title: Robust Road Detection and Tracking in Challenging Scenarios Based on Markov Random Fields With Unsupervised Learning

abstract: This paper presents a robust stereo-vision-based drivable road detection and tracking system that was designed to navigate an intelligent vehicle through challenging traffic scenarios and increment road safety in such scenarios with advanced driver-assistance systems (ADAS). This system is based on a formulation of stereo with homography as a maximum a posteriori (MAP) problem in a Markov random held (MRF). Under this formulation, we develop an alternating optimization algorithm that alternates between computing the binary labeling for road/nonroad classification and learning the optimal parameters from the current input stereo pair itself. Furthermore, online extrinsic camera parameter reestimation and automatic MRF parameter tuning are performed to enhance the robustness and accuracy of the proposed system. In the experiments, the system was tested on our experimental intelligent vehicles under various real challenging scenarios. The results have substantiated the effectiveness and the robustness of the proposed system with respect to various challenging road scenarios such as heterogeneous road materials/textures, heavy shadows, changing illumination and weather conditions, and dynamic vehicle movements.


title: X-ray detectors for digital radiography

abstract: Digital radiography offers the potential of improved image quality as well as providing opportunities for advances in medical image management, computer-aided diagnosis and teleradiology. Image quality is intimately linked to the precise and accurate acquisition of information from the x-ray beam transmitted by the patient, i.e. to the performance of the x-ray detector. Detectors for digital radiography must meet the needs of the specific radiological procedure where they will be used. Key parameters are spatial resolution, uniformity of response, contrast sensitivity, dynamic range, acquisition speed and frame rate. The underlying physical considerations defining the performance of x-ray detectors for radiography will be reviewed. Some of the more promising existing and experimental detector technologies which may be suitable for digital radiography will be considered. Devices that can be employed in full-area detectors and also those more appropriate for scanning x-ray systems will be discussed. These include various approaches based on phosphor x-ray converters, where light quanta are produced as an intermediate stage, as well as direct x-ray-to-charge conversion materials such as zinc cadmium telluride, amorphous selenium and crystalline silicon.


title: Cyber-physical-human system for connected car-based e-tourism : Approach and case study scenario

abstract: Cyber-physical-human systems find new applications in various areas of human lives. The paper proposes usage of a system of this class to achieve a synergy between the connected car and e-tourism ideas. The developed approach is presented with more attention paid to the situational awareness and behavioral awareness. The approach is illustrated via a case study aimed to organization of infomobile support of tourists taking a ride in a car with a driver. The connected car technologies are used to integrate car information system with tourist's personal information device to deliver speech, image and video-based tour guiding synchronized with the ride.


title: Experts, amateurs, and real estate: An anchoring-and-adjustment perspective on property pricing decisions

abstract: Abstract The studies described in this paper investigated the use of a decisional heuristic—anchoring-and-adjustment—in an information-rich, real world setting. In order to assess the generalizability of laboratory research on this decision heuristic, students and real estate agents toured and made pricing decisions about real estate properties. It was hypothesized that manipulated listing prices would anchor values assigned to the properties. Results were consistent with the use of an anchoring-and-adjustment value estimation strategy in information-rich, real world settings. Implications for the understanding of judgmental expertise are discussed.


Author ' s personal copy Age differences in perceptions of online community participation among non-users : An extension of the Technology Acceptance Model


title: Dual-Band Filter Design With Flexible Passband Frequency and Bandwidth Selections

abstract: In this paper, improved dual-band filter design is studied. The dual-band resonators are composed of shunt open- and short-circuited stubs. In order to fulfil the requirements of dual-band inverters, a structure of stepped-impedance asymmetric coupled lines is proposed and its equivalent circuit is also derived. The dual-band filter is then designed based on this equivalent circuit. This type of filter can achieve relatively large practical passband center frequency ratios (in theory infinite), and it has more freedom of bandwidth ratio. The circuit size is also reduced. Detailed design procedure is presented and, finally, a filter example is given to validate the theoretical study


title: Bridge A Big Win for AI Planning

abstract: A computer program that uses AI planning techniques is now the world champion computer program in the game of Contract Bridge. As reported in The New York Times and The Washington Post, this program -- a new version of Great Game Products' BRIDGE BARON program -- won the Baron Barclay World Bridge Computer Challenge, an international competition hosted in July 1997 by the American Contract Bridge League. It is well known that the game tree search techniques used in computer programs for games such as Chess and Checkers work differently from how humans think about such games. In contrast, our new version of the BRIDGE BARON emulates the way in which a human might plan declarer play in Bridge by using an adaptation of hierarchical task network planning. This article gives an overview of the planning techniques that we have incorporated into the BRIDGE BARON and discusses what the program's victory signifies for research on AI planning and game playing.


title: Executive Functioning as a Potential Mediator of Age-Related Cognitive Decline in Normal Adults.

abstract: Critical requirements for the hypothesis that executive functioning is a potential mediator of age-related effects on cognitive functioning are that variables assumed to reflect executive functioning represent a distinct construct and that age-related effects on other types of cognitive functioning are reduced when measures of executive functioning are statistically controlled. These issues were investigated in a study involving 261 adults between 18 and 84 years of age. Although age-related effects on various cognitive abilities were substantially reduced after statistical control of the variance in measures hypothesized to represent executive functioning, there was only weak evidence for the existence of distinct constructs corresponding to executive functioning or to aspects of executive control concerned with inhibition, updating, or time sharing. In recent years there has been considerable interest within neuropsychology and in certain areas of cognitive psychology in the concept of executive functioning, broadly defined as control processes responsible for planning, assembling, coordinating, sequencing, and monitoring other cognitive operations. The executive functioning concept is loosely based on an analogy to a business executive who is not necessarily a specialist in any particular domain but instead is responsible for supervising and managing many different domains. Because disruption of central control processes might result in the impairment of relevant behavior even if the component processes are intact, executive functioning clearly has the potential to affect performance in a wide variety of cognitive variables. The concept of executive functioning could be informative about adult age differences in cognitive functioning for at least two reasons. First, executive functioning encompasses concepts such as inhibition, working memory (updating), and attentional capacity (time sharing) that have played a prominent role in theories of cognitive aging. However, past research has largely viewed these concepts as independent of one another and has not examined the possibility that they represent different aspects of a single construct. And second, executive functioning is often associated with the frontal lobes, and in fact, the terms frontal functions and executive functions are frequently used interchangeably in the literature. Executive functioning is therefore relevant to the frontal hypothesis of aging, in which many age-related cognitive deficits are postulated to be associated with a deterioration of the frontal


Testing a Citation and Text-Based Framework for Retrieving Publications for Literature Reviews


title: Development of smart ECG machine using LabVIEW for biomedical engineering students

abstract: Electrocardiography machine is a useful device to determine the rate and regularity of heart beats. A real ECG machines with complex and multiple functions make a biomedical engineering students difficult to discover. This work has been demonstrated to enable students and end-users to learn the ECG basics principle. The ECG with a less complex circuits performing multiple data and will enable the signal processing analysis. LabVIEW is used to validate and compiling data analysis.


title: Vibration analysis of large bulb tubular pump house under pressure pulsations

abstract: Abstract A 3D finite element model of the Huaiyin third pumping station of the Eastern Route of the South-to-North Water Transfer is described in this paper. Two methods were used in the calculation and vibration analysis of the pumping station in both the time domain and the frequency domain. The pressure pulsation field of the whole flow passage was structured on the basis of pressure pulsations recorded at some locations of the physical model test. Dynamic time-history analysis of the pump house under pressure pulsations was carried out. At the same time, according to spectrum characteristics of the pressure pulsations at measuring points and results of free vibration characteristics analysis of the pump house, the spectrum analysis method of random vibration was used to calculate dynamic responses of the pump house. Results from both methods are consistent, which indicates that they are both reasonable. The results can be used for reference in anti-vibration safety evaluation of the Huaiyin third pumping station.


title: Cloud-Native Applications

abstract: The term “cloud-native” refers to a set of technologies and design patterns that have become the standard for building large-scale cloud applications. In this editorial we describe basic properties of successful cloud applications including dynamic scalability, extreme fault tolerance, seamless upgradeability and maintenance and security. To make it possible to build applications that meet these requirements we describe the microservice architecture and serverless computing foundation that are central to cloud-native design.


title: The use of MMR, diversity-based reranking for reordering documents and producing summaries

abstract: This paper presents a method for combining ::: query-relevance with information-novelty in the context ::: of text retrieval and summarization. The Maximal ::: Marginal Relevance (MMR) criterion strives to reduce ::: redundancy while maintaining query relevance in ::: re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results ::: indicate some benefits for MMR diversity ranking ::: in document retrieval and in single document summarization. ::: The latter are borne out by the recent results of the ::: SUMMAC conference in the evaluation of summarization ::: systems. However, the clearest advantage is demonstrated ::: in constructing non-redundant multi-document ::: summaries, where MMR results are clearly superior to ::: non-MMR passage selection.


