title: Implementation of optimal control for ball and beam system

abstract: The ball and beam system is a widely used tool for learning classical as well as advanced control techniques. The system with highly non linear characteristics is an excellent tool to represent unstable systems. The paper presents an optimal control strategy for controlling the position of ball on the beam. The system is an open loop unstable system. The control problem is a challenging one as the ball position continuously changes with the beam angle. The control strategy includes PID control for both ball position control and servo position control. The control parameters are tuned using Genetic Algorithm. The results are verified experimentally using real time ball and beam system.


title: Intrusion sensor data fusion in an intelligent intrusion detection system architecture

abstract: Most modern intrusion detection systems employ multiple intrusion sensors to maximize their trustworthiness. The overall security view of the multi-sensor intrusion detection system can serve as an aid to appraise the trustworthiness in the system. This paper presents our research effort in that direction by describing a decision engine for an intelligent intrusion detection system (IIDS) that fuses information from different intrusion detection sensors using an artificial intelligence technique. The decision engine uses fuzzy cognitive maps (FCMs) and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process. In this paper, we report on the workings of the decision engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research (CCSR), Mississippi State University.


title: SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

abstract: We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.


title: Graph-based Approach to Automatic Taxonomy Generation (GraBTax)

abstract: We propose a novel graph-based approach for constructing concept hierarchy from a large text corpus. Our algorithm, GraBTax, incorporates both statistical co-occurrences and lexical similarity in optimizing the structure of the taxonomy. To automatically generate topic-dependent taxonomies from a large text corpus, GraBTax first extracts topical terms and their relationships from the corpus. The algorithm then constructs a weighted graph representing topics and their associations. A graph partitioning algorithm is then used to recursively partition the topic graph into a taxonomy. For evaluation, we apply GraBTax to articles, primarily computer science, in the CiteSeerX digital library and search engine. The quality of the resulting concept hierarchy is assessed by both human judges and comparison with Wikipedia categories.


title: Leadership: Past, Present, and Future

abstract: In this introductory chapter, the editors of the present volume provide readers with the background necessary to understand the chapters that follow. They first acquaint readers with the concept of leadership and why leadership is necessary. Then, the editors briefly trace the history of leadership research and examine its major schools, most of which are reviewed in this book. Their historical overview is also necessary as an organizing framework because chapter authors frequently refer to elements of the history of leadership research. Next, the editors discuss emerging issues in leadership research and how findings are being consolidated. Finally, they provide an overview of the book and a summary of each chapter.


title: Cyclodextrins: application in different routes of drug administration.

abstract: The objective of this review article is to explain the use of cyclodextrin in the different routes of drug administration. The article gives the chemistry of cyclodextrins and addresses the issue of the mechanism of drug release from cyclodextrin complexes. Dilution, competitive displacement, protein binding, change in ionic strength and temperature and drug uptake by tissues are the different release mechanisms of the drug from the drug-cyclodextrin complex discussed here. Use and its limitations in the different drug delivery systems like nasal, ophthalmic, transdermal and rectal drug delivery are explained. The application of the cyclodextrins in the oral drug delivery is detailed in this review. Many studies have shown that cyclodextrins are useful additives in the different routes of drug administration because of increased aqueous solubility, stability, bioavailability and reduced drug irritation.


title: A low-noise self-calibrating dynamic comparator for high-speed ADCs

abstract: This paper presents a low offset voltage, low noise dynamic latched comparator using a self-calibrating technique. The new calibration technique does not require any amplifiers for the offset voltage cancellation and quiescent current. It achieves low offset voltage of 1.69 mV at 1 sigma in low power consumption, while 13.7 mV is measured without calibration. Furthermore the proposed comparator requires only one phase clock while conventionally two phase clocks were required leading to relaxed clock. Moreover, a low input noise of 0.6 mV at 1 sigma, three times lower than the conventional one, is obtained. Prototype comparators are realized in 90 nm 10M1P CMOS technology. Experimental and simulated results show that the comparator achieves 1.69 mV offset at 250 MHz operating, while dissipating 40 muW/GHz ( 20 fJ/conv. ) from a 1.0 V supply.


title: Interpretable Deep Learning applied to Plant Stress Phenotyping

abstract: Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identification, classification and quantification of biotic and abiotic stresses in crop research and production. Up until now, this has been predominantly done manually by visual inspection and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intra-rater cognitive variability. Here, we demonstrate the ability of a machine learning framework to identify and classify a diverse set of foliar stresses in the soybean plant with remarkable accuracy. We also present an explanation mechanism using gradient-weighted class activation mapping that isolates the visual symptoms used by the model to make predictions. This unsupervised identification of unique visual symptoms for each stress provides a quantitative measure of stress severity, allowing for identification, classification and quantification in one framework. The learnt model appears to be agnostic to species and make good predictions for other (non-soybean) species, demonstrating an ability of transfer learning.


title: Preventing Location-Based Identity Inference in Anonymous Spatial Queries

abstract: The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.


T 2 * mapping and B 0 orientation-dependence at 7 T reveal cyto- and myeloarchitecture organization of the human cortex


title: Empirical Validation of Neural Network Models for Agile Software Effort Estimation based on Story Points

abstract: Abstract Now-a-days agile software development process has become famous in industries and substituting the traditional methods of software development. However, an accurate estimation of effort in this paradigm still remains a dispute in industries. Hence, the industry must be able to estimate the effort necessary for software development using agile methodology efficiently. For this, different techniques like expert opinion, analogy, disaggregation etc. are adopted by researchers and practitioners. But no proper mathematical model exists for this. The existing techniques are ad-hoc and are thus prone to be incorrect. One popular approach of calculating effort of agile projects mathematically is the Story Point Approach (SPA). In this study, an effort has been made to enhance the prediction accuracy of agile software effort estimation process using SPA. For doing this, different types of neural networks (General Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), Group Method of Data Handling (GMDH) Polynomial Neural Network and Cascade-Correlation Neural Network) are used. Finally performance of the models generated using various neural networks are compared and analyzed.


title: A Multiband RF Antenna Duplexer on CMOS: Design and Performance

abstract: An RF duplexer has been fabricated on a CMOS IC for use in 3G/4G cellular transceivers. The passive circuit sustains large voltage swings in the transmit path, and isolates the receive path from the transmitter by more than 45 dB across a bandwidth of 200 MHz in 3G/4G bands I, II, III, IV, and IX. A low noise amplifier embedded into the duplexer demonstrates a cascade noise figure of 5 dB with more than 27 dB of gain. The duplexer inserts 2.5 dB of loss between power amplifier and antenna.


title: Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models

abstract: In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics. We propose a method that combines Generative Adversarial Networks (GANs) and reinforcement learning (RL) in order to accomplish exactly that. While RL biases the data generation process towards arbitrary metrics, the GAN component of the reward function ensures that the model still remembers information learned from data. We build upon previous results that incorporated GANs and RL in order to generate sequence data and test this model in several settings for the generation of molecules encoded as text sequences (SMILES) and in the context of music generation, showing for each case that we can effectively bias the generation process towards desired metrics.


Geomarketing on a Pay-per-Use Basis


title: Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions

abstract: Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.


title: A de novo t(10;19)(q22.3;q13.33) leads to ZMIZ1/PRR12 reciprocal fusion transcripts in a girl with intellectual disability and neuropsychiatric alterations

abstract: We report a girl with intellectual disability (ID), neuropsychiatric alterations, and a de novo balanced t(10;19)(q22.3;q13.33) translocation. After chromosome sorting, fine mapping of breakpoints by array painting disclosed disruptions of the zinc finger, MIZ-type containing 1 (ZMIZ1) (on chr10) and proline-rich 12 (PRR12) (on chr19) genes. cDNA analyses revealed that the translocation resulted in gene fusions. The resulting hybrid transcripts predict mRNA decay or, if translated, formation of truncated proteins, both due to frameshifts that introduced premature stop codons. Though other molecular mechanisms may be operating, these results suggest that haploinsufficiency of one or both genes accounts for the patient's phenotype. ZMIZ1 is highly expressed in the brain, and its protein product appears to interact with neuron-specific chromatin remodeling complex (nBAF) and activator protein 1 (AP-1) complexes which play a role regulating the activity of genes essential for normal synapse and dendrite growth/behavior. Strikingly, the patient's phenotype overlaps with phenotypes caused by mutations in SMARCA4 (BRG1), an nBAF subunit presumably interacting with ZMIZ1 in brain cells as suggested by our results of coimmunoprecipitation in the mouse brain. PRR12 is also expressed in the brain, and its protein product possesses domains and residues thought to be related in formation of large protein complexes and chromatin remodeling. Our observation from E15 mouse brain cells that a Prr12 isoform was confined to nucleus suggests a role as a transcription nuclear cofactor likely involved in neuronal development. Moreover, a pilot transcriptome analysis from t(10;19) lymphoblastoid cell line suggests dysregulation of genes linked to neurodevelopment processes/neuronal communication (e.g., NRCAM) most likely induced by altered PRR12. This case represents the first constitutional balanced translocation disrupting and fusing both genes and provides clues for the potential function and effects of these in the central nervous system.


