title: Monitoring web browsing behavior with differential privacy

abstract: Monitoring web browsing behavior has benefited many data mining applications, such as top-K discovery and anomaly detection. However, releasing private user data to the greater public would concern web users about their privacy, especially after the incident of AOL search log release where anonymization was not correctly done. In this paper, we adopt differential privacy, a strong, provable privacy definition, and show that differentially private aggregates of web browsing activities can be released in real-time while preserving the utility of shared data. Our proposed algorithms utilize the rich correlation of the time series of aggregated data and adopt a state-space approach to estimate the underlying, true aggregates from the perturbed values by the differential privacy mechanism. We evaluate our algorithms with real-world web browsing data. Utility evaluations with three metrics demonstrate that the quality of the private, released data by our solutions closely resembles that of the original, unperturbed aggregates.


Role-Assignment in Multi-Agent Coordination


title: Compositional Vector Space Models for Knowledge Base Completion

abstract: Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z)→ containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recurrent neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11%, and a method leveraging pre-trained embeddings by 7%.


title: DAPs: Deep Action Proposals for Action Understanding

abstract: Object proposals have contributed significantly to recent advances in object understanding in images. Inspired by the success of this approach, we introduce Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates that our approach outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize, i.e. to retrieve good quality temporal proposals of actions unseen in training.


title: A very large Brazilian pedigree with 11778 Leber's hereditary optic neuropathy.

abstract: Purpose: We conducted extensive epidemiological, neuro-ophthalmological, psychophysical, and blood examinations on a newly discovered, very large pedigree with molecular analysis showing mtDNA mutation for Leber’s hereditary optic neuropathy (LHON). Methods: Four patients representing four index cases from a remote area of Brazil were sent to Sao Paulo, where complete ophthalmological examinations strongly suggested LHON. Molecular analysis of their blood demonstrated that they were LHON, homoplasmic 11778, J-haplogroup. They had an extensive family that all lived in one rural area in Brazil. To investigate this family, we drew on a number of international experts to form a team that traveled to Brazil. This field team also included several members of the Federal University of Sao Paulo, and together we evaluated 273 of the 295 family members that were still alive. We conducted epidemiological interviews emphasizing possible environmental risk factors, comprehensive neuro-ophthalmological examinations, psychophysical tests, Humphrey visual field studies, fundus photography, and blood testing for both mitochondrial genetic analysis and nuclear gene linkage analysis. Results: The person representing the first-generation case immigrated from Verona, Italy, to Colatina. Subsequent generations demonstrated penetrance rates of 71%, 60%, 34%, 15%, and 9%. The percentages of males were 60%, 50%, 64%, 100%, and 100%. Age at onset varied from 10 to 64 years, and current visual acuities varied from LP to 20/400. Conclusions: Almost 95% of a nearly 300-member pedigree with LHON 11778 were comprehensively studied. Analysis of environmental risk factors and a nuclear modifying factor from this group may help address the perplexing mystery of LHON: Why do only some of the genetically affected individuals manifest the disease? This fully described database may also provide an excellent opportunity for future clinical trials of any purported neuroprotective agent.


title: Calculation of Leakage Inductance for High-Frequency Transformers

abstract: Frequency-dependent leakage inductance is often observed. The high-frequency eddy current effects cause a reduction in leakage inductance. The proximity effect between adjacent layers is responsible for the reduction of leakage inductance. This paper gives a detailed analysis of high-frequency leakage inductance and proposes an accurate prediction methodology. High-frequency leakage inductances in several interleaved winding configurations are also discussed. Interleaved winding configurations actually give a smaller degree of reduction of leakage induction at high frequency. Finite-element analysis simulation and measurement validate the models.


Accelerator Architecture for Combinatorial Optimization Problems


title: Walklets: Multiscale Graph Embeddings for Interpretable Network Classification

abstract: We present Walklets, a novel approach for learning multiscale representations of vertices in a network. These representations clearly encode multiscale vertex relationships in a continuous vector space suitable for multi-label classification problems. Unlike previous work, the latent features generated using Walklets are analytically derivable, and human interpretable. ::: Walklets uses the offsets between vertices observed in a random walk to learn a series of latent representations, each which captures successively larger relationships. This variety of dependency information allows the same representation strategy to model phenomenon which occur at different scales. ::: We demonstrate Walklets' latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that Walklets outperforms new methods based on neural matrix factorization, and can scale to graphs with millions of vertices and edges.


title: Multi-user computation offloading as Multiple Knapsack Problem for 5G Mobile Edge Computing

abstract: We present an overview of NP-hard problems and methods related to deployment, resource sharing, load balancing and fairness among multiple users in 5G mobile networks. We provide a general model of the system considering the E2E computational latency of Mobile Edge Computing (MEC) applications. An evaluation of a multi-user MEC offloading model reducible to the Multiple Knapsack Problem (MKP) is performed. We study the behaviour of an exact and a heuristic algorithm. We also show the performance results of these in a real-life small-cell deployment scenario.


title: Parallel Structure from Motion from Local Increment to Global Averaging

abstract: In this paper, we tackle the accurate and consistent Structure from Motion (SfM) problem, in particular camera registration, far exceeding the memory of a single computer in parallel. Different from the previous methods which drastically simplify the parameters of SfM and sacrifice the accuracy of the final reconstruction, we try to preserve the connectivities among cameras by proposing a camera clustering algorithm to divide a large SfM problem into smaller sub-problems in terms of camera clusters with overlapping. We then exploit a hybrid formulation that applies the relative poses from local incremental SfM into a global motion averaging framework and produce accurate and consistent global camera poses. Our scalable formulation in terms of camera clusters is highly applicable to the whole SfM pipeline including track generation, local SfM, 3D point triangulation and bundle adjustment. We are even able to reconstruct the camera poses of a city-scale data-set containing more than one million high-resolution images with superior accuracy and robustness evaluated on benchmark, Internet, and sequential data-sets.


title: Breast and Body Contouring for Transgender and Gender Nonconforming Individuals.

abstract: Even after long-term hormone therapy, many transwomen still have challenges in the size and shape of their breasts and also of the contour of their trunk areas. Though distinct anthropomorphic differences in skeletal structure exist and pose limitations to an ideal result, considerable improvements can be obtained through breast augmentation and trunk feminization. There are challenges that are unique to the transfeminine chest and trunk, and important considerations for the priority of fat grafting to the buttocks and hips when grafted fat is in short supply.


title: Asymmetrical interleaving strategy for multi-channel PFC

abstract: The continuously increasing efficiency and power density requirement of the AC-DC front-end converter posed a big challenge for today's power factor correction (PFC) circuit design. The multi-channel interleaved PFC is a promising candidate to achieve the goals. In this paper, the multi-channel interleaving impact on the EMI filter design and the output capacitor life time is investigated. By properly choosing the interleaving channel number and the switching frequency, the EMI filter size and cost can be effectively reduced. Further more; multi-channel PFC with asymmetrical interleaving strategy is introduced, and the additional benefit on the EMI filter is identified. At the output side, different interleaving schemes impact on the output capacitor ripple cancellation effect is also investigated and compared.


title: A Deep Learning Approach for Cancer Detection and Relevant Gene Identification

abstract: Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.


title: CLINICAL NEUROSCIENCE NEUROREPORT Brain^computer interface using fMRI: spatial navigation by thoughts

abstract: A brain–computer interface (BCI) is a way of conveying an individual's thoughts to control computer or electromechanical hardware. Capitalizing on the ability to characterize brain activity in a reproducible manner, we explored the possibility of using real-time fMRI to interpret the spatial distribution of brain function as BCI commands. Using a high-field (3 T) MRI scanner, brain activities associated with four distinct covert functional tasks were detected and subsequently translated into predetermined computer commands for moving four directional cursors. The proposed fMRI-BCI method allowed volunteer subjects to navigate through a simple 2D maze solely through their thought processes.


