Miracle-dz/UniFAR-FTUs huggingface dataset:
The application of non-orthogonal multiple access in wireless powered communication networks

This work investigates the application of non-orthogonal multiple access (NOMA) scheme for the uplink of wireless powered communication networks (WPCN). We maximize the sum rate by jointly designing of the time allocation, the downlink energy beamforming and receiver beamforming. To solve this nonconvex problem, a method of two stages optimization is proposed. In the first stage, we apply the series approximations to obtain the optimal energy beamforming and receive beamforming with fixed time allocation. In the second stage, one-dimensional search is considered to obtain the optimal time allocation. For the approximation part, successive convex approximation (SCA) method is used to convert a mixed integer non-linear program into its convex approximation problem. The numerical results illustrate that the proposed algorithm converges to the local optimum solution and the proposed scheme outperforms the fixed time allocation scheme and orthogonal multiple access (OMA).
Mode of Onset Predicts Etiological Diagnosis of Arterial Ischemic Stroke in Children

Background and Purpose— In children, early differentiation among various etiologies of arterial ischemic stroke (AIS) is important. Cerebral arteriopathy is a frequently identified cause of childhood stroke. Children with arteriopathies require a different therapeutic approach from children with AIS of nonarteriopathic origin. We aimed to investigate the association between temporal features of the onset of neurological symptoms and stroke etiology in children with AIS. Methods— From a consecutive cohort of children (6 months to 18 years) with a confirmed diagnosis of AIS at one center, we selected all patients with transient cerebral arteriopathy (n=10), postvaricella angiopathy (n=20), dissection (n=8), cardio-embolic (n=8), and cryptogenic stroke (n=10). We retrospectively reviewed medical charts for mode of onset and classified the onset as either abrupt, reaching maximum severity of symptoms within 30 minutes, or nonabrupt, including a progressing, stuttering, or recurring course. We compared the mode of onset in patients with known cerebral arteriopathy to those with nonarteriopathic stroke using multivariate logistic regression modeling. Results— There were no significant differences for age, gender, location of infarction, seizures, and headache between the arteriopathic and nonarteriopathic group. Most children with nonarteriopathic AIS had an abrupt onset (72%), compared with 32% in children with arteriopathic stroke. With nonabrupt onset, the odds of having an arteriopathic etiology was 6.1 (95% CI, 1.6 to 22.8; P=0.007) after correction for possible confounders. Conclusions— Mode of onset predicts etiological diagnosis of childhood AIS and may guide prioritization of ancillary investigations and choice of treatment. A nonabrupt onset of symptoms is associated with arteriopathic stroke, particularly with presumed inflammatory arteriopathies.
Noise adaptive front-end normalization based on Vector Taylor Series for Deep Neural Networks in robust speech recognition

Deep Neural Networks (DNNs) have been successfully applied to various speech tasks during recent years. In this paper, we investigate the use of DNNs for noise-robust speech recognition and demonstrate their superior capabilities of modeling acoustic variations over the conventional Gaussian Mixture Models (GMMs). We then propose to compensate the normalization front-end of the DNNs using the GMM-based Vector Taylor Series (VTS) model compensation technique, which has been successfully applied in the GMM-based ASR systems to handle noisy speech. To fully benefit from both the powerful modeling capability of the DNN and the effective noise compensation of the VTS, an adaptive training algorithm is further developed. The preliminary experimental results on the AURORA 2 task have demonstrated the effectiveness of our approach. The adaptively trained system has been shown to outperform the GMM-based VTS adaptive training by relatively 18.8% using the MFCC features and 21.9% using the FBank features.
IIIT Hyderabad ’ s CLIR experiments for FIRE -

This paper discourses our CLIR experiments performed for the FIRE workshop. We had submitted our runs for Adhoc monolingual document retrieval in Hindi and English, and Ad-hoc cross-lingual document retrieval from Hindi to English, and English to Hindi. In this paper, we describe our English to Hindi and Hindi to English CLIR systems and the experiments conducted on them using the FIRE2008 dataset. We had used a dictionary-based approach of query translation and transliteration of named entities in the queries using a mapping-based, Compressed Word Format(CWF) algorithm[1]. Disjunctive Query formulation with different scoring weights based on the part of the topic from which they originated gave us overall better performance.
Following the path towards intelligently connected devices for on-line, real-time cardiac arrhythmia detection and classification

