title: High-Frequency Behavior of Graphene-Based Interconnects—Part I: Impedance Modeling

abstract: This paper presents the first detailed methodology for the accurate evaluation of high-frequency impedance of graphene-based structures relevant to on-chip interconnect and inductor applications. Going beyond the simplifying assumptions of Ohm's law, the effects of electric-field variation within a mean free path and current dependency on the nonlocal electric-field are taken into account to accurately capture the high-frequency behavior of graphene ribbons (GRs). At the same time, a simplified approach that may be adopted at lower frequencies is also explained. Starting from the basic Boltzmann equation and combining with the unique dispersion relation for graphene in its hexagonal Brillouin zone, the current density across the GR structure is derived. First, a semi-infinite slab of GR is analyzed using the theory of Fourier integrals, which is followed by the development of a rigorous methodology for practical finite structures based on a self-consistent numerical calculation of the derived current density using the Green's function approach.


title: Discounting Time and Time Discounting: Subjective Time Perception and Intertemporal Preferences

abstract: Abstract Consumers often make decisions about outcomes and events that occur over time. This research examines consumers' sensitivity to the prospective duration relevant to their decisions and the implications of such sensitivity for intertemporal trade-offs, especially the degree of present bias (i.e., hyperbolic discounting). The authors show that participants' subjective perceptions of prospective duration are not sufficiently sensitive to changes in objective duration and are nonlinear and concave in objective time, consistent with psychophysical principles. More important, this lack of sensitivity can explain hyperbolic discounting. The results replicate standard hyperbolic discounting effects with respect to objective time but show a relatively constant rate of discounting with respect to subjective time perceptions. The results are replicated between subjects (Experiment 1) and within subjects (Experiments 2), with multiple time horizons and multiple descriptors, and with different measurement ord...


title: Incorporating Word Embeddings into Open Directory Project based Large-scale Classification

abstract: Recently, implicit representation models, such as embedding or deep learning, have been successfully adopted to text classification task due to their outstanding performance. However, these approaches are limited to small- or moderate-scale text classification. Explicit representation models are often used in a large-scale text classification, like the Open Directory Project (ODP)-based text classification. However, the performance of these models is limited to the associated knowledge bases. In this paper, we incorporate word embeddings into the ODP-based large-scale classification. To this end, we first generate category vectors, which represent the semantics of ODP categories by jointly modeling word embeddings and the ODP-based text classification. We then propose a novel semantic similarity measure, which utilizes the category and word vectors obtained from the joint model and word embeddings, respectively. The evaluation results clearly show the efficacy of our methodology in large-scale text classification. The proposed scheme exhibits significant improvements of 10% and 28% in terms of macro-averaging F1-score and precision at k, respectively, over state-of-the-art techniques.


title: RCC8 binary constraint network can be consistently extended

abstract: The RCC8 constraint language developed by Randell et al. has been popularly adopted by the Qualitative Spatial Reasoning and GIS communities. The recent observation that RCC8 composition table describes only weak composition instead of composition raises questions about Renz and Nebel's maximality results about the computational complexity of reasoning with RCC8.This paper shows that any consistent RCC8 binary constraint network (RCC8 network for short) can be consistently extended. Given Θ, an RCC8 network, and z, a fresh variable, suppose xTy ∈ Θ and T is contained in the weak composition of R and S. This means that we can add two new constraints xRz and zSy to Θ without changing the consistency of the network. The result guarantees the applicability to RCC8 of one key technique, (Theorem 5) of [J. Renz, B. Nebel, On the complexity of qualitative spatial reasoning: A maximal tractable fragment of the Region Connection Calculus. Artificial Intelligence 108 (1999) 69-123], which allows the transfer of tractability of a set of RCC8 relations to its closure under composition, intersection, and converse.


title: Analytical Design Methodology for Litz-Wired High-Frequency Power Transformers

abstract: In the last quarter of a century, high-frequency (HF) transformer design has been one of the major concerns to power electronics designers in order to increase converter power densities and efficiencies. Conventional design methodologies are based on iterative processes and rules of thumb founded more on expertise than on theoretical developments. This paper presents an analytical design methodology for litz-wired HF power transformers that provides a deep insight into the transformer design problem making it a powerful tool for converter designers. The most suitable models for the calculation of core and winding losses and the transformer thermal resistance are first selected and then validated with a 5-kW 50-kHz commercial transformer for a photovoltaic application. Based on these models, the design methodology is finally proposed, reducing the design issue to directly solve a five-variable nonlinear optimization problem. The methodology is illustrated with a detailed design in terms of magnetic material, core geometry, and primary and secondary litz-wire sizing. The optimal design achieves a 46.5% power density increase and a higher efficiency of 99.70% when compared with the commercial one.


title: Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)

abstract: We have developed a high-resolution fluorescence microscopy method based on high-accuracy localization of photoswitchable fluorophores. In each imaging cycle, only a fraction of the fluorophores were turned on, allowing their positions to be determined with nanometer accuracy. The fluorophore positions obtained from a series of imaging cycles were used to reconstruct the overall image. We demonstrated an imaging resolution of 20 nm. This technique can, in principle, reach molecular-scale resolution.


title: DRC: a dual route cascaded model of visual word recognition and reading aloud

abstract: This article describes the Dual Route Cascaded (DRC) model, a computational model of visual word recognition and reading aloud. The DRC is a computational realization of the dual-route theory of reading, and is the only computational model of reading that can perform the 2 tasks most commonly used to study reading: lexical decision and reading aloud. For both tasks, the authors show that a wide variety of variables that influence human latencies influence the DRC model's latencies in exactly the same way. The DRC model simulates a number of such effects that other computational models of reading do not, but there appear to be no effects that any other current computational model of reading can simulate but that the DRC model cannot. The authors conclude that the DRC model is the most successful of the existing computational models of reading.


title: Subword-augmented Embedding for Cloze Reading Comprehension

abstract: Representation learning is the foundation of machine reading comprehension. In state-of-the-art models, deep learning methods broadly use word and character level representations. However, character is not naturally the minimal linguistic unit. In addition, with a simple concatenation of character and word embedding, previous models actually give suboptimal solution. In this paper, we propose to use subword rather than character for word embedding enhancement. We also empirically explore different augmentation strategies on subword-augmented embedding to enhance the cloze-style reading comprehension model reader. In detail, we present a reader that uses subword-level representation to augment word embedding with a short list to handle rare words effectively. A thorough examination is conducted to evaluate the comprehensive performance and generalization ability of the proposed reader. Experimental results show that the proposed approach helps the reader significantly outperform the state-of-the-art baselines on various public datasets.


title: Research of Sandstorm in Chinese Historical Period:An overview

abstract: With more and more attention on ecological environment, there has been a rising interest in research of sandstorm in Chinese historical period. The paper made an overview of research of sandstorm in Chinese historical period. There are different academic viewpoints in cause, character and law of sandstorm in Chinese historical period. Deepening of research relies on the introduction of new technique and view.


title: VEGAS: Visual influEnce GrAph Summarization on Citation Networks

abstract: Visually analyzing citation networks poses challenges to many fields of the data mining research. How can we summarize a large citation graph according to the user’s interest? In particular, how can we illustrate the impact of a highly influential paper through the summarization? Can we maintain the sensory node-link graph structure while revealing the flow-based influence patterns and preserving a fine readability? The state-of-the-art influence maximization algorithms can detect the most influential node in a citation network, but fail to summarize a graph structure to account for its influence. On the other hand, existing graph summarization methods fold large graphs into clustered views, but can not reveal the hidden influence patterns underneath the citation network. In this paper, we first formally define the Influence Graph Summarization problem on citation networks. Second, we propose a matrix decomposition based algorithm pipeline to solve the IGS problem. Our method can not only highlight the flow-based influence patterns, but also easily extend to support the rich attribute information. A prototype system called VEGAS implementing this pipeline is also developed. Third, we present a theoretical analysis on our main algorithm, which is equivalent to the kernel k-mean clustering. It can be proved that the matrix decomposition based algorithm can approximate the objective of the proposed IGS problem. Last, we conduct comprehensive experiments with real-world citation networks to compare the proposed algorithm with classical graph summarization methods. Evaluation results demonstrate that our method significantly outperforms the previous ones in optimizing both the quantitative IGS objective and the quality of the visual summarizations.


title: Low Loss Tunable Superconducting Dual-Mode Filter at L-Band Using Semiconductor Varactors

abstract: This letter presents a superconducting continuously tunable bandpass filter with dual-mode resonator and Gallium arsenide (GaAs) varactors. The even- and odd-modes of the dual-mode resonator can be tuned simultaneously using a single bias voltage. The interstage and external couplings are realized with interdigital coupling structure to enhance the coupling strength. A four-pole dual-mode superconducting filter at L-band with a fractional bandwidth of approximately 20% is successfully designed. Two transmission zeros are generated in the upper stopband to increase the stopband rejection. The tuning range of the filter is 16% from 784.92 to 918.23 MHz. The insertion loss at 918.23 MHz is 0.69 dB with a reverse bias voltage of 9 V. The measurements of the filter match well with the simulated ones.


