title: A Review of Automatic Drum Transcription

abstract: In Western popular music, drums and percussion are an important means to emphasize and shape the rhythm, often defining the musical style. If computers were able to analyze the drum part in recorded music, it would enable a variety of rhythm-related music processing tasks. Especially the detection and classification of drum sound events by computational methods is considered to be an important and challenging research problem in the broader field of music information retrieval. Over the last two decades, several authors have attempted to tackle this problem under the umbrella term automatic drum transcription (ADT). This paper presents a comprehensive review of ADT research, including a thorough discussion of the task-specific challenges, categorization of existing techniques, and evaluation of several state-of-the-art systems. To provide more insights on the practice of ADT systems, we focus on two families of ADT techniques, namely methods based on non-negative matrix factorization and recurrent neural networks. We explain the methods’ technical details and drum-specific variations and evaluate these approaches on publicly available data sets with a consistent experimental setup. Finally, the open issues and underexplored areas in ADT research are identified and discussed, providing future directions in this field.


title: Anxiety in children and adolescents with autism spectrum disorders

abstract: article i nfo Anxiety and poor stress management are common concerns in clinical samples of children with autism spectrum disorders (ASD). Anxiety may worsen during adolescence, as young people face an increasingly complex social milieu and often become more aware of their differences and interpersonal difficulties. This review summarizes the state of research on the prevalence, phenomenology, and treatment of anxiety in youth with autism and related conditions such as Asperger's Disorder. Using search words autism, asperger (s) ,o rpervasive developmental disorder and anxiety or anxious to find reports published between 1990 and 2008, this review identified 40 papers. The results of the review suggest that anxiety, whether measured categorically or dimensionally, is indeed common in children and adolescents with autism spectrum disorders and may be a source of additional morbidity. The assessment of anxiety disorders in ASD should be conducted using multiple informants and modalities, as children with ASD often do not display age-typical symptoms of anxiety. To date, relatively few controlled intervention studies using well-characterized samples have been conducted despite preliminary evidence for efficacy of select pharmacological and psychosocial approaches. Recommendations for future applied research are presented and clinical implications are explored.


title: A Neural Network Approach for Mixing Language Models

abstract: The performance of Neural Network (NN)-based language models is steadily improving due to the emergence of new architectures, which are able to learn different natural language characteristics. This paper presents a novel framework, which shows that a significant improvement can be achieved by combining different existing heterogeneous models in a single architecture. This is done through 1) a feature layer, which separately learns different NN-based models and 2) a mixture layer, which merges the resulting model features. In doing so, this architecture benefits from the learning capabilities of each model with no noticeable increase in the number of model parameters or the training time. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art feedforward as well as recurrent neural network architectures.


title: Development of Low Profile Cavity Backed Crossed Slot Antennas for Planar Integration

abstract: Single fed low profile cavity backed crossed slot antennas for dual frequency dual linear polarization and circular polarization applications are first presented in this paper. By employing the substrate integrated waveguide (SIW) technique in the antenna designs, the low profile backed cavity structure can be realized by using only a single layer of low cost printed circuit board (PCB) substrate. A single grounded coplanar waveguide (GCPW) is employed as the feeding element to excite the TE 120 and TE 210 modes in the SIW cavity. A crossed slot structure is used as the radiating element in order to radiate the desired dual linearly or circularly polarized wave. From the measurement results, it is seen that these novel antennas retain the advantages of conventional metallic cavity backed antennas, including high gain, high front-to-back ratio (FTBR), and low cross polarization level (CPL). Furthermore, the proposed antennas also possess the advantages of low profile, light weight, low fabrication cost, and easy integration with planar circuits.


title: Microwave and millimeter wave power amplifiers: Technology, applications, benchmarks, future trends

abstract: Solid State Transistor Device Technology is ubiquitous in communications, radar, electronic warfare, and instrumentation applications. This presentation will cover Si LDMOS, PHEMT, InP HEMT/MHEMT and GaN HEMT. Content includes principles of operation, structures, characteristics, classes of operation, and device state of the art benchmarks. The art of power amplifier design is approached from a historical perspective. Power amplifiers utilizing these device technologies covering UHF through sub-millimeter wave are described including amplifier state of the art benchmarks. Future trends are highlighted and summarized.


title: Using learning analytics to evaluate a video-based lecture series.

abstract: BACKGROUND ::: The video-based lecture (VBL), an important component of the flipped classroom (FC) and massive open online course (MOOC) approaches to medical education, has primarily been evaluated through direct learner feedback. Evaluation may be enhanced through learner analytics (LA) - analysis of quantitative audience usage data generated by video-sharing platforms. ::: ::: ::: METHODS AND RESULTS ::: We applied LA to an experimental series of ten VBLs on electroencephalography (EEG) interpretation, uploaded to YouTube in the model of a publicly accessible MOOC. Trends in view count; total percentage of video viewed and audience retention (AR) (percentage of viewers watching at a time point compared to the initial total) were examined. The pattern of average AR decline was characterized using regression analysis, revealing a uniform linear decline in viewership for each video, with no evidence of an optimal VBL length. Segments with transient increases in AR corresponded to those focused on core concepts, indicative of content requiring more detailed evaluation. We propose a model for applying LA at four levels: global, series, video, and feedback. ::: ::: ::: DISCUSSION AND CONCLUSIONS ::: LA may be a useful tool in evaluating a VBL series. Our proposed model combines analytics data and learner self-report for comprehensive evaluation.


title: An empirical comparison of model-based and capture and replay approaches for performance testing

abstract: A variety of testing tools has been developed to support and automate the software testing activity. Some of them may use different techniques such as Model-based Testing (MBT) or Capture and Replay (CR). Model-based Testing is a technique for automatic generation of testing artifacts based on software models. One of the main benefits of using MBT is related to the easiness of maintaining models over code; hence, it is likely that using models as a source for automatic generation of scripts requires less effort and reduces the number of faults. Otherwise, CR-based tools basically record the user interaction with the System Under Test (SUT) and then playback the recorded test. This paper presents our effort on setting up and running an experimental study performed in order to evaluate the effort to use MBT and CR-based tools to generate performance scripts. Thus, we apply an MBT and a CR approaches for the purpose of evaluation with respect to the effort to generate scripts and scenarios from the perspective of the performance testers and the performance test engineers in the context of undergraduates, M.Sc. and Ph.D. students, performance testers and performance test engineers for the generation of performance test scripts and scenarios. Our results indicate that, for simple testing tasks, the effort of using a CR-based tool was lower than using an MBT tool, but as the complexity or size of the activities of the testing tasks increases, the advantage of using MBT increased significantly.


title: The length of the male urethra

abstract: PURPOSE ::: Catheter-based medical devices are an important component of the urologic armamentarium. To our knowledge, there is no population-based data regarding normal male urethral length. We evaluated the length of the urethra in men with normal genitourinary anatomy undergoing either Foley catheter removal or standard cystoscopy. ::: ::: ::: MATERIALS AND METHODS ::: Male urethral length was obtained in 109 men. After study permission was obtained, the subject's penis was placed on a gentle stretch and the catheter was marked at the tip of the penis. The catheter was then removed and the distance from the mark to the beginning of the re-inflated balloon was measured. Alternatively, urethral length was measured at the time of cystoscopy, on removal of the cystoscope. Data on age, weight, and height was obtained in patients when possible. ::: ::: ::: RESULTS ::: The mean urethral length was 22.3 cm with a standard deviation of 2.4 cm. Urethral length varied between 15 cm and 29 cm. No statistically significant correlation was found between urethral length and height, weight, body mass index (BMI), or age. ::: ::: ::: CONCLUSIONS ::: Literature documenting the length of the normal male adult urethra is scarce. Our data adds to basic anatomic information of the male urethra and may be used to optimize genitourinary device design.


