title: Image Similarity Using Mutual Information of Regions

abstract: Mutual information (MI) has emerged in recent years as an effective similarity measure for comparing images. One drawback of MI, however, is that it is calculated on a pixel by pixel basis, meaning that it takes into account only the relationships between corresponding indi- vidual pixels and not those of each pixel's respective neighborhood. As a result, much of the spatial information inherent in images is not utilized. In this paper, we propose a novel extension to MI called regional mutual information (RMI). This extension efficiently takes neighborhood regions of corresponding pixels into account. We demonstrate the usefulness of RMI by applying it to a real-world problem in the medical domain— intensity-based 2D-3D registration of X-ray projection images (2D) to a CT image (3D). Using a gold-standard spine image data set, we show that RMI is a more robust similarity meaure for image registration than MI.


title: GANs beyond divergence minimization

abstract: Generative adversarial networks (GANs) can be interpreted as an adversarial game between two players, a discriminator D and a generator G, in which D learns to classify real from fake data and G learns to generate realistic data by "fooling" D into thinking that fake data is actually real data. Currently, a dominating view is that G actually learns by minimizing a divergence given that the general objective function is a divergence when D is optimal. However, this view has been challenged due to inconsistencies between theory and practice. In this paper, we discuss of the properties associated with most loss functions for G (e.g., saturating/non-saturating f-GAN, LSGAN, WGAN, etc.). We show that these loss functions are not divergences and do not have the same equilibrium as expected of divergences. This suggests that G does not need to minimize the same objective function as D maximize, nor maximize the objective of D after swapping real data with fake data (non-saturating GAN) but can instead use a wide range of possible loss functions to learn to generate realistic data. We define GANs through two separate and independent D maximization and G minimization steps. We generalize the generator step to four new classes of loss functions, most of which are actual divergences (while traditional G loss functions are not). We test a wide variety of loss functions from these four classes on a synthetic dataset and on CIFAR-10. We observe that most loss functions converge well and provide comparable data generation quality to non-saturating GAN, LSGAN, and WGAN-GP generator loss functions, whether we use divergences or non-divergences. These results suggest that GANs do not conform well to the divergence minimization theory and form a much broader range of models than previously assumed.


Mathematical Methodology to Predict Energy Generation of Grid Connected PV Power Plants in India


title: Processes to Integrate Design With Downstream Applications Through Product Shapes Adaptation

abstract: In the aeronautics industry, various stages of a product development process such as physical phenomena (like stress, strain, and thermal ones) or virtual reality simulations are regarded as product views and based on different digital shapes of the same physical component. The shape adaptation processes needed to progress from one stage of the product development process to another one, the multiple shapes needed for a simulation at a given stage of this process are analyzed and shape adaptation operators are presented. Then, it is shown why geometric models adaptation processes from the design stage to downstream applications are important in an industrial design context, and why this is a concurrent engineering enabler. Subsequently, we show how shapes adaptation processes can be achieved through their appropriate formalization and representation using direct acyclic graphs connected to the scene structure of a simulation and the product structure used as input. A process data structure is also proposed to describe shape adaptation processes and ease their reuse. Finally, based on two EADS case studies, we illustrate how methods and criteria can be set up to produce adapted shapes from computer-aided design data in a controlled and integrated manner.


title: Fully integrated ultra-low voltage DC-DC converter with voltage quadrupling LC tank oscillator for energy harvesting applications

abstract: This paper presents a novel fully integrated ultra-low voltage DC-DC converter and its multi-stage architecture. DC-DC converter frequency has been analytically derived using model analysis and validated in Cadence environment. The proposed voltage quadrupling LC tank oscillator eliminates the buffer circuits utilized in the traditional DC-DC converter, hence improves the performance metrics such as efficiency and output power capacity. The circuit was designed in 180nm standard CMOS process and was simulated to self-start and boost 70 mV to deliver 1.4 V to a 1 MO load. The minimum start-up voltage of the DC-DC converter is 60 mV. The optimized 2-stage DC-DC converter can yield 1.1 V output and 560 μW load power with 43% peak efficiency at 0.2 V input. The 3-stage and 4-stage can deliver 1524 μW and 1193 μW load power, 1.64 V and 2.18 V output voltage with 40% and 34% peak efficiency respectively at 0.2 V input.


title: Localization and navigation using QR code for mobile robot in indoor environment

abstract: Localization and navigation are fundamental issues to mobile robotics. An approach for localization and navigation for a mobile robot in indoor environment is proposed in this paper. In this approach, QR codes are used as landmarks to provide global pose references. Label and location information is stored in QR codes which are strategically placed in the operating environment. The mobile robot is equipped with an industrial camera pointing to the ceiling to read QR codes at high speed. The pose of the robot is estimated according to the positional relationship between QR codes and the robot. For the purpose of collision-free navigation, a laser range finder (LRF) is applied to build a map in unknown environment and detect obstacles. Dijkstra algorithm and Dynamic Window Approach (DWA) are applied in path planning based on a 2D grid map. Experimental results show that this approach has good feasibility and effectiveness.


A Multi-resolution Data Structure for Two-dimensional Morse Functions


title: Extracting usability and user experience information from online user reviews

abstract: Internet review sites allow consumers to write detailed reviews of products potentially containing information related to user experience (UX) and usability. Using 5198 sentences from 3492 online reviews of software and video games, we investigate the content of online reviews with the aims of (i) charting the distribution of information in reviews among different dimensions of usability and UX, and (ii) extracting an associated vocabulary for each dimension using techniques from natural language processing and machine learning. We (a) find that 13%-49% of sentences in our online reviews pool contain usability or UX information; (b) chart the distribution of four sets of dimensions of usability and UX across reviews from two product categories; (c) extract a catalogue of important word stems for a number of dimensions. Our results suggest that a greater understanding of users' preoccupation with different dimensions of usability and UX may be inferred from the large volume of self-reported experiences online, and that research focused on identifying pertinent dimensions of usability and UX may benefit further from empirical studies of user-generated experience reports.


title: GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models

abstract: Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Key components of each Bayes filter are probabilistic prediction and observation models. This paper shows how non-parametric Gaussian process (GP) regression can be used for learning such models from training data. We also show how Gaussian process models can be integrated into different versions of Bayes filters, namely particle filters and extended and unscented Kalman filters. The resulting GP-BayesFilters can have several advantages over standard (parametric) filters. Most importantly, GP-BayesFilters do not require an accurate, parametric model of the system. Given enough training data, they enable improved tracking accuracy compared to parametric models, and they degrade gracefully with increased model uncertainty. These advantages stem from the fact that GPs consider both the noise in the system and the uncertainty in the model. If an approximate parametric model is available, it can be incorporated into the GP, resulting in further performance improvements. In experiments, we show different properties of GP-BayesFilters using data collected with an autonomous micro-blimp as well as synthetic data.


title: MIST: Mitigating Host-Side Interference for Storage Traffic in Virtualized Data Centers

abstract: In today's data centers, storage hardware is getting virtualized and shared across multiple tenants. The storage virtualization is expected to significantly increase resource utilization, and consequently provide infrastructure providers with tremendous cost savings. In a shared environment, however, demanding tenants can severely impact the IO performance of co-located tenants. Therefore, in order to properly materialize expected benefits, it is important to provide virtualized IO traffic with performance isolation and a certain level of assured performance. Unfortunately, most prior work typically provided partial solutions by focusing on a single performance bottleneck, e.g., IO scheduler. In this paper, we present the design and implementation of MIST that can significantly mitigate the impact of IO performance interference. To achieve the goal, we devise a novel interference detection metric, Icpu, and use a collection of control mechanisms that exploit the knobs provided by modern operating systems. Through the evaluation based on our MIST prototype, we demonstrate that MIST can provide effective protection for IO traffic against a wide range of interfering workloads. Moreover, we show that MIST has low performance overhead for its control actions.