Design and Simulation of three phase Inverter for grid connected Photovoltic systems


title: Robots, communication, and language: An overview of the lingodroid project

abstract: The foundation of a language for a mobile agent – robot or human – is the representation of spatial and temporal concepts. These concepts include where and when events, objects and agents are located in space and time. This paper presents an overview of the Lingodroid project, in which real and simulated robots play language games to ground concepts for effective communication about their world. A series of location language games is described, with typical lexicons presented. We present future plans to extend the abilities of the robots to ground concepts for objects attributes, actions, and time.


title: Study on method of detecting preceding vehicle based on monocular camera

abstract: This article describes systemically the method of detecting the preceding vehicle based on a monocular camera. The main content is as follows: first, a primary area of interest is found by the lane borderlines that are identified in a camera image, and a likelihood target vehicle is searched by the gray difference between the target vehicle and the background; second, an identifying area of interest is found again based on the area of a likelihood target vehicle, a target vehicle is affirmed by a symmetry character of the vehicle outline and a position of the vehicle symmetrical axis is ascertained; third, the object vehicle is tracked by Kalman forecast principle in the sequence images; fourth, a method of detecting distance in a frame of image is introduced. The calibration of the camera's interior parameters and the results of some experiments are given.


title: A climbing parallel robot: a robot to climb along tubular and metallic structures

abstract: This article presents the promising applications that can be obtained from parallel robots for climbing. It explores the most important aspects of the mechanical design and control of climbing parallel robots (CPRs) based on the Stewart-Gough's (S-G) platform for tasks in tubular or metallic structures.


title: Longitudinal Residual Stress Analysis in AA2024-T3 Friction Stir Welding

abstract: Friction Stir Welding (FSW) is an innovative solid-state joining process, which is gaining a great deal of attention in several applicative sectors. The opportune definition of process parameters, i.e. minimizing residual stresses, is crucial to improve joint reliability in terms of static and dynamic performance. Longitudinal residual stresses, induced by FSW in AA2024-T3 butt joints, have been inferred by means of a recently developed technique, namely the contour method. Two approaches to stress measurement have been adopted; the former is based on the assumption of uniform material properties, the latter takes into account microstructural effects and material properties variations in the welding zones. The influence of process parameters, namely rotating and welding speeds, on stress distribution is also discussed.


title: A Lip Contour Extraction Method Using Localized Active Contour Model with Automatic Parameter Selection

abstract: Lip contour extraction is crucial to the success of a lipreading system. This paper presents a lip contour extraction algorithm using localized active contour model with the automatic selection of proper parameters. The proposed approach utilizes a minimum-bounding ellipse as the initial evolving curve to split the local neighborhoods into the local interior region and the local exterior region, respectively, and then compute the localized energy for evolving and extracting. This method is robust against the uneven illumination, rotation, deformation, and the effects of teeth and tongue. Experiments show its promising result in comparison with the existing methods.


title: Uppaal SMC tutorial

abstract: This tutorial paper surveys the main features of Uppaal SMC, a model checking approach in Uppaal family that allows us to reason on networks of complex real-timed systems with a stochastic semantic. We demonstrate the modeling features of the tool, new verification algorithms and ways of applying them to potentially complex case studies.


title: Real-time fiber-level cloth rendering

abstract: Modeling cloth with fiber-level geometry can produce highly realistic details. However, rendering fiber-level cloth models not only has a high memory cost but it also has a high computation cost even for offline rendering applications. In this paper we present a real-time fiber-level cloth rendering method for current GPUs. Our method procedurally generates fiber-level geometric details on-the-fly using yarn-level control points for minimizing the data transfer to the GPU. We also reduce the rasterization operations by collectively representing the fibers near the center of each ply that form the yarn structure. Moreover, we employ a level-of-detail strategy to minimize or completely eliminate the generation of fiber-level geometry that would have little or no impact on the final rendered image. Furthermore, we introduce a simple yarn-level ambient occlusion approximation and self-shadow computation method that allows lighting with self-shadows using relatively low-resolution shadow maps. We demonstrate the effectiveness of our approach by comparing our simplified fiber geometry to procedurally generated references and display knitwear containing more than a hundred million individual fiber curves at real-time frame rates with shadows and ambient occlusion.


title: Gut Microbiota, Immune System, and Bone

abstract: The gut microbiota (GM) is the whole of commensal, symbiotic, and pathogenic microorganisms living in our intestine. The GM–host interactions contribute to the maturation of the host immune system, modulating its systemic response. It is well documented that GM can interact with non-enteral cells such as immune cells, dendritic cells, and hepatocytes, producing molecules such as short-chain fatty acids, indole derivatives, polyamines, and secondary bile acid. The receptors for some of these molecules are expressed on immune cells, and modulate the differentiation of T effector and regulatory cells: this is the reason why dysbiosis is correlated with several autoimmune, metabolic, and neurodegenerative diseases. Due to the close interplay between immune and bone cells, GM has a central role in maintaining bone health and influences bone turnover and density. GM can improve bone health also increasing calcium absorption and modulating the production of gut serotonin, a molecule that interacts with bone cells and has been suggested to act as a bone mass regulator. Thus, GM manipulation by consumption of antibiotics, changes in dietary habits, and the use of pre- and probiotics may affect bone health. This review summarizes evidences on the influence of GM on immune system and on bone turnover and density and how GM manipulation may influence bone health.


title: Active Reinforcement Learning with Monte-Carlo Tree Search

abstract: Active Reinforcement Learning (ARL) is a twist on RL where the agent observes reward information only if it pays a cost. This subtle change makes exploration substantially more challenging. Powerful principles in RL like optimism, Thompson sampling, and random exploration do not help with ARL. We relate ARL in tabular environments to Bayes-Adaptive MDPs. We provide an ARL algorithm using Monte-Carlo Tree Search that is asymptotically Bayes optimal. Experimentally, this algorithm is near-optimal on small Bandit problems and MDPs. On larger MDPs it outperforms a Q-learner augmented with specialised heuristics for ARL. By analysing exploration behaviour in detail, we uncover obstacles to scaling up simulation-based algorithms for ARL.


title: A new network model for extracting text keywords

abstract: Text keywords are defined as meaningful and important words in a document, which provide a precise overview of its content and reflect the author’s writing intention. Keyword extraction methods have received a lot of attentions, among which is the network-based method. However, existing network-based keyword extraction methods only consider the connections between words in a document, while ignoring the impact of sentences. Since a sentence is made of many words, while words affect one another in a sentence, neglecting the influence of sentences will result in the loss of information. In this paper, we introduce a word network whose nodes represent words in a document, and define that any keyword extraction method based on a word network is called as a Word-net method. Then, we propose a new network model which considers the influence of sentences, and a new word-sentence method based on the new model. Experimental results demonstrate that our method outperforms the Word-net method, the classical term frequency-inverse document frequency (TF-IDF) method, most frequent method and TextRank method. The precision, recall, and F-measure of our result are respectively 7.95, 8.27 and 6.54% higher than the Word-net result, and the average precision of our result is 17.56% higher than the TF-IDF result. A two-way analysis of variance is employed to validate the empirical analysis, which indicates that keyword extraction methods and keyword numbers have statistically significant effects on the evaluation of metric values.


title: Requirements engineering and the creative process in the video game industry

abstract: The software engineering process in video game development is not clearly understood, hindering the development of reliable practices and processes for this field. An investigation of factors leading to success or failure in video game development suggests that many failures can be traced to problems with the transition from preproduction to production. Three examples, drawn from real video games, illustrate specific problems: 1) how to transform documentation from its preproduction form to a form that can be used as a basis for production;, 2) how to identify implied information in preproduction documents; and 3) how to apply domain knowledge without hindering the creative process. We identify 3 levels of implication and show that there is a strong correlation between experience and the ability to identify issues at each level. The accumulated evidence clearly identifies the need to extend traditional requirements engineering techniques to support the creative process in video game development.


