Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Spectral Clustering, With Application To Speech Separation

Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive new cost functions for spectral clustering based on measures of error between a given partition and a solution of the spectral relaxation of a minimum normalized cut problem. Minimizing these cost functions with respect to the partition leads to new spectral clustering algorithms. Minimizing with respect to the similarity matrix leads to algorithms for learning the similarity matrix from fully labelled data sets. We apply our learning algorithm to the blind one-microphone speech separation problem, casting the problem as one of segmentation of the spectrogram.
Exploring the climate sensitivity of tourists to South Africa through TripAdvisor reviews

ABSTRACT Climate is considered a key determinant of where and when tourists travel, and their enjoyment of the trip. Climate change threats to tourism include sudden damages from natural disasters, and more gradual changes in temperature, precipitation amount and seasonality, sunshine hours and humidity. Tourism Climatic Indices (TCI) consider the latter, classifying the contemporary suitability of destinations for tourism, and projecting changes in suitability under climate change. Scores are seldom compared to tourists’ objective experiences of climate during vacations, or tourists’ sensitivity to climate. This study assesses 5898 TripAdvisor reviews to determine the frequency of climate mentions in unstructured reviews, and the distribution of climate mentions relative to the TCI. These findings are compared to TCI results for 19 destinations in South Africa. A total of 464 TripAdvisor reviews, accounting for 7.9% of the sample, mentioned climatic conditions, highlighting the climatic suitability of South Africa for tourism. The distribution of climatic conditions mentioned in these reviews largely aligns with the TCI, but slight modifications particularly for wind speed are suggested for an improved modelling in the South African context. TripAdvisor reviews are argued to provide a valuable source of information for the tourism sector to facilitate effective adaptation to climate change.
Endothelial Cell–Dependent Regulation of Arteriogenesis

Rationale: Arteriogenesis is the process of formation of arterial conduits. Its promotion is an attractive therapeutic strategy in occlusive atherosclerotic diseases. Despite the functional and clinical importance of arteriogenesis, the biology of the process is poorly understood. Synectin, a gene previously implicated in the regulation of vascular endothelial cell growth factor signaling, offers a unique opportunity to determine relative contributions of various cell types to arteriogenesis. Objective: We investigated the cell-autonomous effects of a synectin knockout in arterial morphogenesis. Methods and Results: A floxed synectin knockin mouse line was crossbred with endothelial-specific (Tie2, Cdh5, Pdgfb) and smooth muscle myosin heavy chain–specific Cre driver mouse lines to produce cell type–specific deletions. Ablation of synectin expression in endothelial, but not smooth muscle cells resulted in the presence of developmental arterial morphogenetic defects (smaller size of the arterial tree, reduced number of arterial branches and collaterals) and impaired arteriogenesis in adult mice. Conclusions: Synectin modulates developmental and adult arteriogenesis in an endothelial cell–autonomous fashion. These findings show for the first time that endothelial cells are central to both developmental and adult arteriogenesis and provide a model for future studies of factors involved in this process.
Consensus algorithms design for constrained heterogeneous multi-agent systems

This paper addresses the output consensus problem of heterogeneous multi-agent systems. Specifically, we propose consensus algorithms that account for input saturations using only the available output measurements. Our approach takes roots from a simple design procedure built around ideal first order consensus schemes (full state availability and no input saturations) and extends to the more challenging case including systems heterogeneity, input saturations and partial state availability. Numerical examples are provided to illustrate the effectiveness of the obtained theoretical results.
2014 Cost of Wind Energy Review

This report uses representative commercial projects to estimate the levelized cost of energy (LCOE) for both land-based and offshore wind plants in the United States for 2014. Scheduled to be published on an annual basis, the analysis relies on both market and modeled data to maintain an up-to-date understanding of wind generation cost trends and drivers. It is intended to provide insight into current component-level costs and a basis for understanding variability in the LCOE across the industry. Data and tools developed by the National Renewable Energy Laboratory (NREL) are used in this analysis to inform wind technology cost projections, goals, and improvement opportunities.
Bifurcation Analysis of a Prey-Predator Coevolution Model

We show in this paper how numerical bifurcation analysis can be used to study the evolution of genetically transmitted phenotypic traits. For this, we consider the standard Rosenzweig--MacArthur prey-predator model [The American Naturalist, 97 (1963), pp. 209--223] and, following the so-called adaptive dynamics approach, we derive from it a second-order evolutionary model composed of two ODEs, one for the prey trait and one for the predator trait. Then, we perform a detailed bifurcation analysis of the evolutionary model with respect to various environmental and demographic parameters. Surprisingly, the evolutionary dynamics turn out to be much richer than the population dynamics. Up to three evolutionary attractors can be present, and the bifurcation diagrams contain numerous global bifurcations and codimension-2 bifurcation points. Interesting biological properties can be extracted from these bifurcation diagrams. In particular, one can conclude that evolution of the traits can be cyclic and easily prom.
A Note on Set Cover Inapproximability Independent of Universe Size