title: A Folded-Monopole Model for Electrically Small NRI-TL Metamaterial Antennas

abstract: An improved model for analyzing electrically small NRI-TL antennas is proposed, that highlights the methods that enable these antennas to offer a good impedance match and a high radiation efficiency compared to previously reported designs. An even-odd mode analysis reveals that the antenna supports a predominately even-mode current on the vertical vias, allowing the antenna to be modeled using a multiple folded monopole topology, which provides a substantial increase in the radiation resistance of the antenna. This, together with the top-loading effect of the microstrip line on the vias, enables the antenna to be matched to 50 Omega without the use of an external matching network, while maintaining a high radiation efficiency. The validity of the proposed model is confirmed with a fabricated prototype, that consists of four microstrip zero-degree NRI-TL unit cells with dimensions of lambda0/10 times lambda0/10 times lambda/20 over a 0.45lambda0 times 0.45lambda0 ground plane. The antenna's performance is verified by full-wave simulations and experimental data obtained at 3.1 GHz, which yield a vertical linear electric field polarization, a measured -10dB return-loss bandwidth of 53 MHz and a measured efficiency of 70%.


title: An overview of augmented and virtual reality applications in radiotherapy and future developments enabled by modern tablet devices

abstract: Purpose: We review augmented (AR) and virtual reality (VR) applications in radiotherapy as found in the scientific literature and highlight future developments enabled by the use of small mass-produced devices and portability of techniques developed in other fields to radiotherapy. Analysis: The application of AR and VR within radiotherapy is still in its infancy, with the notable exception of training and teaching applications. The relatively high cost of equipment needed to generate a realistic 3D effect seems one factor that has slowed down its use, but also the sheer amount of image data is relatively recent, were radiotherapy professionals are only beginning to explore how to use this to its full potential. This increased availability of 3D data in radiotherapy will drive the application of AR and VR in radiotherapy to efficiently recognise and extract key features in the data to act on in clinical decision making. Conclusion: The development of small mass-produced tablet devices coming on the market will allow the user to interact with computer-generated information more easily, facilitating the application of AR and VR. The increased connectivity enabling virtual presence of remote multidisciplinary team meetings heralds significant changes to how radiotherapy professionals will work, to the benefit of our patients.


title: The Utility and Practice of Electrodiagnostic Testing in the Pediatric Population: An AANEM Consensus Statement

abstract: Nerve conduction studies and needle electromyography, collectively known as electrodiagnostic (EDX) studies, have been available for pediatric patients for decades, but the accessibility of this diagnostic modality and the approach to testing vary significantly depending on the physician and institution. The maturation of molecular diagnostic approaches and other diagnostic technologies such as neuromuscular ultrasound indicate that an analysis of current needs and practices for EDX studies in the pediatric population is warranted. The American Association of Neuromuscular & Electrodiagnostic Medicine (AANEM) convened a consensus panel to perform literature searches, share collective experiences, and develop a consensus statement. The panel found that electrodiagnostic studies continue to have high utility for the diagnosis of numerous childhood neuromuscular disorders, and that standardized approaches along with the use of high-quality reference values are important to maximize the diagnostic yield of these tests in infants, children, and adolescents. This article is protected by copyright. All rights reserved.


title: T-bar reconstruction of constricted ears and a new classification

abstract: For the correction of constricted ears, many techniques are described in the literature, the majority based on Tanzer's classification of 1975. The improvements in ear reconstruction published by Brent, Nagata, Firmin and Park make better outcomes possible. It is therefore that a new classification for constricted ears is proposed, together with an alternative technique for correction of group IIA and IIB deformities, using a T-strut of costal cartilage to reconstruct the underdeveloped or missing superior crus of the antihelix.


title: Text-based CAPTCHA strengths and weaknesses

abstract: We carry out a systematic study of existing visual CAPTCHAs based on distorted characters that are augmented with anti-segmentation techniques. Applying a systematic evaluation methodology to 15 current CAPTCHA schemes from popular web sites, we find that 13 are vulnerable to automated attacks. Based on this evaluation, we identify a series of recommendations for CAPTCHA designers and attackers, and possible future directions for producing more reliable human/computer distinguishers.


title: Event-related synchronization (ERS) in the alpha band--an electrophysiological correlate of cortical idling: a review.

abstract: Abstract EEG desynchronization is a reliable correlate of excited neural structures or activated cortical areas. EEG synchronization within the alpha band may be an electrophysiological correlate of deactivated cortical areas. Such areas are not processing sensory information or motor output and can be considered to be in an idling state. One example of such an idling cortical area is the enhancement of mu rhythms in the primary hand area during visual processing or during foot movement. In both circumstances, the neurons in the hand area are not needed for visual processing or preparation for foot movement. As a result of this, an enhanced hand area mu rhythm can be observed.


title: Feature Learning for Image Classification Via Multiobjective Genetic Programming

abstract: Feature extraction is the first and most critical step in image classification. Most existing image classification methods use hand-crafted features, which are not adaptive for different image domains. In this paper, we develop an evolutionary learning methodology to automatically generate domain-adaptive global feature descriptors for image classification using multiobjective genetic programming (MOGP). In our architecture, a set of primitive 2-D operators are randomly combined to construct feature descriptors through the MOGP evolving and then evaluated by two objective fitness criteria, i.e., the classification error and the tree complexity. After the entire evolution procedure finishes, the best-so-far solution selected by the MOGP is regarded as the (near-)optimal feature descriptor obtained. To evaluate its performance, the proposed approach is systematically tested on the Caltech-101, the MIT urban and nature scene, the CMU PIE, and Jochen Triesch Static Hand Posture II data sets, respectively. Experimental results verify that our method significantly outperforms many state-of-the-art hand-designed features and two feature learning techniques in terms of classification accuracy.


title: Transference effect of vertical and horizontal plyometrics on sprint performance of high-level U-20 soccer players

abstract: AbstractThe aim of this study was to investigate the effects of adding vertical/horizontal plyometrics to the soccer training routine on jumping and sprinting performance in U-20 soccer players. The vertical jumping group (VJG) performed countermovement jumps (CMJ), while the horizontal jumping group (HJG) executed horizontal jumps (HJ). Training interventions comprised 11 sessions, with volume varying between 32 and 60 jumps per session. The analysis of covariance revealed that CMJ height and peak force improved only in the VJG, and that HJ distance and peak force improved in both groups. Velocity in 20 m (VEL 20 m) did not improve in either group; however, velocity in 10 m (VEL 10 m) presented a moderate positive effect size (ES = 0.66) in the HJG, while the ES was large (1.63) for improvement in the 10–20 m acceleration in the VJG, and it was largely negative (−1.09) in the HJG. The transference effect coefficients (calculated by the equation: TEC = result gain (ES) in untrained exercise/result gain (E...


title: Modeling Consumer Purchasing Behavior in Social Shopping Communities with Clickstream Data

abstract: Social shopping communities (SSCs) evolve from a linkage of social networking and online shopping. Apart from direct shopping features in shopbots (e.g., search fields), SSCs additionally offer user-generated social shopping features. These include recommendation lists, ratings, styles (i.e., assortments arranged by users), tags, and user profiles. Purchases can be made by following a link to a participating online shop ("click-out"). SSCs are experiencing high growth rates in consumer popularity (e.g., Polyvore attracts more than 6 million unique visitors per month). Thus, this business model has received considerable venture capital in recent years. By analyzing clickstream data, we investigate which factors, especially social shopping features, are significant for predicting purchasing behavior within SSCs. Our logit model includes about 2.73 million visiting sessions and shows that social shopping features exert a significant impact, both positive and negative. Tags and high ratings have a positive impact on a click-out. In contrast, the more lists and styles used, the less likely the user is to make a click-out. Yet, lists and styles seem to enhance site stickiness and browsing. Moreover, the more direct shopping features that are used, the less likely the user is to conduct a click-out. Increasing transaction costs and information overload could be potential reasons. We also found that community members are more likely to make a click-out than ordinary users. This implies that community members are more profitable.


title: Patterns in food use and compliance with NCI dietary guidelines.

abstract: We developed multidimensional measures of diet by identifying seven gender-specific patterns among 1,475 male and 780 female subjects selected as neighborhood controls in a case-control study of cancer at various sites. The purpose of this study was to examine these patterns for compliance with the National Cancer Institute (NCI) Dietary Guidelines. Diets were scored and each subject assigned tertile ranks for each pattern. Mean values for indicators of compliance were compared across tertiles for each pattern. No one pattern signified compliance with all guidelines. High-fat patterns in both genders and the low-cost pattern in women were the best indicators of poor compliance with NCI guidelines.