title: Design and Preliminary Validation of The Player Experience Inventory

abstract: We present the design and preliminary results of the validation of the Player Experience Inventory (PXI). Based on the input of 64 experts in the field of player-computer interaction, we designed and refined this new scale. Our scale is based on the MDA framework (and on Means-End theory, underlying MDA). The PXI incorporates two subscales, one with dimensions at the functional level (i.e., dynamics) and one at the psycho-social level (i.e., aesthetics). The initial results, via principal factor analysis, suggest the scale can be used accurately to evaluate player experience. This work is our first step towards presenting a new, validated survey instrument for player experience evaluation.


title: Rock/water interaction in dielectric properties; experiments with hydrophobic sandstones

abstract: The dielectric constant of a partially saturated sandstone varies as a function of the level of water saturation. Experimental data indicate that rock/water interaction, at low saturations, has a large effect on the measured dielectric response. To theoretically predict the dielectric constant of the rock/water/air system, this rock/water interaction must be accounted for by including the effect of the water wetting the rock solid. Alternatively, if the rock/water interaction can be eliminated, a three-component (dry rock, water, and air) mixing law can be used to model the dielectric behavior. In this laboratory study, a chemical treatment is used to change four water-wet sandstones into hydrophobic sandstones. In three hydrophobic samples the rock/water interaction, seen in the data for the water-wet samples, is eliminated and the dielectric constant of the partially saturated sandstones can be simply modeled as a dry rock/water/air system. This experimental study illustrates the importance of the chemical state of the rock surfaces in determining the dielectric behavior of sandstones.


title: Optimized Projection for Sparse Representation Based Classification

abstract: Dimensionality reduction(DR) methods have commonly been used as a principled way to understand the high-dimensional data such as face images. In this paper, we propose a new supervised DR method called Optimized Projection for Sparse Representation based Classification(OP-SRC). SRC assumes that any new sample will approximately lie in the linear span of the training samples sharing the same class label. The decision of SRC is based on the reconstruction residual. OP-SRC aims to reduce the within-class reconstruction residual and simultaneously increases the between-class reconstruction residual. Therefore, SRC performs well in the OP-SRC transformed space. The feasibility and effectiveness of the proposed method is verified on Yale and ORL with promising results.


title: Analgesic effect of a low-level laser therapy (830 nm) in early orthodontic treatment

abstract: The aim of this study was to evaluate the pain sensation that orthodontic patients experience when elastic separators are placed between molars and premolars and to determine the degree of analgesic efficacy of low-level laser therapy (LLLT) compared to a placebo treatment. The study was conducted with 20 volunteers who were fitted with elastic separators between the maxillary molars and premolars. One quadrant was randomly chosen to be irradiated with an 830-nm laser, 100 mW, beam diameter of 7 mm, 250 mW/cm2 applied for 20 s per point (5 J/cm2). Three points were irradiated in the buccal face and three were irradiated in the palate. The same procedure was applied in the contralateral quadrant with a placebo light. A visual analogue scale was used to assess pain 5 min, 6 h, 24 h, 48 h, and 72 h after placement of the separators. Maximum pain occurred 6–24 h after placement of the elastic separators. Pain intensity was significantly lower in the laser-treated quadrant (mean, 7.7 mm) than in the placebo-treated quadrant (mean, 14.14 mm; p = 0.0001). LLLT at these parameters can reduce pain in patients following placement of orthodontic rubber separators.


title: Muscle contributions to propulsion and support during running

abstract: Muscles actuate running by developing forces that propel the body forward while supporting the body’s weight. To understand how muscles contribute to propulsion (i.e., forward acceleration of the mass center) and support (i.e., upward acceleration of the mass center) during running we developed a threedimensional muscle-actuated simulation of the running gait cycle. The simulation is driven by 92 musculotendon actuators of the lower extremities and torso and includes the dynamics of arm motion. We analyzed the simulation to determine how each muscle contributed to the acceleration of the body mass center. During the early part of the stance phase, the quadriceps muscle group was the largest contributor to braking (i.e., backward acceleration of the mass center) and support. During the second half of the stance phase, the soleus and gastrocnemius muscles were the greatest contributors to propulsion and support. The arms did not contribute substantially to either propulsion or support, generating less than 1% of the peak mass center acceleration. However, the arms effectively counterbalanced the vertical angular momentum of the lower extremities. Our analysis reveals that the quadriceps and plantarflexors are the major contributors to acceleration of the body mass center during running.


title: SoundCompass: A Distributed MEMS Microphone Array-Based Sensor for Sound Source Localization

abstract: Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.


title: Adapting Distributed Real-time and Embedded Pub/Sub Middleware

abstract: Enterprise distributed real-time and embedded (DRE) publish/subscribe (pub/sub) systems manage resources and data that are vital to users. Cloud computing---where computing resources are provisioned elastically and leased as a service---is an increasingly popular deployment paradigm. Enterprise DRE pub/sub systems can leverage cloud computing provisioning services to execute needed functionality when on-site computing resources are not available. Although cloud computing provides flexible on-demand computing and networking resources, enterprise DRE pub/sub systems often cannot accurately characterize their behavior a priori for the variety of resource configurations cloud computing supplies (e.g., CPU and network bandwidth), which makes it hard for DRE systems to leverage conventional cloud computing platforms. ::: ::: This paper provides two contributions to the study of how autonomic configuration of DRE pub/sub middleware can provision and use on-demand cloud resources effectively. We first describe how supervised machine learning can configure DRE pub/sub middleware services and transport protocols autonomically to support end-to-end quality-of-service (QoS) requirements based on cloud computing resources. We then present results that empirically validate how computing and networking resources affect enterprise DRE pub/sub system QoS. These results show how supervised machine learning can configure DRE pub/sub middleware adaptively in < 10 μsec with bounded time complexity to support key QoS reliability and latency requirements.


title: Basal forebrain amnesia: does the nucleus accumbens contribute to human memory?

abstract: OBJECTIVE—To analyse amnesia caused by basal forebrain lesions. ::: METHODS—A single case study of a patient with amnesia after bleeding into the anterior portion of the left basal ganglia. Neuropsychological examination included tests of attention, executive function, working memory, recall, and recognition of verbal and non-verbal material, and recall from remote semantic and autobiographical memory. The patient's MRI and those of other published cases of basal forebrain amnesia were reviewed to specify which structures within the basal forebrain are crucial for amnesia. ::: RESULTS—Attention and executive function were largely intact. There was anterograde amnesia for verbal material which affected free recall and recognition. With both modes of testing the patient produced many false positive responses and intrusions when lists of unrelated words had been memorised. However, he confabulated neither on story recall nor in day to day memory, nor in recall from remote memory. The lesion affected mainly the nucleus accumbens, but encroached on the inferior limb of the capsula interna and the most ventral portion of the nucleus caudatus and globus pallidus, and there was evidence of some atrophy of the head of the caudate nucleus. The lesion spared the nucleus basalis Meynert, the diagnonal band, and the septum, which are the sites of cholinergic cell concentrations. ::: CONCLUSIONS—It seems unlikely that false positive responses were caused by insufficient strategic control of memory retrieval. This speaks against a major role of the capsular lesion which might disconnect the prefrontal cortex from the thalamus. It is proposed that the lesion of the nucleus accumbens caused amnesia.


title: Models of stochastic choice and decision theories: why both are important for analyzing decisions

abstract: We select a menu of seven popular decision theories and embed each theory in five models of stochastic choice, including tremble, Fechner and random utility model. We find that the estimated parameters of decision theories differ significantly when theories are combined with different models. Depending on the selected model of stochastic choice we obtain different rankings of decision theories with regard to their goodness of fit to the data. The fit of all analyzed decision theories improves significantly when they are embedded in a Fechner model of heteroscedastic truncated errors or a random utility model. Copyright © 2009 John Wiley & Sons, Ltd.


title: Image Sharpness Assessment by Sparse Representation

abstract: Recent advances in sparse representation show that overcomplete dictionaries learned from natural images can capture high-level features for image analysis. Since atoms in the dictionaries are typically edge patterns and image blur is characterized by the spread of edges, an overcomplete dictionary can be used to measure the extent of blur. Motivated by this, this paper presents a no-reference sparse representation-based image sharpness index. An overcomplete dictionary is first learned using natural images. The blurred image is then represented using the dictionary in a block manner, and block energy is computed using the sparse coefficients. The sharpness score is defined as the variance-normalized energy over a set of selected high-variance blocks, which is achieved by normalizing the total block energy using the sum of block variances. The proposed method is not sensitive to training images, so a universal dictionary can be used to evaluate the sharpness of images. Experiments on six public image quality databases demonstrate the advantages of the proposed method.