Objective Criteria: Guiding and Evaluating Dental Implant Esthetics


title: Draco: A System for Uniform and Fine-grained Access Control for Web Code on Android

abstract: In-app embedded browsers are commonly used by app developers to display web content without having to redirect the user to heavy-weight web browsers. Just like the conventional web browsers, embedded browsers can allow the execution of web code. In addition, they provide mechanisms (viz., JavaScript bridges) to give web code access to internal app code that might implement critical functionalities and expose device resources. This is intrinsically dangerous since there is currently no means for app developers to perform origin-based access control on the JavaScript bridges, and any web code running in an embedded browser is free to use all the exposed app and device resources. Previous work that addresses this problem provided access control solutions that work only for apps that are built using hybrid frameworks. Additionally, these solutions focused on protecting only the parts of JavaScript bridges that expose permissions-protected resources. In this work, our goal is to provide a generic solution that works for all apps that utilize embedded web browsers and protects all channels that give access to internal app and device resources. Towards realizing this goal, we built Draco, a uniform and fine-grained access control framework for web code running on Android embedded browsers (viz., WebView). Draco provides a declarative policy language that allows developers to define policies to specify the desired access characteristics of web origins in a fine-grained fashion, and a runtime system that dynamically enforces the policies. In contrast with previous work, we do not assume any modifications to the Android operating system, and implement Draco in the Chromium Android System WebView app to enable seamless deployment. Our evaluation of the the Draco runtime system shows that Draco incurs negligible overhead, which is in the order of microseconds.


title: Identification of Zika virus epitopes reveals immunodominant and protective roles for dengue virus cross-reactive CD8+ T cells

abstract: ZIKV-specific and ZIKV/DENV cross-reactive human HLA epitopes are identified in naive and DENV-immune mice, revealing altered immunodominance in the DENV-immune setting and a protective role for epitope-specific CD8+ T cells against ZIKV.


title: An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning

abstract: Many universities implement e-learning for various reasons. It is obvious that the number of e-learning opportunities provided by higher educational institutes continues to grow in Korea. Yet little research has been done to verify the process of how university students adopt and use e-learning. A sample of 628 university students took part in the research. The structural equation modeling (SEM) technique was employed with the LISREL program to explain the adoption process. The general structural model, which included e-learning selfefficacy, subjective norm, system accessibility, perceived usefulness, perceived ease of use, attitude, and behavioral intention to use e-learning, was developed based on the technology acceptance model (TAM). The result proved TAM to be a good theoretical tool to understand users’ acceptance of e-learning. E-learning selfefficacy was the most important construct, followed by subjective norm in explicating the causal process in the model.


title: Cyber-physical system design contracts

abstract: This paper introduces design contracts between control and embedded software engineers for building Cyber-Physical Systems (CPS). CPS design involves a variety of disciplines mastered by teams of engineers with diverse backgrounds. Many system properties influence the design in more than one discipline. The lack of clearly defined interfaces between disciplines burdens the interaction and collaboration. We show how design contracts can facilitate interaction between 2 groups: control and software engineers. A design contract is an agreement on certain properties of the system. Every party specifies requirements and assumptions on the system and the environment. This contract is the central point of inter-domain communication and negotiation. Designs can evolve independently if all parties agree to a contract or designs can be modified iteratively in negotiation processes. The main challenge lies in the definition of a concise but sufficient contract. We discuss design contracts that specify timing and functionality, two important properties control and software engineers have to agree upon. Various design approaches have been established and implemented successfully to address timing and functionality. We formulate those approaches as design contracts and propose guidelines on how to choose, derive and employ them. Modeling and simulation support for the design contracts is discussed using an illustrative example.


title: Creating Evolving User Behavior Profiles Automatically

abstract: Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behavior. Also, because a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper, we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme. We also develop further the recursive formula of the potential of a data point to become a cluster center using cosine distance, which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.


Headphone with Active Noise Control using Analog Adaptive Filters


title: Efficient resource allocation for device-to-device communication underlaying LTE network

abstract: Device-to-device (D2D) communication as an underlaying cellular network empowers user-driven rich multimedia applications and also has proven to be network efficient offloading eNodeB traffic. However, D2D transmitters may cause significant amount of interference to the primary cellular network when radio resources are shared between them. During the downlink (DL) phase, primary cell UE (user equipment) may suffer from interference by the D2D transmitter. On the other hand, the immobile eNodeB is the victim of interference by the D2D transmitter during the uplink (UL) phase when radio resources are allocated randomly. Such interference can be avoided otherwise diminish if radio resource allocated intelligently with the coordination from the eNodeB. In this paper, we formulate the problem of radio resource allocation to the D2D communications as a mixed integer nonlinear programming (MINLP). Such an optimization problem is notoriously hard to solve within fast scheduling period of the Long Term Evolution (LTE) network. We therefore propose an alternative greedy heuristic algorithm that can lessen interference to the primary cellular network utilizing channel gain information. We also perform extensive simulation to prove the efficacy of the proposed algorithm.


title: The use of Web analytics for digital marketing performance measurement

abstract: Abstract This study proposes that the benefits gained from marketing performance measurement are determined by how an organization exploits the metrics system under specific circumstances. For this purpose, the authors review performance measurement literature and apply it to the use of Web analytics, which offers companies a metrics system to measure digital marketing performance. By performing an in-depth investigation of the use of Web analytics in industrial companies, the study shows that an organization's efforts to use marketing metrics systems and the resulting outcomes cannot be understood without considering the reasoning behind the chosen metrics, the processing of metrics data, and the organizational context surrounding the use of the system. Given the continuously growing importance of digital marketing in the industrial sector, this study illustrates how industrial companies characterized by complex selling processes can harness Web analytics to demonstrate how digital marketing activities benefit their businesses.


title: Predicting the Effect of Diabetes on Kidney using Classification in Tanagra

abstract: As numerous data mining tools & techniques continue to develop along with the healthcare domain, the applications of data mining in healthcare sector will undoubtedly play a growing role in the world. There exist a large number of useful techniques in data mining like classification, association, clustering, regression etc. that helps to discover new trends in huge healthcare databases. This paper comes out with the application of classification and prediction technique to find the effect of diabetes on kidney. The implementation is done using the application of C4.5 Algorithm in Tanagra. This paper demonstrated the utility of classification on a dataset containing records of both diabetic and non- diabetic patients using data mining tool Tanagra. Using Kidney Function Tests (KFT), the effect of diabetes on kidney is determined. After comparing manually the result of Tanagra with the actual output, we discovered that Tanagra is very near to the output. Finally, the performance of classifier in Tanagra is evaluated in terms of recall, precision and error rate.


title: Scapular-focused treatment in patients with shoulder impingement syndrome: a randomized clinical trial

abstract: The purpose of this clinical trial is to compare the effectiveness of a scapular-focused treatment with a control therapy in patients with shoulder impingement syndrome. Therefore, a randomized clinical trial with a blinded assessor was used in 22 patients with shoulder impingement syndrome. The primary outcome measures included self-reported shoulder disability and pain. Next, patients were evaluated regarding scapular positioning and shoulder muscle strength. The scapular-focused treatment included stretching and scapular motor control training. The control therapy included stretching, muscle friction, and eccentric rotator cuff training. Main outcome measures were the shoulder disability questionnaire, diagnostic tests for shoulder impingement syndrome, clinical tests for scapular positioning, shoulder pain (visual analog scale; VAS), and muscle strength. A large clinically important treatment effect in favor of scapular motor control training was found in self-reported disability (Cohen’s d = 0.93, p = 0.025), and a moderate to large clinically important improvement in pain during the Neer test, Hawkins test, and empty can test (Cohen’s d 0.76, 1.04, and 0.92, respectively). In addition, the experimental group demonstrated a moderate (Cohen’s d = 0.67) improvement in self-experienced pain at rest (VAS), whereas the control group did not change. The effects were maintained at three months follow-up.


title: Efficient and Low Latency Detection of Intruders in Mobile Active Authentication

abstract: Active authentication (AA) refers to the problem of continuously verifying the identity of a mobile device user for the purpose of securing the device. We address the problem of quickly detecting intrusions with lower false detection rates in mobile AA systems with higher resource efficiency. Bayesian and MiniMax versions of the quickest change detection (QCD) algorithms are introduced to quickly detect intrusions in mobile AA systems. These algorithms are extended with an update rule to facilitate low-frequency sensing which leads to low utilization of resources. Effectiveness of the proposed framework is demonstrated using three publicly available unconstrained face and touch gesture-based AA datasets. It is shown that the proposed QCD-based intrusion detection methods can perform better than many state-of-the-art AA methods in terms of latency and low false detection rates. Furthermore, it is shown that employing the proposed resource-efficient extension further improves the performance of the QCD-based setup.


title: Adding Noun Phrase Structure to the Penn Treebank

abstract: The Penn Treebank does not annotate within base noun phrases (NPs), committing only to flat structures that ignore the complexity of English NPs. This means that tools trained on Treebank data cannot learn the correct internal structure of NPs. This paper details the process of adding gold-standard bracketing within each noun phrase in the Penn Treebank. We then examine the consistency and reliability of our annotations. Finally, we use this resource to determine NP structure using several statistical approaches, thus demonstrating the utility of the corpus. This adds detail to the Penn Treebank that is necessary for many NLP applications.