title: 3D Printed Reversible Shape Changing Components with Stimuli Responsive Materials

abstract: The creation of reversibly-actuating components that alter their shapes in a controllable manner in response to environmental stimuli is a grand challenge in active materials, structures, and robotics. Here we demonstrate a new reversible shape-changing component design concept enabled by 3D printing two stimuli responsive polymers—shape memory polymers and hydrogels—in prescribed 3D architectures. This approach uses the swelling of a hydrogel as the driving force for the shape change, and the temperature-dependent modulus of a shape memory polymer to regulate the time of such shape change. Controlling the temperature and aqueous environment allows switching between two stable configurations – the structures are relatively stiff and can carry load in each – without any mechanical loading and unloading. Specific shape changing scenarios, e.g., based on bending, or twisting in prescribed directions, are enabled via the controlled interplay between the active materials and the 3D printed architectures. The physical phenomena are complex and nonintuitive, and so to help understand the interplay of geometric, material, and environmental stimuli parameters we develop 3D nonlinear finite element models. Finally, we create several 2D and 3D shape changing components that demonstrate the role of key parameters and illustrate the broad application potential of the proposed approach.


title: Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

abstract: This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.


title: MEDIA CONSUMPTION AND PUBLIC ATTITUDES TOWARD CRIME AND JUSTICE : THE RELATIONSHIP BETWEEN FEAR OF CRIME , PUNITIVE ATTITUDES , AND PERCEIVED POLICE

abstract: Public knowledge of crime and justice is largely derived from the media. This paper examines the influence of media consumption on fear of crime, punitive attitudes and perceived police effectiveness. This research contributes to the literature by expanding knowledge on the relationship between fear of crime and media consumption. This study also contributes to limited research on the media’s influence on punitive attitudes, while providing a much-needed analysis of the relationship between media consumption and satisfaction with the police. Employing OLS regression, the results indicate that respondents who are regular viewers of crime drama are more likely to fear crime. However, the relationship is weak. Furthermore, the results indicate that gender, education, income, age, perceived neighborhood problems and police effectiveness are statistically related to fear of crime. In addition, fear of crime, income, marital status, race, and education are statistically related to punitive attitudes. Finally, age, fear of crime, race, and perceived neighborhood problems are statistically related to perceived police effectiveness.


title: Understanding the dynamics of engaging interaction in public spaces

abstract: We present an analysis of three interactive installations in public spaces, in terms of their support of engagement as an evolving process. In particular, we focus on how engagement unfolds as a dynamic process that may be understood in terms of evolving relations between cultural, physical, content-related, and social elements of interactive environments. These elements are explored through the literature on engagement with interaction design, and it is argued that, although valuable contributions have been made towards understanding engagement with interactive environments, the ways in which engagement unfolds as a dynamic process remains relatively unexplored. We propose that we may understand engagement as a product of the four above-mentioned elements, and in our analysis we provide concrete examples of how engagement plays out in practice by analyzing the emergence, transformation and relations between these elements.


title: What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

abstract: We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users' needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers' querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3-4% of the total search traffic. ::: Since questions are such a small part of the query stream, and are more likely to be unique than shorter queries, click-through information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy. ::: To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer.


title: Report on the 2nd International Consortium on Hallucination Research: Evolving Directions and Top-10 “hot spots” in Hallucination Research

abstract: This article presents a report on the 2nd meeting of the International Consortium on Hallucination Research, held on September 12th and 13th 2013 at Durham University, UK. Twelve working groups involving specialists in each area presented their findings and sought to summarize the available knowledge, inconsistencies in the field, and ways to progress. The 12 working groups reported on the following domains of investigation: cortical organisation of hallucinations, nonclinical hallucinations, interdisciplinary approaches to phenomenology, culture and hallucinations, subtypes of auditory verbal hallucinations, a Psychotic Symptoms Rating Scale multisite study, visual hallucinations in the psychosis spectrum, hallucinations in children and adolescents, Research Domain Criteria behavioral constructs and hallucinations, new methods of assessment, psychological therapies, and the Hearing Voices Movement approach to understanding and working with voices. This report presents a summary of this meeting and outlines 10 hot spots for hallucination research, which include the in-depth examination of (1) the social determinants of hallucinations, (2) translation of basic neuroscience into targeted therapies, (3) different modalities of hallucination, (4) domain convergence in cross-diagnostic studies, (5) improved methods for assessing hallucinations in nonclinical samples, (6) using humanities and social science methodologies to recontextualize hallucinatory experiences, (7) developmental approaches to better understand hallucinations, (8) changing the memory or meaning of past trauma to help recovery, (9) hallucinations in the context of sleep and sleep disorders, and (10) subtypes of hallucinations in a therapeutic context.


title: Musical instrument recognition using cepstral coefficients and temporal features

abstract: In this paper, a system for pitch independent musical instrument recognition is presented. A wide set of features covering both spectral and temporal properties of sounds was investigated, and their extraction algorithms were designed. The usefulness of the features was validated using test data that consisted of 1498 samples covering the full pitch ranges of 30 orchestral instruments from the string, brass and woodwind families, played with different techniques. The correct instrument family was recognized with 94% accuracy and individual instruments in 80% of cases. These results are compared to those reported in other work. Also, utilization of a hierarchical classification framework is considered.


title: Opportunities and Challenges of Software-Defined Mobile Networks in Network Security

abstract: Software-defined mobile network (SDMN) architecture integrates software-defined networks, network functions virtualization, and cloud computing principles in mobile networking environments to transform rigid and disparate legacy mobile networks into scalable and dynamic ecosystems. However, because SDMN architecture separates the control and data planes, it will significantly change the way security is managed and applied for mobile networks. In this article, the authors discuss the security challenges, vulnerabilities, and opportunities that need to be investigated and addressed for future SDMNs. It also highlights how common security threats in IP networks such as the Internet are now applicable in new open and IP-based SDMNs.


title: Event-Enhanced Learning for KG Completion

abstract: Statistical learning of relations between entities is a popular approach to address the problem of missing data in Knowledge Graphs. In this work we study how relational learning can be enhanced with background of a special kind: event logs, that are sequences of entities that may occur in the graph. Events naturally appear in many important applications as background. We propose various embedding models that combine entities of a Knowledge Graph and event logs. Our evaluation shows that our approach outperforms state-of-the-art baselines on real-world manufacturing and road traffic Knowledge Graphs, as well as in a controlled scenario that mimics manufacturing processes.


title: Lattice-based access control models

abstract: Lattice-based access control models were developed in the early 1970s to deal with the confidentiality of military information. In the late 1970s and early 1980s, researchers applied these models to certain integrity concerns. Later, application of the models to the Chinese Wall policy, a confidentiality policy unique to the commercial sector, was demonstrated. A balanced perspective on lattice-based access control models is provided. Information flow policies, the military lattice, access control models, the Bell-LaPadula model, the Biba model and duality, and the Chinese Wall lattice are reviewed. The limitations of the models are identified. >


title: X-linked spinal muscular atrophy (SMAX2) caused by de novo c.1731C>T substitution in the UBA1 gene

abstract: Infantile X-linked spinal muscular atrophy (SMAX2) is a rare form of spinal muscular atrophy manifesting as severe hypotonia, areflexia, arthrogryposis, facial weakness and cryptorchidism, and frequently accompanied by bone fractures. We present a male patient with SMAX2 who presented with typical symptoms at birth, preceded by reduced fetal movements in the second and third trimesters of pregnancy. Clinical examination revealed a myopathic face with a characteristic tent-shaped open mouth, tongue fibrillations, profound muscle weakness, areflexia, multiple contractures, mild skeletal abnormalities and cryptorchidism. In the first days of the patient's life, fractures of the right femur and right humerus were found; however, calcium-phosphate metabolism and densitometric examination were normal. Molecular analysis revealed a de novo c.1731C>T substitution in the UBA1 gene, which was localized in exon 15, the specific hot spot for mutation.


