title: Yield and Reliability Improvement Techniques for Emerging Nonvolatile STT-MRAM

abstract: The emerging spin transfer torque magnetic random access memory (STT-MRAM) promises many attractive features, such as nonvolatile, high speed and low power etc, which enable it to be a promising candidate for the next-generation logic and memory circuits. However with the continuous scaling technology process, the chip yield and reliability of STT-MRAM face severe challenges due to the increasing permanent and transient faults. Due to the intrinsic fault features and the targeted application requirements of STT-MRAM, traditional fault tolerant design solutions, such as error correction code (ECC), redundancy repair (RR), and fault masking (FM) techniques, cannot be employed straightforwardly for STT-MRAM. In this paper, we propose a synergistic technique framework, named sECC, that integrates both the ECC and FM techniques to address simultaneously the permanent and transient faults. With such approach, permanent faults are masked while transient faults are corrected with the same codeword. Moreover taking into consideration the fact that most permanent faults are sparse [about 60%–70% single isolated faults (SIFs)], we propose further integrating the RR and sECC (named iRRsECC) to optimize the system performance. In this scenario, all the SIFs are masked and the transient faults are corrected with the proposed sECC, while other permanent faulty types (e.g., faulty rows or columns) are repaired with redundant rows or columns. A simulation tool is developed to evaluate the proposed techniques and the evaluation results show their good performance in terms of repair rate and hardware overhead.


title: The Rubber Hand Illusion Revisited: Visuotactile Integration and Self-Attribution.

abstract: Watching a rubber hand being stroked, while one’s own unseen hand is synchronously stroked, may cause the rubber hand to be attributed to one’s own body, to “feel like it’s my hand.” A behavioral measure of the rubber hand illusion (RHI) is a drift of the perceived position of one’s own hand toward the rubber hand. The authors investigated (a) the influence of general body scheme representations on the RHI in Experiments 1 and 2 and (b) the necessary conditions of visuotactile stimulation underlying the RHI in Experiments 3 and 4. Overall, the results suggest that at the level of the process underlying the build up of the RHI, bottom-up processes of visuotactile correlation drive the illusion as a necessary, but not sufficient, condition. Conversely, at the level of the phenomenological content, the illusion is modulated by top-down influences originating from the representation of one’s own body. Watching a rubber hand being stroked synchronously with one’s own unseen hand causes the rubber hand to be attributed to one’s own body, to “feel like it’s my hand.” Attribution can be measured quantitatively as a drift of the perceived position of one’s own hand toward the rubber hand. In one study, after 30 min of synchronous stimulation on the rubber hand and the participant’s hand, participants mislocated the perceived position of their own hand (Botvinick & Cohen, 1998). Participants judged the position of their hand to be closer to the rubber hand, as if their hand had drifted toward the fake hand. Botvinick and Cohen suggested that the rubber hand illusion (RHI) reflected a three-way interaction between vision, touch, and proprioception: Vision captured touch, resulting in a mislocalization of the tactile percept toward the spatial location of the visual percept. This visual–tactile correlation influenced the felt position of one’s own hand. The end result of the RHI, namely, a visual adaptation of proprioceptive position, is somewhat similar to prism adaptation (Welch, Widawski, Harrington, & Warren, 1979). However, the key feature of the RHI is that it results from correlated visual and tactile inputs. This interplay between vision and touch has been at the center of research on multisensory integration (for a review, see Maravita, Spence, & Driver, 2003). It has been consistently shown that vision usually plays a dominant role over touch and proprioception (Ernst & Banks, 2002). For example, even noninformative vision of body parts exerts a top-down influence on tactile percepts by improving the spatial resolution of touch (Haggard, Taylor-Clarke, & Kennett, 2003; Kennett, Taylor-Clarke, & Haggard, 2001). The RHI can be used as a method of investigating not only multisensory integration and the interplay between vision, touch, and proprioception, but also the way we perceive our bodily self. The necessary condition for the inducement of the illusion is the presence of synchronized and spatially congruent visual and tactile stimulation. At the phenomenological level, the illusory experience seems like a form of incorporation of a foreign object, as if the rubber hand becomes a bodily auxiliary (Merleau-Ponty, 1945/ 1962), possibly in similar ways to the incorporation of tools (Iriki, Tanaka, & Iwamura, 1996). Introspective evidence from the original experiment (Botvinick & Cohen, 1998) suggested that participants felt not only as if they were feeling the touch at the location where the rubber hand was seen to be touched but also as if the rubber hand was their own hand. In a sense, their tactile sensations were projected onto the rubber hand, which eventually felt like part of their own body. These observations might reflect the involvement of two separate components. First, there is a bottom-up process of integrating synchronized visual and tactile percepts, which is a necessary condition for producing the RHI. Second, this process produces persistent, vivid phenomenological changes in body representation, namely, the experience that the rubber hand is part of one’s own body. Moreover, the content of the changed body representation might be quite different from, and goes beyond, the perception of correlated visual and tactile stimulation. These observations suggest that the RHI could involve an interaction between general body scheme representations and localized visuotactile integration. Two issues are in need of clarification. First, how do correlated visual and tactile stimuli come to influence the perceived position of the hand? Second, why do correlated visual and tactile stimuli produce this strange phenomenal experience of ownership, that is, do normal participants who know that this is a fake hand feel as if the rubber hand belongs to their body? We report four experiments. In Experiments 1 and 2, we were principally interested in investigating the modulation exerted by general body scheme representations on the inducement of the illusion. To that extent, in Experiment 1 we manipulated (a) the posture of the viewed rubber hand and (b) the identity of the viewed object, and in Experiment 2 we manipulated the felt and


title: Support vector hazards machine: a counting process framework for learning risk scores for censored outcomes

abstract: Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for di_erent at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate nonlinearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.


title: An Asymptotically Efficient Estimator for TDOA and FDOA Positioning of Multiple Disjoint Sources in the Presence of Sensor Location Uncertainties

abstract: This correspondence considers the problem of locating multiple disjoint sources using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) in the presence of sensor position and velocity errors. Previous work applies to one source only with suboptimum performance for near source, or requires joint estimation of the source and sensor locations that could be computation demanding. Through nonlinearly transforming the measurements and converting them with respect to the inaccurate sensor locations, we have developed an algebraic solution to this problem. The solution is shown analytically to achieve the Cramer-Rao lower bound (CRLB) performance over small noise region and does not require joint estimation with sensor locations.


title: Compressive sensing meets unreliable link: sparsest random scheduling for compressive data gathering in lossy WSNs

abstract: Compressive Sensing (CS) has been recognized as a promising technique to reduce and balance the transmission cost in wireless sensor networks (WSNs). Existing efforts mainly focus on applying CS to reliable WSNs, namely, each wireless link is 100% reliable. However, our experimental results show that traditional compressive data gathering (CDG) could result in arbitrarily bad recovery performance, when the wireless links are lossy. In this paper, we study the impact of packet loss on compressive data gathering and ways to improve its robustness using sparsest random scheduling (SRS). The key idea of our scheme is to treat each sampling value as one CS measurement, which helps us to reduce the impact of packet loss on the recovery accuracy. Our scheme also outperforms the tradition CDG in reliable WSNs in that our scheme has significantly lowered transmission cost. To achieve this, we present a sparsest measurement matrix where each row has only one nonzero element. More importantly, we propose a representation basis to sparsify the gathering data, and prove that our measurement matrix satisfies the restricted isometric property (RIP) with high probability. Extensive experimental results show our scheme can recover the data accurately with packet loss ratio up to $15\%$, while traditional CDG can hardly recover the data under similar or even better conditions.


title: Structured Adversarial Training for Unsupervised Monocular Depth Estimation

abstract: The problem of estimating scene-depth from a single image has seen great progress lately. Recent unsupervised methods are based on view-synthesis and learn depth by minimizing photometric reconstruction error. In this paper, we introduce Structured Adversarial Training (StrAT) to this problem. We generate multiple novel views using depth (or disparity), with the stereo-baseline changing in an increasing order. Adversarial training that goes from easy examples to harder ones produces richer losses and better models. The impact of StrAT is shown to exceed traditional data augmentation using random new views. The combination of an adversarial framework, multiview learning, and structured adversarial training produces state-of-the-art performance on unsupervised depth estimation for monocular images. The StrAT framework can benefit several problems that use adversarial training.