title: Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

abstract: Learning policies on data synthesized by models can in principle quench the thirst of reinforcement learning algorithms for large amounts of real experience, which is often costly to acquire. However, simulating plausible experience de novo is a hard problem for many complex environments, often resulting in biases for model-based policy evaluation and search. Instead of de novo synthesis of data, here we assume logged, real experience and model alternative outcomes of this experience under counterfactual actions, actions that were not actually taken. Based on this, we propose the Counterfactually-Guided Policy Search (CF-GPS) algorithm for learning policies in POMDPs from off-policy experience. It leverages structural causal models for counterfactual evaluation of arbitrary policies on individual off-policy episodes. CF-GPS can improve on vanilla model-based RL algorithms by making use of available logged data to de-bias model predictions. In contrast to off-policy algorithms based on Importance Sampling which re-weight data, CF-GPS leverages a model to explicitly consider alternative outcomes, allowing the algorithm to make better use of experience data. We find empirically that these advantages translate into improved policy evaluation and search results on a non-trivial grid-world task. Finally, we show that CF-GPS generalizes the previously proposed Guided Policy Search and that reparameterization-based algorithms such Stochastic Value Gradient can be interpreted as counterfactual methods.


title: High efficiency transformerless MOSFET inverter for grid-tied photovoltaic system

abstract: This paper proposes a new high efficiency single phase transformerless grid-tied photovoltaic (PV) inverter by using super-junction MOSFETs as main power switches. No reverse recovery issues are required for the main power switches and the blocking voltages across the switches are half of the DC input voltage in the proposed topology. Therefore, the super-junction MOSFETs have been used to improve the efficiency. Two additional switches with the conventional full H-bridge topology, make sure the disconnection of PV module from the grid at the freewheeling mode. As a result, the high frequency common mode (CM) voltage which leads leakage current is minimized. PWM dead time is not necessary for the proposed topology which reduces the distortion of the AC output current. The efficiency at light load is increased by using MOSFET as main power switches which increases the European Union (EU) efficiency of the proposed topology. The proposed inverter can also operate with high frequency by retaining high efficiency which enables reduced cooling system. The total semiconductor device losses for the proposed topology and several existing topologies are calculated and compared. Finally, the proposed new topology is simulated by MATLAB/Simulink software to validate the accuracy of the theoretical explanation. It is being manufactured to verify the experimental results.


title: A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches

abstract: This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on fault diagnosis techniques and their applications. Comments on the advantages and constraints of various diagnosis techniques, including model-based, signal-based, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of fault diagnosis is presented.


title: Shape and motion from image streams under orthography: a factorization method

abstract: Inferring scene geometry and camera motion from a stream of images is possible in principle, but is an ill-conditioned problem when the objects are distant with respect to their size. We have developed a factorization method that can overcome this difficulty by recovering shape and motion under orthography without computing depth as an intermediate step. An image stream can be represented by the 2FxP measurement matrix of the image coordinates of P points tracked through F frames. We show that under orthographic projection this matrix is of rank 3. Based on this observation, the factorization method uses the singular-value decomposition technique to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively. Two of the three translation components are computed in a preprocessing stage. The method can also handle and obtain a full solution from a partially filled-in measurement matrix that may result from occlusions or tracking failures. The method gives accurate results, and does not introduce smoothing in either shape or motion. We demonstrate this with a series of experiments on laboratory and outdoor image streams, with and without occlusions.


title: Seamless surface mappings

abstract: We introduce a method for computing seamless bijective mappings between two surface-meshes that interpolates a given set of correspondences. A common approach for computing a map between surfaces is to cut the surfaces to disks, flatten them to the plane, and extract the mapping from the flattenings by composing one flattening with the inverse of the other. So far, a significant drawback in this class of techniques is that the choice of cuts introduces a bias in the computation of the map that often causes visible artifacts and wrong correspondences. In this paper we develop a surface mapping technique that is indifferent to the particular cut choice. This is achieved by a novel type of surface flattenings that encodes this cut-invariance, and when optimized with a suitable energy functional results in a seamless surface-to-surface map. We show the algorithm enables producing high-quality seamless bijective maps for pairs of surfaces with a wide range of shape variability and from a small number of prescribed correspondences. We also used this framework to produce three-way, consistent and seamless mappings for triplets of surfaces.


title: Overview of emerging nonvolatile memory technologies

abstract: Nonvolatile memory technologies in Si-based electronics date back to the 1990s. Ferroelectric field-effect transistor (FeFET) was one of the most promising devices replacing the conventional Flash memory facing physical scaling limitations at those times. A variant of charge storage memory referred to as Flash memory is widely used in consumer electronic products such as cell phones and music players while NAND Flash-based solid-state disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. The integration limit of Flash memories is approaching, and many new types of memory to replace conventional Flash memories have been proposed. Emerging memory technologies promise new memories to store more data at less cost than the expensive-to-build silicon chips used by popular consumer gadgets including digital cameras, cell phones and portable music players. They are being investigated and lead to the future as potential alternatives to existing memories in future computing systems. Emerging nonvolatile memory technologies such as magnetic random-access memory (MRAM), spin-transfer torque random-access memory (STT-RAM), ferroelectric random-access memory (FeRAM), phase-change memory (PCM), and resistive random-access memory (RRAM) combine the speed of static random-access memory (SRAM), the density of dynamic random-access memory (DRAM), and the nonvolatility of Flash memory and so become very attractive as another possibility for future memory hierarchies. Many other new classes of emerging memory technologies such as transparent and plastic, three-dimensional (3-D), and quantum dot memory technologies have also gained tremendous popularity in recent years. Subsequently, not an exaggeration to say that computer memory could soon earn the ultimate commercial validation for commercial scale-up and production the cheap plastic knockoff. Therefore, this review is devoted to the rapidly developing new class of memory technologies and scaling of scientific procedures based on an investigation of recent progress in advanced Flash memory devices.


title: Histological investigation of the reproductive cycles of the limpets Patella vulgata and Patella ulyssiponensis

abstract: This study devised a staging system for, and monitored, the gonad development of the limpet species Patella vulgata and Patella ulyssiponensis on the South West coast of Ireland using histological techniques. Maturation began in the males of both species in January and in the females it began in March. There was no statistical difference in gonad development between sexes and between species. Spawning in the male P. vulgata occurred from September to December 2003 and in September and October 2004. In female P. vulgata spawning occurred from October to December 2003, no spawning of females was observed in 2004. In male P. ulyssiponensis spawning occurred in November and December 2003 and from September 2004 to December 2004. Spawning was observed from November 2003 to January 2004 and in September 2004 in female P. ulyssiponensis. Sex ratios also varied between the species and between months sampled. Nevertheless more males were observed in both species.


title: Boosting the Actor with Dual Critic

abstract: This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic. Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for learning the critic that is directly related to the objective function of the actor. We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm. We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.


title: Low light image enhancement based on two-step noise suppression

abstract: In low light condition, the signal-to-noise ratio (SNR) is low and thus the captured images are seriously degraded by noise. Since low light images contain much noise in flat and dark regions, contrast enhancement without considering noise characteristics causes serious noise amplification. In this paper, we propose low light image enhancement based on two-step noise suppression. First, we perform noise aware contrast enhancement using noise level function (NLF). NLF is used to get a noise aware histogram which prevents noise amplification, and we use the noise aware histogram in contrast enhancement. However, the increase of intensity by contrast enhancement reduces the visibility threshold, which makes noise visible by human eyes. Second, we utilize a just noticeable difference (JND) model from luminance adaptation to suppress noise based on human visual perception. Experimental results show that the proposed method successfully enhances contrast in low light images while minimizing noise amplification.


title: Neurobiology of intelligence: science and ethics

abstract: Human mental abilities, such as intelligence, are complex and profoundly important, both in a practical sense and for what they imply about the human condition. Understanding these abilities in mechanistic terms has the potential to facilitate their enhancement. There is strong evidence that the lateral prefrontal cortex, and possibly other areas, support intelligent behaviour. Variations in intelligence and brain structure are heritable, but are also influenced by factors such as education, family environment and environmental hazards. Cognitive, psychometric, genetic and neuroimaging studies are converging, and the emergence of mechanistic models of intelligence is inevitable. These exciting scientific advances encourage renewed responsiveness to the social and ethical implications of conducting such research.