title: An Integrated Vehicle Navigation System Utilizing Lane-Detection and Lateral Position Estimation Systems in Difficult Environments for GPS

abstract: A navigation filter combines measurements from sensors currently available on vehicles - Global Positioning System (GPS), inertial measurement unit, inertial measurement unit (IMU), camera, and light detection and ranging (lidar) - for achieving lane-level positioning in environments where stand-alone GPS can suffer or fail. Measurements from the camera and lidar are used in two lane-detection systems, and the calculated lateral distance (to the lane markings) estimates of both lane-detection systems are compared with centimeter-level truth to show decimeter-level accuracy. The navigation filter uses the lateral distance measurements from the lidar- and camera-based systems with a known waypoint-based map to provide global measurements for use in a GPS/Inertial Navigation System (INS) system. Experimental results show that the inclusion of lateral distance measurements and a height constraint from the map creates a fully observable system even with only two satellite observations and, as such, greatly enhances the robustness of the integrated system over GPS/INS alone. Various scenarios are presented, which affect the navigation filter, including satellite geometry, number of satellites, and loss of lateral distance measurements from the camera and lidar systems.


title: Health Systems' Responsiveness and Its Characteristics: A Cross-Country Comparative Analysis

abstract: This paper investigates the influence of aggregate country-level characteristics on health system responsiveness, using data on 62 countries present in the World Health Survey. While evidence exists on variations in reported levels of health system responsiveness across countries, the literature is sparse on the determinants of responsiveness, particularly of system wide characteristics (World Health Report, 2000). We attempt to bridge this gap in the literature by considering simultaneously several plausible country-level characteristics as potential determinants of health system responsiveness. These characteristics refer to the way health care systems are organised and funded, the socio-demographic traits of the populations served and the economic, cultural and institutional characteristics of countries. We pay particular attention to the role of health care expenditures per capita while controlling for potential confounding factors. Data on responsiveness and socio-demographic characteristics of respondents are taken from the World Health Survey, a survey launched by the World Health Organization in 2001. Information on the country-level characteristics are obtained from the United Nations Development Program (UNDP), the World Value Survey and the Polity IV Project database. The empirical analysis is performed by adopting a two step procedure. First, we increase the crosscountry comparability of the data by adjusting for variation in the way survey respondents rate an objective level of responsiveness using the hierarchical ordered probit (hopit) model. Secondly, we investigate the influence of health spending per capita and other country characteristics on the adjusted country-level measures of responsiveness. Our results suggest that the most relevant determinants of responsiveness appear to be health expenditure per capita, health care expenditure in the public sector and population levels of education.


title: Roadmap Generation using a Multi-stage Ensemble of Deep Neural Networks with Smoothing-Based Optimization

abstract: Road detection from aerial images is a challenging task for humans and machines alike. Occlusion, the lack of visual cues and slim class borders for other road-like structures (such as pathways or private alleys) make the problem inherently ambiguous, requiring logic that goes beyond the input image. We propose a three-stage method for the task of road segmentation - first, an ensemble of multiple U-Net like CNNs generate binary road masks. Second, another CNN learns to refine roads segmentations based on the fusion of the road maps from the first stage. Third, missing links are added based on the inferred graph to improve segmentation.


title: An Approach for Combining Multiple Weighting Schemes and Ranking Methods in Graph-Based Multi-Document Summarization

abstract: Automatic text summarization aims to reduce the document text size by building a brief and voluble summary that has the most important ideas in that document. Through the years, many approaches were proposed to improve the automatic text summarization results; the graph-based method for sentence ranking is considered one of the most important approaches in this field. However, most of these approaches rely on only one weighting scheme and one ranking method, which may cause some limitations in their systems. In this paper, we focus on combining multiple graph-based approaches to improve the results of generic, extractive, and multi-document summarization. This improvement results in more accurate summaries, which could be used as a significant part of some natural language applications. We develop and experiment with two graph-based approaches that combine four weighting schemes and two ranking methods in one graph framework. To combine these methods, we propose taking the average of their results using the arithmetic mean and the harmonic mean. We evaluate our proposed approaches using DUC 2003 & DUC 2004 dataset and measure the performance using ROUGE evaluation toolkit. Our experiments demonstrate that using the harmonic mean in combining weighting schemes outperform the arithmetic mean and show a good improvement over the baselines and many state-of-the-art systems.


title: Exploiting Data Deduplication to Accelerate Live Virtual Machine Migration

abstract: As one of the key characteristics of virtualization, live virtual machine (VM) migration provides great benefits for load balancing, power management, fault tolerance and other system maintenance issues in modern clusters and data centers. Although Pre-Copy is a widespread used migration algorithm, it does transfer a lot of duplicated memory image data from source to destination, which results in longer migration time and downtime. This paper proposes a novel VM migration approach, named Migration with Data Deduplication (MDD), which introduces data deduplication into migration. MDD utilizes the self-similarity of run-time memory image, uses hash based fingerprints to find identical and similar memory pages, and employs Run Length Encode (RLE) to eliminate redundant memory data during migration. Experiment demonstrates that compared with Xen's default Pre-Copy migration algorithm, MDD can reduce 56.60% of total data transferred during migration, 34.93% of total migration time, and 26.16% of downtime on average.


title: Evolving Game Skill-Depth using General Video Game AI agents

abstract: Most games have, or can be generalised to have, a number of parameters that may be varied in order to provide instances of games that lead to very different player experiences. The space of possible parameter settings can be seen as a search space, and we can therefore use a Random Mutation Hill Climbing algorithm or other search methods to find the parameter settings that induce the best games. One of the hardest parts of this approach is defining a suitable fitness function. In this paper we explore the possibility of using one of a growing set of General Video Game AI agents to perform automatic play-testing. This enables a very general approach to game evaluation based on estimating the skill-depth of a game. Agent-based play-testing is computationally expensive, so we compare two simple but efficient optimisation algorithms: the Random Mutation Hill-Climber and the Multi-Armed Bandit Random Mutation Hill-Climber. For the test game we use a space-battle game in order to provide a suitable balance between simulation speed and potential skill-depth. Results show that both algorithms are able to rapidly evolve game versions with significant skill-depth, but that choosing a suitable resampling number is essential in order to combat the effects of noise.


title: Smart cevices for smart environments: Device-free passive detection in real environments

abstract: Device-free Passive (DfP) localization is a system envisioned to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. A DfP system allows using nominal WiFi equipment for intrusion detection, without using any extra hardware, adding smartness to any WiFi-enabled device. In this paper, we focus on the detection function of the DfP system in a real environment. We show that the performance of our previously developed algorithms for detection in a controlled environments, which achieved 100% recall and precision, degrades significantly when tested in a real environment. We present an alternative algorithm, based on the maximum likelihood estimator (MLE), that has a significant performance increase in a real environment. Our results show that the recall of the system increases by more than 10% when using the proposed MLE without affecting the system's precision.


title: A localization algorithm for distorted or rotated QR code

abstract: Due to the efficient encoding of Chinese characters and quickly recognition, the Matrix QR code is widely used in numerous industries. Exact localization is the premise and crux of QR code recognition while existing localization algorithms have weak robustness when the QR code is distorted or rotated. A new adaptive and robust localization algorithm for QR code is proposed in this paper. First, we use the adaptive threshold algorithm to solve the influence of uneven illumination. On this basis, we propose a localization method which is based on contour tracking and contour sifting. The experiments of several sample sets show a high detection rate while the QR code is rotated freely or distorted slightly.


title: A Resource-Allocating Network for Function Interpolation

abstract: We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.


title: A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu

abstract: Satellite remote sensing can be an effective alternative for mapping cyanobacterial scums and aquatic macrophyte distribution over large areas compared with traditional ship’s site-specific samplings. However, similar optical spectra characteristics between aquatic macrophytes and cyanobacterial scums in red and near infrared (NIR) wavebands create a barrier to their discrimination when they co-occur. We developed a new cyanobacteria and macrophytes index (CMI) based on a blue, a green, and a shortwave infrared band to separate waters with cyanobacterial scums from those dominated by aquatic macrophytes, and a turbid water index (TWI) to avoid interference from high turbid waters typical of shallow lakes. Combining CMI, TWI, and the floating algae index (FAI), we used a novel classification approach to discriminate lake water, cyanobacteria blooms, submerged macrophytes, and emergent/floating macrophytes using MODIS imagery in the large shallow and eutrophic Lake Taihu (China). Thresholds for CMI, TWI, and FAI were determined by statistical analysis for a 2010–2016 MODIS Aqua time series. We validated the accuracy of our approach by in situ reflectance spectra, field investigations and high spatial resolution HJ-CCD data. The overall classification accuracy was 86% in total, and the user’s accuracy was 88%, 79%, 85%, and 93% for submerged macrophytes, emergent/floating macrophytes, cyanobacterial scums and lake water, respectively. The estimated aquatic macrophyte distributions gave consistent results with that based on HJ-CCD data. This new approach allows for the coincident determination of the distributions of cyanobacteria blooms and aquatic macrophytes in eutrophic shallow lakes. We also discuss the utility of the approach with respect to masking clouds, black waters, and atmospheric effects, and its mixed-pixel effects.


