title: The Effect of Preincubation of Seed Crystals of Uric Acid and Monosodium Urate with Undiluted Human Urine to Induce Precipitation of Calcium Oxalate in Vitro: Implications for Urinary

abstract: BACKGROUND: Previous studies demonstrated that crystals of uric acid (UA) and sodium urate (NaU) can induce the precipitation of calcium oxalate (CaOx) from its inorganic metastable solutions, but similar effects have not been unequivocally shown to occur in urine. The aim of this investigation was to determine whether preincubation of these seeds with urine alter their ability to induce deposition of CaOx from solution and thus provide a possible explanation for discrepancy of results obtained from aqueous inorganic solutions and undiluted urine. MATERIALS AND METHODS: The effects of commercial seed crystals of UA, NaU and CaOx (6 mg/100 ml) on CaOx crystallization were compared in a solution with the same crystals that had been preincubated for 3 hours with healthy male urine. A Coulter Counter was used to follow the crystallization and decrease in soluble (14) C-oxalate was measured to determine the deposition of CaOx. The precipitated particles were examined by scanning electron microscopy (SEM). The preincubated seeds were demineralized and proteins released were analyzed by sodium dodecylsulphate polyacrylamide gel electrophoresis (SDS-PAGE). RESULTS: Analysis of (14) C-oxalate data revealed that while treated UA seeds did not affect CaOx deposition, those of NaU and CaOx inhibited the process by 51.9 (p<0.05) and 8.5% (p<0.05) relative to their respective untreated counterparts. Particle size analysis showed that the average modal sizes of particles precipitated in the presence of treated seed crystals of UA, NaU, and CaOx were very similar to those deposited in the presence of their respective untreated controls. These findings were confirmed by SEM which also showed that seed crystals of UA and NaU, untreated and treated, were attached like barnacles upon the surfaces of CaOx crystals which themselves were bigger than those precipitated in the presence of CaOx seeds. SDS-PAGE analysis of the demineralized treated seeds showed that they all selectively adsorbed urinary proteins, and perhaps other urinary macromolecules and low molecular weight components, on their surface. CONCLUSIONS: It was concluded that preincubation with urine, such as occurs in vivo, only slightly reduces the ability of seed crystals of CaOx, but not of UA, to cause deposition of CaOx. The most striking effect was on NaU seeds where the preincubation quite dramatically attenuated their promotory effect on the mineral deposition. This may explain the discrepancy between findings of studies carried out in inorganic solutions and undiluted human urine. This stresses the invalidity of directly extrapolating results obtained in inorganic solutions to likely effects in urine and more importantly, on stone formation.


title: Software landscape and application visualization for system comprehension with ExplorViz

abstract: Hierarchical and multi-layer visualization of large software landscapes with ExplorViz, generated from monitoring traces.Reusable design and execution of a controlled experiment comparing a flat landscape visualization to our hierarchical landscape visualization in system comprehension tasks.Controlled experiments for comparing ExplorViz with the Extravis trace visualization approach, for employing physical 3D-printed ExplorViz models, and for exploring 3D ExplorViz models in virtual reality. Display Omitted Context: The number of software applications deployed in organizations is constantly increasing. Those applications often several hundreds form large software landscapes.Objective: The comprehension of such landscapes and their applications is often impeded by, for instance, architectural erosion, personnel turnover, or changing requirements. Therefore, an efficient and effective way to comprehend such software landscapes is required.Method: In our ExplorViz visualization, we introduce hierarchical abstractions aiming at solving system comprehension tasks fast and accurately for large software landscapes. Besides hierarchical visualization on the landscape level, ExplorViz provides multi-level visualization from the landscape to the level of individual applications. The 3D application-level visualization is empirically evaluated with a comparison to the Extravis approach, with physical models and in virtual reality. To evaluate ExplorViz, we conducted four controlled experiments. We provide packages containing all our experimental data to facilitate the verifiability, reproducibility, and further extensibility of our results.Results: We observed a statistical significant increase in task correctness of the hierarchical visualization compared to the flat visualization. The time spent did not show any significant differences. For the comparison with Extravis, we observed that solving program comprehension tasks using ExplorViz leads to a significant increase in correctness and in less or similar time spent. The physical models improved the team-based program comprehension process for specific tasks by initiating gesture-based interaction, but not for all tasks. The participants of our virtual reality experiment with ExplorViz rated the realized gestures for translation, rotation, and selection as highly usable. However, our zooming gesture was less favored.Conclusion: The results backup our claim that our hierarchical and multi-level approach enhances the current state of the art in landscape and application visualization for better software system comprehension, including new forms of interaction with physical models and virtual reality.


title: Algorithmic Persuasion with No Externalities

abstract: We study the algorithmics of information structure design --- a.k.a. persuasion or signaling --- in a fundamental special case introduced by Arieli and Babichenko: multiple agents, binary actions, and no inter-agent externalities. Unlike prior work on this model, we allow many states of nature. We assume that the principal's objective is a monotone set function, and study the problem both in the public signal and private signal models, drawing a sharp contrast between the two in terms of both efficacy and computational complexity. When private signals are allowed, our results are largely positive and quite general. First, we use linear programming duality and the equivalence of separation and optimization to show polynomial-time equivalence between (exactly) optimal signaling and the problem of maximizing the objective function plus an additive function. This yields an efficient implementation of the optimal scheme when the objective is supermodular or anonymous. Second, we exhibit a (1-1/e)-approximation of the optimal private signaling scheme, modulo an additive loss of e, when the objective function is submodular. These two results simplify, unify, and generalize results of [Arieli and Babichenko, 2016] and [Babichenko and Barman, 2016], extending them from a binary state of nature to many states (modulo the additive loss in the latter result). Third, we consider the binary-state case with a submodular objective, and simplify and slightly strengthen the result of [Babichenko and Barman, 2016] to obtain a (1-1/e)-approximation via a scheme which (i) signals independently to each receiver and (ii) is "oblivious" in that it does not depend on the objective function so long as it is monotone submodular. When only a public signal is allowed, our results are negative. First, we show that it is NP-hard to approximate the optimal public scheme, within any constant factor, even when the objective is additive. Second, we show that the optimal private scheme can outperform the optimal public scheme, in terms of maximizing the sender's objective, by a polynomial factor.


title: The healthcare environment—The importance of aesthetic surroundings: Health professionals' experiences from a surgical ward in Finland

abstract: There is a growing acceptance of the vital role patient centered design plays in shaping the environment. Art can be of use in every area of health care. It is within the power of each person to share and interpret experience by means of the arts, by viewing the work of others, and by using feelings and imagination. Health professionals [n = 24 of a total of n = 35], answered the Wheel Questionnaire test instrument. It measures structure, motivation/engagement, and degree of emotional investment in a situation. Participants were requested to describe, in their own words, their perception of the enrichment of the ward interiordesign and the double-sided photos with a short poetic text, and the photo-book placed at each patient room. The results demonstrate that participants are motivated, structured and emotionally engaged when describing the new enrichment. The domains and themes are: Social interaction domain; rising thoughts and conversations. Comfort domain; atmosphere. Aesthetic do main; enrichment of the working environment. It could be concluded that the surgical ward environmental enrichment stimulated conversations between health professionals and between health professionals and patients and should be regarded as an important aspect in hospital planning.


title: Active Regression with Adaptive Huber Loss

abstract: This paper addresses the scalar regression problem through a novel solution to exactly optimize the Huber loss in a general semi-supervised setting, which combines multi-view learning and manifold regularization. We propose a principled algorithm to 1) avoid computationally expensive iterative schemes while 2) adapting the Huber loss threshold in a data-driven fashion and 3) actively balancing the use of labelled data to remove noisy or inconsistent annotations at the training stage. In a wide experimental evaluation, dealing with diverse applications, we assess the superiority of our paradigm which is able to combine robustness towards noise with both strong performance and low computational cost.


title: Is Saki #delicious? The Food Perception Gap on Instagram and Its Relation to Health

abstract: Food is an integral part of our life and what and how much we eat crucially affects our health. Our food choices largely depend on how we perceive certain characteristics of food, such as whether it is healthy, delicious or if it qualifies as a salad. But these perceptions differ from person to person and one person's "single lettuce leaf" might be another person's "side salad". Studying how food is perceived in relation to what it actually is typically involves a laboratory setup. Here we propose to use recent advances in image recognition to tackle this problem. Concretely, we use data for 1.9 million images from Instagram from the US to look at systematic differences in how a machine would objectively label an image compared to how a human subjectively does. We show that this difference, which we call the "perception gap", relates to a number of health outcomes observed at the county level. To the best of our knowledge, this is the first time that image recognition is being used to study the "misalignment" of how people describe food images vs. what they actually depict.