title: FDR3 — A Modern Refinement Checker for CSP

abstract: FDR3 is a complete rewrite of the CSP refinement checker FDR2, incorporating a significant number of enhancements. In this paper we describe the operation of FDR3 at a high level and then give a detailed description of several of its more important innovations. This includes the new multi-core refinement-checking algorithm that is able to achieve a near linear speed up as the number of cores increase. Further, we describe the new algorithm that FDR3 uses to construct its internal representation of CSP processes—this algorithm is more efficient than FDR2’s, and is able to compile a large class of CSP processes to more efficient internal representations. We also present experimental results that compare FDR3 to related tools, which show it is unique (as far as we know) in being able to scale beyond the bounds of main memory.


title: Analysis of Federated Business Models: An Application to the Business Model Canvas, ArchiMate, and e3value

abstract: Enterprise models contribute to the understanding, communication and analysis of how business processes are performed, the goals they achieve, the information they use, as well as the applications that support the business, and the underlying technological infrastructure. The analysis of these different domains requires combining different enterprise models, often described with different domain specific modelling languages. This paper explores the application of computational semantic techniques to specify, integrate and analyse multiple enterprise models. The models are individually specified and federated using ontological schemas, and analysed using computational inference and graph analysis. The paper describes (1) how to specify enterprise models as ontological schemas, (2) how to integrate the ontological schemas using transformation maps, and (3) how to analyse the integrated models. This solution is demonstrated through the specification, integration and analysis of a business model landscape comprising three enterprise modelling languages: the Business Model Canvas, e3value, and Archi Mate.


title: Traffic sign detection and classification using colour feature and neural network

abstract: Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour based segmentation are applied to find out if a traffic sign is present. If present, the sign will be highlighted, normalized in size and then classified. Neural network is used for classification purposes. For evaluation purpose, four type traffic signs such as Stop Sign, No Entry Sign, Give Way Sign, and Speed Limit Sign are used. Altogether 300 sets images, 75 sets for each type are used for training purposes. 200 images are used testing. The experimental results show the detection rate is above 90% and the accuracy of recognition is more than 88%.


title: Packing Unequal Circles into a Square Container by Partitioning Narrow Action Spaces and Circle Items

abstract: We address the NP-hard problem of finding a non-overlapping dense packing pattern for n Unequal Circle items in a two-dimensional Square Container (PUC-SC) such that the size of the container is minimized. Based on our previous work on an Action Space based Global Optimization (ASGO) that approximates each circle item as a square item to efficiently find the large unoccupied spaces, we propose an optimization algorithm based on the Partitioned Action Space and Partitioned Circle Items (PAS-PCI). The PAS is to partition the narrow action space on the long side to find two equal action spaces to fully utilize the unoccupied spaces. The PCI is to partition the circle items into four groups based on size for the basin hopping strategy. Experiments on two sets of benchmark instances show the effectiveness of the proposed method. In comparison with our previous algorithm ASGO on the 68 tested instances that ASGO published, PAS-PCI not only gains smaller containers in 64 instances and matches the other 4 but also runs faster in most instances. In comparison with the best record of the Packomania website on a total of 98 instances, PAS-PCI finds smaller containers on 82 and matches the other 16. Note that we updated 19 records for (47-48, 51-54, 57, 61-72) that had been kept unchanged since 2013.


VISIBLE: Video-Assisted Keystroke Inference from Tablet Backside Motion


title: A 45–75MHz 197–452µW oscillator with 164.6dB FoM and 2.3psrms period jitter in 65nm CMOS

abstract: This paper presents a novel oscillator architecture that uses a low frequency temperature stable clock generated by a low power RC relaxation oscillator to improve the temperature stability of a low noise ring oscillator. Fabricated in 65nm CMOS process, the prototype oscillator consumes 197 to 452µW across an output frequency range of 45-to-75MHz. At 70MHz, the measured period jitter is 2.3ps rms with phase noise of −104dBc/Hz at 100kHz offset, which translates to an FoM of 164.6dB.


title: A Distributed FMCW Radar System Based on Fiber-Optic Links for Small Drone Detection

abstract: This paper discusses a distributed frequency modulation continuous wave radar system. This K-band radar system has high sensitivity, linearity, and flatness to detect low-radar cross section targets and measure their range and velocity. To reduce the leakage between a transmitter and a receiver, the system uses not RF cables but fiber-optic links that have low distortion characteristics and low propagation loss. The transmitter and the receiver are each mounted on a designed fixture to reduce the ground reflections. In addition, they are located on different platforms to reduce the leakage signal flowing directly from the transmitter to the receiver. Measurements in terms of the range and the velocity of a small drone have been carried out to evaluate the proposed distributed radar system. The results show that we can clearly detect the small drone within a 500 m range, which demonstrates the high sensitivity of the system and high isolation between the transmitter and the receiver.


title: Robust ground plane tracking in cluttered environments from egocentric stereo vision

abstract: Estimating the ground plane is often one of the first steps in geometric reasoning processes as it offers easily accessible context knowledge. Especially unconstrained platforms that capture video from egocentric viewpoints can benefit from such knowledge in various ways. A key requirement here is keeping orientation, which can be greatly achieved by keeping track of the ground. We present an approach to keep track of the ground plane in cluttered inner-urban environments using stereo vision in real-time. We fuse a planar model fit in low-resolution disparity data with the direction of the vertical vanishing point. Our experiments show how this effectively decreases the error of plane attitude estimation compared to classic least-squares fitting and allows to track the plane with camera configurations in which the ground is not visible. We evaluate the approach using ground-truth from an inertial measurement unit and demonstrate long-term stability on a dataset of challenging inner city scenes.


title: Thrombolysis for Acute Ischemic Stroke

abstract: Graeme J. Hankey MD, FRACP, FRCP Section Editor: ::: ::: Recombinant tissue plasminogen activator (rtPA) is licensed for thrombolytic therapy in ischemic stroke if administered within 3 hours of stroke onset in selected patients (to be extended to 4.5 hours in some countries). However, many patients who could benefit from rtPA are not offered it and there is evidence that rtPA could benefit many patients who do not meet current license criteria. Furthermore, the patient characteristics that might assist in stratifying risk and benefit and the latest time at which thrombolysis may be effective remain unknown. ::: ::: The objectives of this study were to assess the safety and efficacy of thrombolytic agents used in acute treatment of ischemic stroke, factors that might influence risk or benefit, and estimate if current data can identify the latest time window for treatment. ::: ::: We used the Cochrane Stroke Group Trials Register, MEDLINE, and EMBASE; contact with trialists, and hand-searching of pertinent journals (all to October 2008). ::: ::: Selection criteria were randomized trials of any thrombolytic agent compared with a control in patients with definite ischemic stroke. ::: ::: Two reviewers applied the inclusion criteria, extracted the data (published and unpublished), and assessed trial quality. We calculated ORs and 95% CIs for all main outcomes using fixed effects methods (additionally by random effects methods where significant between-trial heterogeneity was present), calculated the effect per 1000 patients …


title: Trajectory learning from human demonstrations via manifold mapping

abstract: This work proposes a framework that enables arbitrary robots with unknown kinematics models to imitate human demonstrations to acquire a skill, and reproduce it in real-time. The diversity of robots active in non-laboratory environments is growing constantly, and to this end we present an approach for users to be able to easily teach a skill to a robot with any body configuration. Our proposed method requires a motion trajectory obtained from human demonstrations via a Kinect sensor, which is then projected onto a corresponding human skeleton model. The kinematics mapping between the robot and the human model is learned by employing Local Procrustes Analysis, which enables the transfer of the demonstrated trajectory from the human model to the robot. Finally, the transferred trajectory is modeled using Dynamic Movement Primitives, allowing it to be reproduced in real time. Experiments in simulation on a 4 degree of freedom robot show that our method is able to correctly imitate various skills demonstrated by a human.