title: New facts in finance

abstract: The last 15 years have seen a revolution in the way financial economists understand the world around us. We once thought that stock and bond returns were essentially unpredictable. Now we recognize that stock and bond returns have a substantial predictable component at long horizons. We once thought the capital asset pricing model (CAPM) provided a good description of why average returns on some stocks, portfolios, funds or strategies were higher than others. Now we recognize that the average returns of many investment opportunities cannot be explained by the CAPM, and multifactor models' have supplanted the CAPM to explain them. We once thought that long-term interest rates reflected expectations of future short term rates and that interest rate differentials across countries reflected expectations of exchange-rate depreciation. Now, we see time-varying risk premia in bond and foreign exchange markets as well as in stock markets. Once, we thought that mutual fund average returns were well explained by the CAPM. Now, we recognize ``value'' and other high return strategies in funds, and slight persistence in fund performance. In this article, I survey these new facts. I show how they are related. Each case uses price variables to infer market expectations of future returns; each case notices that an offsetting adjustment (to dividends, interest rates, or exchange rates) seems to be absent or sluggish. Each case suggests that financial markets offer rewards in the form of average returns for holding risks related to recessions and financial distress, in addition to the risks represented by overall market movements.


title: Mechanisms , Pathophysiology , and Therapy of Arterial Stiffness

abstract: Arterial stiffness is a growing epidemic associated with increased risk of cardiovascular events, dementia, and death. Decreased compliance of the central vasculature alters arterial pressure and flow dynamics and impacts cardiac performance and coronary perfusion. This article reviews the structural, cellular, and genetic contributors to arterial stiffness, including the roles of the scaffolding proteins, extracellular matrix, inflammatory molecules, endothelial cell function, and reactive oxidant species. Additional influences of atherosclerosis, glucose regulation, chronic renal disease, salt, and changes in neurohormonal regulation are discussed. A review of the hemodynamic impact of arterial stiffness follows. A number of lifestyle changes and therapies that reduce arterial stiffness are presented, including weight loss, exercise, salt reduction, alcohol consumption, and neuroendocrine-directed therapies, such as those targeting the renin-angiotensin aldosterone system, natriuretic peptides, insulin modulators, as well as novel therapies that target advanced glycation end products.


title: N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials

abstract: We describe N-body networks, a neural network architecture for learning the behavior and properties of complex many body physical systems. Our specific application is to learn atomic potential energy surfaces for use in molecular dynamics simulations. Our architecture is novel in that (a) it is based on a hierarchical decomposition of the many body system into subsytems, (b) the activations of the network correspond to the internal state of each subsystem, (c) the "neurons" in the network are constructed explicitly so as to guarantee that each of the activations is covariant to rotations, (d) the neurons operate entirely in Fourier space, and the nonlinearities are realized by tensor products followed by Clebsch-Gordan decompositions. As part of the description of our network, we give a characterization of what way the weights of the network may interact with the activations so as to ensure that the covariance property is maintained.


title: A case against convexity in conceptual spaces

abstract: The notion of conceptual space, proposed by Gardenfors as a framework for the representation of concepts and knowledge, has been highly influential over the last decade or so. One of the main theses involved in this approach is that the conceptual regions associated with properties, concepts, verbs, etc. are convex. The aim of this paper is to show that such a constraint—that of the convexity of the geometry of conceptual regions—is problematic; both from a theoretical perspective and with regard to the inner workings of the theory itself. On the one hand, all the arguments provided in favor of the convexity of conceptual regions rest on controversial assumptions. Additionally, his argument in support of a Euclidean metric, based on the integral character of conceptual dimensions, is weak, and under non-Euclidean metrics the structure of regions may be non-convex. Furthermore, even if the metric were Euclidean, the convexity constraint could be not satisfied if concepts were differentially weighted. On the other hand, Gardenfors’ convexity constraint is brought into question by the own inner workings of conceptual spaces because: (i) some of the allegedly convex properties of concepts are not convex; (ii) the conceptual regions resulting from the combination of convex properties can be non-convex; (iii) convex regions may co-vary in non-convex ways; and (iv) his definition of verbs is incompatible with a definition of properties in terms of convex regions. Therefore, the mandatory character of the convexity requirement for regions in a conceptual space theory should be reconsidered.


title: Culture, Mind, and the Brain: Current Evidence and Future Directions

abstract: Current research on culture focuses on independence and interdependence and documents numerous East-West psychological differences, with an increasing emphasis placed on cognitive mediating mechanisms. Lost in this literature is a time-honored idea of culture as a collective process composed of cross-generationally transmitted values and associated behavioral patterns (i.e., practices). A new model of neuro-culture interaction proposed here addresses this conceptual gap by hypothesizing that the brain serves as a crucial site that accumulates effects of cultural experience, insofar as neural connectivity is likely modified through sustained engagement in cultural practices. Thus, culture is “embrained,” and moreover, this process requires no cognitive mediation. The model is supported in a review of empirical evidence regarding (a) collective-level factors involved in both production and adoption of cultural values and practices and (b) neural changes that result from engagement in cultural practices. Future directions of research on culture, mind, and the brain are discussed.


title: Detecting and investigating crime by means of data mining: a general crime matching framework

abstract: Abstract Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify crime characteristics. Actually crime analysis includes exploring and detecting crimes and their relationships with criminals. The high volume of crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of crime analysis and finally we will use the clustering results in order to perform crime matching process.


title: What is the right way to represent document images?

abstract: In this article we study the problem of document image representation based on visual features. We propose a comprehensive experimental study that compares three types of visual document image representations: (1) traditional so-called shallow features, such as the RunLength and the Fisher-Vector descriptors, (2) deep features based on Convolutional Neural Networks, and (3) features extracted from hybrid architectures that take inspiration from the two previous ones. ::: We evaluate these features in several tasks (i.e. classification, clustering, and retrieval) and in different setups (e.g. domain transfer) using several public and in-house datasets. Our results show that deep features generally outperform other types of features when there is no domain shift and the new task is closely related to the one used to train the model. However, when a large domain or task shift is present, the Fisher-Vector shallow features generalize better and often obtain the best results.


title: Extensible pattern matching via a lightweight language extension

abstract: Pattern matching of algebraic data types (ADTs) is a standard feature in typed functional programming languages, but it is well known that it interacts poorly with abstraction. While several partial solutions to this problem have been proposed, few have been implemented or used. This paper describes an extension to the .NET language F# called active patterns, which supports pattern matching over abstract representations of generic heterogeneous data such as XML and term structures, including where these are represented via object models in other .NET languages. Our design is the first to incorporate both ad hoc pattern matching functions for partial decompositions and "views" for total decompositions, and yet remains a simple and lightweight extension. We give a description of the language extension along with numerous motivating examples. Finally we describe how this feature would interact with other reasonable and related language extensions: existential types quantified at data discrimination tags, GADTs, and monadic generalizations of pattern matching.


title: Consensus on Current Injectable Treatment Strategies in the Asian Face

abstract: Background ::: The desire for and use of nonsurgical injectable esthetic facial treatments are increasing in Asia. The structural and anatomical features specific to the Asian face, and differences from Western populations in facial aging, necessitate unique esthetic treatment strategies, but published recommendations and clinical evidence for injectable treatments in Asians are scarce.


title: ModelSec: A Generative Architecture for Model-Driven Security

abstract: Increasingly, the success of software systems depends largely on how their security requirements are satisfied. However, developers are challenged in implement- ing these requirements, mainly because of the gap between the specification and imple- mentation, and the technical complexities of the current software infrastructures. Re- cently, Model-Driven Security has emerged as a new software development area aimed at overcoming these difficulties. This new paradigm takes advantage of the benefits of the model driven software development techniques for modeling and implementing security concerns. Following this trend, this paper proposes a model driven security approach named ModelSec that offers a generative architecture for managing security requirements, from the requirement elicitation to the implementation stage. This ar- chitecture automatically generates security software artifacts (e.g. security rules) by means of a model transformation chain composed of two-steps. Firstly, a security in- frastructure dependent model is derived from three models, which express the security restrictions, the design decisions and the information needed on the target platform. Then, security software artifacts are produced from the previously generated model. A Domain-Specific Language for security requirements management has been built, which is based on a metamodel specifically designed for this purpose. An application example that illustrates the approach and the Eclipse tools implemented to support it are also shown.


