Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilience Evaluation Approach of Transportation Networks

To analyze the resilience of transportation networks, it is proposed to use a quantificational resilience evaluation approach. First, we represent transportation networks by an undirected graph with the nodes as cities and edges as traffic roads. Because the survival ability of transportation of a pair of cities depends on the number of independent paths between them, the resilience of a city node can be evaluated by the weighted average number of reliable independent paths with all other city nodes in the networks. The network resilience can then be calculated by the weighted sum of all node resilience. Based on the recommended approaches, the resilience of a transportation network is evaluated and analyzed. Several interesting conclusions are drawn from the computational results.
Infrared Target Tracking Using Multi-Feature Joint Sparse Representation

This paper proposed a novel sparse representation-based infrared target tracking method using multi-feature fusion to compensate for incomplete description of single feature. In the proposed method, we extract the intensity histogram and the data on-Local Entropy and Local Contrast Mean Difference information for feature representation. To combine various features, particle candidates and multiple feature descriptors of dictionary templates were encoded as kernel matrices. Every candidate particle was sparsely represented as a linear combination of a set of atom vectors of a dictionary. Then, the sparse target template representation model was efficiently constructed using a kernel trick method. Finally, under the framework of particle filter the weights of particles were determined by sparse coefficient reconstruction errors for tracking. For tracking, a template update strategy employing Adaptive Structural Local Sparse Appearance Tracking (ASLAS) was implemented. The experimental results on benchmark data set demonstrate the better performance over many existing ones.
CYR61 (CCN1) is a metastatic biomarker of gastric cardia adenocarcinoma

Gastric cardia adenocarcinoma (GCA) is the most aggressive subtype of gastric cancer with a high metastatic rate. In this report, we collected tumor tissue samples from 214 GCA cases and examined expression of CYR61, a target gene product of the Hippo-YAP/TAZ pathway, in the GCA tumors by immunohistochemical (IHC) staining using the tissue microarray assay (TMA). The results have shown that CYR61 is overexpressed in 44% of the GCA tumor samples. Expression of CYR61 is inversely correlated with cumulative survival of GCA patients (p<0.001) and significantly associated only with metastatic pathological categories (with N category, p=0.052; with TNM stage, p=0.001). Furthermore, knockdown of CYR61 in gastric cancer AGS cells impairs the cancer cell migration and invasion, suggesting a driver role of CYR61 in metastasis. Thus, our studies have established CYR61 as a metastatic biomarker for prediction of poor prognosis of GCA and provided a potential molecular target for anti-metastatic therapy of GCA.
Low-resource keyword search strategies for tamil

We propose strategies for a state-of-the-art keyword search (KWS) system developed by the SINGA team in the context of the 2014 NIST Open Keyword Search Evaluation (OpenKWS14) using conversational Tamil provided by the IARPA Babel program. To tackle low-resource challenges and the rich morphological nature of Tamil, we present highlights of our current KWS system, including: (1) Submodular optimization data selection to maximize acoustic diversity through Gaussian component indexed N-grams; (2) Keywordaware language modeling; (3) Subword modeling of morphemes and homophones.
Smart data collection in large scale sparse WSNs

Mobile wireless sensor networks (MWSNs), a special class of WSN in which one or more elements of the network is mobile, has been extensively studied over the past 15 years. In MWSNs, mobility plays an important role in improving the performance of the network in terms of connectivity, lifetime, and data collection. We exploit controlled mobility to develop a smart data collection scheme in MWSNs. We consider a sensor network where static nodes are deployed and organized into clusters on the basis of distance from each other. These nodes sense their environment and transmit data to their corresponding cluster heads, which in turn send it to a mobile data collector (MDC) when it is in communication range. In this paper, we first show the factors affecting the data collection process of MDC in such an environment. We then present an adaptive algorithm and control parameters that the MDC uses for autonomously controlling its motion. These parameters allow the speed of the MDC to be adjusted at run time in order to adaptively improve the data collection process. Built-in intelligence helps our system adapting to the changing requirements of data collection. Our scheme shows significant advantages for sparsely deployed, large scale sensor networks and heterogeneous networks (where sensors have variable sampling rates). The simulation results show a significant increase in data collection rate and reduction in the overall time and number of laps that the MDC spends for data gathering.
Microarchitectural Characterization of Production JVMs and Java Workloads

