title: An adaptive coding model of neural function in prefrontal cortex.

abstract: The prefrontal cortex has a vital role in effective, organized behaviour. Both functional neuroimaging in humans and electrophysiology in awake monkeys indicate that a fundamental principle of prefrontal function might be adaptive neural coding — in large regions of the prefrontal cortex, neurons adapt their properties to carry specifically information that is relevant to current concerns, producing a dense, distributed representation of related inputs, actions, rewards and other information. A model based on such adaptive coding integrates the role of the prefrontal cortex in working memory, attention and control. Adaptive coding points to new perspectives on several basic questions, including mapping of cognitive to neurophysiological functions, the influences of task content and difficulty, and the nature of frontal lobe specializations.


title: Deep Multimodal Learning for Affective Analysis and Retrieval

abstract: Social media has been a convenient platform for voicing opinions through posting messages, ranging from tweeting a short text to uploading a media file, or any combination of messages. Understanding the perceived emotions inherently underlying these user-generated contents (UGC) could bring light to emerging applications such as advertising and media analytics. Existing research efforts on affective computation are mostly dedicated to single media, either text captions or visual content. Few attempts for combined analysis of multiple media are made, despite that emotion can be viewed as an expression of multimodal experience. In this paper, we explore the learning of highly non-linear relationships that exist among low-level features across different modalities for emotion prediction. Using the deep Bolzmann machine (DBM), a joint density model over the space of multimodal inputs, including visual, auditory, and textual modalities, is developed. The model is trained directly using UGC data without any labeling efforts. While the model learns a joint representation over multimodal inputs, training samples in absence of certain modalities can also be leveraged. More importantly, the joint representation enables emotion-oriented cross-modal retrieval, for example, retrieval of videos using the text query “crazy cat”. The model does not restrict the types of input and output, and hence, in principle, emotion prediction and retrieval on any combinations of media are feasible. Extensive experiments on web videos and images show that the learnt joint representation could be very compact and be complementary to hand-crafted features, leading to performance improvement in both emotion classification and cross-modal retrieval.


title: 3D reconstruction using an n-layer heightmap

abstract: We present a novel method for 3D reconstruction of urban scenes extending a recently introduced heightmap model. Our model has several advantages for 3D modeling of urban scenes: it naturally enforces vertical surfaces, has no holes, leads to an efficient algorithm, and is compact in size. We remove the major limitation of the heightmap by enabling modeling of overhanging structures. Our method is based on an an n-layer heightmap with each layer representing a surface between full and empty space. The configuration of layers can be computed optimally using a dynamic programming method. Our cost function is derived from probabilistic occupancy, and incorporates the Bayesian Information Criterion (BIC) for selecting the number of layers to use at each pixel. 3D surface models are extracted from the heightmap. We show results from a variety of datasets including Internet photo collections. Our method runs on the GPU and the complete system processes video at 13 Hz.


title: iDASH: improved dynamic adaptive streaming over HTTP using scalable video coding

abstract: HTTP-based delivery for Video on Demand (VoD) has been gaining popularity within recent years. Progressive Download over HTTP, typically used in VoD, takes advantage of the widely deployed network caches to relieve video servers from sending the same content to a high number of users in the same access network. However, due to a sharp increase in the requests at peak hours or due to cross-traffic within the network, congestion may arise in the cache feeder link or access link respectively. Since the connection characteristics may vary over the time, with Dynamic Adaptive Streaming over HTTP (DASH), a technique that has been recently proposed, video clients may dynamically adapt the requested video quality for ongoing video flows, to match their current download rate as good as possible. In this work we show the benefits of using the Scalable Video Coding (SVC) for such a DASH environment.


title: Carbon Nanotube Electron Emitter for X-ray Imaging

abstract: The carbon nanotube field emitter array was grown on silicon substrate through a resist-assisted patterning (RAP) process. The shape of the carbon nanotube array is elliptical with 2.0 × 0.5 mm2 for an isotropic focal spot size at anode target. The field emission properties with triode electrodes show a gate turn-on field of 3 V/µm at an anode emission current of 0.1 mA. The author demonstrated the X-ray source with triode electrode structure utilizing the carbon nanotube emitter, and the transmitted X-ray image was of high resolution.


title: Encoding Cultural Heritage Information for the Semantic Web . Procedures for Data Integration through CIDOC-CRM Mapping

abstract: This paper describes the background and methods for the production of CIDOC-CRM compliant data sets from diverse collections of source data. The construction of such data sets is based on data in column format, typically exported for databases, as well as free text, typically created through scanning and OCR processing or transcription.


title: Entity query feature expansion using knowledge base links

abstract: Recent advances in automatic entity linking and knowledge base construction have resulted in entity annotations for document and query collections. For example, annotations of entities from large general purpose knowledge bases, such as Freebase and the Google Knowledge Graph. Understanding how to leverage these entity annotations of text to improve ad hoc document retrieval is an open research area. Query expansion is a commonly used technique to improve retrieval effectiveness. Most previous query expansion approaches focus on text, mainly using unigram concepts. In this paper, we propose a new technique, called entity query feature expansion (EQFE) which enriches the query with features from entities and their links to knowledge bases, including structured attributes and text. We experiment using both explicit query entity annotations and latent entities. We evaluate our technique on TREC text collections automatically annotated with knowledge base entity links, including the Google Freebase Annotations (FACC1) data. We find that entity-based feature expansion results in significant improvements in retrieval effectiveness over state-of-the-art text expansion approaches.


title: Webcam-based Visual Gaze Estimation

abstract: In this paper we combine a state of the art eye center locator and a new eye corner locator into a system which estimates the visual gaze of a user in a controlled environment (e.g. sitting in front of a screen). In order to reduce to a minimum the computational costs, the eye corner locator is built upon the same technology of the eye center locator, tweaked for the specific task. If high mapping precision is not a priority of the application, we claim that the system can achieve acceptable accuracy without the requirements of additional dedicated hardware. We believe that this could bring new gaze based methodologies for human-computer interactions into the mainstream.


title: A Research Roadmap of Cognitive Vision

abstract: Peter Auer Aude Billard Horst Bischof Isabelle Bloch Pia Boettcher Heinrich Bulthoff Hilary Buxton Henrik Christensen Tony Cohn Patrick Courtney Andrew Crookell James Crowley Sven Dickinson Christof Eberst Jan-Olof Eklundh Bob Fisher Wolfgang Forstner John Gilby Goesta Granlund Vaclav Hlavac Josef Kittler Walter Kropatsch Ales Leonardis Jim Little Giorgio Metta Hans-Hellmut Nagel Bernhard Nebel Bernd Neumann Heinrich Niemann Lucas Paletta Axel Pinz Fiora Pirri Gerhard Sagerer Giulio Sandini Bernt Schiele Rebecca Simpson Gerald Sommer John Tsotsos Monique Thonnat David Vernon Markus Vincze


title: Attention Incorporate Network: A network can adapt various data size

abstract: In traditional neural networks for image processing, the inputs of the neural networks should be the same size such as 224*224*3. But how can we train the neural net model with different input size? A common way to do is image deformation which accompany a problem of information loss (e.g. image crop or wrap). Sequence model(RNN, LSTM, etc.) can accept different size of input like text and audio. But one disadvantage for sequence model is that the previous information will become more fragmentary during the transfer in time step, it will make the network hard to train especially for long sequential data. In this paper we propose a new network structure called Attention Incorporate Network(AIN). It solve the problem of different size of inputs including: images, text, audio, and extract the key features of the inputs by attention mechanism, pay different attention depends on the importance of the features not rely on the data size. Experimentally, AIN achieve a higher accuracy, better convergence comparing to the same size of other network structure


title: Progress and Prospects of Long Noncoding RNAs (lncRNAs) in Hepatocellular Carcinoma

abstract: Hepatocellular carcinoma (HCC) is one of the most frequently occurring cancers with poor prognosis, and novel diagnostic or prognostic biomarkers and therapeutic targets for HCC are urgently required. With the advance of high-resolution microarrays and massively parallel sequencing technology, lncRNAs are suggested to play critical roles in the tumorigenesis and development of human HCC. To date, dysregulation of many HCC-related lncRNAs such as HULC, HOTAIR, MALAT1, and H19 have been identified. From transcriptional "noise" to indispensable elements, lncRNAs may re-write the central dogma. Also, lncRNAs found in body fluids have demonstrated their utility as fluid-based noninvasive markers for clinical use and as therapeutic targets for HCC. Even though several lncRNAs have been characterized, the underlying mechanisms of their contribution to HCC remain unknown, and many important questions about lncRNAs need resolving. A better understanding of the molecular mechanism in HCC-related lncRNAs will provide a rationale for novel effective lncRNA-based targeted therapies. In this review, we highlight the emerging roles of lncRNAs in HCC, and discuss their potential clinical applications as biomarkers for the diagnosis, prognosis, monitoring and treatment of HCC.