title: Kernel Regression for Image Processing and Reconstruction

abstract: In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples.


title: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

abstract: Abstract Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h 2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90–0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor-moderate levels for WM and raw data (0.51–0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.


title: Scalable all-pairs similarity search in metric spaces

abstract: Given a set of entities, the all-pairs similarity search aims at identifying all pairs of entities that have similarity greater than (or distance smaller than) some user-defined threshold. In this article, we propose a parallel framework for solving this problem in metric spaces. Novel elements of our solution include: i) flexible support for multiple metrics of interest; ii) an autonomic approach to partition the input dataset with minimal redundancy to achieve good load-balance in the presence of limited computing resources; iii) an on-the- fly lossless compression strategy to reduce both the running time and the final output size. We validate the utility, scalability and the effectiveness of the approach on hundreds of machines using real and synthetic datasets.


title: BioPerf: a benchmark suite to evaluate high-performance computer architecture on bioinformatics applications

abstract: The exponential growth in the amount of genomic data has spurred growing interest in large scale analysis of genetic information. Bioinformatics applications, which explore computational methods to allow researchers to sift through the massive biological data and extract useful information, are becoming increasingly important computer workloads. This paper presents BioPerf a benchmark suite of representative bioinformatics applications to facilitate the design and evaluation of high-performance computer architectures for these emerging workloads. Currently, the BioPerf suite contains codes from 10 highly popular bioinformatics packages and covers the major fields of study in computational biology such as sequence comparison, phylogenetic reconstruction, protein structure prediction, and sequence homology & gene finding. We demonstrate the use of BioPerf by providing simulation points of pre-compiled Alpha binaries and with a performance study on IBM Power using IBM Mambo simulations cross-compared with Apple G5 executions. The BioPerf suite (available from www.bioperf.org) includes benchmark source code, input datasets of various sizes, and information for compiling and using the benchmarks. Our benchmark suite includes parallel codes where available.


title: Nonparametric estimation of the likelihood ratio and divergence functionals

abstract: We develop and analyze a nonparametric method for estimating the class of f-divergence functionals, and the density ratio of two probability distributions. Our method is based on a non-asymptotic variational characterization of the f-divergence, which allows us to cast the problem of estimating divergences in terms of risk minimization. We thus obtain an M-estimator for divergences, based on a convex and differentiable optimization problem that can be solved efficiently. We analyze the consistency and convergence rates for this M-estimator given conditions only on the ratio of densities.


Perspectives on assessment of stem cell therapy in stroke by 18F-FDG PET.


title: A Neural Network-based method for continuous blood pressure estimation from a PPG signal

abstract: There is a relation, not always linear, between the blood pressure and the pulse duration, obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, in this paper the Artificial Neural Networks (ANNs) are used. Training data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care waveform database for better representation of possible pulse and pressure variation. In total there were analyzed more than 15000 heartbeats and 21 parameters were extracted from each of them that define the input vector for the ANN. The comparison between estimated and reference values shows better accuracy than the linear regression method and satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.


title: Cache-Sensitive Skip List: Efficient Range Queries on Modern CPUs

abstract: Due to ever falling prices and advancements in chip technologies, many of today’s databases can be entirely kept in main memory. However, reusing existing disk-based index structures for managing data in memory leads to suboptimal performance due to inefficient cache usage and negligence of the capabilities of modern CPUs. Accordingly, a number of main-memory optimized index structures have been proposed, yet most of them focus entirely on single-key lookups, neglecting the equally important range queries. We present Cache-Sensitive Skip Lists (CSSL) as a novel index structure that is optimized for range queries and exploits modern CPUs. CSSL is based on a cache-friendly data layout and traversal algorithm that minimizes cache misses, branch mispredictions, and allows to exploit SIMD instructions for search. In our experiments, CSSL’s range query performance surpasses all competitors significantly. Even for lookups, it is only surpassed by the recently presented ART index structure. We therefore see CSSL as a serious alternative for mixed key/range workloads on main-memory databases.


title: New Wilkinson Power Dividers and Their Integration Applications to Four-Way and Filtering Dividers

abstract: This paper involved developing two (Type I and Type II) equal-split Wilkinson power dividers (WPDs). The Type I divider can use two short uniform-impedance transmission lines, one resistor, one capacitor, and two quarter-wavelength ( \(\lambda/4 \) ) transformers in its circuit. Compared with the conventional equal-split WPD, the proposed Type I divider can relax the two \(\lambda/4 \) transformers and the output ports layout restrictions of the conventional WPD. To eliminate the number of impedance transformers, the proposed Type II divider requires only one impedance transformer attaining the optimal matching design and a compact size. A compact four-way equal-split WPD based on the proposed Type I and Type II dividers was also developed, facilitating a simple layout, and reducing the circuit size. Regarding the divider, to obtain favorable selectivity and isolation performance levels, two Butterworth filter transformers were integrated in the proposed Type I divider to perform filter response and power split functions. Finally, a single Butterworth filter transformer was integrated in the proposed Type II divider to demonstrate a compact filtering WPD.


title: What's New in the Linux Network Stack?

abstract: In this paper, interesting features of the Linux kernel’s network stack are analyzed, which were introduced during the development cycles from Linux v3.7 to Linux v3.16. Special attention is given to the low-latency device polling, introduced in Linux v3.11, the netfilter’s SYNPROXY target, introduced in Linux v3.12 and the new Nftables framework, introduced in Linux v3.13. At the end a trend is presented, which shows the direction in which the Linux network stack is evolving.


title: Procedural voronoi foams for additive manufacturing

abstract: Microstructures at the scale of tens of microns change the physical properties of objects, making them lighter or more flexible. While traditionally difficult to produce, additive manufacturing now lets us physically realize such microstructures at low cost. In this paper we propose to study procedural, aperiodic microstructures inspired by Voronoi open-cell foams. The absence of regularity affords for a simple approach to grade the foam geometry --- and thus its mechanical properties --- within a target object and its surface. Rather than requiring a global optimization process, the microstructures are directly generated to exhibit a specified elastic behavior. The implicit evaluation is akin to procedural textures in computer graphics, and locally adapts to follow the elasticity field. This allows very detailed structures to be generated in large objects without having to explicitly produce a full representation --- mesh or voxels --- of the complete object: the structures are added on the fly, just before each object slice is manufactured. We study the elastic behavior of the microstructures and provide a complete description of the procedure generating them. We explain how to determine the geometric parameters of the microstructures from a target elasticity, and evaluate the result on printed samples. Finally, we apply our approach to the fabrication of objects with spatially varying elasticity, including the implicit modeling of a frame following the object surface and seamlessly connecting to the microstructures.


title: A memory model for scientific algorithms on graphics processors

abstract: We present a memory model to analyze and improve the performance of scientific algorithms on graphics processing units (GPUs). Our memory model is based on texturing hardware, which uses a 2D block-based array representation to perform the underlying computations. We incorporate many characteristics of GPU architectures including smaller cache sizes, 2D block representations, and use the 3C's model to analyze the cache misses. Moreover. we present techniques to improve the performance of nested loops on GPUs. In order to demonstrate the effectiveness of our model, we highlight its performance on three memory-intensive scientific applications - sorting, fast Fourier transform and dense matrix-multiplication. In practice, our cache-efficient algorithms for these applications are able to achieve memory throughput of 30-50 GB/s on a NVIDIA 7900 GTX GPU. We also compare our results with prior GPU-based and CPU-based implementations on high-end processors. In practice, we are able to achieve 2-5 x performance improvement.


title: Complex reconstruction of facial deformity and function after severe gunshot injury: one case report

abstract: In this report, we described clinical outcomes of a multi-stage surgery integrating multiple techniques in restoration of facial morphology and function of a 17-year-old boy with severe gunshot injuries. This multi-stage surgery was applied in treatment of one rare case of gunshot-caused complicated facial deformities involving most parts of the face (labrum, left nose wing, nasal columella, nasal septum, maxillary alveolar process, hard palate, soft palate, bilateral maxillary bones, left zygoma, suborbital bone defects) and clinical efficacy upon restoring facial form and function were retrospectively evaluated. The patient was diagnosed with massive facial defects and deformities caused by gunshot, which led to feeding difficulty, severe articulation disorders and serious facial disfigurement. To reconstruct facial form and restore functions of mastication and articulation, multiple examinations and surgical procedures including mirror imaging, rapid prototyping technique, porous titanium implants, microscopic surgical technique, dental implants, osteomyocutaneous flap, muscular flap, shifting and repairing of adjacent tissue flaps and free bone graft reconstruction were undertaken. Postoperatively, reconstruction of severe facial disfigurement and restoring basic functions including articulation and feeding for the first time and relatively sound clinical outcomes have been obtained, which may add clinical evidence to the treatment of similar cases of severe facial deformities.