Fitness Landscapes and Memetic Algorithm Design


title: Recursive Monte Carlo Search for Imperfect Information Games

abstract: Perfect information Monte Carlo (PIMC) search is the method of choice for constructing strong Al systems for trick-taking card games. PIMC search evaluates moves in imperfect information games by repeatedly sampling worlds based on state inference and estimating move values by solving the corresponding perfect information scenarios. PIMC search performs well in trick-taking card games despite the fact that it suffers from the strategy fusion problem, whereby the game's information set structure is ignored because moves are evaluated opportunistically in each world. In this paper we describe imperfect information Monte Carlo (IIMC) search, which aims at mitigating this problem by basing move evaluation on more realistic playout sequences rather than perfect information move values. We show that RecPIMC - a recursive IIMC search variant based on perfect information evaluation - performs considerably better than PIMC search in a large class of synthetic imperfect information games and the popular card game of Skat, for which PIMC search is the state-of-the-art cardplay algorithm.


title: TCM (Traditional Chinese Medicine) telemedicine with enterprise ontology support - a form of consensus-certified collective human intelligence

abstract: We propose the novel enterprise ontology-driven information system (IS) development (EOD-ISD) approach to automate the construction and customization of telemedicine systems. The system builder simply constructs the meta-interface (MI) specification, which is made up of icons selected from the icon library. With the MI, the EOD-ISD generator uses the enterprise ontology as the master and basis to automatically generate the target system. The enterprise ontology is a form of consensus-certified collective human intelligence with explicit semantics. In the context of our research the enterprise ontology is locally owned by the establishment that has isolated the desired subset from the global ontology of the relevant knowledge domain (e.g. Traditional Chinese Medicine). From the enterprise ontology, which serves as the standard vocabulary for the establishment, different local systems can be customized anytime and anywhere simply with the MI specifications provided. The EOD-ISD approach was verified successfully with different TCM telemedicine diagnostic/prescription (D/P) system prototypes, customized from the proprietary enterprise TCM ontology core (TCM onto-core) of the Nong's Company Ltd.


title: A Reinforcement Learning Approach to Job-shop Scheduling

abstract: We apply reinforcement learning methods to learn domain-specific heuristics for job shop scheduling. A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule. The temporal difference algorithm TD(λ) is applied to tram a neural network to learn a heuristic evaluation function over states. This evaluation function is used by a one-step lookahead search procedure to find good solutions to new scheduling problems. We evaluate this approach on synthetic problems and on problems from a NASA space shuttle pay load processing task. The evaluation function is trained on problems involving a small number of jobs and then tested on larger problems. The TD scheduler performs better than the best known existing algorithm for this task--Zwehen's iterative repair method based on simulated annealing. The results suggest that reinforcement learning can provide a new method for constructing high-performance scheduling systems.


title: An AC Input Switching-Converter-Free LED Driver With Low-Frequency-Flicker Reduction

abstract: This paper presents a novel switching-converter-free ac–dc light-emitting diode (LED) driver with low-frequency-flicker reduction for general lighting applications. The proposed driving solution can minimize the system size as it enables the monolithic integration of the controller and power transistors while both the bulky off-chip electrolytic capacitors and magnetics are eliminated. Moreover, the driver can effectively reduce the harmful optical flicker at the double-line-frequency by employing a novel quasi-constant power control scheme while maintaining high efficiency and a good power factor (PF). The proposed driver is implemented with a single controller integrated circuit chip, which includes the controller and high-voltage power transistors, and the off-chip diode bridge and valley-fill circuit. The chip is fabricated with a 0.35- $\mu \text{m}$ 120-V high-voltage CMOS process and occupies 1.85 mm2. The driver can provide up to 7.8-W power to the LED and achieves 87.6% peak efficiency and an over 0.925 PF with only 17.3% flicker from a 110-Vac 60-Hz input.


title: The Goal Structuring Notation – A Safety Argument Notation

abstract: In Europe, over recent years, the responsibility for ensuring system safety has shifted onto the developers and operators to construct and present well reasoned arguments that their systems achieve acceptable levels of safety. These arguments (together with supporting evidence) are typically referred to as a “safety case”. This paper describes the role and purpose of a safety case. Safety arguments within safety cases are often poorly communicated. This paper presents a technique called GSN (Goal Structuring Notation) that is increasingly being used in safety-critical industries to improve the structure, rigor, and clarity of safety arguments. The paper also describes a number of extensions, based upon GSN, which can be used to assist the maintenance, construction, reuse and assessment of safety cases. The aim of this paper is to describe the current industrial use and research into GSN such that its applicability to other types of Assurance Case, in addition to safety cases, can also be considered.


title: Towards Hardware Accelerated Reinforcement Learning for Application-Specific Robotic Control

abstract: Reinforcement Learning (RL) is an area of machine learning in which an agent interacts with the environment by making sequential decisions. The agent receives reward from the environment based on how good the decisions are and tries to find an optimal decision-making policy that maximises its longterm cumulative reward. This paper presents a novel approach which has showon promise in applying accelerated simulation of RL policy training to automating the control of a real robot arm for specific applications. The approach has two steps. First, design space exploration techniques are developed to enhance performance of an FPGA accelerator for RL policy training based on Trust Region Policy Optimisation (TRPO), which results in a 43% speed improvement over a previous FPGA implementation, while achieving 4.65 times speed up against deep learning libraries running on GPU and 19.29 times speed up against CPU. Second, the trained RL policy is transferred to a real robot arm. Our experiments show that the trained arm can successfully reach to and pick up predefined objects, demonstrating the feasibility of our approach.


title: Data-flow Aware CNN Accelerator with Hybrid Wireless Interconnection

abstract: Deep convolution neural networks (CNNs) are computationally intensive machine learning algorithms with a large amount of data that impose various challenges for their hardware implementation. To meet the high computing demands of CNNs, many accelerator designs are proposed that revolve around achieving high parallelization, increasing on-chip data reuse and efficient memory hierarchy, etc. However, very few works have attempted to address the communication challenges in these massively parallel accelerators architectures, which is the most anticipated performance bottleneck. Traditional interconnections like bus, crossbar and even Network-on-Chip ( $N$ o $C$ ) topologies like mesh fail to achieve the peak performance required by the large number of processing elements on accelerators. In this work, we address the communication bottlenecks of accelerators by extensively studying the application data-flow. We propose an efficient accelerator architecture that employs broadcast enabled low latency wireless links along with traditional wired links to efficiently support the data-flow of accelerators and achieve high communication performance. Evaluation of the proposed design shows that it achieves 28 % latency reduction, 19x bandwidth improvement and 35% network energy saving as compared to baseline wired networks.


A Blockchain based Access Control Scheme


title: High quality depth map upsampling for 3D-TOF cameras

abstract: This paper describes an application framework to perform high quality upsampling on depth maps captured from a low-resolution and noisy 3D time-of-flight (3D-ToF) camera that has been coupled with a high-resolution RGB camera. Our framework is inspired by recent work that uses nonlocal means filtering to regularize depth maps in order to maintain fine detail and structure. Our framework extends this regularization with an additional edge weighting scheme based on several image features based on the additional high-resolution RGB input. Quantitative and qualitative results show that our method outperforms existing approaches for 3D-ToF upsampling. We describe the complete process for this system, including device calibration, scene warping for input alignment, and even how the results can be further processed using simple user markup.


title: Jacobian analysis for lower mobility parallel robots based on actuating wrenches

abstract: The lower mobility parallel robots have been applied in lots of industrial fields, and the Jacobian plays an important role in the robotic performance analysis such as velocity analysis, precision analysis, and stiffness analysis. As for lower mobility parallel robots with the special limb structures, a novel approach based on actuating wrenches is proposed for the Jacobian analysis, which is much concise compared with the existing methods and significant for the proposed physical concept of the actuating wrench. The characteristics of the special limb structures are explored, and these structures are frequently adopted to constitute the lower mobility parallel robots that are extensively utilized in industry. The Jacobian matrix is derived just through the actuating wrenches. Not only the velocity Jacobian but also the force Jacobian is analyzed, and some incorrect deduction between the two Jacobian matrices appeared in the published works is clarified. Examples show that if the origin of the global coordinate frame is set at the moving platform, the Jacobian analysis can be much simplified.