Multidimensional Bin Packing and Other Related Problems : A Survey ∗


title: Assessing the impact of life changes: Development of the Life Experiences Survey.

abstract: This article describes the development of a new instrument, the Life Experiences Survey, for the measurement of life changes. It was designed to eliminate certain shortcomings of previous life stress measures and allows for separate assessment of positive and negative life experiences as well as individualize d ratings of the impact of events. Several studies bearing on the usefulness of the Life Experiences Survey are presented, and the implications of the findings are discussed. During recent years, numerous studies have investigated the relationship between life stress and susceptibility to physical and psychological problems. Most of these studies have been based on the assumptions that (a) life changes require adaptation on the part of the individual and are stressful, and (b) persons experiencing marked degrees of life change during the recent past are susceptible to physical and psychiatric problems. There is considerable evidence that a relationship exists between life stress, operationally defined in terms of self-reported life changes, and physical illness (Dohrenwcnd & Dohrenwend, 1974b). Rahe and Lind (1971) have reported a relationship between life stress and sudden cardiac death. Theorcll and Rahe (1971) and Edwards (1971) have provided data suggestive of a link between life stress and myocardial infarction. Holmes (1970) and Rahe (1968) both found a relationship between life stress and major and minor health


title: Effects of hypoxia on cultured chemoreceptors of the rat carotid body: DNA synthesis and mitotic activity in glomus cells.

abstract: Enlargement of the carotid body, an arterial chemosensory organ, is known to occur under natural conditions of chronic hypoxia or during hypoxic lung disease (Edwards et al 1971a,b; Dhillon et al 1984). For example, natives or animals living at high altitude (e.g. the Peruvian Andes) have enlarged carotid bodies and a high incidence of carotid body tumors (Edwards et al 1971b). This enlargement is due to both hyperplasia and hypertrophy of various cell types, including endothelial cells of the vasculature and the arterial chemoreceptors (Dhillon et al 1984; Bee et al 1986), which recent evidence indicates are the glomus or type 1 cells (Lopez-Barneo et al 1988; Biscoe and Duchen 1990; Stea and Nurse 1991 a,b). These cells are considered members of the sympathoadrenal sublineage of neural crest derivatives, and show several ultrastructural and biochemical similarities to other neuroendocrine cells of this lineage, i.e. adrenalmedullary chromaffin cells and the small intensely fluorescent (S1F) cells in sympathetic ganglia (Kobayashi 1971; McDonald and Blewett 1981; Doupe et al 1985). The mechanisms by which a simple environmental stimulus, i.e. hypoxia, triggers hyperplasia or cell division in the neuronal-like glomus cells, especially in the adult carotid body, are of general neurobiological interest since the inability of mature neurons to divide severely limits the repair capacity of the nervous system.


title: Exploring Learner Perception and Use of Task-Based Interactional Feedback in FTF and CMC Modes

abstract: Theoretical claims about the benefits of corrective feedback have been largely premised on learners’ noticing of feedback (e.g., Gass & Mackey, 2006 ; Long, 1996 ; Schmidt, 1990 , 1995 ; Swain, 1995 ), and findings have demonstrated that both the feedback target (Mackey, Gass, & McDonough, 2000 ) and the mode of provision (Lai & Zhao, 2006 ) can affect learners’ accurate perception of feedback. The current study extended this research by investigating learners’ perception and use of feedback provided in task-based interaction in both computer-mediated (CMC) and face-to-face (FTF) modes. Utilizing stimulated recall, the study examined if 24 intermediate-level learners of Spanish as a foreign language accurately noticed feedback as feedback, if they noticed the feedback target, and if the environment in which they interacted (CMC vs. FTF) made a difference in their accuracy. The study also investigated if modality affected opportunities for modified output immediately following feedback and if learners used those opportunities differently according to mode. Results demonstrated that, overall, learners did notice feedback as feedback in both modes. Contrary to expectations, there were no statistical differences between modes in feedback perception accuracy. Significant differences were found, however, in learners’ opportunities for and use of feedback depending on the interaction environment and the type of error being addressed.


title: Finding Structure in Time

abstract: Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves; the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands; indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context-dependent while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction.


title: Deep Sentiment Features of Context and Faces for Affective Video Analysis

abstract: Given the huge quantity of hours of video available on video sharing platforms such as YouTube, Vimeo, etc. development of automatic tools that help users find videos that fit their interests has attracted the attention of both scientific and industrial communities. So far the majority of the works have addressed semantic analysis, to identify objects, scenes and events depicted in videos, but more recently affective analysis of videos has started to gain more attention. In this work we investigate the use of sentiment driven features to classify the induced sentiment of a video, i.e. the sentiment reaction of the user. Instead of using standard computer vision features such as CNN features or SIFT features trained to recognize objects and scenes, we exploit sentiment related features such as the ones provided by Deep-SentiBank, and features extracted from models that exploit deep networks trained on face expressions. We experiment on two recently introduced datasets: LIRIS-ACCEDE and MEDIAEVAL-2015, that provide sentiment annotations of a large set of short videos. We show that our approach not only outperforms the current state-of-the-art in terms of valence and arousal classification accuracy, but it also uses a smaller number of features, requiring thus less video processing.


title: Upper Body Pose Estimation with Temporal Sequential Forests

abstract: Our objective is to efficiently and accurately estimate human upper body pose in gesture videos. To this end, we build on the recent successful applications of random forests (RF) classifiers and regressors, and develop a pose estimation model with the following novelties: (i) the joints are estimated sequentially, taking account of the human kinematic chain. This means that we don't have to make the simplifying assumption of most previous RF methods - that the joints are estimated independently; (ii) by combining both classifiers (as a mixture of experts) and regressors, we show that the learning problem is tractable and that more context can be taken into account; and (iii) dense optical flow is used to align multiple expert joint position proposals from nearby frames, and thereby improve the robustness of the estimates. The resulting method is computationally efficient and can overcome a number of the errors (e.g. confusing left/right hands) made by RF pose estimators that infer their locations independently. We show that we improve over the state of the art on upper body pose estimation for two public datasets: the BBC TV Signing dataset and the ChaLearn Gesture Recognition dataset.


title: ECPP: Efficient Conditional Privacy Preservation Protocol for Secure Vehicular Communications

abstract: In this paper, we introduce an efficient conditional privacy preservation (ECPP) protocol in vehicular ad hoc networks (VANETs) to address the issue on anonymous authentication for safety messages with authority traceability. The proposed protocol is characterized by the generation of on-the-fly short-time anonymous keys between on-board units (OBUs) and roadside units (RSUs), which can provide fast anonymous authentication and privacy tracking while minimizing the required storage for short-time anonymous keys. We demonstrate the merits gained by the proposed protocol through extensive analysis.


title: A Design Technique for Faster Dadda Multiplier

abstract: In this work faster Baugh-Wooley multiplication has been achieved by using a combination of two design techniques: partition of the partial products into two parts for independent parallel column compression and acceleration of the final addition using a hybrid adder proposed in this work. Based on the proposed techniques 8, 16, 32 and 64-bit Dadda based Baugh-Wooley multipliers has been developed and compared with the regular Baugh-Wooley multiplier. The performance of the proposed multiplier is analyzed by evaluating the delay, area and power, with 180 nm process technologies on interconnect and layout using industry standard design and layout tools. The result analysis shows that the 64-bit proposed multiplier is as much as 26.9% faster than the regular Baugh-Wooley multiplier and requires only 2.21% more power. Also the power-delay product of the proposed design is significantly lower than that of the regular Baugh-Wooley multiplier.