title: A new fast associative classification algorithm for detecting phishing websites

abstract: Display Omitted A new fast Associative classification mining approach is developed.The applicability of well-known associative classification techniques on detecting phishing websites is investigated.Experimental results using different associative classification algorithms was performed. Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper, we propose a new AC algorithm called the Fast Associative Classification Algorithm (FACA). We investigate our proposed algorithm against four well-known AC algorithms (CBA, CMAR, MCAR, and ECAR) on real-world phishing datasets. The bases of the investigation in our experiments are classification accuracy and the F1 evaluation measures. The results indicate that FACA is very successful with regard to the F1 evaluation measure compared with the other four well-known algorithms (CBA, CMAR, MCAR, and ECAR). The FACA also outperformed the other four AC algorithms with regard to the accuracy evaluation measure.


Training Pac-Man bots using reinforcement learning and case-based reasoning


title: Office emergencies: an outline of causes, symptoms, and treatment.

abstract: Conscientious dental practitioners are aware of the increasing chances for medical emergencies to arise in the dental office. New treatment modalities and drugs have increased potential life spans, but have also presented greater responsibilities to the dentist for properly managing these patients. Additionally, widely diversified sources are available to the dentist for acquiring and assembling all necessary information to train his personnel and organize equipment and drugs so he is constantly ready to handle any emergency. We have attempted to give a simple, thorough outline that provides basic information on causes, symptoms, and treatment of major medical emergencies, but that in no way reflects the only means of treating the specific emergency. We feel the manner presented allows one to regiment his diagnosis and treatment rapidly and make modifications to the list as needed.


title: One-step Consensus with Zero-Degradation

abstract: In the asynchronous distributed system model, consensus is obtained in one communication step if all processes propose the same value. Assuming f<n/3, this is regardless of the failure detector output. A zero-degrading protocol reaches consensus in two communication steps in every stable run, i.e., when the failure detector makes no mistakes and its output does not change. We show that no leader-based consensus protocol can be simultaneously one-step and zero-degrading. We propose two approaches to circumvent the impossibility result and present corresponding consensus protocols. Further, we present an atomic broadcast protocol that has a latency of 3delta in every stable run and a latency of 2delta in case of no collisions. Finally, we evaluate its performance in a cluster of workstations


title: New service bonds and customer value in customer relationship management: The case of museum visitors

abstract: The ability to manage customer relationships successfully provides tourism organizations with an opportunity to increase their value proposition and obtain competitive advantage. However, no study has attempted to investigate the role of new service bonds (i.e., the newness and meaningfulness of new services provided by organizations) as a relationship tactic for customer value for long-term sustainability, particularly in the context of the museum industry, which is relatively under-researched in the tourism management field. This study addresses this research gap by exploring the effects of new service bonds on customer commitment through the creation of knowledge and relational values, particularly in the museum industry. Two hundred ninety-six customers of six major museums in Hong Kong were surveyed. Findings show that both traditional relationship tactics and new service bonds affect customers' perceived relationship investment of a museum, on which new service bonds have a stronger effect. Both knowledge and relational value mechanisms are the underlying mechanisms that partially mediate the relationship between relationship investment and customer commitment.


title: The gut microbiota shapes intestinal immune responses during health and disease

abstract: Immunological dysregulation is the cause of many non-infectious human diseases such as autoimmunity, allergy and cancer. The gastrointestinal tract is the primary site of interaction between the host immune system and microorganisms, both symbiotic and pathogenic. In this Review we discuss findings indicating that developmental aspects of the adaptive immune system are influenced by bacterial colonization of the gut. We also highlight the molecular pathways that mediate host-symbiont interactions that regulate proper immune function. Finally, we present recent evidence to support that disturbances in the bacterial microbiota result in dysregulation of adaptive immune cells, and this may underlie disorders such as inflammatory bowel disease. This raises the possibility that the mammalian immune system, which seems to be designed to control microorganisms, is in fact controlled by microorganisms.


title: Outstanding Issues in Anaphora Resolution (Invited Talk)

abstract: This paper argues that even though there has been considerable advance in the research in anaphora resolution over the last 10 years, there are still a number of outstanding issues. The paper discusses several of these issues and outlines some of the work underway to address them with particular reference to the work carried out by the author's research team.


title: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

abstract: Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.


title: BoS cost savings and LCOE reduction for a 10 MW PV system with the 500 kW transformerless inverter

abstract: This paper presents the cost advantages that a high-performance, 500 kW, transformerless PV inverter can have for a solar PV system. Higher efficiency, smaller size, and lower installation costs impact balance-of -system (BoS) costs and reduce the levelized cost of energy (LCOE) compared with traditional, transformer-based PV inverters. These cost advantages should be considered as integrators and end-users in China and the U.S. select and install inverters in PV systems. This paper focuses on the effect of a more efficient PV inverter on BoS cost and LCOE, analyzing real PV grid-connected, transformerless inverter applications as well as unipolar and bipolar PV array configurations. Some standards and specifications for the PV inverters are introduced, such as CEC-weighted and EU efficiency. Using the Solar Advisory Model (SAM) and PVSYST software with PV module, inverter, and weather data, a simulation to calculate the LCOE and system performance rate of a bipolar PV array with a transformerless inverter is presented for a 10 MW PV system in Dunhuang, China.


title: SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

abstract: Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES to predict chemical properties, without the need for additional explicit feature engineering. Using Bayesian optimization methods to tune the network architecture, we show that an optimized SMILES2vec model can serve as a general-purpose neural network for predicting distinct chemical properties including toxicity, activity, solubility and solvation energy, while also outperforming contemporary MLP neural networks that uses engineered features. Furthermore, we demonstrate proof-of-concept of interpretability by developing an explanation mask that localizes on the most important characters used in making a prediction. When tested on the solubility dataset, it identified specific parts of a chemical that is consistent with established first-principles knowledge with an accuracy of 88%. Our work demonstrates that neural networks can learn technically accurate chemical concept and provide state-of-the-art accuracy, making interpretable deep neural networks a useful tool of relevance to the chemical industry.


title: Testing Deep Neural Networks

abstract: Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional software test coverage metrics cannot be applied directly to DNNs. In this paper, inspired by the MC/DC coverage criterion, we propose a family of four novel test criteria that are tailored to structural features of DNNs and their semantics. We validate the criteria by demonstrating that the generated test inputs guided via our proposed coverage criteria are able to capture undesired behaviours in a DNN. Test cases are generated using a symbolic approach and a gradient-based heuristic search. By comparing them with existing methods, we show that our criteria achieve a balance between their ability to find bugs (proxied using adversarial examples) and the computational cost of test case generation. Our experiments are conducted on state-of-the-art DNNs obtained using popular open source datasets, including MNIST, CIFAR-10 and ImageNet.


title: Computing the Types of the Relationships Between Autonomous Systems

abstract: We investigate the problem of computing the types of the relationships between Internet Autonomous Systems. We refer to the model introduced by Gao [IEEE/ACM TRANSACTIONS ON NETWORKING, 9(6):733-645, 2001] and Subramanian et al. (IEEE Infocom, 2002) that bases the discovery of such relationships on the analysis of the AS paths extracted from the BGP routing tables. We characterize the time complexity of the above problem, showing both NP-completeness results and efficient algorithms for solving specific cases. Motivated by the hardness of the general problem, we propose approximation algorithms and heuristics based on a novel paradigm and show their effectiveness against publicly available data sets. The experiments provide evidence that our algorithms perform significantly better than state-of-the-art heuristics.


title: Research Challenges for the Internet of Things: What Role Can OR Play?

