Miracle-dz/UniFAR-FTUs huggingface dataset:
Data recovery in heterogeneous wireless sensor networks based on low-rank tensors

An effective way to reduce the energy consumption of energy constrained wireless sensor networks is reducing the number of collected data, which causes the recovery problem. In this paper, we propose a novel data recovery method based on low-rank tensors for the heterogeneous wireless sensor networks with various sensor types. The proposed method represents the collected high-dimensional data as low-rank tensors to effectively exploit the spatiotemporal correlation that exists in the various data. Furthermore, an algorithm based on the alternating direction method of multipliers is developed to solve the resultant optimization problem efficiently. Experimental results demonstrate that the proposed method significantly outperforms the sparsity constraint method and matrix completion method for each type of signals.
ONTOLOGY FOR CONTEXT-AWARE VISUALIZATION OF SPATIAL DATA IN MOBILE DEVICES

Recently, the visualization of the spatial data is gaining importance in the ubiquitous computing. In geoinformatics such as distributed GIS or Location Based Services, context models are responsible for the robust communication between the mobile user and the system. Context-aware systems are considerable issue in ubiquitous systems, reflecting delicate effect of the designed context leads us to ontologies. To develop a user-adaptive ontological model, users’ different situations should be defined properly. In Location Based Services (LBS), much intelligent system ignores affect of user’s states and roles except user’s location. However different user profiles need customized visualization style in order to provide a relevant spatial data. In this paper, the aim of the research is to define new mobile contextual ontologies (classes and properties) which obey relevance theories so as to define any user’s relevant visualization profile on the mobile devices. Therefore a kind of intelligent system proposed here can reason over the complete semantic model of the OWL language by an inference engine. A contextual ontology (OWL-DL) for relevancy has been edited in an ontology editor and knowledge acquisition system. Consistency of taxonomies has been checked in a reasoning engine for OWL-DL. Consequently inference engine retrieves criteria for relevant visualization profile with its reasoning algorithms or defined SWRL (Semantic Web Rule Language) as a server. * Corresponding author.
A Monte-Carlo path planner for dynamic and partially observable environments

In this paper, we present a Monte-Carlo policy rollout technique (called MOCART-CGA) for path planning in dynamic and partially observable real-time environments such as Real-time Strategy games. The emphasis is put on fast action selection motivating the use of Monte-Carlo techniques in MOCART-CGA. Exploration of the space is guided by using corridors which direct simulations in the neighbourhood of the best found moves. MOCART-CGA limits how many times a particular state-action pair is explored to balance exploration of the neighbourhood of the state and exploitation of promising actions. MOCART-CGA is evaluated using four standard pathfinding benchmark maps, and over 1000 instances. The empirical results show that MOCART-CGA outperforms existing techniques, in terms of search time, in dynamic and partially observable environments. Experiments have also been performed in static (and partially observable) environments where MOCART-CGA still requires less time to search than its competitors, but typically finds lower quality plans.
IPIP: A new approach to inverse planning for HDR brachytherapy by directly optimizing dosimetric indices.

PURPOSE Many planning methods for high dose rate (HDR) brachytherapy require an iterative approach. A set of computational parameters are hypothesized that will give a dose plan that meets dosimetric criteria. A dose plan is computed using these parameters, and if any dosimetric criteria are not met, the process is iterated until a suitable dose plan is found. In this way, the dose distribution is controlled by abstract parameters. The purpose of this study is to develop a new approach for HDR brachytherapy by directly optimizing the dose distribution based on dosimetric criteria. METHODS The authors developed inverse planning by integer program (IPIP), an optimization model for computing HDR brachytherapy dose plans and a fast heuristic for it. They used their heuristic to compute dose plans for 20 anonymized prostate cancer image data sets from patients previously treated at their clinic database. Dosimetry was evaluated and compared to dosimetric criteria. RESULTS Dose plans computed from IPIP satisfied all given dosimetric criteria for the target and healthy tissue after a single iteration. The average target coverage was 95%. The average computation time for IPIP was 30.1 s on an Intel(R) Core 2 Duo CPU 1.67 GHz processor with 3 Gib RAM. CONCLUSIONS IPIP is an HDR brachytherapy planning system that directly incorporates dosimetric criteria. The authors have demonstrated that IPIP has clinically acceptable performance for the prostate cases and dosimetric criteria used in this study, in both dosimetry and runtime. Further study is required to determine if IPIP performs well for a more general group of patients and dosimetric criteria, including other cancer sites such as GYN.
Teaching and the Self.