title: EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

abstract: Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.


title: Sparse depth super resolution

abstract: We describe a method to produce detailed high resolution depth maps from aggressively subsampled depth measurements. Our method fully uses the relationship between image segmentation boundaries and depth boundaries It uses an image combined with a low resolution depth map. 1) The image is segmented with the guidance of sparse depth samples 2) Each segment has its depth field reconstructed independently using a novel smoothing method. 3) For videos, time-stamped samples from near frames are incorporated. The paper shows reconstruction results of super resolution from x4 to x100, while previous methods mainly work on x2 to xl6. The method is tested on four different datasets and six video sequences, covering quite different regimes, and it outperforms recent state of the art methods quantitatively and qualitatively We also demonstrate that depth maps produced by our method can be used by applications such as hand trackers, while depth maps from other methods have problems.


title: Foundations of Modern Query Languages for Graph Databases

abstract: We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges; and property graphs, where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions. We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed, and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.


title: Heat event detection in dairy cows with collar sensors: An unsupervised machine learning approach

abstract: The detection of heat (estrus) events in pasture-based dairy cows fitted with on-animal sensors was investigated using an unsupervised learning. Accelerometer data from the cow collar sensors were used in this approach where the aim was to identify increased activity level (restlessness, increased walking for mating) and to find association with recorded heat events. High dimensional time series data from accelerometers were first segmented in windows followed by feature extractions. The extracted features are standard deviation, amplitude, energy and Fast Fourier Transform (FFT). K-means clustering algorithm was then applied across the windows for grouping. The groups were labeled in terms of activity intensities: high, medium and low. An activity index level (AIxL) was derived from the activity intensity labels. We compared the AIxL with recorded heat events and observed significant associations between the increased activities through high AIxL values and the observed heat events.


title: Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT)

abstract: Abstract The technological improvement coupled with the growing use of smartphones has, among other functions, facilitated purchase and payment transactions through the mobile phone. This phenomenon occurs worldwide and provides individuals more flexibility and convenience in carrying out their daily activities. This article aims to evaluate the intention of adopting a future mobile payment service from the perspective of current Brazilian consumers of mobile phones, based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was carried out with mobile customers of a telecommunications company that operates in southeastern Brazil, with a valid sample of 605 respondents. Using structural equation modeling, 76% of behavioral intention was explained through performance expectation, effort expectation, social influence and perceived risk. Perceived cost was found not statistically significant at the level of 5%. This result serves as a guide to participants in the payments market to develop a service for mobile payments of good performance, easy to use, secure and promotes the action of the social circle of the individual at a fair price, in other words, that meets needs and expectations of today's mobile phone users. As well as serves as a stimulus to the development of communication and marketing strategies that highlight these positive attributes and awaken the intention of adoption of the service by the wider range of people as possible.


title: Vaginal Labiaplasty: Defense of the Simple “Clip and Snip” and a New Classification System

abstract: Vaginal labiaplasty has become a more fre- quently performed procedure as a result of the publicity and education possible with the internet. Some of our patients have suffered in silence for years with large, protruding labia minora and the tissue above the clitoris that is disfiguring and uncomfortable and makes inter- course very difficult and painful. We propose four classes of labia protrusion based on size and location: Class 1 is normal, where the labia majora and minora are about equal. Class 2 is the protrusion of the minora beyond the majora. Class 3 includes a clitoral hood. Class 4 is where the large labia minora extends to the perineum. There are two principal means of reconstructing this area. Simple ampu- tation may be possible for Class 2 and Class 4. Class 2 and Class 3 may be treated with a wedge resection and flap advancement that preserves the delicate free edge of the labia minora (Alter, Ann Plast Surg 40:287, 1988). Class 4 may require a combination of both amputation of the clitoral hood and/or perineal extensions and rotation flap advancement over the labia minora. Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: Finding optimal solutions to Rubik’s Cube using pattern databases

abstract: We have found the first optimal solutions to random instances of Rubik's Cube. The median optimal solution length appears to be 18 moves. The algorithm used is iterative-deepening-A* (IDA*), with a lower-bound heuristic function based on large memory-based lookup tables, or "pattern databases" (Culberson and Schaeffer 1996). These tables store the exact number of moves required to solve various subgoals of the problem, in this case subsets of the individual movable cubies. We characterize the effectiveness of an admissible heuristic function by its expected value, and hypothesize that the overall performance of the program obeys a relation in which the product of the time and space used equals the size of the state space. Thus, the speed of the program increases linearly with the amount of memory available. As computer memories become larger and cheaper, we believe that this approach will become increasingly cost-effective.


Domain Adaptation with Adversarial Neural Networks and Auto-encoders


CUHK & SIAT Submission for THUMOS 15 Action Recognition Challenge


title: Watershed Modeling Using GIS Technology: A Critical Review

abstract: Understanding and managing water resource problems involves complex processes and interactions within the watershed surface and subsurface. The imposition of total maximum daily load (TMDL) regulations on the pollutant influx to a watershed has created a strong demand for new assessment tools. The spatial scales relevant to transport of the pollutants may span many orders of magnitude, ranging from field plots to regional hydrological systems. As the demand for and development of watershed modeling capabilities have evolved, geographic information systems (GIS) in tandem with remote sensing technologies have played an essential role supporting both data collection and analysis. This paper reviews the current and future trends of GIS and remote sensing technologies in watershed modeling. The primary focus of this discussion is on spatial data availability and management, and further opportunities for model development.


title: How to Screen a Data Stream - Quality-Driven Load Shedding in Sensor Data Streams.

abstract: As most data stream sources exhibit bursty data rates, data stream management systems must recurrently cope with load spikes that exceed the average workload to a considerable degree. To guarantee low-latency processing results, load has to be shed from the stream, when data rates overstress system resources. There exist numerous load shedding strategies to delete excess data. However, the consequent data loss leads to incomplete and/or inaccurate results during the ongoing stream processing. In this paper, we present a novel quality-driven load shedding approach that screens the data stream to find and discard data items of minor quality. The data quality of stream processing results is maximized under the adverse condition of data overload. After an introduction to data quality management in data streams, we define three data quality-driven load shedding algorithms, which minimize the approximation error of aggregations and maximize the completeness of join processing results, respectively. Finally, we demonstrate their superiority over existing load shedding techniques at real-life weather data.


title: High frequency data in financial markets: Issues and applications☆

abstract: Abstract The development of high frequency data bases allows for empirical investigations of a wide range of issues in the financial markets. In this paper, we set out some of the many important issues connected with the use, analysis, and application of high-frequency data sets. These include the effects of market structure on the availability and interpretation of the data, methodological issues such as the treatment of time, the effects of intra-day seasonals, and the effects of time-varying volatility, and the information content of various market data. We also address using high frequency data to determine the linkages between markets and to determine the applicability of temporal trading rules. The paper concludes with a discussion of the issues for future research.


title: Is the internet a better public sphere? Comparing old and new media in the USA and Germany:

abstract: Normative theorists of the public sphere, such as Jurgen Habermas, have been very critical of the ‘old’ mass media, which were seen as unable to promote free and plural societal communication. The advent of the internet, in contrast, gave rise to hopes that it would make previously marginalized actors and arguments more visible to a broader public. To assess these claims, this article compares the internet and mass media communication. It distinguishes three levels of both the offline and the online public sphere, which differ in their structural prerequisites, in their openness for participation and in their influence on the wider society. Using this model, the article compares the levels that are most strongly structured and most influential for the wider society: the mass media and communication as organized by search engines. Using human genome research and analysing Germany and the USA, the study looks at which actors, evaluations and frames are present in the print mass media and on websites, and finds that internet communication does not differ significantly from the offline debate in the print media.