In the set cover problem we are given a collection of m sets whose union covers [n] = {1, . , n} and must find a minimum-sized subcollection whose union still covers [n]. We investigate the approximability of set cover by an approximation ratio that depends only on m and observe that, for any constant c < 1/2, set cover cannot be approximated to within O(2 1−1/(log log m)c m) unless SAT can be decided in slightly subexponential time. The main ingredients in the observation are the Ω(log n) hardness of approximation proof of Lund and Yannakakis and a hardness result for label cover due to Dinur and Safra.
Electromagnetic performance of interior permanent magnet machines with eccentricity

This paper comparatively investigates the electromagnetic performance, such as airgap flux density, flux linkage, radial stress, back-EMF and output torque of two rotational symmetrical and asymmetric interior permanent magnet BLDC and BLAC machines having static/rotating eccentricities by FE model and a simple analytical model. It shows that the static/rotating eccentricities hardly affect the back-EMF and output torque of symmetrical machine, although they significantly change its airgap flux density and radial stress. On the contrary, both eccentricities can significantly distort the back-EMF and output torque of asymmetric machines. Asymmetric machines can have larger torque ripple with eccentricity than symmetrical machines, although asymmetric machines produce smoother torque without eccentricity. The static and rotating eccentricities result in many different orders of the back-EMF and torque components.
Phosphatidylinositol 3-Kinase-and AKT-dependent Pathways Cytotoxic Death in Human Glioblastoma via Scatter Factor / Hepatocyte Growth Factor Protects against Updated

We have shown recently that the multifunctional growth factor, scatter factor/hepatocyte growth factor (SF/HGF), and its receptor c-met enhance the malignancy of human glioblastoma through an autocrine stimulatory loop (R. Abounaderet al., J. Natl. Cancer Inst.,91: 1548–1556, 1999). This report examines the effects of SF/HGF:c-met signaling on human glioma cell responses to DNA-damaging agents. Pretreating U373 human glioblastoma cells with recombinant SF/HGF partially abrogated their cytotoxic responses to gamma irradiation, cisplatin, camptothecin, Adriamycin, and Taxol in vitro. This cytoprotective effect of SF/HGF occurred at least in part through an inhibition of apoptosis, as evidenced by diminished terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling index and reduced DNA laddering. Anti-c-met U1/ribozyme gene transfer inhibited the ability of SF/HGF to protect against single-strand DNA breakage, DNA fragmentation, and glioblastoma cell death caused by DNA-damaging agents, demonstrating a requirement for c-met receptor function. Phosphorylation of the cell survival-promoting kinase Akt (protein kinase B) resulted from SF/HGF treatment of U373 cells, and both Akt phosphorylation and cell survival induced by SF/HGF were inhibited by phosphatidylinositol 3-kinase inhibitors but not by inhibitors of mitogen-activated protein kinase kinase or protein kinase C. Cytoprotection by SF/HGF in vitro was also inhibited by transient expression of dominant-negative Akt. Transgenic SF/HGF expression by intracranial 9L gliosarcomas reduced tumor cell sensitivity to gamma irradiation, confirming the cytoprotective effect of SF/HGF in vivo. These findings demonstrate that c-met receptor activation by SF/HGF protects certain glioblastoma cells from DNA-damaging agents by activating phosphoinositol 3-kinase-dependent and Akt-dependent antiapoptotic pathways.
Statistical analysis of time behavior of total aerosol content in the stratosphere on the basis of data from Siberian Lidar station in period 2000-2016

We present the results of statistical analysis of time series of integrated (over stratosphere) aerosol backscattering coefficient, obtained at the Siberian Lidar Station in the period 2000-2016. The analysis had the following purposes: to identify the distribution functions of variations on the basis of histograms of experimental values; to determine the time intervals of interrelation in the total aerosol content on the basis of autocorrelation functions; to extract the periodic components in the time series with the use of procedures of fast Fourier transform.
A Packet Scheduling To Enhance Quality of Service in IEEE 802.16