the Protestant Ethic of hard work, obedience to authority, thrift, and self-restraint as the most meaningful basis for the worship of God. With the end of the Civil War came industrialism and a gradual turning to the pursuit of the "almighty dollar" as the newly emerging focus of our energies. Fromm' points out that man has freed himself from the old bonds of slavery and feudalism and become an individual, enjoying certain rights and liberties. However, he has not gained freedom in the positive sense of realizing his intellectual and artistic possibilities. He has gained too much freedom from traditional controls,
Recombinant Galectin-1 and Its Genetic Delivery Suppress Collagen-Induced Arthritis via T Cell Apoptosis

Galectin-1 (GAL-1), a member of a family of conserved β-galactoside–binding proteins, has been shown to induce in vitro apoptosis of activated T cells and immature thymocytes. We assessed the therapeutic effects and mechanisms of action of delivery of GAL-1 in a collagen-induced arthritis model. A single injection of syngeneic DBA/1 fibroblasts engineered to secrete GAL-1 at the day of disease onset was able to abrogate clinical and histopathological manifestations of arthritis. This effect was reproduced by daily administration of recombinant GAL-1. GAL-1 treatment resulted in reduction in anticollagen immunoglobulin (Ig)G levels. The cytokine profile in draining lymph node cells and the anticollagen IgG isotypes in mice sera at the end of the treatment clearly showed inhibition of the proinflammatory response and skewing towards a type 2–polarized immune reaction. Lymph node cells from mice engaged in the gene therapy protocol increased their susceptibility to antigen-induced apoptosis. Moreover, GAL-1–expressing fibroblasts and recombinant GAL-1 revealed a specific dose-dependent inhibitory effect in vitro in antigen-dependent interleukin 2 production to an Aq-restricted, collagen type 2–specific T cell hybridoma clone. Thus, a correlation between the apoptotic properties of GAL-1 in vitro and its immunomodulatory properties in vivo supports its therapeutic potential in the treatment of T helper cell type 1–mediated autoimmune disorders.
Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

Speckle noise (salt and pepper) is inherent to synthetic aperture radar (SAR), which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA), a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR) images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS) Phased-Array Synthetic-Aperture Radar (PALSAR) sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondonia State (municipality of Candeias do Jamari), containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF) or noise-adjusted principal components (NAPCs). Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.
How Do We Evaluate the Quality of Computational Editing Systems?

One problem common to all researchers in the field of virtual cinematography and editing is to be able to assess the quality of the output of their systems. There is a pressing requirement for appropriate evaluations of proposed models and techniques. Indeed, though papers are often accompanied with example videos, showing subjective results and occasionally providing qualitative comparisons with other methods or with human-created movies, they generally lack an extensive evaluation. The goal of this paper is to survey evaluation methodologies that have been used in the past and to review a range of other interesting methodologies as well as a number of questions related to how we could better evaluate and compare future systems.
Classification of Driver's Cognitive Responses Using Nonparametric Single-trial EEG Analysis