title: Dynamic programming and influence diagrams

abstract: The concept of a super value node is developed to extend the theory of influence diagrams to allow dynamic programming to be performed within this graphical modeling framework. The operations necessary to exploit the presence of these nodes and efficiently analyze the models are developed. The key result is that by representing value function separability in the structure of the graph of the influence diagram, formulation is simplified and operations on the model can take advantage of the separability. From the decision analysis perspective, this allows simple exploitation of separability in the value function of a decision problem. This allows algorithms to be designed to solve influence diagrams that automatically recognize the opportunity for applying dynamic programming. From the decision processes perspective, influence diagrams with super value nodes allow efficient formulation and solution of nonstandard decision process structures. They also allow the exploitation of conditional independence between state variables. >


title: High speed true random number generator based on open loop structures in FPGAs

abstract: Most hardware ''True'' Random Number Generators (trng) take advantage of the thermal agitation around a flip-flop metastable state. In Field Programmable Gate Arrays (fpga), the classical trng structure uses at least two oscillators, build either from pll or ring oscillators. This creates good trng albeit limited in frequency by the interference rate which cannot exceed a few Mbit/s. This article presents an architecture allowing higher bit rates while maintaining provable unconditional security. This speed requirement becomes stringent for secure communication applications such as the cryptographic quantum key distribution protocols. The proposed architecture is very simple and generic as it is based on an open loop structure with no specific component such as pll.


title: A sentiment analysis of who participates, how and why, at social media sport websites: How differently men and women write about football

abstract: Purpose ::: ::: ::: ::: ::: Due to an immense rise of social media in recent years, the purpose of this paper is to investigate who, how and why participates in creating content at football websites. Specifically, it provides a sentiment analysis of user comments from gender perspective, i.e. how differently men and women write about football. The analysis is based on user comments published on Facebook pages of the top five 2015-2016 Premier League football clubs during the 1st and the 19th week of the season. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This analysis uses a data collection via social media website and a sentiment analysis of the collected data. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Results show certain unexpected similarities in social media activities between male and female football fans. A comparison of the user comments from Facebook pages of the top five 2015-2016 Premier League football clubs revealed that men and women similarly express hard emotions such as anger or fear, while there is a significant difference in expressing soft emotions such as joy or sadness. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This paper provides an original insight into qualitative content analysis of male and female comments published at social media websites of the top five Premier League football clubs during the 1st and the 19th week of the 2015-2016 season.


title: Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition

abstract: The task of recommending items, like movies, to users is a core feature of many social networks. Standard approaches either use item or user similarity to suggest the next items users might be interested in. Recently, multivariate models like matrix factorization have become popular to combine the advantages of both perspectives. In addition, extensions have been proposed to capture the dynamics of user interests over time, like trends or recurrent user needs. While offering good predictive performance, so far those models do not exploit possibly available rich semantic context. Typically, only one implicit feature, like user ratings, is tracked to give personalized recommendations. However, with semantic data sources, like linked data, wealthy background knowledge becomes available that could be leveraged to improve predictive performance. We argue, that a more flexible framework is needed to model and learn a greater class of recommendation scenarios where rich context is available. Thus, we propose a generic approach which generalizes state-of-the-art methods based on pair wise interaction tensor factorization by leveraging arbitrary background knowledge related to the recommendation situation. Our experiments on streamed semantic data from a social network show that by adding varying sets of context - like user information, sequential information or time information - the ranking of potential items can be personalized and the predictive performance can be improved.


title: Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation

abstract: We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.


title: Detecting and Tracking Political Abuse in Social Media

abstract: We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96% accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections.


title: Theory of Extraordinary Optical Transmission through Subwavelength Hole Arrays

abstract: We present a fully three-dimensional theoretical study of the extraordinary transmission of light through subwavelength hole arrays in optically thick metal films. Good agreement is obtained with experimental data. An analytical minimal model is also developed, which conclusively shows that the enhancement of transmission is due to tunneling through surface plasmons formed on each metal-dielectric interface. Different regimes of tunneling (resonant through a “surface plasmon molecule,” or sequential through two isolated surface plasmons) are found depending on the geometrical parameters defining the system.


title: Clinical trial management of participant recruitment, enrollment, engagement, and retention in the SMART study using a Marketing and Information Technology (MARKIT) model.

abstract: Advances in information technology and near ubiquity of the Internet have spawned novel modes of communication and unprecedented insights into human behavior via the digital footprint. Health behavior randomized controlled trials (RCTs), especially technology-based, can leverage these advances to improve the overall clinical trials management process and benefit from improvements at every stage, from recruitment and enrollment to engagement and retention. In this paper, we report the results for recruitment and retention of participants in the SMART study and introduce a new model for clinical trials management that is a result of interdisciplinary team science. The MARKIT model brings together best practices from information technology, marketing, and clinical research into a single framework to maximize efforts for recruitment, enrollment, engagement, and retention of participants into a RCT. These practices may have contributed to the study's on-time recruitment that was within budget, 86% retention at 24 months, and a minimum of 57% engagement with the intervention over the 2-year RCT. Use of technology in combination with marketing practices may enable investigators to reach a larger and more diverse community of participants to take part in technology-based clinical trials, help maximize limited resources, and lead to more cost-effective and efficient clinical trial management of study participants as modes of communication evolve among the target population of participants.


title: A fast Nonlinear Model Predictive Control strategy for real-time motion control of mechanical systems

abstract: Widespread application of real-time, Nonlinear Model Predictive Control (NMPC) algorithms to systems of large scale or with fast dynamics is challenged by the high associated computational cost, in particular in presence of long prediction horizons. In this paper, a fast NMPC strategy to reduce the on-line computational cost is proposed. A Curvature-based Measure of Nonlinearity (CMoN) of the system is exploited to reduce the required number of sensitivity computations, which largely contribute to the overall computational cost. The proposed scheme is validated by a simulation study on the chain of masses motion control problem, a toy example that can be easily extended to an arbitrary dimension. Simulations have been run with long prediction horizons and large state dimensions. Results show that sensitivity computations are significantly reduced with respect to other sensitivity updating schemes, while preserving control performance.


title: Occupation times of random walks in confined geometries: from random trap model to diffusion-limited reactions.

abstract: We consider a random walk in confined geometry, starting from a site and eventually reaching a target site. We calculate analytically the distribution of the occupation time on a third site, before reaching the target site. The obtained distribution is exact and completely explicit in the case or parallelepipedic confining domains. We discuss implications of these results in two different fields: The mean first passage time for the random trap model is computed in dimensions greater than 1 and is shown to display a nontrivial dependence with the source and target positions. The probability of reaction with a given imperfect center before being trapped by another one is also explicitly calculated, revealing a complex dependence both in geometrical and chemical parameters.


title: TestU01: A C library for empirical testing of random number generators

abstract: We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several others tests proposed in the literature, and some original ones. Predefined tests suites for sequences of uniform random numbers over the interval (0, 1) and for bit sequences are available. Tools are also offered to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator's period length, before the generator starts to fail the test systematically. Finally, the library provides various types of generators implemented in generic form, as well as many specific generators proposed in the literature or found in widely used software. The tests can be applied to instances of the generators predefined in the library, or to user-defined generators, or to streams of random numbers produced by any kind of device or stored in files. Besides introducing TestU01, the article provides a survey and a classification of statistical tests for RNGs. It also applies batteries of tests to a long list of widely used RNGs.


title: Paper prototyping - what is it good for?: a comparison of paper- and computer-based low-fidelity prototyping

abstract: This study investigated the differences between computer-based and paper-based low-fidelity prototypes. It researched whether subjects confronted with these two kinds of prototypes differ in their willingness to criticize a system and to give suggestions for its improvement. The chosen approach was an empirical study including test sessions using both kinds of prototypes. Quantitative and qualitative methods were applied to measure and to explain possible differences.


title: Advancing government-wide Enterprise Architecture - A meta-model approach

abstract: Enterprise Architecture (EA) is regarded as one of the means to ensure information technology interoperability, standard and reuse within an organizational boundary. In the public sector, however, there rises another kind of issues on how to integrate agency EA practices into the whole of government view. The conception of Government-wide EA (GEA) is the results of this consideration, and how to implement GEA has become a major challenge among IT policymakers form many countries. The present article deals with the applied case of modeling EA in the public sector. More specifically the article focuses on the methods and applications of modeling information integration architecture at government-wide level. A meta-modeling approach is used to design GEA database and to communicate with agencies regarding how they should manage agency-level EA information. The article consists of brief history of the EA program of the Korean Government, introduction of the KGEA Meta-model and its evolutionary history, and current status with some major outcomes. Methodological and managerial implications are also discussed.


title: Nonparametric Blind Super-resolution

abstract: Super resolution (SR) algorithms typically assume that the blur kernel is known (either the Point Spread Function 'PSF' of the camera, or some default low-pass filter, e.g. a Gaussian). However, the performance of SR methods significantly deteriorates when the assumed blur kernel deviates from the true one. We propose a general framework for "blind" super resolution. In particular, we show that: (i) Unlike the common belief, the PSF of the camera is the wrong blur kernel to use in SR algorithms. (ii) We show how the correct SR blur kernel can be recovered directly from the low-resolution image. This is done by exploiting the inherent recurrence property of small natural image patches (either internally within the same image, or externally in a collection of other natural images). In particular, we show that recurrence of small patches across scales of the low-res image (which forms the basis for single-image SR), can also be used for estimating the optimal blur kernel. This leads to significant improvement in SR results.