title: Saturated fatty acids induce endoplasmic reticulum stress and apoptosis independently of ceramide in liver cells

abstract: Accumulation of lipids in nonadipose tissues can lead to cell dysfunction and cell death, a phenomenon known as lipotoxicity. However, the signaling pathways and mechanisms linking lipid accumulation to cell death are poorly understood. The present study examined the hypothesis that saturated fatty acids disrupt endoplasmic reticulum (ER) homeostasis and promote apoptosis in liver cells via accumulation of ceramide. H4IIE liver cells were exposed to varying concentrations of saturated (palmitate or stearate) or unsaturated (oleate or linoleate) fatty acids. ER homeostasis was monitored using markers of the ER stress response pathway, including phosphorylation of IRE1alpha and eIF2alpha, splicing of XBP1 mRNA, and expression of molecular chaperone (e.g., GRP78) and proapoptotic (CCAAT/enhancer-binding protein homologous protein) genes. Apoptosis was monitored using caspase activity and DNA laddering. Palmitate and stearate induced ER stress, caspase activity, and DNA laddering. Inhibition of caspase activation prevented DNA laddering. Unsaturated fatty acids did not induce ER stress or apoptosis. Saturated fatty acids increased ceramide concentration; however, inhibition of de novo ceramide synthesis did not prevent saturated fatty acid-induced ER stress and apoptosis. Unsaturated fatty acids rescued palmitate-induced ER stress and apoptosis. These data demonstrate that saturated fatty acids disrupt ER homeostasis and induce apoptosis in liver cells via mechanisms that do not involve ceramide accumulation.


title: Advanced n-channel LDMOS with ultralow specific on-resistance by 0.18 μm epitaxial BCD technology

abstract: An advanced n-channel LDMOS (nLDMOS) by 0.18 μm epitaxial BCD Technology is proposed with the best-in-class performance. Thin drift region oxidation by independent LOCOS process is adopted and optimized to improve the trade-off between breakdown voltage (BV) and specific ON-resistance (R on, sp ). Both P-Body and N-Drift region vertical doping optimization is developed accordingly to improve the reliability of the device. Both 20 V and 30 V nLDMOS devices are designed, and experimental results show that ultralow R on, sp has been demonstrated (i.e., R on, sp = 6.5 mΩ-mm2 for BV = 27.6 V, R on, sp = 9.6 mΩ mm2 for BV = 37.3 V, respectively). Moreover, the electrical safe operating area (SOA) and hot-carrier injection (HCI) are also improved.


title: CCMF, Computational Context Modeling Framework - An Ontological Approach to Develop Context-Aware Web Applications

abstract: The purpose of software is to help people to perform their activities and fulfill their objectives. In this regard, the human-software relationship could be enhanced if software could adapt to changes automatically during its utilization (Brezillon, 1999). Context is defined by (Dey, 2001) as any type of information which characterizes an entity. An entity is any person, place or object that is relevant to the interaction between users and software. According to (Dey & Abowd, 1999) context-awareness is the capability of software to use context to offer services to users. For instance, a context-aware system may trigger an alarm in a device near to a user to remind him of the departure time of a planned trip (Yamato et al. 2011). In this example, context is used to provide a service to the user: the reminding of a personal activity. It is not, however, a trivial task to associate context with software in the same level of abstraction as humans do when they communicate with each other. (Dey & Abowd, 1999) considers that such ability is naturally inherited from the richness of the human languages and the common understanding of how the world works and from an implicit understanding of daily situations. Thus, with the intention of enhancing the offering of services from software to humans, it is important to transmit these capacities to computational environments.


title: Glucose detection using SERS with multi-branched gold nanostructures in aqueous medium

abstract: Gold nanoparticles (AuNPs), multi-branched gold nanoparticles (MBGNs), and silica-coated MBGNs (MBGNs-silica) were studied for rhodamine B (RB) and α-glucose detection at low concentration. The MBGNs SPR band in the NIR, which is tunable, is useful for SERS that was demonstrated using rhodamine B and α-glucose as probe molecules with detection limits of 50 pM and 5 mM (90 mg dL−1), respectively, much lower than that using regular AuNPs. The SERS signals of RB and α-glucose using MBGNs-silica are further enhanced with respect to AuNPs and MBGNs, which is attributed to the aggregation of the MBGNs and a stronger interaction. In the case of α-glucose, the functionalization process performed to both, α-glucose molecules and MBGNs, improves the interaction and allows measurements at low concentration.


title: TS fuzzy controller applied to the DTC-SVM scheme for three-phase induction motor

abstract: In this paper is proposed a Takagi-Sugeno Fuzzy controller (TSF) applied to the direct torque control scheme with space vector modulation. In conventional DTC-SVM scheme, two PI controllers are used to generate the reference stator voltage vector. To improve the drawback of this conventional DTC-SVM scheme is proposed the TSF controller to substitute both PI controllers. The proposed controller calculates the reference quadrature components of the stator voltage vector. The rule base for the proposed controller is defined in function of the stator flux error and the electromagnetic torque error using trapezoidal and triangular membership functions. Constant switching frequency and low torque ripple are obtained using space vector modulation technique. Performance of the proposed DTC-SVM with TSF controller is analyzed in terms of several performance measures such as rise time, settling time and torque ripple considering different operating conditions. The simulation results shown that the proposed scheme ensure fast torque response and low torque ripple validating the proposed scheme.


Biological versus cultural evolution: Beyond a false dichotomy: Comment on “Modeling the cultural evolution of language” by Luc Steels


title: Scalable Spatiotemporal Crowdsourcing for Smart Cities based on Particle Filtering

abstract: In mobile crowdsourcing, workers are financially motivated to perform as many self-selected tasks as possible to maximize their revenue. Unfortunately, the existing task scheduling approaches in mobile crowdsourcing fail to consider task execution duration and do not scale for massive tasks and large geographic areas (e.g., a whole city). In this paper, we study on the geo-task scheduling problem (GTS) under the various spatial and temporal constraints in real-world mobile crowdsourcing applications, including task execution duration and task expiration time. Given the location of a worker, the goal of our study is to find an optimal task execution sequence that maximizes the number of tasks that could be finished. Since the exact solution to the maximum task scheduling is computationally intractable, we propose two sub-optimal approaches (LCPF and NUD-IC) based on the particle filtering and the DBSCAN clustering.


title: Privacy and security: Key requirements for sustainable IoT growth

abstract: As IoT moves beyond a catchphrase and starts to provide meaningful solutions in multiple fields, three of its critical pillars are now well understood: • Transducers are needed as means of interacting with the environment and machines, and in converting stimuli to data and vice versa. These sensors and actuators form the basis of contextual awareness. • Given that many end-node IoT devices are size and power constrained, local low-power computing is essential. The need for power-efficient end-node and edge computing becomes more apparent when latency, network bandwidth, and real time analytics are considered. • Low power communication links to transmit the data between IoT devices and local aggregators or cloud resources form the third pillar. Missing in this picture, and not fully appreciated yet, is the fourth pillar of IoT: privacy and security (P&S). If IoT is all about data, how P&S is treated will determine IoT's fate: a second phase of rapid proliferation or ultimate demise and collapse. Recent breaches in P&S are starting to change the industry's view on this issue. Even IoT end nodes that are low cost and have limited functionality pose significant risk to the entire system when their security is breached. This is due to the networking nature of the IoT that exposes a massive attack surface, making these devices ideal attack points for causing disruptions and stealing sensitive data. PC-era Internet security has been an expensive afterthought that has cost industry and consumers billions of dollars. Therefore, we should approach IoT differently, making P&S a key requirement at the design phase itself, and address all life-cycle aspects from initial deployment to in-field updates, to end-of-life decommissioning. This is a system level challenge that requires complete end-end HW/SW solutions, developed in partnership with the entire ecosystem.


title: HapLand: A Scalable Robust Emotion Regulation Haptic System Testbed

abstract: Emotion regulation is crucial for healthy adaptation, while emotion dysregulation can lead to the development of mental health disorders such as anxiety or depression. Everyone, one way or another, sometimes fails to effectively regulate their emotions, making this a challenging problem with broad potential impact. In our research, we are exploring three ways in which haptics-enabled wearables can facilitate effective emotion regulation, formulated based on Gross's model of emotion regulation. We hypothesize that an individual-level biofeedback haptic could foster healthier and more effective patterns of emotion regulation. We have designed and implemented HapLand, a scalable, robust biofeedback haptic system testbed to facilitate research-based haptics-enabled wearables design for the purpose of emotion regulation. In this paper, we give an overview of HapLand and our plans for using HapLand for future research.


title: Towards Robust Linguistic Analysis using OntoNotes

abstract: Large-scale linguistically annotated corpora have played a crucial role in advancing the state of the art of key natural language technologies such as syntactic, semantic and discourse analyzers, and they serve as training data as well as evaluation benchmarks. Up till now, however, most of the evaluation has been done on monolithic corpora such as the Penn Treebank, the Proposition Bank. As a result, it is still unclear how the state-of-the-art analyzers perform in general on data from a variety of genres or domains. The completion of the OntoNotes corpus, a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information, makes it possible to perform such an evaluation. This paper presents an analysis of the performance of publicly available, state-of-the-art tools on all layers and languages in the OntoNotes v5.0 corpus. This should set the benchmark for future development of various NLP components in syntax and semantics, and possibly encourage research towards an integrated system that makes use of the various layers jointly to improve overall performance.


