title: ImageNet Training in Minutes.

abstract: Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires 10^18 single precision operations in total. On the other hand, the world's current fastest supercomputer can finish 2 * 10^17 single precision operations per second (Dongarra et al 2017, this https URL). If we can make full use of the supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in one minute. However, the current bottleneck for fast DNN training is in the algorithm level. Specifically, the current batch size (e.g. 512) is too small to make efficient use of many processors. For large-scale DNN training, we focus on using large-batch data-parallelism synchronous SGD without losing accuracy in the fixed epochs. The LARS algorithm (You, Gitman, Ginsburg, 2017, arXiv:1708.03888) enables us to scale the batch size to extremely large case (e.g. 32K). We finish the 100-epoch ImageNet training with AlexNet in 11 minutes on 1024 CPUs. About three times faster than Facebook's result (Goyal et al 2017, arXiv:1706.02677), we finish the 90-epoch ImageNet training with ResNet-50 in 20 minutes on 2048 KNLs without losing accuracy. State-of-the-art ImageNet training speed with ResNet-50 is 74.9% top-1 test accuracy in 15 minutes. We got 74.9% top-1 test accuracy in 64 epochs, which only needs 14 minutes. Furthermore, when we increase the batch size to above 16K, our accuracy is much higher than Facebook's on corresponding batch sizes. Our source code is available upon request.


title: Formal Verification of Financial Algorithms

abstract: Many deep issues plaguing today’s financial markets are symptoms of a fundamental problem: The complexity of algorithms underlying modern finance has significantly outpaced the power of traditional tools used to design and regulate them. At Aesthetic Integration, we have pioneered the use of formal verification for analysing the safety and fairness of financial algorithms. With a focus on financial infrastructure (e.g., the matching logics of exchanges and dark pools and FIX connectivity between trading systems), we describe the landscape, and illustrate our Imandra formal verification system on a number of real-world examples. We sketch many open problems and future directions along the way.


title: STELLA: a domain-specific tool for structured grid methods in weather and climate models

abstract: Many high-performance computing applications solving partial differential equations (PDEs) can be attributed to the class of kernels using stencils on structured grids. Due to the disparity between floating point operation throughput and main memory bandwidth these codes typically achieve only a low fraction of peak performance. Unfortunately, stencil computation optimization techniques are often hardware dependent and lead to a significant increase in code complexity. We present a domain-specific tool, STELLA, which eases the burden of the application developer by separating the architecture dependent implementation strategy from the user-code and is targeted at multi- and manycore processors. On the example of a numerical weather prediction and regional climate model (COSMO) we demonstrate the usefulness of STELLA for a real-world production code. The dynamical core based on STELLA achieves a speedup factor of 1.8x (CPU) and 5.8x (GPU) with respect to the legacy code while reducing the complexity of the user code.


"It's Friendship, Developing Friendship": A Teacher Action Research Study on Reading Buddies


title: Relapse prevention with levomilnacipran ER in adults with major depressive disorder: A multicenter, randomized, double‐blind, placebo‐controlled study

abstract: BACKGROUND ::: Levomilnacipran extended release (ER) is a serotonin and norepinephrine reuptake inhibitor approved for major depressive disorder (MDD) in adults. This study was designed to evaluate relapse prevention with levomilnacipran ER in patients with MDD. ::: ::: ::: METHODS ::: Patients (≥18 years) with MDD (N = 644) received 20 weeks of open-label treatment with levomilnacipran ER 40, 80, or 120 mg/d (8 weeks flexible dosing; 12 weeks fixed dosing). Patients with a Montgomery-Åsberg Depression Rating Scale (MADRS) total score ≤12 from the end of week 8 to week 20 were randomized to 26 weeks of double-blind treatment with levomilnacipran ER (same dosage; n = 165) or placebo (n = 159). The primary efficacy endpoint was time to relapse, defined as insufficient therapeutic response (≥2-point increase from randomization in Clinical Global Impression of Severity score, risk of suicide, need for hospitalization due to worsening of depression, or need for alternative antidepressant treatment as determined by the investigator) or an MADRS total score ≥18 at 2 consecutive visits. ::: ::: ::: RESULTS ::: In the double-blind intent-to-treat population, levomilnacipran ER-treated patients had a significantly longer time to relapse compared with placebo (hazard ratio = 0.56; 95% CI, 0.33-0.92; P = 0.0212). Crude relapse rates were 14.5% (levomilnacipran ER) and 24.5% (placebo). Double-blind treatment-emergent adverse events (AEs) were reported for 58.8% and 56.0% of levomilnacipran ER and placebo patients, respectively; 3.0% and 1.3% discontinued due to AEs, and 1.2% and 0.6% had serious AEs, respectively. ::: ::: ::: CONCLUSION ::: Levomilnacipran ER (40-120 mg/d) was effective in preventing relapse in patients with MDD. Safety and tolerability results were consistent with levomilnacipran ER acute studies.


title: Cleanix: a Parallel Big Data Cleaning System

abstract: For big data, data quality problem is more serious. Big data cleaning system requires scalability and the abilityof handling mixed errors. Motivated by this, we develop Cleanix, a prototype system for cleaning relational Big Data. Cleanix takes data integrated from multiple data sources and cleans them on a shared-nothing machine cluster. The backend system is built on-top-of an extensible and flexible data-parallel substrate the Hyracks framework. Cleanix supports various data cleaning tasks such as abnormal value detection and correction, incomplete data filling, de-duplication, and conflict resolution. In this paper, we show the organization, data cleaning algorithms as well as the design of Cleanix.


title: From the Test Benches to the First Prototype of the muFly Micro Helicopter

abstract: The goal of the European project muFly is to build a fully autonomous micro helicopter, which is comparable to a small bird in size and mass. The rigorous size and mass constraints infer various problems related to energy efficiency, flight stability and overall system design. In this research, aerodynamics and flight dynamics are investigated experimentally to gather information for the design of the helicopter’s propulsion group and steering system. Several test benches are designed and built for these investigations. A coaxial rotor test bench is used to measure the thrust and drag torque of different rotor blade designs. The effects of cyclic pitching of the swash plate and the passive stabilizer bar are studied on a test bench measuring rotor forces and moments with a 6–axis force sensor. The gathered knowledge is used to design a first prototype of the muFly helicopter. The prototype is described in terms of rotor configuration, structure, actuator and sensor selection according to the project demands, and a first version of the helicopter is shown. As a safety measure for the flight tests and to analyze the helicopter dynamics, a 6DoF vehicle test bench for tethered helicopter flight is used.


title: Influencing mental models of security: a research agenda

abstract: Over 80 million households in the United States have a home computer and an Internet connection. The vast majority of these are administered by people who have little computer security knowledge or training, and many users try to avoid making security decisions because they feel they don't have the knowledge and skills to maintain proper security. Nevertheless, home computer users still make security-related decisions on a regular basis -- for example, whether or not to click on a shady link in an email message -- without even knowing that's what they are doing. Their decisions are guided by how they think about computer security, or their "mental models," which do not have to be technically correct to lead to desirable security behaviors [44]. In other words, sometimes even "wrong" mental models produce good security decisions. By eliminating the constraint that nontechnical users must become more like computer security experts to properly protect themselves, we believe that we can create more effective ways of helping home computer users make good security decisions. To that end, we propose a research agenda that will help us learn how to shape the mental models of regular non-technical computer users.


title: An Innovative, Open, Interoperable Citizen Engagement Cloud Platform for Smart Government and Users' Interaction

abstract: This paper introduces an open, interoperable, and cloud-computing-based citizen engagement platform for the management of administrative processes of public administrations, which also increases the engagement of citizens. The citizen engagement platform is the outcome of a 3-year Italian national project called PRISMA (Interoperable cloud platforms for smart government). The aim of the project is to constitute a new model of digital ecosystem that can support and enable new methods of interaction among public administrations, citizens, companies, and other stakeholders surrounding cities. The platform has been defined by the media as a flexible (enable the addition of any kind of application or service) and open (enable access to open services) Italian"cloud"that allows public administrations to access to a vast knowledge base represented as linked open data to be reused by a stakeholder community with the aim of developing new applications ("Cloud Apps") tailored to the specific needs of citizens. The platform has been used by Catania and Syracuse municipalities, two of the main cities of southern Italy, located in the Sicilian region. The fully adoption of the platform is rapidly spreading around the whole region (local developers have already used available application programming interfaces (APIs) to create additional services for citizens and administrations) to such an extent that other provinces of Sicily and Italy in general expressed their interest for its usage. The platform is available online and, as mentioned above, is open source and provides APIs for full exploitation.