title: The Developmental Psychopathology of Worry

abstract: Although childhood generalized anxiety disorder is generally understudied, worry, the cardinal feature of GAD, appears to be relatively common in youth. Despite its prevalence, there are few conceptual models of the development of clinical worry in children. The current review provides a framework for integrating the developmental psychopathology perspective, models of worry in adults, and data available on worry in children. General risk factors for the development of worry are considered, as well as potential pathways including genetics, temperament, cognitive, emotional and parenting influences, as well as the influence of cognitive development. Based on this review, it appears unlikely that main effects models will be able to explain the development of GAD or clinical worry in children and that a broad, complex model incorporating a number of factors and their interactions will best describe etiological and maintaining factors. With this perspective in mind, a number of suggestions for future work are offered.


title: Structure-aware shape processing

abstract: Shape structure is about the arrangement and relations between shape parts. Structure-aware shape processing goes beyond local geometry and low level processing, and analyzes and processes shapes at a high level. It focuses more on the global inter and intra semantic relations among the parts of shape rather than on their local geometry. With recent developments in easy shape acquisition, access to vast repositories of 3D models, and simple-to-use desktop fabrication possibilities, the study of structure in shapes has become a central research topic in shape analysis, editing, and modeling. A whole new line of structure-aware shape processing algorithms has emerged that base their operation on an attempt to understand such structure in shapes. The algorithms broadly consist of two key phases: an analysis phase, which extracts structural information from input data; and a (smart) processing phase, which utilizes the extracted information for exploration, editing, and synthesis of novel shapes. In this course, we will organize, summarize, and present the key concepts and methodological approaches towards efficient structure-aware shape processing. We discuss common models of structure, their implementation in terms of mathematical formalism and algorithms, and explain the key principles in the context of a number of state-of-the-art approaches. Further, we attempt to list the key open problems and challenges, both at the technical and at the conceptual level, to make it easier for new researchers to better explore and contribute to this topic. Our goal is to both give the practitioner an overview of available structure-aware shape processing techniques, as well as identify future research questions in this important, emerging, and fascinating research area.


title: A research manifesto for services science

abstract: The services sector has grown over the last 50 years to dominate economic activity in most advanced industrial economies, yet scientific understanding of modern services is rudimentary. Here, we argue for a services science discipline to integrate across academic silos and advance service innovation more rapidly.


title: Modeling and analysis of the effects of PLL phase noise on FMCW radar performance

abstract: The phase noise of a phase-locked loop (PLL) has a great impact on the performance of frequency-modulated continuous-wave (FMCW) radar. To examine the effects of the phase noise on FMCW radar performance, a model of an FMCW radar with a noisy PLL is developed. A filter-based technique for modeling the PLL phase noise is described. The radar model shows that PLL in-band phase noise affects the spatial resolution of the FMCW radar, whereas PLL out-of-band phase noise limits the maximum range. Finally, we propose a set of design constraints for PLL based on the model simulation results.


title: Printed Monopole Slot Antenna for Internal Multiband Mobile Phone Antenna

abstract: A new internal multiband mobile phone antenna formed by two printed monopole slots of different lengths cut at the edge of the system ground plane of the mobile phone is presented. The antenna can generate two wide bands centered at about 900 and 2100 MHz to cover the GSM850/GSM900/DCS/PCS/UMTS bands and the 2.4-GHz WLAN band. Further, the antenna has a simple planar structure and occupies a small area of only. It is also promising to bend the antenna into an L shape to reduce its volume occupied inside the mobile phone. Good radiation characteristics are obtained over the two wide operating bands.


n-3 Polyunsaturated Fatty Acids Suppress Mitochondrial Translocation toQ : 1 the Immunologic Synapse and Modulate Calcium SigQ : 2 naling in T Cells


title: Real-time visualization using a 2D/3D imaging MMWave radar

abstract: This paper describes a novel approach to the real-time visualization of 3D imagery obtained from a 3D millimeter wave radar. The radar system uses two scanning beams to provide all weather 3D distance measurements of objects appearing on the ground. This information is displayed using our high-end 3D visualization engine capable of delivering models of up to 100,000 polygons with 30 frames per second. The resulting 3D models can then be viewed from any angle and subsequently processed to integrate match them against 3D model data stored in a synthetic database. The resulting Synthetic Radar Vision System will provide a truly novel way to obtained all weather 3D images. The paper will focus on the real-time imaging and display aspects of our solution, and will discuss technical details of the radar design itself. Engineering challenges will be outlined in the context of a practical application.


title: From speech to SQL queries: a speech understanding system

abstract: In this pap er, we describe our speech understanding system and we test it on ::: two different applications. The proposed system is a task specific one and it concern ::: especially oral database consultation tasks. In this work, we consider that the ::: automatic speech understanding problem could be seen as an association problem ::: between two different languages. At the entry , the request expressed in natural ::: language and at the end, just before the interpretation stage, the same request is ::: expressed in term of concepts. A concept represents a given meaning, it is defined ::: by a set of words sharing the same semantic properties. In this pap er, we propose a ::: new Bayesian network based method to automatically extract the underlined concepts. ::: We also propose and compare three approaches for the vector representation ::: of words. We finish this pap er by a description of the post-processing step during ::: which we generate corresponding SQL queries to the pronounced sentences and we ::: connect our understanding system to a speech recognition engine. This step allows ::: us to validate our speech understanding approach by obtaining with the two treated ::: applications the rates of 78% and 81% of well formed SQL requests.


title: Multiple-Degree-of-Freedom Counterbalance Robot Arm Based on Slider-Crank Mechanism and Bevel Gear Units

abstract: Low-cost but high-performance robot arms are required for robot arms to come into widespread use. To provide sufficient torques to support the robot mass and payload, most arms use expensive motors and speed reducers, which are the main reasons for their high price. As a solution to these problems, a multiple-degree-of-freedom (DOF) counterbalance mechanism and robot arm were developed in our previous study, which can compensate for the gravitational torque due to the robot mass. However, there are durability and reliability problems associated with wire-based mechanisms. To solve these, in this study, we proposed a new counterbalance mechanism based on a spring and slider-crank mechanism, along with a double-parallelogram mechanism composed of bevel gear units. Moreover, a 6-DOF counterbalance robot arm was built to verify the performance of the proposed mechanism. Simulation and experimental results showed that the proposed mechanism effectively decreased the torque required to support the robot mass, thus allowing the prospective use of low-cost motors and speed reducers for high-performance robots.


NEWS FRAMING AND PUBLIC OPINION: A MEDIATIONAL ANALYSIS OF FRAMING EFFECTS ON POLITICAL ATTITUDES Manuscript under review


title: Multi-Scale Context Attention Network for Image Retrieval

abstract: Recent attempts on the Convolutional Neural Network (CNN) based image retrieval usually adopt the output of a specific convolutional or fully connected layer as feature representation. Though superior representation capability has yielded better retrieval performance, the scale variation and clutter distracting remain to be two challenging problems in CNN based image retrieval. In this work, we propose a Multi-Scale Context Attention Network (MSCAN) to generate global descriptors, which is able to selectively focus on the informative regions with the assistance of multi-scale context information. We model the multi-scale context information by an improved Long Short-Term Memory (LSTM) network across different layers. As such, the proposed global descriptor is equipped with the scale aware attention capability. Experimental results show that our proposed method can effectively capture the informative regions in images and retain reliable attention responses when encountering scale variation and clutter distracting. Moreover, we compare the performance of the proposed scheme with the state-of-the-art global descriptors, and extensive results verify that the proposed MSCAN can achieve superior performance on several image retrieval benchmarks.


title: Nonlinear Capacitors Power Dissipation and its Graphical Visualization

abstract: This is a brief review paper on nonlinear capacitors and how it may have a major difference in energy dissipation when Compared with linear capacitors. In this paper we will discuss the energy dissipation difference between nonlinear and linear capacitors and apply the Graphene capacitor C-V curve as an example and its expected power dissipation then we will discuss other nonlinear capacitors dissipation


title: pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference

abstract: Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This paper proposes new methods for learning and using embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are computed as a compositional function of each word's representation, which is learned by maximizing the pointwise mutual information (PMI) with the contexts in which the the two words co-occur. We add these representations to the cross-sentence attention layer of existing inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or replacing existing word embeddings. Experiments show a gain of 2.72% on the recently released SQuAD 2.0 and 1.3% on MultiNLI. Our representations also aid in better generalization with gains of around 6-7% on adversarial SQuAD datasets, and 8.8% on the adversarial entailment test set by Glockner et al.


title: Constituent Parsing as Sequence Labeling

abstract: We introduce a method to reduce constituent parsing to sequence labeling. For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.