Fabrication and Characterization of a CMOS-MEMS Humidity Sensor


The challenge of consolation: nurses’ experiences with spiritual and existential care for the dying-a phenomenological hermeneutical study


title: A CMOS Time-to-Digital Converter (TDC) Based On a Cyclic Time Domain Successive Approximation Interpolation Method

abstract: This paper describes a time-to-digital converter (TDC) with ~1.2 ps resolution and ~327 mus dynamic range suitable for laser range-finding application for example. The resolution of ~1.2 ps is achieved with interpolation based on a cyclic time domain successive approximation (CTDSA) method that resolves the time difference between two non-repetitive signals using binary search. The method utilizes a pair of digital-to-time converters (DTC), the propagation delay difference between which is implemented by digitally controlling the unit load capacitors of their delay cells, thus enabling sub-gate delay timing resolution. The rms single-shot precision, i.e., standard deviation sigma-value of the TDC is 3.2 ps, which is achieved by using an external integral nonlinearity look-up table (INL-LUT) for the interpolators. The power consumption is 33 mW at 100 MHz with a 3.3 V operating voltage. The prototypes were fabricated in a 0.35 mum CMOS process.


title: CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

abstract: Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. By means of this, our approach achieves 64.3% and 65.3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.


A Survey and Taxonomy of Location Systems for Ubiquitous Computing


title: Can machine learning techniques predict customer dissatisfaction? A feasibility study for the automotive industry

abstract: The automotive industry is in the strongest competition ever, as this sector gets disrupted by new arising competitors. Providing services to maximum customer satisfaction will be one of the most crucial competitive advantages in the future. Around 1 Terabyte of objective data is created every hour today. This volume will significantly grow in the future by the increasing number of connected services within the automotive industry. However, customer satisfaction determination is solely based on subjective questionnaires today without taking the vast amount of objective sensor and service process data into account. This work presents an industrial application that fills this lack of research and thus provides a solution with a high practical impact to survive in the tough competition of the automotive industry. Therefore, the work addresses these fundamental business questions: 1) Can dissatisfied customers be classified based on data that is produced during every service visit? 2) Can the dissatisfaction indicators be derived from service process data? A machine learning problem is set up that compared 5 classifiers and analyzed data from 19,008 real service visits from an automotive company. The 105 extracted features were drawn from the most significant available sources: warranty, diagnostic, dealer system and general vehicle data. The best result for customer dissatisfaction classification was 88.8% achieved with the SVM classifier (RBF kernel). Furthermore, the 46 most potential indicators for dissatisfaction were identified by the evolutionary feature selection. Our system was capable of classifying customer dissatisfaction solely based on the objective data that is generated by almost every service visit. As the amount of these data is continuously growing, we expect that the presented data-driven approach can achieve even better results in the future with a higher amount of data.


title: Dual-Band Dual-Polarized Compact Bowtie Antenna Array for Anti-Interference MIMO WLAN

abstract: Smart antennas have received increasing interest for mitigating interference in the multiple-input-multiple-output (MIMO) wireless local area network (WLAN). In this paper, a dual-band dual-polarized compact bowtie dipole antenna array is proposed to support anti-interference MIMO WLAN applications. In the antenna array, there are 12 antennas, six for horizontal polarization and six for vertical polarization. In order to achieve dual linear polarizations and beam switching, six horizontal antennas are placed in a sequential, rotating arrangement on a horizontal substrate panel with an equal inclination angle of 60 ° to form a symmetrical structure, while the other six antennas for vertical polarization are inserted through slots made on the horizontal substrate panel. Furthermore, six pairs of meandered slits are introduced to reduce the mutual coupling between horizontal antennas in the lower band. A prototype of the array with a dimension of 150 × 150 × 60 mm3 is manufactured and exhibits the characteristics of high isolation, good front-to-back ratio, and average gains of 4.5 and 5 dBi over the 2.4- and 5-GHz band, respectively. The MIMO performance of the array is analyzed and evaluated by mutual coupling, the total active reflection coefficient (TARC) and the envelope correlation coefficient. The anti-interference capability of the array is also investigated by the experiment.


title: A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function

abstract: The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this paper, we consider constructive approximation on any finite interval of $\mathbb{R}$ by neural networks with only one neuron in the hidden layer. We construct algorithmically a smooth, sigmoidal, almost monotone activation function $\sigma$ providing approximation to an arbitrary continuous function within any degree of accuracy. This algorithm is implemented in a computer program, which computes the value of $\sigma$ at any reasonable point of the real axis.


title: Mining MEDLINE: Abstracts, Sentences, or Phrases?

abstract: A growing body of works address automated mining of biochemical knowledge from digital repositories of scientific literature, such as MEDLINE. Some of these works use abstracts as the unit of text from which to extract facts. Others use sentences for this purpose, while still others use phrases. Here we compare abstracts, sentences, and phrases in MEDLINE using the standard information retrieval performance measures of recall, precision, and effectiveness, for the task of mining interactions among biochemical terms based on term co-occurrence. Results show statistically significant differences that can impact the choice of text unit.


title: Achieving Transformative Sustainability Learning: Engaging Head, Hands and Heart.

abstract: Purpose – The current UN Decade of Education for Sustainable Development echoes many scholars' calls to re‐envision education for sustainability. Short of a complete overhaul of education, the paper seeks to propose learning objectives that can be integrated across existing curricula. These learning objectives are organized by head, hands and heart – balancing cognitive, psychomotor and affective domains. University programs and courses meeting these learning objectives exhibit an emergent property here termed transformative sustainability learning (TSL).Design/methodology/approach – Theoretically, TSL grew from traditions of sustainability education and transformative education. Practically, TSL emerged from experimental learning collaborations sponsored by the University of British Columbia in 2003 and 2004 in an effort to enable explicit transitions to sustainability‐oriented higher education. Primarily through action research, these community‐based, applied learning experiences constituted cyclical pr...


title: Centaur: locating devices in an office environment

abstract: We consider the problem of locating devices such as laptops, desktops, smartphones etc. within an office environment, without requiring any special hardware or infrastructure. We consider two widely-studied approaches to indoor localization: (a) those based on Radio Frequency (RF) measurements made by devices with WiFi or cellular interfaces, and (b) those based on Acoustic Ranging (AR) measurements made by devices equipped with a speaker and a microphone. A typical office environment today comprises devices that are amenable to either one or both these approaches to localization. In this paper we ask the question, "How can we combine RF and AR based approaches in synergy to locate a wide range of devices, leveraging the benefits of both approaches?" The key contribution of this paper is Centaur, a system that fuses RF and AR based localization techniques into a single systematic framework that is based on Bayesian inference. Centaur is agnostic to the specific RF or AR technique used, giving users the flexibility of choosing their preferred RF or AR schemes. We also make two additional contributions: making AR more robust in non-line-of-sight settings (EchoBeep) and adapting AR to localize speaker-only devices (DeafBeep). We evaluate the performance of our AR enhancements and that of the Centaur framework through microbenchmarks and deployment in an office environment.


title: Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild

abstract: We present a framework for robust face detection and landmark localisation of faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark Localisation Competition'. The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation. To achieve a high detection rate, we use two publicly available CNN-based face detectors and two proprietary detectors. We aggregate the detected face bounding boxes of each input image to reduce false positives and improve face detection accuracy. A cascaded shape regressor, trained using faces with a variety of pose variations, is then employed for pose estimation and image pre-processing. Last, we train the final cascaded shape regressor for fine-grained landmark localisation, using a large number of training samples with limited pose variations. The experimental results obtained on the 300W and Menpo benchmarks demonstrate the superiority of our framework over state-of-the-art methods.