title: OpenFlow Communications and TLS Security in Software-Defined Networks

abstract: The adoption of Software-Defined Networking (SDN), a networking approach where data traffic control and execution are made independent of each other, is an ongoing process that some companies are considering as an option but have not embraced yet due to different factors. Incorporating this new paradigm into an existing network defines a shift in networking technology with different benefits expected to derive from this implementation. These benefits include (1) the ability to use customised business specific applications, (2) reduce overhead costs on legacy network infrastructure, taking full control of network, (3) reduce network application update time, increase productivity, and (4) apply increased security among others. However, the security of SDN itself has been a subject of debate. This is mainly because, the communication standard used by SDN, known as OpenFlow, and developed by the Open Network Foundation, does not enforce the implementation of the Transport Layer Security (TLS) but defines it only as optional. This could then make the network infrastructure vulnerable and therefore affect the overall security of a company. Security plays a significant part in an organisation and it is one of the determinants of the success of SDN. OpenFlow security relies on the implementation of TLS, which has been proven vulnerable, and therefore bringing to mind the question on how secure organisation's data is when the implementation of secure data transfer is treated with laxity. This paper focuses on securing OpenFlow communication in SDN by summarising TLS security flaws and recommending ways of improving TLS security thereby securing OpenFlow communication.


title: Partially supervised clustering for image segmentation

abstract: All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tendency to recommend solutions that equalize cluster populations. The semi-supervised c-means algorithms introduced in this paper attempt to overcome these problems domains where a few data from each clas can be labeled. Segmentation of magnetic resonance images is a problem of this type and we use it to illustrate the new algorithm. Our examples show that the semi-supervised approach provides MRI segmentations that are superior to ordinary fuzzy c-means and to the crisp k-nearest neighbor rule and further, that the new method ameliorates (P1)-(P3).


title: A Time-Of-Flight Depth Sensor - System Description, Issues and Solutions

abstract: This paper describes a CMOS-based time-of-flight depth sensor and presents some experimental data while addressing various issues arising from its use. Our system is a single-chip solution based on a special CMOS pixel structure that can extract phase information from the received light pulses. The sensor chip integrates a 64x64 pixel array with a high-speed clock generator and ADC. A unique advantage of the chip is that it can be manufactured with an ordinary CMOS process. Compared with other types of depth sensors reported in the literature, our solution offers significant advantages, including superior accuracy, high frame rate, cost effectiveness and a drastic reduction in processing required to construct the depth maps. We explain the factors that determine the resolution of our system, discuss various problems that a time-of-flight depth sensor might face, and propose practical solutions.


title: The use of Atypical antipsychotics in French psychiatric hospitals

abstract: OBJECTIVE ::: To study the prescribing practices of atypical antipsychotic drugs in French psychiatric hospitals. ::: ::: ::: METHOD ::: A 1-day cross-sectional observational survey was performed in seven psychiatric hospitals to study prescribing practices of the four atypical antipsychotics (AAP) marketed in France. These hospitals are members of the PIC network, a pharmacists' working group based in southwestern France. ::: ::: ::: MAIN OUTCOME MEASURES ::: Type and dosages of prescribed atypical antipsychotics, indication and concomitant prescriptions. ::: ::: ::: RESULTS ::: The study included 1475 adult inpatients' prescriptions with an antipsychotic drug; 647 prescriptions included an AAP with risperidone and olanzapine accounting for about 70% cases. AAP prescriptions concerned psychotic patients in 65% of cases. Patients receiving an AAP in this indication were more likely to be male, were younger, and received higher daily doses than patients treated for other troubles. They were also more likely to receive associated neuroleptic and anticholinergic antiparkinsonian agents. There were 59 prescriptions (9.1%) for bipolar disorders. Clozapine wasn't used in this indication. In 76.3% of cases, mood stabilizers were associated to the AAP prescribed in this indication. ::: ::: ::: CONCLUSION ::: This observational survey underlines the significant place taken by AAP for the treatment of psychiatric diseases in hospital prescribing practices. They show usage patterns of these drugs: dose, indications and concomitant medications, in line or not with current recommendations and reference guidelines. In bipolar disorders, AAPs seem more likely to be associated to an ongoing therapy using mood stabilizer than to replace it.


title: Describing Upper-Body Motions Based on Labanotation for Learning-from-Observation Robots

abstract: We have been developing a paradigm that we call learning-from-observation for a robot to automatically acquire a robot program to conduct a series of operations, or for a robot to understand what to do, through observing humans performing the same operations. Since a simple mimicking method to repeat exact joint angles or exact end-effector trajectories does not work well because of the kinematic and dynamic differences between a human and a robot, the proposed method employs intermediate symbolic representations, tasks, for conceptually representing what-to-do through observation. These tasks are subsequently mapped to appropriate robot operations depending on the robot hardware. In the present work, task models for upper-body operations of humanoid robots are presented, which are designed on the basis of Labanotation. Given a series of human operations, we first analyze the upper-body motions and extract certain fixed poses from key frames. These key poses are translated into tasks represented by Labanotation symbols. Then, a robot performs the operations corresponding to those task models. Because tasks based on Labanotation are independent of robot hardware, different robots can share the same observation module, and only different task-mapping modules specific to robot hardware are required. The system was implemented and demonstrated that three different robots can automatically mimic human upper-body operations with a satisfactory level of resemblance.


title: Planning for Crowdsourcing Hierarchical Tasks

abstract: We show how machine vision, learning, and planning can be combined to solve hierarchical consensus tasks. Hierarchical consensus tasks seek correct answers to a hierarchy of subtasks, where branching depends on answers at preceding levels of the hierarchy. We construct a set of hierarchical classification models that aggregate machine and human effort on different subtasks and use these inferences in planning. Optimal solution of hierarchical tasks is intractable due to the branching of task hierarchy and the long horizon of these tasks. We study Monte Carlo planning procedures that can exploit task structure to constrain the policy space for tractability. We evaluate the procedures on data collected from Galaxy Zoo II in allocating human effort and show that significant gains can be achieved.


title: An integrated fire detection and suppression system based on widely available video surveillance

abstract: An integrated system based on video surveillance is presented for automatic fire detection and suppression. The system is composed of two modules, including fire detection and fire suppression. The fire detection module makes full use of traditional CCD cameras for fire recognition. Some spatio-temporal features, such as color and motion, are extracted to detect fire in real time by utilizing sequential image processing techniques. Once a fire is detected, the system will control the fire suppression module to extinguish the fire automatically. It mainly consists of control device, mobile device, and water gun. Experiments performed in a large space hall show that the integrated system can detect a fire about a few seconds after ignition and automatically suppress the fire quickly.


title: A Comprehensive Implementation of Conceptual Spaces

abstract: The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points and concepts are represented by regions in a (potentially) high-dimensional space. Based on our recent formalization, we present a comprehensive implementation of the conceptual spaces framework that is not only capable of representing concepts with inter-domain correlations, but that also offers a variety of operations on these concepts.


title: Implementing Mobile Ad Hoc Networking (MANET) over Legacy Tactical Radio Links

abstract: Mobile Ad Hoc Networking (MANET) is a key technology enabler in the tactical communication domain for the Network Centric Warfare.[1] A self-forming, self-healing, infrastructure-less network consisting of mobile nodes is very attractive for beyond line of sight (BLOS) voice and data range extension as well as tactical networking applications in general. Current research and development mostly focus on implementing MANET over new wideband data waveforms. However, a large number of currently fielded tactical radios and the next generation software defined radios (SDR) support various legacy tactical radio waveforms. A mobile ad hoc network over such legacy tactical radio links not only provides war fighters mission critical networking applications such as Situational Awareness and short payload messaging, the MANET nodes can also support voice and legacy data interoperation with the existing fielded legacy radios. Furthermore, the small spectrum footprint of current narrowband tactical radio waveforms can be complementary to the new wideband data waveforms for providing networking access in a spectrum constrained environment. This paper first describes the networking usage requirements for MANET over legacy narrowband tactical waveforms. Next, the common characteristics of legacy tactical radio waveforms and the implications of such characteristics for the MANET implementation are discussed. Then an actual MANET implementation over a legacy tactical radio waveform on a SDR is presented with the results of actual field tests. Finally, several improvements to this implementation are proposed.