An Architectural Trail to Threaded-Code Systems


title: Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries

abstract: Objective A system that translates narrative text in the medical domain into structured representation is in great demand. The system performs three sub-tasks: concept extraction, assertion classification, and relation identification. ::: ::: Design The overall system consists of five steps: (1) pre-processing sentences, (2) marking noun phrases (NPs) and adjective phrases (APs), (3) extracting concepts that use a dosage-unit dictionary to dynamically switch two models based on Conditional Random Fields (CRF), (4) classifying assertions based on voting of five classifiers, and (5) identifying relations using normalized sentences with a set of effective discriminating features. ::: ::: Measurements Macro-averaged and micro-averaged precision, recall and F-measure were used to evaluate results. ::: ::: Results The performance is competitive with the state-of-the-art systems with micro-averaged F-measure of 0.8489 for concept extraction, 0.9392 for assertion classification and 0.7326 for relation identification. ::: ::: Conclusions The system exploits an array of common features and achieves state-of-the-art performance. Prudent feature engineering sets the foundation of our systems. In concept extraction, we demonstrated that switching models, one of which is especially designed for telegraphic sentences, improved extraction of the treatment concept significantly. In assertion classification, a set of features derived from a rule-based classifier were proven to be effective for the classes such as conditional and possible. These classes would suffer from data scarcity in conventional machine-learning methods. In relation identification, we use two-staged architecture, the second of which applies pairwise classifiers to possible candidate classes. This architecture significantly improves performance.


title: Towards the holy grail: Combining system dynamics and discrete-event simulation in healthcare

abstract: The idea of combining discrete-event simulation and system dynamics has been a topic of debate in the operations research community for over a decade. Many authors have considered the potential benefits of such an approach from a methodological or practical standpoint. However, despite numerous examples of models with both discrete and continuous parameters in the computer science and engineering literature, nobody in the OR field has yet succeeded in developing a genuinely hybrid approach which truly integrates the philosophical approach and technical merits of both DES and SD in a single model. In this paper we consider some of the reasons for this and describe two practical healthcare examples of combined DES/SD models, which nevertheless fall short of the "holy grail" which has been so widely discussed in the literature over the past decade.


Design of a Visualization Design Environment ( VDE )


title: Planar microwave lens based beam-forming phased antenna array system using non-coplanar SIW fed bowtie antenna

abstract: A novel beam-forming phased array system using a substrate integrated waveguide (SIW) fed Yagi-Uda array antenna is presented. This phase array antenna employs an integrated waveguide structure lens as a beam forming network (BFN). A prototype phased array system is designed with 7 beam ports, 9 array ports, and 8 dummy ports. A 10 GHz SIW-fed Bow-tie linear array antenna is proposed with a nonplanar structure to scan over (−24°, +24°) with SIW lens.


title: Virtual reality for education?

abstract: It is still unclear what, if any, impact virtual reality will have on public education. The virtual reality gorilla system is being used as a testbed to study if and how virtual reality might be use ful as an aid in educating middle school children, and to investigate the issues that arise when building virtual reality systems for knowledge acquisition and concept formation.


title: Electron microscopy of myelin: Structure preservation by high-pressure freezing

abstract: Electron microscopic visualization of nervous tissue morphology is crucial when aiming to understand the biogenesis and structure of myelin in healthy and pathological conditions. However, accurate interpretation of electron micrographs requires excellent tissue preservation. In this short review we discuss the recent utilization of tissue fixation by high-pressure freezing and freeze-substitution, which now supplements aldehyde fixation in the preparation of samples for electron microscopy of myelin. Cryofixation has proven well suited to yield both, improved contrast and excellent preservation of structural detail of the axon/myelin-unit in healthy and mutant mice and can also be applied to other model organisms, including aquatic species. This article is part of a Special Issue entitled SI: Myelin Evolution.


title: Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising

abstract: Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.


title: Synthesis of aminoalkylpyrazoles and-isoxazoles from cyclic β-(trifluoroacetyl) enamines

abstract: A method for the synthesis of cyclic β-(trifluoroacetyl) enamines was proposed. Reactions of the latter with hydrazine and hydroxylamine gave pyrazoles and isoxazoles, respectively, containing trifluoromethyl and ω-aminoalkyl fragments. Addition of hydrazine (hydroxylamine) to the above amino enones was regiospecific, the regiochemistry of the heterocyclization of trifluoromethyl ketones being different from that for nonfluorinated analogs.


title: A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes

abstract: The question of representation of 3D geometry is of vital importance when it comes to leveraging the recent advances in the field of machine learning for geometry processing tasks. For common unstructured surface meshes state-of-the-art methods rely on patch-based or mapping-based techniques that introduce resampling operations in order to encode neighborhood information in a structured and regular manner. We investigate whether such resampling can be avoided, and propose a simple and direct encoding approach. It does not only increase processing efficiency due to its simplicity - its direct nature also avoids any loss in data fidelity. To evaluate the proposed method, we perform a number of experiments in the challenging domain of intrinsic, non-rigid shape correspondence estimation. In comparisons to current methods we observe that our approach is able to achieve highly competitive results.


title: Towards human-like artificial intelligence using StarCraft 2

abstract: On our path towards artificial general intelligence, video games have become excellent tools for research. Reinforcement learning (RL) algorithms are particularly successful in this domain, with the added benefit of having fairly well established biological foundations. To improve how artificial intelligence research and the cognitive sciences can inform each other, we argue the StarCraft II Learning Environment is an ideal candidate for an environment where humans and artificial agents can be tested on the same tasks. We present an upcoming study using this environment, where the goal is to investigate how RL can be extended to enable abstract human abilities such as moments of insight. We claim this is valuable for advancing our understanding of both artificial and natural intelligence, thereby leading to improved models of player behaviour and for general video game playing.


Synthetic and systems biology for microbial production of commodity chemicals


title: Ambiguity Detection: Towards a Tool Explaining Ambiguity Sources

abstract: [Context and motivation] Natural language is the main representation means of industrial requirements documents, which implies that requirements documents are inherently ambiguous. There exist guidelines for ambiguity detection, such as the Ambiguity Handbook [1]. In order to detect ambiguities according to the existing guidelines, it is necessary to train analysts.


title: LETOR: A benchmark collection for research on learning to rank for information retrieval

abstract: LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several state-of-the-art learning to rank algorithms on LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics.


Audit support systems and decision aids: Current practice and opportunities for future research


title: Cell-wall differentiation during growth of electrically polarised protoplasts of Physcomitrella

abstract: Protoplasts of Physcomitrella patens have been grown in continuous electric field of 50 V cm(-1), resulting in a predictable pattern of filament emergence. The events preceding the visible formation of a polar axis have been examined by electron microscopy. The first sign of polarity is the formation of a thickened inner wall layer over the potential growth site. Elongation of the filament is preceded by the appearance of a layer of heavily stained amorphous material at the external surface of the thickened wall. This material marks the region of initial extension of the filament, but it is not produced once extension has begun, and further growth of the filament results in the retention of the material as an annular ring at its base. The wall of the filament has a complex thickened structure which is a result of the osmotic conditions under which the protoplasts are grown. These results are discussed in terms of the development of the polar axis.


Globally Coherent Text Generation with Neural Checklist Models


title: Generation of Synthetic Electronic Medical Record Text

abstract: Machine learning (ML) and Natural Language Processing (NLP) have achieved remarkable success in many fields and have brought new opportunities and high expectation in the analyses of medical data. The most common type of medical data is the massive free-text electronic medical records (EMR). It is widely regarded that mining such massive data can bring up important information for improving medical practices as well as for possible new discoveries on complex diseases. However, the free EMR texts are lacking consistent standards, rich of private information, and limited in availability. Also, as they are accumulated from everyday practices, it is often hard to have a balanced number of samples for the types of diseases under study. These problems hinder the development of ML and NLP methods for EMR data analysis. To tackle these problems, we developed a model to generate synthetic text of EMRs called Medical Text Generative Adversarial Network or mtGAN. It is based on the GAN framework and is trained by the REINFORCE algorithm. It takes disease features as inputs and generates synthetic texts as EMRs for the corresponding diseases. We evaluate the model from micro-level, macro-level and application-level on a Chinese EMR text dataset. The results show that the method has a good capacity to fit real data and can generate realistic and diverse EMR samples. This provides a novel way to avoid potential leakage of patient privacy while still supply sufficient well-controlled cohort data for developing downstream ML and NLP methods. It can also be used as a data augmentation method to assist studies based on real EMR data.


title: ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

abstract: Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.


title: Decision support system for age-related macular degeneration using discrete wavelet transform

abstract: Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, \(k\)-nearest neighbor (\(k\)-NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.


title: On the Integration of Test Adequacy, Test Case Prioritization, and Statistical Fault Localization

abstract: Testing and debugging account for at least 30% of the project effort. Scientific advancements in individual activities or their integration may bring significant impacts to the practice of software development. Fault localization is the foremost debugging sub-activity. Any effective integration between testing and debugging should address how well testing and fault localization can be worked together productively. How likely does a testing technique provide test suites for effective fault localization? To what extent may such a test suite be prioritized so that the test cases having higher priority can be effectively used in a standalone manner to support fault localization? In this paper, we empirically study these two research questions in the context of test data adequacy, test case prioritization and statistical fault localization. Our preliminary postmortem analysis results on 16 test case prioritization techniques and four statistical fault localizations show that branch-adequate test suites on the Siemens suite are unlikely to support effective fault localization. On the other hand, if such a test suite is effective, around 60% of the test cases can be further prioritized to support effective fault localization, which indicates that the potential savings in terms of effort can be significant.