title: Recognition of visual activities and interactions by stochastic parsing

abstract: This paper describes a probabilistic syntactic approach to the detection and recognition of temporally extended activities and interactions between multiple agents. The fundamental idea is to divide the recognition problem into two levels. The lower level detections are performed using standard independent probabilistic event detectors to propose candidate detections of low-level features. The outputs of these detectors provide the input stream for a stochastic context-free grammar parsing mechanism. The grammar and parser provide longer range temporal constraints, disambiguate uncertain low-level detections, and allow the inclusion of a priori knowledge about the structure of temporal events in a given domain. We develop a real-time system and demonstrate the approach in several experiments on gesture recognition and in video surveillance. In the surveillance application, we show how the system correctly interprets activities of multiple interacting objects.


title: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms for power economic dispatch

abstract: In this paper we describe an efficient approach for solving the economic dispatch problem using Genetic Algorithms (GAs). We recommend the use of adaptive probabilities crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. In the Adaptive Genetic Algorithm (AGA), the probabilities of crossover and mutation, pc and pm, are varied depending on the fitness values of the solutions. By using adaptively varying, pc and pm, we also provide a solution to the problem of deciding the optimal values of pc and pm, i.e., pc and pm need not be specified at all. We compare the performance of the AGA with that of the Standard GA (SGA) in optimizing the penalty function by using a sequential unconstrained minimization technique (SUMT). In this work, the AGA has been applied to a practical 14-bus system to show its feasibility and capabilities. The Numerical and graphical results show that the proposed approach is faster and more robust than the simple static


title: A literature review on management of supply and distribution in the UK online grocery retailing: a resource-based view

abstract: Although much has been discovered about online grocery retailing of Asda, Sainsbury’s, Tesco and Waitrose (ASTW) in the UK; research that provides a resource-based view (RBV) of stores and managerial capabilities advantages in supporting supply and distribution (SD necessitate further investigation; and indicate critical success factors to grocery e-retailing.


Deep Convolutional Neural Networks for Lung Cancer Detection


title: FPGA-Based Assessment of Midori and Gift Lightweight Block Ciphers

abstract: Lightweight block ciphers are today of paramount importance to provide security services in constrained environments. Recent studies have questioned the security properties of Present, which makes it evident the need to study alternative ciphers. In this work we provide hardware architectures for Midori and Gift, and compare them against implementations for Present and Gimli under fair conditions. The hardware description for our designs is made publicly available.


title: On the dynamics of a quadruped robot model with impedance control: Self-stabilizing high speed trot-running and period-doubling bifurcations

abstract: United States. Defense Advanced Research Projects Agency. Maximum Mobility and Manipulation (M3) Program


title: Dynamics of a stressful encounter: Cognitive appraisal, coping, and encounter outcomes.

abstract: Despite the importance that is attributed to coping as a factor in psychological and somatic health outcomes, little is known about actual coping processes, the variables that influence them, and their relation to the outcomes of the stressful encounters people experience in their day-to-day lives. This study uses an intraindividu al analysis of the interrelations among primary appraisal (what was at stake in the encounter), secondary appraisal (coping options), eight forms of problem- and emotionfocused coping, and encounter outcomes in a sample of community-res iding adults. Coping was strongly related to cognitive appraisal; the forms of coping that were used varied depending on what was at stake and the options for coping. Coping was also differentially related to satisfactory and unsatisfactory encounter outcomes. The findings clarify the functional relations among appraisal and coping variables and the outcomes of stressful encounters. The recent burgeoning of research on coping is indicative of a growing conviction that coping is a major factor in the relation between stressful events and adaptational outcomes such as depression, psychological symptoms, and somatic illness (e.g., Andrews, Tennant, Hewson, & Vaillant, 1978; Baum, Fleming, & Singer, 1983; Billings & Moos, 1981, 1984; Collins, Baum, & Singer, 1983; Coyne, Aldwin, & Lazarus, 1981; Felton, Revenson, & Hinrichsen, 1984; Menaghan, 1982; Mitchell, Cronkite, & Moos, 1983; Pearlin & Schooler, 1978; Schaefer, 1983; Shinn, Rosario, M0rch, & Chestnut, 1984; Taylor, Wood, & Lichtman, 1983; Vaillant, 1977). This new body of research is characterized by an interest in the actual coping processes that people use to manage the demands of stressful events, as distinct from traitoriented research, which focuses on personality dispositions from which coping processes are usually inferred, but not actually studied (e.g., Byrne, Steinberg, & Schwartz, 1968; Gaines, Smith, & Skolnick, 1977; Kobasa, Maddi, & Courington, 1981; Kobasa, Maddi, & Kahn, 1982). A critical difference between the trait-oriented and the processoriented approaches is the significance given to the psychological and environmental context in which coping takes place. In the trait-oriented approach, it is assumed that coping is primarily a property of the person, and variations in the stressful situation are of little importance. In contrast, the context is critical in the process-oriented approach because coping is assessed as a response to the psychological and environmental demands of specific stressful encounters. However, although coping processes are usually assessed contextually, with few exceptions (e.g.,


title: Memory Augmented Policy Optimization for Program Synthesis with Generalization.

abstract: This paper presents Memory Augmented Policy Optimization (MAPO): a novel policy optimization formulation that incorporates a memory buffer of promising trajectories to reduce the variance of policy gradient estimates for deterministic environments with discrete actions. The formulation expresses the expected return objective as a weighted sum of two terms: an expectation over a memory of trajectories with high rewards, and a separate expectation over the trajectories outside the memory. We propose 3 techniques to make an efficient training algorithm for MAPO: (1) distributed sampling from inside and outside memory with an actor-learner architecture; (2) a marginal likelihood constraint over the memory to accelerate training; (3) systematic exploration to discover high reward trajectories. MAPO improves the sample efficiency and robustness of policy gradient, especially on tasks with a sparse reward. We evaluate MAPO on weakly supervised program synthesis from natural language with an emphasis on generalization. On the WikiTableQuestions benchmark we improve the state-of-the-art by 2.5%, achieving an accuracy of 46.2%, and on the WikiSQL benchmark, MAPO achieves an accuracy of 74.9% with only weak supervision, outperforming several strong baselines with full supervision. Our code is open sourced at this https URL


title: Deep Learning Approximation for Stochastic Control Problems

abstract: Many real world stochastic control problems suffer from the "curse of dimensionality". To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics. The objective function for the control problem plays the role of the loss function for the deep neural network. We test this approach using examples from the areas of optimal trading and energy storage. Our results suggest that the algorithm presented here achieves satisfactory accuracy and at the same time, can handle rather high dimensional problems.


title: Coarse-to-Fine Natural Language Processing

abstract: State-of-the-art natural language processing models are anything but compact. Syntactic parsers have huge grammars, machine translation systems have huge transfer tables, and so on across a range of tasks. With such complexity come two challenges. First, how can we learn highly complex models? Second, how can we efficiently infer optimal structures within them? ::: Hierarchical coarse-to-fine methods address both questions. Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Because each refinement introduces only limited complexity, both learning and inference can be done in an incremental fashion. In this dissertation, we describe several coarse-to-fine systems. ::: In the domain of syntactic parsing, complexity is in the grammar. We present a latent variable approach which begins with an X-bar grammar and learns to iteratively refine grammar categories. For example, noun phrases might be split into subcategories for subjects and objects, singular and plural, and so on. This splitting process admits an efficient incremental inference scheme which reduces parsing times by orders of magnitude. Furthermore, it produces the best parsing accuracies across an array of languages, in a fully language-general fashion. ::: In the domain of acoustic modeling for speech recognition, complexity is needed to model the rich phonetic properties of natural languages. Starting from a mono-phone model, we learn increasingly refined models that capture phone internal structures, as well as context-dependent variations in an automatic way. Our approaches reduces error rates compared to other baseline approaches, while streamlining the learning procedure. ::: In the domain of machine translation, complexity arises because there and too many target language word types. To manage this complexity, we translate into target language clusterings of increasing vocabulary size. This approach gives dramatic speed-ups while additionally increasing final translation quality.


title: Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions

abstract: Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms.


title: Backscattering particle immunoassays in wire-guide droplet manipulations

abstract: A simpler way for manipulating droplets on a flat surface was demonstrated, eliminating the complications in the existing methods of open-surface digital microfluidics. Programmed and motorized movements of 10 μL droplets were demonstrated using stepper motors and microcontrollers, including merging, complicated movement along the programmed path, and rapid mixing. Latex immunoagglutination assays for mouse immunoglobulin G, bovine viral diarrhea virus and Escherichia coli were demonstrated by merging two droplets on a superhydrophobic surface (contact angle = 155 ± 2°) and using subsequent back light scattering detection, with detection limits of 50 pg mL-1, 2.5 TCID50 mL-1 and 85 CFU mL-1, respectively, all significantly lower than the other immunoassay demonstrations in conventional microfluidics (~1 ng mL-1 for proteins, ~100 TCID50 mL-1 for viruses and ~100 CFU mL-1 for bacteria). Advantages of this system over conventional microfluidics or microwell plate assays include: (1) minimized biofouling and repeated use (>100 times) of a platform; (2) possibility of nanoliter droplet manipulation; (3) reprogrammability with a computer or a game pad interface.


title: Domain Adaptation for Statistical Classifiers

abstract: The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.