title: An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting

abstract: Short-term electric load forecasting (STLF) is an essential tool for power generation planning, transmission dispatching, and day-to-day utility operations. A number of techniques are used and reported in the literature to build an accurate forecasting model. Out of them Artificial Neural Networks (ANN) are proven most promising technique for STLF model building. Many learning schemes are being used to boost the ANN performance with improved results. This motivated us to explore better optimization approaches to devise a more suitable prediction technique. In this study, we propose a new hybrid model for STLF by combining greater optimization ability of artificial bee colony (ABC) algorithm with ANN. The ABC is used as an alternative learning scheme to get optimized set of neuron connection weights for ANN. This formulation showed improved convergence rate without trapping into local minimum. Forecasting results obtained by this new approach have been presented and compared with other mature and competitive approaches, which confirms its applicability in forecasting domain.


title: Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing

abstract: We study traces taken from UMass DieselNet, a Disruption-Tolerant Network consisting of WiFi nodes attached to buses. As buses travel their routes, they encounter other buses and in some cases are able to establish pair-wise connections and transfer data between them. We analyze the bus-to-bus contact traces to characterize the contact process between buses and its impact on DTN routing performance. We find that the all-bus-pairs aggregated inter-contact times show no discernible pattern. However, the inter-contact times aggregated at a route level exhibit periodic behavior.Based on analysis of the deterministic inter-meeting times for bus pairs running on route pairs, and consideration of the variability in bus movement and the random failures to establish connections, we construct generative route-level models that capture the above behavior. Through trace-driven simulations of epidemic routing, we find that the epidemic performance predicted by traces generated with this finer-grained route-level model is much closer to the actual performance that would be realized in the operational system than traces generated using the coarse-grained all-bus-pairs aggregated model. This suggests the importance in choosing the rightlevel of model granularity when modelingmobility-related measures such as inter-contact times in DTNs.


title: Stochastic Pre-classification for SDN Data Plane Matching

abstract: The Software Defined Networking (SDN) approach has numerous advantages, including the ability to program the network through simple abstractions, provide a centralized view of network state, and respond to changing network conditions. One of the main challenges in designing SDN enabled switches is efficient packet classification in the data plane. As the complexity of SDN applications increases, the data plane becomes more susceptible to Denial of Service (DoS) attacks, which can result in increased delays and packet loss. Accordingly, there is a strong need for network architectures that operate efficiently in the presence of malicious traffic. In particular, there is a need to protect authorized flows from DoS attacks. In this work we utilize a probabilistic data structure to pre-classify traffic with the aim of decoupling likely legitimate traffic from malicious traffic by leveraging the locality of packet flows. We validate our approach by examining a fundamental SDN application: software defined network firewall. For this application, our architecture dramatically reduces the impact of unknown/malicious flows on established/legitimate flows. We explore the effect of stochastic pre-classification in prioritizing data plane classification. We show how pre-classification can be used to increase the effective Quality of Service (QoS) for established flows and reduce the impact of adversarial traffic.


title: Fully Convolutional Networks for Semantic Segmentation

abstract: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.


title: Truncation Error of Sampling-Theorem Expansions

abstract: Upper bounds are obtained for the error, termed truncation error, which arises in reconstituting a band-limited function by summing over only a finite number (instead of the requisite infinite number) of samples of this function in an appropriate sampling-theorem expansion. Upper bounds are given for the truncation errors of the Cardinal and Fogel sampling expansions and for "self-truncating" versions of these two sampling expansions; these latter sampling expansions are "self-truncating" in the sense that the upper bounds on their truncation errors are almost always much lower than the upper bounds on the truncation errors of their prototype sampling expansions. All of the upper bounds are given as functions of three parameters: M, the maximum magnitude of the band-limited function; q, the per unit guard band (assuming that the band-limited function is sampled at a rate greater than the Nyquist rate); and N, the measure of the number of samples in the finite summation of terms of the sampling expansion.


title: Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations

abstract: There is an increasing awareness of the advantages of multi-modal neuroimaging. Networks obtained from different modalities are usually treated in isolation, which is however contradictory to accumulating evidence that these networks show non-trivial interdependencies. Even networks obtained from a single modality, such as frequency-band specific functional networks measured from magnetoencephalography (MEG) are often treated independently. Here, we discuss how a multilayer network framework allows for integration of multiple networks into a single network description and how graph metrics can be applied to quantify multilayer network organisation for group comparison. We analyse how well-known biases for single layer networks, such as effects of group differences in link density and/or average connectivity, influence multilayer networks, and we compare four schemes that aim to correct for such biases: the minimum spanning tree (MST), effective graph resistance cost minimisation, efficiency cost optimisation (ECO) and a normalisation scheme based on singular value decomposition (SVD). These schemes can be applied to the layers independently or to the multilayer network as a whole. For correction applied to whole multilayer networks, only the SVD showed sufficient bias correction. For correction applied to individual layers, three schemes (ECO, MST, SVD) could correct for biases. By using generative models as well as empirical MEG and functional magnetic resonance imaging (fMRI) data, we further demonstrated that all schemes were sensitive to identify network topology when the original networks were perturbed. In conclusion, uncorrected multilayer network analysis leads to biases. These biases may differ between centres and studies and could consequently lead to unreproducible results in a similar manner as for single layer networks. We therefore recommend using correction schemes prior to multilayer network analysis for group comparisons.


Evading Machine Learning Malware Detection


title: Learning to count with deep object features

abstract: Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.


title: Face Recognition System based on SURF and LDA Technique

abstract: In the past decade, Improve the quality in face recognition system is a challenge. It is a challenging problem and widely studied in the different type of imag- es to provide the best quality of faces in real life. These problems come due to illumination and pose effect due to light in gradient features. The improvement and optimi- zation of human face recognition and detection is an im- portant problem in the real life that can be handles to optimize the error rate, accuracy, peak signal to noise ratio, mean square error, and structural similarity Index. Now-a-days, there several methods are proposed to recognition face in different problem to optimize above parameters. There occur many invariant changes in hu- man faces due to the illumination and pose variations. In this paper we proposed a novel method in face recogni- tion to improve the quality parameters using speed up robust feature and linear discriminant analysis for opti- mize result. SURF is used for feature matching. In this paper, we use linear discriminant analysis for the edge dimensions reduction to live faces from our data-sets. The proposed method shows the better result as compare to the previous result on the basis of comparative analysis because our method show the better quality and better results in live images of face.


title: A Maturity Model for Implementing ITIL V3 in Practice

abstract: ITIL is the most popular "best practices" framework for managing Information Technology (IT) services. However, implementing ITIL is not only very difficult but also there are no recommendations and guidelines for it. As a result, ITIL implementations are usually long, expensive and risky. In a previous paper we proposed a maturity model to assess an ITIL implementation and provided a roadmap for improvement based on priorities, dependencies, and guidelines. In this paper, we demonstrate a practical application of the proposed model with a questionnaire to assess the Incident Management and Configuration Management processes as well as the Service Desk Function. We evaluate the questionnaires in 13 assessments in five Portuguese organizations and then implemented a prototype to support the assessments. We finally draw conclusions that could be very useful for organizations that are considering ITIL implementation.


title: Characterizing Private Clouds: A Large-Scale Empirical Analysis of Enterprise Clusters

abstract: There is an increasing trend in the use of on-premise clusters within companies. Security, regulatory constraints, and enhanced service quality push organizations to work in these so called private cloud environments. On the other hand, the deployment of private enterprise clusters requires careful consideration of what will be necessary or may happen in the future, both in terms of compute demands and failures, as they lack the public cloud's flexibility to immediately provision new nodes in case of demand spikes or node failures. In order to better understand the challenges and tradeoffs of operating in private settings, we perform, to the best of our knowledge, the first extensive characterization of on-premise clusters. Specifically, we analyze data ranging from hardware failures to typical compute/storage requirements and workload profiles, from a large number of Nutanix clusters deployed at various companies. We show that private cloud hardware failure rates are lower, and that load/demand needs are more predictable than in other settings. Finally, we demonstrate the value of the measurements by using them to provide an analytical model for computing durability in private clouds, as well as a machine learning-driven approach for characterizing private clouds' growth.


title: Discovering regions of different functions in a city using human mobility and POIs

abstract: The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The results justify the advantages of our approach over baseline methods solely using POIs or human mobility.