An Interactive Tool for Learning Linear and Differential Cryptanalysis of SPNs


title: Treating patellar tendinopathy with Fascial Manipulation.

abstract: According to Fascial Manipulation theory, patellar tendon pain is often due to uncoordinated quadriceps contraction caused by anomalous fascial tension in the thigh. Therefore, the focus of treatment is not the patellar tendon itself, but involves localizing the cause of this incoordination, considered to be within the muscular fascia of the thigh region. Eighteen patients suffering from patellar tendon pain were treated with the Fascial Manipulation technique. Pain was assessed (in VAS) before (VAS 67.8/100) and after (VAS 26.5/100) treatment, plus a follow-up evaluation at 1 month (VAS 17.2/100). Results showed a substantial decrease in pain immediately after treatment (p<0.0001) and remained unchanged or improved in the short term. The results show that the patellar tendon may be only the zone of perceived pain and that interesting results can be obtained by treating the muscular fascia of the quadriceps muscle, whose alteration may cause motor incoordination and subsequent pathology.


title: Syntactic and Semantic Modulation of Neural Activity during Auditory Sentence Comprehension

abstract: In previous functional neuroimaging studies, left anterior temporal and temporal-parietal areas responded more strongly to sentences than to randomly ordered lists of words. The smaller response for word lists could be explained by either (1) less activation of syntactic processes due to the absence of syntactic structure in the random word lists or (2) less activation of semantic processes resulting from failure to combine the content words into a global meaning. To test these two explanations, we conducted a functional magnetic resonance imaging study in which word order and combinatorial word meaning were independently manipulated during auditory comprehension. Subjects heard six different stimuli: normal sentences, semantically incongruent sentences in which content words were randomly replaced with other content words, pseudoword sentences, and versions of these three sentence types in which word order was randomized to remove syntactic structure. Effects of syntactic structure (greater activation to sentences than to word lists) were observed in the left anterior superior temporal sulcus and left angular gyrus. Semantic effects (greater activation to semantically congruent stimuli than either incongruent or pseudoword stimuli) were seen in widespread, bilateral temporal lobe areas and the angular gyrus. Of the two regions that responded to syntactic structure, the angular gyrus showed a greater response to semantic structure, suggesting that reduced activation for word lists in this area is related to a disruption in semantic processing. The anterior temporal lobe, on the other hand, was relatively insensitive to manipulations of semantic structure, suggesting that syntactic information plays a greater role in driving activation in this area.


title: Secure Biometric-Based Authentication Scheme Using Chebyshev Chaotic Map for Multi-Server Environment

abstract: Multi-server environment is the most common scenario for a large number of enterprise class applications. In this environment, user registration at each server is not recommended. Using multi-server authentication architecture, user can manage authentication to various servers using single identity and password. We introduce a new authentication scheme for multi-server environments using Chebyshev chaotic map. In our scheme, we use the Chebyshev chaotic map and biometric verification along with password verification for authorization and access to various application servers. The proposed scheme is light-weight compared to other related schemes. We only use the Chebyshev chaotic map, cryptographic hash function and symmetric key encryption-decryption in the proposed scheme. Our scheme provides strong authentication, and also supports biometrics & password change phase by a legitimate user at any time locally, and dynamic server addition phase. We perform the formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to show that the presented scheme is secure. In addition, we use the formal security analysis using the Burrows-Abadi-Needham (BAN) logic along with random oracle models and prove that our scheme is secure against different known attacks. High security and significantly low computation and communication costs make our scheme is very suitable for multi-server environments as compared to other existing related schemes.


title: The Gremlin Graph Traversal Machine and Language

abstract: Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph, a traversal, and a set of traversers. The traversers move about the graph according to the instructions specified in the traversal, where the result of the computation is the ultimate locations of all halted traversers. A Gremlin machine can be executed over any supporting graph computing system such as an OLTP graph database and/or an OLAP graph processor. Gremlin, as a graph traversal language, is a functional language implemented in the user's native programming language and is used to define the traversal of a Gremlin machine. This article provides a mathematical description of Gremlin and details its automaton and functional properties. These properties enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, hybrid depth- and breadth-first evaluation, as well as the existence of a Universal Gremlin Machine and its respective entailments.


title: CREDIT SCORING IN THE ERA OF BIG DATA

abstract: TABLE OF CONTENTS TABLE OF CONTENTS I. INTRODUCTION II. TRADITIONAL CREDIT-ASSESSMENT TOOLS III. ALGORITHMS, MACHINE LEARNING, AND THE ALTERNATIVE CREDIT-SCORING MARKET A. Introduction to basic terminology and concepts B. How traditional credit-modeling tools compare to alternative, "big-data" tools C. Using machine learning to build a big-data credit-scoring model--how it works and potential problems IV. THE INADEQUACIES IN THE EXISTING LEGAL FRAMEWORK FOR CREDIT SCORING A. The Fair Credit Reporting Act (FCRA) B. The Equal Credit Opportunity Act (ECOA) V. THE CHALLENGES OF ALTERNATIVE CREDIT-SCORING AND A LEGISLATIVE FRAMEWORK FOR CHANGE A. Existing transparency rules are inadequate B. The burden of ensuring accuracy should not fall to the consumer C. Better tools are needed to detect and prevent discrimination by proxy D. Credit-assessment tools should not be used to target vulnerable consumers VI. CONCLUSION VII. ANNEXES I. INTRODUCTION One day in late 2008, Atlanta businessman Kevin Johnson returned home from his vacation to find an unpleasant surprise waiting in his mailbox. It was a letter from his credit card company, American Express, informing him that his credit limit had been lowered from $10,800 to a mere $3,800. (1) While Kevin was shocked that American Express would make such a drastic change to his limit, he was even more surprised by the company's reasoning. By any measure, Kevin had been an ideal customer. Kevin, who is black, was running a successful Atlanta public relations firm, was a homeowner, and had always paid his bills on time, rarely carrying a balance on his card. (2) Kevin's father, who had worked in the credit industry, had taught him the importance of responsible spending and, "because of his father's lessons, [Kevin had] scrupulously maintained his credit since college." (3) Yet his stellar track record and efforts to maintain "scrupulous" credit seemed to matter little, if at all, to American Express. The company had deemed him a risk simply because, as the letter put it, "[o]ther customers who ha[d] used their card at establishments where [Kevin] recently shopped have a poor repayment history with American Express." (4) When Kevin sought an explanation, the company was unwilling to share any information on which of businesses--many of them major retailers--contributed to American Express's decision to slash Kevin's limit by more than 65 percent. (5) Kevin Johnson was an early victim of a new form of credit assessment that some experts have labeled "behavioral analysis" or "behavioral scoring," (6) but which might also be described as "creditworthiness by association." Rather than being judged on their individual merits and actions, consumers may find that access to credit depends on a lender's opaque predictions about a consumer's friends, neighbors, and people with similar interests, income levels, and backgrounds. This data-centric approach to credit is reminiscent of the racially discriminatory and now illegal practice of "redlining," by which lenders classified applicants on the basis their zip codes, and not their individual capacities to borrow responsibly. (7) Since 2008, lenders have only intensified their use of big-data profiling techniques. With increased use of smartphones, social media, and electronic means of payment, every consumer leaves behind a digital trail of data that companies--including lenders and credit scorers--are eagerly scooping up and analyzing as a means to better predict consumer behavior. (8) The credit-scoring industry has experienced a recent explosion of start-ups that take an "all data is credit data" approach that combines conventional credit information with thousands of data points mined from consumers' offline and online activities. (9) Many companies also use complex algorithms to detect patterns and signals within a vast sea of information about consumers' daily lives. …


title: Neural correlates of flow using auditory evoked potential suppression

abstract: "Flow" is a hyper-engaged state of consciousness most commonly described in athletics, popularly termed "being in the zone." Quantitative research into flow has been hampered by the disruptive nature of gathering subjective reports. Here we show that a passive probe (suppression of Auditory Evoked Potential in EEG) that allowed our participants to remain engaged in a first-person shooting game while we continually tracked the depth of their immersion corresponded with the participants' subjective experiences, and with their objective performance levels. Comparing this time-varying record of flow against the overall EEG record, we identified neural correlates of flow in the anterior cingulate cortex and the temporal pole. These areas displayed increased beta band activity, mutual connectivity, and feedback connectivity with primary motor cortex. These results corroborate the notion that the flow state is an objective and quantifiable state of consciousness, which we identify and characterize across subjective, behavioral and neural measures.