Understanding and comparing Java Virtual Machine (JVM) performance at a microarchitectural level can identify JVM performance anomalies and potential opportunities for optimization. The two primary tools for microarchitectural performance analysis are hardware performance counters and cycle accurate simulators. Unfortunately, the nondeterminism, complexity, and size of modern JVMs make these tools difficult to apply and therefore the microarchitectural performance of JVMs remains under-studied. We propose and use new methodologies for measuring unmodified production JVMs using both performance counters and a cycle accurate simulator. Our experimental design controls nondeterminism within a single measurement by using multiple iterations after a steady state is reached. We also use call-backs provided by the production JVMs to isolate application performance from the garbage collector, and where supported, the JIT. Finally, we use conventional statistical approaches to understand the effect of the remaining sources of measurement noise such as nondeterministic JIT optimization plans. This paper describes these methodologies and then reports on work in progress using these methodologies to compare IBM J9, BEA JRockit, and Sun HotSpot JVM performance with hardware performance counters and simulation. We examine one benchmark in detail to give a flavor of the depth and type of analyses possible with this methodology.
Control of nonlinear chained systems: from the Routh-Hurwitz stability criterion to time-varying exponential stabilizers

We show how any linear feedback that stabilizes the origin of a linear chain of integrators induces a simple, continuous time-varying feedback that exponentially stabilizes the origin of a nonlinear chained-form system. The design method is related to a method developed by M'Closkey and Murray (1997) to transform smooth feedback yielding slow polynomial convergence into continuous homogeneous ones that give exponential convergence.
Compressed Sensing of Analog Signals

A traditional assumption underlying most data converters i s that the signal should be sampled at a rate which exceeds twice the highest frequency. This statement is base d on a worst-case scenario in which the signal occupies the entire available bandwidth. In practice, many signals p o ses a sparse structure so that a large part of the bandwidth is not exploited. In this paper, we consider a fram ework for utilizing this sparsity in order to sample such analog signals at a low rate. More specifically, we consi der continuous-time signals that lie in a shift-invariant (SI) space generated by m kernels, so that any signal in the space can be expressed as an infinite linear combination of the shifted kernels. If the period of the underlying SI spa ce is equal toT , then such signals can be perfectly reconstructed from samples at a rate of m/T . Here we treat the case in which only k out of them generators are active, meaning that the signal actually lies in a lower dime nsional space spanned by k generators. However, we do not know whichk are chosen. By relying on results developed in the context of compressed sensing (CS) of finite-length vectors, we develop a general framework for sa mpling such signals at a rate much lower than m/T . The distinguishing feature of our results is that in contras t to the problems treated in the context of CS, here we consider sampling of analog-signals for which no underly ing finite-dimensional model exists. Our approach combines ideas from analog sampling in a subspace with a rece ntly developed block diagram that converts an infinite set of sparse equations to a finite counterpart. Usin g these two components we formulate our problem within the framework of finite CS and then rely on efficient and stable algorithms developed in that context. Our results allow to extend much of the recent literature on CS to the truly analog domain. Department of Electrical Engineering, Technion—Israel In stitute of Technology, Haifa 32000, Israel. Phone: +972-48293256, fax: +9724-8295757, E-mail: yonina@ee.technion. ac.il. This work w as supported in part by the Israel Science Foundation under G rant no. 1081/07 and by the European Commission in the framework of the FP7 Net work of Excellence in Wireless COMmunications NEWCOM++ (co ntract no. 216715). 2
Strongly singular integrals along curves

In this article we obtain $L^2$ bounds for strongly singular integrals along curves in $\R^d;$ our results both generalise and extend to higher dimensions those obtained by Chandarana in the plane. Moreover, we show that the operators in question are bounded from $L\log L$ to weak $L^1$ at the critical exponent $\alpha=0. $
A multimodal and multilevel system for robotics treatment of autism in children

Several studies suggest that robots can play a relevant role to address Autistic Spectrum Disorder (ASD). This paper presents a humanoid social robot-assisted behavioral system based on a therapeutic multilevel treatment protocol customized to improve eye contact, joint attention, symbolic play, and basic emotion recognition. In the system, the robot acts as a social mediator, trying to elicit specific behaviors in child, taking into account his/her multimodal signals. Statistical differences in eye contact and facial expression imitation behaviors after the use of the system are reported as preliminary results.
Web Browser Support for Problem-based Learning

This project was intended to develop the investigative competencies of undergraduate Biological Science students. It has developed a problem-solving approach to designing student practicals in Immunology, changing the learning process from being teacher-focussed to learner-focussed and providing students through situated learning with the necessary skills to make decisions about what they do in the laboratory. A suite of problems created can be solved by the students’ development of their own laboratory protocols, using learning support materials generated during the project. The knowledge base consists of topic summaries, key references and detailed experimental protocols. A hypermedia version in HTML includes visuals of experimental outcomes and an image and audio glossary linked to the theory and experiments. The computer-based material produced in 1995 enhanced the use of paper resources on which the system was trialled in 1994 and broadened the scope and potential outcomes of the project to include use of the resources of the World Wide Web.
Risk Aversion and Monetary Policy in a Global Context