abstract: The Internet of Things (IoT) is an extension of the Internet in which large numbers of “things”, including sensors, actuators and processors, in addition to human users, are networked and able to provide high resolution data on their environment and exercise a degree of control over it. It is still at an early stage of development, and many problems/research challenges must be solved before it is widely adopted. Many of these are technical, including interoperability and scalability, as billions of heterogeneous devices will be connected, but deciding on how to invest in the IoT is a challenge for business, and there are also major social, legal and ethical challenges, including security and privacy of data collection, which must be resolved. As the future IoT will be a multi-national, multi-industry, multi-technology infrastructure, the paper reviews the global standardization efforts that are underway to facilitate its worldwide creation and adoption. The main purpose of the paper is to give a broad survey, based on published literature, of the methods of Operations Research (OR), both the mathematical tools and techniques of “hard” OR, and the various approaches of Systems Thinking, including “soft” OR, which may assist in dealing with these problems. A subset of these is described in greater depth to better convey what might be involved in applying OR and Systems Thinking to the IoT. It is suggested that OR has a role to play in balancing the technical and non-technical research challenges which confront the IoT.


title: Analysis of Financial Time Sequence using Hidden Markov Models

abstract: Financial time sequence analysis has been a popular research topic in the field of finance, data science and machine learning. It is a highly challenging due to the extreme complexity within the sequences. Mostly existing models are failed to capture its intrinsic information, factor and tendency. To improve the previous approaches, in this paper, we propose a Hidden Markov Model (HMMs) based approach to analyze the financial time sequence. The fluctuation of financial time sequence was predicted through introducing a dual-state HMMs. Dual-state HMMs models the sequence and produces the features which will be delivered to SVMs for prediction. Note that we cast a financial time sequence prediction problem to a classification problem. To evaluate the proposed approach, we use Shanghai Composite Index as the dataset for empirically experiments. The dataset was collected from 550 consecutive trading days, and is randomly split to the training set and test set. The extensively experimental results show that: when analyzing financial time sequence, the mean-square error calculated with HMMs was obviously smaller error than the compared GARCH approach. Therefore, when using HMM to predict the fluctuation of financial time sequence, it achieves higher accuracy and exhibits several attractive advantageous over GARCH approach.


title: Fully Integrated FPGA-Based Controller for Synchronous Motor Drive

abstract: The aim of this paper is to present a fully integrated solution for synchronous motor control. The implemented controller is based on Actel Fusion field-programmable gate array (FPGA). The objective of this paper is to evaluate the ability of the proposed fully integrated solution to ensure all the required performances in such applications, particularly in terms of control quality and time/area performances. To this purpose, a current control algorithm of a permanent-magnet synchronous machine has been implemented. This machine is associated with a resolver position sensor. In addition to the current control closed loop, all the necessary motor control tasks are implemented in the same device. The analog-to-digital conversion is ensured by the integrated analog-to-digital converter (ADC), avoiding the use of external converters. The resolver processing unit, which computes the rotor position and speed from the resolver signals, is implemented in the FPGA matrix, avoiding the use of external resolver-to-digital converter (RDC). The sine patterns used for the Park transformation are stored in the integrated flash memory blocks.


title: Puzzling face verification algorithms for privacy protection

abstract: This paper presents a new approach for deidentifying face images, i.e. for preventing automatic matching with public face collections. The overall motivation is to offer tools for privacy protection on social networks. We address this question by drawing a parallel between face de-identification and oracle attacks in digital watermarking. In our case, the identity of the face is seen as the watermark to be removed. Inspired by oracle attacks, we forge de-identified faces by superimposing a collection of carefully designed noise patterns onto the original face. The modification of the image is controlled to minimize the probability of good recognition while minimizing the distortion. In addition, these de-identified images are – by construction – made robust to counter attacks such as blurring. We present an experimental validation in which we de-identify LFW faces and show that resulting images are still recognized by human beings while deceiving a state-of-the-art face recognition algorithm.


title: Local and Global Contrast Adaptation in Retinal Ganglion Cells

abstract: Retinal ganglion cells react to changes in visual contrast by adjusting their sensitivity and temporal filtering characteristics. This contrast adaptation has primarily been studied under spatially homogeneous stimulation. Yet, ganglion cell receptive fields are often characterized by spatial subfields, providing a substrate for nonlinear spatial processing. This raises the question whether contrast adaptation follows a similar subfield structure or whether it occurs globally over the receptive field even for local stimulation. We therefore recorded ganglion cell activity in isolated salamander retinas while locally changing visual contrast. Ganglion cells showed primarily global adaptation characteristics, with notable exceptions in certain aspects of temporal filtering. Surprisingly, some changes in filtering were most pronounced for locations where contrast did not change. This seemingly paradoxical effect can be explained by a simple computational model, which emphasizes the importance of local nonlinearities in the retina and suggests a reevaluation of previously reported local contrast adaptation.


Pump it up: computer animation of a biomechanically based model of muscle using the finite element method


title: Tracking control of mobile robot using ANFIS

abstract: This study addresses a nonlinear trajectory tracking control problem for a kinematics Model of nonholonomic mobile robot with considering next 2 time path curvature. The tracking control of mobile robot using two cascade controllers is presented. The first fuzzy controller produces a variable which shows curvature of the path and is considered as one of the inputs of the second fuzzy controller. Adaptive Neuro Fuzzy Inference System (ANFIS) is applied as second stage controller for the solution of the path tracking problem of mobile robots. The inputs value to fuzzy logic layer are V C , C, dR & dθ the robot current linear velocity, trajectory curvature, distance from the robot actual position to the next desired position, and difference between the angles of the dθ and the robot actual heading, respectively. A gradient descent learning algorithm is used to adjust the parameters. That present controller is compared with previous work to confirm its effectiveness.


title: An experiment investigating the links among online dating profile attractiveness, ideal endorsement, and romantic media

abstract: First-ever experiment investigating online dating profiles, attraction, and romantic beliefs.Exposure to the conditions featuring any ideal content produced stronger endorsement of romantic beliefs.Consuming romantic media predicted stronger endorsement of romantic beliefs and higher ratings of profile attractiveness. This study was the first-ever experiment to test how ideal expressions in hypothetical online dating profiles and exposure to romantic media are related to profile attractiveness, romantic beliefs, and endorsement of ideal partner characteristics. The sample contained 249 undergraduate students from a small, southwestern university. The study is a one-way experiment, with five manipulations and one control group. Results revealed that exposure to the conditions featuring any ideal content produced stronger endorsement of romantic beliefs, but not ideal partner characteristics. Consuming romantic media predicted stronger endorsement of romantic beliefs and higher ratings of profile attractiveness. Results support the heuristic processing model of cultivation.


title: Changing trends in biotechnology of secondary metabolism in medicinal and aromatic plants

abstract: Main conclusion ::: Medicinal and aromatic plants are known to produce secondary metabolites that find uses as flavoring agents, fragrances, insecticides, dyes and drugs. Biotechnology offers several choices through which secondary metabolism in medicinal plants can be altered in innovative ways, to overproduce phytochemicals of interest, to reduce the content of toxic compounds or even to produce novel chemicals. Detailed investigation of chromatin organization and microRNAs affecting biosynthesis of secondary metabolites as well as exploring cryptic biosynthetic clusters and synthetic biology options, may provide additional ways to harness this resource. ::: ::: Plant secondary metabolites are a fascinating class of phytochemicals exhibiting immense chemical diversity. Considerable enigma regarding their natural biological functions and the vast array of pharmacological activities, amongst other uses, make secondary metabolites interesting and important candidates for research. Here, we present an update on changing trends in the biotechnological approaches that are used to understand and exploit the secondary metabolism in medicinal and aromatic plants. Bioprocessing in the form of suspension culture, organ culture or transformed hairy roots has been successful in scaling up secondary metabolite production in many cases. Pathway elucidation and metabolic engineering have been useful to get enhanced yield of the metabolite of interest; or, for producing novel metabolites. Heterologous expression of putative plant secondary metabolite biosynthesis genes in a microbe is useful to validate their functions, and in some cases, also, to produce plant metabolites in microbes. Endophytes, the microbes that normally colonize plant tissues, may also produce the phytochemicals produced by the host plant. The review also provides perspectives on future research in the field.


title: Affordances and Product Design to Support Environmentally Conscious Behavior

abstract: We developed an affordance-based methodology to support environmentally conscious behavior (ECB) that conserves resources such as materials, energy, etc. While studying concepts that aim to support ECB, we noted that characteristics of products that enable ECB tend to be more accurately described as affordances than functions. Therefore, we became interested in affordances, and specifically how affordances can be used to design products that support ECB. Affordances have been described as possible ways of interacting with products, or context-dependent relations between artifacts and users. Other researchers have explored affordances in lieu of functions as a basis for design, and developed detailed deductive methods of discovering affordances in products. We abstracted desired affordances from patterns and principles we observed to support ECB, and generated concepts based on those affordances. As a possible shortcut to identifying and implementing relevant affordances, we introduced the affordance-transfer method. This method involves altering a product's affordances to add desired features from related products. Promising sources of affordances include lead-user and other products that support resource conservation. We performed initial validation of the affordance-transfer method and observed that it can improve the usefulness of the concepts that novice designers generate to support ECB.