title: Mean-Variance Loss for Deep Age Estimation from a Face

abstract: Age estimation has wide applications in video surveillance, social networking, and human-computer interaction. Many of the published approaches simply treat age estimation as an exact age regression problem, and thus do not leverage a distribution's robustness in representing labels with ambiguity such as ages. In this paper, we propose a new loss function, called mean-variance loss, for robust age estimation via distribution learning. Specifically, the mean-variance loss consists of a mean loss, which penalizes difference between the mean of the estimated age distribution and the ground-truth age, and a variance loss, which penalizes the variance of the estimated age distribution to ensure a concentrated distribution. The proposed mean-variance loss and softmax loss are jointly embedded into Convolutional Neural Networks (CNNs) for age estimation. Experimental results on the FG-NET, MORPH Album II, CLAP2016, and AADB databases show that the proposed approach outperforms the state-of-the-art age estimation methods by a large margin, and generalizes well to image aesthetics assessment.


title: Internet of things for planting in smart farm hydroponics style

abstract: Recently, the farmers gain more profits by producing the quality product. The effects of the global warming make more difficult planning in uncontrolled environment. On the other hand, the yield does not match customers' needs. For these reasons, planting in a greenhouse is easy to maintain and to control important factors such as light, temperature, and humidity. Using of sensors coming in a greenhouse as Wireless Sensor Networks System are one efficiency of technology used in agricultural development by sending data to the cloud and controlling values such as temperature, light, etc. The results of this study will be useful for the farmer and related organizations applying in the farm.


title: Mobile Phones and Financial Services in Developing Countries: A Review of Concepts, Methods, Issues, Evidence and Future Research Directions

abstract: Abstract Research concerning mobile phones and financial services in developing countries has undergone rapid growth in recent years. This paper seeks to improve understanding of the current state of knowledge by reviewing the content of 43 research articles. A framework is developed that differentiates research activity according to a lifecycle model that incorporates financial needs, design, adoption and impact. The review finds that research to date has resulted in a high level of practitioner involvement, providing valuable links from the mobile phone industry to the research community but, as a consequence, research has become too narrowly defined. Thus, issues of assessing financial need and the measurement of impact have been comparatively neglected, while application design and adoption studies have received greater attention. This paper suggests a future direction for research and practice within the mainstream of micro-financial services and finance for the poor, correcting this imbalance, and c...


title: Prefrontal and medial temporal lobe interactions in long-term memory

abstract: Cognitive neuroscience has made considerable progress in understanding the involvement of the medial temporal and frontal lobes in long-term memory. Whereas the medial temporal lobe has traditionally been associated with the encoding, storage and retrieval of long-term memories, the prefrontal cortex has been linked with cognitive control processes such as selection, engagement, monitoring and inhibition. However, there has been little attempt to understand how these regions might interact during encoding and retrieval, and little consideration of the anatomical connections between them. Recent advances in functional neuroimaging, neurophysiology, crossed-lesion neuropsychology and computational modelling highlight the importance of understanding how the medial temporal and frontal lobes interact to allow successful remembering, and provide an opportunity to explore these interactions.


title: e-Health monitoring applications: What about Data Quality?

abstract: Data quality analysis remains a difficult issue on several domains (e.g. geographic, software, databases, etc.). This is particularly the case on e-Health monitoring applications for chronic patients, where the need of data quality to ensure correct decision making is very important. Patients monitoring refers to a continuous observation of patient’s condition (physiological and physical) traditionally performed by one or several body sensors. In fact, significant actions and decisions are based on data coming from such sensors (e.g. remote diagnosis, consultations, hospitalization…). Providing high data quality helps to guarantee a correct processing and interpretation of information, as well as the appropriate intervention of medical services. In this paper, we explore the principles and issues of data quality in this particular domain providing primary research clues and motivation about this subject. We underline the necessity of the analysis of data quality on e-Health applications, especially concerning remote monitoring and assistance of patients with chronic diseases.


title: A comparison of attitudes toward meat and animals among strict and semi-vegetarians

abstract: Abstract A number of studies have documented a phenomenon whereby individuals self-identify as vegetarians but then simultaneously acknowledge that they eat red meat, chicken, and/or fish. Despite being a consistent and fairly robust effect, there has been little attempt to explain these semi-vegetarians, why they would define themselves in a category whose membership criteria they violate, and ways they might differ from strict vegetarians. The present research highlights possible reasons for the discrepancy and focuses on several dimensions that may demarcate semi-from strict vegetarians: belief in human–animal similarity and liking of and disgust toward meat. Survey results indicated that semi-vegetarians (n = 57) were less likely to dislike meat and to find meat disgusting than were strict vegetarians (n = 157), even accounting for diet motives. There were no differences between the groups in their beliefs about human–animal similarity although semi-vegetarians who consumed a wider range of animal products perceived marginally less human–animal similarity than those who consumed only fish. The results suggest that semi-vegetarians are distinct from strict vegetarians primarily in their evaluation of and disgust toward meat, likely as a cause or consequence of their occasional consumption of animal flesh.


title: Optimizing Learned Bloom Filters by Sandwiching

abstract: We provide a simple method for improving the performance of the recently introduced learned Bloom filters, by showing that they perform better when the learned function is sandwiched between two Bloom filters.


title: Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics

abstract: Timbre spaces have been used in music perception to study the perceptual relationships between instruments based on dissimilarity ratings. However, these spaces do not generalize to novel examples and do not provide an invertible mapping, preventing audio synthesis. In parallel, generative models have aimed to provide methods for synthesizing novel timbres. However, these systems do not provide an understanding of their inner workings and are usually not related to any perceptually relevant information. Here, we show that Variational Auto-Encoders (VAE) can alleviate all of these limitations by constructing generative timbre spaces. To do so, we adapt VAEs to learn an audio latent space, while using perceptual ratings from timbre studies to regularize the organization of this space. The resulting space allows us to analyze novel instruments, while being able to synthesize audio from any point of this space. We introduce a specific regularization allowing to enforce any given similarity distances onto these spaces. We show that the resulting space provide almost similar distance relationships as timbre spaces. We evaluate several spectral transforms and show that the Non-Stationary Gabor Transform (NSGT) provides the highest correlation to timbre spaces and the best quality of synthesis. Furthermore, we show that these spaces can generalize to novel instruments and can generate any path between instruments to understand their timbre relationships. As these spaces are continuous, we study how audio descriptors behave along the latent dimensions. We show that even though descriptors have an overall non-linear topology, they follow a locally smooth evolution. Based on this, we introduce a method for descriptor-based synthesis and show that we can control the descriptors of an instrument while keeping its timbre structure.