Low Cost Heart Multi Parameter Monitoring and Arrhythmia Analysis Using MATLAB


title: A reconfigurable in-phase/out-of-phase and power-dividing ratio power divider

abstract: This paper presents a varactor-based power divider with reconfigurable power-dividing ratio and reconfigurable in-phase or out-of-phase phase relation between outputs. By properly controlling the tuning varactors, the power divider can be either in phase or out of phase and each with a wide range of tunable power-dividing ratio. The proposed microstrip power divider was prototyped and experimentally characterized. Measured and simulated results are in good agreement.


title: Efficient Scale Invariant Human Detection Using Histogram of Oriented Gradients for IoT Services

abstract: Recent advancements in computer vision, multimedia and Internet of Things (IoT) have shown that human detection methods are useful for applications of intelligent transportation system in smart environment. However, detection of a human in real world remains a challenging problem. Histogram of oriented gradients (HOG) based human detection gives an emphasis towards finding an effective solution to the problems of significant changes in view point, fixed resolution human but are expensive to compute. The proposed algorithm aims to reduce the computations using approximation methods and adapts for varying scale. The features are modeled at different scales for training the classifier. Experiments have been conducted on human datasets to demonstrate the superior performance of the proposed approach in human detection and discussions are made to integrate and increase personalization for building smart environment using IoT.


title: A thin-film-based wearable antenna array for breast microwave imaging and diagnosis

abstract: In this paper, a wearable antenna array for ultra-wideband (UWB) radar based microwave imaging and diagnosis application is proposed and investigated. This paper describes a wearable antenna array which is constructed by a number of thin-film flexible monopole antennas having 3.8GHz impendence bandwidth covering frequency ranges from 2.8GHz to 6.6GHz. The proposed antenna array is modelled and the performance evaluation is carried out with Computer Simulation Technology Microwave Studio (CST MWS). Simulation results of the proposed antennas coupled to several of chest phantoms in different situations are presented and discussed.


title: Side lobe suppression of Vivaldi antenna using shorting pin structure

abstract: This paper was executed in order to design side lobe suppression of Vivaldi antenna using shorting pin structure at S-band frequency. Nowadays, Vivaldi antenna has been used to satellite communications, remote sensing and radio telescope. There are a variety of designs such as tapered slot Vivaldi antenna, antipodal Vivaldi antenna and balanced antipodal Vivaldi antenna. Tapered slot Vivaldi antenna has been selected to design the project. The design of the antenna is start with patch antenna by using basis of transmission line model (TLM) formula to calculate the length and width of the patch. It is shaped into Vivaldi antenna. For the final design, the shorting pin structure will connect the patch on top side and the ground plane to absorb the unbalanced current. The shorting pin will reduce the side lobe. The antenna was simulated by using CST Microwave Studio. Based on the simulation results, the Vivaldi antenna shows a good performance as the return loss less than − 10 dB, voltage standing wave ratio, gain, directivity and radiation pattern was obtained the desired result as for a resonant frequency of 3 GHz. The radiation pattern of Vivaldi antenna with shorting pin structure reducing as compared with the design without shorting pin structure. The final antenna design shows that it has a few main lobe magnitudes where the antenna can focus and direct the energy for transmission or receiving of the energy signal.


title: Cognitive behavioral principles in managing chronic disease.

abstract: Medical problems that previously carried considerable mortality risk can now be managed more effectively. As a result, chronic medical illnesses have become more prevalent in recent years. With the increase in life expectancy comes a set of psychological challenges that face the chronically ill. ::: ::: Chronic disease is associated with high levels of uncertainty. Patients need to change their behavior as part of a new lifestyle of self-care. They also have to endure debilitating and demanding treatments. These are some of the factors that make adjustment to chronic medical illness psychologically demanding. ::: ::: It is generally accepted that around a quarter of patients with chronic medical problems have clinically significant psychological symptoms. In some cases, these psychological symptoms themselves are associated with physical morbidity. For example, when medical factors are controlled for, the risk of myocardial infarction increases 4- to 5-fold as a result of the presence of depressive symptoms.1 Even in the absence of overt psychological or psychiatric disorder, patients have to regulate often-complex and ever-changing thoughts, feelings, and behaviors. ::: ::: In this article, I outline how primary care physicians can incorporate some principles from cognitive therapy into their management of patients with chronic disease. Cognitive therapy is effective in managing chronic mental health problems2 and many of the long-term symptoms of chronic physical illnesses, including chronic pain.3,4


title: Electromagnetic Vortex Imaging Using Uniform Concentric Circular Arrays

abstract: Electromagnetic vortex imaging based on orbital angular momentum (OAM) modulation has the ability of azimuthal resolution without relative motion, which needs various OAM modes. However, the main-lobe directions of different OAM beams differ from each other so that the echo energy is limited. In this letter, several concentric circular arrays are employed to adjust the directivity, each array generating a beam carrying special OAM mode. With this scheme, the target can be illuminated by main lobes of all OAM beams simultaneously. Our proposal can benefit the image reconstruction in noisy environment. Moreover, this scheme can successfully reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution. Simulation results demonstrate the effectiveness of this scheme.


title: An improvement of rotation invariant 3D-shape based on functions on concentric spheres

abstract: In this paper, we consider 3D-shape descriptors generated by using functions on a sphere. The descriptors are engaged for retrieving polygonal mesh models. Invariance of descriptors with respect to rotation of a model can be achieved either by using the principle component analysis (PCA) or defining features in which the invariance exists. The contribution of the paper is twofold: firstly, we define a new rotation invariant feature vector based on functions on concentric spheres, which outperforms a recently proposed descriptor; secondly, we compare the two approaches for achieving rotation invariance as well as options to use a single function or several functions on concentric spheres to generate feature vectors. We conclude that descriptors, which use the PCA, outperform others, while capturing the internal structure of a 3D-model with functions on concentric spheres can improve retrieval effectiveness.


title: Vehicle detection using normalized color and edge map

abstract: This paper presents a novel vehicle detection approach for detecting vehicles from static images using color and edges. Different from traditional methods which use motion features to detect vehicles, this method introduces a new color transform model to find important "vehicle color" from images for quickly locating possible vehicle candidates. Since vehicles have different colors under different lighting conditions, there were seldom works proposed for detecting vehicles using colors. This paper proves that the new color transform model has extreme abilities to identify vehicle pixels from backgrounds even though they are lighted under various illumination conditions. Each detected pixel corresponds to a possible vehicle candidate. Then, two important features including edge maps and coefficients of wavelet transform are used for constructing a multi-channel classifier to verify this candidate. According to this classifier, we can perform an effective scan to detect all desired vehicles from static images. Since the color feature is first used to filter out most background pixels, this scan can be extremely quickly achieved. Experimental results show that the integrated scheme is very powerful in detecting vehicles from static images. The average accuracy of vehicle detection is 94.5%.


title: The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences

abstract: What are the positive and negative consequences of telecommuting? How do these consequences come about? When are these consequences more or less potent? The authors answer these questions through construction of a theoretical framework and meta-analysis of 46 studies in natural settings involving 12,883 employees. Telecommuting had small but mainly beneficial effects on proximal outcomes, such as perceived autonomy and (lower) work–family conflict. Importantly, telecommuting had no generally detrimental effects on the quality of workplace relationships. Telecommuting also had beneficial effects on more distal outcomes, such as job satisfaction, performance, turnover intent, and role stress. These beneficial consequences appeared to be at least partially mediated by perceived autonomy. Also, high-intensity telecommuting (more than 2.5 days a week) accentuated telecommuting’s beneficial effects on work–family conflict but harmed relationships with coworkers. Results provide building blocks for a more complete theoretical and practical treatment of telecommuting.


title: Survey of Polygonal Surface Simplification Algorithms

abstract: Abstract : This paper surveys methods for simplifying and approximating polygonal surfaces. A polygonal surface is a piecewise-linear surface in 3-D defined by a set of polygons; typically a set of triangles. Methods from computer graphics, computer vision, cartography, computational geometry, and other fields are classified, summarized, and compared both practically and theoretically. The surface types range from height fields (bivariate functions), to manifolds, to non-manifold self-intersecting surfaces. Piecewise-linear curve simplification is also briefly surveyed.


title: Health systems changes after decentralisation: progress, challenges and dynamics in Pakistan

abstract: Decentralisation is widely practised but its scrutiny tends to focus on structural and authority changes or outcomes. Politics and process of devolution implementation needs to be better understood to evaluate how national governments use the enhanced decision space for bringing improvements in the health system and the underlying challenges faced. We use the example of Pakistan’s radical, politically driven provincial devolution to analyse how national structures use decentralisation opportunities for improved health planning, spending and carrying out transformations to the health system. Our narrative draws on secondary data sources from the PRIMASYS study, supplemented with policy roundtable notes from Pakistan. Our analysis shows that in decentralised Pakistan, health became prioritised for increased government resources and achieved good budgetary use, major strides were made contextualised sector-wide health planning and legislations, and a proliferation seen in governance measures to improve and regulate healthcare delivery. Despite a disadvantaged and abrupt start to devolution, high ownership by politicians and bureaucracy in provincial governments led to resourcing, planning and innovations. However, effective translation remained impeded by weak institutional capacity, feeble federal–provincial coordination and vulnerability to interference by local elites. Building on this illustrative example, we propose (1) political management of decentralisation for effective national coordination, sustaining stable leadership and protecting from political interfere by local elites; (2) investment in stewardship capacity in the devolved structures as well as the central ministry to deliver on new roles.


title: Design Principles for Nanoparticle Plasmon-Enhanced Organic Solar Cells

abstract: Plasmonic metallic nanoparticles are coupled to the organic solar cells to overcome the trade-off between the light absorption and carrier collection. They are usually located inside or outside of the active layers. However, no detailed comparison was reported on the light absorption difference when nanoparticles are located inside or outside of the active layers. In this paper, we compare light-trapping abilities of Ag nanospheres in organic solar cells when they are located inside and outside of the photoactive layer. We show that large-sized nanoparticles are preferred when they are placed outside of the active layer while small-sized nanoparticles are favored in the case of embedding nanoparticles in the homogenous active layer.