title: Kawasaki’s Disease

abstract: Kawasaki’s disease can cause cardiac changes. Such changes have implications for dental treatment.


title: QuizRDF: search technology for the semantic Web

abstract: An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RDF annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.


title: Building the Capacity for School Improvement

abstract: Effective school improvement involves building the capacity for change and development. This necessitates pressure and support from both external and internal sources. The need for 'change agentry' is well established in the literature however relatively little has been written about the role of the LEA as an external agent of change. This paper focuses upon the role of the LEA in building capacity for school improvement. It focuses upon a highly successful school improvement project and draws upon related empirical evidence to explore the LEA's role as an agent of change. The article concludes by suggesting that the current responsibilities placed upon the LEA are not conducive to ways of working with schools that will build the capacity for improvement.


title: FPGA acceleration of multilevel ORB feature extraction for computer vision

abstract: In this paper, we present the first multilevel implementation of the Harris-Stephens corner detector and the ORB feature extractor running on FPGA hardware, for computer vision and robotics applications. ORB is a fundamental component of many robotics applications, and requires significant computation. The design has been validated both in behavioural simulation and in implementation on an Arria V FPGA connected to a desktop PC via PCI-Express. A Linux kernel-mode driver and userspace library allow integration of the acceleration hardware into C++ programs. The device has significantly higher throughput than a CPU implementation (150 MPixel/s vs 27 MPixel/s) and a GPU implementation (40 MPixel/s), with much lower power draw (5.3 W vs 145 W). This throughput is equivalent to 72 fps at 1920 × 1080 or 488 fps at 640 × 480.


title: A comprehensive framework for evaluation in design science research

abstract: Evaluation is a central and essential activity in conducting rigorous Design Science Research (DSR), yet there is surprisingly little guidance about designing the DSR evaluation activity beyond suggesting possible methods that could be used for evaluation. This paper extends the notable exception of the existing framework of Pries-Heje et al [11] to address this problem. The paper proposes an extended DSR evaluation framework together with a DSR evaluation design method that can guide DSR researchers in choosing an appropriate strategy for evaluation of the design artifacts and design theories that form the output from DSR. The extended DSR evaluation framework asks the DSR researcher to consider (as input to the choice of the DSR evaluation strategy) contextual factors of goals, conditions, and constraints on the DSR evaluation, e.g. the type and level of desired rigor, the type of artifact, the need to support formative development of the designed artifacts, the properties of the artifact to be evaluated, and the constraints on resources available, such as time, labor, facilities, expertise, and access to research subjects. The framework and method support matching these in the first instance to one or more DSR evaluation strategies, including the choice of ex ante (prior to artifact construction) versus ex post evaluation (after artifact construction) and naturalistic (e.g., field setting) versus artificial evaluation (e.g., laboratory setting). Based on the recommended evaluation strategy(ies), guidance is provided concerning what methodologies might be appropriate within the chosen strategy(ies).


A Personal View of Average-Case Complexity


title: A 2.5 GHz low phase noise oscillator design in 65nm CMOS technology with reduced current consumption

abstract: A 2.5 GHz low phase noise oscillator is presented in this paper. The oscillator was design using a Solidly Mounted BAW Resonator (SMR BAW) as resonant element. The resonator exhibits a parallel resonance Q factor around 1300 at 2.54 GHz. The core oscillator was designed using STMicroelectronics 65 nm CMOS technology. It exhibits 632 mV output (zero-to-pic) with phase noise performance of -92 dBc/Hz, -109 dBc/Hz, and -130 dBc/Hz at 2 KHz, 10 KHz and 100 KHz respectively. It consumes 1 mA from a 1.2 V source.


title: Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems

abstract: Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of 'bonus' payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each 'arm' representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We provide a full analysis of this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature).


title: Adversarial Label Flips Attack on Support Vector Machines

abstract: To develop a robust classification algorithm in the adversarial setting, it is important to understand the adversary's strategy. We address the problem of label flips attack where an adversary contaminates the training set through flipping labels. By analyzing the objective of the adversary, we formulate an optimization framework for finding the label flips that maximize the classification error. An algorithm for attacking support vector machines is derived. Experiments demonstrate that the accuracy of classifiers is significantly degraded under the attack.


title: TransNet: Translation-Based Network Representation Learning for Social Relation Extraction

abstract: Conventional network representation learning (NRL) models learn low-dimensional vertex representations by simply regarding each edge as a binary or continuous value. However, there exists rich semantic information on edges and the interactions between vertices usually preserve distinct meanings, which are largely neglected by most existing NRL models. In this work, we present a novel Translation-based NRL model, TransNet, by regarding the interactions between vertices as a translation operation. Moreover, we formalize the task of Social Relation Extraction (SRE) to evaluate the capability of NRL methods on modeling the relations between vertices. Experimental results on SRE demonstrate that TransNet significantly outperforms other baseline methods by 10% to 20% on [email protected]1. The source code and datasets can be obtained from https://github.com/thunlp/TransNet.


title: Image Segmentation Algorithms Overview

abstract: The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms.


title: Taxonomy of Challenges for Digital Forensics

abstract: Since its inception, over a decade ago, the field of digital forensics has faced numerous challenges. Despite different researchers and digital forensic practitioners having studied and analysed various known digital forensic challenges, as of 2013, there still exists a need for a formal classification of these challenges. This article therefore reviews existing research literature and highlights the various challenges that digital forensics has faced for the last 10 years. In conducting this research study, however, it was difficult for the authors to review all the existing research literature in the digital forensic domain; hence, sampling and randomization techniques were employed to facilitate the review of the gathered literature. Taxonomy of the various challenges is subsequently proposed in this paper based on our review of the literature. The taxonomy classifies the large number of digital forensic challenges into four well-defined and easily understood categories. The proposed taxonomy can be useful, for example, in future developments of automated digital forensic tools by explicitly describing processes and procedures that focus on addressing specific challenges identified in this paper. However, it should also be noted that the purpose of this paper was not to propose any solutions to the individual challenges that digital forensics face, but to serve as a survey of the state of the art of the research area.


title: Reconstruction of high contrast images for dynamic scenes

abstract: High Dynamic Range (HDR) imaging requires one to composite multiple, differently exposed images of a scene in the irradiance domain and perform tone mapping of the generated HDR image for displaying on Low Dynamic Range (LDR) devices. In the case of dynamic scenes, standard techniques may introduce artifacts called ghosts if the scene changes are not accounted for. In this paper, we consider the blind HDR problem for dynamic scenes. We develop a novel bottom-up segmentation algorithm through superpixel grouping which enables us to detect scene changes. We then employ a piecewise patch-based compositing methodology in the gradient domain to directly generate the ghost-free LDR image of the dynamic scene. Being a blind method, the primary advantage of our approach is that we do not assume any knowledge of camera response function and exposure settings while preserving the contrast even in the non-stationary regions of the scene. We compare the results of our approach for both static and dynamic scenes with that of the state-of-the-art techniques.


title: Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories

abstract: Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain experts or large number of annotators. This is very costly and not necessarily optimal regarding recognition performance, and most importantly, it limits the applicability of attribute-based models to large scale data sets. To tackle this problem, we propose an end-to-end unsupervised attribute learning approach. We utilize online text corpora to automatically discover a salient and discriminative vocabulary that correlates well with the human concept of semantic attributes. Moreover, we propose a deep convolutional model to optimize class-attribute associations with a linguistic prior that accounts for noise and missing data in text. In a thorough evaluation on ImageNet, we demonstrate that our model is able to efficiently discover and learn semantic attributes at a large scale. Furthermore, we demonstrate that our model outperforms the state-of-the-art in zero-shot learning on three data sets: ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable attribute-based learning on ImageNet and will share the attributes and associations for future research.


title: Evasion and Hardening of Tree Ensemble Classifiers

abstract: Classifier evasion consists in finding for a given instance $x$ the nearest instance $x'$ such that the classifier predictions of $x$ and $x'$ are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixed Integer Linear Program solver and finds the optimal evading instance under an expressive set of constraints. Our second algorithm trades off optimality for speed by using symbolic prediction, a novel algorithm for fast finite differences on tree ensembles. On a digit recognition task, we demonstrate that both gradient boosted trees and random forests are extremely susceptible to evasions. Finally, we harden a boosted tree model without loss of predictive accuracy by augmenting the training set of each boosting round with evading instances, a technique we call adversarial boosting.