title: Digital Natives? New and Old Media and Children's Outcomes

abstract: The current generation of young children has been described as 'digital natives', having been born into a ubiquitous digital media environment. They are envisaged as educationally independent of the guided interaction provided by 'digital immigrants': parents and teachers. This article uses data from the Longitudinal Study of Australian Children (LSAC) to study the development of vocabulary and traditional literacy in children aged from 0 to 8 years; their access to digital devices; parental mediation practices; children's use of digital devices as recorded in time-diaries; and, finally, the association between patterns of media use and family contexts on children's learning. The analysis shows the importance of the parental context in framing media use for acquiring vocabulary, and suggests that computer (but not games) use is associated with more developed language skills. Independently of these factors, raw exposure to television is not harmful to learning.


title: A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part I: Model of the Intrinsic Channel Region

abstract: This paper presents a circuit-compatible compact model for the intrinsic channel region of the MOSFET-like single-walled carbon-nanotube field-effect transistors (CNFETs). This model is valid for CNFET with a wide range of chiralities and diameters and for CNFET with either metallic or semiconducting carbon-nanotube (CNT) conducting channel. The modeled nonidealities include the quantum confinement effects on both circumferential and axial directions, the acoustical/optical phonon scattering in the channel region, and the screening effect by the parallel CNTs for CNFET with multiple CNTs. In order to be compatible with both large-(digital) and small-signal (analog) applications, a complete transcapacitance network is implemented to deliver the real-time dynamic response. This model is implemented with an HSPICE. Using this model, we project a 13 times CV/I improvement of the intrinsic CNFET with (19, 0) CNT over the bulk n-type MOSFET at the 32-nm node. The model described in this paper serves as a starting point toward the complete CNFET-device model incorporating the additional device/circuit-level non-idealities and multiple CNTs reported in the paper of Deng and Wong.


title: Self-programming networks: Architecture and algorithms

abstract: The rapid growth and wide adoption of cloud computing platforms have made them very complex to operate and manage. The operators of cloud platforms and the underlying data center networks have desired to make them much more programmable. This has culminated in the development of the Software-Defined Networking paradigm. But you cannot program what you do not understand: the volume, velocity and richness of network applications seem beyond the ability of direct human comprehension. What is needed is a sensing, inference and learning system which can observe the data emitted by a network during the course of its operation, reconstruct the network's evolution, infer key performance metrics, continually learn the best responses to rapidly-changing load and operating conditions, and help the network adapt to them in real-time. This white paper describes Self-Programming Networks (SPNs), an ongoing research effort at Stanford for making networks autonomous; that is, to enable networks to sense and monitor themselves, and program and control themselves. We present a NIC-centric architecture for SPNs, describe systems and algorithms for (i) fine-grained network measurement using packet and probe timestamps taken at the edge, and (ii) nanosecond-level clock synchronization in real-time and at scale. We also describe how these enhancements can enable new applications on top of an SPN. While our initial work has been on developing the sensing and monitoring capabilities of SPNs, we describe future work on learning and control.


title: Open Evaluation Tool for Layout Analysis of Document Images

abstract: This paper presents an open tool for standardizing the evaluation process of the layout analysis task of document images at pixel level. We introduce a new evaluation tool that is both available as a standalone Java application and as a RESTful web service. This evaluation tool is free and open-source in order to be a common tool that anyone can use and contribute to. It aims at providing as many metrics as possible to investigate layout analysis predictions, and also provides an easy way of visualizing the results. This tool evaluates document segmentation at pixel level, and supports multi-labeled pixel ground truth. Finally, this tool has been successfully used for the ICDAR 2017 competition on Layout Analysis for Challenging Medieval Manuscripts.


title: 1st International Workshop "From Dependable to Resilient, from Resilient to Antifragile Ambients and Systems" (ANTIFRAGILE 2014) Toward Antifragile Cloud Computing Infrastructures

abstract: Cloud computing systems are rapidly growing in scale and complexity. They are also changing dynamically as a result of dynamic addition and removal of system components, different execution environments, common updates and upgrades, runtime repairs, mobility of devices and more. Such large-scale, complex and dynamic cloud environments are prone to failures and performance anomalies. Thus, dependability and resilience in cloud computing are of paramount importance to guarantee availability and reliability of services and application execution, even in the presence of large number of faulty components. Antifragility is the key to such techniques. It proposes that some systems could be strengthened by changes and faults instead of be weakened by them. In contrast to classical resilience methods, antifragile techniques aim to build systems that handle unpredictable and irregular events, while growing and getting stronger. Most of the classical resilience techniques are not sufficient to build highly available cloud infrastructures. In fact, they just resist shocks and stay the same. They should be complemented by some other aspects like learning from failure to built more elastic and stronger cloud infrastructures. This may represent the idea of building antfragile cloud systems. In this paper, we discuss the existing resilience techniques and propose a solution to design antifragile systems in cloud computing environments. c


title: Is Happiness Good for Your Personality? Concurrent and Prospective Relations of the Big Five With Subjective Well-Being

abstract: The present research examined longitudinal relations of the Big Five personality traits with three core aspects of subjective well-being: life satisfaction, positive affect, and negative affect. Latent growth models and autoregressive models were used to analyze data from a large,nationally representative sample of 16,367Australian residents.Concurrent and change correlations indicated that higher levels of subjective well-being were associated with higher levels of Extraversion, Agreeableness, and Conscientiousness, and with lower levels of Neuroticism. Moreover, personality traits prospectively predicted change in well-being, and well-being levels prospectively predicted personality change. Specifically, prospective trait effects indicated that individuals who were initially extraverted,agreeable,conscientious,and emotionally stable subsequently increased in well-being. Prospective well-being effects indicated that individuals with high initial levels of well-being subsequently became more agreeable, conscientious, emotionally stable, and introverted. These findings challenge the common assumption that associations of personality traits with subjective well-being are entirely, or almost entirely, due to trait influences on well-being. They support the alternative hypothesis that personality traits and well-being aspects reciprocally influence each other over time. People with different personality traits tend to experience different degrees of subjective well-being. Specifically, in terms of the Big Five trait dimensions—Extraversion, Agreeableness, Conscientiousness, Neuroticism (vs. Emotional Stability), and Openness to Experience—individuals who are more extraverted, agreeable, conscientious, and emotionally stable tend to experience greater satisfaction with life, more frequent positive affect, and less frequent negative affect (DeNeve & Cooper, 1998; Steel, Schmidt, & Shultz, 2008). Do such associations between personality traits and well-being aspects indicate that traits influence subjective well-being, that well-being influences traits, or both? The present research was conducted to test these possibilities by examining concurrent and prospective relations of the Big Five with life satisfaction, positive affect, and negative affect, using longitudinal data from a large, nationally representative sample.


title: Towards Understanding Malware Behaviour by the Extraction of API Calls

abstract: One of the recent trends adopted by malware authors is to use packers or software tools that instigate code obfuscation in order to evade detection by antivirus scanners. With evasion techniques such as polymorphism and metamorphism malware is able to fool current detection techniques. Thus, security researchers and the anti-virus industry are facing a herculean task in extracting payloads hidden within packed executables. It is a common practice to use manual unpacking or static unpacking using some software tools and analyse the application programming interface (API) calls for malware detection. However, extracting these features from the unpacked executables for reverse obfuscation is labour intensive and requires deep knowledge of low-level programming that includes kernel and assembly language. This paper presents an automated method of extracting API call features and analysing them in order to understand their use for malicious purpose. While some research has been conducted in arriving at file birthmarks using API call features and the like, there is a scarcity of work that relates to features in malcodes. To address this gap, we attempt to automatically analyse and classify the behavior of API function calls based on the malicious intent hidden within any packed program. This paper uses four-step methodology for developing a fully automated system to arrive at six main categories of suspicious behavior of API call features.


title: NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data

abstract: Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.


title: SFace: An Efficient Network for Face Detection in Large Scale Variations

abstract: Face detection serves as a fundamental research topic for many applications like face recognition. Impressive progress has been made especially with the recent development of convolutional neural networks. However, the issue of large scale variations, which widely exists in high resolution images/videos, has not been well addressed in the literature. In this paper, we present a novel algorithm called SFace, which efficiently integrates the anchor-based method and anchor-free method to address the scale issues. A new dataset called 4K-Face is also introduced to evaluate the performance of face detection with extreme large scale variations. The SFace architecture shows promising results on the new 4K-Face benchmarks. In addition, our method can run at 50 frames per second (fps) with an accuracy of 80% AP on the standard WIDER FACE dataset, which outperforms the state-of-art algorithms by almost one order of magnitude in speed while achieves comparative performance.


title: Gender-Inclusive Design: Sense of Belonging and Bias in Web Interfaces

abstract: We interact with dozens of web interfaces on a daily basis, making inclusive web design practices more important than ever. This paper investigates the impacts of web interface design on ambient belonging, or the sense of belonging to a community or culture. Our experiment deployed two content-identical webpages for an introductory computer science course, differing only in aesthetic features such that one was perceived as masculine while the other was gender-neutral. Our results confirm that young women exposed to the masculine page are negatively affected, reporting significantly less ambient belonging, interest in the course and in studying computer science broadly. They also experience significantly more concern about others' perception of their gender relative to young women exposed to the neutral page, while no similar effect is seen in young men. These results suggest that gender biases can be triggered by web design, highlighting the need for inclusive user interface design for the web.


