Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving the Maximum Sum Rate Using D.C. Programming in Cellular Networks

Intercell interference is the major limitation to the performance of future cellular systems. Despite the joint detection and joint transmission techniques aiming at interference cancellation which suffer from the limited possible cooperation among the nodes, power allocation is a promising approach for optimizing the system performance. If the interference is treated as noise, the power allocation optimization problem aiming at maximizing the sum rate with a total power constraint is nonconvex and up to now an open problem. In the present paper, the solution is found by reformulating the nonconvex objective function of the sum rate as a difference of two concave functions. A globally optimum power allocation is found by applying a branch-and-bound algorithm to the new formulation. In principle, the algorithm partitions the feasible region recursively into subregions where for every subregion the objective function is upper and lower bounded. For each subregion, a linear program is applied for estimating the upper bound of the sum rate which is derived from a convex maximization formulation of the problem with piecewise linearly approximated constraints. The performance is investigated by system-level simulations. The results show that the proposed algorithm outperforms the known conventional suboptimum schemes. Furthermore, it is shown that the algorithm asymptotically converges to a globally optimum power allocation.
Panel and multivariate methods for tests of trend equivalence in climate data series

We explain panel and multivariate regressions for comparing trends in climate data sets. They impose minimal restrictions on the covariance matrix and can embed multiple linear comparisons, which is a convenience in applied work. We present applications comparing post‐1979 modeled and observed temperature trends in the tropical lower‐ and mid‐troposphere. Results are sensitive to the sample length. In data spanning 1979–1999, observed trends are not significantly different from zero or from model projections. In data spanning 1979–2009, the observed trends are significant in some cases but tend to differ significantly from modeled trends. Copyright © 2010 Royal Meteorological Society
Design considerations for energy-efficient Multi-Granular Optical Networks

Multi-Granular Optical Networks (MG-ONs) offer enhanced bandwidth utilization, reduced transmission cost and increased scalability in the optical Internet backbone. There have been several proposals for optimized design of MG-ONs as power consumption in the Internet backbone has recently become an important concern. Although recent research has shown that multi-granular switching concept can guarantee energy savings when compared to the traditional traffic grooming, MG-ON design still calls for legitimate and self-contained design specifications. In this paper, we revisit the Routing and MultiGranular Path Assignment (RMGPA) problem by having the objective of minimized power consumption. Heuristic solutions of RMGPA problem show that crucial power and cost savings can be achieved with the proper selection of the wavelength capacity, waveband size and number of wavebands per fiber with a certain fiber capacity.
A New Benchmark For Evaluation Of Graph-Theoretic Algorithms

We propose a new graph-theoretic benchmark in this paper. The benchmark is developed to address shortcomings of an existing widely-used graph benchmark. We thoroughly studied a large number of traditional and contemporary graph algorithms reported in the literature to have clear understanding of their algorithmic and run-time characteristics. Based on this study, we designed a suite of kernels, each of which represents a specific class of graph algorithms. The kernels are designed to capture the typical run-time behavior of target algorithms accurately, while limiting computational and spatial overhead to ensure its computation finishes in reasonable time. We expect that the developed benchmark will serve as a much needed tool for evaluating different architectures and programming models to run graph algorithms.
Detect Climatic Factors Contributing to Dengue Outbreak based on Wavelet, Support Vector Machines and Genetic Algorithm

Dengue Fever, existing throughout the contemporary history of mankind, poses an endemic threat to most tropical regions. Dengue virus is transmitted to humans mainly by the Aedes Aegypti mosquito. It has been observed that there are significantly more Aedes Aegypti mosquitoes present in tropical areas than in other climate areas. As such, it is commonly believed that the tropical climate suits the life-cycle of the mosquito. Thus, studying the correlation between the climatic factors and trend of dengue cases is helpful in conceptualising a more effective pre-emptive control measure towards dengue outbreak. In this paper, we proposed to use Wavelet transformation for data pre-processing before employing a Support Vector Machines (SVM)-based Genetic Algorithm to select the most important features. After which, regression based on SVM is used to perform forecasting of the model. The analytical result drawn from this model based on dengue data in Singapore shows improvement in prediction performance of dengue cases ahead. This can help the health control agency to improve its strategic planning for disease control to combat dengue outbreak. The experimental result arising from this model also suggests strong correlation between the monsoon seasonality and dengue virus transmission. It also confirms with previous work that mean temperature and monthly seasonality have minimal contribution to the outbreak.
Price Fairness: Good and Service Differences and the Role of Vendor Costs