title: A 5 GHz Differential Colpitts CMOS VCO Using the Bottom PMOS Cross-Coupled Current Source

abstract: A low phase noise differential Colpitts voltage-controlled oscillator (VCO) with the bottom series PMOS cross-coupled current source is presented. The core Colpitts VCO adopts a pair of PMOS as the biased current source at the bottom, instead of a conventional NMOS topology, to achieve a better phase noise performance as the PMOS has lower flicker and white noise than those of NMOS and the output power spectral density of PMOS operated in bottom-biased type is further less than that in top-biased one in the same power consumption. The fabricated VCO operates from 4.9 to 5.46 GHz with 10.6% tuning range when the power consumption is below 6.4 mW with a supply voltage of 1.8 V. The measured phase noise at 100 kHz offset is -100.3 dBc/Hz at 5.46 GHz and achieves a good FOM performance of -187 dBc/Hz.


title: Many Languages, One Parser

abstract: We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser's performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.


title: Churn prediction for high-value players in casual social games

abstract: Predicting when players will leave a game creates a unique opportunity to increase players' lifetime and revenue contribution. Players can be incentivized to stay, strategically cross-linked to other games in the company's portfolio or, as a last resort, be passed on to other companies through in-game advertisement. This paper focuses on predicting churn for highvalue players of casual social games and attempts to assess the business impact that can be derived from a predictive churn model. We compare the prediction performance of four common classification algorithms over two casual social games, each with millions of players. Furthermore, we implement a hidden Markov model to explicitly address temporal dynamics. We find that a neural network achieves the best prediction performance in terms of area under curve (AUC). In addition, to assess the business value of churn prediction, we design and implement an A/B test on one of the games, using free in-game currency as an incentive to retain players. Test results indicate that contacting players shortly before the predicted churn event substantially improves the effectiveness of communication with players. They further show that giving out free in-game currency does not significantly impact the churn rate or monetization of players. This suggests that players can only be retained by remarkably changing their gameplay experience ahead of the churn event and that cross-linking may be the more effective measure to deal with churning players.


title: Unpredictable Random Number Generator Based on the Performance Data Helper Interface

abstract: Performance evaluation and monitoring is essential for guiding performance improvement efforts and tuning software products -- such as operating systems or applications -- based on the analysis of hardware events and hardware performance counters (HPC). Nevertheless, hardware performance counters are noisy by their very nature. The causes of variations in the counter values are so complex that are nearly impossible to determine. Hence, while being a major issue in the process of accurately evaluating software products, the unpredictability exhibited by HPCs offers a high potential for random number generation. In the present paper we propose an unpredictable random number generator based on the variations in the HPC values provided by the Performance Data Helper (PDH) interface on Windows operating systems and analyze the randomness quality and throughput of the generator in order to determine its suitability for integration in cryptographic applications. Experimental results show a high statistical quality of the generated sequences for the best performing counters -- measured using the NIST statistical test suite, and a throughput level at approximately 10-80 kB/s.


title: Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

abstract: This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification -- EG2, CS-ID3, and IDX -- and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.


title: Charticulator: Interactive Construction of Bespoke Chart Layouts

abstract: We present Charticulator , an interactive authoring tool that enables the creation of bespoke and reusable chart layouts. Charticulator is our response to most existing chart construction interfaces that require authors to choose from predefined chart layouts, thereby precluding the construction of novel charts. In contrast, Charticulator transforms a chart specification into mathematical layout constraints and automatically computes a set of layout attributes using a constraint-solving algorithm to realize the chart. It allows for the articulation of compound marks or glyphs as well as links between these glyphs, all without requiring any coding or knowledge of constraint satisfaction. Furthermore, thanks to the constraint-based layout approach, Charticulator can export chart designs into reusable templates that can be imported into other visualization tools. In addition to describing Charticulator's conceptual framework and design, we present three forms of evaluation: a gallery to illustrate its expressiveness, a user study to verify its usability, and a click-count comparison between Charticulator and three existing tools. Finally, we discuss the limitations and potentials of Charticulator as well as directions for future research. Charticulator is available with its source code at https://charticulator.com .


title: Brain-Computer Interfacing: An Introduction

abstract: The idea of interfacing minds with machines has long captured the human imagination. Recent advances in neuroscience and engineering are making this a reality, opening the door to restoring and potentially augmenting human physical and mental capabilities. Medical applications such as cochlear implants for the deaf and deep brain stimulation for Parkinson's disease are becoming increasingly commonplace. Brain- computer interfaces (BCIs) (also known as brain- machine interfaces or BMIs) are now being explored in applications as diverse as security, lie detection, alertness monitoring, telepresence, gaming, education, art, and human augmentation. This introduction to the field is designed as a textbook for upper- level undergraduate and first year graduate courses in neural engineering or brain- computer interfacing for students from a wide range of disciplines. It can also be used for self- study and as a reference by neuroscientists, computer scientists, engineers, and medical practitioners. Key features include: Essential background in neuroscience, brain recording and stimulation technologies, signal processing, and machine learning Detailed description of the major types of BCIs in animals and humans, including invasive, semi-invasive, noninvasive, stimulating, and bidirectional BCIs In-depth discussion of BCI applications and BCI ethics Questions and exercises in each chapter Supporting Web site with annotated list of book- related links


title: Scheduling and replenishment plan for an integrated deteriorating inventory model with stock-dependent selling rate

abstract: An integrated approach is used to clarify the critical issues of scheduling and replenishment planning in an advanced inventory system under stock-dependent selling rate environment. This is because integration removes the barriers between enterprises and improves overall performance. A close collaboration such as just-in-time (JIT) operations also improves product quality of the supply chain. Different from the single-stage inventory model, this study develops an integrated two-stage production-inventory deteriorating model for the buyer and the supplier with stock-dependent selling rate, as well as considering imperfect items and JIT multiple deliveries. Our paper proposes a significant method using a time-weighted-inventory approach to analyze the supplier’s saw-tooth holding cost. We derive the optimal number of inspection, optimal deliveries and the optimal delivery-time interval. A numerical example is also presented to illustrate the theory. The results show that the fixed demand rate, the holding cost and the unit inspection cost are critical in the management of the deteriorating inventory model.


Connotation Frames of Power and Agency in Modern Films.


title: Gaussian particle filter for tracking hip angle in gait cycles

abstract: Hip angle has attracted increasing attention recently because of its wide spectrum of applications, including gait analysis, clinical performance analysis and animation. Accurate and robust estimation of hip angle in ambulatory environment remains a challenge because the non-linear nature of thigh movement has not been well studied yet. We propose to use Hybrid Dynamic Bayesian Network (HDBN) to model the nonlinear hip angle dynamics. Based on the model, Gaussian Particle Filter (GPF) is designed to estimate the hip angle during gait cycles from the measurements of the wearable accelerometers that are attached to the thighs. The experiments have been conducted with four subjects and the results have shown that the proposed methods can achieve robust hip angle tracking for different subjects with significant accuracy improvement over the previous work on the ambulatory gait analysis.


title: E-books or textbooks: Students prefer textbooks

abstract: Previous research has demonstrated that the experience of reading e-books is not equivalent to reading textbooks. This study examines factors influencing preference for e-books as well as reported use of e-book content. Although the present student cohort is the most technologically savvy to ever enter universities, students do not prefer e-books over textbooks regardless of their gender, computer use or comfort with computers. No significant correlations existed between the number of e-books previously used and overall preference of e-books: Participants who had previously used an e-book still preferred print texts for learning. Despite the ability to easily access supplemental content through e-books via hyperlinks and other features, students were more likely to use special features in print books than in e-books.


title: Spatial topic modeling in online social media for location recommendation

abstract: Mobile networks enable users to post on social media services (e.g., Twitter) from anywhere. The activities of mobile users involve three major entities: user, post, and location. The interaction of these entities is the key to answer questions such as who will post a message where and on what topic? In this paper, we address the problem of profiling mobile users by modeling their activities, i.e., we explore topic modeling considering the spatial and textual aspects of user posts, and predict future user locations. We propose the first ST (Spatial Topic) model to capture the correlation between users' movements and between user interests and the function of locations. We employ the sparse coding technique which greatly speeds up the learning process. We perform experiments on two real life data sets from Twitter and Yelp. Through comprehensive experiments, we demonstrate that our proposed model consistently improves the average precision@1,5,10,15,20 for location recommendation by at least 50% (Twitter) and 300% (Yelp) against existing state-of-the-art recommendation algorithms and geographical topic models.