A study into the believability of animated characters in the context of bullying intervention


title: Automatic speech recognition An evaluation of Google Speech

abstract: The use of speech recognition is increasing rapidly and is now available in smart TVs, desktop computers, every new smart phone, etc. allowing us to talk to computers naturally. With the use in home appliances, education and even in surgical procedures accuracy and speed becomes very important.This thesis aims to give an introduction to speech recognition and discuss its use in robotics. An evaluation of Google Speech, using Google’s speech API, in regards to word error rate and translation speed, as well as a comparison between Google Speech and Pocketsphinx is made.The results show that Google Speech presented lower error rates on general purpose sentences but, due to the high average translation speed and the inability to specify vocabulary, was not suitable for voice-controlled moving robots.


title: Almost Envy-Freeness with General Valuations

abstract: The goal of fair division is to distribute resources among competing players in a"fair"way. Envy-freeness is the most extensively studied fairness notion in fair division. Envy-free allocations do not always exist with indivisible goods, motivating the study of relaxed versions of envy-freeness. We study the envy-freeness up to any good (EFX) property, which states that no player prefers the bundle of another player following the removal of any single good, and prove the first general results about this property. We use the leximin solution to show existence of EFX allocations in several contexts, sometimes in conjunction with Pareto optimality. For two players with valuations obeying a mild assumption, one of these results provides stronger guarantees than the currently deployed algorithm on Spliddit, a popular fair division website. Unfortunately, finding the leximin solution can require exponential time. We show that this is necessary by proving an exponential lower bound on the number of value queries needed to identify an EFX allocation, even for two players with identical valuations. We consider both additive and more general valuations, and our work suggests that there is a rich landscape of problems to explore in the fair division of indivisible goods with different classes of player valuations.


Weakly-supervised Transfer for 3D Human Pose Estimation in the Wild.


title: Multiview LSA: Representation Learning via Generalized CCA

abstract: Multiview LSA (MVLSA) is a generalization of Latent Semantic Analysis (LSA) that supports the fusion of arbitrary views of data and relies on Generalized Canonical Correlation Analysis (GCCA). We present an algorithm for fast approximate computation of GCCA, which when coupled with methods for handling missing values, is general enough to approximate some recent algorithms for inducing vector representations of words. Experiments across a comprehensive collection of test-sets show our approach to be competitive with the state of the art.


title: Comparative study of a new dermal filler Uma Jeunesse® and Juvéderm®.

abstract: INTRODUCTION ::: Innovation in technology has resulted in the emergence of better, longer-lasting hyaluronic acid implants with fewer side effects. The new dermal implant Uma Jeunesse® was compared to Juvéderm® in this split-face study. ::: ::: ::: METHODS ::: Uma Jeunesse® is crosslinked with butanediol diglycidyl ether (BDDE) using a new crosslinking technology. Uma Jeunesse® and Juvéderm® Ultra 3 were injected in a split-face study on 17 healthy volunteers, whose ages ranged from 33-58 years. There were 14 women and three men with medium to deep nasolabial folds. All subjects randomly received either Uma Jeunesse® or Juvéderm®) Ultra 3 on one half of their face. Patients were followed up for 9 months. ::: ::: ::: RESULTS ::: Juvéderm® was easier to inject with lesser injection pain because of lidocaine, but late postinjection pain was much less with Uma Jeunesse® as compared to Juvéderm®. Overall rate of early and late complications as well as adverse events was lower with Uma Jeunesse® than Juvéderm(®) . After 9 months of follow-up, Uma Jeunesse® lasted in tissues for longer as compared to Juvéderm(®) even in patients injected for the first time (P<0.0001). Patient acceptability rate of Uma Jeunesse® was also much higher. Perception of pain during injection was lesser with Juvéderm® probably because of the presence of lidocaine. ::: ::: ::: CONCLUSION ::: The new dermal implant Uma Jeunesse® is a safe and patient-friendly product which resides in the tissues for longer with maintenance of aesthetic effect over and beyond 6 months, reaching 9 months in over 80% of patients, and Juvéderm® injection is less painful.


title: Probase: a probabilistic taxonomy for text understanding

abstract: Knowledge is indispensable to understanding. The ongoing information explosion highlights the need to enable machines to better understand electronic text in human language. Much work has been devoted to creating universal ontologies or taxonomies for this purpose. However, none of the existing ontologies has the needed depth and breadth for universal understanding. In this paper, we present a universal, probabilistic taxonomy that is more comprehensive than any existing ones. It contains 2.7 million concepts harnessed automatically from a corpus of 1.68 billion web pages. Unlike traditional taxonomies that treat knowledge as black and white, it uses probabilities to model inconsistent, ambiguous and uncertain information it contains. We present details of how the taxonomy is constructed, its probabilistic modeling, and its potential applications in text understanding.


title: Implementation of a Chinese Word Segmentation in Big Data Environment

abstract: A Chinese word segmentation is implement in big data environment in this paper. We develop a word segmentation system based on Double-Array Trie tree for dictionary and improving N-Gram algorithm for OOV words discovery. This paper also present a improving Omni-segmentation by using the feature of Double-Array Trie tree. Encapsulate this Chinese word segmentation system to an Elasticsearch plugin. Practical results show the system has a better performance compare with the existing system.


title: Characteristic impedance and propagation of the first higher order microstrip mode in frequency and time domain

abstract: This paper experimentally and theoretically confirms the validity of the definition proposed by Das (1996) for computing the complex characteristic impedance of the first higher order (EH/sub 1/) microstrip mode. The normalized complex propagation constant and complex characteristic impedance of the microstrip obtained by the rigorous full-wave integral-equation method are also presented. To better understand the circuit behavior of the leaky mode at the respective frequencies, the results are analyzed in both frequency and transformed steepest descent plane. A differential time-domain reflectometry (TDR) experiment shows that the experimental results are in excellent agreement with the time-domain plots obtained theoretically by the inverse discrete Fourier transform of the transmission line modeled by the dispersive characteristic. The propagation characteristics of the echoed signals in the time domain, which are reflected from the open end of the leaky line, are analyzed in detail using the corresponding group velocity of the EH/sub 1/ mode. The time-to-frequency conversion of the measured TDR data reveals that the reflection, leaky, and propagation zones coexist simultaneously for the EH, mode propagation. The conversion also accurately assesses the attenuation constant of the EH, mode if the attenuation is not too high. The Fourier transform of the TDR responses also simultaneously yields the input reflection coefficient (S/sub 11/) and the complex characteristic impedance. The complex characteristic impedance extracted from the TDR responses also agrees closely with the theoretical data.


title: Optimization of Passive Low Power Wireless Electromagnetic Energy Harvesters

abstract: This work presents the optimization of antenna captured low power radio frequency (RF) to direct current (DC) power converters using Schottky diodes for powering remote wireless sensors. Linearized models using scattering parameters show that an antenna and a matched diode rectifier can be described as a form of coupled resonator with different individual resonator properties. The analytical models show that the maximum voltage gain of the coupled resonators is mainly related to the antenna, diode and load (remote sensor) resistances at matched conditions or resonance. The analytical models were verified with experimental results. Different passive wireless RF power harvesters offering high selectivity, broadband response and high voltage sensitivity are presented. Measured results show that with an optimal resistance of antenna and diode, it is possible to achieve high RF to DC voltage sensitivity of 0.5 V and efficiency of 20% at −30 dBm antenna input power. Additionally, a wireless harvester (rectenna) is built and tested for receiving range performance.


title: Consistent multilabel classification

abstract: Multilabel classification is rapidly developing as an important aspect of modern predictive modeling, motivating study of its theoretical aspects. To this end, we propose a framework for constructing and analyzing multilabel classification metrics which reveals novel results on a parametric form for population optimal classifiers, and additional insight into the role of label correlations. In particular, we show that for multilabel metrics constructed as instance-, micro- and macro-averages, the population optimal classifier can be decomposed into binary classifiers based on the marginal instance-conditional distribution of each label, with a weak association between labels via the threshold. Thus, our analysis extends the state of the art from a few known multilabel classification metrics such as Hamming loss, to a general framework applicable to many of the classification metrics in common use. Based on the population-optimal classifier, we propose a computationally efficient and general-purpose plug-in classification algorithm, and prove its consistency with respect to the metric of interest. Empirical results on synthetic and benchmark datasets are supportive of our theoretical findings.