title: Korean pop takes off! Social media strategy of Korean entertainment industry

abstract: The Korean Wave (K-wave), or Hallyu, referred as “the phenomenon of Korean pop culture, such as TV dramas, films, pop music, fashion, and online games being widely embraced and shared among the people of Japan, China, Hong Kong, Taiwan, and other Asian countries”. After the drama-oriented craze of the first K-wave, Korean pop music (shortly K-pop) took the integral part of the second K-wave. In addition, with the rapid spread of social media like YouTube and Twitter, K-pop has expanded its fandom outside of Asia to the West. The world-wide success of K-pop contributes to improve the `Korea' image and make a positive impact on Korean economy. It is reported that around 1,000 entertainment agencies are active in Korea, while there exist the “big three” record labels and entertainment agencies: SM Entertainment, YG Entertainment and JYP Entertainment. In this case, we address the world-wide phenomenon of K-pop popularity and the role of social media on recent boom of K-pop. Focusing the major agencies above, we present lessons on how to manage social media strategically: align strategic business model with social media; maximize various social media channels; engage customers with on-and offline promotions; and stimulate audience with exclusive contents.


title: Hybrid Job-driven Scheduling for Virtual MapReduce Clusters

abstract: It is cost-efficient for a tenant with a limited budget to establish a virtual MapReduce cluster by renting multiple virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant's perspective. JoSS provides not only job-level scheduling, but also map-task level scheduling and reduce-task level scheduling. JoSS classifies MapReduce jobs based on job scale and job type and designs an appropriate scheduling policy to schedule each class of jobs. The goal is to improve data locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations of JoSS are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations with current scheduling algorithms supported by Hadoop. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-data locality, and network overhead without incurring significant overhead. In addition, the two variations are separately suitable for different MapReduce-workload scenarios and provide the best job performance among all tested algorithms.


title: A 600MS/s 30mW 0.13µm CMOS ADC array achieving over 60dB SFDR with adaptive digital equalization

abstract: At high conversion speed, time interleaving provides a viable way of achieving analog-to-digital conversion with low power consumption, especially when combined with the successive-approximation-register (SAR) architecture that is known to scale well in CMOS technology. In this work, we showcase a digital background-equalization technique to treat the path-mismatch problem as well as individual ADC nonlinearities in time-interleaved SAR ADC arrays. This approach was first introduced to calibrate the linearity errors in pipelined ADCs [1], and subsequently extended to treat SAR ADCs [2]. In this prototype, we demonstrate the effectiveness of this technique in a compact SAR ADC array, which achieves 7.5 ENOB and a 65dB SFDR at 600MS/s while dissipating 23.6mW excluding the on-chip DLL, and exhibiting one of the best conversion FOMs among ADCs with similar sample rates and resolutions [3].


title: Introduction of speech log-spectral priors into dereverberation based on Itakura-Saito distance minimization

abstract: It has recently been shown that a multi-channel linear prediction can effectively achieve blind speech dereverberation based on maximum-likelihood (ML) estimation. This approach can estimate and cancel unknown reverberation processes from only a few seconds of observation. However, one problem with this approach is that speech distortion may increase if we iterate the dereverberation more than once based on Itakura-Saito (IS) distance minimization to further reduce the reverberation. To overcome this problem, we introduce speech log-spectral priors into this approach, and reformulate it based on maximum a posteriori (MAP) estimation. Two types of priors are introduced, a Gaussian mixture model (GMM) of speech log spectra, and a GMM of speech mel-frequency cepstral coefficients. In the formulation, we also propose a new versatile technique to integrate such log-spectral priors with the IS distance minimization in a computationally efficient manner. Preliminary experiments show the effectiveness of the proposed approach.


title: Visualizing Social Media Sentiment in Disaster Scenarios

abstract: Recently, social media, such as Twitter, has been successfully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media during disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social media streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualization of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geographic visualization. In particular, we provide an entropy-based metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how visual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.


title: Memory persistency

abstract: Emerging nonvolatile memory technologies (NVRAM) promise the performance of DRAM with the persistence of disk. However, constraining NVRAM write order, necessary to ensure recovery correctness, limits NVRAM write concurrency and degrades throughput. We require new memory interfaces to minimally describe write constraints and allow high performance and high concurrency data structures. These goals strongly resemble memory consistency. Whereas memory consistency concerns the order that memory operations are observed between numerous processors, persistent memory systems must constrain the order that writes occur with respect to failure. We introduce memory persistency, a new approach to designing persistent memory interfaces, building on memory consistency. Similar to memory consistency, memory persistency models may be relaxed to improve performance. We describe the design space of memory persistency and desirable features that such a memory system requires. Finally, we introduce several memory persistency models and evaluate their ability to expose NVRAM write concurrency using two implementations of a persistent queue. Our results show that relaxed persistency models accelerate system throughput 30-fold by reducing NVRAM write constraints


title: MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs

abstract: We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,895 images from 14,982 studies, where each study is manually labeled by radiologists as either normal or abnormal. On this dataset, we train a 169-layer densely connected convolutional network to detect and localize abnormalities. To evaluate our model robustly and to get an estimate of radiologist performance, we collect additional labels from board-certified Stanford radiologists on the test set, consisting of 209 musculoskeletal studies. We compared our model and radiologists on the Cohen's kappa statistic, which expresses the agreement of our model and of each radiologist with the gold standard, defined as the majority vote of a disjoint group of radiologists. We find that our model achieves performance comparable to that of radiologists. Model performance is higher than the best radiologist performance in detecting abnormalities on finger studies and equivalent on wrist studies. However, model performance is lower than best radiologist performance in detecting abnormalities on elbow, forearm, hand, humerus, and shoulder studies, indicating that the task is a good challenge for future research. To encourage advances, we have made our dataset freely available at https://stanfordmlgroup.github.io/projects/mura


An introduction to inertial navigation


title: Design of Multi-loop PID Controllers Based on the Generalized IMC-PID Method with Mp Criterion

abstract: A new method of designing multi-loop PID controllers is presented in this paper. By using the generalized IMC-PID method for multi-loop systems, the optimization problem involved in finding the PID parameters is efficiently simplified to find the optimum closed-loop time constant in a reduced search space. A weighted sum Mp criterion is proposed as a performance cost function to cope with both the performance and robustness of a multi-loop control system. Several illustrative examples are included to demonstrate the improved performance of the multi-loop PID controllers obtained by the proposed design method.


title: The effect of a coordinator on cardiac rehabilitation in a district general hospital

abstract: Abstract The purpose of this study was to compare the recorded evidence for cardiac rehabilitation (CR) before and after the appointment of a CR coordinator in a large district general hospital in the north of England. An audit proforma was used to collect data recorded in the case notes of 200 patients admitted to hospital during this period with the diagnosis of first myocardial infarction. Recorded CR activity was significantly higher ( P P


title: Error detection and accuracy estimation in automatic speech recognition using deep bidirectional recurrent neural networks

abstract: Deep bidirectional RNNs (DBRNNs) are applied to ASR error detection and accuracy estimation.DBRNNs take longer bidirectional context of input feature vectors into account.DBRNNs model highly nonlinear relationships between input feature vectors and output labels.DBRNNs greatly outperform CRFs and other structures of neural networks. Recurrent neural networks (RNNs) have recently been applied as the classifiers for sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied to error detection in automatic speech recognition (ASR), which is a sequential labeling problem. We investigate three types of ASR error detection tasks, i.e. confidence estimation, out-of-vocabulary word detection and error type classification. We also estimate ASR accuracy, i.e. percent correct and word accuracy, from the error type classification results. Experimental results using English and Japanese lecture speech corpora show that the DBRNNs greatly outperform conditional random fields (CRFs) and the other NN structures, i.e. deep feedforward NNs (DNNs) and deep unidirectional RNNs (DURNNs). These performance improvements are because the DBRNNs can take the longer bidirectional context of input feature vectors into account and can model highly nonlinear relationships between the input feature vectors and output labels. In detailed analyses, the DBRNNs show a better generalization ability than the CRFs. These results are thanks to the ability of the DBRNNs to represent (embed) the words in a low-dimensional continuous value vector space. In addition, the superiority of the DBRNNs to the DNNs and DURNNs indicates that the average length of the context of the input feature vectors required for ASR error detection is only a few time steps, however, it will change (lengthen) depending on the situation.