Accidents caused by errors and failures in human performance among traffic fatalities have a high rate causing death and become an important issue in public security. The key problem causing these car accidents is mainly because that the drivers failed to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for ongoing assessment of driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. Three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), discriminant analysis feature extraction (DAFE) are applied to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data can be classified with fewer features and their classification results are compared by utilizing three different classifiers including Gaussian classifier (GC), k nearest neighbor classification (KNNC), and naive Bayes classifier (NBC). Experimental results show that the successful rate of nonparametric weighted feature extraction combined with Gaussian classifier is higher more than 10% compared with other combinations. It also demonstrates the feasibility of detecting and analyzing single-trail ERP signals that represent operators' cognitive states and responses to task events.
HIV Replication at Low Copy Number and its Correlation with the HIV Reservoir: A Clinical Perspective

The efficacy of combination therapy (antiretroviral therapy - ARV) is demonstrated by the high rates of viral suppression achieved in most treated HIV patients. Whereas contemporary treatments may continuously suppress HIV replication, they do not eliminate the latent reservoir, which can reactivate HIV infection if ARV is discontinued. The persistence of HIV proviral DNA and infectious viruses in CD4+ T cells and others cells has long been considered a major obstacle in eradicating the HIV virus in treated patients. Moreover, recent studies have demonstrated the persistence of HIV replication at low copies in most patients on suppressive ARV. The source of this ‘residual viraemia’ and whether it declines over years of therapy remain unknown. Similarly, little is known regarding the biological relationships between the HIV reservoir and viral replication at low copies. The question of whether this ‘residual viraemia’ represents active replication or the release of non-productive virus from the reservoir has not been adequately resolved. From a clinical perspective, both the quantification of the HIV reservoir and the detection of low levels of replication in full-responder patients on prolonged ARV may provide important information regarding the effectiveness of treatment and the eradication of HIV. To date, the monitoring of these two parameters has been conducted only for research purposes; the routine use of standardised tests procedure is lacking. This review aims to assess the current data regarding the correlation between HIV replication at low copies and the HIV reservoir and to provide useful information for clinicians.
Normal modes of vibration in bovine pancreatic trypsin inhibitor and its mechanical property

The normal mode analysis of conformational fluctuation is carried out for a small globular protein, bovine pancreatic trypsin inhabitor. Results are analyzed mainly to reveal the mechanical construction of the protein molecule. We take dihedral angles, including peptide ω angles, as independent variables for the normal mode analysis. There are 306 such angles in this molecule. Motions in modes with frequencies lower than 120 cm−1 are showned to involve atoms in the whole protein molecule, and spatial change of displacement vectors is continues, i.e., those of atoms near in space are similar. To quantitate the observation of the continuity, a correlation function of direction vectors of atomic displacement is calculated. From this function we define a Quantity that is interpreted as the wave length of an equivalent elastic plane wave. From this Quantity we deduce eefective young's modulus for each mode. For the mode with the lowest frequency 4.4 cm−1, it terned out to be 0.8 × 109 dyn cm−2, the value two orders magnitude softer than, for instance, α‐helices. Prompted by this observation, the four lowest frequency modes and also the harmonic motions in the thermal equilibrium are analyzed further mainly to detect relatively rigid structural elements in the molecule. From this analysis emerges a mechanical picture of the protein molecule that is made up of relatively rigid elements held together by very soft parts.
Query interactions in database workloads

Database workloads consist of mixes of queries that run concurrently and interact with each other. In this paper, we demonstrate that query interactions can have a significant impact on database system performance. Hence, we argue that it is important to take these interactions into account when characterizing workloads, designing test cases, or developing performance tuning algorithms for database systems. To capture and model query interactions, we propose using an experimental approach that is based on sampling the space of possible interactions and fitting statistical models to the sampled data. We discuss using such an approach for database testing and tuning, and we present some opportunities and research challenges.
Achieving weighted-fairnessin message rate-based congestion control for DSRC systems