Prior research suggests that consumers are forgiving of a price increase that is commensurate with increased vendor costs. We argue that the perceived fairness of the price increase will also depend on the alignability of the cost and price increases, such that alignable increases will be perceived as more acceptable than nonalignable increases. Moreover, we predict that when a cost increase is nonalignable, consumers will be more receptive to a service price increase than a goods price increase. Evidence from a series of experiments supports both predictions.
WhereNext : Prediction of human mobility based on location of interest ( Hotspot )

With the increased popularity of mobile devices carrying ability to gather location coordinates and with the use of internet in the form of 3G and LTE services, it has now become possible to track someone carrying these devices. Once such data is gathered over period of time, it can help to predict the next location with the help of historical patterns of movements. It is a common human tendency to travel to locations of great interest for an individual (home, work, etc) or to locations which are of common interest for a group in large (for e.g. shopping malls, airports, bus stations, road intersections, sports arenas, etc). While travelling from one place to another, individuals follow specific paths. Goal of this project is to find next intermediate location that an individual could possibly take while travelling from one place to another.
Enlarging Scarce In-domain English-Croatian Corpus for SMT of MOOCs Using Serbian

Massive Open Online Courses have been growing rapidly in size and impact. Yet the language barrier constitutes a major growth impediment in reaching out all people and educating all citizens. A vast majority of educational material is available only in English, and state-of-the-art machine translation systems still have not been tailored for this peculiar genre. In addition, a mere collection of appropriate in-domain training material is a challenging task. In this work, we investigate statistical machine translation of lecture subtitles from English into Croatian, which is morphologically rich and generally weakly supported, especially for the educational domain. We show that results comparable with publicly available systems trained on much larger data can be achieved if a small in-domain training set is used in combination with additional in-domain corpus originating from the closely related Serbian language.
A Hybrid System for Emotion Extraction from Suicide Notes

The reasons that drive someone to commit suicide are complex and their study has attracted the attention of scientists in different domains. Analyzing this phenomenon could significantly improve the preventive efforts. In this paper we present a method for sentiment analysis of suicide notes submitted to the i2b2/VA/Cincinnati Shared Task 2011. In this task the sentences of 900 suicide notes were labeled with the possible emotions that they reflect. In order to label the sentence with emotions, we propose a hybrid approach which utilizes both rule based and machine learning techniques. To solve the multi class problem a rule-based engine and an SVM model is used for each category. A set of syntactic and semantic features are selected for each sentence to build the rules and train the classifier. The rules are generated manually based on a set of lexical and emotional clues. We propose a new approach to extract the sentence's clauses and constitutive grammatical elements and to use them in syntactic and semantic feature generation. The method utilizes a novel method to measure the polarity of the sentence based on the extracted grammatical elements, reaching precision of 41.79 with recall of 55.03 for an f-measure of 47.50. The overall mean f-measure of all submissions was 48.75% with a standard deviation of 7%.
Degradation of an intramitochondrial protein by the cytosolic proteasome

Mitochondrial uncoupling protein 2 (UCP2) is implicated in a wide range of pathophysiological processes, including immunity and diabetes mellitus, but its rapid degradation remains uncharacterized. Using pharmacological proteasome inhibitors, immunoprecipitation, dominant negative ubiqbiquitiuitin mutants, cellular fractionation and siRNA techniques, we demonstrate the involvement of the ubiquitin-proteasome system in the rapid degradation of UCP2. Importantly, we resolve the issue of whether intramitochondrial proteins can be degraded by the cytosolic proteasome by reconstituting a cell-free system that shows rapid proteasome-inhibitor-sensitive UCP2 degradation in isolated, energised mitochondria presented with an ATP regenerating system, ubiquitin and 26S proteasome fractions. These observations provide the first demonstration that a mitochondrial inner membrane protein is degraded by the cytosolic ubiquitin-proteasome system.
Lysosomal Proteolysis Inhibition Selectively Disrupts Axonal Transport of Degradative Organelles and Causes an Alzheimer's-Like Axonal Dystrophy