An Automatic Test Data Generation Tool using Machine Learning


title: Enhanced Algorithms for the IEEE 802.11p Deployment in Vehicular Ad Hoc Networks

abstract: The Enhanced Distributed Channel Access (EDCA) and the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithms are used in the IEEE 802.11p standard to support Quality of Service (QoS) and reliable communication in Vehicular Ad hoc Networks (VANETs). An essential part of the CSMA/CA process is when a packet in an Access Category (AC) waits for an Arbitrary Inter- Frame Space (AIFS) period of time before engaging in the the backoff phase. According to the standard, AIFS values are fixed and deterministic, they do not guarantee strict priority for ACs especially when these values are combined with the Contention Window (CW) size of the backoff phase. On the other hand, the AIFS values are not adjustable, they do not adapt to the current status of the medium. In this paper, we propose two algorithms for AIFS value assignment, the Strict Priority Algorithm (SPA) and the Adaptive AIFS Algorithm (A3). With SPA, the AIFS values are fixed, but they are determined according to a mathematical formula that ensures strict priority level among the ACs. With A3, the AIFS values are adaptively changing depending on the value of the collision probability. AIFS still maintains a strict priority level assignment among the ACs.


title: On self-play computation of equilibrium in poker

abstract: We compare performance of the genetic algorithm and the counterfactual regret minimization algorithm in computing the near-equilibrium strategies in the simplified poker games. We focus on the von Neumann poker and the simplified version of the Texas Hold'Em poker, and test outputs of the considered algorithms against analytical expressions defining the Nash equilibrium strategies. We comment on the performance of the studied algorithms against opponents deviating from equilibrium.


title: Iteratively Learning from the Best

abstract: We study a simple generic framework to address the issue of bad training data; both bad labels in supervised problems, and bad samples in unsupervised ones. Our approach starts by fitting a model to the whole training dataset, but then iteratively improves it by alternating between (a) revisiting the training data to select samples with lowest current loss, and (b) re-training the model on only these selected samples. It can be applied to any existing model training setting which provides a loss measure for samples, and a way to refit on new ones. We show the merit of this approach in both theory and practice. We first prove statistical consistency, and linear convergence to the ground truth and global optimum, for two simpler model settings: mixed linear regression, and gaussian mixture models. We then demonstrate its success empirically in (a) saving the accuracy of existing deep image classifiers when there are errors in the labels of training images, and (b) improving the quality of samples generated by existing DC-GAN models, when it is given training data that contains a fraction of the images from a different and unintended dataset. The experimental results show significant improvement over the baseline methods that ignore the existence of bad labels/samples.


title: A 30-MHz Voltage-Mode Buck Converter Using Delay-Line-Based PWM Control

abstract: A 30-MHz voltage-mode buck converter using a delay-line-based pulse-width-modulation controller is proposed in this brief. Two voltage-to-delay cells are used to convert the voltage difference to delay-time difference. A charge pump is used to charge or discharge the loop filter, depending on whether the feedback voltage is larger or smaller than the reference voltage. A delay-line-based voltage-to-duty-cycle (V2D) controller is used to replace the classical ramp-comparator-based V2D controller to achieve wide duty cycle. A type-II compensator is implemented in this design with a capacitor and resistor in the loop filter. The prototype buck converter was fabricated using a 0.18- ${\mu }\text{m}$ CMOS process. It occupies an active area of 0.834 mm2 including the testing PADs. The tunable duty cycle ranges from 11.9%–86.3%, corresponding to 0.4 V–2.8 V output voltage with 3.3 V input. With a step of 400 mA in the load current, the settling time is around 3 ${\mu }\text{s}$ . The peak efficiency is as high as 90.2% with 2.4 V output and the maximum load current is 800 mA.


title: $A^2$-Nets: Double Attention Networks

abstract: Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly inefficient. In this work, we propose the double attention block'', a novel component that aggregates and propagates informative global features from the entire spatio-temporal space of input images/videos, enabling subsequent convolution layers to access features from the entire space efficiently. The component is designed with a double attention mechanism in two steps, where the first step gathers features from the entire space into a compact set through second-order attention pooling and the second step adaptively selects and distributes features to each location via another attention. The proposed double attention block is easy to adopt and can be plugged into existing deep neural networks conveniently. We conduct extensive ablation studies and experiments on both image and video recognition tasks for evaluating its performance. On the image recognition task, a ResNet-50 equipped with our double attention blocks outperforms a much larger ResNet-152 architecture on ImageNet-1k dataset with over 40% less the number of parameters and less FLOPs. On the action recognition task, our proposed model achieves the state-of-the-art results on the Kinetics and UCF-101 datasets with significantly higher efficiency than recent works.


title: You May Not Need Attention

abstract: In NMT, how far can we get without attention and without separate encoding and decoding? To answer that question, we introduce a recurrent neural translation model that does not use attention and does not have a separate encoder and decoder. Our eager translation model is low-latency, writing target tokens as soon as it reads the first source token, and uses constant memory during decoding. It performs on par with the standard attention-based model of Bahdanau et al. (2014), and better on long sentences.


title: Can Deep Learning Identify Tomato Leaf Disease?

abstract: This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.


title: Automatic lesion segmentation for melanoma diagnostics in macroscopic images

abstract: Detailed segmentation of pigmented skin lesions is an important requirement in computer aided applications for melanoma assessment. In particular, accurate segmentation is necessary for image-guided evaluation of skin lesions characteristics. In this paper, we present a new approach of histogram thresholding for detailed segmentation of skin lesions based on histogram analysis of the saturation color component in the hue-saturation-value (HSV) color space. The proposed technique is specifically developed with the aim to handle the complex variability of features for macroscopic color images taken in uncontrolled environment. A dataset of 30 cases with manual segmentation was used for evaluation. We compare our results with two of most important existing segmentation techniques. For similarity report between automatic and manual segmentation we used dice similarity coefficient (DSC), the true detection rate (TDR), and the false positive rate (FPR). Experimental results show that the proposed method has high precision and low computational complexity.


An approach to selecting keywords to track on twitter during a disaster


title: Performance-oriented Cloud Provisioning: Taxonomy and Survey

abstract: Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.


title: Exploiting Unlabeled Data for Neural Grammatical Error Detection

abstract: Identifying and correcting grammatical errors in the text written by non-native writers have received increasing attention in recent years. Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because human annotation is labor-intensive, time-consuming, and expensive. In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models. The basic idea is to cast error detection as a binary classification problem and derive positive and negative training examples from unlabeled data. We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected. Experiments show that the proposed approach significantly outperforms SVM and convolutional networks with fixed-size context window.


title: Do Multi-Sense Embeddings Improve Natural Language Understanding?

abstract: Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations. Yet while ‘multi-sense’ methods have been proposed and tested on artificial wordsimilarity tasks, we don’t know if they improve real natural language understanding tasks. In this paper we introduce a multisense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language understanding. We then test the performance of our model on part-of-speech tagging, named entity recognition, sentiment analysis, semantic relation identification and semantic relatedness, controlling for embedding dimensionality. We find that multi-sense embeddings do improve performance on some tasks (part-of-speech tagging, semantic relation identification, semantic relatedness) but not on others (named entity recognition, various forms of sentiment analysis). We discuss how these differences may be caused by the different role of word sense information in each of the tasks. The results highlight the importance of testing embedding models in real applications.