OD A RCH : Protecting Legacy Applications with a Purely Hardware TCB


title: Enhanced Knowledge Management by Synchronizing Mind Maps and Fuzzy Cognitive Maps

abstract: This paper presents a conceptual approach to enhance knowledge management by synchronizing mind maps and fuzzy cognitive maps. Using mind maps takes advantage of human creativity, while fuzzy cognitive maps can store and retrieve information expressed in natural language. Applying the concepts of cognitive computing makes it possible to gather and extract relevant information from a data pool. Therefore, this approach is intended to provide a framework that enhances knowledge management. To demonstrate the potential of this framework, a use case concerning the development of a smart city app is presented.


title: Classification-Reconstruction Learning for Open-Set Recognition

abstract: Open-set classification is a problem of handling ‘unknown’ classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns. In contrast, we train networks for joint classification and reconstruction of input data. This enhances the learned representation so as to preserve information useful for separating unknowns from knowns, as well as to discriminate classes of knowns. Our novel Classification-Reconstruction learning for Open-Set Recognition (CROSR) utilizes latent representations for reconstruction and enables robust unknown detection without harming the known-class classification accuracy. Extensive experiments reveal that the proposed method outperforms existing deep open-set classifiers in multiple standard datasets and is robust to diverse outliers.


title: Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

abstract: Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.


title: One Way Hash Functions and DES

abstract: One way hash functions are a major tool in cryptography. DES is the best known and most widely used encryption function in the commercial world today. Generating a one-way hash function which is secure if DES is a "good" block cipher would therefore be useful. We show three such functions which are secure if DES is a good random block cipher.


title: Chinese AIS Network and Impact on the Maritime Management

abstract: AIS (Automatic Identification System) has more and more been used on a ship to identify a target and improve the navigation safety at sea as a new type of radio navaid system. AIS network has also been constructed at the main harbor in many countries to enhance the traffic safety and the maritime management. In the paper, AIS system is briefly described. It stresses on Chinese AIS network frame. The applications and traffic management are discussed. It is analyzed that AIS network brings to the benefits and the influences on the traffic safety and the maritime management in China. Expectation of application prospect of the network is conducted.


title: Alert Detection in System Logs

abstract: We present Nodeinfo, an unsupervised algorithm for anomaly detection in system logs. We demonstrate Nodeinfo's effectiveness on data from four of the world's most powerful supercomputers: using logs representing over 746 million processor-hours, in which anomalous events called alerts were manually tagged for scoring, we aim to automatically identify the regions of the log containing those alerts. We formalize the alert detection task in these terms, describe how Nodeinfo uses the information entropy of message terms to identify alerts, and present an online version of this algorithm, which is now in production use. This is the first work to investigate alert detection on (several) publicly-available supercomputer system logs, thereby providing a reproducible performance baseline.


title: Augmented segmentation and visualization for presentation videos

abstract: We investigate methods of segmenting, visualizing, and indexing presentation videos by both audio and visual data. The audio track is segmented by speaker, and augmented with key phrases which are extracted using an Automatic Speech Recognizer (ASR). The video track is segmented by visual dissimilarities and changes in speaker gesturing, and augmented by representative key frames. An interactive user interface combines a visual representation of audio, video, text, key frames, and allows the user to navigate presentation videos. User studies with 176 students of varying knowledge were conducted on 7.5 hours of student presentation video (32 presentations). Tasks included searching for various portions of presentations, both known and unknown to students, and summarizing presentations given the annotations. The results are favorable towards the video summaries and the interface, suggesting faster responses by a factor of 20% compared to having access to the actual video. Accuracy of responses remained the same on average. Follow-up surveys present a number of suggestions towards improving the interface, such as the incorporation of automatic speaker clustering and identification, and the display of an abstract topological view of the presentation. Surveys also show alternative contexts in which students would like to use the tool in the classroom environment.


title: Commonsense Causal Reasoning Using Millions of Personal Stories

abstract: The personal stories that people write in their Internet weblogs include a substantial amount of information about the causal relationships between everyday events. In this paper we describe our efforts to use millions of these stories for automated commonsense causal reasoning. Casting the commonsense causal reasoning problem as a Choice of Plausible Alternatives, we describe four experiments that compare various statistical and information retrieval approaches to exploit causal information in story corpora. The top performing system in these experiments uses a simple co-occurrence statistic between words in the causal antecedent and consequent, calculated as the Pointwise Mutual Information between words in a corpus of millions of personal stories.


title: Video search reranking through random walk over document-level context graph

abstract: Multimedia search over distributed sources often result in recurrent images or videos which are manifested beyond the textual modality. To exploit such contextual patterns and keep the simplicity of the keyword-based search, we propose novel reranking methods to leverage the recurrent patterns to improve the initial text search results. The approach, context reranking, is formulated as a random walk problem along the context graph, where video stories are nodes and the edges between them are weighted by multimodal contextual similarities. The random walk is biased with the preference towards stories with higher initial text search scores - a principled way to consider both initial text search results and their implicit contextual relationships. When evaluated on TRECVID 2005 video benchmark, the proposed approach can improve retrieval on the average up to 32% relative to the baseline text search method in terms of story-level Mean Average Precision. In the people-related queries, which usually have recurrent coverage across news sources, we can have up to 40% relative improvement. Most of all, the proposed method does not require any additional input from users (e.g., example images), or complex search models for special queries (e.g., named person search).


title: The impact of immediately placed and restored single-tooth implants on hard and soft tissues in the anterior maxilla.

abstract: The purpose of this literature review is to systematically evaluate the impact of immediate implant placement and restoration (IIPR) on hard and soft tissues and to identify clinical parameters which influence the outcome.An electronic search of the PubMed database was performed from January 2000 to September 2015. A further hand search was conducted in selected journals and only abstracts published in English were considered for review. Human clinical trials with at least 10 participants and which reported hard and soft tissue outcomes were assessed. Randomised controlled trials (RCT), prospective, prospective comparative and retrospective studies were considered. The effects of the following clinical parameters on hard and soft tissue outcomes were analysed: type of implant, primary stability, gingival biotype, flapless surgery, tooth extraction, spatial arrangement of the implant, socket grafting, the gap between implant surface and alveolar wall and the loading protocol.17 studies (four RCT, six prospective, two comparative prospective, three controlled cohort and two retrospective studies) were included with 626 censored IIPR in 609 patients. A total of 411(65.56 %) implants were placed flapless vs 215 implants after raising a mucoperiosteal flap. Five studies defined raising a mucoperiostal flap as a mandatory part of the surgical protocol. The mean of the remaining gap in between the implant surface and the alveolar wall, the so-called "jump space", was reported for 170 implants ranging from 1.38 mm to 2.25 mm. Two hundred and one implant sites were not grafted, 405 were grafted, mostly with bone substitutes; for 20 no information was available. For 419 implants, a minimum insertion torque of ≥ 32 Ncm or an ISQ value of ≥ 60 was reached; for 53 implants an insertion torque of 25 Ncm was accepted. The implants were mostly placed palatinally of the jaw bone. The vertical position of the platform was reported either to be 0.5 to 1.0 mm below the vestibular bone crest or 3 to 4 mm apical to the adjacent cementoenamel junction of the neighbouring tooth. Post-insertion healing with a non-functional occlusion occurred for 97.8% of the implants. The final single crowns were inserted 3 to 6 months after implant placement. The IIPR resulted in a high success (97.96 %) and survival rate (98.25%) after a mean followup period of 31.2 months. The soft-tissue biotype was evaluated in 379 (60.5%) sites as thick. The mean crestal bone and the mean interproximal mucosa level changes were less than 1 mm compared to the baseline. The midfacial periimplant mucosal level change was less than 0.95 mm. This level was reached for both thin and thick soft-tissue biotypes, without a significant difference. Only in one study did the thin biotypes show a significantly higher recession.The systematic review revealed promising results for immediately placed and immediately restored implants (IIPR) in the anterior maxilla. The possible options of flapless surgery and absence of grafting of the socket allows a minimal surgical intervention. However, a strict patient selection seemed mandatory for all included clinical trials.