title: Free vibration analysis of orthotropic doubly-curved shallow shells based on the gradient elasticity

abstract: Abstract The comprehensive free vibration analysis of doubly-curved shallow shells which are made of an orthotropic material is presented. The size effect is taken into consideration using the gradient elasticity theory. Novozhilov’s linear shallow shell theory is used and it is assumed that the shell is simply supported. The governing equations of the doubly-curved shallow shells with consideration of the length scales are developed. Analytical solutions to the equations are proposed to obtain the frequencies of the shells. Numerical results are presented for various shell curvatures including spherical, cylindrical and hyperbolic paraboloidal shells and compared with those available in the literature.


title: Comparative analysis of gene expression between CMS-D8 restored plants and normal non-restoring fertile plants in cotton by differential display

abstract: CMS-D8 and its restorer were developed by introducing the cytoplasm and nuclear gene Rf2from the wild diploid Gossypium trilobum (D8) into the cultivated tetraploid Upland cotton (Gossypium hirsutum). No information is available on how the Rf2 gene interacts with CMS-associated genes and how CMS-D8 cytoplasm affects nuclear gene expression. The objective of this study was to identify differentially expressed genes in anther tissues between the non-restoring fertile maintainer ARK8518 (rf2rf2) and its isogenic heterozygous D8 restorer line, ARK8518R (Rf2rf2) with D8 cytoplasm, by mRNA differential display (DD). Out of more than 3,000 DDRT-PCR bands amplified by 31 primer combinations from 12 anchor primers and 8 arbitrary decamer primers, approximately 100 bands were identified as being qualitatively differentially displayed. A total of 38 cDNA fragments including 12 preferentially expressed cDNA bands in anther were isolated, cloned and sequenced. Reverse northern blot analysis showed that only 4 genes, including genes encoding a Cys-3-His zinc finger protein and aminopeptidase, were up-regulated, while 22 genes, including genes for phosphoribosylanthranilate transferase (PAT), starch synthase (SS), 4-coumarate-CoA ligase, electron transporter, calnexin, arginine decarboxylase, and polyubiquitin, were down-regulated in the heterozygous restorer ARK8518R. The down-regulation of SS explains the lack of starch accumulation in sterile rf2 pollen grains in the heterozygous restored plants. The molecular mechanism of CMS and its restoration, specifically the possible roles of SS and PAT genes in relation to restoration of Rf2 to CMS-D8, are discussed. This investigation represents the first account of such an analysis in cotton.


title: Transfer learning for automated optical inspection

abstract: One of the challenges in applying convolutional neural networks to automated optical inspection is the lack of sufficient training data. In this paper we show that transfer learning can be successfully applied using image data from an entirely different domain. Focusing on optical inspection of texture images, we transfer weights from a source network trained with arbitrary unrelated images from the ImageNet dataset. Inspection experiments using our method show that one epoch of fine-tuning is sufficient to achieve 99.95% classification accuracy, while conventional transfer learning without fine-tuning achieves only 78.76%. An in-depth analysis of the effects of fine-tuning reveals that after fine-tuning, most of the unnecessary features encoded in the weights of the source network are deactivated, while meaningful features of the target data are amplified to capture new variations in the target domain.


Feature-Oriented Artistic Styles Transfer Based on Effective Texture Synthesis


title: Building Encoder and Decoder with Deep Neural Networks: On the Way to Reality

abstract: Deep learning has been a groundbreaking technology in various fields as well as in communications systems. In spite of the notable advancements of deep neural network (DNN) based technologies in recent years, the high computational complexity has been a major obstacle to apply DNN in practical communications systems which require real-time operation. In this sense, challenges regarding practical implementation must be addressed before the proliferation of DNN-based intelligent communications becomes a reality. To the best of the authors' knowledge, for the first time, this article presents an efficient learning architecture and design strategies including link level verification through digital circuit implementations using hardware description language (HDL) to mitigate this challenge and to deduce feasibility and potential of DNN for communications systems. In particular, DNN is applied for an encoder and a decoder to enable flexible adaptation with respect to the system environments without needing any domain specific information. Extensive investigations and interdisciplinary design considerations including the DNN-based autoencoder structure, learning framework, and low-complexity digital circuit implementations for real-time operation are taken into account by the authors which ascertains the use of DNN-based communications in practice.


title: RNA velocity of single cells

abstract: RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.RNA velocity, estimated in single cells by comparison of spliced and unspliced mRNA, is a good indicator of transcriptome dynamics and will provide a useful tool for analysis of developmental lineage.


title: Real-Time Defocus Rendering With Level of Detail and Sub-Sample Blur

abstract: This paper presents a GPU-based rendering algorithm for real-time defocus blur effects, which significantly improves the accumulation buffering. The algorithm combines three distinctive techniques: 1 adaptive discrete geometric level of detail LOD, made popping-free by blending visibility samples across the two adjacent geometric levels; 2 adaptive visibility/shading sampling via sample reuse; 3 visibility supersampling via height-field ray casting. All the three techniques are seamlessly integrated to lower the rendering cost of smooth defocus blur with high visibility sampling rates, while maintaining most of the quality of brute-force accumulation buffering.


title: A Partial Least Squares Regression-Based Fusion Model for Predicting the Trend in Drowsiness

abstract: This paper proposes a new technique of modeling driver drowsiness with multiple eyelid movement features based on an information fusion technique - partial least squares regression (PLSR), with which to cope with the problem of strong collinear relations among eyelid movement features and, thus, predicting the tendency of the drowsiness. With a set of electro- oculogram signals measured in an experiment conducted in Sweden, 14 typical eyelid movement features are first extracted. Then, statistical analyses from 20 subjects indicate that the eyelid movement parameters can characterize a driver's degree of drowsiness. The intrinsic quantitative relationships between eyelid movement features and driver drowsiness degree are modeled by PLSR analysis. The developed model provides a framework for integrating multiple sleepiness features together and defining the contribution of each feature to the decision and prediction result. The predictive precision and robustness of the model thus established are validated, which show that it provides a novel way of fusing multifeatures together for enhancing our capability of detecting and predicting the state of drowsiness.


title: Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback

abstract: Albeit the implicit feedback based recommendation problem--when only the user history is available but there are no ratings--is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS applies a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate various contextual information into the model while maintaining its computational efficiency. We present two context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types that are typically purchased repetitively or once. Experiments performed on five implicit datasets (LastFM 1K, Grocery, VoD, and "implicitized" Netflix and MovieLens 10M) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.


title: Constraint-handling in nature-inspired numerical optimization: Past, present and future

abstract: Abstract In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented.


title: Learning Convolutional Neural Networks for Graphs

abstract: Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be undirected, directed, and with both discrete and continuous node and edge attributes. Analogous to image-based convolutional networks that operate on locally connected regions of the input, we present a general approach to extracting locally connected regions from graphs. Using established benchmark data sets, we demonstrate that the learned feature representations are competitive with state of the art graph kernels and that their computation is highly efficient.


title: One-shot learning by inverting a compositional causal process

abstract: People can learn a new visual class from just one example, yet machine learning algorithms typically require hundreds or thousands of examples to tackle the same problems. Here we present a Hierarchical Bayesian model based on com-positionality and causality that can learn a wide range of natural (although simple) visual concepts, generalizing in human-like ways from just one image. We evaluated performance on a challenging one-shot classification task, where our model achieved a human-level error rate while substantially outperforming two deep learning models. We also tested the model on another conceptual task, generating new examples, by using a "visual Turing test" to show that our model produces human-like performance.


title: The Internet of Things--A survey of topics and trends

abstract: The Internet of Things is a paradigm where everyday objects can be equipped with identifying, sensing, networking and processing capabilities that will allow them to communicate with one another and with other devices and services over the Internet to accomplish some objective. Ultimately, IoT devices will be ubiquitous, context-aware and will enable ambient intelligence. This article reports on the current state of research on the Internet of Things by examining the literature, identifying current trends, describing challenges that threaten IoT diffusion, presenting open research questions and future directions and compiling a comprehensive reference list to assist researchers.