Wireless Broadband Network like the standardized IEEE 802.16, popularly referred to as WiMAX is known for its differentiation of Quality-of-Service (QoS) feature based on the traffic requirement. However the QoS differentiation process is often unfair especially to traffic classes which do not require high QoS. Such traffic classes are often starved to the extent that they experience extremely low throughput. In this paper, we look into a scheduling algorithm for providing QoS in Wireless Broadband Networks which alleviates this unfairness. The algorithm implements two queuing theory principles - Earliest Deadline First (EDF) and Weighted Fair Queuing (WFQ) to assure QoS to various classes of traffic. We propose an enhancement to the EDF queuing principle to ensure that lower priority traffic do not starve. We simulated the algorithm in dot net framework using the programming language C#. Our simulation results show considerable improvement in the throughput of low priority traffic while retaining the packet delay constraint of the high priority traffic classes.
Imaging An Evolving Black Hole By Leveraging Shared Structure

High quality black hole videos can provide key evidence of astrophysical processes that single static images cannot provide. However, reconstructing a video of a black hole is a highly ill-posed problem, requiring additional structural constraints to produce a plausible solution. Traditional structural constraints on the spatial or temporal structure are subject to human bias. In our work, we adapt recently developed techniques to solve realistic black hole video reconstruction without direct priors on the spatial or temporal structure, mitigating human bias. In particular, we solve a set of per-frame imaging inverse problems by relying on the shared structure across different underlying frames of the black hole as regularization. We encode this shared structure through a deep generative neural network, requiring that the reconstructed frames all lie within the range of this shared generator. We demonstrate our framework on a set of synthetic measurements of a simulated video of the supermassive black hole M87*, showing that we can substantially outperform both traditional and modern imaging methods and even achieve a level of superresolution in the reconstructed frames.
Influences on thermal fatigue reliability of QFP component solder joints based on physic-of-failure method

Physics-of-failure (POF) method based reliability prediction technology is one of the key technologies in prognostic and health management (PHM) field. Based on CalceSARA, a physics-of-failure based reliability assessment software developed by University of Maryland, influences on solder joints thermal fatigue reliability of QFP component were researched in detail, and the influences of six types of parameters such as package material, lead span, lead material, lead thickness, solder height and solder joints bond area on thermal fatigue life of solder joints were analyzed. The results indicated that under temperature cycle loading, the fatigue life of solder joints is positively correlated with solder height and solder joints bond area, and is negatively correlated with lead span and lead thickness, and the fatigue life of components with plastic package and copper alloy lead is longer than components with ceramic package and iron-nickel alloy lead. This study can provide guideline and reference for selection and usage of electronic components in reliability design.
ε-Solidity in Geometric Data Translation

Classical theory of solid modeling relies on the notion of regular sets and presupposes exactness in both geometric data and algorithms. In contrast, modeling, exchange and translation of geometric models in engineering applications usually involve data approximations and algorithms with different numerical precisions. We argue that an appropriate formulation of the geometric data translation problems requires finite size neighborhoods, leading to the notion of ε-topological operations. These operations are then used to formulate the definitions of ε-regularity and ε-solidity that extend and subsume the corresponding classical concepts as exact special cases. The proposed theory allows systematic classification and investigation of problems in geometric data translation. In particular, it explains why the current methods for validity checking of boundary representations are neither necessary nor sufficient for maintaining ε-solidity in the presence of numerical inaccuracies, whereas geometric healing procedures may be avoided in many common situations. Furthermore, the proposed theory suggests how the classical solid modeling paradigm should be extended in order to deal with the outstanding problems in geometric robustness, validation, and data translation.
Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Medicinal plants are essential to healthcare since ancient times and are integral to developing drugs and other medical treatments. More than 25% of medicines in developed countries are produced from medicinal plants, while in developing countries, approximately 80% of individuals receive primary healthcare from these plants. Traditionally, these plants are identified manually by experts, which is tedious, time-consuming, subjective and dependent on the availability of experts. Furthermore, a wrong detection can result in serious health issues or death. This signifies the need for a more reliable approach to identifying medicinal plants, which is accurate and practical. Several automated methods were proposed previously, utilizing deep learning and traditional machine learning (TML) techniques, but they require singular leaf images and failed to achieve sufficient accuracy when demonstrated in a different setting. Capturing singular leaf images for each plant is also time-consuming and laborious. This paper presents a robust, accurate and practical system to identify medicinal plants from smartphone-captured plant images in the site of plants. The proposed system utilized a cascaded architecture to extract features using a pre-trained ResNet50 model, which were optimized using Particle Swarm Optimization (PSO) to classify the plants using a Support Vector Machine (SVM). The proposed ResNet50-PSO-SVM network classified seven medicinal plants with 99.60% accuracy, outperforming the state-of-the-art (99%). The system was demonstrated for three different smartphones, classifying an image in 0.15 seconds with 97.79% accuracy on average. The system’s high accuracy, rapid identification time and robustness ensured its practical use.
Calculation of Smith’s Mean Measure of Divergence for Intergroup Comparisons Using Nonmetric Data