Meta-Learning for Medical Image Classification


title: An Associative Classification Data Mining Approach for Detecting Phishing Websites

abstract: Phishing websites are fake websites that are created by dishonest people to mimic webpages of real websites. Victims of phishing attacks may expose their financial sensitive information to the attacker whom might use this information for financial and criminal activities. Various approaches have been proposed to detect phishing websites, among which, approaches that utilize data mining techniques had shown to be more effective. The main goal of data mining is to analyze a large set of data to identify unsuspected relation and extract understandable useful patterns. Associative Classification (AC) is a promising data mining approach that integrates association rule and classification to build classification models (classifiers). This paper, proposes a new AC algorithm called Phishing Associative Classification (PAC), for detecting phishing websites. PAC employed a novel methodology in construction the classifier which results in generating moderate size classifiers. The algorithm improved the effectiveness and efficiency of a known algorithm called MCAR, by introducing a new prediction procedure and adopting a different rule pruning procedure. The conducted experiments compared PAC with 4 well-known data mining algorithms, these are: covering algorithm (Prism), decision tree (C4.5), associative Classification (CBA) and MCAR. Experiments are performed on a dataset that consists of 1010 website. Each Website is represented using 17 features categorized into 4 sets. The features are extracted from the website contents and URL. The results on each features set show that PAC is either equivalent or more effective than the compared algorithms. When all features are considered, PAC outperformed the compared algorithms and correctly identified 99.31% of the tested websites. Furthermore, PAC produced less number of rules than MCAR, and therefore, is more efficient.


title: Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization.

abstract: In this work we apply a technique called non-negative matrix factorization (NMF) to the problem of analyzing hundreds of x-ray microdiffraction (microXRD) patterns from a combinatorial materials library. An in-house scanning x-ray microdiffractometer is used to obtain microXRD patterns from 273 different compositions on a single composition spread library. NMF is then used to identify the unique microXRD patterns present in the system and quantify the contribution of each of these basis patterns to each experimental diffraction pattern. As a baseline, the results of NMF are compared to the results obtained using principle component analysis. The basis patterns found using NMF are then compared to reference patterns from a database of known structural patterns in order to identify known structures. As an example system, we explore a region of the Fe-Ga-Pd ternary system. The use of NMF in this case reduces the arduous task of analyzing hundreds of microXRD patterns to the much smaller task of identifying only nine microXRD patterns.


abstract: Advanced Compiler Design and Implementation by Steven Muchnick Preface 1 Introduction to Advanced Topics 1.1 Review of Compiler Structure 1.2 Advanced Issues in Elementary Topics 1.3 The Importance of Code Optimization 1.4 Structure of Optimizing Compilers 1.5 Placement of Optimizations in Aggressive Optimizing Compilers 1.6 Reading Flow Among the Chapters 1.7 Related Topics Not Covered in This Text 1.8 Target Machines Used in Examples 1.9 Number Notations and Data Sizes 1.10 Wrap-Up 1.11 Further Reading 1.12 Exercises 2 Informal Compiler Algorithm Notation (ICAN) 2.1 Extended Backus-Naur Form Syntax Notation 2.2 Introduction to ICAN 2.3 A Quick Overview of ICAN 2.4 Whole Programs 2.5 Type Definitions 2.6 Declarations 2.7 Data Types and Expressions 2.8 Statements 2.9 Wrap-Up 2.10 Further Reading 2.11 Exercises 3 Symbol-Table Structure 3.1 Storage Classes, Visibility, and Lifetimes 3.2 Symbol Attributes and Symbol-Table Entries 3.3 Local Symbol-Table Management 3.4 Global Symbol-Table Structure 3.5 Storage Binding and Symbolic Registers 3.6 Approaches to Generating Loads and Stores 3.7 Wrap-Up 3.8 Further Reading 3.9 Exercises 4 Intermediate Representations 4.1 Issues in Designing an Intermediate Language 4.2 High-Level Intermediate Languages 4.3 Medium-Level Intermediate Languages 4.4 Low-Level Intermediate Languages 4.5 Multi-Level Intermediate Languages 4.6 Our Intermediate Languages: MIR, HIR, and LIR 4.7 Representing MIR, HIR, and LIR in ICAN 4.8 ICAN Naming of Data Structures and Routines that Manipulate Intermediate Code 4.9 Other Intermediate-Language Forms 4.10 Wrap-Up 4.11 Further Reading 4.12 Exercises 5 Run-Time Support 5.1 Data Representations and Instructions 5.2 Register Usage 5.3 The Local Stack Frame 5.4 The Run-Time Stack 5.5 Parameter-Passing Disciplines 5.6 Procedure Prologues, Epilogues, Calls, and Returns 5.7 Code Sharing and Position-Independent Code 5.8 Symbolic and Polymorphic Language Support 5.9 Wrap-Up 5.10 Further Reading 5.11 Exercises 6 Producing Code Generators Automatically 6.1 Introduction to Automatic Generation of Code Generators 6.2 A Syntax-Directed Technique 6.3 Introduction to Semantics-Directed Parsing 6.4 Tree Pattern Matching and Dynamic Programming 6.5 Wrap-Up 6.6 Further Reading 6.7 Exercises 7 Control-Flow Analysis 7.1 Approaches to Control-Flow Analysis 7.2 Depth-First Search, Preorder Traversal, Postorder Traversal, and Breadth-First Search 7.3 Dominators 7.4 Loops and Strongly Connected Components 7.5 Reducibility 7.6 Interval Analysis and Control Trees 7.7 Structural Analysis 7.8 Wrap-Up 7.9 Further Reading 7.10 Exercises 8 Data-Flow Analysis 8.1 An Example: Reaching Definitions 8.2 Basic Concepts: Lattices, Flow Functions, and Fixed Points 8.3 Taxonomy of Data-Flow Problems and Solution Methods 8.4 Iterative Data-Flow Analysis 8.5 Lattices of Flow Functions 8.6 Control-Tree-Based Data-Flow Analysis 8.7 Structural Analysis 8.8 Interval Analysis 8.9 Other Approaches 8.10 Du-Chains, Ud-Chains, and Webs 8.11 Static Single-Assignment (SSA) Form 8.12 Dealing with Arrays, Structures, and Pointers 8.13 Automating Construction of Data-Flow Analyzers 8.14 More Ambitious Analyses 8.15 Wrap-Up 8.16 Further Reading 8.17 Exercises 9 Dependence Analysis and Dependence Graph 9.1 Dependence Relations 9.2 Basic-Block Dependence DAGs 9.3 Dependences in Loops 9.4 Dependence Testing 9.5 Program-Dependence Graphs 9.6 Dependences Between Dynamically Allocated Objects 9.7 Wrap-Up 9.8 Further Reading 9.9 Exercises 10 Alias Analysis 10.1 Aliases in Various Real Programming Languages 10.2 The Alias Gatherer 10.3 The Alias Propagator 10.4 Wrap-Up 10.5 Further Reading 10.6 Exercises 11 Introduction to Optimization 11.1 Global Optimizations Discussed in Chapters 12 Through 18 11.2 Flow Sensitivity and May vs. Must Information 11.3 Importance of Individual Optimizations 11.4 Order and Repetition of Optimizations 11.5 Further Reading 11.6 Exercises 12 Early Optimizations 12.1 Constant-Expression Evaluation (Constant Folding) 12.2 Scalar Replacement of Aggregates 12.3 Algebraic Simplifications and Reassociation 12.4 Value Numbering 12.5 Copy Propagation 12.6 Sparse Conditional Constant Propagation 12.7 Wrap-Up 12.8 Further Reading 12.9 Exercises 13 Redundancy Elimination 13.1 Common-Subexpression Elimination 13.2 Loop-Invariant Code Motion 13.3 Partial-Redundancy Elimination 13.4 Redundancy Elimination and Reassociation 13.5 Code Hoisting 13.6 Wrap-Up 13.7 Further Reading 13.8 Exercises 14 Loop Optimizations 14.1 Induction-Variable Optimizations 14.2 Unnecessary Bounds-Checking Elimination 14.3 Wrap-Up 14.4 Further Reading 14.5 Exercises 15 Procedure Optimizations 15.1 Tail-Call Optimization and Tail-Recursion Elimination 15.2 Procedure Integration 15.3 In-Line Expansion 15.4 Leaf-Routine Optimization and Shrink Wrapping 15.5 Wrap-Up 15.6 Further Reading 15.7 Exercises 16 Register Allocation 16.1 Register Allocation and Assignment 16.2 Local Methods 16.3 Graph Coloring 16.4 Priority-Based Graph Coloring 16.5 Other Approaches to Register Allocation 16.6 Wrap-Up 16.7 Further Reading 16.8 Exercises 17 Code Scheduling 17.1 Instruction Scheduling 17.2 Speculative Loads and Boosting 17.3 Speculative Scheduling 17.4 Software Pipelining 17.5 Trace Scheduling 17.6 Percolation Scheduling 17.7 Wrap-Up 17.8 Further Reading 17.9 Exercises 18 Control-Flow and Low-Level Optimizations 18.1 Unreachable-Code Elimination 18.2 Straightening 18.3 If Simplifications 18.4 Loop Simplifications 18.5 Loop Inversion 18.6 Unswitching 18.7 Branch Optimizations 18.8 Tail Merging or Cross Jumping 18.9 Conditional Moves 18.10 Dead-Code Elimination 18.11 Branch Prediction 18.12 Machine Idioms and Instruction Combining 18.13 Wrap-Up 18.14 Further Reading 18.15 Exercises 19 Interprocedural Analysis and Optimization 19.1 Interprocedural Control-Flow Analysis: The Call Graph 19.2 Interprocedural Data-Flow Analysis 19.3 Interprocedural Constant Propagation 19.4 Interprocedural Alias Analysis 19.5 Interprocedural Optimizations 19.6 Interprocedural Register Allocation 19.7 Aggregation of Global References 19.8 Other Issues in Interprocedural Program Management 19.9 Wrap-Up 19.10 Further Reading 19.11 Exercises 20 Optimization of the Memory Hierarchy 20.1 Impact of Data and Instruction Caches 20.2 Instruction-Cache Optimization 20.3 Scalar Replacement of Array Elements 20.4 Data-Cache Optimization 20.5 Scalar vs. Memory-Oriented Optimizations 20.6 Wrap-Up 20.7 Further Reading 20.8 Exercises 21 Case Studies of Compilers and Future Trends 21.1 the Sun Compilers for SPARC 21.2 The IBM XL Compilers for the POWER and PowerPC Architectures 21.3 Digital Equipment's Compilers for Alpha 21.4 The Intel Reference Compilers for the Intel 386 Architecture 21.5 Future Trends in Compiler Design and Implementation 21.6 Further Reading A Guide to Assembly Languages Used in This Book A.1 Sun SPARC Versions 8 and 9 Assembly Language A.2 IBM POWER and PowerPC Assembly Language A.3 DEC Alpha Assembly Language A.4 Intel 386 Architecture Assembly Language A.5 Hewlett-Packard's PA-RISC Assembly Language B Representation of Sets, Sequences, Trees, DAGs, and Functions B.1 Representation of Sets B.2 Representation of Sequences B.3 Representation of Trees and DAGs B.4 Representation of Functions B.5 Further Reading C Software Resources View Appendix C with live links to download sites C.1 Finding and Accessing Software on the Internet C.2 Machine Simulators C.3 Compilers C.4 Code-Generator Generators: BURG and IBURG C.5 Profiling Tools Bibliography Indices