title: Immediate effects of scapular stabilizing exercise in chronic stroke patient with winging and elevated scapula: a case study

abstract: [Purpose] The purpose of this study was to investigate the effect of scapular stabilizing exercise in a stroke patient with winging and elevated scapula. [Subject and Methods] The subject was a 46-year-old female with a history of stroke. She had right side hemiplegia with winging and elevated scapula on the right side, and had compensatory motions of the neck and shoulder when using the paretic upper extremity. The subject participated in scapular stabilizing exercises for four days. This exercise program consisted of scapular protraction-retraction in an upright seated position. Scapular position was measured as distance between scapular medial border and thoracic vertebrae 3, 4. Upper extremity function was measured as time required for lifting and lowering a cup with the affected arm. [Results] After intervention, distance between scapular medial border and spinouse process of T3, 4 decreased. Time required for lifting and lowering a cup with the affected arm decreased. Compensatory motions of the neck and shoulder joint decreased. [Conclusion] Despite the short period, scapular stabilizing exercises had a positive effect on scapular position and upper extremity function.


title: Protecting Data on Smartphones and Tablets from Memory Attacks

abstract: Smartphones and tablets are easily lost or stolen. This makes them susceptible to an inexpensive class of memory attacks, such as cold-boot attacks, using a bus monitor to observe the memory bus, and DMA attacks. This paper describes Sentry, a system that allows applications and OS components to store their code and data on the System-on-Chip (SoC) rather than in DRAM. We use ARM-specific mechanisms originally designed for embedded systems, but still present in today's mobile devices, to protect applications and OS subsystems from memory attacks.


title: Consistent penetration depth estimation for deformable collision response

abstract: Penalty approaches can be used to efficiently resolve collisions of dynamically simulated rigid and deformable objects. These approaches compute penalty forces based on the penetration depth of intersecting objects and there exist many algorithms for estimating the exact penetration depth. However, in discrete-time simulations, this information can cause non-plausible collision responses in case of large penetrations or due to the object discretization. In this paper, we present a method to compute consistent penetration depth information in order to reduce collision response artifacts inherent to existing penetration depth approaches. The method considers a set of close surface features to avoid discontinuous penetration depths. Further, a propagation scheme is applied in case of large penetrations to avoid non-plausible, inconsistent penetration depth information.


title: The End of Corporate Imperialism

abstract: As they search for growth, multinational corporations will have no choice but to compete in the big emerging markets of China, India, Indonesia, and Brazil. But while it is still common to question how such corporations will change life in those markets, Western executives would be smart to turn the question around and ask how multinationals themselves will be transformed by these markets. To be successful, MNCs will have to rethink every element of their business models, the authors assert in this seminal HBR article from 1998. During the first wave of market entry in the 1980s, multinationals operated with what might be termed an imperialist mind-set, assuming that the emerging markets would merely be new markets for their old products. But this mind-set limited their success: What is truly big and emerging in countries like China and India is a new consumer base comprising hundreds of millions of people. To tap into this huge opportunity, MNCs need to ask themselves five basic questions: Who is in the emerging middle class in these countries? How do the distribution networks operate? What mix of local and global leadership do you need to foster business opportunities? Should you adopt a consistent strategy for all of your business units within one country? Should you take on local partners? The transformation that multinational corporations must undergo is not cosmetic--simply developing greater sensitivity to local cultures will not do the trick, the authors say. To compete in the big emerging markets, multinationals must reconfigure their resources, rethink their cost structures, redesign their product development processes, and challenge their assumptions about who their top-level managers should be.


title: Bone remodeling during fracture repair : The cellular picture

abstract: Abstract Fracture healing is a complex event that involves the coordination of a variety of different processes. Repair is typically characterized by four overlapping stages: the initial inflammatory response, soft callus formation, hard callus formation, initial bony union and bone remodeling. However, repair can also be seen to represent a juxtaposition of two distinct forces: anabolism or tissue formation, and catabolism or remodeling. These anabolic/catabolic concepts are useful for understanding bone repair without giving the false impression of temporally distinct stages that operate independently. They are also relevant when considering intervention. In normal bone development, bone remodeling conventionally refers to the removal of calcified bone tissue by osteoclasts. However, in the context of bone repair there are two phases of tissue catabolism: the removal of the initial cartilaginous soft callus, followed by the eventual remodeling of the bony hard callus. In this review, we have attempted to examine catabolism/remodeling in fractures in a systematic fashion. The first section briefly summarizes the traditional four-stage view of fracture repair in a physiological manner. The second section highlights some of the limitations of using a temporal rather than process-driven model and summarizes the anabolic/catabolic paradigm of fracture repair. The third section examines the cellular participants in soft callus remodeling and in particular the role of the osteoclast in endochondral ossification. Finally, the fourth section examines the effects of delaying osteoclast-dependent hard callus remodeling and also poses questions regarding the crosstalk between anabolism and catabolism in the latter stages of fracture repair.


title: Motivational Incongruence and Well-Being at the Workplace: Person-Job Fit, Job Burnout, and Physical Symptoms

abstract: Person-environment fit has been identified as a key prerequisite for employee well-being. We investigated to what extent a misfit between motivational needs and supplies at the workplace affects two key health outcomes: burnout and physical symptoms. Individual needs (implicit affiliation and power motives) and environment supplies (motive specific job characteristics) were assessed in an online survey of full time employees (n = 97), using a picture story exercise measuring implicit motives and a scale listing affiliation and power related job characteristics. Outcomes were assessed using the Maslach Burnout Inventory and a checklist of physical symptoms. We conducted polynomial regressions with response surface analysis. Results reveal that motivational incongruence with respect to the affiliation motive was related to high job burnout, while motivational incongruence concerning the power motive predicted increased physical symptoms. This was true for both those with a strong affiliation or power motive and low corresponding job characteristics and those with a weak affiliation or power motive and job characteristics demanding the respective motive. Results hint at potential interventions towards preventing or remedying a lack of needs-supply fit and reducing the risk of impairments of well-being.


title: High electromechanical coupling piezoelectric micro-machined ultrasonic transducer (PMUT) elements for medical imaging

abstract: This paper presents a multi-cell lead zirconate titanate (PZT) pMUT element geometrically optimized for high effective electromechanical coupling (ke ff 2) and acoustic power and manufactured via a commercially viable, array compatible and wafer-scale process. A top-down fabrication approach facilitates achievement of a high fill factor (≈1250 transducers/mm2) multi-cell element array, and more substantially, results in a frequency based k eff 2 measurement of 9±1.4% per element, a nearly two fold improvement over the previous state-of-the-art.


title: A Survey of Context-Aware Mobile Computing Research

abstract: Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity). Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology. Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users. In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context. Through this survey, it is clear that context-aware research is an old but rich area for research. The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality.


title: DVB-S2: The Second Generation Standard for Satellite Broad-Band Services

abstract: DVB-S2 is the second-generation specification for satellite broad-band applications, developed by the Digital Video Broadcasting (DVB) Project in 2003. The system is structured as a toolkit to allow the implementation of the following satellite applications: TV and sound broadcasting, interactivity (i.e., Internet access), and professional services, such as TV contribution links and digital satellite news gathering. It has been specified around three concepts: best transmission performance approaching the Shannon limit, total flexibility, and reasonable receiver complexity. Channel coding and modulation are based on more recent developments by the scientific community: low density parity check codes are adopted, combined with QPSK, 8PSK, 16APSK, and 32APSK modulations for the system to work properly on the nonlinear satellite channel. The framing structure allows for maximum flexibility in a versatile system and also synchronization in worst case configurations (low signal-to-noise ratios). Adaptive coding and modulation, when used in one-to-one links, then allows optimization of the transmission parameters for each individual user,dependant on path conditions. Backward-compatible modes are also available,allowing existing DVB-S integrated receivers-decoders to continue working during the transitional period. The paper provides a tutorial overview of the DVB-S2 system, describing its main features and performance in various scenarios and applications.


title: Integrating Object Affordances with Artificial Visual Attention

abstract: Affordances, e.g., grasping possibilities, play a role in the guidance of human attention. We report experiments on the integration of affordance estimation with artificial visual attention in a prototypical model. Furthermore, Growing Neural Gas is discussed as a potential framework for future attention models that deeply integrate affordance, saliency and further attentional mechanisms.


title: SQLScript: Efficiently Analyzing Big Enterprise Data in SAP HANA

abstract: Today, not only Internet companies such as Google, Facebook or Twitter do have Big Data but also Enterprise Information Systems store an ever growing amount of data (called Big Enterprise Data in this paper). In a classical SAP system landscape a central data warehouse (SAP BW) is used to integrate and analyze all enterprise data. In SAP BW most of the business logic required for complex analytical tasks (e.g., a complex currency conversion) is implemented in the application layer on top of a standard relational database. While being independent from the underlying database when using such an architecture, this architecture has two major drawbacks when analyzing Big Enterprise Data: (1) algorithms in ABAP do not scale with the amount of data and (2) data shipping is required. To this end, we present a novel programming language called SQLScript to efficiently support complex and scalable analytical tasks inside SAP’s new main-memory database HANA. SQLScript provides two major extensions to the SQL dialect of SAP HANA: A functional and a procedural extension. While the functional extension allows the definition of scalable analytical tasks on Big Enterprise Data, the procedural extension provides imperative constructs to orchestrate the analytical tasks. The major contributions of this paper are two novel functional extensions: First, an extended version of the MapReduce programming model for supporting parallelizable user-defined functions (UDFs). Second, compared to recursion in the SQL standard, a generalized version of recursion to support graph analytics as well as machine learning tasks.


MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries


title: Pattern reconfigurable wideband circularly-polarized quadrifilar helix with broadside and backfire radiation patterns

abstract: This paper introduces a pattern reconfigurable wideband circularly-polarized (CP) quadrifilar helical antenna (QHA) with both broadside and backfire radiation modes. A compact 1∶4 power divider network is proposed to feed the QHA for obtaining the wide bandwidth. To realize pattern diversity, PIN diodes on the power divider are to reconfigure the sequences of the output phases such that the direction of radiation pattern can be controlled alternatively for the broadside or the backfire radiation. The fabricated antenna prototype is compact and light-weight. Measured results show that the broadside and backfire radiation modes can be switched by applying opposite voltages on the two DC bias. The proposed QHA has the wide impedance bandwidth of 43% and the wide 3-dB AR bandwidth of 38%. The measured broadside and backfire radiations have the wide 3-dB gain beamwidth of 140° and 120° with the maximum gain of 2.3 and 3.0 dBic respectively. The proposed antenna can be used in many applications as subway and railway communications, base station for narrow street coverage, RFID or satellite systems.


Melody-to-Chord using paired model and multitask learning language modeling


title: All-in-one: Multi-task Learning for Rumour Verification

abstract: Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stance classification, leading to the final outcome of determining the veracity of a rumour. In previous work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification. We examine the connection between the dataset properties and the outcomes of the multi-task learning models used.


title: AGIL: Learning Attention from Human for Visuomotor Tasks

abstract: When intelligent agents learn visuomotor behaviors from human demonstrations, they may benefit from knowing where the human is allocating visual attention, which can be inferred from their gaze. A wealth of information regarding intelligent decision making is conveyed by human gaze allocation; hence, exploiting such information has the potential to improve the agents' performance. With this motivation, we propose the AGIL (Attention Guided Imitation Learning) framework. We collect high-quality human action and gaze data while playing Atari games in a carefully controlled experimental setting. Using these data, we first train a deep neural network that can predict human gaze positions and visual attention with high accuracy (the gaze network) and then train another network to predict human actions (the policy network). Incorporating the learned attention model from the gaze network into the policy network significantly improves the action prediction accuracy and task performance.


title: An evaluation of optical flow algorithms for background oriented schlieren imaging

abstract: The background oriented schlieren method (BOS) allows for accurate flow measurements with a simple experimental configuration. To estimate per-pixel displacement vectors between two images, BOS systems traditionally borrow window-based algorithms from particle image velocimetry. In this paper, we evaluate the performance of more recent optical flow methods in BOS settings. We also analyze the impact of different background patterns, suggesting the use of a pattern with detail at many scales. Experiments with both synthetic and real datasets show that the performance of BOS systems can be significantly improved through a combination of optical flow algorithms and multiscale background.


title: Divide and Conquer Kernel Ridge Regression

abstract: We study a decomposition-based scalable approach to performing kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subsets of equal size, computes an independent kernel ridge regression estimator for each subset, then averages the local solutions into a global predictor. This partitioning leads to a substantial reduction in computation time versus the standard approach of performing kernel ridge regression on all N samples. Our main theorem establishes that despite the computational speed-up, statistical optimality is retained: if m is not too large, the partition-based estimate achieves optimal rates of convergence for the full sample size N. As concrete examples, our theory guarantees that m may grow polynomially in N for Sobolev spaces, and nearly linearly for finite-rank kernels and Gaussian kernels. We conclude with simulations complementing our theoretical results and exhibiting the computational and statistical benefits of our approach.


title: Identity-Based Authentication for Cloud Computing

abstract: Cloud computing is a recently developed new technology for complex systems with massive-scale services sharing among numerous users. Therefore, authentication of both users and services is a significant issue for the trust and security of the cloud computing. SSL Authentication Protocol (SAP), once applied in cloud computing, will become so complicated that users will undergo a heavily loaded point both in computation and communication. This paper, based on the identity-based hierarchical model for cloud computing (IBHMCC) and its corresponding encryption and signature schemes, presented a new identity-based authentication protocol for cloud computing and services. Through simulation testing, it is shown that the authentication protocol is more lightweight and efficient than SAP, specially the more lightweight user side. Such merit of our model with great scalability is very suited to the massive-scale cloud.


title: Evidence for the occurrence of two sympatric sibling species within the Anopheles (Kerteszia) cruziicomplex in southeast Brazil and the detection of asymmetric introgression between them using a multilocus analysis

abstract: BackgroundAnopheles (Kerteszia) cruzii (Diptera: Culicidae) is a primary vector of human and simian malaria parasites in southern and southeastern Brazil. Earlier studies using chromosome inversions, isoenzymes and a number of molecular markers have suggested that An. cruzii is a species complex.ResultsIn this study, a multilocus approach using six loci, three circadian clock genes and three encoding ribosomal proteins, was carried out to investigate in more detail the genetic differentiation between the An. cruzii populations from Florianópolis–Santa Catarina (southern Brazil) and Itatiaia–Rio de Janeiro States (southeastern Brazil). The analyses were performed first comparing Florianópolis and Itatiaia, and then comparing the two putative sympatric incipient species from Itatiaia (Itatiaia A and Itatiaia B). The analysis revealed high FST values between Florianópolis and Itatiaia (considering Itatiaia A and B together) and also between the sympatric Itatiaia A and Itatiaia B, irrespective of their function. Also, using the IM program, no strong indication of migration was found between Florianópolis and Itatiaia (considering Itatiaia A and B together) using all loci together, but between Itatiaia A and Itatiaia B, the results show evidence of migration only in the direction of Itatiaia B.ConclusionsThe results of the multilocus analysis indicate that Florianópolis and Itatiaia represent different species of the An. cruzii complex that diverged around 0.6 Mya, and also that the Itatiaia sample is composed of two sympatric incipient species A and B, which diverged around 0.2 Mya. Asymmetric introgression was found between the latter two species despite strong divergence in some loci.


title: 3D reconstruction from projection matrices in a C-arm based 3D-angiography system

abstract: 3D reconstruction of arterial vessels from planar radiographs obtained at several angles around the object has gained increasing interest. The motivating application has been interventional angiography. In order to obtain a three-dimensional reconstruction from a C-arm mounted X-Ray Image Intensifier (XRII) traditionally the trajectory of the source and the detector system is characterized and the pixel size is estimated. The main use of the imaging geometry characterization is to provide a correct 3D-2D mapping between the 3D voxels to be reconstructed and the 2D pixels on the radiographic images.


title: Local privacy and statistical minimax rates

abstract: We study the tradeoff between privacy guarantees and the utility of statistical estimators under local differential privacy, where data remains private even from the statistician. We prove bounds on information-theoretic quantities that influence estimation rates as a function of the amount of privacy preserved. Our bounds can be viewed as quantitative data-processing inequalities that, combined with minimax techniques such as Le Cam's and Fano's methods, allow for precise characterization of statistical rates under local privacy constraints.


title: Distribution-oriented Aesthetics Assessment for Image Search

abstract: Aesthetics has become increasingly prominent for image search to enhance user satisfaction. Therefore, image aesthetics assessment is emerging as a promising research topic in recent years. In this paper, distinguished from existing studies relying on a single label, we propose to quantify the image aesthetics by a distribution over quality levels. The distribution representation can effectively characterize the disagreement among the aesthetic perceptions of users regarding the same image. Our framework is developed on the foundation of label distribution learning, in which the reliability of training examples and the correlations between quality levels are fully taken into account. Extensive experiments on two benchmark datasets well verified the potential of our approach for aesthetics assessment. The role of aesthetics in image search was also rigorously investigated.