title: Acting Optimally in Partially Observable Stochastic Domains

abstract: In this paper, we describe the partially observable Markov decision process (POMDP) approach to finding optimal or near-optimal control strategies for partially observable stochastic environments, given a complete model of the environment. The POMDP approach was originally developed in the operations research community and provides a formal basis for planning problems that have been of interest to the AI community. We found the existing algorithms for computing optimal control strategies to be highly computationally inefficient and have developed a new algorithm that is empirically more efficient. We sketch this algorithm and present preliminary results on several small problems that illustrate important properties of the POMDP approach.


title: FairTest: Discovering Unwarranted Associations in Data-Driven Applications

abstract: In a world where traditional notions of privacy are increasingly challenged by the myriad companies that collect and analyze our data, it is important that decision-making entities are held accountable for unfair treatments arising from irresponsible data usage. Unfortunately, a lack of appropriate methodologies and tools means that even identifying unfair or discriminatory effects can be a challenge in practice. We introduce the unwarranted associations (UA) framework, a principled methodology for the discovery of unfair, discriminatory, or offensive user treatment in data-driven applications. The UA framework unifies and rationalizes a number of prior attempts at formalizing algorithmic fairness. It uniquely combines multiple investigative primitives and fairness metrics with broad applicability, granular exploration of unfair treatment in user subgroups, and incorporation of natural notions of utility that may account for observed disparities. We instantiate the UA framework in FairTest, the first comprehensive tool that helps developers check data-driven applications for unfair user treatment. It enables scalable and statistically rigorous investigation of associations between application outcomes (such as prices or premiums) and sensitive user attributes (such as race or gender). Furthermore, FairTest provides debugging capabilities that let programmers rule out potential confounders for observed unfair effects. We report on use of FairTest to investigate and in some cases address disparate impact, offensive labeling, and uneven rates of algorithmic error in four data-driven applications. As examples, our results reveal subtle biases against older populations in the distribution of error in a predictive health application and offensive racial labeling in an image tagger.


title: Explaining Soccer Match Outcomes with Goal Scoring Opportunities Predictive Analytics

abstract: In elite soccer, decisions are often based on recent results and emotions. In this paper, we propose a method to determine the expected winner of a match in elite soccer. The expected result of a soccer match is determined by estimating the probability of scoring for the individual goal scoring opportunities. The outcome of a match is then obtained by integrating these probabilities. In our experimental study, we show that the probabilities of goal scoring opportunities accurately match reality.


title: Collaborative Deep Learning Across Multiple Data Centers

abstract: Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method.


title: Automated color prediction of paddy crop leaf using image processing

abstract: In India a majority of the population in rural areas is working in the agriculture field for their livelihood. They not only have to struggle for the better yield against the natural disasters but also have to tackle the losses of the net output because of land fertilization specifications and unskilled labour too. In the event of inadequate utilities and resources, in the face of unpredictable crises, their gain opportunities and livelihood are proportionally and adversely affected. However in this era of technology, the scenario may get changed as the Information and Communication and related fields of technology are providing a great for such type of crisis handling. Here in this paper, the method which may be used to compare the crop leaf color with the leaf color chart (LCC), has been proposed for getting a detail about the requirement of plant, before enough to get the yield affected. By making use of image processing technology a simple and robust method for the color prediction of paddy crop plant has been discussed along with the mathematical modelling which may provide a great platform to the advisory bodies in the agriculture field for the atomization of the crop health problems and solutions.


title: Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing

abstract: With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0 , Industrial Internet , manufacturing based on Cyber-Physical System , and Made in China 2025 . However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.


title: Co-segmentation by Composition

abstract: Given a set of images which share an object from the same semantic category, we would like to co-segment the shared object. We define 'good' co-segments to be ones which can be easily composed (like a puzzle) from large pieces of other co-segments, yet are difficult to compose from remaining image parts. These pieces must not only match well but also be statistically significant (hard to compose at random). This gives rise to co-segmentation of objects in very challenging scenarios with large variations in appearance, shape and large amounts of clutter. We further show how multiple images can collaborate and "score" each others' co-segments to improve the overall fidelity and accuracy of the co-segmentation. Our co-segmentation can be applied both to large image collections, as well as to very few images (where there is too little data for unsupervised learning). At the extreme, it can be applied even to a single image, to extract its co-occurring objects. Our approach obtains state-of-the-art results on benchmark datasets. We further show very encouraging co-segmentation results on the challenging PASCAL-VOC dataset.


title: A game theoretical approach for image denoising

abstract: How to adaptively choose optimal neighborhoods is very important to pixel-domain image denoising algorithms since too many neighborhoods may cause over-smooth artifacts and too few neighborhoods may not be able to efficiently remove the noise. While the Stein's principle is shown to be able to estimate the true mean square error (MSE) for determining the optimal neighborhoods, there exists a trade-off between the accuracy of the estimate and the minimum of the true MSE. In this paper, we study the impact of this trade-off and formulate the image denoising problem as a coalition formation game. In the game, every pixel is treated as a player, who tries to seek partners to form a coalition to achieve better denoising results. By forming a coalition, every player in the coalition can obtain a gain of improving the accuracy of the Stein's estimate while incurring a cost of increasing the minimum of the true MSE. We also propose a heuristically distributed approach for coalition formation. Finally, experimental results show that the proposed game theoretical approach can achieve better performance than the nonlocal method in terms of both PSNR and visual quality.


title: A novel Bayesian network-based fault prognostic method for semiconductor manufacturing process

abstract: Fault prognostic in various levels of production of semiconductor chips is considered to be a great challenge. To reduce yield loss during the manufacturing process, tool abnormalities should be detected as early as possible during process monitoring. In this paper, we propose a novel fault prognostic method based on Bayesian networks. The network is designed such that it can process both discrete and continuous variables, to represent the correlations between critical deviations and to process quality control data based on divide-and-conquer strategy. Such a network enables us to perform high-precision multi-step prognostic on the status of the fabrication process given the current state of the sensory info. Additionally, we introduce a layer-wise approach for efficient learning of the Bayesian-network parameters. We evaluate the accuracy of our prognostic model on a wafer fabrication dataset where our model performs precise next-step fault prognostic by using the control sensory data.


title: Real-time volume imaging using a crossed electrode array

abstract: This paper describes a unique crossed electrode array for real-time volume ultrasound imaging. By placing orthogonal linear array electrode patterns on the opposite sides of a hemispherically shaped composite transducer substrate, a 2-D array can be fabricated using a small fraction of the elements required for a traditional 2-D array. The performance of the array is investigated using a computer simulation of the radiation pattern. We show that by using a 288-element crossed electrode pattern it is possible to collect large field of view volume images (60° x 60° sector) at real-time frame rates (>20 volume images/s), with image contrast and resolution comparable to what can be obtained using a conventional 128-element linear phased array.


title: An Empirical Evaluation of the MuJava Mutation Operators

abstract: Mutation testing is used to assess the fault-finding effectiveness of a test suite. Information provided by mutation testing can also be used to guide the creation of additional valuable tests and/or to reveal faults in the implementation code. However, concerns about the time efficiency of mutation testing may prohibit its widespread, practical use. We conducted an empirical study using the MuClipse automated mutation testing plug-in for Eclipse on the back end of a small web-based application. The first objective of our study was to categorize the behavior of the mutants generated by selected mutation operators during successive attempts to kill the mutants. The results of this categorization can be used to inform developers in their mutant operator selection to improve the efficiency and effectiveness of their mutation testing. The second outcome of our study identified patterns in the implementation code that remained untested after attempting to kill all mutants.


title: Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis

abstract: We formulate and study distributed estimation algorithms based on diffusion protocols to implement cooperation among individual adaptive nodes. The individual nodes are equipped with local learning abilities. They derive local estimates for the parameter of interest and share information with their neighbors only, giving rise to peer-to-peer protocols. The resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment. It improves performance in terms of transient and steady-state mean-square error, as compared with traditional noncooperative schemes. Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived, presenting a very good match with simulations.