title: High-frequency trading in a limit order book

abstract: We study a stock dealer’s strategy for submitting bid and ask quotes in a limit order book. The agent faces an inventory risk due to the diffusive nature of the stock’s mid-price and a transactions risk due to a Poisson arrival of market buy and sell orders. After setting up the agent’s problem in a maximal expected utility framework, we derive the solution in a two step procedure. First, the dealer computes a personal indifference valuation for the stock, given his current inventory. Second, he calibrates his bid and ask quotes to the market’s limit order book. We compare this ”inventory-based” strategy to a ”naive” best bid/best ask strategy by simulating stock price paths and displaying the P&L profiles of both strategies. We find that our strategy has a P&L profile that has both a higher return and lower variance than the benchmark strategy.


title: SoftLight: Adaptive visible light communication over screen-camera links

abstract: Screen-camera links for Visible Light Communication (VLC) are diverse, as the link quality varies according to many factors, such as ambient light and camera's performance. This paper presents SoftLight, a channel coding approach that considers the unique channel characteristics of VLC links and automatically adapts the transmission data rate to the link qualities of various scenarios. SoftLight incorporates two new ideas: (1) an expanded color modulation interface that provides soft hint about its confidence in each demodulated bit and establishes a bit-level VLC erasure channel, and (2) a rateless coding scheme that achieves bit-level rateless transmissions with low computation complexity and tolerates the false positive of bits provided by the soft hint enabled erasure channel. SoftLight is orthogonal to the visual coding schemes and can be applied atop any barcode layouts. We implement SoftLight on Android smartphones and evaluate its performance under a variety of environments. The experiment results show that SoftLight can correctly transmit a 22-KByte photo between two smartphones within 0.6 second and improves the average goodput of the state-of-the-art screen-camera VLC solution by 2.2.


title: Not All Browsers Are Created Equal: Comparing Web Browser Fingerprintability

abstract: Browsers and their users can be tracked even in the absence of a persistent IP address or cookie. Unique and hence identifying pieces of information, making up what is known as a fingerprint, can be collected from browsers by a visited website, e.g. using JavaScript. However, browsers vary in precisely what information they make available, and hence their fingerprintability may also vary. In this paper, we report on the results of experiments examining the fingerprintable attributes made available by a range of modern browsers. We tested the most widely used browsers for both desktop and mobile platforms. The results reveal significant differences between browsers in terms of their fingerprinting potential, meaning that the choice of browser has significant privacy implications.


title: Case study of the Miner Botnet

abstract: Malware and botnets are one of the most serious threats to today's Internet security. In this paper, we characterise the so-called &Miner Botnet”. It received major media attention after massive distributed denial of service attacks against a wide range of German and Russian websites, mainly during August and September 2011. We use our insights on this botnet to outline current botnet-related money-making concepts and to show that multiple activities of this botnet are actually centred on the virtual anonymised currency Bitcoin, thus justifying the name. Furthermore, we provide a binary-level analysis of the malware's design and components to illustrate the modularity of the previously mentioned concepts. We give an overview of the structure of the command-and-control protocol as well as of the botnet's architecture. Both centralised as well as distributed infrastructure aspects realised through peer-to-peer are present to run the botnet, the latter for increasing its resiliency. Finally, we provide the results of our ongoing tracking efforts that started in September 2011, focusing on the development of the botnet's size and geographic distribution. In addition we point out the challenge that is generally connected with size measurements of botnets due to the reachability of individual nodes and the persistence of IP addresses over time.


title: The Semantic Web takes Wing: Programming Ontologies with Tawny-OWL

abstract: The Tawny-OWL library provides a fully-programmatic environment for ontology building; it enables the use of a rich set of tools for ontology development, by recasting development as a form of programming. It is built in Clojure - a modern Lisp dialect, and is backed by the OWL API. Used simply, it has a similar syntax to OWL Manchester syntax, but it provides arbitrary extensibility and abstraction. It builds on existing facilities for Clojure, which provides a rich and modern programming tool chain, for versioning, distributed development, build, testing and continuous integration. In this paper, we describe the library, this environment and the its potential implications for the ontology development process.


title: Deep Fried Convnets

abstract: The fully-connected layers of deep convolutional neural networks typically contain over 90% of the network parameters. Reducing the number of parameters while preserving predictive performance is critically important for training big models in distributed systems and for deployment in embedded devices. In this paper, we introduce a novel Adaptive Fastfood transform to reparameterize the matrix-vector multiplication of fully connected layers. Reparameterizing a fully connected layer with d inputs and n outputs with the Adaptive Fastfood transform reduces the storage and computational costs costs from O(nd) to O(n) and O(n log d) respectively. Using the Adaptive Fastfood transform in convolutional networks results in what we call a deep fried convnet. These convnets are end-to-end trainable, and enable us to attain substantial reductions in the number of parameters without affecting prediction accuracy on the MNIST and ImageNet datasets.


title: Reducing Common-Mode Noise in Two-Switch Forward Converter

abstract: This paper addresses the common-mode (CM) electromagnetic interference noise issues in the two-switch forward converter. The two-switch forward converter has low CM noise compared to other topologies because its symmetric primary-side circuit has two out-of-phase dv/dts that cancel each other. However, parasitic capacitances of the circuit significantly affect the symmetry and degrade the noise reduction. Moreover, the secondary-side circuit of the converter is not symmetric and still contributes to the CM noise. In this paper, the parasitic capacitances of the converter are first modeled. Different transformer structures and their parasitic capacitances are characterized. A general balance technique is introduced to further reduce the CM noise of the converter. For each transformer structure, balance can be achieved with proper connection of the windings terminals and control of the parasitic capacitances to minimize the CM noise of the converter. Experimental results validated the proposed techniques.


title: Design of a sliding mode control for a DC-to-DC buck-boost converter

abstract: This paper studies DC-to-DC buck-boost converter, as well as of the most important features asociated with the sliding mode control, such as adding robustness to the system with respect to variations of its parameters and external disturbances applied to this converter.


title: Inductive Logic Programming: Techniques and Applications

abstract: Part 1 Empirical inductive logic programming: introduction empirical ILP systems - an overview LINUS - using attribute-value learners in an ILP framework experiments in learning relations with LINUS ILP as search for program clauses. Part 2 Learning relations from imperfect data: handling imperfect data in ILP using heuristics to handle noise in ILP mFOIL - extending noise-handling in FOIL experiments in learning relations from noisy examples. Part 3 Applications of inductive logic programming: learning rules for early diagnosis of rheumatic diseases finite element mesh design an overview of selected ILP applications.


title: Privacy-Preserving Fraud Detection Across Multiple Phone Record Databases

abstract: Subscription fraud, i.e., customers signing up to a service with no intent to pay, causes significant losses in the telecommunication industry. Telecom operators have developed strategies to identify those fraudsters, but fraudsters tend to migrate from one carrier to another. Data sharing between telecoms would increase fraud detection rates, but phone records are protected by law and telecom operators might be reluctant to share information about fraudsters because they see it as giving a competitive advantage. We propose several protocols to enable fraud detection across multiple databases without revealing additional information. We also propose a model to generate phone records, with which we evaluate how the choice of parameters affects detection performance. We show feasibility, performance and costs with implementations of our protocols.


title: Statistical analysis of the jointly-optimized acoustic echo cancellation BF-AEC structure

abstract: This work presents a statistical analysis of a beamformer-assisted acoustic echo canceler (AEC). A new formulation leads to analytical models that can also be used to predict the transient performance of adaptive wideband beamformers. Monte Carlo simulations illustrate the accuracy of the model, which is then used to provide design guidelines. Application of the new model confirms previous experimental findings that the same cancellation performance of a single-microphone AEC can be achieved with a shorter AEC when the possibility of spatial filtering is available.


title: IEC 61850 Standard Based MMS Communication Stack Design Using OOP

abstract: This paper deals with the MMS communication stack library based on IEC 61850 standard. IEC 61850 standard is communication protocol of substation automation for converting conventional copper-wired type to Ethernet based open architecture in digital method. With IEC 61850 standard, interoperability can be maximized. With IEC 61850, optimal use and prediction of troubles are done through real-time surveillance by substation system. Starting from European market, substation automation systems (Sass) based on IEC61850 are spreading trend. Trying to stay with the trend, communication stack library is needed. IEC 61850 standard consists of object, we adopt the strategy of using object-oriented programming (OOP) based on C++ language. Therefore we can take advantages of maintenance, management, alteration and reuse. For improving power of OOP method, we adopt design pattern, especially singleton pattern. With these two strategies, this paper layers the library into a dozen or more layer to provide developers ease of use. We simulate this library by making Microsoft Windows programs.