title: The workload onparallel supercomputers: modeling the characteristics of rigid jobs

abstract: The analysis of workloads is important for understanding how systems are used. In addition, workload models are needed as input for the evaluation of new system designs, and for the comparison of system designs. This is especially important in costly large-scale parallel systems. Luckily, workload data are available in the form of accounting logs. Using such logs from three different sites, we analyze and model the job-level workloads with an emphasis on those aspects that are universal to all sites. As many distributions turn out to span a large range, we typically first apply a logarithmic transformation to the data, and then fit it to a novel hyper-Gamma distribution or one of its special cases. This is a generalization of distributions proposed previously, and leads to good goodness-of-fit scores. The parameters for the distribution are found using the iterative EM algorithm. The results of the analysis have been codified in a modeling program that creates a synthetic workload based on the results of the analysis.


title: The paratendineous tissues: an anatomical study of their role in the pathogenesis of tendinopathy

abstract: The aim of this paper was to examine the macroscopic and microscopic characteristics of the paratendineous tissues (paratenon, epitenon and endotenon) of the calcaneal tendon to better understand their role in the pathogenesis of "tendinopathy". Ten non-embalmed legs from cadavers were used. Histological and immunohistochemical studies were done at the middle third of the tendon. Magnetic resonance images of the hind foot were made in 60 living subjects to analyze the morphological alterations of tendon and paratenon. The paratenon is a thick fibrous layer with few elastic fibers, continuous with the crural fascia, well vascularized and innervated. It forms a sheath around the tendon similar to a synovial layer, but less organized. Indeed, it has no complete epithelium, but only some cells producing hyaluronan, called fasciacytes. Crural fascia and paratenon can be clearly observed by MRI, appearing as homogeneous, low signal intensity bands, sharply defined in the context of subcutaneous tissue in T1-weighted sequences. The mean thickness of the crural fascia was 1.11 mm in healthy subjects and 1.30 mm in patients (p < 0.005). The mean value of paratenon thickness in patients was 1.34 mm, 0.85 in healthy (p < 0.0001). The paratenon is more highly vascularized and innervated than the tendon, supporting the hypothesis that it is the origin of pain in tendinopathy. The imaging study suggests that, an increase in the thickness of the paratenon more than 1.35 mm is predictive of paratendinopathy, even before tendon damage.


title: Quantum-Inspired Immune Clonal Algorithm for Global Optimization

abstract: Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum NOT gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.


title: Multimodal affect recognition in learning environments

abstract: We propose a multi-sensor affect recognition system and evaluate it on the challenging task of classifying interest (or disinterest) in children trying to solve an educational puzzle on the computer. The multimodal sensory information from facial expressions and postural shifts of the learner is combined with information about the learner's activity on the computer. We propose a unified approach, based on a mixture of Gaussian Processes, for achieving sensor fusion under the problematic conditions of missing channels and noisy labels. This approach generates separate class labels corresponding to each individual modality. The final classification is based upon a hidden random variable, which probabilistically combines the sensors. The multimodal Gaussian Process approach achieves accuracy of over 86%, significantly outperforming classification using the individual modalities, and several other combination schemes.


title: The Many Faces of Formative Assessment.

abstract: In this research paper we consider formative assessment (FA) and discuss ways in which it has been implemented in four different university courses. We illustrate the different aspects of FA by deconstructing it and then demonstrating effectiveness in improving both teaching and student achievement. It appears that specifically “what is done” was less important since there were positive achievement gains in each study. While positive gains were realized with use of technology, gains were also realized with implementation of nontechnology dependent techniques. Further, gains were independent of class size or subject matter.


title: Computational Thinking and Expository Writing in the Middle School

abstract: To broaden participation in computing we need to look beyond traditional domains of inquiry and expertise. We present results from a demonstration project in which interactive journalism was used to infuse computational thinking into the standard curriculum and regular classroom experience at a middle school with a diverse population. Outcomes indicate that we were able to develop positive attitudes about computational thinking and programming among students and teachers who did not necessarily view themselves as “math types.” By partnering with language arts, technology and math teachers at Fisher Middle School, Ewing New Jersey, we introduced the isomorphism between the journalistic process and computational thinking to 7th and 8th graders. An intense summer institute, first with the teachers and then with students recruited from the school, immersed them in the “newsroom of the future” where they researched and wrote news stories, shot and edited video, and developed procedural animations in Scratch to support their storylines. An afterschool club sustained the experience. The teachers adapted interactive journalism and Scratch programming to enrich standard language arts curriculum and are infusing computational thinking in classroom experiences throughout the school.


title: Human Activity Recognition in Smart Environments

abstract: This paper presents a method for image based human activity recognition, in a smart environment. We use background subtraction and skeletisation as image processing techniques, combined with Artificial Neural Networks for human posture classification and Hidden Markov Models for activity interpretation. By this approach we successfully recognized basic human actions such as walking, rotating, sitting and bending up/down, lying and falling. The method can be applied in smart houses, for elderly people who live alone.


title: Evaluation of AHRS algorithms for inertial personal localization in industrial environments

abstract: This paper presents a comparison among several state-of-the-art Attitude and Heading Reference Systems (AHRS). These algorithms can be used for 3D orientation and position estimation of users or devices. The robust performance of these AHRS algorithms is of paramount importance, specially in environments with potential external perturbations, such as industrial environments. The comparison among AHRS algorithms presented in this paper also includes an algorithm recently proposed by the authors (DLR-AHRS). In this paper the performance of the different AHRS will be studied, including the effect of magnetic perturbations on the performance of orientation estimation, and the effect of using different patterns of motion when the sensor is carried by a user at different locations (pocket, foot/shoe, hand). These AHRS algorithms are also compared with the Kalman-based commercially available AHRS algorithm of Xsens. The performance of the AHRS algorithms depends strongly on the strategies used to reject perturbations (sudden accelerations or deformations of the Earth magnetic field) and the ability of the systems to estimate the biases of the gyroscopes.


title: A STUDY ON MILLIMETER-WAVE IMAGING OF CONCEALED OBJECTS: APPLICATION USING BACK-PROJECTION ALGORITHM

abstract: Millimeter-wave (MMW) imaging is a powerful tool for the detection of objects concealed under clothing. Several factors including difierent kinds of objects, variety of covering materials and their thickness, accurate imaging of near-fleld scattered data afiect the success of detection. To practice with such considerations, this paper presents the two-dimensional (2D) images of difierent targets hidden under various fabric sheets. The W-band inverse synthetic aperture radar (ISAR) data of various target-covering situations are acquired and imaged by applying both the focusing operator based inversion algorithm and the spherical back-projection algorithm. Results of these algorithms are demonstrated and compared to each other to assess the performance of the MMW imaging in detecting the concealed objects of both metallic and dielectric types.


title: A survey of Agent-Oriented Software Engineering

abstract: Agent-Oriented Software Engineering is the one of the most recent contributions to the field of Software Engineering. It has several benefits compared to existing development approaches, in particular the ability to let agents represent high-level abstractions of active entities in a software system. This paper gives an overview of recent research and industrial applications of both general high-level methodologies and on more specific design methodologies for industry-strength software engineering.


title: Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries

abstract: Objective In this paper, we focus on three aspects: (1) to annotate a set of standard corpus in Chinese discharge summaries; (2) to perform word segmentation and named entity recognition in the above corpus; (3) to build a joint model that performs word segmentation and named entity recognition. ::: ::: Design Two independent systems of word segmentation and named entity recognition were built based on conditional random field models. In the field of natural language processing, while most approaches use a single model to predict outputs, many works have proved that performance of many tasks can be improved by exploiting combined techniques. Therefore, in this paper, we proposed a joint model using dual decomposition to perform both the two tasks in order to exploit correlations between the two tasks. Three sets of features were designed to demonstrate the advantage of the joint model we proposed, compared with independent models, incremental models and a joint model trained on combined labels. ::: ::: Measurements Micro-averaged precision (P), recall (R), and F-measure (F) were used to evaluate results. ::: ::: Results The gold standard corpus is created using 336 Chinese discharge summaries of 71 355 words. The framework using dual decomposition achieved 0.2% improvement for segmentation and 1% improvement for recognition, compared with each of the two tasks alone. ::: ::: Conclusions The joint model is efficient and effective in both segmentation and recognition compared with the two individual tasks. The model achieved encouraging results, demonstrating the feasibility of the two tasks.