title: Discovering the invisible city: Location-based games for learning in smart cities

abstract: In this paper we discuss how location-based mobile games can be designed for learning in modern technology enhanced public spaces. We start with the description of the design process and we identify the main challenges faced. We elaborate the case of the game Invisible City: Rebels vs. Spies, a game to be played in a city centre using mobile devices. Through this case we highlight the adaptation of an original party game into a mobile form, the issues we faced and the key aspects conductive to learning in a smart city. It is claimed that creating mobile city games for learning is a new challenge, as our city landscapes are augmented with an increasing number of layers of digital information in which a new generation of city games are played.


title: Nostalgia and the emotional tone and content of song lyrics.

abstract: Emotion and topic were manipulated in original song lyrics. Participants completed Batcho's (1995) and Holbrook's (1993) nostalgia surveys and rated 6 sets of lyrics for happiness, sadness, anger, nostalgia, meaning, liking, and relevance. Nostalgic lyrics were characterized by bittersweet reverie, loss of the past, identity, and meaning. Contrary to theories linking nostalgia to pathology, participants who scored high on Batcho's measure of personal nostalgia preferred happy lyrics, found them more meaningful, and related more closely to them. Consistent with theories relating nostalgia to social connectedness, high-nostalgia respondents preferred other-directed to solitary themes. Historical nostalgia was associated with relating more closely to sad lyrics.


title: Sniffing Through Millions of Blocks for Bad Smells

abstract: Code smells codify poor coding patterns known to degrade software quality. Block-based languages have proven to be a viable educational and end-user programming paradigm with increasing adoption across a broad spectrum of users and domains. This rising popularity of this programming paradigm calls for a serious look at the program quality written in block-based languages. While code smells in the context of text-based languages have been studied extensively, the research community lacks a comprehensive understanding of code smells in block-based software. To address this problem, we present the results of a large-scale study of code smells prevalent in programs written in the highly popular Scratch programming language. We analyzed programs submitted to the public Scratch repository in 2016, considering a million programs altogether. We discovered interesting relationships between the prevalence of certain smells and the levels of proficiency of the programmers commonly introducing them. Our findings not only can help block-based programmers improve the quality of their software, but also establish the requirements for refactoring support in this programming domain.


title: Comparative Study on Agile software development methodologies

abstract: Today-s business environment is very much dynamic, and organisations are constantly changing their software requirements to adjust with new environment. They also demand for fast delivery of software products as well as for accepting changing requirements. In this aspect, traditional plan-driven developments fail to meet up these requirements. Though traditional software development methodologies, such as life cycle-based structured and object oriented approaches, continue to dominate the systems development few decades and much research has done in traditional methodologies, Agile software development brings its own set of novel challenges that must be addressed to satisfy the customer through early and continuous delivery of the valuable software. It is a set of software development methods based on iterative and incremental development process, where requirements and development evolve through collaboration between self-organizing, cross-functional teams that allows rapid delivery of high quality software to meet customer needs and also accommodate changes in the requirements. In this paper, we significantly identify and describe the major factors, that Agile development approach improves software development process to meet the rapid changing business environments. We also provide a brief comparison of agile development methodologies with traditional systems development methodologies, and discuss current state of adopting agile methodologies. We speculate that from the need to satisfy the customer through early and continuous delivery of the valuable software, Agile software development is emerged as an alternative to traditional plan-based software development methods. The purpose of this paper, is to provide an in-depth understanding, the major benefits of agile development approach to software development industry, as well as provide a comparison study report of ASDM over TSDM.


title: Testing and Validating Machine Learning Classifiers by Metamorphic Testing ✩

abstract: Abstract: Machine learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no ''test oracle'' to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique ''metamorphic testing'', which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program.


title: Fast Nonparametric Conditional Density Estimation

abstract: Conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx). This is important for many tasks, including handling multi-modality and generating prediction intervals. Though fundamental and widely applicable, nonparametric conditional density estimators have received relatively little attention from statisticians and little or none from the machine learning community. None of that work has been applied to greater than bivariate data, presumably due to the computational difficulty of data-driven bandwidth selection. We describe the double kernel conditional density estimator and derive fast dual-tree-based algorithms for bandwidth selection using a maximum likelihood criterion. These techniques give speedups of up to 3.8 million in our experiments, and enable the first applications to previously intractable large multivariate datasets, including a redshift prediction problem from the Sloan Digital Sky Survey.


title: Evaluating Shape Representations for Maya Glyph Classification

abstract: Shape representations are critical for visual analysis of cultural heritage materials. This article studies two types of shape representations in a bag-of-words-based pipeline to recognize Maya glyphs. The first is a knowledge-driven Histogram of Orientation Shape Context (HOOSC) representation, and the second is a data-driven representation obtained by applying an unsupervised Sparse Autoencoder (SA). In addition to the glyph data, the generalization ability of the descriptors is investigated on a larger-scale sketch dataset. The contributions of this article are four-fold: (1) the evaluation of the performance of a data-driven auto-encoder approach for shape representation; (2) a comparative study of hand-designed HOOSC and data-driven SA; (3) an experimental protocol to assess the effect of the different parameters of both representations; and (4) bridging humanities and computer vision/machine learning for Maya studies, specifically for visual analysis of glyphs. From our experiments, the data-driven representation performs overall in par with the hand-designed representation for similar locality sizes on which the descriptor is computed. We also observe that a larger number of hidden units, the use of average pooling, and a larger training data size in the SA representation all improved the descriptor performance. Additionally, the characteristics of the data and stroke size play an important role in the learned representation.


title: Particle swarm optimization-based algorithms for TSP and generalized TSP

abstract: A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented. An uncertain searching strategy and a crossover eliminated technique are used to accelerate the convergence speed. Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm. Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by employing the generalized chromosome. Two local search techniques are used to speed up the convergence. Numerical results show the effectiveness of the proposed algorithms.


title: Fatal air embolism during female autoerotic practice

abstract: Air embolism has been described in pregnant women and is a classic cause of death during attempted abortion by syringing. Death was caused by the introduction of an amount of air sufficient to cause a substantial air lock in the heart, pulmonary trunk or arteries. If the surface of the placenta has been stripped by the syringe, venous spaces are opened and air can pass into the circulation. Orogenital sex with vaginal insufflation can also cause air embolism during pregnancy. We report a case of air embolism in a 40-year-old non-pregnant woman subsequent to vaginal insertion of a foreign body (carrot) for an autoerotic purpose. The mechanism is roughly similar to syringing, the foreign body acting like a piston to displace a sufficient amount of air. In the present case, the endometrium stripping was probably due to the presence of an intrauterine device. As such risks may often be encountered and as, to our knowledge, no similar case has previously been reported, we may speculate if such air embolisms are underdiagnosed or dissimulated by the partners when questioned by the medical rescue teams.


The Measurement of Textual Coherence with Latent Semantic Analysis


title: A BigData approach for classification and prediction of student result using MapReduce

abstract: In recent years the amount of data stored in educational database is growing rapidly. The stored database contains hidden information which if used aids improvement of student's performance and behaviour. In this paper predictive modelling approach is used for extracting this hidden information. Data is collected, a predictive model is formulated, predictions are made, and the model is validated as additional data becomes available. The predictive models will help the instructor to understand how well or how poorly the students in his/her class will perform, and hence the instructor can choose proper pedagogical and instructional interventions to enhance student learning outcomes. The implementation is done in Hadoop framework with MapReduce and Revolutionary R Enterprise RRE.


title: Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network

abstract: Face super-resolution methods usually aim at producing visually appealing results rather than preserving distinctive features for further face identification. In this work, we propose a deep learning method for face verification on very low-resolution face images that involves identity-preserving face super-resolution. Our framework includes a super-resolution network and a feature extraction network. We train a VGG-based deep face recognition network (Parkhi et al. 2015) to be used as feature extractor. Our super-resolution network is trained to minimize the feature distance between the high resolution ground truth image and the super-resolved image, where features are extracted using our pre-trained feature extraction network. We carry out experiments on FRGC, Multi-PIE, LFW-a, and MegaFace datasets to evaluate our method in controlled and uncontrolled settings. The results show that the presented method outperforms conventional super-resolution methods in low-resolution face verification.