Feature Selection using Multiobjective Optimization for Aspect based Sentiment Analysis


title: Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO

abstract: Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.


title: Analysis of array CGH data : from signal ratio to gain and loss of DNA regions

abstract: MOTIVATION ::: Genomic DNA regions are frequently lost or gained during tumor progression. Array Comparative Genomic Hybridization (array CGH) technology makes it possible to assess these changes in DNA in cancers, by comparison with a normal reference. The identification of systematically deleted or amplified genomic regions in a set of tumors enables biologists to identify genes involved in cancer progression because tumor suppressor genes are thought to be located in lost genomic regions and oncogenes, in gained regions. Array CGH profiles should also improve the classification of tumors. The achievement of these goals requires a methodology for detecting the breakpoints delimiting altered regions in genomic patterns and assigning a status (normal, gained or lost) to each chromosomal region. ::: ::: ::: RESULTS ::: We have developed a methodology for the automatic detection of breakpoints from array CGH profile, and the assignment of a status to each chromosomal region. The breakpoint detection step is based on the Adaptive Weights Smoothing (AWS) procedure and provides highly convincing results: our algorithm detects 97, 100 and 94% of breakpoints in simulated data, karyotyping results and manually analyzed profiles, respectively. The percentage of correctly assigned statuses ranges from 98.9 to 99.8% for simulated data and is 100% for karyotyping results. Our algorithm also outperforms other solutions on a public reference dataset. ::: ::: ::: AVAILABILITY ::: The R package GLAD (Gain and Loss Analysis of DNA) is available upon request.


title: The Entrepreneurial Gap: How Managers Adjust Span of Accountability and Span of Control to Implement Business Strategy

abstract: This study focuses on the relationship between business strategy, organization structure, and diagnostic control systems. The project analyzes data from 75 field studies to illustrate how managers adjust span of accountability and span of control to motivate different levels of innovation and entrepreneurial behavior. Six propositions are derived inductively about when, why, and how managers make these choices.


title: How many tiers?: pricing in the internet transit market

abstract: ISPs are increasingly selling "tiered" contracts, which offer Internet connectivity to wholesale customers in bundles, at rates based on the cost of the links that the traffic in the bundle is traversing. Although providers have already begun to implement and deploy tiered pricing contracts, little is known about how to structure them. While contracts that sell connectivity on finer granularities improve market efficiency, they are also more costly for ISPs to implement and more difficult for customers to understand. Our goal is to analyze whether current tiered pricing practices in the wholesale transit market yield optimal profits for ISPs and whether better bundling strategies might exist. In the process, we deliver two contributions: 1) we develop a novel way of mapping traffic and topology data to a demand and cost model, and 2) we fit this model on three large real-world networks: an European transit ISP, a content distribution network, and an academic research network, and run counterfactuals to evaluate the effects of different bundling strategies. Our results show that the common ISP practice of structuring tiered contracts according to the cost of carrying the traffic flows (e.g., offering a discount for traffic that is local) can be suboptimal and that dividing contracts based on both traffic demand and the cost of carrying it into only three or four tiers yields near-optimal profit for the ISP.


title: Data Glove Embedded with 6-DOF Inertial Sensors for Hand Rehabilitation

abstract: A hand injury can have great impact on a person's daily life. However, the current manual evaluations of hand functions are imprecise and inconvenient. In this research, a data glove embedded with 6-axis inertial sensors is proposed. With the proposed angle calculating algorithm, accurate bending angles are measured to estimate the real-time movements of hands. This proposed system can provide physicians with an efficient tool to evaluate the recovery of patients and improve the quality of hand rehabilitation.


title: Authentic leaders creating healthy work environments for nursing practice.

abstract: Implementation of authentic leadership can affect not only the nursing workforce and the profession but the healthcare delivery system and society as a whole. Creating a healthy work environment for nursing practice is crucial to maintain an adequate nursing workforce; the stressful nature of the profession often leads to burnout, disability, and high absenteeism and ultimately contributes to the escalating shortage of nurses. Leaders play a pivotal role in retention of nurses by shaping the healthcare practice environment to produce quality outcomes for staff nurses and patients. Few guidelines are available, however, for creating and sustaining the critical elements of a healthy work environment. In 2005, the American Association of Critical-Care Nurses released a landmark publication specifying 6 standards (skilled communication, true collaboration, effective decision making, appropriate staffing, meaningful recognition, and authentic leadership) necessary to establish and sustain healthy work environments in healthcare. Authentic leadership was described as the "glue" needed to hold together a healthy work environment. Now, the roles and relationships of authentic leaders in the healthy work environment are clarified as follows: An expanded definition of authentic leadership and its attributes (eg, genuineness, trustworthiness, reliability, compassion, and believability) is presented. Mechanisms by which authentic leaders can create healthy work environments for practice (eg, engaging employees in the work environment to promote positive behaviors) are described. A practical guide on how to become an authentic leader is advanced. A research agenda to advance the study of authentic leadership in nursing practice through collaboration between nursing and business is proposed.


title: Scratching middle schoolers' creative itch

abstract: Each July since 2003, the author has directed summer camps that introduce middle school boys and girls to the basic ideas of computer programming. Prior to 2009, the author used Alice 2.0 to introduce object-based computing. In 2009, the author decided to offer these camps using Scratch, primarily to engage repeat campers but also for variety. This paper provides a detailed overview of this outreach, and documents its success at providing middle school girls with a positive, engaging computing experience. It also discusses the merits of Alice and Scratch for such outreach efforts; and the use of these visually oriented programs by students with disabilities, including blind students.


title: Development of animated facial expressions to express emotions in a robot: RobotIcon

abstract: Robotics is a field which does not only become more and more important in industry and production but is becoming part of many peoples every day life and as such, the interaction between robots and humans is an essential field of research. In this work, we present a face — being an important part of a natural communication — and show that animated facial expressions can be used to express the robot's emotions. To do so, we developed a set of animated facial expressions to be shown on the screen of the MASHI robot, a telepresence robot with a wide-screen display mounted on top. A survey was conducted to evaluate the recognition value of the facial expressions as well as to evaluate whether displaying the full face or rather just the eye-region affects this recognition rate. The evaluation of this survey shows that most of our emotions are recognized better if a mouth is provided. Yet, the recognition rate is still acceptable without displaying the mouth. In summary, all emotions were well recognized and therefore, displaying these on robots like MASHI will contribute to a more natural human-robot interaction.


title: Plausible Shading Decomposition For Layered Photo Retouching

abstract: Photographers routinely compose multiple manipulated photos of the same scene (layers) into a single image, which is better than any individual photo could be alone. Similarly, 3D artists set up rendering systems to produce layered images to contain only individual aspects of the light transport, which are composed into the final result in post-production. Regrettably, both approaches either take considerable time to capture, or remain limited to synthetic scenes. In this paper, we suggest a system to allow decomposing a single image into a plausible shading decomposition (PSD) that approximates effects such as shadow, diffuse illumination, albedo, and specular shading. This decomposition can then be manipulated in any off-the-shelf image manipulation software and recomposited back. We perform such a decomposition by learning a convolutional neural network trained using synthetic data. We demonstrate the effectiveness of our decomposition on synthetic (i.e., rendered) and real data (i.e., photographs), and use them for common photo manipulation, which are nearly impossible to perform otherwise from single images.


title: Catching Metaphors

abstract: Metaphors are ubiquitous in language and developing methods to identify and deal with metaphors is an open problem in Natural Language Processing (NLP). In this paper we describe results from using a maximum entropy (ME) classifier to identify metaphors. Using the Wall Street Journal (WSJ) corpus, we annotated all the verbal targets associated with a set of frames which includes frames of spatial motion, manipulation, and health. One surprising finding was that over 90% of annotated targets from these frames are used metaphorically, underscoring the importance of processing figurative language. We then used this labeled data and each verbal target's PropBank annotation to train a maximum entropy classifier to make this literal vs. metaphoric distinction. Using the classifier, we reduce the final error in the test set by 5% over the verb-specific majority class baseline and 31% over the corpus-wide majority class baseline.