title: A Call for the Integration of Trauma-Informed Care Among Intellectual and Developmental Disability Organizations

abstract: Research exploring the occurrence of trauma among adults with intellectual and developmental disabilities (I/DD) has grown over the past decade. Yet there is a dearth of literature investigating the impact of organizational factors on the trauma expe- rience despite this population's need for organizational supports. Trauma-informed care (TIC), a systems-focused model for service delivery, is a fast-developing interest among the broader field of trauma in the general population. It recognizes the prevalence and impact of trauma, and creates a culture of safety, trustworthiness, choice, collaboration, and empowerment. The author synthesized relevant literature from both the intellectual and developmental disabilities areas and integrated this with TIC and trauma literature drawn from the general population. Explored are the implications of organizations for service delivery and the potential assimila- tion of TIC within I/DD organizations. The effectiveness of TIC applications and their potential barriers are discussed and related to the philosophy of quality of life and organizational culture. The author notes that some individuals with I/DD comprise a vulner- able subgroup of the population that in large part relies upon the support of organizational services to foster quality of life. Given the implications of the focus on quality of life, he posits that TIC presents as a viable response for organizations, complimenting and augmenting current efforts.


title: Repellent and Fumigant Actions of the Essential Oils from Elettaria cardamomum (L.) Maton, Salvia officinalis (L.) Linnaeus, and Lippia origanoides (V.) Kunth Against Tribolium castaneum and Ulomoides dermestoides

abstract: AbstractColeoptera poses a threat to quality standards in crop production and storage, where it can cause large production losses as a fundamental organism for the transmission of plant diseases. Several synthetic products have been used for their control, with reported adverse effects on the health of exposed animals and humans. The use of essential oils (EOs) is an excellent alternative for the control of pests in crop production and storage, due to their high rate of safety for people and the environment. In the present study, EOs were obtained from three plant species cultivated in Colombia characterized by GC/MS Elettaria cardamomum, Salvia officinalis and Lippia origanoides (Carvacrol chemotype). These three plant species were evaluated for repellent and fumigant functions against Tribolium castaneum and Ulomoides dermestoides. The main component in E. cardamomum and S. officinalis was 1.8-cineole (23.4 and 30.9 %), whereas in L. origanoides it was carvacrol (32.3 %). EOs depicted concentration-depe...


title: S2E: a platform for in-vivo multi-path analysis of software systems

abstract: This paper presents S2E, a platform for analyzing the properties and behavior of software systems. We demonstrate S2E's use in developing practical tools for comprehensive performance profiling, reverse engineering of proprietary software, and bug finding for both kernel-mode and user-mode binaries. Building these tools on top of S2E took less than 770 LOC and 40 person-hours each. S2E's novelty consists of its ability to scale to large real systems, such as a full Windows stack. S2E is based on two new ideas: selective symbolic execution, a way to automatically minimize the amount of code that has to be executed symbolically given a target analysis, and relaxed execution consistency models, a way to make principled performance/accuracy trade-offs in complex analyses. These techniques give S2E three key abilities: to simultaneously analyze entire families of execution paths, instead of just one execution at a time; to perform the analyses in-vivo within a real software stack--user programs, libraries, kernel, drivers, etc.--instead of using abstract models of these layers; and to operate directly on binaries, thus being able to analyze even proprietary software. Conceptually, S2E is an automated path explorer with modular path analyzers: the explorer drives the target system down all execution paths of interest, while analyzers check properties of each such path (e.g., to look for bugs) or simply collect information (e.g., count page faults). Desired paths can be specified in multiple ways, and S2E users can either combine existing analyzers to build a custom analysis tool, or write new analyzers using the S2E API.


title: HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks

abstract: A recent approach in modeling and analysis of the supply and demand in heterogeneous wireless cellular networks has been the use of two independent Poisson point processes (PPPs) for the locations of base stations (BSs) and user equipments (UEs). This popular approach has two major shortcomings. First, although the PPP model may be a fitting one for the BS locations, it is less adequate for the UE locations mainly due to the fact that the model is not adjustable (tunable) to represent the severity of the heterogeneity (non-uniformity) in the UE locations. Besides, the independence assumption between the two PPPs does not capture the often-observed correlation between the UE and BS locations. ::: This paper presents a novel heterogeneous spatial traffic modeling which allows statistical adjustment. Simple and non-parameterized, yet sufficiently accurate, measures for capturing the traffic characteristics in space are introduced. Only two statistical parameters related to the UE distribution, namely, the coefficient of variation (the normalized second-moment), of an appropriately defined inter-UE distance measure, and correlation coefficient (the normalized cross-moment) between UE and BS locations, are adjusted to control the degree of heterogeneity and the bias towards the BS locations, respectively. This model is used in heterogeneous wireless cellular networks (HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic on the network performance. This network is called HetHetNet since it has two types of heterogeneity: heterogeneity in the infrastructure (supply), and heterogeneity in the spatial traffic distribution (demand).


title: Optimized Design of N-Phase Passive Mixer-First Receivers in Wideband Operation

abstract: Recent developments in CMOS passive mixers have demonstrated a number of new and useful capabilities, including dynamic RF port impedance control and filter up-conversion, while also allowing low noise figure and high out-of-band linearity, all of which track wide-ranging LO frequencies and tunable baseband bandwidth. However, these circuits also bring a unique set of challenges and requirements. Here we extend a previously derived LTI model for such mixers to the general N-phase case, and discuss basic limits in performance specifications including impedance matching, noise figure and linearity. We then extend this analysis to include high-frequency effects, especially as they relate to transistor properties. We show that essentially all of the key specification of such mixers can be described in terms of an impedance ratio, a characteristic cut-off frequency, the number of phases of the mixer and some process-related parameters. Finally, we discuss how these properties relate to power consumption of LO circuitry.


title: Direct Measurements of the Inherent Frequency Stability of Quartz Crystal Resonators

abstract: A technique is presented that allows one to measure directly the fluctuations of the natural resonant frequency of quartz crystal resonators in a passive circuit. This technique greatly aids in modeling the noise in both crystal resonators and crystal controlled oscillator circuits. Definite changes of slope in the spectral density of the frequency fluctuations,as a function of frequency offset from the natural resonant frequen of the crystals indicate that several mezanisms are contributing to the frequency instabilities in crystals. Our measurements also indicate that the electronics in the oscillators seriously degrade the frequency stability for sample times less than 100 S. The effects are especially dramatic for times less than 1 S.


title: Negative-Sequence Current Injection for Fast Islanding Detection of a Distributed Resource Unit

abstract: This paper presents an active islanding detection method for a distributed resource (DR) unit which is coupled to a utility grid through a three-phase voltage-sourced converter (VSC). The method is based on injecting a negative-sequence current through the VSC controller and detecting and quantifying the corresponding negative-sequence voltage at the point of common coupling of the VSC by means of a unified three-phase signal processor (UTSP). UTSP is an enhanced phase-locked loop system which provides high degree of immunity to noise, and thus enable islanding detection based on injecting a small (3%) negative-sequence current. The negative-sequence current is injected by a negative-sequence controller which is adopted as the complementary of the conventional VSC current controller. Based on simulation studies in the PSCAD/EMTDC environment, performance of the islanding detection method under UL1741 anti-islanding test is evaluated, and its sensitivity to noise, grid short-circuit ratio, grid voltage imbalance, and deviations in the UL1741 test parameters are presented. The studies show that based on negative-sequence current injection of about 2% to 3%, islanding can be detected within 60 ms even for the worst case scenario.


title: Assessing the complexity of software architecture

abstract: A central activity of software architecture design is decomposing the system into subsystems (i.e. components) that work together to satisfy the required functionality. The purpose of this activity is to reduce problem complexity into smaller manageable parts. Complexity can never be totally eliminated; however the designer/architect can reduce it.The decomposition process is an art form; the architect must decide whether to assign a specific functionality to a given component or to defer some or all of the functionality to other components, with a goal of minimizing complexity. Deferring work decreases the responsibilities of a component (intracomponent complexity) but also may increase the dependencies between components (inter-component complexity).In this paper, our goal is to formulate an approach that identifies and measures those complexity factors that reflect interand intra-complexity for the purpose of introducing a new metric for assessing the overall complexity of software architecture. To accomplish this, we have chosen to use Full Function Points (FFP) methodology, which is the latest form of Functional Size Measure (FSM), as a building block for measuring complexity.However, since FFP was designed to measure the size of architecture; it fails to address some important issues with regard to complexity. Therefore, we identify those areas of weakness for FFP and exploit them to measure overall system complexity. The main feature of the approach is the integration of Full Function Points measure with a specification of the architecture to evaluate its overall complexity.