title: FFQ: A Fast Single-Producer/Multiple-Consumer Concurrent FIFO Queue

abstract: With the spreading of multi-core architectures, operating systems and applications are becoming increasingly more concurrent and their scalability is often limited by the primitives used to synchronize the different hardware threads. In this paper, we address the problem of how to optimize the throughput of a system with multiple producer and consumer threads. Such applications typically synchronize their threads via multi-producer/multi-consumer FIFO queues, but existing solutions have poor scalability, as we could observe when designing a secure application framework that requires high-throughput communication between many concurrent threads. In our target system, however, the items enqueued by different producers do not necessarily need to be FIFO ordered. Hence, we propose a fast FIFO queue, FFQ, that aims at maximizing throughput by specializing the algorithm for single-producer/multiple-consumer settings: each producer has its own queue from which multiple consumers can concurrently dequeue. Furthermore, while we provide a wait-free interface for producers, we limit ourselves to lock-free consumers to eliminate the need for helping. We also propose a multi-producer variant to show which synchronization operations we were able to remove by focusing on a single producer variant. Our evaluation analyses the performance using micro-benchmarks and compares our results with other state-of-the-art solutions: FFQ exhibits excellent performance and scalability.


title: Strategic firm commitments and rewards for customer relationship management in online retailing

abstract: Abstract Academic studies offer a generally positive portrait of the effect of customer relationship management (CRM) on firm performance, but practitioners question its value. The authors argue that a firm's strategic commitments may be an overlooked organizational factor that influences the rewards for a firm's investments in CRM. Using the context of online retailing, the authors consider the effects of two key strategic commitments of online retailers on the performance effect of CRM: their bricks-and-mortar experience and their online entry timing. They test the proposed model with a multimethod approach that uses manager ratings of firm CRM and strategic commitments and third-party customers' ratings of satisfaction from 106 online retailers. The findings indicate that firms with moderate bricks-and-mortar experience are better able to leverage CRM for superior customer satisfaction outcomes than firms with either low or high bricks-and-mortar experience. Likewise, firms with moderate online experie...


title: Analysis of Inductor Current Sharing in Nonisolated and Isolated Multiphase dc–dc Converters

abstract: Current sharing among inductors is an important issue in both isolated and nonisolated dc-dc converters. In this paper, average current sharing is in-depth modeled and analyzed for both interleaved multiphase buck converters and isolated current-doubler dc-dc converters. The features and comparison of various current-sharing techniques used in isolated and nonisolated dc-dc converters are presented, and the corresponding design guidelines are provided based on the theoretical analysis. The analysis reveals that the state-of-the-art current-sharing technique for multiphase buck converters cannot be directly applied to isolated current-doubler rectifier (CDR) dc-dc converters to achieve balanced inductor currents. Passive and active current-sharing methods are proposed for isolated current-doubler dc-dc converters to balance two inductor currents. Experimental results are presented to verify the modeling analysis and the proposed current-sharing techniques for CDRs in isolated dc-dc converters.


title: Auditory paradigm for a P300 BCI system using spatial hearing

abstract: The present paper proposes an auditory BCI paradigm for systems based on P300 signals which are generated by auditory stimuli characterized by different sound typologies and locations. A Head Related Transfer Function approach is adopted to virtualize auditory stimuli. When virtualized audio is used, the user has to focus the attention both on the type and location of the stimulus, thus generating P300 signals whose amplitude is higher than that generated without audio virtualization. Classification is performed by Support Vector Machines in which gaussian radial basis functions are used as kernel functions. The system has been validated with 14 users, who were asked to choose one among five common spoken words, previously virtualized and transmitted to stereophonic headphones. Classification results prove that the proposed auditory BCI system performed similarly to common visual BCI P300 systems, representing then an alternative to visual BCI for users with visual impairments.


title: Combined Automatic Lane-Keeping and Driver's Steering Through a 2-DOF Control Strategy

abstract: In this paper, we address the problem of combining automatic lane-keeping and driver's steering for either obstacle avoidance or lane-change maneuvers for passing purposes or any other desired maneuvers, through a closed-loop control strategy. The automatic lane-keeping control loop is never opened, and no on/off switching strategy is used. During the driver's maneuver, the vehicle lateral dynamics are controlled by the driver himself through the vehicle steering system. When there is no driver's steering action, the vehicle center of gravity tracks the center of the traveling lane thanks to the automatic lane-keeping system. At the beginning (end) of the maneuver, the lane-keeping task is released (resumed) safely and smoothly. The performance of the proposed closed-loop structure is shown both by means of simulations and through experimental results obtained along Italian highways.


title: Adopting Code Reviews for Agile Software Development

abstract: Code reviews have many benefits, most importantly to find bugs early in the development phase and to enforce coding standards. Still, it is widely accepted that formal code reviews are time-consuming and the practical applicability in agile development is controversial. This work presents a continuous differential-based method and tool for code reviews. By using a continuous approach to code reviews, the review overhead can be reduced and the effectiveness and applicability in agile environments shall be improved.


title: A search engine for 3D models

abstract: As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245p better performance than related shape-matching methods during precision--recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).


title: Ant colony optimization: Introduction and recent trends

abstract: Abstract Ant colony optimization is a technique for optimization that was introduced in the early 1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. First, we deal with the biological inspiration of ant colony optimization algorithms. We show how this biological inspiration can be transfered into an algorithm for discrete optimization. Then, we outline ant colony optimization in more general terms in the context of discrete optimization, and present some of the nowadays best-performing ant colony optimization variants. After summarizing some important theoretical results, we demonstrate how ant colony optimization can be applied to continuous optimization problems. Finally, we provide examples of an interesting recent research direction: The hybridization with more classical techniques from artificial intelligence and operations research.


title: Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

abstract: A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.


title: Genetic diagnosis by whole exome capture and massively parallel DNA sequencing

abstract: Protein coding genes constitute only approximately 1% of the human genome but harbor 85% of the mutations with large effects on disease-related traits. Therefore, efficient strategies for selectively sequencing complete coding regions (i.e., “whole exome”) have the potential to contribute to the understanding of rare and common human diseases. Here we report a method for whole-exome sequencing coupling Roche/NimbleGen whole exome arrays to the Illumina DNA sequencing platform. We demonstrate the ability to capture approximately 95% of the targeted coding sequences with high sensitivity and specificity for detection of homozygous and heterozygous variants. We illustrate the utility of this approach by making an unanticipated genetic diagnosis of congenital chloride diarrhea in a patient referred with a suspected diagnosis of Bartter syndrome, a renal salt-wasting disease. The molecular diagnosis was based on the finding of a homozygous missense D652N mutation at a position in SLC26A3 (the known congenital chloride diarrhea locus) that is virtually completely conserved in orthologues and paralogues from invertebrates to humans, and clinical follow-up confirmed the diagnosis. To our knowledge, whole-exome (or genome) sequencing has not previously been used to make a genetic diagnosis. Five additional patients suspected to have Bartter syndrome but who did not have mutations in known genes for this disease had homozygous deleterious mutations in SLC26A3. These results demonstrate the clinical utility of whole-exome sequencing and have implications for disease gene discovery and clinical diagnosis.


title: Piecewise Planar Modeling for Step Detection using Stereo Vision

abstract: A mobility aid for the visually impaired should be able to detect and warn about nearby obstacles. Reliable detection of curbs and steps is critical to meet this goal. This paper describes a stereo-vision based algorithm that estimates the underlying planar geometry of the 3D scene to generate hypotheses for the presence of steps. Tensor voting is used to calculate globally consistent normals at each data point and a clustering algorithm is described to generate a piecewise planar model of the scene. Results demonstrate the improvement in plane clustering using tensor voting and the ability of the algorithm to find sufficient evidence for the presence of curbs and steps.


title: LSB-steganography framework for stereoscopic images based on BJND

abstract: Nowadays, stereoscopic images are commonly used for 3D image generation, these are composed by a pair of images viewed independently by each eye, which creates a feeling of immersion and in depth perception. 3D vision technologies have multiple applications in many different areas such as medicine, entertainment, computer vision, etc.; being accessible to a wider group of people. However, there is so few researches that seek to hide information in this type of 3D content. This paper proposes a new method to hide information into stereoscopic images using a LSB-Steganography technique and a Binocular Just Noticeable Difference model (BJND) embedding the maximum payload capacity avoiding visual artifacts and inaccurate 3D generation. Results demonstrate that it is possible to embed up to 1.87 Mb into the stereoscopic images keeping high imperceptibility, obtaining average values for PSNR and SSIM between the original stereoscopic pair and the modified of 31.12dB and 0.9693, respectively. Quantity Bad Pixels (QBP, δ d = 0. 5) and SSIM are calculated for evaluating accurate 3D generation, obtaining average values of 41.26% and of 0.7721 between the ground-truth and the disparity map of the modified stereoscopic pair.