title: Fast Pedestrian Detection by Cascaded Random Forest with Dominant Orientation Templates

abstract: In this paper, we present a new pedestrian detection method combining Random Forest and Dominant Orientation Templates(DOT) to achieve state-of-the-art accuracy and, more importantly, to accelerate run-time speed. DOT can be considered as a binary version of Histogram of Oriented Gradients(HOG) and therefore provides time-efficient properties. However, since discarding magnitude information, it degrades the detection rate, when it is directly incorporated. We propose a novel template-matching split function using DOT for Random Forest. It divides a feature space in a non-linear manner, but has a very low complexity up to binary bit-wise operations. Experiments demonstrate that our method provides much superior speed with comparable accuracy to state-ofthe-art pedestrian detectors. By combining a holistic and a patch-based detectors in a cascade manner, we accelerate the detection speed of Hough Forest, a prior-art using Random Forest and HOG, by about 20 times. The obtained speed is 5 frames per second for 640×480 images with 24 scales.


title: On being loud and proud: Non-conformity and counter-conformity to group norms

abstract: Most experiments on conformity have been conducted in relation to judgments of physical reality; surprisingly few papers have experimentally examined the influence of group norms on social issues with a moral component. In response to this, participants were told that they were either in a minority or in a majority relative to their university group in terms of their attitudes toward recognition of gay couples in law (Expt 1: N = 205) and a government apology to Aborigines (Expt 2: N = 110). In both experiments, it was found that participants who had a weak moral basis for their attitude conformed to the group norm on private behaviours. In contrast, those who had a strong moral basis for their attitude showed non-conformity on private behaviours and counter-conformity on public behaviours. Incidences of nonconformity and counter-conformity are discussed with reference to theory and research on normative influence.


title: Fast Effective Rule Induction

abstract: Abstract Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error rates higher than those of C4.5 and C4.5rules. We then propose a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5rules with respect to error rates, but much more efficient on large samples. RIPPERk obtains error rates lower than or equivalent to C4.5rules on 22 of 37 benchmark problems, scales nearly linearly with the number of training examples, and can efficiently process noisy datasets containing hundreds of thousands of examples.


title: On the interplay of Internet of Things and Cloud Computing

abstract: The Internet of Things (IoT) is a novel paradigm relying on the interaction of smart objects (things) with each other and with physical and/or virtual resources through the Internet. Despite the recent advances that have made IoT a reality, there are several challenges to be addressed towards exploiting its full potential and promoting tangible benefits to society, environment, economy, and individual citizens. Recently, Cloud Computing has been advocated as a promising approach to tackle some of the existing challenges in IoT while leveraging its adoption and bringing new opportunities. With the combination of IoT and Cloud Computing, the cloud becomes an intermediate layer between smart objects and applications that make use of data and resources provided by these objects. On the one hand, IoT can benefit from the almost unlimited resources of Cloud Computing to implement management and composition of services related to smart objects and their provided data. On the other hand, the cloud can benefit from IoT by broadening its operation scope to deal with real-world objects. In spite of this synergy, the literature still lacks of a broad, comprehensive overview on what has been investigated on the integration of IoT and Cloud Computing and what are the open issues to be addressed in future research and development. The goal of this work is to fill this gap by systematically collecting and analyzing studies available in the literature aiming to: (i) obtain a comprehensive understanding on the integration of IoT and Cloud Computing paradigms; (ii) provide an overview of the current state of research on this topic; and (iii) identify important gaps in the existing approaches as well as promising research directions. To achieve this goal, a systematic mapping study was performed covering papers recently published in journals, conferences, and workshops, available at five relevant electronic databases. As a result, 35 studies were selected presenting strategies and solutions on how to integrate IoT and Cloud Computing as well as scenarios, research challenges, and opportunities in this context. Besides confirming the increasing interest on the integration of IoT and Cloud Computing, this paper reports the main outcomes of the performed systematic mapping by both presenting an overview of the state of the art on the investigated topic and shedding light on important challenges and potential directions to future research.


title: Channel estimation for OFDM transmission in multipath fading channels based on parametric channel modeling

abstract: We present an improved channel estimation algorithm for orthogonal frequency-division multiplexing mobile communication systems using pilot subcarriers. This algorithm is based on a parametric channel model where the channel frequency response is estimated using an L-path channel model. In the algorithm, we employ the ESPRIT (estimation of signal parameters by rotational invariance techniques) method to do the initial multipath time delays acquisition and propose an interpath interference cancellation delay locked loop to track the channel multipath time delays. With the multipath time delays information, a minimum mean square error estimator is derived to estimate the channel frequency response. It is demonstrated that the use of the parametric channel model can effectively reduce the signal subspace dimension of the channel correlation matrix for the sparse multipath fading channels and, consequently, improve the channel estimation performance.


title: Heterogeneous hypergraph embedding for document recommendation

abstract: Abstract Nowadays, more and more users are using online tagging services to organize their resources, e.g. Web bookmarks and bibliographies. Tags not only facilitate organization and retrieval of resources, but also provide valuable semantic descriptions for both resources and users’ interests. This work is focused on document recommendation using tagging data. Previous works either model the 3-order relation user, tag, document > in tagging data by an ordinary graph or model different types of relations by a homogeneous hypergraph. The former scheme would lead to serious information loss, and the latter one fails to discern the influence of different types of relations. In this paper, we propose a heterogeneous hypergraph model which fully exploits high-order relational information in tagging data and, meanwhile, customizes the influence of different types of relations. A novel heterogeneous hypergraph embedding framework is developed for document recommendation. The framework is general and can incorporate various relations among users, tags and resources. Experimental results on two real-world datasets show the superiority of the proposed method over traditional methods.


title: Zero intelligence in economics and finance

abstract: This paper reviews the Zero Intelligence (ZI) methodology for investigating markets. This approach models individual traders, operating within a market mechanism, who behave without strategy, in order to determine the impact of the market mechanism and consequently the effect of trader behaviour. The paper considers the major contributions and models within this area from both the economics and finance communities before examining the strengths and weaknesses of this methodology.


title: Adolescent sexual risk behavior: a multi-system perspective.

abstract: Abstract Adolescents are at high risk for a number of negative health consequences associated with early and unsafe sexual activity, including infection with human immunodeficiency virus, other sexually transmitted diseases, and unintended pregnancy. As a result, researchers have attempted to identify those factors that influence adolescent sexual risk behavior so that meaningful prevention and intervention programs may be developed. We propose that research efforts so far have been hampered by the adoption of models and perspectives that are narrow and do not adequately capture the complexity associated with the adolescent sexual experience. In this article, we review the recent literature (i.e., 1990–1999) pertaining to the correlates of adolescent sexual risk-taking, and organize the findings into a multisystemic perspective. Factors from the self, family, and extrafamilial systems of influence are discussed. We also consider several methodological problems that limit the literature's current scope, and consider implications of the adoption of a multisystemic framework for future research endeavors. We conclude with a discussion of the implications of the available research for practitioners working to reduce sexual risk behavior among adolescents.


title: Offline signature recognition: Artificial neural network approach

abstract: This paper discusses the offline signature recognition system in which features are extracted using Discrete Wavelet Transform (DWT) and Principal component analysis (PCA) and the artificial neural network (ANN) is used for the recognition of signature. ANN is used to recognize whether a signature is original or fraud. The user provide the scanned image of signature for computer database, modifies there quality by image preprocessing. Features are extracted from preprocessed image and extracted features are used as input of ANN training and recognition.


title: A computational approach to politeness with application to social factors

abstract: We propose a computational framework for identifying linguistic aspects of politeness. Our starting point is a new corpus of requests annotated for politeness, which we use to evaluate aspects of politeness theory and to uncover new interactions between politeness markers and context. These findings guide our construction of a classifier with domain-independent lexical and syntactic features operationalizing key components of politeness theory, such as indirection, deference, impersonalization and modality. Our classifier achieves close to human performance and is effective across domains. We use our framework to study the relationship between politeness and social power, showing that polite Wikipedia editors are more likely to achieve high status through elections, but, once elevated, they become less polite. We see a similar negative correlation between politeness and power on Stack Exchange, where users at the top of the reputation scale are less polite than those at the bottom. Finally, we apply our classifier to a preliminary analysis of politeness variation by gender and community.


title: Circularly Polarized S-Band Satellite Antenna With Parasitic Elements and Its Arrays

abstract: A structural modification of S-band turnstile antenna with cylindrical parasitic elements around a main radiator is introduced and investigated with respect to its gain, axial-ratio (AR) beamwidth, and radiation pattern. The overall structure is composed of a main radiator of two crossed bowtie-shaped dipole antennas and a feeding network using a Wilkinson power divider with phase differences between two parts. A final optimization of the parameter values for the enhancement of AR beamwidth is carried out from the parametric studies. The proposed antenna has not only a broad 5-dB AR bandwidth of 12.3% ranging from 2025 to 2290 MHz, but also a stable peak gain of 7.6 dBic with circular polarization (CP) characteristic.


title: A Novel SVM Based Food Recognition Method for Calorie Measurement Applications

abstract: Emerging food classification methods play an important role in nowadays food recognition applications. For this purpose, a new recognition algorithm for food is presented, considering its shape, color, size, and texture characteristics. Using various combinations of these features, a better classification will be achieved. Based on our simulation results, the proposed algorithm recognizes food categories with an approval recognition rate of 92.6%, in average.