title: OpenStack extensions for QoS and energy efficiency in edge computing

abstract: Mobility has always been a big challenge in cellular networks, because it is responsible for traffic fluctuations that eventually result into inconstant resource usage and the need for proper Quality of Service management. When applications get deployed at the network edge, the challenges even grow because software is harder to hand-over than traffic streams. Cloud technologies have been designed with different specifications, and should be properly revised to balance efficiency and effectiveness in distributed and capillary infrastructures. In this paper, we propose some extensions to OpenStack for power management and Quality of Service. Our framework provides additional APIs for setting the service level and interacting with power-saving mechanisms. It is designed to be easily integrated with modern software orchestration tools and workload consolidation algorithms. We report real measurements from an experimental proof-of-concept.


title: The golden age of software architecture

abstract: Since the late 1980s, software architecture has emerged as the principled understanding of the large-scale structures of software systems. From its roots in qualitative descriptions of empirically observed useful system organizations, software architecture has matured to encompass a broad set of notations, tools, and analysis techniques. Whereas initially the research area interpreted software practice, it now offers concrete guidance for complex software design and development. It has made the transition from basic research to an essential element of software system design and construction. This retrospective examines software architecture's growth in the context of a technology maturation model, matching its significant accomplishments to the model's stages to gain perspective on where the field stands today. This trajectory has taken architecture to its golden age.


title: Multi-resolution point cloud generation based on heterogeneous sensor fusion system

abstract: Various three-dimensional environment representation techniques exist depending on the type of sensor, and the level of expression varies depending on the characteristics of the sensor. Our sensor system is composed of a three-dimensional lasers scanner and a stereo camera. A rigid transformation matrix representing a three-dimensional relative pose is obtained by calibration between a stereo camera and laser scanner and creates a multi-resolution point cloud. By combining a stereo camera with a high resolution and a laser scanner with wide range and low-resolution, it is able to complement each other and achieve better results.


title: Dual-Band Bandpass Filter With Independently Tunable Center Frequencies and Bandwidths

abstract: This paper presented a novel approach for the design of a tunable dual-band bandpass filter (BPF) with independently tunable passband center frequencies and bandwidths. The newly proposed dual-band filter principally comprised two dual-mode single band filters using common input/output lines. Each single BPF was realized using a varactorloaded transmission line resonator. To suppress the harmonics over a broad bandwidth, a defected ground structure was used at the input/output feeding lines. From the experimental results, it was found that the proposed filter exhibited the first passband center frequency tunable range from 1.48 to 1.8 ㎓ with a 3-㏈ fractional bandwidth (FBW) variation from 5.76% to 8.55%, while the second passband center’s frequency tunable range was 2.40 to 2.88 ㎓ with a 3-㏈ FBW variation from 8.28% to 12.42%. The measured results of the proposed filters showed a rejection level of 19 ㏈ up to more than 10 times the highest center frequency of the first passband.


abstract: Projections of charged particle beam current density (profiles) are frequently used as a measure of beam position and size. In conventional practice only two projections, usually horizontal and vertical, are measured. This puts a severe limit on the detail of information that can be achieved. A third projection provides a significant improvement. The Algebraic Reconstruction Technique (ART) uses three or more projections to reconstruct 3-dimensional density profiles. At the 200 MeV H- linac, we have used this technique to measure beam density, and it has proved very helpful, especially in helping determine if there is any coupling present in x-y phase space. We will present examples of measurements of current densities using this technique.


title: Identifying the Prevalence of the "Dark Triad" Personality Traits in Law Students: Eradicating an Unwarranted Stereotype

abstract: The personalities of lawyers are often categorized to be immoral, at least more so than those of other professionals. An abundance of literature parallels this generalization and depicts lawyers’ personalities as narcissistic, psychopathic and high in Machiavellian attitudes. Together, these three traits form the “Dark Triad” of personality. 53 law students from 3 law schools in Ontario completed a survey measuring their levels on the “Dark Triad” traits. Furthermore, each participant revealed their gender, year of study, and the field of law they aspired to enter. Results show that the law students from the present sample do not display any “Dark Triad” trait significantly greater than the general population. Furthermore, it found that male law students were not significantly “darker” than female law students, on average. However, 1 year law students were found to score significantly higher on the narcissism trait than both second and third-year students. There were no other significant differences between the students in different years of schooling. Lastly, those wishing to enter criminal law scored significantly higher on the Machiavellianism trait than those wishing to enter tax law. No other fields of law differed significantly on any of the three traits. These findings suggest that law students, and perhaps lawyers themselves, are not deserving of the pejorative stereotype assigned to them. Furthermore, it suggests that male law students are no more responsible for the assigned stereotype than females. Next, the present research implies that law school, or perhaps the aging process, may actually serve to reduce the narcissism trait suggested to be present in law students. Finally, the research proposes that those aspiring to enter criminal law may be more responsible for the assigned stereotype whereas those aspiring to enter tax law experience the most apparent wrongful stereotyping. The “Dark Triad” of Personality Traits in Lawyers 3 Identifying the Prevalence of the “Dark Triad” Personality Traits in Law Students: Eradicating an Unwarranted Stereotype There exists a view in popular culture that lawyers and law students display immoral and “dark” personality traits as a general entity. Friedman (1989) highlights the abundance of jokes regarding lawyers, few of which placed them in a favourable light. Friedman goes on to claim that the most common impression held by the layperson regarding the generic lawyer is that they come across as vile, obsessed with money and callous. A further impediment to the perceptions of lawyers exists because the ordinary person only interacts with a lawyer in times of serious trouble (Friedman, 1989). The depiction of lawyers in mass media contributes to the negative stereotype of lawyers. Friedman specifically exemplifies the television show “L.A. Law” as a reflection of the real changes in the personalities of lawyers, specifically business lawyers. The show depicts lawyers as loud and flamboyant, and as those who displayed these traits proudly before the public (Friedman, 1989). In contrast, Friedman describes the “gentlemanly” lawyers as those who keep low profiles, he specifically mentions the disdain held by the latter regarding the former. The present research question becomes clear with the seminal quote presented by Friedman, “The Bar (the society of lawyers) worries about ‘professionalism’” (p. 1602), or as implied by Friedman’s tone, the lack-thereof. Clearly, if the presiding entity representing lawyers is worried about the impression given off by its constituents, the measurement of whether these traits actually exist, or are instead born from a stereotyped view of the public, is worthy of investigation. Furthermore, if such a problem is found to exist, does the root of the problem lay in the profession itself, the people it attracts, or the schools who teach the curriculum? If the stereotype is found to be substantiated, there appears to be a requisite overhaul of the lawyer profession, however, if the views are found to be significantly exaggerated, there instead requires an overhaul of the perception of lawyers. Regardless of the answer to the aforementioned The “Dark Triad” of Personality Traits in Lawyers 4 question, the root cause of the issue, whether accurately substantiated or unfairly stereotyped, needs to be addressed. This paper starts from square-one; does the problem exist at the law


title: Real-Time Dual-Frequency ( L 1 / L 5 ) GPS / WAAS Software Receiver

abstract: This paper demonstrates a real-time software receiver supporting GPS/WAAS dual-frequency (L1/L5) processing. The software receiver is implemented on a widely-available recent generation multi-core processor and is capable of post-processing datasets from collection hardware. Key contributions of this software receiver are that 1) it is perhaps the first receiver capable positioning solely on L5 ranging despite there being only one operational GPS L5 signal and 2) it estimates ionosphere delay using L1-L5 measurements The architecture of such a software receiver needs to be carefully developed so that it can process signals from each individual frequency. Multitasking and synchronization mechanisms were developed to support the tracking of multiple channels in real time. To achieve real-time capability, parallel operations are necessary to reduce computation complexity. Bit-wise operations are exploited and implemented by Single Instruction Multiple Data (SIMD) instructions in the software correlator. An assistance mechanism between L1 and L5 is built for shortening the acquisition time and increasing the sensitivity of tracking L5 data-free channel. Currently, positioning using GPS/WAAS L5 signal needs assistance from L1 signal because the ephemeris on GPS L5 is currently not provided. However, using the L1 GPS ephemeris from L1 should consider the time offset or interfrequency bias (IFB) between L1 and L5 which results from the difference of antenna phase center and other hardware on the satellite. Besides, a time offset exists between WAAS and GPS time. These time offsets are solved by adding another unknown into position solution. The solutions of the time offset are used to correct the pseudorange of L5 and then perform positioning by three methods 1) L1 only 2) L5 only 3) iono-free combination. The results of positioning by the dual-frequency software receiver are illustrated and the positioning accuracy is analyzed in this paper.