title: Accounting for Language Changes Over Time in Document Similarity Search

abstract: Given a query document, ranking the documents in a collection based on how similar they are to the query is an essential task with extensive applications. For collections that contain documents whose creation dates span several decades, this task is further complicated by the fact that the language changes over time. For example, many terms add or lose one or more senses to meet people’s evolving needs. To address this problem, we present methods that take advantage of two types of information to account for the language change. The first is the citation network that often exists within the collection, which can be used to link related documents with significantly different creation dates (and hence different language use). The second is the changes in the usage frequency of terms that occur over time, which can indicate changes in their senses and uses. These methods utilize the preceding information while estimating the representation of both documents and terms within the context of nonprobabilistic static and dynamic topic models. Our experiments on two real-world datasets that span more than 40 years show that our proposed methods improve the retrieval performance of existing models and that these improvements are statistically significant.


title: Psychoneuroimmunology: Psychological influences on immune function and health

abstract: This review focuses on human psychoneuroimmunology studies published in the past decade. Issues discussed include the routes through which psychological factors influence immune function, how a stressor’s duration may influence the changes observed, individual difference variables, the ability of interventions to modulate immune function, and the health consequences of psychosocially mediated immune dysregulation. The importance of negative affect and supportive personal relationships are highlighted. Recent data suggest that immune dysregulation may be one core mechanism for a spectrum of conditions associated with aging, including cardiovascular disease, osteoporosis, arthritis, Type 2 diabetes, certain cancers, and frailty and functional decline; production of proinflammatory cytokines that influence these and other conditions can be stimulated directly by negative emotions and indirectly by prolonged infection.


title: A Novel Cross Layer Intrusion Detection System in MANET

abstract: Intrusion detection System forms a vital component of internet security. To keep pace with the growing trends, there is a critical need to replace single layer detection technology with multi layer detection. Different types of Denial of Service (DoS) attacks thwart authorized users from gaining access to the networks and we tried to detect as well as alleviate some of those attacks. In this paper, we have proposed a novel cross layer intrusion detection architecture to discover the malicious nodes and different types of DoS attacks by exploiting the information available across different layers of protocol stack in order to improve the accuracy of detection. We have used cooperative anomaly intrusion detection with data mining technique to enhance the proposed architecture. We have implemented fixed width clustering algorithm for efficient detection of the anomalies in the MANET traffic and also generated different types of attacks in the network. The simulation of the proposed architecture is performed in OPNET simulator and we got the result as we expected.


title: Pixel level jointed sparse representation with RPCA image fusion algorithm

abstract: This paper presents a pixel level image fusion technique based on integrating the sparse representation with robust principle component analysis algorithm (RPCA) to promote relevant information, eliminate noise and preserve edges. In this method, the input images are first decomposed into their common components and sparse or uncommon components using RPCA algorithm. Secondly, the sparse representation using OMP algorithm with a trained dictionary is adapted to fuse those two components by adding the common low rank sparse coefficient to the maximum norm of uncommon sparse coefficients. Finally, we reconstruct the fused image from the fused sparse coefficients and adaptive dictionary. This proposed image fusion technique is evaluated and compared with OMP based on visual inspection and quality measures. The conjoint OMP-RPCA simultaneous method shows promising results and best performance in a variety of different image types including medical, multifocus and infrared and visible light images.


title: Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data

abstract: Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal observational data are often non-causal and occur due to confounding. The focus of this paper is to investigate incorporating information from additional databases to complement the longitudinal observational database analysis. We investigate the detection of prescription drug side effects as this is an example of a causal relationship. In previous work a framework was proposed for detecting side effects only using longitudinal data. In this paper we combine a measure of association derived from mining a spontaneous reporting system database to previously proposed analysis that extracts domain expertise features for causal analysis of a UK general practice longitudinal database. The results show that there is a significant improvement to the performance of detecting prescription drug side effects when the longitudinal observation data analysis is complemented by incorporating additional drug safety sources into the framework. The area under the receiver operating characteristic curve (AUC) for correctly classifying a side effect when other data were considered was 0.967, whereas without it the AUC was 0.923 However, the results of this paper may be biased by the evaluation and future work should overcome this by developing an unbiased reference set.


title: A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe

abstract: Reference EPFL-CONF-229229 URL: http://proceedings.mlr.press/v54/locatello17a.html Record created on 2017-06-21, modified on 2017-07-11


title: A portable Wireless Sensor Network system for real-time environmental monitoring

abstract: Environmental contaminations such as fine particulate matter (PM2.5) and ultraviolet (UV) are expanding public health concerns globally. Existing centralized monitoring stations, however, are unable to properly estimate human exposure due to the low resolution spatiotemporal data. Wireless Sensor Network (WSN) uses real-time capable instrumentation could not only provide fine-grained readings of multiple environmental factors, but also create real-time mapping of the contaminants. In this paper we present a WSN system which is capable of sensing multiple environmental factors, collecting data from multiple dispersed sensor nodes and displaying the aggregated data in real-time. Each individual sensor node is capable of probing multiple factors, including temperature, humidity, atmospheric pressure, PM2.5, UV radiation, and geographical location. Sensor data are transmitted to a server, which then be stored into a database through Wi-Fi networks. Each sensor node is portable enough to be carried for personal use, enabling broad potential application of our system.


Modeling memory and perception


title: 3DensiNet: A Robust Neural Network Architecture towards 3D Volumetric Object Prediction from 2D Image

abstract: 3D volumetric object generation/prediction from single 2D image is a quite challenging but meaningful task in 3D visual computing. In this paper, we propose a novel neural network architecture, named "3DensiNet", which uses density heat-map as an intermediate supervision tool for 2D-to-3D transformation. Specifically, we firstly present a 2D density heat-map to 3D volumetric object encoding-decoding network, which outperforms classical 3D autoencoder. Then we show that using 2D image to predict its density heat-map via a 2D to 2D encoding-decoding network is feasible. In addition, we leverage adversarial loss to fine tune our network, which improves the generated/predicted 3D voxel objects to be more similar to the ground truth voxel object. Experimental results on 3D volumetric prediction from 2D images demonstrates superior performance of 3DensiNet over other state-of-the-art techniques in handling 3D volumetric object generation/prediction from single 2D image.


title: Parallel Transport Unfolding: A Connection-based Manifold Learning Approach

abstract: Manifold learning offers nonlinear dimensionality reduction of high-dimensional datasets. In this paper, we bring geometry processing to bear on manifold learning by introducing a new approach base...


title: Accuracy-based Curriculum Learning in Deep Reinforcement Learning

abstract: In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show that an agent trained with various accuracy requirements sampled randomly learns more efficiently than when asked to be very accurate at all times. Then we show that adaptive selection of accuracy requirements, based on a local measure of competence progress, automatically generates a curriculum where difficulty progressively increases, resulting in a better learning efficiency than sampling randomly.


title: Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction

abstract: Automated driving requires decision making in dynamic and uncertain environments. The uncertainty from the prediction originates from the noisy sensor data and from the fact that the intention of human drivers cannot be directly measured. This problem is formulated as a partially observable Markov decision process (POMDP) with the intended route of the other vehicles as hidden variables. The solution of the POMDP is a policy determining the optimal acceleration of the ego vehicle along a preplanned path. Therefore, the policy is optimized for the most likely future scenarios resulting from an interactive, probabilistic motion model for the other vehicles. Considering possible future measurements of the surrounding cars allows the autonomous car to incorporate the estimated change in future prediction accuracy in the optimal policy. A compact representation results in a low-dimensional state-space. Thus, the problem can be solved online for varying road layouts and number of vehicles. This is done with a point-based solver in an anytime fashion on a continuous state-space. Our evaluation is threefold: At first, the convergence of the algorithm is evaluated and it is shown how the convergence can be improved with an additional search heuristic. Second, we show various planning scenarios to demonstrate how the introduction of different considered uncertainties results in more conservative planning. At the end, we show online simulations for the crossing of complex (unsignalized) intersections. We can demonstrate that our approach performs nearly as good as with full prior information about the intentions of the other vehicles and clearly outperforms reactive approaches.


title: Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks

abstract: Recently, there has been considerable interest in new tiered network cellular architectures, which would likely use many more cell sites than found today. Two major challenges will be i) providing backhaul to all of these cells and ii) finding efficient techniques to leverage higher frequency bands for mobile access and backhaul. This paper proposes the use of outdoor millimeter wave communications for backhaul networking between cells and mobile access within a cell. To overcome the outdoor impairments found in millimeter wave propagation, this paper studies beamforming using large arrays. However, such systems will require narrow beams, increasing sensitivity to movement caused by pole sway and other environmental concerns. To overcome this, we propose an efficient beam alignment technique using adaptive subspace sampling and hierarchical beam codebooks. A wind sway analysis is presented to establish a notion of beam coherence time. This highlights a previously unexplored tradeoff between array size and wind-induced movement. Generally, it is not possible to use larger arrays without risking a corresponding performance loss from wind-induced beam misalignment. The performance of the proposed alignment technique is analyzed and compared with other search and alignment methods. The results show significant performance improvement with reduced search time.