In the hallmark neuritic dystrophy of Alzheimer's disease (AD), autophagic vacuoles containing incompletely digested proteins selectively accumulate in focal axonal swellings, reflecting defects in both axonal transport and autophagy. Here, we investigated the possibility that impaired lysosomal proteolysis could be a basis for both of these defects leading to neuritic dystrophy. In living primary mouse cortical neurons expressing fluorescence-tagged markers, LC3-positive autophagosomes forming in axons rapidly acquired the endo-lysosomal markers Rab7 and LAMP1 and underwent exclusive retrograde movement. Proteolytic clearance of these transported autophagic vacuoles was initiated after fusion with bidirectionally moving lysosomes that increase in number at more proximal axon levels and in the perikaryon. Disrupting lysosomal proteolysis by either inhibiting cathepsins directly or by suppressing lysosomal acidification slowed the axonal transport of autolysosomes, late endosomes, and lysosomes and caused their selective accumulation within dystrophic axonal swellings. Mitochondria and other organelles lacking cathepsins moved normally under these conditions, indicating that the general functioning of the axonal transport system was preserved. Dystrophic swellings induced by lysosomal proteolysis inhibition resembled in composition those in several mouse models of AD and also acquired other AD-like features, including immunopositivity for ubiquitin, amyloid precursor protein, and hyperphosphorylated neurofilament proteins. Restoration of lysosomal proteolysis reversed the affected movements of proteolytic Rab7 vesicles, which in turn essentially cleared autophagic substrates and reversed the axonal dystrophy. These studies identify the AD-associated defects in neuronal lysosomal proteolysis as a possible basis for the selective transport abnormalities and highly characteristic pattern of neuritic dystrophy associated with AD.
Application of multi-scale fuzzy entropy for roller bearing fault detection and fault classification based on VPMCD

Roller bearing is an integral component in various types of rotating machinery. Bearing fault detection is very important to prevent failure, increase safety, reduce production idle time and decrease maintenance cost. In this paper, Multi-scale Fuzzy Entropy(MFE) is used for fault detection of roller bearing and Variable predictive model-based class discrimination (VPMCD) is used as multi-fault classifier. Fuzzy entropy is calculated for complexity measure of time series constructed from motor vibration signal. Usually, as vibration signals tend to be non-linear, fuzzy entropy calculated for single scale may not contain all the fault information. Hence it is essential to calculate entropy for multiple scales. As a multi-fault classifier VPMCD has been used to classify bearing faults. Fault features created using MFE are used as an input for VPMCD classifier. VPMCD is applied here for roller bearing fault classification. The effect of motor rotational speed on the MFE values is investigated. Experimental analysis is conducted to evaluate performance of this method. The results of this experiment indicate that MFE and VPMCD together can achieve good accuracy and reliability in bearing fault detection and classification.
Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Abstract A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and “difference” images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15–20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.
Quantitation of DNA double-strand break resection intermediates in human cells

5′ strand resection at DNA double strand breaks (DSBs) is critical for homologous recombination (HR) and genomic stability. Here we develop a novel method to quantitatively measure single-stranded DNA intermediates in human cells and find that the 5′ strand at endonuclease-generated break sites is resected up to 3.5 kb in a cell cycle–dependent manner. Depletion of CtIP, Mre11, Exo1 or SOSS1 blocks resection, while depletion of 53BP1, Ku or DNA-dependent protein kinase catalytic subunit leads to increased resection as measured by this method. While 53BP1 negatively regulates DNA end processing, depletion of Brca1 does not, suggesting that the role of Brca1 in HR is primarily to promote Rad51 filament formation, not to regulate end resection.
Mediating Effects of Place Attachment and Satisfaction on the Relationship between Tourists’ Emotions and Intention to Recommend

This study develops a model based on the developmental theory of place attachment. The model considers the influence of tourists’ emotions on place attachment and the mediating effects of satisfaction and place attachment on the relationship between tourists’ emotions and intention to recommend. The model was tested using data collected from 464 international tourists at the end of their trip to Thailand. Results show that positive emotions, negative emotions and satisfaction are significant determinants of place attachment. In particular, negative emotions display a positive relationship with place attachment. In addition, only satisfaction mediates the relationship between tourists’ emotions and intention to recommend. Findings highlight the need for researchers to incorporate emotions in modeling place attachment and offer implications for marketers promoting Thailand as a tourist destination.
Identification of Proteins Interacting with Cytoplasmic High-Mobility Group Box 1 during the Hepatocellular Response to Ischemia Reperfusion Injury