Cardiac arrhythmia detection and classification is of outmost importance for early diagnosis to reduce significantly the rates of morbidity and mortality of patients with heart disease. In particular for patients with silent cardiac symptomatology, the advances in wearable sensing technology offer a promising solution for on-line, real-time detection of intermittent tachyarrhythmia events that otherwise may evolve undetected. In this paper the authors examine some of the key issues that outline the path towards integrating various aspects for ECG signal acquisition and analysis with current trends in wearable sensing technology and present recent results towards on-line real-time arrhythmia classification considering the IoMT (Internet of Medical Things) approach.
Instability of Toroidal Magnetic Field in Jets and Plerions

Astrophysical jets and pulsar-fed supernova remnants (plerions) are expected to develop highly organized magnetic structures dominated by concentric loops of toroidal field, Bϕ. It has been argued that such structures could explain the polarization properties of some jets and contribute to their lateral confinement through magnetic tension forces. A concentric toroidal field geometry is also central to the Rees-Gunn model for the Crab Nebula, the archetypal plerion, and leads to the deduction that the Crab pulsar's wind must have a weak magnetic field. Yet this kind of equilibrium between magnetic and gas pressure forces, the "equilibrium Z-pinch" of the controlled fusion literature, is well known to be susceptible to disruptive localized instabilities, even when the magnetic field is weak and/or boundary conditions (e.g., a dense external medium) slow or suppress global modes. Thus, the magnetic field structures imputed to the interiors of jets and plerions are unlikely to persist for very long. To determine the growth rates of Z-pinch instabilities under astrophysical conditions, I derive a dispersion relation that is valid for the relativistic fluids of which jets and plerions may be composed, in the ideal magnetohydrodynamics (MHD) limit. The dominant instabilities are kink (m = 1) and pinch (m = 0) modes. The former generally dominate, destroying the concentric field structure and probably driving the system toward a more chaotic state in which the mean field strength is independent of radius (and in which resistive dissipation of the field may be enhanced). I estimate the timescales over which the field structure is likely to be rearranged and relate these to distances along relativistic jets and radii from the central pulsar in a plerion. I conclude that the central tenet of the Rees-Gunn model for the Crab Nebula, the existence of a concentric toroidal field well outside the pulsar wind's termination shock, is physically unrealistic. With this assumption gone, there is no dynamical reason to conclude that the magnetic energy flux carried by the pulsar wind is much weaker than the kinetic energy flux. Abandoning the principal conclusion of Rees & Gunn would resolve a long-standing puzzle in pulsar wind theory.
DEM generation and analysis on rugged terrain using ENVISAT/ASAR ENVISAT/ASAR multi-angle insar data

The suitable incidence angle is one of the important parameters on DEM mapping precision when interferometric SAR used to generate DEM on mountainous terrain. In this paper, from real ENVISAT/ASAR multiple angle interferometric data, SRTM DEM with 90m horizontal resolution and 1:50000 digital topographic DEM, the concrete impact of incidence angle on DEM accuracy has been analysed and discussed from qualitative and quantitative views。 The result indicates: (1)For ASAR multi-incidence angle interferometric data, the effects of the layover and foreshortening on DEM precision are significantly at lower incidence angle, and difference of DEM accuracy is large, for example, the accuracy difference between IS2 and IS4 ASAR DEM is more than 6m. So, the incidence angle must be selected rigidly when InSAR are used for rugged terrain mapping. (2) Temporal decorrelation is serious with 35 days time interval, the overall evaluation of DEM precision are difficult. If the InSAR data that have wider incidence range and have not temporal decorrelation can be obtained, the further and overall evaluation and analysis that radar layover, shadow and foreshortening are how to affect the DEM precision can be investigated.
WillCam: a digital camera visualizing users. interest