title: Mining causality graph for automatic web-based service diagnosis

abstract: It is crucial for Internet company to provide highly reliable web-based services. The web-based services always have many components running in the large-scale infrastructure with complex interactions. As an indispensable part of high reliability, the diagnosis remains to be a thorny problem. With the growth of system scale and complexity, it becomes even more difficult. In this paper, we propose an automatic diagnosis system based on causality graph to help system operators find the root causes. The causality graph is mainly extracted from the historical data of the monitoring system, and the method consists of four steps. 1) It utilizes a data mining method to extract the initial causality graph. 2) Once a failure happens, it lists top-k suspects with a ranking algorithm based on the causality graph. 3) Then system operators check the suspects and label them either right or wrong. 4) A supervised learning algorithm takes the labels as the input to tune the causality graph, in order to improve the diagnosis accuracy on step 2 iteratively. This method requires neither knowledge about the design and implementation details of the web-based service, nor instrumenting the services' source code. Our controlled experiments show that the root causes can be ranked in top 3 with 100% accuracy after countable learning iterations.


title: Textual Spatial Cosine Similarity

abstract: When dealing with document similarity many methods exist today, like cosine similarity. More complex methods are also available based on the semantic analysis of textual information, which are computationally expensive and rarely used in the real time feeding of content as in enterprise- wide search environments. To address these real-time constraints, we developed a new measure of document similarity called Textual Spatial Cosine Similarity, which is able to detect similitude at the semantic level using word placement information contained in the document. We will see in this paper that two degenerate cases exist for this model, which coincide with Cosine Similarity on one side and with a paraphrasing detection model to the other.


title: Constructivism and computer-mediated communication in distance education

abstract: Abstract The fields of learning theory and instructional design are in the midst of a scientific revolution in which their objectivist philosophical foundations are being replaced by a constructivist epistemology. This article describes the assumptions of a constructivist epistemology, contrasts them with objectivist assumptions, and then describes instructional systems that can support constructive learning at a distance.


title: Assessment of the hypothalamic-pituitary-adrenal axis over a 24-hour diurnal period and in response to neuroendocrine challenges in women with and without childhood sexual abuse and posttraumatic stress disorder.

abstract: BACKGROUND ::: Preclinical studies showed that early stress results in long-term alterations in the hypothalamic-pituitary-adrenal (HPA) axis. We performed a comprehensive assessment of the HPA axis in women with and without a history of early childhood sexual abuse and posttraumatic stress disorder (PTSD). ::: ::: ::: METHODS ::: Fifty-two women with and without a history of early childhood sexual abuse and PTSD underwent a comprehensive assessment of the HPA axis, including measurement of cortisol in plasma every 15 min over a 24-hour period and cortisol and corticotropin (ACTH) following corticotropin-releasing factor (CRF) and ACTH challenge. ::: ::: ::: RESULTS ::: Abused women with PTSD had lower levels of cortisol during the afternoon hours (12:00-8:00 PM) of a 24-hour period compared with non-PTSD women. Their ACTH response to a CRF challenge was blunted compared with nonabused non-PTSD (but not abused non-PTSD) women. There were no differences in cortisol response to CRF and ACTH challenges between the groups. Increased PTSD symptom levels were associated with low afternoon cortisol levels. ::: ::: ::: CONCLUSIONS ::: These findings suggest that early abuse is associated with increased CRF drive as evidenced by decreased pituitary sensitivity to CRF, whereas in abuse with PTSD there is a specific hypocortisolemia that is most pronounced in the afternoon hours.


title: MAX-MIN Ant System

abstract: Abstract Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this area has mainly focused on the development of algorithmic variants which achieve better performance than Ant System. In this paper, we present MAX – MIN Ant System ( MM AS ), an Ant Colony Optimization algorithm derived from Ant System. MM AS differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to MM AS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MM AS is currently among the best performing algorithms for these problems.


title: A systematic review of neurobiological and clinical features of mindfulness meditations

abstract: Background Mindfulness meditation (MM) practices constitute an important group of meditative practices that have received growing attention. The aim of the present paper was to systematically review current evidence on the neurobiological changes and clinical benefits related to MM practice in psychiatric disorders, in physical illnesses and in healthy subjects. Method A literature search was undertaken using Medline, ISI Web of Knowledge, the Cochrane collaboration database and references of retrieved articles. Controlled and cross-sectional studies with controls published in English up to November 2008 were included. Results Electroencephalographic (EEG) studies have revealed a significant increase in alpha and theta activity during meditation. Neuroimaging studies showed that MM practice activates the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) and that long-term meditation practice is associated with an enhancement of cerebral areas related to attention. From a clinical viewpoint, Mindfulness-Based Stress Reduction (MBSR) has shown efficacy for many psychiatric and physical conditions and also for healthy subjects, Mindfulness-Based Cognitive Therapy (MBCT) is mainly efficacious in reducing relapses of depression in patients with three or more episodes, Zen meditation significantly reduces blood pressure and Vipassana meditation shows efficacy in reducing alcohol and substance abuse in prisoners. However, given the low-quality designs of current studies it is difficult to establish whether clinical outcomes are due to specific or non-specific effects of MM. Discussion Despite encouraging findings, several limitations affect current studies. Suggestions are given for future research based on better designed methodology and for future directions of investigation.


title: Music information retrieval meets music education

abstract: This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described: music transcription, solo and accompaniment track creation, and generation of performance instructions. As a representative example of a music learning system developed within the MIR community, the Songs2See software is outlined. Finally, challenges and directions for future research are described.


Task-Oriented Dialog Agents Using Memory-Networks and Ensemble Learning


title: Conservative edge sparsification for graph SLAM node removal

abstract: This paper reports on optimization-based methods for producing a sparse, conservative approximation of the dense potentials induced by node marginalization in simultaneous localization and mapping (SLAM) factor graphs. The proposed methods start with a sparse, but overconfident, Chow-Liu tree approximation of the marginalization potential and then use optimization-based methods to adjust the approximation so that it is conservative subject to minimizing the Kullback-Leibler divergence (KLD) from the true marginalization potential. Results are presented over multiple real-world SLAM graphs and show that the proposed methods enforce a conservative approximation, while achieving low KLD from the true marginalization potential.


title: A generalizable approach for multi-view 3D human pose regression

abstract: Despite the significant improvement in the performance of monocular pose estimation approaches and their ability to generalize to unseen environments, multi-view (MV) approaches are often lagging behind in terms of accuracy and are specific to certain datasets. This is mainly due to the fact that (1) contrary to real world single-view (SV) datasets, MV datasets are often captured in controlled environments to collect precise 3D annotations, which do not cover all real world challenges, and (2) the model parameters are learned for specific camera setups. To alleviate these problems, we propose a two-stage approach to detect and estimate 3D human poses, which separates SV pose detection from MV 3D pose estimation. This separation enables us to utilize each dataset for the right task, i.e. SV datasets for constructing robust pose detection models and MV datasets for constructing precise MV 3D regression models. In addition, our 3D regression approach only requires 3D pose data and its projections to the views for building the model, hence removing the need for collecting annotated data from the test setup. Our approach can therefore be easily generalized to a new environment by simply projecting 3D poses into 2D during training according to the camera setup used at test time. As 2D poses are collected at test time using a SV pose detector, which might generate inaccurate detections, we model its characteristics and incorporate this information during training. We demonstrate that incorporating the detector's characteristics is important to build a robust 3D regression model and that the resulting regression model generalizes well to new MV environments. Our evaluation results show that our approach achieves competitive results on the Human3.6M dataset and significantly improves results on a MV clinical dataset that is the first MV dataset generated from live surgery recordings.


title: Explicit Solution for Constrained Stochastic Linear-Quadratic Control with Multiplicative Noise

abstract: We study in this paper a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered in our model destroys the elegant structure of the conventional LQ formulation and has blocked the derivation of an explicit control policy so far in the literature. We successfully derive in this paper the analytical control policy for such a class of problems by utilizing the state separation property induced from its structure. We reveal that the optimal control policy is a piece-wise affine function of the state and can be computed off-line efficiently by solving two coupled Riccati equations. Under some mild conditions, we also obtain the stationary control policy for infinite time horizon. We demonstrate the implementation of our method via some illustrative examples and show how to calibrate our model to solve dynamic constrained portfolio optimization problems.