title: Robust vehicle localization in urban environments using probabilistic maps

abstract: Autonomous vehicle navigation in dynamic urban environments requires localization accuracy exceeding that available from GPS-based inertial guidance systems. We have shown previously that GPS, IMU, and LIDAR data can be used to generate a high-resolution infrared remittance ground map that can be subsequently used for localization [4]. We now propose an extension to this approach that yields substantial improvements over previous work in vehicle localization, including higher precision, the ability to learn and improve maps over time, and increased robustness to environment changes and dynamic obstacles. Specifically, we model the environment, instead of as a spatial grid of fixed infrared remittance values, as a probabilistic grid whereby every cell is represented as its own gaussian distribution over remittance values. Subsequently, Bayesian inference is able to preferentially weight parts of the map most likely to be stationary and of consistent angular reflectivity, thereby reducing uncertainty and catastrophic errors. Furthermore, by using offline SLAM to align multiple passes of the same environment, possibly separated in time by days or even months, it is possible to build an increasingly robust understanding of the world that can be then exploited for localization. We validate the effectiveness of our approach by using these algorithms to localize our vehicle against probabilistic maps in various dynamic environments, achieving RMS accuracy in the 10cm-range and thus outperforming previous work. Importantly, this approach has enabled us to autonomously drive our vehicle for hundreds of miles in dense traffic on narrow urban roads which were formerly unnavigable with previous localization methods.


title: Weakly Supervised Facial Analysis with Dense Hyper-Column Features

abstract: Weakly supervised methods have recently become one of the most popular machine learning methods since they are able to be used on large-scale datasets without the critical requirement of richly annotated data. In this paper, we present a novel, self-taught, discriminative facial feature analysis approach in the weakly supervised framework. Our method can find regions which are discriminative across classes yet consistent within a class and can solve many face related problems. The proposed method first trains a deep face model with high discriminative capability to extract facial features. The hypercolumn features are then used to give pixel level representation for better classification performance along with discriminative region detection. In addition, calibration approaches are proposed to enable the system to deal with multi-class and mixed-class problems. The system is also able to detect multiple discriminative regions from one image. Our uniform method is able to achieve competitive results in various face analysis applications, such as occlusion detection, face recognition, gender classification, twins verification and facial attractiveness analysis.


title: Unconstrained face verification using deep CNN features

abstract: In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder than the LFW and Youtube Face (YTF) datasets. The deep convolutional neural network (DCNN) is trained using the CASIA-WebFace dataset. Results of experimental evaluations on the IJB-A and the LFW datasets are provided.


title: Car Dynamics using Quarter Model and Passive Suspension, Part I: Effect of Suspension Damping and Car Speed

abstract: Quarter-car model is in use for years to study the car dynamics. The objection of this paper is to examine the dynamics of a car passing a circular hump for sake maintaining ride comfort for the passengers. Passive suspension elements are considered with suspension damping coefficient in the range 1 to 15 kNs/m. Car speed in the range 5 to 25 km/h is considered when passing the hump. Important phenomenon evolved from the analysis of the car dynamics which is performed using MATLAB. The mathematical model of the quarter-car model is derived in the state form and the dynamics are evaluated in terms of the sprung mass displacement and acceleration. The effect of suspension damping and car speed on the sprung-mass displacement and acceleration is examined. The study shows that for ride comfort the car speed has not to exceed 6.75 km/h when passing a circular hump depending on the suspension damping. .


title: Leiomyoma originating from axilla: A rare case report and differential diagnosis

abstract: Introduction:Leiomyoma is a form of benign tumor originated in hypertrophy of the smooth muscles, which is most prevalent in the uterus and gastrointestinal tract. However, Leiomyoma originating from smooth muscle at the vessels lying on deep soft tissue is very rare. Case Report:Our case was a rare case of leiomyoma originating from the axillary region, which was initially diagnosed as a fibroadenoma on radiological examination. The mass was separated from surrounding tissues and totally resected. Pathologically, hematoxylin–eosin-stained biopsy tissue showed the typical findings of leiomyoma. Postoperative follow-up observation was done for 1 year, without any complications or recurrence. Conclusion:Notably, a leiomyoma in the axillary region is difficult to differentiate from other benign or malignant tumors on preoperative radiological examinations such as ultrasonography or computed tomgraphy. Therefore, when an indefinite asymptomatic mass that is not lymphadenopathy or common benign tumor is identified in the axillary region, leiomyoma can be considered as one of the differential diagnoses.


title: Neural network based spectral mask estimation for acoustic beamforming

abstract: We present a neural network based approach to acoustic beamforming. The network is used to estimate spectral masks from which the Cross-Power Spectral Density matrices of speech and noise are estimated, which in turn are used to compute the beamformer coefficients. The network training is independent of the number and the geometric configuration of the microphones. We further show that it is possible to train the network on clean speech only, avoiding the need for stereo data with separated speech and noise. Two types of networks are evaluated. One small feed-forward network with only one hidden layer and one more elaborated bi-directional Long Short-Term Memory network. We compare our system with different parametric approaches to mask estimation and using different beamforming algorithms. We show that our system yields superior results, both in terms of perceptual speech quality and with respect to speech recognition error rate. The results for the simple feed-forward network are especially encouraging considering its low computational requirements.


title: Deep salience map guided arbitrary direction scene text recognition

abstract: Irregular scene text such as curved, rotated or perspective texts commonly appear in natural scene images due to different camera view points, special design purposes etc. In this work, we propose a text salience map guided model to recognize these arbitrary direction scene texts. We train a deep Fully Convolutional Network (FCN) to calculate the precise salience map for texts. Then we estimate the positions and rotations of the text and utilize this information to guide the generation of CNN sequence features. Finally the sequence is recognized with a Recurrent Neural Network (RNN) model. Experiments on various public datasets show that the proposed approach is robust to different distortions and performs superior or comparable to the state-of-the-art techniques.


College Admissions and the Stability of Marriage


title: Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances

abstract: Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective.


title: Noncontact Accurate Measurement of Cardiopulmonary Activity Using a Compact Quadrature Doppler Radar Sensor

abstract: The designed sensor enables accurate reconstruction of chest-wall movement caused by cardiopulmonary activities, and the algorithm enables estimation of respiration, heartbeat rate, and some indicators of heart rate variability (HRV). In particular, quadrature receiver and arctangent demodulation with calibration are introduced for high linearity representation of chest displacement; 24-bit ADCs with oversampling are adopted for radar baseband acquisition to achieve a high signal resolution; continuous-wavelet filter and ensemble empirical mode decomposition (EEMD) based algorithm are applied for cardio/pulmonary signal recovery and separation so that accurate beat-to-beat interval can be acquired in time domain for HRV analysis. In addition, the wireless sensor is realized and integrated on a printed circuit board compactly. The developed sensor system is successfully tested on both simulated target and human subjects. In simulated target experiments, the baseband signal-to-noise ratio (SNR) is 73.27 dB, high enough for heartbeat detection. The demodulated signal has 0.35% mean squared error, indicating high demodulation linearity. In human subject experiments, the relative error of extracted beat-to-beat intervals ranges from 2.53% to 4.83% compared with electrocardiography (ECG) R-R peak intervals. The sensor provides an accurate analysis for heart rate with the accuracy of 100% for p = 2% and higher than 97% for p = 1%.


title: Intrinsic localized modes in arrays of atomic-molecular Bose-Einstein condensates

abstract: The existence of strongly localized matter solitons, intrinsic localized modes (ILM's), in an array of atomic-molecular Bose-Einstein condensates (AMBEC's) is shown. The theory is based on the Wannier function expansion of the system order parameter and predicts the possibility of strong localization of the atomic and molecular components whose relative populations are determined by the Raman detuning parameter and by the atom-molecule conversion rate. ILM's can possess different symmetries and spatial distributions of the components. In this context AMBEC arrays can be viewed as potential compressors and separators of atomic and molecular condensates.


title: Graphene photo-detector enhanced by plasmonic coupling

abstract: A graphene-gold photo-detector produced enhanced photocurrent due to absorbed surface plasmons with p-polarized near IR excitation light, when compared with s-polarized light. Charge doping was characterized by Raman spectroscopy at different positions on the graphene.