Generalized Cannon's algorithm for parallel matrix multiplication


title: Non-contact Method for Producing Tactile Sensation Using Airborne Ultrasound

abstract: This paper describes a new tactile device which produces stress fields in 3D space. Combined with 3D stereoscopic displays, this device is expected to provide high-fidelity tactile feedback for the interaction with 3D visual objects. The principle is based on a non-linear phenomenon of ultrasound, acoustic radiation pressure. We fabricated a prototype device to confirm the feasibility as a tactile display. The prototype consists of 91 airborne ultrasound transducers packed in the hexagonal arrangement, a 12 channel driving circuit, and a PC. The transducers which were in the same distance from the center of the transducer array were connected to form a 12 channel annular array. The measured total output force within the focal region was 0.8 gf. The spatial resolution was 20 mm. The prototype could produce sufficient vibrations up to 1 kHz.


title: Experimental Study of 1.55- $\mu$ m EML-Based Optical IM/DD PAM-4/8 Short Reach Systems

abstract: We experimentally evaluate high-speed intensity modulation/direct detection (IM/DD) transmissions with a 1.55- $\mu \text{m}$ broadband electro-absorption modulated laser and pulse amplitude modulations (PAM). We demonstrate 80 Gb/s/ $\lambda $ PAM-4 and 96 Gb/s/ $\lambda $ PAM-8 transmissions with low-complexity digital equalizers at the receiver. Performance comparison with different types of equalizers are performed, including linear symbol-spaced feed-forward equalizer (FFE), fractional (half-symbol) spaced FFE and decision feedback equalizer (DFE), with different tap number. It is found that for both cases, a 6-tap symbol-spaced FFE is sufficient to achieve a stable performance with bit-error-rate below the 7% overhead hard decision forward error correction (7%-OH HD-FEC) threshold over a 4 km standard single mode fiber link. Practical considerations including comparison between adaptive and static equalizer implementation and tolerable fiber chromatic dispersion are discussed.


title: Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

abstract: Social data in digital form, including user-generated content, expressed or implicit relations between people, and behavioral traces, are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding ``what the world thinks'' about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naive usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them.


title: Detecting Malicious Javascript in PDF through Document Instrumentation

abstract: An emerging threat vector, embedded malware inside popular document formats, has become rampant since 2008. Owed to its wide-spread use and Javascript support, PDF has been the primary vehicle for delivering embedded exploits. Unfortunately, existing defenses are limited in effectiveness, vulnerable to evasion, or computationally expensive to be employed as an on-line protection system. In this paper, we propose a context-aware approach for detection and confinement of malicious Javascript in PDF. Our approach statically extracts a set of static features and inserts context monitoring code into a document. When an instrumented document is opened, the context monitoring code inside will cooperate with our runtime monitor to detect potential infection attempts in the context of Javascript execution. Thus, our detector can identify malicious documents by using both static and runtime features. To validate the effectiveness of our approach in a real world setting, we first conduct a security analysis, showing that our system is able to remain effective in detection and be robust against evasion attempts even in the presence of sophisticated adversaries. We implement a prototype of the proposed system, and perform extensive experiments using 18623 benign PDF samples and 7370 malicious samples. Our evaluation results demonstrate that our approach can accurately detect and confine malicious Javascript in PDF with minor performance overhead.


title: Massive MIMO and waveform design for 5th generation wireless communication systems

abstract: This article reviews existing related work and identifies the main challenges in the key 5G area at the intersection of waveform design and large-scale multiple antenna systems, also known as Massive MIMO. The property of self-equalization is introduced for Filter Bank Multicarrier (FBMC)-based Massive MIMO, which can reduce the number of subcarriers required by the system. It is also shown that the blind channel tracking property of FBMC can be used to address pilot contamination - one of the main limiting factors of Massive MIMO systems. Our findings shed light into and motivate for an entirely new research line towards a better understanding of waveform design with emphasis on FBMC-based Massive MIMO networks.


title: ZygOS: Achieving Low Tail Latency for Microsecond-scale Networked Tasks

abstract: This paper focuses on the efficient scheduling on multicore systems of very fine-grain networked tasks, which are the typical building block of online data-intensive applications. The explicit goal is to deliver high throughput (millions of remote procedure calls per second) for tail latency service-level objectives that are a small multiple of the task size. We present ZYGOS, a system optimized for μs-scale, in-memory computing on multicore servers. It implements a work-conserving scheduler within a specialized operating system designed for high request rates and a large number of network connections. ZYGOS uses a combination of shared-memory data structures, multi-queue NICs, and inter-processor interrupts to rebalance work across cores. For an aggressive service-level objective expressed at the 99th percentile, ZYGOS achieves 75% of the maximum possible load determined by a theoretical, zero-overhead model (centralized queueing with FCFS) for 10μs tasks, and 88% for 25μs tasks. We evaluate ZYGOS with a networked version of Silo, a state-of-the-art in-memory transactional database, running TPC-C. For a service-level objective of 1000μs latency at the 99th percentile, ZYGOS can deliver a 1.63x speedup over Linux (because of its dataplane architecture) and a 1.26x speedup over IX, a state-of-the-art dataplane (because of its work-conserving scheduler).


title: Density-based clustering validation

abstract: One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly containing noise objects. In these cases relative validity indices proposed for globular cluster validation may fail. In this paper we propose a relative validation index for density-based, arbitrarily shaped clusters. The index assesses clustering quality based on the relative density connection between pairs of objects. Our index is formulated on the basis of a new kernel density function, which is used to compute the density of objects and to evaluate the within- and between-cluster density connectedness of clustering results. Experiments on synthetic and real world data show the effectiveness of our approach for the evaluation and selection of clustering algo rithms and their respective appropriate parameters.


title: 0.5-V input digital LDO with 98.7% current efficiency and 2.7-µA quiescent current in 65nm CMOS

abstract: Digital LDO is proposed to provide the low noise and tunable power supply voltage to the 0.5-V near-threshold logic circuits. Because the conventional LDO feedback-controlled by the operational amplifier fail to operate at 0.5V, the digital LDO eliminates all analog circuits and is controlled by digital circuits, which enables the 0.5-V operation. The developed digital LDO in 65nm CMOS achieved the 0.5-V input voltage and 0.45-V output voltage with 98.7% current efficiency and 2.7-µA quiescent current at 200-µA load current. Both the input voltage and the quiescent current are the lowest values in the published LDO's, which indicates the good energy efficiency of the digital LDO at 0.5-V operation.


Are audio or textual training data more important for ASR in less-represented languages?


title: Voice biometrics: Deep learning-based voiceprint authentication system

abstract: Speaker identification systems are becoming more important in today's world. This is especially true as devices rely on the user to speak commands. In this article, an analysis of how a text-independent voice identification system can be built is presented. Extracting the Mel-Frequency Cepstral Coefficients is evaluated and a support vector machine is trained and tested on two different data sets, one from LibriSpeech and one from in-house recorded audio files. The results show the ability for such systems to be utilized in both speaker identification and speaker verification tasks.


title: Design and control of JAIST active robotic walker

abstract: This paper presents the design and control of a novel assistive robotic walker that we call “JAIST active robotic walker (JARoW)”. JARoW is developed to provide potential users with sufficient ambulatory capability in an efficient, cost-effective way. Specifically, our focus is placed on how to allow easier maneuverability by creating a natural interface between the user and JARoW. For the purpose, we develop a rotating infrared sensor to detect the user’s lower limb movement. The implementation details of the JARoW control algorithms based on the sensor measurements are explained, and the effectiveness of the proposed algorithms is verified through experiments. Our results confirmed that JARoW can autonomously adjust its motion direction and velocity according to the user’s walking behavior without requiring any additional user effort.