title: ONLI: An ontology-based system for querying DBpedia using natural language paradigm

abstract: Abstract The Semantic Web has emerged as an extension of the current Web, in which Web content has well-defined meaning through the addition of logic-based metadata. However, current mechanisms for information retrieval from semantic knowledge bases restrict their use to only experienced users. To address this gap, the natural language processing (NLP) is deemed to be very intuitive from a use point of view, due to it hides the formality of a knowledge base as well as the executable query language. This paper presents a novel ontology-based information retrieval system for DBpedia called ONLI (Ontology-based Natural Language Interface). ONLI proposes the use of an ontology model in order to represent both the syntactic question’s structure and the question’s context. This model allows inferring the answer type expected by the user through an established question’s classification. These features allow reducing the search space thus increasing the probability of providing the correct answer. From this perspective, ONLI was evaluated in terms of their ability to find the correct answer into DBpedia’s content, achieving promising results and proving to be very useful to non-experienced users.


title: High Dimensional Semiparametric Gaussian Copula Graphical Models

abstract: In this paper, we propose a semiparametric approach, named nonparanormal skeptic, for efficiently and robustly estimating high dimensional undirected graphical models. To achieve modeling flexibility, we consider Gaussian Copula graphical models (or the nonparanormal) as proposed by Liu et al. (2009). To achieve estimation robustness, we exploit nonparametric rank-based correlation coefficient estimators, including Spearman's rho and Kendall's tau. In high dimensional settings, we prove that the nonparanormal skeptic achieves the optimal parametric rate of convergence in both graph and parameter estimation. This celebrating result suggests that the Gaussian copula graphical models can be used as a safe replacement of the popular Gaussian graphical models, even when the data are truly Gaussian. Besides theoretical analysis, we also conduct thorough numerical simulations to compare different estimators for their graph recovery performance under both ideal and noisy settings. The proposed methods are then applied on a large-scale genomic dataset to illustrate their empirical usefulness. The R language software package huge implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran. r-project.org/.


title: A Survey on Big Data Management and Job Scheduling

abstract: Big data has gained its popularity in the recent years due to the fact that there is a need for sophisticated method to collect, process, analyze and visualize huge volumes of data generated by our digital and computing world. Several challenges in handling petabytes of information, commonly named as Big data needs to be addressed in more efficient way. Big data management (BDM) is the process of collecting, storing, analysing and visualization of large volumes of data, which can be in the form of structured, unstructured and semistructured formats. Problems such as data acquisition, data storage, data retrieval, data analysis, and data visualization can no longer be handled by traditional database systems. The primary purpose of this paper is to provide a comprehensive survey on Big data management and to provide an overview on various algorithms related to job scheduling in Hadoop and the latest advancements. These research directions can lead to exploration of Big data domain and result in development of optimal techniques and scheduling algorithms to address problems faced in Big data.


title: Pixel-level image fusion with simultaneous orthogonal matching pursuit

abstract: Pixel-level image fusion integrates the information from multiple images of one scene to get an informative image which is more suitable for human visual perception or further image-processing. Sparse representation is a new signal representation theory which explores the sparseness of natural signals. Comparing to the traditional multiscale transform coefficients, the sparse representation coefficients can more accurately represent the image information. Thus, this paper proposes a novel image fusion scheme using the signal sparse representation theory. Because image fusion depends on local information of source images, we conduct the sparse representation on overlapping patches instead of the whole image, where a small size of dictionary is needed. In addition, the simultaneous orthogonal matching pursuit technique is introduced to guarantee that different source images are sparsely decomposed into the same subset of dictionary bases, which is the key to image fusion. The proposed method is tested on several categories of images and compared with some popular image fusion methods. The experimental results show that the proposed method can provide superior fused image in terms of several quantitative fusion evaluation indexes.


Effective Representing of Information Network by Variational Autoencoder


title: Visuospatial selective attention in chickens

abstract: Voluntary control of attention promotes intelligent, adaptive behaviors by enabling the selective processing of information that is most relevant for making decisions. Despite extensive research on attention in primates, the capacity for selective attention in nonprimate species has never been quantified. Here we demonstrate selective attention in chickens by applying protocols that have been used to characterize visual spatial attention in primates. Chickens were trained to localize and report the vertical position of a target in the presence of task-relevant distracters. A spatial cue, the location of which varied across individual trials, indicated the horizontal, but not vertical, position of the upcoming target. Spatial cueing improved localization performance: accuracy (d′) increased and reaction times decreased in a space-specific manner. Distracters severely impaired perceptual performance, and this impairment was greatly reduced by spatial cueing. Signal detection analysis with an “indecision” model demonstrated that spatial cueing significantly increased choice certainty in localizing targets. By contrast, error-aversion certainty (certainty of not making an error) remained essentially constant across cueing protocols, target contrasts, and individuals. The results show that chickens shift spatial attention rapidly and dynamically, following principles of stimulus selection that closely parallel those documented in primates. The findings suggest that the mechanisms that control attention have been conserved through evolution, and establish chickens—a highly visual species that is easily trained and amenable to cutting-edge experimental technologies—as an attractive model for linking behavior to neural mechanisms of selective attention.


title: CMOS logic design with independent-gate FinFETs

abstract: Fin-type field-effect transistors (FinFETs) are promising substitutes for bulk CMOS in nano-scale circuits. In this paper, it is observed that in spite of improved device characteristics, high active leakage may remain a problem for FinFET logic circuits. Leakage is found to contribute 31.3% of total power consumption in power-optimized FinFET logic circuits. Various FinFET logic design styles, based on independent control of FinFET gates, are studied. A new low-leakage logic style is presented. Leakage (total) power savings of 64.7% (14.5%) under tight delay constraints and 91.2% (37.2%) under relaxed delay constraints, through the judicious use of FinFET logic styles, are demonstrated.


title: Critical infrastructures: a perspective from systems of systems

abstract: This paper introduces a system of systems (SoS)-based perspective for critical infrastructure (CI) and derives implications for future development of the CI field. At a most fundamental level, CIs provide goods and services essential to public wellbeing. The importance of the CI field has never been more pronounced as the 21st century continues to unfold, beset by unprecedented levels of complexity, emergence, interdependence, and uncertainty. There are legions of threats to CIs and so too are the emerging and fragmented array of methods and tools to address different concerns within the field. This paper suggests that since SoS is concerned with integration and coordination of multiple systems to act in unison to achieve higher levels of performance, it offers a unique perspective on how we might address different concerns at systems level in the CI field. The paper examines that state of knowledge for the CI field and introduces an organizing construct within which the CI field might be framed.


title: DEEP-SEE FACE: A Mobile Face Recognition System Dedicated to Visually Impaired People

abstract: In this paper, we introduce the DEEP-SEE FACE framework, an assistive device designed to improve cognition, interaction, and communication of visually impaired (VI) people in social encounters. The proposed approach jointly exploits computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks in order to detect, track, and recognize, in real-time, various persons existent in the video streams. The major contribution of the paper concerns a global, fixed-size face representation that takes into the account of various video frames while remaining independent of the length of the image sequence. To this purpose, we introduce an effective weight adaptation scheme that is able to determine the relevance assigned to each face instance, depending on the frame degree of motion/camera blur, scale variation, and compression artifacts. Another relevant contribution involves a hard negative mining stage that helps us differentiating between known and unknown face identities. The experimental results, carried out on a large-scale data set, validate the proposed methodology with an average accuracy and recognition rates superior to 92%. When tested in real life, indoor/outdoor scenarios, the DEEP-SEE FACE prototype proves to be effective and easy to use, allowing the VI people to access visual information during social events.


title: Flexible Hybrid Electronics Technology Using Die-First FOWLP for High-Performance and Scalable Heterogeneous System Integration

abstract: A technological platform is established for scalable flexible hybrid electronics based on a novel fan-out wafer-level packaging (FOWLP) methodology. Small dielets are embedded in flexible substrates we call FlexTrate. These dielets can be interconnected through high-density wirings formed in wafer-level processing. We demonstrate homogeneous integration of 625 (25 by 25) 1-mm2 Si dielets and heterogeneous integration of GaAs and Si dielets with various thicknesses in a biocompatible polydimethylsiloxane (PDMS). In this paper, 8- $\mu \text{m}$ -pitch die-to-die interconnections are successfully implemented over a stress buffer layer formed on the PDMS. In addition, coplanarity between the PDMS and embedded dielets, die shift concerned in typical die-first FOWLP, and the bendability of the resulting FlexTrate are characterized.