The Use of Drones in Forestry


title: The use of graphs as decision aids in relation to information overload and managerial decision quality

abstract: Nowadays, people with limited information processing capability are unable to cope with an exponentially increasing amount of information, leading to a phenomenon called information overload. This phenomenon has widely been recognized to have adverse effects on decision quality. The purpose of this study is to investigate the effectiveness of using graphs as decision aids to reduce the adverse effects of information overload on decision quality. The results obtained from an experiment simulating a real business prediction task with a sample of business managers indicated that prediction accuracy deteriorated under information overload. However, the mode of presentation format alone did not have any significant impact on prediction accuracy; nor did the interactions of information load and the mode of presentation format. The implications of these results are discussed.


title: Compact and Broadband Rectangular Waveguide Power Divider/Combiner Using Microstrip-Fed Antisymmetric Tapered Probe

abstract: In this paper, a compact and broadband rectangular waveguide power divider using the T-junction with antisymmetric tapered probe is proposed. The power divider is compact due to the adoption of the microstrip-fed antisymmetric tapered probe. In addition, the power divider has a broadband performance that the transmission coefficients of the two output ports are around -3.2 dB and the reflection coefficient of the input port is smaller than -15 dB from 8.48 to 12.04 GHz, almost covering the whole X-band (8.2-12.4 GHz). To advance this power divider as a power divider/combiner, a Wilkinson power divider is used to replace the T-junction. The power divider/combiner using the Wilkinson power divider with antisymmetric tapered probe is compact and has a broadband performance that shows equal power division, small reflection, and good isolation for all ports in the whole X-band. The transmission coefficients of the two output ports are around -3.3 dB and the reflection coefficient of the input port is smaller than -15 dB. In addition, the reflection coefficients of the two output ports are smaller than -12.6 dB and the isolation between the two output ports is smaller than -17.5 dB.


title: A Radiation Hard Bandgap Reference Circuit in a Standard 0.13 $\mu$m CMOS Technology

abstract: With ongoing CMOS evolution, the gate-oxide thickness steadily decreases, resulting in an increased radiation tolerance of MOS transistors. Combined with special layout techniques, this yields circuits with a high inherent robustness against X-rays and other ionizing radiation. In bandgap voltage references, the dominant radiation-susceptibility is then no longer associated with the MOS transistors, but is dominated by the diodes. This paper gives an analysis of radiation effects in both MOS devices and diodes and presents a solution to realize a radiation-hard voltage reference circuit in a standard CMOS technology. A demonstrator circuit was implemented in a standard 0.13 mum CMOS technology. Measurements show correct operation with supply voltages in the range from 1.4 V down to 0.85 V, a reference voltage of 405 mV plusmn 7.5 mV ( sigma = 6 mV chip-to-chip statistical spread), and a reference voltage shift of only plusmn1.5 mV (around 0.8%) under irradiation up to 44 Mrad (Si).


title: Improving emergency preparedness with simulation of cascading events scenarios

abstract: Natural or man-made disasters can trigger other negative events leading to tremendous increase of fatalities and damages. In case of Low Probability High consequences events, decision makers are faced with very difficult choices and the availability of a tool to support emergency decisions would be very much beneficial. Within EU CRISMA project a concept model and tool for evaluating cascading effects into scenario-based analyses was implemented. This paper describes the main concepts of the model and demonstrates its application with reference to two earthquake-triggered CE scenarios, including (the first) the falling of an electric cable, ignition and spreading of forest fire and (the second) the happening of a second earthquake in a sequence. Time dependent seismic vulnerability of buildings and population exposure are also considered for updating impact estimation during an earthquake crisis.


title: Chinese wall security model for workflow management systems with dynamic security policy

abstract: Secure workflow management systems (WfMSs) are required to support major security features such as authentication, confidentiality, data integrity, and nonrepudiation. The Chinese wall security model (CWSM) was designed to provide access controls that mitigate conflict of interest in commercial organizations, and is especially important for large-scale interenterprise workflow applications. This paper describes how to implement the CWSM in a WfMS. We first demonstrate situations in which an access control model is not sufficient for this if the WfMS does not keep the run-time history of data accesses and company information is mutable, and we then propose an application programming interface (API) to solve this problem, also providing support for the intrinsic dynamic access control mechanism defined in the CWSM (i.e., the dynamic binding of subjects and elements in the company data set). This API can also specify several requirements of the dynamic security policy that arise when applying the CWSM in WfMSs. Finally we discuss how to implement a run-time system to implement CWSM policies specified by this API in a WfMS.


title: Under-Foliage Object Imaging Using SAR Tomography and Polarimetric Spectral Estimators

abstract: This paper addresses the imaging of objects located under a forest cover using Polarimetric SAR Tomography (POLTOM-SAR) at L band. High-Resolution (HR) spectral estimators, able to accurately separate multiple scattering centers in the vertical direction, are used to separate the response of objects and vehicles embedded in a volumetric background. Polarimetric spectral analysis techniques are introduced, and are shown to both improve the estimation accuracy of the vertical position of artificial scatterers and natural environments and provide optimal polarimetric features that may be used to further characterize the media under analysis. The effectiveness of the proposed approaches is demonstrated using tomograms derived from fully polarimetric L-band airborne data acquired by DLR?s E-SAR system over the test site of Dornstetten, Germany.


title: Exploration of older and younger British adults’ performance on The Awareness of Social Inference Test (TASIT)

abstract: Background: Social perception is an important skill. One assessment that is commonly used to assess social perception abilities is The Awareness of Social Inference Test (TASIT). The only normative data available for this test are for Australian younger adults. Despite no normative data being available for British adults, the test is widely used in the UK with older and younger adults. There is a growing body of research that suggests that older adults have difficulty with skills associated with social perception. There is therefore a need to determine whether British adults, and more specifically British older adults, perform similarly to the Australian normative TASIT scores available in the manual. ::: ::: Aims: To explore the differences between older and younger British adults’ performance on TASIT, and to determine whether younger and older British adults perform similarly to the data from Australian adults in TASIT manual. ::: ::: Methods & Procedures: TASIT was administered to a total of 42 native British English speaking participants. The participants were split into two age groups 18–45 and 60–90 years. Comparisons were made between the two groups and the Australian data in TASIT manual. ::: ::: Outcomes & Results: The younger British and Australian adults obtained similar scores on all parts of TASIT. The older British adults though, obtained significantly lower scores than the Australian younger adults on all parts of TASIT and when education was controlled for they obtained significantly lower scores than the British younger adults. ::: ::: Conclusions & Implications: The findings are discussed in the light of previous research that has found that older adults are worse than younger adults at social inferences. The findings of the current study suggest that caution should be used when using TASIT with older British adults to assess social perception abilities.


title: A Method for Mapping Sensor Data to SSN Ontology

abstract: Along with the continuous development of the sensor network technology, sensors from all over the world are constantly producing sensor data. However, the sensor data from different source is hard to work together for lack of semantic. Fortunately, SSN ontology provide a way to represent sensor data semantically, but how to transform sensor data into the instance of SSN ontology conveniently is still an issue to be considered. This paper proposed a solution to map sensor data to SSN ontology automatically based on a predefined XML-based document. We design a mapping language SASML (Sensors Annotation and Semantic Mapping Language) which provide a schema to annotate sensors and sources so as to generate a XML document for mapping. Then, an algorithm (namely SDRM) is designed to automatically transform sensor data, which described by SASML, to RDF conforming to SSN ontology, according to the mapping document and the element correspondences between the SASML and SSN ontology. Further, a case study about sensor data from greenhouse is presented to illustrate our method, and a prototype is also developed to demonstrate the feasibility and effectiveness.


title: Making Serverless Computing More Serverless

abstract: In serverless computing, developers define a function to handle an event, and the serverless framework horizontally scales the application as needed. The downside of this function-based abstraction is it limits the type of application supported and places a bound on the function to be within the physical resource limitations of the server the function executes on. In this paper we propose a new abstraction for serverless computing: a developer supplies a process and the serverless framework seamlessly scales out the process's resource usage across the datacenter. This abstraction enables processing to not only be more general purpose, but also allows a process to break out of the limitations of a single server – making serverless computing more serverless. To realize this abstraction, we propose ServerlessOS, comprised of three key components: (i) a new disaggregation model, which leverages disaggregation for abstraction, but enables resources to move fluidly between servers for performance; (ii) a cloud orchestration layer which manages fine-grained resource allocation and placement throughout the application's lifetime via local and global decision making; and (iii) an isolation capability that enforces data and resource isolation across disaggregation, effectively extending Linux cgroup functionality to span servers.