title: Deep driven fMRI decoding of visual categories

abstract: Deep neural networks have been developed drawing inspiration from the brain visual pathway, implementing an end-to-end approach: from image data to video object classes. However building an fMRI decoder with the typical structure of Convolutional Neural Network (CNN), i.e. learning multiple level of representations, seems impractical due to lack of brain data. As a possible solution, this work presents the first hybrid fMRI and deep features decoding approach: collected fMRI and deep learnt representations of video object classes are linked together by means of Kernel Canonical Correlation Analysis. In decoding, this allows exploiting the discriminatory power of CNN by relating the fMRI representation to the last layer of CNN (fc7). We show the effectiveness of embedding fMRI data onto a subspace related to deep features in distinguishing semantic visual categories based solely on brain imaging data.


title: Write a Classifier: Predicting Visual Classifiers from Unstructured Text

abstract: People typically learn through exposure to visual concepts associated with linguistic descriptions. For instance, teaching visual object categories to children is often accompanied by descriptions in text or speech. In a machine learning context, these observations motivates us to ask whether this learning process could be computationally modeled to learn visual classifiers. More specifically, the main question of this work is how to utilize purely textual description of visual classes with no training images, to learn explicit visual classifiers for them. We propose and investigate two baseline formulations, based on regression and domain transfer, that predict a linear classifier. Then, we propose a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the parameters of a linear classifier. We also propose a generic kernelized models where a kernel classifier is predicted in the form defined by the representer theorem. The kernelized models allow defining and utilizing any two Reproducing Kernel Hilbert Space (RKHS) kernel functions in the visual space and text space, respectively. We finally propose a kernel function between unstructured text descriptions that builds on distributional semantics, which shows an advantage in our setting and could be useful for other applications. We applied all the studied models to predict visual classifiers on two fine-grained and challenging categorization datasets (CU Birds and Flower Datasets), and the results indicate successful predictions of our final model over several baselines that we designed.


title: Extracorporeal Septoplasty for the Markedly Deviated Septum

abstract: Objective To describe a technique of extracorporeal septal reconstruction to correct the markedly deviated nasal septum. Design Retrospective medical chart review of 2119 patients undergoing extracorporeal septoplasty from January 1, 1981, through July 31, 2004, by the author in a tertiary care facial plastic surgery center. The main outcomes measured included surgical complications, revision rate, and the surgeon’s subjective determination of functional and aesthetic outcomes. Results Of the 2119 patients, 2 cohorts were available for review. From January 1, 1981, to July 31, 1987, the author performed the operation on 459 patients. Fifty-seven complications (12%) occurred, with irregular contour of the dorsum or saddling noted in 38 (8%). Twenty patients (4%) elected to have revision septoplasty. From January 1, 1996, to December 31, 1996, the author supervised residents performing extracorporeal septoplasty in 108 patients. Fourteen postoperative complications (13%) occurred, with dorsal irregularity noted in 12 (11%). Eight patients (7%) elected to have revision septoplasty. Conclusions Extracorporeal septal reconstruction is an important surgical option for the correction of the markedly deviated nasal septum. Fixation of the straightened and replanted septum at the nasal spine and dorsal septum border with the upper lateral cartilages is essential. Spreader grafts for stabilization of the internal nasal valve and dorsal onlay grafts to prevent dorsal irregularity are strongly encouraged.


title: The Impact of Network Topologies on the Performance of the In-Vehicle Network

abstract: Today's vehicles contain hundreds of circuits, sensors and around 80-120 Electronic Control Units(ECUs). The communication is needed among many circuits and functions of a vehicle. In earlier vehicle systems, this type of communication was handled via a dedicated wire through point-to-point connections. If all possible combinations of switches, sensors, ECUs and other electronic devices in fully featured vehicles are accumulated, the resulting number of connections and dedicated wiring is enormous. Hence networking of these components is necessary to reduce the complexity of electronics inside a vehicle. In-vehicle networking provides a more efficient method for today's complex in-vehicle communications. This paper focuses on the comparison of the performance of ring and star network topologies on the basis of bus load.


title: Adaptive spectrum searching in cognitive radio networks

abstract: The channel sensing order in cognitive radio networks is very important and has a significant impact on the cognitive users operation. In this paper, we propose four adaptive spectrum searching schemes to determine the channel sensing order taking into consideration the primary users activities. These schemes use previous sensing results to generate a criterion to find the channel sensing sequence that guarantees the fast discovery of a free channel. Two main parameters are estimated and used to sequence the channels: the probability of a channel to be free, and how long the channel will be free. The simulation results show that the delay associated with finding free channel is reduced. Moreover, the number of triggered spectrum search processes is reduced when taking the period of the opportunity into consideration.


title: Algorithms on strings, trees, and sequences

abstract: Linear-Time Construction of Suffix Trees We will present two methods for constructing suffix trees in detail, Ukkonen’s method and Weiner’s method. Weiner was the first to show that suffix trees can be built in linear time, and his method is presented both for its historical importance and for some different technical ideas that it contains. However, lJkkonen’s method is equally fast and uses far less space (i.e., memory) in practice than Weiner’s method Hence Ukkonen is the method of choice for most problems requiring the construction of a suffix tree. We also believe that Ukkonen’s method is easier to understand. Therefore, it will be presented first A reader who wishes to study only one method is advised to concentrate on it. However, our development of Weiner’s method does not depend on understanding Ukkonen’s algorithm, and the two algorithms can be read independently (with one small shared section noted in the description of Weiner’s method).


title: An entropy-based uncertainty measure of process models

abstract: In managing business processes, the process uncertainty and variability are significant factors causing difficulties in prediction and decision making, which evokes and augments the importance and need of process measures for systematic analysis. We propose an entropy-based process measure to quantify the uncertainty of business process models. The proposed measure enables capturing the dynamic behavior of processes, in contrast to previous work which focused on providing measures for the static aspect of process models. Research highlights? We propose an entropy-based uncertainty measure for business process models. ? The process uncertainty is defined in terms of the transition and execution of tasks. ? The proposed measure enables capturing the dynamic behavior of processes. ? We provide the explicit forms of measure for primitive control-flow patterns. ? It is illustrated by a process model fractionated into separate blocks of primitive patterns.


title: Lack of uniformity among United States recommendations for diagnosis and management of acute, uncomplicated cystitis

abstract: Acute, uncomplicated cystitis is one of the most common bacterial infections seen in clinical practice. Quality improvement and antibiotic stewardship efforts to optimize cystitis management rely on clinicians managing patients in a manner recommended by experts and guidelines. However, it is unclear if recent recommendations for cystitis from experts and guidelines from US medical societies that provide recommendations are well aligned. We examined recommendations and guidelines for acute, symptomatic cystitis in women published in US medical societies’ journals from January 1, 2008, to December 31, 2016, within the fields of family medicine, obstetrics and gynecology, internal medicine, female pelvic medicine and reconstructive surgery, and infectious diseases. All recommendations endorsed the use of symptoms and urine dipstick to diagnose cystitis. Some societies did not recommend urine dipstick in patients with recurrent urinary tract infection (UTI), classic UTI symptoms, or a lack of underlying conditions or competing diagnoses. All endorsed nitrofurantoin, trimethoprim-sulfamethoxazole, and fosfomycin as first-line agents. Some guidelines classified fluoroquinolones as second- or third-line, while others considered them first-line treatment for UTI. Avoiding use of amoxicillin and ampicillin, antibiotic agents with high prevalence of resistance in the US, was recommended by some societies. US recommendations differed in their approach to the treatment of acute, uncomplicated cystitis. Lack of uniformity likely contributes to clinical management variance for patients with UTI and hampers quality improvement and antibiotic stewardship efforts aimed at promoting optimal management. Our findings emphasize the need for more consistent recommendations for cystitis management.


title: Breaking NLI Systems with Sentences that Require Simple Lexical Inferences

abstract: We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences.


title: Bluetooth based home automation system using cell phone

abstract: Technology is a never ending process. To be able to design a product using the current technology that will be beneficial to the lives of others is a huge contribution to the community. This paper presents the design and implementation of a low cost but yet flexible and secure cell phone based home automation system. The design is based on a stand alone Arduino BT board and the home appliances are connected to the input/ output ports of this board via relays. The communication between the cell phone and the Arduino BT board is wireless. This system is designed to be low cost and scalable allowing variety of devices to be controlled with minimum changes to its core. Password protection is being used to only allow authorised users from accessing the appliances at home.