With the increased usage of digital cameras and camera-enabled mobile phones in recent years, large numbers of photos are being taken. Many of the photos that are taken are used little, if at all. Researchers and companies have developed systems where photos can be annotated or tagged to facilitate storage and retrieval. However, people are often unwilling to spend the time and effort to carry out annotation. To solve this problem, we focus on real-time annotation where photographs are annotated at the time when they are taken. We propose a novel digital camera,. WillCam. , which enables users to capture various information, such as location, temperature, ambient noise, and photographer facial expression, in addition to the photo itself. WillCam also helps users express their interest -what object or information in the picture/scene is most important for them- visually.
Research on Chinese web relevance evaluation based on lexical chains

Web relevance is one of the important indicators to reflect website characteristics from the semantic perspective. To users and constructors of the website, whether web relevance is good or not will affect their use and construction of the website. This paper makes use of the keywords extraction method for web page content and web relevance comparison way to do the research of Chinese web relevance evaluation. A small-scale experiment has been done to verify the feasibility of this evaluation method.
A sexual elitist genetic algorithm for providing QoS in distributed virtual environment systems

Architectures based on networked servers have become a de-facto standard for distributed virtual environment (DVE) systems. These systems allow a large number of remote users to share a single 3D virtual scene. In order to provide quality of service in a DVE system, clients should be assigned to servers taking into account system throughput and system latency. This highly complex problem is known as the quality of service (QoS) problem. This paper proposes an elitist sexual genetic algorithm for solving the QoS problem in distributed virtual environment systems. Performance evaluation results show that, due to its ability of both finding good search paths and keeping diversity escaping from local minima, this nature inspired technique can provide significantly better solutions than other heuristic methods with shorter execution times. Therefore, the proposed implementation of GA search method can improve the QoS offered by DVE systems.
Efficiently Inferring Thread Correlations Technical Report TR-09-59203

We present a new analysis for proving properties of finegrained concurrent programs with a shared, mutable, heap in the presence of an unbounded number of objects and threads. The properties we address include memory safety, data structure invariants, partial correctness, and linearizability. Our techniques enable successful verification of programs that were not be handled by previous concurrent shape analysis algorithms. We present our techniques in an abstract framework we call thread-correlation analysis. Thread-correlation analysis infers invariants that capture the correlations between the local states of different threads and the global state (content of the heap). However, inferring such invariants is non-trivial, since it requires reasoning about a quadratic number of interactions between threads. We provide two novel techniques for reducing the cost of applying the abstract transformers. In our experiments, these techniques were able to reduce the time of the analysis by a factor of 35.
Combining Stochastic and Deterministic Search for Pose-Invariant Facial Expression Recognition

We propose a novel method for pose-invariant facial expression recognition from monocular video sequences that combines stochastic and deterministic search processes. We use the simple face model called variable-intensity template, which can be prepared with very little time and effort. We tackle the two issues found in previous work on the variable-intensity template: low accuracy in head pose estimation, and assumption violations due to external intensity changes such as illumination change. We mitigate these issues by introducing the deterministic approach into the stochastic approach implemented as a particle filter. Our experiment demonstrates significant improvements in recognition performance for horizontal and vertical head orientations in the range of ±40 degrees and ±20 degrees, respectively, from the frontal view.
Segmenting focused objects in visual images

Image segmentation that partitions a given image into meaningful regions is an important task of image analysis for representation, interpretation and recognition. If the image consists of man-made objects in well controlled situations such as industrial environments, segmentation can be easy. In applications such as target acquisition in natural scenes, there is no control of the environment, and segmentation becomes extremely difficult.
The direct and indirect effects of audits on the tax revenue in Greece

This paper uses recently unveiled monthly information from the Hellenic Ministry of Finance on tax audits targeted to large enterprises, high wealth individuals and VAT non-filers and finds evidence that an increase in the number of audits can boost revenue performance. A 1 percent increase in the number of tax audits increases the direct revenue yield of audits by about 0.4 percent. The indirect tax revenue effect of an intensification of tax audits is estimated to be 0.1 percent. The intensification of targeted audits, as well as improvements in the collection of taxes and fines can tackle tax evasion and boost revenue performance, contributing, thus, to the fiscal consolidation effort.
Evolutionary theorizing in economics