title: Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

abstract: Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.


title: Taking Human out of Learning Applications: A Survey on Automated Machine Learning

abstract: Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge- and labor-intensive to pursue good learning performance, human experts are heavily involved in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date but also can guide the design for new methods. Subsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.


title: Neurobiology of emotion perception II: implications for major psychiatric disorders

abstract: To date, there has been little investigation of the neurobiological basis of emotion processing abnormalities in psychiatric populations. We have previously discussed two neural systems: 1) a ventral system, including the amygdala, insula, ventral striatum, ventral anterior cingulate gyrus, and prefrontal cortex, for identification of the emotional significance of a stimulus, production of affective states, and automatic regulation of emotional responses; and 2) a dorsal system, including the hippocampus, dorsal anterior cingulate gyrus, and prefrontal cortex, for the effortful regulation of affective states and subsequent behavior. In this critical review, we have examined evidence from studies employing a variety of techniques for distinct patterns of structural and functional abnormalities in these neural systems in schizophrenia, bipolar disorder, and major depressive disorder. In each psychiatric disorder, the pattern of abnormalities may be associated with specific symptoms, including emotional flattening, anhedonia, and persecutory delusions in schizophrenia, prominent mood swings, emotional lability, and distractibility in bipolar disorder during depression and mania, and with depressed mood and anhedonia in major depressive disorder. We suggest that distinct patterns of structural and functional abnormalities in neural systems important for emotion processing are associated with specific symptoms of schizophrenia and bipolar and major depressive disorder.


title: Dementia Caregiver Intervention Research In Search of Clinical Significance

abstract: Purpose: We reviewed intervention studies that reported dementia caregiver outcomes published since 1996, including psychosocial interventions for caregivers and environmental and pharmacological interventions for care recipients. Our goal was to focus on issues of clinical significance in caregiver intervention research in order to move the field toward a greater emphasis on achieving reliable and clinically meaningful outcomes. Design and Methods: MEDLINE, PsycINFO, and Cumulative Index to Nursing & Allied Health databases from 1996 through 2001 were searched to identify articles and book chapters mapping to two medical subject headings: caregivers and either dementia or Alzheimer’s disease . Articles were evaluated on two dimensions, outcomes in four domains thought to be important to the individual or society and the magnitude of reported effects for these outcomes in order to determine if they were large enough to be clinically meaningful. Results: Although many studies have reported small to moderate statistically significant effects on a broad range of outcomes, only a small proportion of these studies achieved clinically meaningful outcomes. Nevertheless, caregiving intervention studies have increasingly shown promise of affecting important public health outcomes in areas such as service utilization, including delayed institutionalization; psychiatric symptomatology, including the successful treatment of major and minor depression; and providing services that are highly valued by caregivers. Implications: Assessment of clinical significance in addition to statistical significance is needed in this research area. Specific recommendations on design, measurement, and conceptual issues are made to enhance the clinical significance of future research.


title: KKse: Safety education system of the child in the kitchen knife cooking

abstract: The Kitchen Knife Safety Educator (KKse) is a safety education system designed to teach children how to correctly use cooking knives. Cooking is important for children to learn about what they eat. In addition, that is also important for daily communication between children and their parents. However, it is dangerous for young children to handle cooking knives. Because of this danger, parents often try to keep their young children away from the kitchen. Our proposed system will contribute to not only improving children's cooking skills, but also improving communication between parents and children. The system composed of a virtual knife with haptic feedback function, a touch/force sensitive virtual food and a two-dimensional force sensitive cutting board. This system was developed to teach a fundamental cutting method, the “thrusting cut”. This paper describes the detail of the system.


title: Research on Money Laundering Detection Based on Improved Minimum Spanning Tree Clustering and Its Application

abstract: To detect suspicious money laundering transaction in the real world financial applications, a new dissimilarity metric was proposed and a novel Money Laundering Detection Algorithm based on Improved Minimum Spanning Tree clustering was put forward in this paper. Suspicious money laundering transaction detection experiment on financial data set from the real world indicates that our algorithm is effective and succinct.


title: Using dynamic time warping distances as features for improved time series classification

abstract: Dynamic time warping (DTW) has proven itself to be an exceptionally strong distance measure for time series. DTW in combination with one-nearest neighbor, one of the simplest machine learning methods, has been difficult to convincingly outperform on the time series classification task. In this paper, we present a simple technique for time series classification that exploits DTW's strength on this task. But instead of directly using DTW as a distance measure to find nearest neighbors, the technique uses DTW to create new features which are then given to a standard machine learning method. We experimentally show that our technique improves over one-nearest neighbor DTW on 31 out of 47 UCR time series benchmark datasets. In addition, this method can be easily extended to be used in combination with other methods. In particular, we show that when combined with the symbolic aggregate approximation (SAX) method, it improves over it on 37 out of 47 UCR datasets. Thus the proposed method also provides a mechanism to combine distance-based methods like DTW with feature-based methods like SAX. We also show that combining the proposed classifiers through ensembles further improves the performance on time series classification.


title: Design of millimeter wave antenna arrays for 5G cellular applications using FEKO

abstract: The technologies under development in the world of mobile broadband networks specifically focusing on the next generation of standards, namely 5G, are targeting the millimeter wave spectrum of 28 GHz and beyond. This paper discusses some of these futuristic technologies that are laying the foundation for the 5G standards, highlighting the concept of massive MIMO that employs antenna arrays and beamforming techniques to address the high data rate demands.


title: The Kinematics of Contact and Grasp

abstract: The kinematics of contact describe the motion of a point of contact over the surfaces of two contacting objects in response to a relative motion of these objects. Using concepts from differential geometry, I derive a set of equations, called the contact equations, that embody this relationship. I employ the contact equations to design the following applications to be executed by an end-effector with tactile sensing capability: ( 1) determining the curvature form of an unknown object at a point of contact; and (2) following the surface of an unknown object. The contact equations also serve as a basis for an investigation of the kinematics of grasp. I derive the relation ship between the relative motion of two fingers grasping an object and the motion of the points of contact over the object surface. Based on this analysis, we explore the following applications: (1) rolling a sphere between two arbitrarily shaped fingers ; (2) fine grip adjustment ( i.e., having two fingers that grasp an unknown object locally optimize their grip for maximum stability).