Asset Allocation


title: The Modic Vertebral Endplate and Marrow Changes: Pathologic Significance and Relation to Low Back Pain and Segmental Instability of the Lumbar Spine

abstract: SUMMARY: Two decades following their description, the significance of Modic vertebral endplate and marrow changes remains a matter of debate. These changes are closely related to the normal degenerative process affecting the lumbar spine, and their prevalence increases with age. However, the exact pathogenesis underlying these changes and their relation to segmental instability of the lumbar spine and to low back pain remain unclear. In this paper, we review the literature relevant to this topic and discuss the currently available evidence regarding the pathologic and clinical significance of Modic changes.


title: Semi-supervised Graph Embedding Approach to Dynamic Link Prediction

abstract: We propose a simple discrete time semi-supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross-sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from past dynamics and the unsupervised loss of predicting the neighborhood context in the current network. Our model is also capable of learning different embeddings for both formation and dissolution dynamics. These key aspects contributes to the predictive performance of our model and we provide experiments with three real--world dynamic networks showing that our method is comparable to state of the art methods in link formation prediction and outperforms state of the art baseline methods in link dissolution prediction.


title: No-Reference JPEG Image Quality Assessment Based on Blockiness and Luminance Change

abstract: When scoring the quality of JPEG images, the two main considerations for viewers are blocking artifacts and improper luminance changes, such as blur. In this letter, we first propose two measures to estimate the blockiness and the luminance change within individual blocks. Then, a no-reference image quality assessment (NR-IQA) method for JPEG images is proposed. Our method obtains the quality score by considering the blocking artifacts and the luminance changes from all nonoverlapping 8 × 8 blocks in one JPEG image. The proposed method has been tested on five public IQA databases and compared with five state-of-the-art NR-IQA methods for JPEG images. The experimental results show that our method is more consistent with subjective evaluations than the state-of-the-art NR-IQA methods. The MATLAB source code of our method is available at http://image.ustc.edu.cn/IQA.html .


title: JEPC: The Java Event Processing Connectivity

abstract: Today, event processing (EP) is the first choice technology for analyzing massive event streams in a timely manner. EP allows to detect user-defined situations of interest, like in streaming position events for example, in near real-time such that actions can be taken immediately. Unfortunately, each specific EP system has its very own API and query language because there are no standards. The exchange of EP systems as well as their use within a federation is challenging, error-prone, and expensive. To overcome these problems, we introduce the Java Event Processing Connectivity (JEPC) that is a middleware for uniform EP functionality in Java. JEPC provides always the same API and query language for EP completely independent of the EP system beneath. Furthermore, we show in detail how JEPC can integrate database systems besides EP systems and evaluate the performance of EP powered by databases systems.


title: Adaptive Suspicious Prevention for Defending DoS Attacks in SDN-Based Convergent Networks

abstract: The convergent communication network will play an important role as a single platform to unify heterogeneous networks and integrate emerging technologies and existing legacy networks. Although there have been proposed many feasible solutions, they could not become convergent frameworks since they mainly focused on converting functions between various protocols and interfaces in edge networks, and handling functions for multiple services in core networks, e.g., the Multi-protocol Label Switching (MPLS) technique. Software-defined networking (SDN), on the other hand, is expected to be the ideal future for the convergent network since it can provide a controllable, dynamic, and cost-effective network. However, SDN has an original structural vulnerability behind a lot of advantages, which is the centralized control plane. As the brains of the network, a controller manages the whole network, which is attractive to attackers. In this context, we proposes a novel solution called adaptive suspicious prevention (ASP) mechanism to protect the controller from the Denial of Service (DoS) attacks that could incapacitate an SDN. The ASP is integrated with OpenFlow protocol to detect and prevent DoS attacks effectively. Our comprehensive experimental results show that the ASP enhances the resilience of an SDN network against DoS attacks by up to 38%.


title: Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing

abstract: Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings. This requires fine-grained analysis of embedding subspaces. Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding. The task we use is fine-grained name typing: given a large corpus, find all types that a name can refer to based on the name embedding. Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes, and allow the direct evaluation of embeddings without confounding factors like sentence context


XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis


title: SME Adoption of Enterprise Systems in the Northwest of England - An Environmental, Technological, and Organizational Perspective.

abstract: A b s t r d C t The attention of software vendors has moved recently to SMEs (smallto medium-sized enterprises), offering them a vast range of enterprise systems (ES), which were formerly adopted by large firms only. From reviewing information technology innovation adoption literature, it can be argued that IT innovations are highly differentiated technologies for which there is not necessarily a single adoption model. Additionally, the question of why one SME adopts an ES while another does not is still understudied. This study intends to fill this gap by investigating the factors impacting SME adoption of ES. A qualitative approach was adopted in this study involving key decision makers in nine SMEs in the Northwest of England. The contribution of this study is twofold: it provides a framework that can be used as a theoretical basis for studying SME adoption ofES, and it empirically examines the impact of the factors within this framework on SME adoption ofES. The findings of this study confirm that factors impacting the adoption ofES are different from factors impacting SME adoption of other previously studied IT innovations. Contrary to large companies that are mainly affected by organizational factors, this study shows that SMEs are not only affected by environmental factors as previously established, but also affected by technological and organizational factors.


title: Detection of Bare PCB Defects by Image Subtraction Method using Machine Vision

abstract: A Printed Circuit Board (PCB) consists of circuit with electronic components mounted on surface. There are three main steps involved in manufacturing process, where the inspection of PCB is necessary to reduce the defects. First step is printing, second step is the components fabrication over the PCB surface and third is the components soldering. In this paper Machine Vision PCB Inspection System is applied at the first step of manufacturing, i.e., the making of bare PCB. We first compare a standard PCB image with a PCB image to be inspected, using a simple subtraction algorithm that can detect the defected regions. Our focus is to detect defects on printed circuit boards. Typical defects that can be detected are over etchings (opens), under-etchings (shorts), holes etc.


title: Interference Impact on Coverage and Capacity for Low Power Wide Area IoT Networks

abstract: In this paper we analyze and discuss the coverage and capacity of Sigfox and LoRaWAN in a large scale urban environments covering 150 km$^2$ in Northern Denmark. First, the study measures and analyzes interference in the European 868 MHz license free industrial, scientific, and medical band, creating a model for the interference. The measured interference in downtown Aalborg has an occurrence rate of 22 % and a generalized extreme value distributed power level. Next, the study compares the coverage of the two Internet of Things network solutions using the existing Telenor cellular site grid both with and without interference from the measured external sources. The study concludes that without interference, both LoRaWAN and Sigfox provides very good indoor coverage of more than 99 %. Furthermore, Sigfox and LoRaWAN can provide uplink and downlink failure rates of less than 1 % for the 95 percentile of the devices for all cells without external interference. Adding the external interference results in an outdoor coverage of 90- 95 % and indoor coverage of 50-80 %. Finally, the uplink and downlink 95 percentile failure rate increases significantly to 50 % for LoRaWAN and exceeds 60 % for Sigfox.


title: Urban design, central London and the ‘crisis’ 2007–2013: business as usual?

abstract: London is changing, to a breathtaking extent. Beneath this fast paced activity, new patterns are forming and divisions that had been relatively unremarked before are now becoming increasingly visible. The ‘square mile’ of the City of London, which is now identified by some dramatically tall buildings, forms a contrast to the traditional urbanism of the City of Westminster, the majority of which is covered by conservation area legislation. This paper will consider this contrast from the perspective of urban design, examining both the wider development context for these changes and the separate design policies of these two historic organizations of local government. One of the key questions to be investigated is how these changes have impacted on the character of central London as a place. Moving on from the well-rehearsed debates about London’s skyline, the paper considers the significance of urban design in the context of a global urban spatial economy. It suggests that central London faces severe dilemmas about its future if the growth scenario continues.