We analyze the relationship between the stance of Eurozone monetary policy and the implicit risk aversion in the European Stock market prices. We use a structural vector autoregression (SVAR) model as Bekaert et al. (2013) do for the U.S. market. We adapt this model for the European Stock market enlarging it to incorporate the effect of the monetary policy of the U.S. Federal Reserve. Our results show that a lax monetary policy decreases risk aversion, bearing out the evidence found in the U.S. market. We also show that Eurozone analysis could lead to erroneous conclusions when we disregard U.S. monetary policy effect.
Towards better understanding of academic search

Academics have relied heavily on search engines to identify and locate research manuscripts that are related to their research areas. Many of the early information retrieval systems and technologies were developed while catering for librarians to help them sift through books and proceedings, followed by recent online academic search engines such as Google Scholar and Microsoft Academic Search. In spite of their popularity among academics and importance to academia, the usage, query behaviors, and retrieval models for academic search engines have not been well studied. To this end, we study the distribution of queries that are received by an academic search engine. Furthermore, we delve deeper into academic search queries and classify them into navigational and informational queries. This work introduces a definition for navigational queries in academic search engines under which a query is considered navigational if the user is searching for a specific paper or document. We describe multiple facets of navigational academic queries, and introduce a machine learning approach with a set of features to identify such queries.
Investigation of the focusing properties of some fractal arrays

Many medical and industrial applications require focusing of microwave radiation in the near field of the radiating antenna. Antenna arrays are suitable and flexibel in such applications. Fractal antennas and arrays are finding much interest in recent years for the benefits they offer over conventional ones. The feasibility of deploying the fractal principle to focused arrays is demonstrated. The properties of fractal array whose elements are spatially distributed according to the binary tree fractal are investigated and compared with those of conventional array. Computer simulations results show that focused fractal array can have similar focusing properties while having favorable ones.
Free-Viewpoint Video for Outdoor Sporting Events

When traditional fixed-viewpoint video of an event is rendered, the only viewpoint available for playback is that of the camera that recorded the event. Free-viewpoint video (FVV) attempts to break this restriction by allowing the specification of the viewpoint at the point of rendering rather than the point of recording. This work proposes to apply the techniques of FVV to video recorded at outdoor sporting events. Sports often rely on the movement of contestants within a delineated playing area. Cameras are set up to try to get a direct view of the boundaries of these areas, as play here is often crucial to the game. However, as the broadcasters typically have a limited number of cameras, they must try to preempt the locations of interesting boundary events. If they get this wrong they risk missing out on crucial footage and also waste a camera that could have provided another angle for general coverage. Similarly, a great deal of tactical interest in sport relies on the spacial configuration of the competitors, such as the relationship that determines whether a player is offside in football. These relationships are often very difficult to determine unless a camera is specifically placed to highlight the desired relationship. As the configuration of the contestants may become interesting at an arbitrary time and location in the playing area, it is impossible to place cameras accurately to guarantee a good viewpoint. As a result it can often be very difficult, if not impossible, to work out the spacial relationship of the contestants from the broadcast coverage of a sporting
Note on a polytropic β-model to fit the X-ray surface brightness of clusters of galaxies

In this note, I suggest that themodel used to fit the X-ray surface brightness profiles of extended sources, like groups and clusters of galaxies, has to be corrected when the counts are collected in a wide energy band comparable to the mean temperature of the source and a significant gradient in the gas temperature is observed. I p resent a revised version of the � model for the X-ray brightness that applies to a intracluste r gas with temperature and density related by a polytropic equation and extends the standard version that is strictly valid for an isothermal gas. Given a temperature gradient observed through an energy window of 1-10 keV typical for the new generation of X-ray observatories, theparameter can change systematically up to 20 per cent from the value obtained under isothermal assumption, i.e. by an amount larger that any statistical uncertainty obtain ed from the present data. Within the virial regions of typical clusters of galaxies, these sy stematic corrections affect the total gravitating mass estimate by 5-10 per cent, the gas mass by 10-30 per cent and the gas fraction value up to 50 per cent, when compared to the measurements obtained under the isothermal assumption.
Routability-driven analytical placement by net overlapping removal for large-scale mixed-size designs

Routability is a challenging cost metric for modern large-scale mixed-size placement. Most existing routability-driven placement algorithms apply whitespace allocation to relieve the routing congestion. Nevertheless, we observe that whitespace allocation might worsen the routability of a placement. To remedy this deficiency, we propose in this paper a new direction/technique, called net overlapping removal, to optimize the routability during placement. Unlike most previous works that allocate whitespace among blocks, our approach moves nets apart from congested regions to improve the chip routability. To apply the net overlapping removal technique, we generalize a net bounding-box based congestion evaluation model to handle practical routing constraints and speed up the routability optimization during placement. We further propose a Gaussian smoothing technique to handle the challenging macro porosity issue, arising in modern mixed-size designs with large macros that require to preserve routing resources for inner routing of the macros. Experimental results show that our approaches lead to significantly better routability and running time than previous works for mixed-size placement.
Artificial Neural Network Approach for More Accurate Solar Cell Electrical Circuit Model