title: A cloud-assisted malware detection and suppression framework for wireless multimedia system in IoT based on dynamic differential game

abstract: As a novel dynamic network service infrastructure, Internet of Things (IoT) has gained remarkable popularity with obvious superiorities in the interoperability and real-time communication. Despite of the convenience in collecting information to provide the decision basis for the users, the vulnerability of embedded sensor nodes in multimedia devices makes the malware propagation a growing serious problem, which would harm the security of devices and their users financially and physically in wireless multimedia system (WMS). Therefore, many researches related to the malware propagation and suppression have been proposed to protect the topology and system security of wireless multimedia network. In these studies, the epidemic model is of great significance to the analysis of malware propagation. Considering the cloud and state transition of sensor nodes, a cloud-assisted model for malware detection and the dynamic differential game against malware propagation are proposed in this paper. Firstly, a SVM based malware detection model is constructed with the data sharing at the security platform in the cloud. Then the number of malware-infected nodes with physical infectivity to susceptible nodes is calculated precisely based on the attributes of WMS transmission. Then the state transition among WMS devices is defined by the modified epidemic model. Furthermore, a dynamic differential game and target cost function are successively derived for the Nash equilibrium between malware and WMS system. On this basis, a saddle-point malware detection and suppression algorithm is presented depending on the modified epidemic model and the computation of optimal strategies. Numerical results and comparisons show that the proposed algorithm can increase the utility of WMS efficiently and effectively.


title: Learning to Represent Programs with Graphs

abstract: Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered. We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures. ::: In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs. We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location. Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases. Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.


title: Analysis of European mitochondrial haplogroups with Alzheimer disease risk

abstract: We examined the association of mtDNA variation with Alzheimer disease (AD) risk in Caucasians (989 cases and 328 controls) testing the effect of individual haplogroups and single nucleotide polymorphisms (SNPs). Logistic regression analyses were used to assess risk of haplogroups and SNPs with AD in both main effects and interaction models. Males classified as haplogroup U showed an increase in risk (OR = 2.30; 95% CI, 1.03–5.11; P = 0.04) of AD relative to the most common haplogroup H, while females demonstrated a significant decrease in risk with haplogroup U (OR = 0.44; 95% CI, 0.24–0.80; P = 0.007). Our results were independent of APOE genotype, demonstrating that the effect of mt variation is not confounded by APOE4 carrier status. We suggest that variations within haplogroup U may be involved in AD expression in combination with environmental exposures or nuclear proteins other than APOE. © 2004 Published by Elsevier Ireland Ltd.


title: DNA-Targeted Precision Medicine; Have we Been Caught Sleeping?

abstract: In the current drive to incorporate molecular markers into treatment-selection for precision medicine, there has been a significant and we believe ill-advised omission of the large and routinely used group of drugs whose mechanism of action is DNA damage.


title: The Educational Potential of Augmented Reality Technology: Experiences of Instructional Designers and Practitioners

abstract: In this study, the educational potential of the augmented reality (AR) technology was investigated in the context of the instructional designers and practitioners' experiences. Within this scope, intrinsic case study design was used in this study. 42 instructional designers and 10 practitioners participated in the research. The data were collected using open-ended questionnaire from instructional designers. Interviews were held with the practitioners through the semi-structured interview form created from the open-ended questionnaire. The qualitative data were analyzed with the content analysis method, using Nvivo 8.0 software. The participants suggested that AR would be more influential in the educational fields of science education, social studies, and health education. Among the most important instructional materials to be developed with AR technology are 3D materials, videos, and animations. The participants suggested that AR could be useful in the development of special applications for other fields of education. For example, AR could be used to create magic books, 3D demonstrations related to different subjects, and experimental simulations in the field of science education. Moreover, the participants suggested that AR could provide system design enhancements to better attract users’ attention, to improve retention, to increase user motivation, and to “concretize” virtually presented information by creating a fuller sense of reality in terms of educational. Lastly, in order to develop an AR application, several points must be considered in the stages of analysis, design, development, and implementation. We present these points for consideration in detail. The results obtained in this study can be used to guide future research studies on AR technology.


title: Open-label observational study of the homeopathic medicine Passiflora Compose for anxiety and sleep disorders

abstract: Background Anxiety and sleep disorders (SDS) are frequently treated with psychotropic drugs. Health authorities in France have been advised to improve access to alternative treatments such as homeopathic medicines. Our aim was to describe the socio-demographic characteristics and clinical progression of patients prescribed homeopathic medicine Passiflora Compose (PC) for anxiety and/or SDS. Material and methods This was an open-label, observational study. Randomly selected general practitioners (GPs) known to prescribe homeopathic medicines recruited consecutive patients (≥18-years) prescribed PC. The following data were recorded at inclusion by the GP: socio-demographic data and anxiety severity (Hamilton anxiety rating scale or HAM-A); and by the patients: level of anxiety (STAI Spielberger self-assessment questionnaire) and SDS (Jenkins sleep scale or JSS). Anxiety and SDS were reassessed after 4 weeks of treatment using the same scales. Results A total of 639 patients (mean age: 46.3 ± 17.5 years; 78.6% female) were recruited by 98 GPs. Anxiety was present in 85.4% (HAM-A) and 93.3% (Spielberger State) at inclusion (mean scores: 17.8 ± 8.91 and 54.59 ± 11.69, respectively) and SDS was present in 74.0% (mean score: 15.24 ± 5.28). A total of 401 (62.7%) patients received PC alone and 167 (26.1%) PC + psychotropics. After 4 weeks, mean anxiety scores decreased by more than 7, 12 and 6 points (HAM-A, Spielberger State and Trait respectively), and SDS score by more than 4 points (JSS). Conclusion Anxiety and/or SDS improved significantly in patients included on this study. PC could be an alternative to the use of psychotropic drugs for first intention treatment of anxiety and SDS. Further studies are needed to confirm those results.


title: “Fitspiration” on Social Media: A Content Analysis of Gendered Images

abstract: Background: “Fitspiration” (also known as “fitspo”) aims to inspire individuals to exercise and be healthy, but emerging research indicates exposure can negatively impact female body image. Fitspiration is frequently accessed on social media; however, it is currently unclear the degree to which messages about body image and exercise differ by gender of the subject. Objective: The aim of our study was to conduct a content analysis to identify the characteristics of fitspiration content posted across social media and whether this differs according to subject gender. Methods: Content tagged with #fitspo across Instagram, Facebook, Twitter, and Tumblr was extracted over a composite 30-minute period. All posts were analyzed by 2 independent coders according to a codebook. Results: Of the 415/476 (87.2%) relevant posts extracted, most posts were on Instagram (360/415, 86.8%). Most posts (308/415, 74.2%) related thematically to exercise, and 81/415 (19.6%) related thematically to food. In total, 151 (36.4%) posts depicted only female subjects and 114/415 (27.5%) depicted only male subjects. Female subjects were typically thin but toned; male subjects were often muscular or hypermuscular. Within the images, female subjects were significantly more likely to be aged under 25 years (P<.001) than the male subjects, to have their full body visible (P=.001), and to have their buttocks emphasized (P<.001). Male subjects were more likely to have their face visible in the post (P=.005) than the female subjects. Female subjects were more likely to be sexualized than the male subjects (P=.002). Conclusions: Female #fitspo subjects typically adhered to the thin or athletic ideal, and male subjects typically adhered to the muscular ideal. Future research and interventional efforts should consider the potential objectifying messages in fitspiration, as it relates to both female and male body image. [J Med Internet Res 2017;19(3):e95]


title: Security attack analysis using attack patterns

abstract: Discovering potential attacks on a system is an essential step in engineering secure systems, as the identified attacks will determine essential security requirements. The prevalence of Socio-Technical Systems (STSs) makes attack analysis particularly challenging. These systems are composed of people and organizations, their software systems, as well as physical infrastructures. As such, a thorough attack analysis needs to consider strategic (social and organizational) aspects of the involved people and organizations, as well as technical aspects affecting software systems and the physical infrastructure, requiring a large amount of security knowledge which is difficult to acquire. In this paper, we propose a systematic approach to efficiently leverage a comprehensive attack knowledge repository (CAPEC) in order to identify realistic and detailed attack behaviors, avoiding severe repercussions of security breaches. In particular, we propose a systematic method to model CAPEC attack patterns, which has been applied to 102 patterns, in order to semi-automatically select and apply such patterns. Using the CAPEC patterns as part of a systematic and tool-supported process, we can efficiently operationalize attack strategies and identify realistic alternative attacks on an STS. We validate our proposal by performing a case study on a smart grid scenario.