title: A Spatial-Context Effect in Recognition Memory

abstract: We designed a novel experiment to investigate the modulation of human recognition memory by environmental context. Human participants were asked to navigate through a four-arm Virtual Reality (VR) maze in order to find and memorize discrete items presented at specific locations in the environment. They were later on tested on their ability to recognize items as previously presented or new. By manipulating the spatial position of half of the studied items during the testing phase of our experiment, we could assess differences in performance related to the congruency of environmental information at encoding and retrieval. Our results revealed that spatial context had a significant effect in the quality of memory. In particular, we found that recognition performance was significantly better in trials in which contextual information was congruent as opposed to those in which it was different. Our results are in line with previous studies that have reported spatial-context effects in recognition memory, further characterizing their magnitude under ecologically valid experimental conditions.


title: Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals

abstract: Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology from observations of signals on its nodes. Since the problem is ill-posed, these approaches make assumptions, such as smoothness of the signals on the graph, or sparsity priors. In this paper, we propose a characterization of the space of valid graphs, in the sense that they can explain stationary signals. To simplify the exposition in this paper, we focus here on the case where signals were i.i.d. at some point back in time and were observed after diffusion on a graph. We show that the set of graphs verifying this assumption has a strong connection with the eigenvectors of the covariance matrix, and forms a convex set. Along with a theoretical study in which these eigenvectors are assumed to be known, we consider the practical case when the observations are noisy, and experimentally observe how fast the set of valid graphs converges to the set obtained when the exact eigenvectors are known, as the number of observations grows. To illustrate how this characterization can be used for graph recovery, we present two methods for selecting a particular point in this set under chosen criteria, namely graph simplicity and sparsity. Additionally, we introduce a measure to evaluate how much a graph is adapted to signals under a stationarity assumption. Finally, we evaluate how state-of-the-art methods relate to this framework through experiments on a dataset of temperatures.


title: Robust multi-pose face tracking by multi-stage tracklet association

abstract: We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on location, size and pose, resulting in short but reliable tracklets. The high-level stage uses different cues for computing a joint similarity measure between tracklets. The facial cue compares facial features of the most frontal face detections in pairs of tracklets. The classifier cue learns a discriminative appearance model for each tracklet, using detection pairs within reliable tracklets and between overlapping tracklets as training data. The constraint cue observes the compatibility of motion of two tracklets. The association of tracklets is globally optimized with the Hungarian algorithm. We validate our approach on two challenging episodes of two TV series and report a Multiple Object Tracking Accuracy (MOTA) of 82% and 68.2%, respectively.


title: Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

abstract: Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.


title: League Championship Algorithm: A New Algorithm for Numerical Function Optimization

abstract: Inspired by the competition of sport teams in a sport league, an algorithm is presented for optimizing nonlinear continuous functions. A number of individuals as sport teams compete in an artificial league for several weeks (iterations). Based on the league schedule in each week, teams play in pairs and the outcome is determined in terms of win or loss, given known the team’s playing strength (fitness value) resultant from a particular team formation (solution). In the recovery period, each team devises the required changes in the formation/playing style (a new solution) for the next week contest and the championship goes on for a number of seasons (stopping condition). Performance of the proposed algorithm is tested in comparison with that of particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmarked functions. Results testify that the new algorithm performs well on all test problems, exceeding or matching the best performance obtained by PSO. This suggests that further developments and practical applications of the proposed algorithm would be worth investigating in the future.


title: Test Cases Generation for Embedded Real-Time Software Based on Extended UML

abstract: Automated testing of embedded real-time software is a hotpot in nowadays software testing area. In this paper, the relations between unified modeling language (UML) and embedded real-time software testing are introduced firstly. And then, based on the extensions of UML diagrams and the scenario technology, we propose the test cases generation for embedded real-time software based on extended UML. Furthermore, combined with the verification of test cases generation process for avionics embedded system, the research thoughts and methods are brought out. The verification result shows that the method proposed in this paper can be used effectively in automated test cases generation of embedded real-time software testing and improve testing efficiency and maintainability so as to reduce the test cost.


title: Protein function in precision medicine: deep understanding with machine learning

abstract: Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.


title: Deep Osmosis: Holistic Distributed Deep Learning in Osmotic Computing

abstract: Emerging availability (and varying complexity and types) of Internet of Things (IoT) devices, along with large data volumes that such devices (can potentially) generate, can have a significant impact on our lives, fuelling the development of critical next-generation services and applications in a variety of application domains (e.g. healthcare, smart grids, finance, disaster management, agriculture, transportation and water management). Deep learning technology, which has in the past been used successfully in computer vision and language modelling is now finding application in new domains driven by availability of diverse and large datasets. One such example is the advances in medical diagnostics and prediction by using Deep Learning technologies to improve human health. However, transferring large data streams (a requirement of Deep Learning technologies for achieving high accuracy) to centralised locations such as Cloud datacentre environments, in a timely and reliable manner, is being seen as a key limitation of expanding the application horizons of such technologies. To this end, various paradigms, including Osmotic Computing, have been proposed that promotes distribution of data analysis tasks across Cloud and Edge computing environments. However, these existing paradigms fail to provide a detailed account of how technologies such as deep learning can be orchestrated and take advantage of the cloud, edge and mobile edge environments in a holistic manner. In other words, the focus of this Blue Skies piece is to analyze the research challenges involved with developing a new class of holistic distributed deep learning algorithms that are “resource and data aware”, and which are able to account for underlying heterogeneous data and data models, resource (cloud vs. edge vs. mobile edge) models and data availability while executing – trading accuracy for execution time, etc.


title: Color2Gray: salience-preserving color removal

abstract: Visually important image features often disappear when color images are converted to grayscale. The algorithm introduced here reduces such losses by attempting to preserve the salient features of the color image. The Color2Gray algorithm is a 3-step process: 1) convert RGB inputs to a perceptually uniform CIE L*a*b* color space, 2) use chrominance and luminance differences to create grayscale target differences between nearby image pixels, and 3) solve an optimization problem designed to selectively modulate the grayscale representation as a function of the chroma variation of the source image. The Color2Gray results offer viewers salient information missing from previous grayscale image creation methods.


title: Knowledge network centrality, formal rank and research performance: evidence for curvilinear and interaction effects

abstract: This study explores the curvilinear (inverted U-shaped) association of three classical dimension of co-authorship network centrality, degree, closeness and betweenness and the research performance in terms of g-index, of authors embedded in a co-authorship network, considering formal rank of the authors as a moderator between network centrality and research performance. We use publication data from ISI Web of Science (from years 2002---2009), citation data using Publish or Perish software for years 2010---2013 and CV's of faculty members. Using social network analysis techniques and Poisson regression, we explore our research questions in a domestic co-authorship network of 203 faculty members publishing in Chemistry and it's sub-fields within a developing country, Pakistan. Our results reveal the curvilinear (inverted U-shaped) association of direct and distant co-authorship ties (degree centrality) with research performance with formal rank having a positive moderating role for lower ranked faculty.


title: An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods

abstract: Inventory control of spare parts is essential to many organizations, since excess inventory leads to high holding costs and stock outs can have a great impact on operations performance. This paper compares different re-order point methods for effective spare parts inventory control, motivated by a case study at a large oil refinery. Different demand modeling techniques and inventory policies are evaluated using real data.


title: A Diversity-Promoting Objective Function for Neural Conversation Models

abstract: Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., "I don't know") regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (response) given input (message) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as the objective function in neural models. Experimental results demonstrate that the proposed MMI models produce more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets and in human evaluations.


title: Theory of spatial position and spatial frequency relations in the receptive fields of simple cells in the visual cortex

abstract: Striate cells showing linear spatial summation obey very general mathematical inequalities relating the size of their receptive fields to the corresponding spatial frequency and orientation tuning characteristics. The experimental data show that, in the preferred direction of stimulus motion, the spatial response profiles of cells in the simple family are well described by the mathematical form of Gabor elementary signals. The product of the uncertainties in signalling spatial position (?x) and spatial frequency (?f) has, therefore, a theoretical minimum value of ?x?f=1/2. We examine the implications that these conclusions have for the relationship between the spatial response profiles of simple cells and the characteristics of their spatial frequency tuning curves. Examples of the spatial frequency tuning curves and their associated spatial response profiles are discussed and illustrated. The advantages for the operation of the visual system of different relationships between the spatial response profiles and the characteristics of the spatial frequency tuning curves are examined. Two examples are discussed in detail, one system having a constant receptive field size and the other a constant bandwidth.


title: What Can You Do?: Studying Social-Agent Orientation and Agent Proactive Interactions with an Agent for Employees

abstract: Personal agent software is now in daily use in personal devices and in some organizational settings. While many advocate an agent sociality design paradigm that incorporates human-like features and social dialogues, it is unclear whether this is a good match for professionals who seek productivity instead of leisurely use. We conducted a 17-day field study of a prototype of a personal AI agent that helps employees find work-related information. Using log data, surveys, and interviews, we found individual differences in the preference for humanized social interactions (social-agent orientation), which led to different user needs and requirements for agent design. We also explored the effect of agent proactive interactions and found that they carried the risk of interruption, especially for users who were generally averse to interruptions at work. Further, we found that user differences in social-agent orientation and aversion to agent proactive interactions can be inferred from behavioral signals. Our results inform research into social agent design, proactive agent interaction, and personalization of AI agents.