title: Using mutual information for selecting features in supervised neural net learning

abstract: This paper investigates the application of the mutual information criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is suitable for assessing the "information content" of features in complex classification tasks, where methods bases on linear relations (like the correlation) are prone to mistakes. The fact that the mutual information is independent of the coordinates chosen permits a robust estimation. Nonetheless, the use of the mutual information for tasks characterized by high input dimensionality requires suitable approximations because of the prohibitive demands on computation and samples. An algorithm is proposed that is based on a "greedy" selection of the features and that takes both the mutual information with respect to the output class and with respect to the already-selected features into account. Finally the results of a series of experiments are discussed. >


title: Near-infrared spectroscopy and imaging: Basic principles and pharmaceutical applications

abstract: Near-infrared (NIR) spectroscopy and imaging are fast and nondestructive analytical techniques that provide chemical and physical information of virtually any matrix. In combination with multivariate data analysis these two methods open many interesting perspectives for both qualitative and quantitative analysis. This review focuses on recent pharmaceutical NIR applications and covers (1) basic principles of NIR techniques including chemometric data processing, (2) regulatory issues, (3) raw material identification and qualification, (4) direct analysis of intact solid dosage forms, and (5) process monitoring and process control.


title: Synaptic behavior and STDP of asymmetric nanoscale memristors in biohybrid systems

abstract: We fabricate and characterize asymmetric memristors which show a very strong single-sided hysteresis. When biased in one direction there is hysteresis and in the opposite direction there is a lack of hysteresis. We demonstrate that this apparent lack is actually hysteresis on a much faster time-scale. We further demonstrate that this form of asymmetric behavior correlates very well to the asymmetric structure and function of an actual synapse. The asymmetric memristor device presented here is necessary to correctly implement spike-timing-dependent-plasticity STDP in mixed memristor/neuron hybrid systems as an artificial synapse. These devices show the required characteristics for implementing the asymmetric form of long-term potentiation (LTP) and long-term depression (LTD) of a synapse between two neurons, where symmetric memristor devices do not. Signals from a presynaptic neuron are sent via its axon across the synapse to the dendrite of a postsynaptic neuron. Postsynaptic neuron signals sent to subsequent neurons have an influence on the strength of any further presynaptic neuron signals received by the postsynaptic neuron across the synapse. These signals are grouped into spike triplets within the framework of STDP and, as we experimentally show here, can be implemented with asymmetric memristors, not standard symmetric memristors.


title: Leading Students to Care: The Use of Clinical Simulations in Ethics

abstract: ABSTRACTAlthough there is considerable normative discussion about the importance of care and caring in the pharmacy literature, little empirical data exist about how best to teach students to become caring professionals. Third-year pharmacy students (N = 50) in a required ethics course participated in four clinical simulations involving ethical issues. Although the primary purpose of the project was to explore the impact of clinical simulations on ethical decision-making, a secondary question surfaced: Do clinical simulations teach students about what it means to care for a patient? Since care is a complex phenomenon, it follows that learning how to care requires multiple teaching strategies and various types of assessment. Evidence of student learning such as: transcriptions of clinical simulations, reflective writing on the simulation experience and learning about caring from interactions with standardized patients are included. Finally, organizing questions for further research in teaching and learning...


title: Potentials and limitations of Green Light Optimal Speed Advisory systems

abstract: The reduction of CO2 emissions is one of the most anticipated features of future transportation systems. Smart traffic lights are believed to contribute to achieving this by either adapting their signal program or by informing approaching drivers. In this paper we investigate the potentials and limitations of the latter, that is, Green Light Optimal Speed Advisory (GLOSA) systems in a realistic, large scale simulation study. We examine the impact of different equipment rates of both traffic lights and vehicles on environmental related metrics but also study how these systems can increase the comfort for drivers by reducing waiting times and the number of stops. We find that at low traffic densities these systems can meet all their goals and lower CO2 emissions by up to 11.5% whereas in dense traffic several side-effects could be observed, including overall longer waiting times and even higher CO2 emissions for unequipped vehicles.


title: Convolutive Blind Separation of Non-Stationary Sources

abstract: Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the problem by explicitly exploiting the nonstationarity of the acoustic sources. Changing cross correlations at multiple times give a sufficient set of constraints for the unknown channels. A least squares optimization allows us to estimate a forward model, identifying thus the multipath channel. In the same manner we can find an FIR backward model, which generates well separated model sources. Furthermore, for more than three channels we have sufficient conditions to estimate underlying additive sensor noise powers. We show the good performance in a real room environments and demonstrate the algorithm's utility for automatic speech recognition.


An Evaluation of Local Shape-Based Features for Pedestrian Detection


title: Cellular stress conditions are reflected in the protein and RNA content of endothelial cell-derived exosomes

abstract: Background: The healthy vascular endothelium, which forms the barrier between blood and the surrounding tissues, is known to efficiently respond to stress signals like hypoxia and inflammation by adaptation of cellular physiology and the secretion of (soluble) growth factors and cytokines. Exosomes are potent mediators of intercellular communication. Their content consists of RNA and proteins from the cell of origin, and thus depends on the condition of these cells at the time of exosome biogenesis. It has been suggested that 20 exosomes protect their target cells from cellular stress through the transfer of RNA and proteins. We hypothesized that endothelium-derived exosomes are involved in the endothelial response to cellular stress, and that exosome RNA and protein content reflect the effects of cellular stress induced by hypoxia, inflammation or hyperglycemia. Methods: We exposed cultured endothelial cells to different types of cellular stress (hypoxia, TNF-a-induced 25 activation, high glucose and mannose concentrations) and compared mRNA and protein content of exosomes produced by these cells by microarray analysis and a quantitative proteomics approach. Results: We identified 1,354 proteins and 1,992 mRNAs in endothelial cell-derived exosomes. Several proteins and mRNAs showed altered abundances after exposure of their producing cells to cellular stress, which were confirmed by immunoblot or qPCR analysis. 30 Conclusion: Our data show that hypoxia and endothelial activation are reflected in RNA and protein exosome composition, and that exposure to high sugar concentrations alters exosome protein composition only to a minor extend, and does not affect exosome RNA composition. Keywords: extracellular vesicles; exosomes; RNA; proteomics; hypoxia; tumor necrosis factor alpha. (Published: 16 April 2012) Citation: Journal of Extracellular Vesicles 2012, 1 : 18396 - DOI: 10.3402/jev.v1i0.18396 To access the supplementary material to this article: Tables SI-SIV and Figures S1-2, please see Supplementary files in the column to the right (under Article Tools)


title: Liposomes in drug delivery: Progress and limitations

abstract: Liposomes are microparticulate lipoidal vesicles which are under extensive investigation as drug carriers for improving the delivery of therapeutic agents. Due to new developments in liposome technology, several liposome-based drug formulations are currently in clinical trial, and recently some of them have been approved for clinical use. Reformulation of drugs in liposomes has provided an opportunity to enhance the therapeutic indices of various agents mainly through alteration in their biodistribution. This review discusses the potential applications of liposomes in drug delivery with examples of formulations approved for clinical use, and the problems associated with further exploitation of this drug delivery system.


title: Topology Review and Derivation Methodology of Single-Phase Transformerless Photovoltaic Inverters for Leakage Current Suppression

abstract: Single-phase voltage source transformerless inverters have been developed for many years and have been successful commercial applications in the distributed photovoltaic (PV) grid-connected systems. Moreover, many advanced industrial topologies and recent innovations have been published in the last few years. The objective of this paper is to classify and review these recent contributions to establish the present state of the art and trends of the transformerless inverters. This can provide a comprehensive and insightful overview of this technology. First, the generation mechanism of leakage current is investigated to divide the transformerless inverters into asymmetrical inductor-based and symmetrical inductor-based groups. Then, the concepts of dc-based and ac-based decoupling networks are proposed to not only cover the published symmetrical inductor-based topologies but also offer an innovative strategy to derive advanced inverters. Furthermore, the transformation principle between the dc-based and ac-based topologies is explored to make a clear picture on the general law and framework for the recent advances and future trend in this area. Finally, a family of clamped highly efficient and reliable inverter concept transformerless inverters is derived and tested to offer some excellent candidates for next-generation high-efficiency and cost-effective PV grid-tie inverters.


title: Performance of Parallel Computing in Bubble Sort Algorithm

abstract: The performance of an algorithm can be improved by using a parallel computing programming approach. In this study, the performance of bubble sort algorithm on various computer specifications has been applied. Experimental results have shown that parallel computing programming can save significant time performance by 61%-65% compared to serial computing programming.