title: A natural language processing approach to automatic plagiarism detection

abstract: The problem of plagiarism has existed for a long time but with the advance of information technology the problem becomes worse. It is because there are many electronic versions of published materials available to everyone. The Web is an important and common source for plagiarism. Some plagiarism detection programs (such as Turnitin) were developed to attempt to deal with this problem. To determine whether an article is copied from the Web or other electronic sources, the plagiarism detection program should calculate the similarity between two articles. However, it is often difficult to detect plagiarism accurately after modification of the copied contents. For example, it is possible to simply replace a word with its synonym (e.g. "program" -- "software ") and change the entire sentence structure. Most plagiarism detection programs can only compare whether two words are the same lexically and count how many matched words are there in a paper. Thus, if the copied materials are modified deliberately, it becomes difficult to detect plagiarism. Application of natural language processing can help to resolve this kind of problem. The underlying syntactic structure and semantic meaning of two sentences can be compared to reveal their similarity. There are several steps in the matching procedure. First, the thesaurus (or the lexical hierarchical structure) is referenced to find out the synonyms, broader terms and narrower terms used in the paper being checked. Then, the paper will be compared with the documents in the database. Wordnet is a typical example of the thesaurus that can be used for this purpose. If it is suspected that the paper contains some contents from the database, the sentences of the paper may be parsed to construct their parsing trees and semantic representations for further detailed comparison. The context free grammar and the case grammar are used to represent the syntactic structure and semantic meaning of sentences in the system. It is found that plagiarism that cannot be detected by the traditional methods can be identified by this new approach.


title: The role of multichannel integration in customer relationship management

abstract: Abstract This paper reviews the strategic role of multichannel integration in customer relationship management (CRM) with the objective proposing a structured approach to the development of an integrated multichannel strategy. Alternative perspectives of CRM are reviewed and it is concluded that adoption of a strategic perspective is essential for success. Multichannel integration is posited as one of the key cross-functional processes in CRM strategy development. The nature of industry channel structure and channel participants, channel options, and alternative channel strategies are reviewed. The customer experience is explored both within and across channels. Analytical tools, such as market structure maps, the customer relationship life cycle, and demand chain analysis, are described. Key steps in building an integrated multichannel strategy are examined. Major challenges faced by enterprises in their adoption of an integrated multichannel approach and areas for future research are discussed.


title: Optimal Design of Broadband Microwave Baluns Using Single-Layer Planar Circuit Technology

abstract: A new design methodology is presented for the planar implementation of a classical Marchand balun. A novel intuitive analysis shows that the Marchand configuration can be designed optimally to eliminate the phenomenon of “trace separation,” which is frequently observed in planar implementations. The new theory shows that this unbalancing effect is caused by the parasitic transmission line formed between the inner strip and ground, which is not considered in Marchand's original coaxial structures. Compact design equations are derived, based on which a new innovative structure is proposed and fabricated. This demonstrates the elimination of trace separation and achieves flat equal port split over a double octave bandwidth, performing up to 10 GHz, using an industry standard single-layer thin-film process having a continuous unpatterned ground plane. Popular planar variants of Marchand's original structures are also designed and fabricated to verify the new design equations. These structures are compared in terms of bandwidth, trace separation, and balanced port impedances.


title: Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image

abstract: In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.


title: Older Adults and Emerging Digital Service Delivery: A Mixed Methods Study on Information and Communications Technology Use, Skills, and Attitudes.

abstract: Based on data from a survey (n = 3291) and 14 qualitative interviews among Danish older adults, this study investigated the use of, and attitudes toward, information communications technology (ICT) and the digital delivery of public services. While age, gender, and socioeconomic status were associated with use of ICT, these determinants lost their explanatory power when we controlled for attitudes and experiences. We identified three segments that differed in their use of ICT and attitudes toward digital service delivery. As nonuse of ICT often results from the lack of willingness to use it rather than from material or cognitive deficiencies, policy measures for bridging the digital divide should focus on skills and confidence rather than on access or ability.


title: THE EFFECT OF SOCIAL MEDIA MARKETING ACTIVITIES ON BRAND AWARENESS, BRAND IMAGE AND BRAND LOYALTY

abstract: The aim of this research is to examine the effect of social media marketing activities on brand awareness, brand image and brand loyalty. In addition, it has been aimed to analyze the effect of brand awareness and brand image on brand loyalty in this research. The population of the research consists of the consumers who actively follow five brands with the highest social score according to the Marketing Turkey social media brand performance data on social media communication channels such as Facebook, Twitter and Instagram. In this research, quantitative method has been used and research data has been obtained via online questionnaires shared on social media from 547 brand followers with applying convenience sampling method. The obtained data have been analyzed by structural equation modeling (SEM). As a result of the analysis, social media marketing activities have been found as effective factors on brand image and brand loyalty, besides it has been determined that the most obvious effect seen on brand awareness. In addition, it has been found out that brand awareness and brand image have a significant effect on brand loyalty. Furthermore, in the research, it has been achieved that the brand awareness has a limited effect on the brand image.


title: Accelerating CUDA graph algorithms at maximum warp

abstract: Graphs are powerful data representations favored in many computational domains. Modern GPUs have recently shown promising results in accelerating computationally challenging graph problems but their performance suffered heavily when the graph structure is highly irregular, as most real-world graphs tend to be. In this study, we first observe that the poor performance is caused by work imbalance and is an artifact of a discrepancy between the GPU programming model and the underlying GPU architecture.We then propose a novel virtual warp-centric programming method that exposes the traits of underlying GPU architectures to users. Our method significantly improves the performance of applications with heavily imbalanced workloads, and enables trade-offs between workload imbalance and ALU underutilization for fine-tuning the performance. Our evaluation reveals that our method exhibits up to 9x speedup over previous GPU algorithms and 12x over single thread CPU execution on irregular graphs. When properly configured, it also yields up to 30% improvement over previous GPU algorithms on regular graphs. In addition to performance gains on graph algorithms, our programming method achieves 1.3x to 15.1x speedup on a set of GPU benchmark applications. Our study also confirms that the performance gap between GPUs and other multi-threaded CPU graph implementations is primarily due to the large difference in memory bandwidth.


title: Downscaling of Organic Field-Effect Transistors with a Polyelectrolyte Gate Insulator

abstract: A polyelectrolyte is used as gate insulator material in organic field-effect transistors with self-aligned inkjet printed sub–micrometer channels. The small separation of the charges in the electri ...


title: Application of ZigBee Wireless Sensor Network in Smart Home System

abstract: Abstract In this paper, current technologies which can be used in the wireless home networking area are reviewed and compared. Then a solution of smart home system based on ZigBee technology is put forward and discussed. The hardware design of ZigBee wireless sensor network based on CC2430 chip and embedded home gateway based on S3C2440 is also given in detail. The software workflow of coordinator and router are discussed and user-defined frame format is further discribed. Experimental results show that the solution of smart home system mentioned above can be served as practical application reliably.


title: Resource Management for OFDMA based Next Generation 802.11ax WLANs

abstract: Recently, IEEE 802.11ax Task Group has adapted OFDMA as a new technique for enabling multi-user transmission. It has been also decided that the scheduling duration should be same for all the users in a multi-user OFDMA so that the transmission of the users should end at the same time. In order to realize that condition, the users with insufficient data should transmit null data (i.e. padding) to fill the duration. While this scheme offers strong features such as resilience to Overlapping Basic Service Set (OBSS) interference and ease of synchronization, it also poses major side issues of degraded throughput performance and waste of devices' energy. In this work, for OFDMA based 802.11 WLANs we first propose practical algorithm in which the scheduling duration is fixed and does not change from time to time. In the second algorithm the scheduling duration is dynamically determined in a resource allocation framework by taking into account the padding overhead, airtime fairness and energy consumption of the users. We analytically investigate our resource allocation problems through Lyapunov optimization techniques and show that our algorithms are arbitrarily close to the optimal performance at the price of reduced convergence rate. We also calculate the overhead of our algorithms in a realistic setup and propose solutions for the implementation issues.


title: Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

abstract: The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation. In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation. In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures. Representations from different sources are integrated with an extra layer to obtain the joint representations for users and items. In the end, both the per-source and the joint representations are trained as a whole using pair-wise learning to rank for top-N recommendation. We analyze how information propagates among different information sources in a gradient-descent learning paradigm, based on which we further propose an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice. By representing users and items into embeddings offline, and using a simple vector multiplication for ranking score calculation online, our framework also has the advantage of fast online prediction compared with other deep learning approaches to recommendation that learn a complex prediction network for online calculation.


title: Cubical Type Theory: a constructive interpretation of the univalence axiom

abstract: This paper presents a type theory in which it is possible to directly manipulate $n$-dimensional cubes (points, lines, squares, cubes, etc.) based on an interpretation of dependent type theory in a cubical set model. This enables new ways to reason about identity types, for instance, function extensionality is directly provable in the system. Further, Voevodsky's univalence axiom is provable in this system. We also explain an extension with some higher inductive types like the circle and propositional truncation. Finally we provide semantics for this cubical type theory in a constructive meta-theory.


title: 22 kV SiC Emitter turn-off (ETO) thyristor and its dynamic performance including SOA

abstract: Ultra-high voltage (>10 kV) power devices based on SiC are gaining significant attentions since Si power devices are typically at lower voltage levels. In this paper, a world record 22kV Silicon Carbide (SiC) p-type ETO thyristor is developed and reported as a promising candidate for ultra-high voltage applications. The device is based on a 2cm2 22kV p type gate turn off thyristor (p-GTO) structure. Its static as well as dynamic performances are analyzed, including the anode to cathode blocking characteristics, forward conduction characteristics at different temperatures, turn-on and turn-off dynamic performances. The turn-off energy at 6kV, 7kV and 8kV respectively is also presented. In addition, theoretical boundary of the reverse biased safe operation area (RBSOA) of the 22kV SiC ETO is obtained by simulations and the experimental test also demonstrated a wide RBSOA.


Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment.


title: Fixed Frequency Pulse-Width Modulation Based Integrated Sliding Mode Controller for Phase-Shifted Full-Bridge Converters

abstract: The conventional approach to designing controllers for the phase-shifted full-bridge (PSFB) converter is to design a linear compensator based on the small-signal model, which cannot provide the converter with strong robustness against parametric disturbances and consistent transient responses over a wide range of operating conditions. This paper proposed a fixed frequency pulse-width-modulation (PWM)-based integrated sliding mode (ISM) controller for the PSFB converter. By analyzing the phase-shifted modulation of the full bridge, the dynamic state-space model of the PSFB is established based on the simplified circuit model. On this basis, the sliding motion in state space, the existence condition for steady-state operation, and the indirect PWM control function are discussed. Meanwhile, a switch control is introduced to the control function, and a robustness condition for parametric disturbances is analyzed. The designed PWM ISM controller is applied to a 1-kW PSFB converter prototype. Experimental results verify the effectiveness of the proposed controller by comparison with the hysteresis-modulation-based sliding mode controller and the increment PID controller.


title: Mobile Health Technology Evaluation The mHealth Evidence Workshop

abstract: Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.


title: Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal Segmentation in Alzheimer’s Disease

abstract: We provide and evaluate an open-source software solution for automatically hippocampal segmentation from T1-weighted (T1w) magnetic resonance imaging (MRI). The method is applied for measuring the hippocampal volume, which allows discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC). The method is based on a fast patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances. These combined similarity measures produces better selection of the patches, and their weights are more robust. The algorithm is trained with the Harmonized Hippocampal Protocol (HarP). The proposal is compared with FreeSurfer and other label fusion methods. To evaluate the performance and the robustness of the proposed label fusion method, we employ two databases of T1w MRI of human brains. For AD vs NC, we obtain a high degree of accuracy, approximately 90 %. For MCI vs NC, we obtain accuracies around 75 %. The average time for the hippocampal segmentation from a T1w MRI is less than 17 minutes.


title: Low power 1-Bit full adder using Full-Swing gate diffusion input technique

abstract: This paper presents a design which provides full swing output for logic 1 and logic 0 for 1-bit full adder cell and reduces power consumption, delay, and area. In this design full adder consists of two XOR gate cells and one cell of 2×1 multiplexer (MUX). The performance of the proposed design compared with the different logic style for full adders through cadence virtuoso simulation based on TSMC 65nm technology models with a supply voltage of 1v and frequency 125MHz. The simulation results showed that the proposed full adder design dissipates low power, while improving delay and area among all the design taken for comparison.


title: Body joints regression using deep convolutional neural networks

abstract: Human pose estimation is a well-known computer vision problem that receives intensive research interest. The reason for such interest is the wide range of applications that the successful estimation of human pose offers. Articulated pose estimation includes real time acquisition, analysis, processing and understanding of high dimensional visual information. Ensemble learning methods operating on hand-engineered features have been commonly used for addressing this task. Deep learning exploits representation learning methods to learn multiple levels of representations from raw input data, alleviating the need to hand-crafted features. Deep convolutional neural networks are achieving the state-of-the-art in visual object recognition, localization, detection. In this paper, the pose estimation task is formulated as an offset joint regression problem. The 3D joints positions are accurately detected from a single raw depth image using a deep convolutional neural networks model. The presented method relies on the utilization of the state-of-the-art data generation pipeline to generate large, realistic, and highly varied synthetic set of training images. Analysis and experimental results demonstrate the generalization performance and the real time successful application of the proposed method.


title: Ethnic Polarization, Potential Conflict, and Civil Wars

abstract: This paper analyzes the relationship between ethnic fractionalization, polarization, and con- ‡ict. In recent years many authors have found empirical evidence that ethnic fractionalization has a negative eect on growth. One mechanism that can explain this nexus is the eect of ethnic heterogeneity on rent-seeking activities and the increase in potential con‡ict, which is negative for investment. However the empirical evidence supporting the eect of ethnic frac- tionalization on the incidence of civil con‡icts is very weak. Although ethnic fractionalization may be important for growth, we argue that the channel is not through an increase in potential ethnic con‡ict. We discuss the appropriateness of indices of polarization to capture con‡ictive dimensions. We develop a new measure of ethnic heterogeneity that satis…es the basic proper- ties associated with the concept of polarization. The empirical section shows that this index of ethnic polarization is a signi…cant variable in the explanation of the incidence of civil wars. This result is robust to the presence of other indicators of ethnic heterogeneity, alternative sources of data for the construction of the indicators, and alternative data for civil wars.


title: User Transparent Data and Task Parallel Multimedia Computing with Pyxis-DT

abstract: The research area of Multimedia Content Analysis (MMCA) considers all aspects of the automated extraction of knowledge from multimedia archives and data streams. To satisfy the increasing computational demands of emerging MMCA problems, there is an urgent need to apply High Performance Computing (HPC) techniques. However, as most MMCA researchers are not also HPC experts, in the field there is a demand~for~programming models and tools that are both efficient and easy~to~use. Today several user transparent library-based parallelization tools exist that aim to satisfy both these requirements. Such tools generally use a data parallel approach in which data structures (e.g. video frames) are scattered among the available nodes in a compute cluster. However, for certain MMCA applications a data parallel approach induces intensive communication, which significantly decreases performance. In these situations, we can benefit from applying alternative approaches. This paper presents Pyxis-DT: a user transparent parallel programming model for MMCA applications that employs both data and task parallelism. Hybrid parallel execution is obtained by run-time construction and execution of a task graph consisting of strictly defined building block operations. Each of these building block operations can be executed in data parallel fashion. Results show that for realistic MMCA applications the concurrent use of data and task parallelism can significantly improve performance compared to using either approach in isolation.


PARA-SNORT : A MULTI-THREAD SNORT ON MULTI-CORE IA PLATFORM


title: Ubisemiquinones of the mitochondrial respiratory chain do not interact with molecular oxygen.

abstract: The present investigation deals with the suggested role of redox-cycling ubisemiquinones in mitochondrial O2-. generation. Due to the functional complexity of electron-transferring ubiquinones in the respiratory chain, model experiments were designed to study whether ubisemiquinones will directly react with oxygen, thereby generating O2-. radicals. Based on the fact that mitochondrial ubiquinone was reported to operate in an aprotic surrounding of the inner mitochondrial membrane, the reactivity of ubisemiquinones with oxygen was tested in water-free acetonitrile. Our results prove that autoxidation of ubisemiquinones requires the addition of protons to the non-polar reaction system. An experimental evaluation of the validity of this finding with respect to mitochondrial ubiquinones is impeded by the biochemical role that oxygen plays in the establishment of ubisemiquinone populations. To differentiate between a possible direct interaction of oxygen on redox-cycling ubisemiquinones and this indirect biochemical O2 effect, we have successfully introduced ferricyanide instead of oxygen to establish mitochondrial ubisemiquinone pools. Ubisemiquinones in this reaction system were not susceptible to oxygen and no O2-. radicals were released unless the inner mitochondrial membrane was protonated by toluene pretreatment. Since the inner mitochondrial membrane is normally not permeable to protons (which is a prerequisite of the chemiosmotic theory of energy conservation) based on our experiments we can exclude the involvement of redox-cycling ubisemiquinones in mitochondrial O2-. generation.


title: Effect size, confidence interval and statistical significance: a practical guide for biologists

abstract: Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.