The implementation of a neural network especially for improving the accuracy of the electrical equivalent circuit parameters of a solar cell is proposed. These electrical parameters mainly depend on solar irradiation and temperature, but their relationship is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the proposed neural network is trained once by using some measured current–voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly. Taking the effect of sunlight irradiance and ambient temperature into consideration, the output current and power characteristics of PV model are simulated and optimized. Finally, the proposed model has been validated with datasheet and experimental data from commercial PV module, Kotak PV-KM0060 (60Wp). The comparison show the higher accuracy of the ANN model than the conventional one diode circuit model for all operating conditions.
Attentional Bias Associated with Habitual Self-Stigma in People with Mental Illness

As habitual self-stigma can have a tremendous negative impact on people with mental illness, it is of paramount importance to identify its risk factors. The present study aims to examine the potential contributory role of attentional bias in habitual self-stigma. People with mental illness having strong (n = 47) and weak (n = 47) habitual self-stigma completed a computerized emotional Stroop task which included stigma-related, positive, and non-affective words as stimuli. The strong habit group was found to exhibit faster color-naming of stigma-related words (compared to non-affective words), whereas the weak habit group showed no difference in the speed of response to different stimuli. These findings suggest that people with stronger habitual self-stigma may be more able to ignore the semantic meaning of stigma-related words and focus on the color-naming task. Moreover, people with stronger habitual self-stigma may have greater attentional avoidance of stigma-related material. The present study is the first to demonstrate a specific relationship between habitual self-stigma and biased processing of stigma-related information. In order to further determine the role and the nature of attentional bias in habitual self-stigma, future research should employ a broader range of experimental paradigms and measurement techniques to examine stigma-related attentional bias in people with mental illness.
Enhanced SVR ensembles for wind power prediction

Wind energy is an important component in the renewable energy mix, but successful integration into existing grid infrastructure is a major challenge. In this context, the accurate prediction of future wind generation power is extremely valuable as it would facilitate more efficient and sustainable provision. In a previous work, we proposed a method for wind power prediction based on an ensemble of support vector regressors. While this approach was very promising, there are still many avenues for further improvement. In this paper, we present two key extensions to the existing methodology and show that these result in significant performance improvements. In the first approach, we seek to vary the parameter values for the component support vector regressors. This is done in two ways: random initialization, and using an evolutionary strategy to select appropriate parameter values. Secondly, we exploit correlations between the input features to reduce the dimensionality and noise. Again, two approaches are tested: a feature selection stage using correlation, and Principal Component Analysis (PCA), which is shown to greatly reduce computational requirements while increasing prediction accuracy. The combination of these two enhancements produces interesting improvements over the previous prediction system, and these are presented and discussed in this paper.
Overnight Closed-Loop Insulin Delivery in Young People With Type 1 Diabetes: A Free-Living, Randomized Clinical Trial

OBJECTIVE To evaluate feasibility, safety, and efficacy of overnight closed-loop insulin delivery in free-living youth with type 1 diabetes. RESEARCH DESIGN AND METHODS Overnight closed loop was evaluated at home by 16 pump-treated adolescents with type 1 diabetes aged 12–18 years. Over a 3-week period, overnight insulin delivery was directed by a closed-loop system, and on another 3-week period sensor-augmented therapy was applied. The order of interventions was random. The primary end point was time when adjusted sensor glucose was between 3.9 and 8.0 mmol/L from 2300 to 0700 h. RESULTS Closed loop was constantly applied over at least 4 h on 269 nights (80%); sensor data were collected over at least 4 h on 282 control nights (84%). Closed loop increased time spent with glucose in target by a median 15% (interquartile range −9 to 43; P < 0.001). Mean overnight glucose was reduced by a mean 14 (SD 58) mg/dL (P < 0.001). Time when glucose was <70 mg/dL was low in both groups, but nights with glucose <63 mg/dL for at least 20 min were less frequent during closed loop (10 vs. 17%; P = 0.01). Despite lower total daily insulin doses by a median 2.3 (interquartile range −4.7 to 9.3) units (P = 0.009), overall 24-h glucose was reduced by a mean 9 (SD 41) mg/dL (P = 0.006) during closed loop. CONCLUSIONS Unsupervised home use of overnight closed loop in adolescents with type 1 diabetes is safe and feasible. Glucose control was improved during the day and night with fewer episodes of nocturnal hypoglycemia.