Explainable Agency for Intelligent Autonomous Systems.


title: Insights into Alzheimer disease pathogenesis from studies in transgenic animal models

abstract: Alzheimer disease is the most common cause of dementia among the elderly, accounting for ~60-70% of all cases of dementia. The neuropathological hallmarks of Alzheimer disease are senile plaques (mainly containing p-amyloid peptide derived from amyloid precursor protein) and neurofibrillary tangles (containing hyperphosphorylated Tau protein), along with neuronal loss. At present there is no effective treatment for Alzheimer disease. Given the prevalence and poor prognosis of the disease, the development of animal models has been a research priority to understand pathogenic mechanisms and to test therapeutic strategies. Most cases of Alzheimer disease occur sporadically in people over 65 years old, and are not genetically inherited. Roughly 5% of patients with Alzheimer disease have familial Alzheimer disease-that is, related to a genetic predisposition, including mutations in the amyloid precursor protein, presenilin 1, and presenilin 2 genes. The discovery of genes for familial Alzheimer disease has allowed transgenic models to be generated through the overexpression of the amyloid precursor protein and/or presenilins harboring one or several mutations found in familial Alzheimer disease. Although none of these models fully replicates the human disease, they have provided valuable insights into disease mechanisms as well as opportunities to test therapeutic approaches. This review describes the main transgenic mouse models of Alzheimer disease which have been adopted in Alzheimer disease research, and discusses the insights into Alzheimer disease pathogenesis from studies in such models. In summary, the Alzheimer disease mouse models have been the key to understanding the roles of soluble b-amyloid oligomers in disease pathogenesis, as well as of the relationship between p-amyloid and Tau pathologies.


title: MSKDE - Using Marching Squares to Quickly Make High Quality Crime Hotspot Maps

abstract: In recent years, violence has considerably increased in the world. In a certain state of Brazil, for example, the homicide rate grew from 16 homicides per 100,000 inhabitants in 2000, to 48 homicides per 100,000 inhabitants in 2014. Police departments worldwide use various types of crime maps, which are generated with diverse techniques, in order to analyze and fight crime. Those types of maps enable decision makers to identify high-risk areas and to allocate resources more effectively. Hotspot maps, in particular, are crime maps often available in visual interactive systems for crime analysis. In order for hotspot maps to be really useful, they need to be very accurate - specially for resource allocation tasks - and to be processed very fast for quick analysis of different scenarios. In this paper, we propose MSKDE - Marching Squares Kernel Density Estimation, a solution for generating fast and accurate hotspot maps. We describe the technique and demonstrate its superior qualities through a careful comparison with the standard Kernel Density Estimation technique, which is widely used for generating hotspot maps.


title: Low cost SDR spectrum analyzer and analog radio receiver using GNU radio, raspberry Pi2 and SDR-RTL dongle

abstract: This paper presents the implementation and test of a low cost Software Defined Radio based on the Raspberry Pi2 computer, the RTL-SDR dongle radio receiver, and the GNU Radio software. Key aspects regarding installation and configuration of GNU radio and RTL-SDR drivers on Raspbian Operating System are addressed. The resulting device can demodulate AM and FM signals in the frequency range from 30MHz to 1.7GHz and also can meet the function of Spectrum Analyzer in moderate sensitivity demanding applications.


title: Text Matching as Image Recognition

abstract: Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.


title: RNA sequencing: advances, challenges and opportunities

abstract: In the few years since its initial application, massively parallel cDNA sequencing, or RNA-seq, has allowed many advances in the characterization and quantification of transcriptomes. Recently, several developments in RNA-seq methods have provided an even more complete characterization of RNA transcripts. These developments include improvements in transcription start site mapping, strand-specific measurements, gene fusion detection, small RNA characterization and detection of alternative splicing events. Ongoing developments promise further advances in the application of RNA-seq, particularly direct RNA sequencing and approaches that allow RNA quantification from very small amounts of cellular materials.


title: Performance of a GaN-HEMT synchronous boost converter in ZVS and hard switching mode

abstract: The loss contribution of a 2.3 kW synchronous GaN-HEMT boost converter for an input voltage of 250 V and an output voltage of 500 V was analyzed. A simulation model which consists of two parts is introduced. First, a physics-based model is used to determine the switching losses. Then, a system simulation is applied to calculate the losses of the specific elements. This approach allows a fast and accurate system evaluation as required for further system optimization. In this work, a hard- and a zero-voltage turn-on switching converter are compared. Measurements were performed to verify the simulation model, showing a good agreement. A peak efficiency of 99 % was achieved for an output power of 1.4 kW. Even with an output power above 400 W, it was possible to obtain a system efficiency exceeding 98 %.


title: The Impact of Telemedicine on Quality of Life in Rural Areas: The Extremadura Model of Specialized Care Delivery

abstract: BACKGROUND ::: Referrals from rural health centers to urban hospitals join waiting lists as outpatients for hospital admission and hospital treatment. This influences quality of life (QoL) of the rural population and retired people who require medical attention without traveling, provided no risks are involved. For this reason, a rural region of Spain has adopted a strategy to deliver telemedicine (TM) specialized care (Extremadura model) as a political decision. ::: ::: ::: OBJECTIVES ::: The present study aimed at objectively assessing QoL on aspects of health and well-being for citizens benefiting from this system. ::: ::: ::: METHODS ::: We performed a randomized study of 800 primary care patients referred for specialized care: 420 regular face-to-face hospital referrals and 380 referred to a hospital specialist at a distance by TM. The study used two questionnaires: a modified version of the classical SF-12v2 short form questionnaire for health and well-being and a specific author-elaborated questionnaire. The latter focused on major patient concerns such as (1) discomfort and pain relief, (2) swift diagnosis, (3) swift treatment, (4) swift decision on hospital admission or not, (5) avoidance of traveling, (6) avoidance of red tape, and (7) personal attention. QoL was assessed twice: before referral to a hospital specialist and 6 months after referral to the same. The results were statistically compared. ::: ::: ::: RESULTS ::: Both groups showed comparable health status with added advantages for TM referrals such as (1) less traveling (p = 0.0001) and (2) faster diagnosis, health examination, and treatment (p = 0.0001). ::: ::: ::: CONCLUSION ::: Telemedicine care by a hospital specialist through videoconferencing was comparable to hospital referral for face-to-face medicine. Teleconsultations managed by nurses had a positive impact on the QoL of rural patients. They did not have to travel and thus diagnoses and examinations to start treatment were initiated faster.


title: A portable digitally controlled oscillator using novel varactors

abstract: This work presents a portable digitally controlled oscillator (DCO) by using two-input NOR gates as a digitally controlled varactor (DCV) in fine-tuning delay cell design. This novel varactor uses the gate capacitance difference of NOR gates under different digital control inputs to establish a DCV. Thus proposed DCO can improve delay resolution 256 times better than a single buffer design. This study also examines different types of NOR/NAND gates (2-input or 3-input) for DCV. The proposed DCO with novel DCV can be implemented with standard cells, and thus it can be ported to different processes in short time. Furthermore, the final circuit layout can be generated using an auto placement and routing (APR) tools. A test chip demonstrates that LSB resolution of the DCO can be improved to 1.55 ps with standard 0.35-/spl mu/m 2P4M CMOS digital cell library. The proposed DCO has good performance in terms of fine resolution, high portability, and short design turnaround cycle compared with conventional DCO designs.