title: A generalized theoretical deterministic particle swarm model

abstract: A number of theoretical studies of particle swarm optimization (PSO) have been done to gain a better understanding of the dynamics of the algorithm and the behavior of the particles under different conditions. These theoretical analyses have been performed for both the deterministic PSO model and more recently for the stochastic model. However, all current theoretical analyses of the PSO algorithm were based on the stagnation assumption, in some form or another. The analysis done under the stagnation assumption is one where the personal best and neighborhood best positions are assumed to be non-changing. While analysis under the stagnation assumption is very informative, it could never provide a complete description of a PSO’s behavior. Furthermore, the assumption implicitly removes the notion of a social network structure from the analysis. This paper presents a generalization to the theoretical deterministic PSO model. Under the generalized model, conditions for particle convergence to a point are derived. The model used in this paper greatly weakens the stagnation assumption, by instead assuming that each particle’s personal best and neighborhood best can occupy an arbitrarily large number of unique positions. It was found that the conditions derived in previous theoretical deterministic PSO research could be obtained as a specialization of the new generalized model proposed. Empirical results are presented to support the theoretical findings.


title: Program element matching for multi-version program analyses

abstract: Multi-version program analyses require that elements of one version of a program be mapped to the elements of other versions of that program. Matching program elements between two versions of a program is a fundamental building block for multi-version program analyses and other software evolution research such as profile propagation, regression testing, and software version merging.In this paper, we survey matching techniques that can be used for multi-version program analyses and evaluate them based on hypothetical change scenarios. This paper also lists challenges of the matching problem, identifies open problems, and proposes future directions.


title: Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary

abstract: We present an approach to learning a model-theoretic semantics for ::: natural language tied to Freebase. Crucially, our approach uses an ::: open predicate vocabulary, enabling it to produce denotations for ::: phrases such as "Republican front-runner from Texas" whose semantics ::: cannot be represented using the Freebase schema. Our approach directly ::: converts a sentence's syntactic CCG parse into a logical form ::: containing predicates derived from the words in the sentence, ::: assigning each word a consistent semantics across sentences. This ::: logical form is evaluated against a learned probabilistic database ::: that defines a distribution over denotations for each textual ::: predicate. A training phase produces this probabilistic database using ::: a corpus of entity-linked text and probabilistic matrix factorization ::: with a novel ranking objective function. We evaluate our approach on a ::: compositional question answering task where it outperforms several ::: competitive baselines. We also compare our approach against manually ::: annotated Freebase queries, finding that our open predicate vocabulary ::: enables us to answer many questions that Freebase cannot.


title: Real-time and low latency embedded computer vision hardware based on a combination of FPGA and mobile CPU

abstract: Recent developments in smartphones create an ideal platform for robotics and computer vision applications: they are small, powerful, embedded devices with low-power mobile CPUs. However, though the computational power of smartphones has increased substantially in recent years, they are still not capable of performing intense computer vision tasks in real time, at high frame rates and low latency. We present a combination of FPGA and mobile CPU to overcome the computational and latency limitations of mobile CPUs alone. With the FPGA as an additional layer between the image sensor and CPU, the system is capable of accelerating computer vision algorithms to real-time performance. Low latency calculation allows for direct usage within control loops of mobile robots. A stereo camera setup with disparity estimation based on the semi global matching algorithm is implemented as an accelerated example application. The system calculates dense disparity images with 752×480 pixels resolution at 60 frames per second. The overall latency of the disparity estimation is less than 2 milliseconds. The system is suitable for any mobile robot application due to its light weight and low power consumption.


title: Show and Recall: Learning What Makes Videos Memorable

abstract: With the explosion of video content on the Internet, there is a need for research on methods for video analysis which take human cognition into account. One such cognitive measure is memorability, or the ability to recall visual content after watching it. Prior research has looked into image memorability and shown that it is intrinsic to visual content, but the problem of modeling video memorability has not been addressed sufficiently. In this work, we develop a prediction model for video memorability, including complexities of video content in it. Detailed feature analysis reveals that the proposed method correlates well with existing findings on memorability. We also describe a novel experiment of predicting video sub-shot memorability and show that our approach improves over current memorability methods in this task. Experiments on standard datasets demonstrate that the proposed metric can achieve results on par or better than the state-of-the art methods for video summarization.


title: Solving Interleaved and Blended Sequential Decision-Making Problems through Modular Neuroevolution

abstract: Many challenging sequential decision-making problems require agents to master multiple tasks, such as defense and offense in many games. Learning algorithms thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well the methods work depends on the nature of the tasks: Interleaved tasks are disjoint and have different semantics, whereas blended tasks have regions where semantics from different tasks overlap. While many methods work well in interleaved tasks, blended tasks are difficult for methods with strict, human-specified task divisions, such as Multitask Learning. In such problems, task divisions should be discovered automatically. To demonstrate the power of this approach, the MM-NEAT neuroevolution framework is applied in this paper to two variants of the challenging video game of Ms. Pac-Man. In the simplified interleaved version of the game, the results demonstrate when and why such machine-discovered task divisions are useful. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. Modular neuroevolution is thus a promising technique for discovering multimodal behavior for challenging real-world tasks.


title: Understanding biofilm resistance to antibacterial agents

abstract: Key PointsBiofilm bacteria have been demonstrated to be significant contributors to human disease, yet our understanding of the nature of biofilm infections and their effective treatment remains underdeveloped. Recent research has begun to unlock many of the mysteries associated with biofilm infections; however, substantial research will be necessary before adequate control of biofilm infections will be generally attainable.Biofilms have been shown to be generally more resistant to antimicrobial chemotherapies than free-living bacteria. No current consensus exists regarding the mechanisms of biofilm resistance to antibacterial agents. Numerous competing theories are presently under investigation, yet the phenomenon of biofilm resistance remains a subject generating many questions but few answers.Multiple factors appear to contribute to the overall resistance of biofilm bacteria. These include reduced metabolic and growth rates, protection by extracellular polymeric substances and specific resistance mechanisms conferred by the altered physiology of biofilm bacteria compared with planktonic bacteria.The failure of antibacterial agents to rapidly penetrate into all areas of a biofilm has been considered as a contributing factor to biofilm resistance. Reports indicate that species-composition of biofilms and the choice of antibiotic have a marked impact on antibiotic penetration.Biofilm cells have been shown by a number of investigators to have reduced growth rates, and this is believed to impact the effectiveness of antibiotics that target rapidly multiplying cells. Therefore, antibiotics such as the fluoroquinolones or macrolides may be better therapeutic choices than β-lactams when treating biofilm infections.Altered physiological states of biofilm cells compared with planktonic cells have been demonstrated for a number of bacteria. The activation of specific resistance genes in biofilms has been demonstrated in a few instances. Specific resistance mechanisms should, therefore, be considered when treating biofilm infections.A number of alternative approaches to antibacterial treatment have been proposed for biofilms. Many of these are non-lethal treatments that are expected to enhance the activity of currently used antibiotics. For example, manipulation of electrical fields has shown some promise in enhancing the activity of certain antibiotics against biofilms. The finding that in certain bacteria autoinducers mediate the production of virulence factors, and possibly also biofilm formation, has led to developmental work on autoinducer-blocking agents. These blocking agents are expected, in certain cases, to limit disease progression at the same time as antibiotics are being administered, and, in other cases, to act to enhance the activity of an antibiotic. Biofilm-dispersion agents are currently under development and are anticipated to result in improved access of antibiotics to bacteria, and possibly to alter the physiological status of the bacteria making them more susceptible to antibiotic exposure.The current state of the field indicates that enhanced antimicrobial resistance is a general trait of biofilms and is the result of numerous specific factors which depend on the species involved, the environment of the biofilm and the antimicrobial agent used. It is expected, therefore, that the effective treatment of biofilms in the future will depend on tailoring treatment strategies to specific infection conditions rather than to any general phenomenon common to all biofilms.AbstractAccording to a public announcement by the US National Institutes of Health, “Biofilms are medically important, accounting for over 80% of microbial infections in the body”. Yet bacterial biofilms remain poorly understood and strategies for their control remain underdeveloped. Standard antimicrobial treatments typically fail to eradicate biofilms, which can result in chronic infection and the need for surgical removal of afflicted areas. The need to create effective therapies to counter biofilm infections presents one of the most pressing challenges in anti-bacterial drug development. In this article, the mechanisms that underlie biofilm resistance to antimicrobial chemotherapy will be examined, with particular attention being given to potential avenues for the effective treatment of biofilms.