title: Chargrid: Towards Understanding 2D Documents

abstract: We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.


title: Memristor SPICE model and crossbar simulation based on devices with nanosecond switching time

abstract: This paper presents a memristor SPICE model that is able to reproduce current-voltage relationships of previously published memristor devices. This SPICE model shows a stronger correlation to various published device data when compared to existing SPICE models. Furthermore, switching characteristics of published memristor devices with switching times in the nanosecond scale were modeled. Therefore, this model can be used to accurately simulate neural systems based on these high-speed memristors. This paper also demonstrates how this model can be used to accurately calculate switching energy of these high-speed devices, leading to more accurate power calculations in memristor based neural systems. Memristor crossbar circuits provide a potential method for developing very high density neural classifiers. This model was able to simulate crossbar circuits containing up to 256 memristors. It is significantly less likely to cause convergence errors when operating in the nanosecond switching regime with a large number of devices when compared with existing SPICE models.


title: Mobile-Based Location Tracking without Internet Connectivity Using Cloud Computing Environment

abstract: With emergence of the smartphone, peoplegain access to several applications that make their workeasier. Likewise, one of the top trended app searcheswhich we do is location tracking or accessing location ofthe other user in the cloud computing environment. Inrecent past, these accesses are done without user'sknowledge and accord. In this paper, we present thestudy about runtime location access of users in the cloudcomputing environment. With this, we have thepossibility of the location accessibility of users both viaonline as well as offline. Our main goal is to get users'location for safety purpose. Through the online, we haveseveral apps which show the user's location but not mucheffective. We introduce the novel Mobile-Based LocationTracking without Internet (MLTWI). Our approachtracks the location of the user by applying latitude andlongitude information. The latitude and longitudeinformation are collected using our designed latitude andlongitude (LL) algorithm. Based on the realexperimental tests and simulation results, we validatedthat our scheme outperforms to known approaches.


title: A statistical framework for genomic data fusion

abstract: Motivation: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. ::: ::: Results: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein--protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins---membrane proteins and ribosomal proteins---performs significantly better than the same algorithm trained on any single type of data. ::: ::: Availability: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm


title: Mentorship Among Female Sport Management Doctoral Students

abstract: The purpose of this study was to ascertain what role mentoring played in female sport management faculty’s decision to pursue doctoral degrees and to investigate and identify factors related to successful transition through the doctoral program. A qualitative, descriptive-interpretive approach utilizing a cross case analysis of current female faculty in sport management was used to discover participants’ subjective views regarding a specific experience or experiences in an effort to provide unique, relevant data (Anda, 2001). This methodology allowed for a greater understanding of the participants and their experiences. Semistructured interviews were conducted with eight participants dichotomized by race: four White and four Black Assistant Professors teaching in undergraduate and graduate programs at various types of Carnegie classified institutions. Collectively, seven major themes and four major personality traits and characteristics developed from verbatim transcriptions of the interviews. The seven t...


title: Impact of ECT on duration of hospitalizations for mania.

abstract: Objective: To assess the impact of the use of electroconvulsive therapy (ECT) on the length of hospital stay of manic patients. Materials and Methods: We reviewed 425 consecutive admissions of 269 patients with manic or mixed affective episodes in a Brazilian private psychiatric hospital. Lengths of stay (LOSs) were compared for admissions in which ECT versus exclusive pharmacologic treatment was administered. A mixed model for repeated measures was developed to control for intraindividual correlations and potential demographic, clinical, and treatment confounder variables. The analyses were repeated using LOS minus the time until ECT was first given in each admission (LOS-tECT) as the response variable. Results: Use of ECT was associated with longer LOS than exclusive pharmacologic treatment (18.78 versus 12.51 days; p<0.001). This effect disappeared when using LOS-tECT as the response variable (11.57 versus 12.16 days; p = 0.530). Violent behavior and in-hospital use of antipsychotics and benzodiazepines were also associated with longer LOS, whereas being married was associated with shorter LOS. Conclusion: The use of ECT during hospitalizations for mania was associated with longer lengths of stay. This effect was caused mostly by delays in commencing ECT treatments rather than by the duration of treatment itself.


title: Synchromesh Torque Estimation in an Electric Vehicle's Clutchless Automated Manual Transmission Using Unknown Input Observer

abstract: This paper studies the estimation of the frictional torque of the synchromesh during the gear shifting operation in an electric vehicle equipped with a clutchless automated manual transmission (AMT). The clutchless drivetrain of the electric vehicle is discussed and the dynamical model of the powertrain from the electric traction motor to the synchromesh system of a two-speed AMT is developed. In order to estimate the frictional torque of the synchromesh, which is indeed an unknown input to the dynamical system, it is assumed to be generated by a fictitious autonomous system. Thereafter, the augmented state- space representation of the actual and fictitious state variables, which forms the basis for the observer design, is provided and the observability of this augmented system is discussed. A deterministic Luenberger observer and a stochastic Kalman-Bucy filter are designed in order to estimate the frictional torque of the synchromesh. The estimation is based on the measuring angular velocities of the electric motor and synchro ring together with the known electromagnetic torque of the traction motor. The performance of the observers is assessed experimentally by means of a test rig. The results demonstrate the satisfactory performance of the stochastic observer when the system encounters process and measurement noise which are likely to happen in practice.


title: Improved t-SNE based manifold dimensional reduction for remote sensing data processing

abstract: In our increasingly “data-abundant” society, remote sensing big data perform massive, high dimension and heterogeneity features, which could result in “dimension disaster” to various extent. It is worth mentioning that the past two decades have witnessed a number of dimensional reductions to weak the spatiotemporal redundancy and simplify the calculation in remote sensing information extraction, such as the linear learning methods or the manifold learning methods. However, the “crowding” and mixing when reducing dimensions of remote sensing categories could degrade the performance of existing techniques. Then in this paper, by analyzing probability distribution of pairwise distances among remote sensing datapoints, we use the 2-mixed Gaussian model(GMM) to improve the effectiveness of the theory of t-Distributed Stochastic Neighbor Embedding (t-SNE). A basic reducing dimensional model is given to test our proposed methods. The experiments show that the new probability distribution capable retains the local structure and significantly reveals differences between categories in a global structure.


Three-dimensional fluid-attenuated inversion recovery magnetic resonance imaging findings and prognosis in sudden sensorineural hearing loss


title: Recursive Recurrent Nets with Attention Modeling for OCR in the Wild

abstract: We present recursive recurrent neural networks with attention modeling (R2AM) for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image feature extraction, (2) an implicitly learned character-level language model, embodied in a recurrent neural network which avoids the need to use N-grams, and (3) the use of a soft-attention mechanism, allowing the model to selectively exploit image features in a coordinated way, and allowing for end-to-end training within a standard backpropagation framework. We validate our method with state-of-the-art performance on challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k.


title: Byzantine-Tolerant Machine Learning

abstract: The growth of data, the need for scalability and the complexity of models used in modern machine learning calls for distributed implementations. Yet, as of today, distributed machine learning frameworks have largely ignored the possibility of arbitrary (i.e., Byzantine) failures. In this paper, we study the robustness to Byzantine failures at the fundamental level of stochastic gradient descent (SGD), the heart of most machine learning algorithms. Assuming a set of $n$ workers, up to $f$ of them being Byzantine, we ask how robust can SGD be, without limiting the dimension, nor the size of the parameter space. ::: We first show that no gradient descent update rule based on a linear combination of the vectors proposed by the workers (i.e, current approaches) tolerates a single Byzantine failure. We then formulate a resilience property of the update rule capturing the basic requirements to guarantee convergence despite $f$ Byzantine workers. We finally propose Krum, an update rule that satisfies the resilience property aforementioned. For a $d$-dimensional learning problem, the time complexity of Krum is $O(n^2 \cdot (d + \log n))$.


title: A Comparison of Link Plus, The Link King, and a “Basic ” Deterministic Algorithm

abstract: The study objective was to compare the accuracy of a deterministic record linkage algorithm and two public domain software applications for record linkage (The Link King and Link Plus). The three algorithms were used to unduplicate an administrative database containing personal identifiers for over 500,000 clients. Subsequently, a random sample of linked records was submitted to four research staff for blinded clerical review. Using reviewers' decisions as the `gold standard', sensitivity and positive predictive values (PPVs) were estimated. Optimally, sensitivity and PPVs in the mid 90s could be obtained from both The Link King and Link Plus. Sensitivity and PPVs using a basic deterministic algorithm were 79 and 98 per cent respectively. Thus the full feature set of The Link King makes it an attractive option for SAS users. Link Plus is a good choice for non-SAS users as long as necessary programming resources are available for processing record pairs identified by Link Plus.


title: Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings

abstract: The present paper reports on the results of automatic noun compound interpretation for English using a deep neural network classifier and a selection of publicly available word embeddings to represent the individual compound constituents. The task at hand consists of identifying the semantic relation that holds between the constituents of a compound (e.g. WHOLE+PART_OR_MEMBER_OF in the case of ‘robot arm’, LOCATION in the case of ‘hillside home’). The experiments reported in the present paper use the noun compound dataset described in Tratz (2011), a revised version of the dataset used by Tratz and Hovy (2010) for training their Maximum Entropy classifier. Our experiments yield results that are comparable to those reported in Tratz and Hovy (2010) in a crossvalidation setting, but outperform their system on unseen compounds by a large margin.