title: CellDesigner: a process diagram editor for gene-regulatory and biochemical networks

abstract: Context is critical if researchers need toknow the specificity or responsiveness of atarget within a pathway.This is somethingthat has yet to be captured by anyinformatics system,but it will invariably betied to greater utility and even IP definitionsin the near future.To a large extent,pathways data and knowledge managementare seen to go hand in hand,both being usedto make important decisions,and bothrelying on context.It is interesting toconsider that perhaps we are witnessing theemergence of a new informatics paradigm,where knowledge becomes a formallydefined and well-shared scientific andeconomic asset.The third topic that was discussed by afew was the development of new ITstrategies to improve informaticsinfrastructure within large companies.Several speakers presented new andeffective methodologies to design and buildcomplex informatics systems that willeventually become the backbone of drugdiscovery platforms.Richard Ashe describedaccelerated software development programswithin GSK (http://www.gsk.com/) that canadapt more effectively to changing discoveryrequirements.Using lightweightmethodologies and web-based servicesrather than monolithic designing principles,projects could be defined and completedfaster within an ever-changing world ofneeds.Roy Dunbar,CIO of Eli Lilly(http://www.lilly.com/) proposes moreeffective collaborations between IT andscientists to create applications that havemore utility.Juergen Seega from AbbottLaboratories (http://abbott.com/) described‘keeping the scientific and IT worlds in sync’,rather than at odds.Taken together,IT isprogressing from being hidden in thebackwaters of software tinkering,tobecoming a major strategic partner withindrug discovery environments.Other issues were addressed,includinggood laboratory practices as related toinformatics,electronic notebooks,intelligent storage and use of high-throughput screening data,and IP portfoliomanagement.Based on the presentations atthe conference,it was clear that theapplications and benefits of informatics forthe pharmaceutical industry are growing,and that informatics promises to be a keyfactor for the future success of drugdiscovery.Systems biology is characterized by synergisticintegration of theory,computational modeling,and experiment [1].Although softwareinfrastructure is one of the most crucialcomponents of systems biology research,there has been no common infrastructureor standard to enable integration ofcomputational resources.To solve thisproblem,the Systems Biology MarkupLanguage (SBML) [2] and Systems BiologyWorkbench (SBW) have been developed[3].SBML is an open,XML-based format forrepresenting biochemical reaction networks,and SBW is a modular,broker-based,message-passing framework for simplifiedintercommunication between applications.Several simulation and analysis softwarepackages already support SBML (Level-1)and SBW,or are being developed to supportthem.Identification of the logic and dynamics ofgene-regulatory and biochemical networks isa major challenge of systems biology.Webelieve that such network building tools andsimulation environments using standardizedtechnologies play an important role in thesoftware platform of systems biology.As onesuch approach,we have developedCellDesigner,a process diagram editor forgene-regulatory and biochemical networks.In the following,we will introduce themain features of CellDesigner.The mostcrucial elements are that it is a system ofBIOSILICO Vol.1, No.5 November 2003


title: A Bayesian Method for Auditing Elections Abstract

abstract: We propose an approach to post-election auditing based on Bayesian principles, and give experimental evidence for its efficiency and effectiveness. We call such an audit a "Bayes audit". It aims to control the probability of miscertification (certifying a wrong election outcome). The miscertification probability is computed using a Bayesian model based on information gathered by the audit so far. ::: ::: A Bayes audit is a single-ballot audit method applicable to any voting system (e.g. plurality, approval, IRV, Borda, Schulze, etc.) as long as the number of ballot types is not too large. The method requires only the ability to randomly sample single ballots and the ability to compute the election outcome for a profile of ballots. A Bayes audit does not require the computation of a "margin of victory" in order to get started. ::: ::: Bayes audits are applicable both to ballot-polling audits, which work just from the paper ballots, and to comparison audits, which work by comparing the paper ballots to their electronic representations. The procedure is quite simple and can be described on a single page. ::: ::: The Bayes audit uses an efficient method (which may be based on the use of gamma variates or on Polya's Urn) for simulating a Bayesian posterior distribution on the tally of a profile of ballots. ::: ::: A Bayes audit is very similar to single-ballot risk-limiting audits. However, since Bayes audits are based on different principles, the precise relationship between risk-limiting audits and Bayes audits remains open. ::: ::: We provide some initial experimental results indicating that Bayes audits are quite efficient, requiring few ballots to be examined, and that the miscertification rate is indeed kept small, even for very close elections.


title: EdgeCloudSim: An environment for performance evaluation of Edge Computing systems

abstract: Edge Computing is a fast growing field of research covering a spectrum of technologies such as Cloudlets, Fog Computing and Mobile Edge Computing (MEC). Edge Computing involves technically more sophisticated setup when compared with the pure Cloud Computing and pure Mobile Computing cases since both computational and network resources should be considered simultaneously. In that respect, it provides a larger design space with many parameters rendering a variety of novel approaches feasible. Given the complexity, Edge Computing designs deserve scientific scrutiny for sound assessment of their feasibility. However, despite increasing research activity, this field lacks a simulation tool compatible with the requirements. Starting from available simulators a significant programming effort is required to obtain a simulation tool meeting the actual needs. To decrease the barriers, a new simulator tool called EdgeCloudSim streamlined for Edge Computing scenarios is proposed in this work. EdgeCloudSim builds upon CloudSim to address the specific demands of Edge Computing research and support necessary functionality in terms of computation and networking abilities. To demonstrate the capabilities of EdgeCloudSim an experiment setup based on different edge architectures is simulated and the effect of the computational and networking system parameters on the results are depicted.


title: Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

abstract: This paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The solution to the problem is formulated as a combination of the opinions of different experts. The experts in this work are two existing techniques for feature location: a scenario-based probabilistic ranking of events and an information-retrieval-based technique that uses latent semantic indexing. The combination of these two experts is empirically evaluated through several case studies, which use the source code of the Mozilla Web browser and the Eclipse integrated development environment. The results show that the combination of experts significantly improves the effectiveness of feature location as compared to each of the experts used independently


title: Subcision-suction method: a new successful combination therapy in treatment of atrophic acne scars and other depressed scars

abstract: BACKGROUND ::: Among therapeutic modalities of acne scars, subcision is a simple, safe procedure with a different and basic mechanism for correcting atrophic and depressed scars. Subcision releases scar surfaces from underlying attachments and induces connective tissue formation beneath the scar directly, without injury to the skin surface. Therefore, subcision is a valuable method, but due to high recurrence rate, its efficacy is mild to moderate. ::: ::: ::: OBJECTIVES ::: To increase the efficacy of subcision, a new complementary treatment of repeated suction sessions was added at the recurrence period of subcised scars. ::: ::: ::: METHODS ::: In this before and after trail, 58 patients with mild to severe acne scars of various types (rolling, superficial and deep boxcar, pitted), chicken pox, traumatic and surgical depressed scars were treated by superficial dermal undermining, with mainly 23-guage needles. The protocol for suctioning was: start of suction on third day after subcision for flat and depressing subcised scars and its continuation at least every other day for 2 weeks. ::: ::: ::: RESULTS ::: Forty-six patients followed the protocol completely, had 60-90% improvement in depth and size of scars (significant improvement) with mean: 71.73%. 28.2% of them had '80% improvement or more' (excellent improvement). Twelve patients started suction late and/or had long interval suction-sessions, had 30-60% improvement (moderate improvement) with mean: 43.75%. ::: ::: ::: CONCLUSION ::: Frequent suctioning at the recurrence period of subcision increases subcision efficacy remarkably and causes significant and persistent improvement in short time, without considerable complication, in depressed scars of the face. Therefore, subcision-suction method is introduced as a new effective treatment.


title: Murine CD4 T cells selected in a highly disparate xenogeneic porcine thymus graft do not show rapid decay in the absence of selecting MHC in the periphery.

abstract: CD4 repopulation can be achieved in T cell-depleted, thymectomized mice grafted with xenogeneic porcine thymus tissue. These CD4 T cells are specifically tolerant of the xenogeneic porcine thymus donor and the recipient, but are positively selected only by porcine MHC. Recent studies suggest that optimal peripheral survival of naive CD4 T cells requires the presence of the same class II MHC in the periphery as that of the thymus in which they were selected. These observations would suggest that T cells selected on porcine thymic MHC would die rapidly in the periphery, where porcine MHC is absent. Persistent CD4 reconstitution achieved in mice grafted with fetal porcine thymus might be due to increased thymic output to compensate for rapid death of T cells in the periphery. Comparison of CD4 T cell decay after removal of porcine or murine thymic grafts ruled out this possibility. No measurable role for peripheral murine class II MHC in maintaining the naive CD4 pool originating in thymic grafts was demonstrable. However, mouse class II MHC supported the conversion to, survival, and/or proliferation of memory-type CD4 cells selected in fetal porcine thymus. Thus, the same MHC as that mediating positive selection in the thymus is not critical for maintenance of the memory CD4 cell pool in the periphery. Our results support the interpretation that xenogeneic thymic transplantation is a feasible strategy to reconstitute CD4 T cells and render recipients tolerant of a xenogeneic donor.