The Mean Measure of Divergence (MMD) is a formula that converts a battery of trait frequencies into a numerical value such that the more dissimilar two samples are, the greater the value. This measure of phenetic distance was developed by the statistician Cedric A. B. Smith and has become popular among dental anthropologists and osteologists for estimating the dissimilarity among groups in order to help reconstruct populations’ movements and structure over time and space. The purpose of the present study is to present the correct formulae and procedures for the MMD given that (1) numerous errors have entered into the literature concerning the formulae themselves, (2) improvements have been described that should be incorporated, and (3) various misunderstandings and misinterpretations have developed that need clarification.
Skin Detection -a Short Tutorial

Skin detection is the process of finding skin-colored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in images. Several computer vision approach es have been developed for skin detection. A skin detector typically transforms a given pix el into an appropriate color space and then use a skin classifier to label the pixel whether it is a ski n or a non-skin pixel. A skin classifier defines a decision boundary of the skin color class in the colo r space based on a training database of skin-colored pixels.
Increased plasma levels of the methylglyoxal in patients with newly diagnosed type 2 diabetes 初诊2型糖尿病患者血浆甲基乙二醛水平升高

Methylglyoxal (MG) is a reactive‐dicarbonyl that is thought to contribute to the development of diabetes either as a precursor for advanced glycation end products or as a direct toxin. The present study was designed to determine plasma MG level in patients with newly diagnosed type 2 diabetes mellitus (T2DM) and to evaluate the relationship between MG and other parameters, such as oxidative stress and metabolic indices.
Describing and visualizing the capacity of a system with behaviour uncertainties

User and system behaviour is difficult to predict for novel systems, and this affects the capacity of the system (the number of active users that can be supported with acceptable response delay). This leads to a range of values, in the form of a feasible or acceptable region for the potential capacity, conditional on the uncertain parameters. This work considers uncertainties in the delay between requests (user think time), network latency, and cache behaviour due to users' locality of reference. The acceptable region is shown to be bounded approximately by linear constraints which are easy to derive. This simple result is useful for sensitivity and scalability analysis, and appears to have been overlooked. It is applied to a Web-based system for telephony, using voiceXML for service ranging from interactive voice response, to voice-based E-mail.
Advanced Technologies for the Improvement of Spray Application Techniques in Spanish Viticulture: An Overview

Spraying techniques have been undergoing continuous evolution in recent decades. This paper presents part of the research work carried out in Spain in the field of sensors for characterizing vineyard canopies and monitoring spray drift in order to improve vineyard spraying and make it more sustainable. Some methods and geostatistical procedures for mapping vineyard parameters are proposed, and the development of a variable rate sprayer is described. All these technologies are interesting in terms of adjusting the amount of pesticides applied to the target canopy.
Automatic distinction of arguments and modifiers: the case of prepositional phrases

The automatic distinction of arguments and modifiers is a necessary step for the automatic acquisition of subcategorisation frames and argument structure. In this work, we report on supervised learning experiments to learn this distinction for the difficult case of prepositional phrases attached to the verb. We develop statistical indicators of linguistic diagnostics for argumenthood, and we approximate them with counts extracted from an annotated corpus. We reach an accuracy of 86.5%, over a baseline of 74%, showing that this novel method is promising in solving this difficult problem.
Information‐centric networking for multimedia, social and peer‐to‐peer communications

Information‐centric networking (ICN) is an emerging networking paradigm, which is considered as a possible solution for the next generation Internet. Several research projects have been funded by the state and the industry towards the specification and implementation of ICN solutions, whereas Internet Research Task Force has established a research group for this reason, the Information‐Centric Networking Research Group. However, the use of ICN in action will be decided on the basis of its performance in several disciplines that, eventually, concern the users expectations from their applications over the future Internet. Currently, statistics suggest that, in the near future, the Internet bandwidth will be mainly consumed by multimedia and peer‐to‐peer applications. Furthermore, social networking applications become more and more popular both to end‐users and businesses. This special issue includes papers illustrating how information‐centric networks can effectively address practical issues rising in ICN deployments considering multimedia applications and social networking technologies. Copyright © 2014 John Wiley & Sons, Ltd.
Experimental Analysis of Coordination Strategies to Support Wireless Sensor Networks Composed by Static Ground Sensors and UAV-Carried Sensors

This paper presents a comparison among different strategies to coordinate the use of heterogeneous wireless sensors aimed for area surveillance. The heterogeneity among the sensor nodes is related to their sensing and mobility capabilities. The goal of the strategies is to provide coordination among the different nodes, in order to make the wireless sensor network perform its missions with higher efficiency. Strategies combine advantages of bio-inspired and utility-based approaches to coordination. Simulations of scenarios with different characteristics were performed and the results are compared and analyzed.