title: Decision trees for predicting the academic success of students

abstract: The aim of this paper is to create a model that successfully classifies students into one of two categories, depending on their success at the end of their first academic year, and finding meaningful variables affecting their success. This model is based on information regarding student success in high school and their courses after completing their first year of study, as well as the rank of preferences assigned to the observed faculty, and attempts to classify students into one of the two categories in line with their academic success. Creating a model required collecting data on all undergraduate students enrolled into their second year at the Faculty of Economics, University of Osijek, as well as data on completion of the state exam. These two datasets were combined and used for the model. Several classification algorithms for constructing decision trees were compared and the statistical significance (t-test) of the results was analyzed. Finally, the algorithm that produced the highest accuracy was chosen as the most successful algorithm for modeling the academic success of students. The highest classification rate of 79% was produced using the REPTree decision tree algorithm, but the tree was not as successful in classifying both classes. Therefore, the average rate of classification was calculated for two models that gave the highest total rate of classification, where a higher percentage is achieved using the model relying on the algorithm J48. The most significant variables were total points in the state exam, points from high school and points in the Croatian language exam.


Distance Estimation Based Filter RSSI for Indoor Wireless Sensor Networks Assist


title: “Women Home and Away”: Transnational Managerial Work and Gender Relations

abstract: This article addresses the intersections, even blurrings, of two “homes” and two “aways” – the personal, ‹private’ home and the corporate ‹public’ ‹away’, and the national home country and corporate base and the transnational work away. Drawing on 40 semi-structured interviews with women and men top and middle managers in seven multinational corporations located in Finland, we examine the complex relations among transnational managerial work, corporate careers and personal, marriage and family-type relations, and their differences for women and men managers. This shows the very different personal and social worlds inhabited by senior women and men managers, and how transnational processes can make those differences even greater.


title: A low cost, adaptive mixed reality system for home-based stroke rehabilitation

abstract: This paper presents a novel, low-cost, real-time adaptive multimedia environment for home-based upper extremity rehabilitation of stroke survivors. The primary goal of this system is to provide an interactive tool with which the stroke survivor can sustain gains achieved within the clinical phase of therapy and increase the opportunity for functional recovery. This home-based mediated system has low cost sensing, off the shelf components for the auditory and visual feedback, and remote monitoring capability. The system is designed to continue active learning by reducing dependency on real-time feedback and focusing on summary feedback after a single task and sequences of tasks. To increase system effectiveness through customization, we use data from the training strategy developed by the therapist at the clinic for each stroke survivor to drive automated system adaptation at the home. The adaptation includes changing training focus, selecting proper feedback coupling both in real-time and in summary, and constructing appropriate dialogues with the stroke survivor to promote more efficient use of the system. This system also allows the therapist to review participant's progress and adjust the training strategy weekly.


title: Structure guided texture inpainting through multi-scale patches and global optimization for image completion

abstract: Automatic image completion can provide convenient editing of consumer images. Most image completion methods find the best patch locally and then copy it to the hole region for texture inpainting. Since the best patch is fixed in size, it is hard to adapt these methods efficiently either to various patterns or to content synthesis. Meanwhile, salient image structures can be estimated and propagated to guide the texture inpainting process for more plausible results. This paper presents a novel image completion method using structure guided texture inpainting. We do not require any interactions to achieve automatic two-stage image completion. In the structure completion stage, the salient structures around the hole region are detected, and then the missing structure curves are completed with Euler spiral. The textures along the structure curves are completed through belief propagation. In the texture inpainting stage, we propose multi-scale patches and global optimization to inpaint the remaining texture in the hole regions guided by the completed structures. First, with defined patch sizes, the hole region is divided into lattice patches, making it possible for multiple patch sizes to render multiscale descriptions of this image. A multi-scale graph is then built for the hole region and formulated as a posterior probability model. Second, using a simulated annealing based Markov chain Monte Carlo method, an inference algorithm is designed to find a global optimization solution for the posterior probability model. The experiments show that our method can automatically complete the hole region and preserve plausible structure shapes of existing ones in various scenarios. The texture inpainting results are more convincing with guidance from the completed structures, and our method can guarantee and accelerate convergence of the global optimization.


title: Jigsaw puzzles with pieces of unknown orientation

abstract: This paper introduces new types of square-piece jigsaw puzzles: those for which the orientation of each jigsaw piece is unknown. We propose a tree-based reassembly that greedily merges components while respecting the geometric constraints of the puzzle problem. The algorithm has state-of-the-art performance for puzzle assembly, whether or not the orientation of the pieces is known. Our algorithm makes fewer assumptions than past work, and success is shown even when pieces from multiple puzzles are mixed together. For solving puzzles where jigsaw piece location is known but orientation is unknown, we propose a pairwise MRF where each node represents a jigsaw piece's orientation. Other contributions of the paper include an improved measure (MGC) for quantifying the compatibility of potential jigsaw piece matches based on expecting smoothness in gradient distributions across boundaries.


title: Comparative Study of the IEEE 34 Node Test Feeder under Practical Simplifications

abstract: The IEEE 34 node distribution test feeder incorporates all possible practical configurations and load profiles, such as three phase asymmetry, single phase laterals and distributed loads, among others. Simplifying assumptions leading to the balanced three phase main feeder and spot loads are common in the papers published in the literature on distribution system planning and operation. The purpose of this paper is to compare the results obtained using such simplifications with exact results given in the IEEE node test feeder data. Typical simplifying scenarios are assumed and the resulting balanced system analyzed. Comparison of voltage profiles and system losses validates the use of simplified balanced three phase system in the determination of equipment sizing, location and control parameters.


title: Advancing NLP via a distributed-messaging approach

abstract: Natural Language Processing (NLP) constitutes a fundamental module for a plethora of domains where unstructured text is a predominant source. Despite the keen interest of both industry and research community in developing NLP tools, current industrial solutions still suffer from two main cons. First, the architectures underlying existing systems do not satisfy critical requirements of large-scale processing, completeness, and versatility. Second, the algorithms typically employed for entity recognition and disambiguation — a core task common to all modern NLP systems — are still not well-suited for deployment in a real industrial environment, for evident issues of efficiency and result interpretability. In this paper we present Hermes, a novel NLP tool that overcomes the two main limitations of existing solutions. By employing an efficient and extendable distributed-messaging architecture, Hermes achieves the critical requirements of large-scale processing, completeness, and versatility. Moreover, our tool includes an entity-disambiguation algorithm enhanced with a two-level hashing-based approximation technique to considerably improve efficiency, as a well as a densest-subgraph-extraction method to increase result interpretability.


title: Demystifying magic: high-level low-level programming

abstract: The power of high-level languages lies in their abstraction over hardware and software complexity, leading to greater security, better reliability, and lower development costs. However, opaque abstractions are often show-stoppers for systems programmers, forcing them to either break the abstraction, or more often, simply give up and use a different language. This paper addresses the challenge of opening up a high-level language to allow practical low-level programming without forsaking integrity or performance. The contribution of this paper is three-fold: 1) we draw together common threads in a diverse literature, 2) we identify a framework for extending high-level languages for low-level programming, and 3) we show the power of this approach through concrete case studies. Our framework leverages just three core ideas: extending semantics via intrinsic methods, extending types via unboxing and architectural-width primitives, and controlling semantics via scoped semantic regimes. We develop these ideas through the context of a rich literature and substantial practical experience. We show that they provide the power necessary to implement substantial artifacts such as a high-performance virtual machine, while preserving the software engineering benefits of the host language. The time has come for high-level low-level programming to be taken more seriously: 1) more projects now use high-level languages for systems programming, 2) increasing architectural heterogeneity and parallelism heighten the need for abstraction, and 3) a new generation of high-level languages are under development and ripe to be influenced.


title: Learning Language Representations for Typology Prediction

abstract: One central mystery of neural NLP is what neural models "know" about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one neural machine translation (NMT) system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages' geographic and phylogenetic neighbors.


title: Different drugs for bad bugs: antivirulence strategies in the age of antibiotic resistance

abstract: Efforts to combat bacterial infections by targeting virulence factors are gaining traction, fuelled by the potential to circumvent the development of antibacterial resistance and recent landmark approvals of antivirulence drugs. Here, Otto and colleagues examine the antivirulence drugs in development, highlighting the most promising targets and strategies, as well as caveats to using this approach.