title: Adapting a Rapidly-Exploring Random Tree for Automated Planning

abstract: Rapidly-exploring random trees (RRTs) are data structures and search algorithms designed to be used in continuous path planning problems. They are one of the most successful state-of-the-art techniques as they offer a great degree of flexibility and reliability. However, their use in other search domains has not been thoroughly analyzed. In this work we propose the use of RRTs as a search algorithm for automated planning. We analyze the advantages that this approach has over previously used search algorithms and the challenges of adapting RRTs for implicit and discrete search spaces.


title: The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

abstract: While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on the ImageNet classification task has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called"perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new Full Reference Image Quality Assessment (FR-IQA) dataset of perceptual human judgments, orders of magnitude larger than previous datasets. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by huge margins. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.


title: Location-aware access to hospital information and services

abstract: Hospital workers are highly mobile; they are constantly changing location to perform their daily work, which includes visiting patients, locating resources, such as medical records, or consulting with other specialists. The information required by these specialists is highly dependent on their location. Access to a patient's laboratory results might be more relevant when the physician is near the patient's bed and not elsewhere. We describe a location-aware medical information system that was developed to provide access to resources such as patient's records or the location of a medical specialist, based on the user's location. The system is based on a handheld computer which includes a trained backpropagation neural-network used to estimate the user's location and a client to access information from the hospital information system that is relevant to the user's current location.


title: Multi-Domain Collaborative Filtering

abstract: Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.


title: A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned

abstract: We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation study's results, discuss the current state-of-the-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.


title: Pedestrian Protection Systems: Issues, Survey, and Challenges

abstract: This paper describes the recent research on the enhancement of pedestrian safety to help develop a better understanding of the nature, issues, approaches, and challenges surrounding the problem. It presents a comprehensive review of research efforts underway dealing with pedestrian safety and collision avoidance. The importance of pedestrian protection is emphasized in a global context, discussing the research programs and efforts in various countries. Pedestrian safety measures, including infrastructure enhancements and passive safety features in vehicles, are described, followed by a systematic description of active safety systems based on pedestrian detection using sensors in vehicle and infrastructure. The pedestrian detection approaches are classified according to various criteria such as the type and configuration of sensors, as well as the video cues and classifiers used in detection algorithms. It is noted that collision avoidance not only requires detection of pedestrians but also requires collision prediction using pedestrian dynamics and behavior analysis. Hence, this paper includes research dealing with probabilistic modeling of pedestrian behavior for predicting collisions between pedestrians and vehicles.


title: Human pigmentation variation: Evolution, genetic basis, and implications for public health

abstract: Pigmentation, which is primarily determined by the amount, the type, and the distribution of melanin, shows a remarkable diversity in human populations, and in this sense, it is an atypical trait. Numerous genetic studies have indicated that the average proportion of genetic variation due to differences among major continental groups is just 10-15% of the total genetic variation. In contrast, skin pigmentation shows large differences among continental populations. The reasons for this discrepancy can be traced back primarily to the strong influence of natural selection, which has shaped the distribution of pigmentation according to a latitudinal gradient. Research during the last 5 years has substantially increased our understanding of the genes involved in normal pigmentation variation in human populations. At least six genes have been identified using genotype/phenotype association studies and/or direct functional assays, and there is evidence indicating that several additional genes may be playing a role in skin, hair, and iris pigmentation. The information that is emerging from recent studies points to a complex picture where positive selection has been acting at different genomic locations, and for some genes only in certain population groups. There are several reasons why elucidating the genetics and evolutionary history of pigmentation is important. 1) Pigmentation is a trait that should be used as an example of how misleading simplistic interpretations of human variation can be. It is erroneous to extrapolate the patterns of variation observed in superficial traits such as pigmentation to the rest of the genome. It is similarly misleading to suggest, based on the "average" genomic picture, that variation among human populations is irrelevant. The study of the genes underlying human pigmentation diversity brings to the forefront the mosaic nature of human genetic variation: our genome is composed of a myriad of segments with different patterns of variation and evolutionary histories. 2) Pigmentation can be very useful to understand the genetic architecture of complex traits. The pigmentation of unexposed areas of the skin (constitutive pigmentation) is relatively unaffected by environmental influences during an individual's lifetime when compared with other complex traits such as diabetes or blood pressure, and this provides a unique opportunity to study gene-gene interactions without the effect of environmental confounders. 3) Pigmentation is of relevance from a public health perspective, because of its critical role in photoprotection and vitamin D synthesis. Fair-skinned individuals are at higher risk of several types of skin cancer, particularly in regions with high UVR incidence, and dark-skinned individuals living in high latitude regions are at higher risk for diseases caused by deficient or insufficient vitamin D levels.


title: Auditory perception of 3D size: Experiments with synthetic resonators

abstract: For representing complex data through auditory display, the ecological approach to sound perception, if combined with the most advanced techniques for physical modeling, seems to form a good strategy to translate information into sound by composition of auditory scenes and to create an object-based auditory information space.We examine the auditory perception of one feature of simulated 3D resonators: the size. In prior publications, we assessed the ability of humans to distinguish the shapes of (spherical and cubic) resonators, and we investigated pitch perception and its relationship to the volume of the enclosures. In this paper, we focus on the latter topic, introducing our most recent experiments and results. We validate previous results in a more controlled environment and, in particular, we investigate whether the specific procedure used in the auditory display may affect the users' performance.The results of the experiment show that even the perception of a basic object property such as size can be affected by the listening procedure and can be decoupled from object shape only in controlled conditions.


title: Do Convolutional Neural Networks Learn Class Hierarchy?

abstract: Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.


title: Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks

abstract: We provide several new depth-based separation results for feed-forward neural networks, proving that various types of simple and natural functions can be better approximated using deeper networks than shallower ones, even if the shallower networks are much larger. This includes indicators of balls and ellipses; non-linear functions which are radial with respect to the L1 norm; and smooth non-linear functions. We also show that these gaps can be observed experimentally: Increasing the depth indeed allows better learning than increasing width, when training neural networks to learn an indicator of a unit ball.


title: Rigid-Motion Scattering for Texture Classification

abstract: A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network. Convolutions are calculated on the rigid-motion group, with wavelets defined on the translation and rotation variables. It preserves joint rotation and translation information, while providing global invariants at any desired scale. Texture classification is studied, through the characterization of stationary processes from a single realization. State-of-the-art results are obtained on multiple texture data bases, with important rotation and scaling variabilities.


title: Generative Adversarial Network in Medical Imaging: A Review

abstract: Abstract Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.


title: Zebrafish: a preclinical model for drug screening.

abstract: The zebrafish embryo has become an important vertebrate model for assessing drug effects. It is well suited for studies in genetics, embryology, development, and cell biology. Zebrafish embryos exhibit unique characteristics, including ease of maintenance and drug administration, short reproductive cycle, and transparency that permits visual assessment of developing cells and organs. Because of these advantages, zebrafish bioassays are cheaper and faster than mouse assays, and are suitable for large-scale drug screening. Here we describe the use of zebrafish bioassays for assessing toxicity, angiogenesis, and apoptosis. Using 18 chemicals, we demonstrated that toxic response, teratogenic effects, and LC(50) in zebrafish are comparable to results in mice. The effects of compounds on various organs, including the heart, brain, intestine, pancreas, cartilage, liver, and kidney, were observed in the transparent animals without complicated processing, demonstrating the efficiency of toxicity assays using zebrafish embryos. Using endogenous alkaline phosphatase staining and a whole-animal enzyme assay, we demonstrated that SU5416 and flavopiridol, compounds shown to have antiangiogenic effects in mammals, inhibit blood vessel growth in zebrafish, and this bioassay is suitable for high-throughput screening using a 96-well microplate reader. We also demonstrated that in vivo acridine orange staining can be used to visualize apoptotic events in embryos treated with brefeldin A, neomycin, or caspase inhibitors. After in vivo staining, acridine orange can be extracted and quantitated using a fluorescence microplate reader, providing a screening system for agents that modulate apoptosis.