This paper reviews the case for an evolutionary approach to problems of economic analysis, ranging from the details of individual firm behavior in the short run through industrial dynamics to the historical evolution of institutions and technologies. We draw upon a substantial body of recent research contributions. We characterize micro behavior as governed by skills and routines that are shaped by learning and selection. We then consider major areas of application of evolutionary thinking, including the analysis of competitive processes in technologically dynamic industries and the evolution of institutions and technologies.
Uniqueness of Solutions for an Elliptic Equation Modeling MEMS

This equation has been proposed as a model for a simple electrostatic Micro-Electromechanical System (MEMS) device consisting of a thin dielectric elastic membrane with boundary supported at 0 below a rigid ground plate located at height z = 1. See [10, 11]. A voltage – directly proportional to the parameter λ – is applied, and the membrane deflects towards the ground plate and a snap-through may occur when it exceeds a certain critical value λ∗, the pull-in voltage.
Implementation of Promising Practices that Support Students With Emotional and Behavioral Disorders

Students with or at-risk for emotional and behavioral disorders pose an immense challenge for educators, and typical practices to address them are punitive and exclusionary, leading to poor academic and social outcomes for students. To address the complex needs of students with intensive emotional and behavioral needs, evidence-based practices and strategies that have been validated through rigorous research are needed. In this article, we describe evidence-based practices for creating positive and effective classrooms environments as well as illustrate implementation factors that are key to successful and sustained use of evidence-based practices in school settings.
A Hybrid of Genetic Algorithm and Gaussian Mixture Model for Features Reduction and Detection of Vocal Fold Pathology

Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. In this paper initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis has been presented. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new GA-based method for feature reduction stage is proposed and compared with conventional methods such as Principal Component Analysis (PCA). Gaussian Mixture Model (GMM) is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with current methods.
Identifying RNA-protein interactions using feature dimension reduction method

In this paper, a new system is proposed to improve the performance of protein-RNA interaction prediction. First of all, the protein sequences are quantized into a high dimension space using an effective sequence encoding scheme. However, the problem caused by such representation is small sample size problem, where the data dimension is much larger than the sample size. To sort out this problem, a new dimension reduction algorithm is introduced. It extracts the essential features from the high dimension feature space and does not suffer from small sample size problem. Then, an efficient classifier is employed to recognize the protein-RNA interaction according to the new features after dimension reduction.
Cct1, a phosphatidylcholine biosynthesis enzyme, is required for Drosophila oogenesis and ovarian morphogenesis

Patterning of the Drosophila egg requires cooperation between the germline cells and surrounding somatic follicle cells. In order to identify genes involved in follicle cell patterning, we analyzed enhancer trap lines expressed in specific subsets of follicle cells. Through this analysis, we have identified tandem Drosophila genes homologous to CTP: phosphocholine cytidylyltransferase (CCT), the second of three enzymes in the CDP-choline pathway, which is used to synthesize phosphatidylcholine. Drosophila Cct1 is expressed at high levels in three specific subsets of follicle cells, and this expression is regulated, at least in part, by the TGF-β and Egfr signaling pathways. Mutations in Cct1 result in a number of defects, including a loss of germline stem cell maintenance, mispositioning of the oocyte, and a shortened operculum, suggesting that Cct1 plays multiple roles during oogenesis. In addition, Cct1 mutants display a novel branched ovariole phenotype, demonstrating a requirement for this gene during ovarian morphogenesis. These data provide the first evidence for a specific role for CCT, and thus for phosphatidylcholine, in patterning during development.
Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in Sao Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.
Parallel Programmability and the Chapel Language

In this paper we consider productivity challenges for parallel programmers and explore ways that parallel language design might help improve end-user productivity. We offer a candidate list of desirable qualities for a parallel programming language, and describe how these qualities are addressed in the design of the Chapel language. In doing so, we provide an overview of Chapel's features and how they help address parallel productivity. We also survey current techniques for parallel programming and describe ways in which we consider them to fall short of our idealized productive programming model.