title: Human-Robot Interaction Through Gesture-Free Spoken Dialogue

abstract: We present an approach to human-robot interaction through gesture-free spoken dialogue. Our approach is based on passive knowledge rarefication through goal disambiguation, a technique that allows a human operator to collaborate with a mobile robot on various tasks through spoken dialogue without making bodily gestures. A key assumption underlying our approach is that the operator and the robot share a common set of goals. Another key idea is that language, vision, and action share common memory structures.We discuss how our approach achieves four types of human-robot interaction: command, goal disambiguation, introspection, and instruction-based learning. We describe the system we developed to implement our approach and present experimental results.


title: An unusual symbiont from the gut of surgeonfishes may be the largest known prokaryote.

abstract: Symbionts first reported from the gut of a Red Sea surgeonfish, Acanthurus nigrofuscus (family Acanthuridae), were subsequently described as Epulopiscium fishelsoni. The taxonomic position of this very large (up to 576 microns in length) microorganism has previously been designated in the literature as either uncertain or eukaryotic. We suggest that similar symbionts from Great Barrier Reef surgeonfish may be prokaryotes, which together with E. fishelsoni from the Red Sea may represent the largest known forms of this cell type. Features identifying the symbionts as prokaryotes include the presence of bacterial-type flagella and a bacterial nucleoid and the absence of a nucleus or any other membrane-bound organelle.


title: Real-Time GPU Resource Management with Loadable Kernel Modules

abstract: Graphics processing unit (GPU) programming environments have matured for general-purpose computing on GPUs. Significant challenges for GPUs include system software support for bounded response times and guaranteed throughput. In recent years, GPU technologies have been applied to real-time systems by extending the operating system modules to support real-time GPU resource management. Unfortunately, such a system extension makes it difficult to maintain the system with version updates because the OS kernel and device drivers must be modified at the source-code level, thereby preventing continuous research and development of GPU technologies for real-time systems. A loadable kernel module (LKM) framework, called Linux Real-Time eXtention with GPUs (Linux-RTXG), for managing real-time GPU resources with Linux without modifying the OS kernel and device drivers is proposed and evaluated experimentally. Linux-RTXG provides mechanisms for interrupt interception and independent synchronization to achieve real-time scheduling and resource reservation capabilities for GPU applications on top of existing device drivers and runtime libraries. Experimental results demonstrate that the overhead incurred by introducing the proposed Linux-RTXG is comparable to that of introducing existing kernel-dependent approaches. In addition, the results demonstrate that multiple GPU applications can be scheduled successfully by Linux-RTXG to meet their priority and quality-of-service requirements in real time.


title: Human spinal locomotor control is based on flexibly organized burst generators

abstract: Constant drive provided to the human lumbar spinal cord by epidural electrical stimulation can cause local neural circuits to generate rhythmic motor outputs to lower limb muscles in people paralysed by spinal cord injury. Epidural spinal cord stimulation thus allows the study of spinal rhythm and pattern generating circuits without their configuration by volitional motor tasks or task-specific peripheral feedback. To reveal spinal locomotor control principles, we studied the repertoire of rhythmic patterns that can be generated by the functionally isolated human lumbar spinal cord, detected as electromyographic activity from the legs, and investigated basic temporal components shared across these patterns. Ten subjects with chronic, motor-complete spinal cord injury were studied. Surface electromyographic responses to lumbar spinal cord stimulation were collected from quadriceps, hamstrings, tibialis anterior, and triceps surae in the supine position. From these data, 10-s segments of rhythmic activity present in the four muscle groups of one limb were extracted. Such samples were found in seven subjects. Physiologically adequate cycle durations and relative extension- and flexion-phase durations similar to those needed for locomotion were generated. The multi-muscle activation patterns exhibited a variety of coactivation, mixed-synergy and locomotor-like configurations. Statistical decomposition of the electromyographic data across subjects, muscles and samples of rhythmic patterns identified three common temporal components, i.e. basic or shared activation patterns. Two of these basic patterns controlled muscles to contract either synchronously or alternatingly during extension- and flexion-like phases. The third basic pattern contributed to the observed muscle activities independently from these extensor- and flexor-related basic patterns. Each bifunctional muscle group was able to express both extensor- and flexor-patterns, with variable ratios across the samples of rhythmic patterns. The basic activation patterns can be interpreted as central drives implemented by spinal burst generators that impose specific spatiotemporally organized activation on the lumbosacral motor neuron pools. Our data thus imply that the human lumbar spinal cord circuits can form burst-generating elements that flexibly combine to obtain a wide range of locomotor outputs from a constant, repetitive input. It may be possible to use this flexibility to incorporate specific adaptations to gait and stance to improve locomotor control, even after severe central nervous system damage.


title: What causes hidradenitis suppurativa?

abstract: Hidradenitis suppurativa (HS)--a rather common, very chronic and debilitating inflammatory skin appendage disorder with a notoriously underestimated burden of disease--has long been a playground for the high priests of nomenclature: Ask a bunch of eminent dermatologists and skin pathologists to publicly share their thoughts on what causes HS, and they will soon get entrenched in a heated debate on whether this historical term is a despicable misnomer. Fortunately, the recently founded Hidradenitis Suppurativa Foundation (HSF; http://www.hs-foundation.org), to which EXP DERMATOL serves as home journal, has broken with this unproductive tradition and has encouraged publication of the current CONTROVERSIES feature. This is exclusively devoted to discussing the pathobiology of this chronic neutrophilic folliculitis of unknown origin. Although traces of terminological bickering remain visible, it does the HS experts in our virtual debate room credit that they engage in a constructive and comprehensive dissection of potential pathogenesis pathways that may culminate in the clinical picture we know under the competing terms HS or acne inversa. These experts sketch more often complementary than mutually exclusive pathogenesis scenarios, and the outlines of a conceivable consensus on the many open pathobiology questions begin to emerge in these CONTROVERSIES. Hopefully, this heralds a welcome new tradition: to get to the molecular heart of HS pathogenesis, which can only be achieved by a renaissance of solid basic HS research, as the key to developing more effective HS therapy.


title: Wideband Magnetoelectric Dipole Antennas With Dual Polarization and Circular Polarization

abstract: A new 45 degrees dual-polarized magnetoelectric dipole antenna is proposed. The antenna is excited by two -shaped probes placed at a convenient location. The measured overlapped impedance bandwidth is 48% with standing-wave ratio (SWR) 1.5 from 1.69 to 2.76 GHz. The measured gains vary from 7.6 to 9.3 dBi and from 7.6 to 9.4 dBi for port 1 and port 2, respectively. The isolation between the two ports is larger than 30 dB. The proposed antenna achieves a low cross-polarization level of less than 21 dB and a low back radiation level of less than 29 dB over the operating frequency range. With a broadband 90 phase shifter and a power divider, the proposed antenna can radiate circularly-polarized (CP) wave and exhibit a wide impedance bandwidth (SWR 2) of 90% from 1.23 to 3.23 GHz, which covers the whole 3-dB axial-ratio (AR) bandwidth of 82% from 1.28 to 3.05 GHz. In this operation frequency band, the proposed CP antenna has a broadside gain of larger than 5 dBi above 1.45 GHz. Considering the common overlapped bandwidth limited by the impedance, AR, and gain, the proposed antenna exhibits an effective bandwidth of 71%.


title: BRICS - Best practice in robotics

abstract: In the past, the process of developing a new robot application has had more of the design of a piece of artwork or of an act of ingenious engineering than of a structured and formalized process. The prime objective of BRICS is to structure and formalize the robot development process itself and to provide tools, models, and functional libraries, which allow reducing the development time by a magnitude. BRICS is working together with academic as well as industrial providers of robotics “components” (hardware and software), to identify and document best practices in the development of complex robotics systems, to refactor (together) the existing components in order to achieve a much higher level of reusability and robustness, and to support the robot development process with a structured tool chain and code repository. BRICS is a joint research project funded by the European Commission ICT Challenge 2 under grant number 231940. First results include the analysis of existing robot development processes, the first steps towards harmonizing robot con- ::: trol interfaces and component models and the set-up of robot systems for best practice analyses.


title: Understanding student differences

abstract: Students have different levels of motivation, different attitudes about teaching and learning, and different responses to specific classroom environments and instructional practices. The more thoroughly instructors understand the differences, the better chance they have of meeting the diverse learning needs of all of their students. Three categories of diversity that have been shown to have important implications for teaching and learning are differences in students' learning styles (characteristic ways of taking in and processing information), approaches to learning (surface, deep, and strategic), and intellectual development levels (attitudes about the nature of knowledge and how it should be acquired and evaluated). This article reviews models that have been developed for each of these categories, outlines their pedagogical implications, and suggests areas for further study.


LabVIEW, the USRP, and their Implications on Software Defined Radio