title: Feature enhancement by bidirectional LSTM networks for conversational speech recognition in highly non-stationary noise

abstract: The recognition of spontaneous speech in highly variable noise is known to be a challenge, especially at low signal-to-noise ratios (SNR). In this paper, we investigate the effect of applying bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks for speech feature enhancement in noisy conditions. BLSTM networks tend to prevail over conventional neural network architectures, whenever the recognition or regression task relies on an intelligent exploitation of temporal context information. We show that BLSTM networks are well-suited for mapping from noisy to clean speech features and that the obtained recognition performance gain is partly complementary to improvements via additional techniques such as speech enhancement by non-negative matrix factorization and probabilistic feature generation by Bottleneck-BLSTM networks. Compared to simple multi-condition training or feature enhancement via standard recurrent neural networks, our BLSTM-based feature enhancement approach leads to remarkable gains in word accuracy in a highly challenging task of recognizing spontaneous speech at SNR levels between -6 and 9 dB.


title: Artificial intelligence in research

abstract: We asked young scientists to describe an example of artificial intelligence or machine learning in research, its broader implications in the field, and the challenges scientists face when using such technologies. Our survey's responses reflected a variety of countries and fields, but only 6% came


title: Hierarchical optimization on manifolds for online 2D and 3D mapping

abstract: In this paper, we present a new hierarchical optimization solution to the graph-based simultaneous localization and mapping (SLAM) problem. During online mapping, the approach corrects only the coarse structure of the scene and not the overall map. In this way, only updates for the parts of the map that need to be considered for making data associations are carried out. The hierarchical approach provides accurate non-linear map estimates while being highly efficient. Our error minimization approach exploits the manifold structure of the underlying space. In this way, it avoids singularities in the state space parameterization. The overall approach is accurate, efficient, designed for online operation, overcomes singularities, provides a hierarchical representation, and outperforms a series of state-of-the-art methods.


title: Energy-Efficient Transmission for Beamforming in Wireless Sensor Networks

abstract: Energy conservation is essential in wireless sensor networks (WSNs) because of limited energy in batteries. Collaborative beamforming uses multiple transmitters to form antenna arrays; the electromagnetic waves from these antenna arrays can create constructive interferences at the receiver and increase the transmission distance. Each transmitter can use lower power and save energy, since the energy consumption is spread over multiple transmitters. However, if the same nodes are always used for collaborative beamforming, these nodes would deplete their energy much sooner and this sensing area will no longer be monitored. To avoid this situation, energy consumption for collaborative beamforming needs to be balanced over the whole network by assigning the transmitters in turns. The transmitters in each round are selected by a scheduler and the energy carried in each node is balanced to increase the number of transmissions. We define the lifetime of a network as the number of transmissions until a certain percentage of the nodes depletes their energy. This paper proposes an algorithm to calculate energy-efficient schedules based on the remaining energy and the phase differences of their signals arriving at the receiver. Compared with an existing algorithm, our algorithm can extend the network lifetime by more than 60%.


title: Variational Auto-encoded Deep Gaussian Processes

abstract: We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.


title: Pilot: Passive Device-Free Indoor Localization Using Channel State Information

abstract: Many emerging applications such as intruder detection and border protection drive the fast increasing development of device-free passive (DfP) localization techniques. In this paper, we present Pilot, a Channel State Information (CSI)-based DfP indoor localization system in WLAN. Pilot design is motivated by the observations that PHY layer CSI is capable of capturing the environment variance due to frequency diversity of wideband channel, such that the position where the entity located can be uniquely identified by monitoring the CSI feature pattern shift. Therefore, a ``passive'' radio map is constructed as prerequisite which include fingerprints for entity located in some crucial reference positions, as well as clear environment. Unlike device-based approaches that directly percepts the current state of entities, the first challenge for DfP localization is to detect their appearance in the area of interest. To this end, we design an essential anomaly detection block as the localization trigger relying on the CSI feature shift when entity emerges. Afterwards, a probabilistic algorithm is proposed to match the abnormal CSI to the fingerprint database to estimate the positions of potential existing entities. Finally, a data fusion block is developed to address the multiple entities localization challenge. We have implemented Pilot system with commercial IEEE 802.11n NICs and evaluated the performance in two typical indoor scenarios. It is shown that our Pilot system can greatly outperform the corresponding best RSS-based scheme in terms of anomaly detection and localization accuracy.


title: The effectiveness of manual stretching in the treatment of plantar heel pain: a systematic review

abstract: BackgroundPlantar heel pain is a commonly occurring foot complaint. Stretching is frequently utilised as a treatment, yet a systematic review focusing only on its effectiveness has not been published. This review aimed to assess the effectiveness of stretching on pain and function in people with plantar heel pain.MethodsMedline, EMBASE, CINAHL, AMED, and The Cochrane Library were searched from inception to July 2010. Studies fulfilling the inclusion criteria were independently assessed, and their quality evaluated using the modified PEDro scale.ResultsSix studies including 365 symptomatic participants were included. Two compared stretching with a control, one study compared stretching to an alternative intervention, one study compared stretching to both alternative and control interventions, and two compared different stretching techniques and durations. Quality rating on the modified Pedro scale varied from two to eight out of a maximum of ten points. The methodologies and interventions varied significantly between studies, making meta-analysis inappropriate. Most participants improved over the course of the studies, but when stretching was compared to alternative or control interventions, the changes only reached statistical significance in one study that used a combination of calf muscle stretches and plantar fascia stretches in their stretching programme. Another study comparing different stretching techniques, showed a statistically significant reduction in some aspects of pain in favour of plantar fascia stretching over calf stretches in the short term.ConclusionsThere were too few studies to assess whether stretching is effective compared to control or other interventions, for either pain or function. However, there is some evidence that plantar fascia stretching may be more effective than Achilles tendon stretching alone in the short-term. Appropriately powered randomised controlled trials, utilizing validated outcome measures, blinded assessors and long-term follow up are needed to assess the efficacy of stretching.


title: Compiler research: the next 50 years

abstract: Research and education in compiler technology is more important than ever.


title: The influence of neuroscience on US Supreme Court decisions about adolescents' criminal culpability

abstract: In the past 8 years, the US Supreme Court has issued landmark opinions in three cases that involved the criminal culpability of juveniles. In the most recent case, in 2012, a ruling prohibited states from mandating life without parole for crimes committed by minors. In these cases, the Court drew on scientific studies of the adolescent brain in concluding that adolescents, by virtue of their inherent psychological and neurobiological immaturity, are not as responsible for their behaviour as adults. This article discusses the Court's rationale in these cases and the role of scientific evidence about adolescent brain development in its decisions. I conclude that the neuroscientific evidence was probably persuasive to the Court not because it revealed something new about the nature of adolescence but precisely because it aligned with common sense and behavioural science.


title: Stacked Generative Adversarial Networks

abstract: In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.


title: Attachment and growth of fibroblast cells on silk fibroin.

abstract: The attachment and growth of fibroblast cells (L-929) on matrices of silk fibroin from Bombyx mori domestic silkworm (DSF) and Antheraea pernyi wild silkworm (WSF) were studied by a cell culture method. The performance of the two kinds of silk fibroin was compared to that of collagen. DSF exhibited as high a cell attachment and growth as collagen did. The cells attached to DSF were extensively spread out and their filopodia were visible in the SEM pictures. WSF, which contains the tripeptide sequence Arg-Gly-Asp (believed to be a specific interaction site for cell-attachment), displayed much higher cell attachment and growth compared to DSF. The cells attached on WSF became virtually flat and their filopodia could be seen, indicating that the cells were very strongly held on the surface.


title: A SURVEY OF CONVOLUTIVE BLIND SOURCE SEPARATION METHODS

abstract: In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks.


title: Retina Vessels Detection Algorithm for Biomedical Symptoms Diagnosis

abstract: paper presents a method for blood vessel detection in digital retinal images. The method uses fuzzy logic approach with block wise gridding. It uses an adaptive approach for vessel detection. The segmentation is produced by classifying each pixel of the image as vessel or nonvessel. The performance of the proposed methodology is evaluated on the publicly available DRIVE database. It also contains manually labeled images by experts. Performance of this method on set of test images shows significant improvement than other solutions present in the literature. The method proves especially accurate results for vessel detection in DRIVE images. The method is simple and has fast implementation. It shows effectiveness and robustness with different image conditions. The vessel detection performance has a sensitivity of 0.8653 with specificity 0.9833. The accuracy of the method is 0.9728 for Drive database. This blood vessel detection and segmentation technique can play a useful clinical role in an automated retinopathy analysis system. Keywordsretinopathy, block wise gridding, retinal image, vessels segmentation.