Abstract Failure Analysis of SGI XFS File System


Web page design: implications of memory, structure and scent for information retrieval


A 5-Gbps CMOS Burst-Mode CDR Circuit With an Analog Phase Interpolator for PONs


title: An oral platelet-activating factor antagonist, Ro-24-4736, protects the rat kidney from ischemic injury.

abstract: The role of platelet-activating factor (PAF) in ischemic acute renal failure was evaluated by administering an oral PAF antagonist (Ro-24-4736) to rats prior to or after interruption of blood flow to both kidneys for 30 min. In animals treated with the PAF antagonist prior to ischemia, renal function was less impaired and histological abnormalities was less pronounced when compared with postischemic kidneys from vehicle-treated animals. Serum creatinine (mg/ dl) 24 h following renal ischemia was 1.58 +/- 0.17 in the PAF antagonist-treated rats compared with 2.19 +/- 0.15 in rats given placebo (P < 0.01). There was less necrosis in the outer medulla of kidneys of PAF antagonist-treated animals (P < 0.01). Tissue myeloperoxidase activity at 48 and 72 h postischemia was lower in kidneys of PAF antagonist-treated rats (P < 0.05). The PAF antagonist was also protective when administered 30 min but not 2 h following the ischemic insult. The coincident use of anti-intercellular adhesion molecule-1 monoclonal antibody did not confer additional protection over that observed with the oral PAF antagonist alone. These data suggest that PAF contributes to the pathophysiology of renal ischemic injury, perhaps by its effects on leukocyte-endothelial interactions. An orally active PAF antagonist can protect against the development of ischemic acute renal failure.


title: Deep Learning for Image Super-resolution: A Survey

abstract: Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. In this survey, we aim to give a survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic way. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.


title: Recognizing textual entailment using a subsequence kernel method

abstract: We present a novel approach to recognizing Textual Entailment. Structural features are constructed from abstract tree descriptions, which are automatically extracted from syntactic dependency trees. These features are then applied in a subsequence-kernel-based classifier to learn whether an entailment relation holds between two texts. Our method makes use of machine learning techniques using a limited data set, no external knowledge bases (e.g. WordNet), and no handcrafted inference rules. We achieve an accuracy of 74.5% for text pairs in the Information Extraction and Question Answering task, 63.6% for the RTE-2 test data, and 66.9% for the RET-3 test data.


title: Efficient Selective-ID Secure Identity-Based Encryption Without Random Oracles

abstract: We construct two efficient Identity Based Encryption (IBE) systems that are selective identity secure without the random oracle model. Selective identity secure IBE is a slightly weaker security model than the standard security model for IBE. In this model the adversary must commit ahead of time to the identity that it intends to attack, whereas in the standard model the adversary is allowed to choose this identity adaptively. Our first secure IBE system extends to give a selective identity Hierarchical IBE secure without random oracles.


title: A Horizontally Polarized Omnidirectional Printed Antenna for WLAN Applications

abstract: The design simulation, fabrication, and measurement of a 2.4-GHz horizontally polarized omnidirectional planar printed antenna for WLAN applications is presented. The antenna adopts the printed Alford-loop-type structure. The three-dimensional (3-D) EM simulator HFSS is used for design simulation. The designed antenna is fabricated on an FR-4 printed-circuit-board substrate. The measured input standing-wave-ratio (SWR) is less than three from 2.40 to 2.483 GHz. As desired, the horizontal-polarization H-plane pattern is quite omnidirectional and the E-plane pattern is also very close to that of an ideal dipole antenna. Also a comparison with the popular printed inverted-F antenna (PIFA) has been conducted, the measured H-plane pattern of the Alford-loop-structure antenna is better than that of the PIFA when the omnidirectional pattern is desired. Further more, the study of the antenna printed on a simulated PCMCIA card and that inserted inside a laptop PC are also conducted. The HFSS model of a laptop PC housing, consisting of the display, the screen, and the metallic box with the keyboard, is constructed. The effect of the laptop PC housing with different angle between the display and keyboard on the antenna is also investigated. It is found that there is about 15 dB attenuation of the gain pattern (horizontal-polarization field) in the opposite direction of the PCMCIA slot on the laptop PC. Hence, the effect of the large ground plane of the PCMCIA card and the attenuation effect of the laptop PC housing should be taken into consideration for the antenna design for WLAN applications. For the proposed antenna, in addition to be used alone for a horizontally polarized antenna, it can be also a part of a diversity antenna


title: IGLOO: Slicing the Features Space to Represent Long Sequences

abstract: We introduce a new neural network architecture, IGLOO, which aims at providing a representation for long sequences where RNNs fail to converge. The structure uses the relationships between random patches sliced out of the features space of some backbone 1 dimensional CNN to find a representation. This paper explains the implementation of the method and provides benchmark results commonly used for RNNs and compare IGLOO to other structures recently published. It is found that IGLOO can deal with sequences of up to 25,000 time steps. For shorter sequences it is also found to be effective and we find that it achieves the highest score in the literature for the permuted MNIST task. Benchmarks also show that IGLOO can run at the speed of the CuDNN optimized GRU or LSTM or faster for most of the tasks presented.


title: CellProfiler 3.0: Next-generation image processing for biology

abstract: CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.


title: Prospective Identification of Tumorigenic Prostate Cancer Stem Cells

abstract: Existing therapies for prostate cancer eradicates the bulk of cells within a tumor. However, most patients go on to develop androgen-independent disease that remains incurable by current treatment strategies. There is now increasing evidence in some malignancies that the tumor cells are organized as a hierarchy originating from rare stem cells that are responsible for maintaining the tumor. We report here the identification and characterization of a cancer stem cell population from human prostate tumors, which possess a significant capacity for self-renewal. These cells are also able to regenerate the phenotypically mixed populations of nonclonogenic cells, which express differentiated cell products, such as androgen receptor and prostatic acid phosphatase. The cancer stem cells have a CD44+/α2β1hi/CD133+ phenotype, and we have exploited these markers to isolate cells from a series of prostate tumors with differing Gleason grade and metastatic states. Approximately 0.1% of cells in any tumor expressed this phenotype, and there was no correlation between the number of CD44+/α2β1hi/CD133+ cells and tumor grade. The identification of a prostate cancer stem cell provides a powerful tool to investigate the tumorigenic process and to develop therapies targeted to the stem cell.


title: An analysis and improvement of congestion control in the CoAP Internet-of-Things protocol

abstract: An Internet of Things (IoT) environment typically consists of lossy and low powered networks (LLN) of interconnected sensors. An IoT messaging protocol that is gaining in popularity and importance is the Constrained Application Protocol (CoAP). CoAP runs at the application layer, handling reliability and congestion control over the unreliable UDP layer. Due to low bandwidth and high scale of communication, congestion can occur among sensors in an LLN, or when communicating to a border router, or when clients from the Internet access the resources in an LLN. While CoAP was originally specified with a primitive congestion control mechanism, to address its various limitations a new congestion control protocol named CoAP Congestion Control Advanced (CoCoA) is being developed. We present an analysis and evaluation of CoCoA, and based on our findings we propose and implement CoCoA 4-state-Strong, an adaptation of CoCoA that uses a 4-state estimator for variable backoffs to significantly improve throughput, even in highly lossy networks, while maintaining a higher goodput.