title: Price Discrimination Bans for Dominant Firms ∗

abstract: Competition authorities and regulatory agencies sometimes impose pricing restrictions on firms with substantial market power — the “dominant” firms. We analyze the welfare effects of a ban on behaviour-based price discrimination in a two-period setting where the market displays a competitive and a sheltered segment. A ban on “higher-prices-to-shelteredconsumers” decreases prices in the sheltered segment, relaxes competition in the competitive segment, increases the rival’s profits, and may harm the dominant firm’s profits. We show that a ban on “higher-prices-to-sheltered-consumers” increases the dominant firm’s share of the first-period market. A ban on “lower-prices-to-rival’s-customers” decreases prices in the competitive segment, lowers the rival’s profits, and augments the consumer surplus. In particular, while second-period competition is relaxed by a ban on “lower-prices-to-rival’scustomers”, first-period competition is intensified substantially, which leads to lower prices “on-average” over the two periods. Our findings indicate that a dynamic two-period analysis may lead to conclusions opposite to those drawn from a static one-period analysis.


title: Personal identification using periocular skin texture

abstract: In this paper, we propose the use of periocular skin texture as a biometric modality. Salient skin texture features are extracted and represented using Local Binary Patterns (LBPs). Matching is performed using CityBlock distance as a measure of similarity. We investigate the use of each periocular region separately in addition to their use in conjunction. Verification and identification experiments involving over 400 subjects were performed using a datasets constructed from the FRGC and FERET datasets. Reported recognition rates of nearly 90%, demonstrate the effectiveness of this novel technique.


title: A Variational Framework for Retinex

abstract: Retinex theory addresses the problem of separating the illumination from the reflectance in a given image and thereby compensating for non-uniform lighting. This is in general an ill-posed problem. In this paper we propose a variational model for the Retinex problem that unifies previous methods. Similar to previous algorithms, it assumes spatial smoothness of the illumination field. In addition, knowledge of the limited dynamic range of the reflectance is used as a constraint in the recovery process. A penalty term is also included, exploiting a-priori knowledge of the nature of the reflectance image. The proposed formulation adopts a Bayesian view point of the estimation problem, which leads to an algebraic regularization term, that contributes to better conditioning of the reconstruction problem. ::: ::: Based on the proposed variational model, we show that the illumination estimation problem can be formulated as a Quadratic Programming optimization problem. An efficient multi-resolution algorithm is proposed. It exploits the spatial correlation in the reflectance and illumination images. Applications of the algorithm to various color images yield promising results.


title: Barriers to Effective Municipal Solid Waste Management in a Rapidly Urbanizing Area in Thailand

abstract: This study focused on determining the barriers to effective municipal solid waste management (MSWM) in a rapidly urbanizing area in Thailand. The Tha Khon Yang Subdistrict Municipality is a representative example of many local governments in Thailand that have been facing MSWM issues. In-depth interviews with individuals and focus groups were conducted with key informants including the municipality staff, residents, and external organizations. The major influences affecting waste management were categorized into six areas: social-cultural, technical, financial, organizational, and legal-political barriers and population growth. SWOT analysis shows both internal and external factors are playing a role in MSWM: There is good policy and a reasonably sufficient budget. However, there is insufficient infrastructure, weak strategic planning, registration, staff capacity, information systems, engagement with programs; and unorganized waste management and fee collection systems. The location of flood prone areas has impacted on location and operation of landfill sites. There is also poor communication between the municipality and residents and a lack of participation in waste separation programs. However, external support from government and the nearby university could provide opportunities to improve the situation. These findings will help inform municipal decision makers, leading to better municipal solid waste management in newly urbanized areas.


title: Adaptive synergies for a humanoid robot hand

abstract: One of the motivations behind the development of humanoid robots is the will to comply with, and fruitfully integrate in the human environment, a world forged by humans for humans, where the importance of the hand shape dominates prominently. This paper presents the novel hand under-actuation framework which goes under the name of synergies. In particular two incarnations of this concept are considered, soft synergies and adaptive synergies. They are presented and their substantial equivalence is demonstrated. After this, it presents the first implementation of THE UNIPI-hand, a prototype which conciliates the idea of adaptive synergies for actuation with an high degree of integration, in a humanoid shape. The hand is validated experimentally through some grasps and measurements. Results are reported also in the attached video.


Human Problem Solving: The State of The Theory in 1970


title: Pose-Invariant Face Alignment with a Single CNN

abstract: Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNN, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, hand-crafted features and slow training speed. To address these issues, we propose a new layer, named visualization layer, that can be integrated into the CNN architecture and enables joint optimization with different loss functions. Extensive evaluation of the proposed method on multiple datasets demonstrates state-of-the-art accuracy, while reducing the training time by more than half compared to the typical cascade of CNNs. In addition, we compare multiple CNN architectures with the visualization layer to further demonstrate the advantage of its utilization.


title: Three-Dimensional Skin Deformation as Force Substitution: Wearable Device Design and Performance During Haptic Exploration of Virtual Environments

abstract: Virtual reality systems would benefit from a compelling force sensory substitute when workspace or stability limitations prevent the use of kinesthetic force feedback systems. We present a wearable fingertip haptic device with the ability to make and break contact in addition to rendering both shear and normal skin deformation to the fingerpad. A delta mechanism with novel bias spring and tether actuator relocation method enables the use of high-end motors and encoders, allowing precise device control: 10 Hz bandwidth and 0.255 mm RMS tracking error were achieved during testing. In the first of two experiments, participants determined the orientation of a stiff region in a surrounding compliant virtual surface with an average angular error of 7.6 degree, similar to that found in previous studies using traditional force feedback. In the second experiment, we evaluated participants’ ability to interpret differences in friction. The Just Noticeable Difference (JND) of surface friction coefficient discrimination using our skin deformation device was 0.20, corresponding with a reference friction coefficient of 0.5. While higher than that found using kinesthetic feedback, this demonstrates that users can perceive differences in surface friction without world-grounded kinesthetic forces. These experiments show that three DoF skin deformation enables both stiffness and friction discrimination capability in the absence of kinesthetic force feedback.


title: Scale registration based on descriptor analysis and B-spline matching

abstract: By adopting different reconstruction techniques, 3D sensors generate different point clouds of various scales for the same object. This paper presents a new algorithm to align point clouds of different scales. First, shape descriptors based on different neighboring sizes are calculated on each point. Secondly, for each neighboring size, principle components analysis is applied on all descriptors to generate the cumulative contribution rates, which are fitted by B-spline curves. Then, finding scale different between two 3D point clouds in 3D space, is reduced to finding scale difference between two sets of 2D B-spline curves in 2D space. Finally, a numerical solution is presented to find the unknown scale, which is experimentally proved to have higher accuracy than existing methods.


title: Robust Graph Hyperparameter Learning for Graph Based Semi-supervised Classification

abstract: Graph-based semi-supervised learning has attracted much attention in recent years. Many successful methods rely on graph structure to propagate labels from labeled data to unlabeled data. Although graph structure affects the performance of the system, only few works address its construction problem. In this work, the graph structure is constructed by adjusting the hyperparameter controlling the connection weights between nodes. The idea is to learn the hyperparameters for graphs that yields low leave-one-out prediction error on labeled data while retaining high stability of the prediction on unlabeled data. The problem is solved efficiently using the gradient-based optimization technique. Experimental results indicate that the proposed technique yields improvement of generalization error as well as stability of the labeling function.


title: A probabilistic approach to socio-geographic reality mining

abstract: As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuously captured by hundreds of millions of people around the world and promises to reveal important behavioral clues about humans in a manner never before possible. Mining patterns of human behavior from large-scale mobile phone data has deep potential impact on society. For example, by understanding a community's movements and interactions, appropriate measures may be put in place to prevent the threat of an epidemic. The study of such human-centric massive datasets requires advanced mathematical models and tools. In this thesis, we investigate probabilistic topic models as unsupervised machine learning tools for large-scale socio-geographic activity mining.


title: Analysis and design of average current mode control using describing function-based equivalent circuit model

abstract: A small signal model for average current mode control based on equivalent circuit is proposed. The model uses the three-terminal equivalent circuit model based on linearized describing function method to include the feedback effect of the side-band frequency components of inductor current. It extends the results obtained in peak-current model control to average current mode control. The proposed small signal model is accurate up to half switching frequency, predicting the sub-harmonic instability. The proposed model is verified using SIMPLIS simulation and hardware experiments, showing good agreement with the measurement results. Based on the proposed model, a new feedback design guideline is presented. The proposed design guideline is compared with several conventional, widely used design criteria to highlight its virtue. By designing the external ramp following the proposed design guideline, quality factor of the double poles at half of switching frequency in control-to-output transfer function can be precisely controlled. This helps the feedback design to achieve widest control bandwidth and proper damping.