Ischemia/reperfusion injury (IRI) occurs inevitably in liver transplantations and frequently during major resections, and can lead to liver dysfunction as well as systemic disorders. High-mobility group box 1 (HMGB1) plays a pathogenic role in hepatic IRI. In the normal liver, HMGB1 is located in the nucleus of hepatocytes; after ischemia reperfusion, it translocates to the cytoplasm and it is further released to the extracellular space. Unlike the well-explored functions of nuclear and extracellular HMGB1, the role of cytoplasmic HMGB1 in hepatic IRI remains elusive. We hypothesized that cytoplasmic HMGB1 interacts with binding proteins involved in the hepatocellular response to IRI. In this study, binding proteins of cytoplasmic HMGB1 during hepatic IRI were identified. Liver tissues from rats with warm ischemia reperfusion (WI/R) injury and from normal rats were subjected to cytoplasmic protein extraction. Co-immunoprecipitation using these protein extracts was performed to enrich HMGB1-protein complexes. To separate and identify the immunoprecipitated proteins in eluates, 2-dimensional electrophoresis and subsequent mass spectrometry detection were performed. Two of the identified proteins were verified using Western blotting: betaine–homocysteine S-methyltransferase 1 (BHMT) and cystathionine γ-lyase (CTH). Therefore, our results revealed the binding of HMGB1 to BHMT and CTH in cytoplasm during hepatic WI/R. This finding may help to better understand the cellular response to IRI in the liver and to identify novel molecular targets for reducing ischemic injury.
Quasinormal modes for the SdS black hole: An Analytical approximation scheme

Quasinormal modes for scalar field perturbations of a Schwarzschild\char21{}de Sitter (SdS) black hole are investigated. An analytical approximation is proposed for the problem. The quasinormal modes are evaluated for this approximate model in the limit when the black hole mass is much smaller than the radius of curvature of the spacetime. The model mirrors some striking features observed in numerical studies of time behavior of scalar perturbations of the SdS black hole. In particular, it shows the presence of two sets of modes relevant at two different time scales, proportional to the surface gravities of the black hole and cosmological horizons, respectively. These quasinormal modes are not complete\char22{}another feature observed in the numerical studies. Refinements of this model to yield more accurate quantitative agreement with numerical results are discussed. Further investigations of this model are outlined, which would provide valuable insight into time behavior of perturbations in the SdS spacetime.
FEATURE EXTRACTION USING RANDOM WALKS

In this paper, a novel idea, which utilizes the metric on a graph, is proposed to extract prominent features for pattern recognition. This proposed model, called “Graphical Metrics Guided Transformation” (GMGT), aims to find projections that can preserve the original metric on the graphic domain in a new Euclidean subspace. With the functional analysis, we present the definition of the metric in the graphical domain and prove that the commute time of random walk is a metric on graphs with the help of real physical model. Furthermore, a new feature extraction algorithm based on GMGT and the commute time is proposed, and is applied to face recognition.
Regulating the Internet in the Interests of Children: Emerging European and International Approaches

This chapter contains sections titled: Introduction Positive and Negative Internet Regulation in the Interests of Children “We Do Not Intend to Regulate the Internet” Children's Rights Offline and Online Internet Regulation: Emerging Principles and Practices Regulating Contact, Content, and Conduct Risks Online Integrating Diverse Policy Initiatives Conclusion References
Making Microsoft Excel™: multimodal presentation of charts

Several solutions, based on aural and haptic feedback, have been developed to enable access to complex on-line information for people with visual impairments. Nevertheless, there are several components of widely used software applications that are still beyond the reach of screen readers and Braille displays. This paper investigates the non-visual accessibility issues associated with the graphing component of Microsoft Excel". The goal is to provide flexible multi-modal navigation schemes which can help visually impaired users in comprehending Excel charts. The methodology identifies the need for 3 strategies used in interaction: exploratory, guided, and summarization. Switching between them supports the development of a mental model of a chart. Aural cues and commentaries are integrated in a haptic presentation to help understanding the presented chart. The methodology has been implemented using the Novint Falcon haptic device.
A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks

The traditional designs for the prototype filters of cosine modulated filterbanks usually involve nonlinear optimizations. We propose limiting the search of the prototype filters to the class of filters obtained using Kaiser windows. The design process is reduced to the optimization of a single parameter. An example is given to show that very good designs can be obtained in spite of the limit of search.