Controlling channel congestion is one of the major challenges in achieving scalability for DSRC safety systems. Message rate-based congestion control schemes, like LInear MEssage Rate Integrated Control (LIMERIC), generally aim to produce unweighted fair outcomes, e.g. equal message rate allocation among nodes in a given area. However, there are practical scenarios where it may be desirable for various vehicles participating in congestion control to converge at weighted fair rates. These situations may reflect relatively static distinctions among vehicles, e.g. allocating higher rates to more accident-prone drivers, or more dynamic factors, e.g. temporary aggressive driving patterns. In this paper we propose a method that achieves weighted fair message rate allocation using a variation of the LIMERIC algorithm for DSRC systems. We provide a mathematical proof which shows the convergence and stability of the proposed technique. We also present NS-2 simulation results that demonstrate the weighted fair rates achieved by several nodes participating in congestion control.
Dynamic hand gesture recognition for human-robot and inter-robot communication

This paper discusses inter-robot and human-robot communication by bare hand dynamic gestures. We use a Bag-of-Features and a local part model approach for bare hand dynamic hand gesture recognition from video. We used dense sampling to extract local 3D multiscale whole-part features. We adopted three dimensional histograms of a gradient orientation (3D HOG) descriptor to represent features. The K-means++ method was applied to cluster the visual words. Dynamic hand gesture classification was completed by using a Bag-of-features (BOF) and non-linear support vector machine (SVM) method. A BOF does not track the order of events. To counter the unordered events of the BOF approach, we used a multiscale local part model to preserve temporal context. Initial experimental results on the newly collected complex dataset show a higher level of recognition. We used the same above mentioned approach for inter-robot communication by using two sample hand models.
Internet Appendix for : “ Issuer Quality and Corporate Bond Returns ”

A.1 Time-series forecasting regressions of excess corporate bond returns A.2 Deriving ISS t B Data Definitions B.1 Compustat measures of issuance B.2 Characteristic definitions C Time-series Robustness Checks C.1 Non-parametric HAC standard errors and small sample inference C.2 Parametric HAC standard errors C.3 Stambaugh bias D Additional Empirical Results D.1 Additional tests of the null that expected returns are non-negative D.2 Unpacking issuer quality D.3 Forecasting equity market and equity factor returns D.4 Subsample forecasting results for log(HYS) D.5 Multivariate forecasting results for BBB bond returns D.6 Quantity and quality of corporate bond offerings D.7 The determinants of the high yield share
6D SLAM with approximate data association

This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. A fast variant of the iterative closest points (ICP) algorithm registers 3D scans taken by a mobile robot into a common coordinate system and thus provides relocalization. Hereby, data association is reduced to the problem of searching for closest points. Approximation algorithms for this searching, namely, approximate kd-trees and box decomposition trees, are presented and evaluated in this paper. A solution to 6D SLAM that considers all free parameters in the robot pose is built based on 3D scan matching
Interactive Democracy: The Social Roots of Global Justice

In this book, Carol Gould tries to envision a future for democracy that is both faithful to what she takes to be its philosophical and normative ground and well-matched to the political challenges of advancing global justice. These challenges arise because the social and institutional world is increasingly complex, with the relevance of state boundaries diminishing significantly in recent decades when it comes to identifying and evaluating agents, acts and effects on the global stage. I begin by reconstructing and summarizing what I take to be her central line of argument.
Large Scale Participatory Acoustic Sensor Data Analysis: Tools and Reputation Models to Enhance Effectiveness

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.
Lower bounds for cutting planes proofs with small coefficients

Abstract We consider small-weight Cutting Planes (CP*) proofs; that is, Cutting Planes (CP) proofs with coefficients up to Poly(n). We use the well known lower bounds for monotone complexity to prove an exponential lower bound for the length of CP* proofs, for a family of tautologies based on the clique function. Because Resolution is a special case of small-weight CP, our method also gives a new and simpler exponential lower bound for Resolution. We also prove the following two theorems: (1) Tree-like CP* proofs cannot polynomially simulate non-tree-like CP* proofs. (2) Tree-like CP* proofs and Bounded-depth-Frege proofs cannot polynomially simulate each other. Our proofs also work for some generalizations of the CP* proof system. In particular, they work for CP* with a deduction rule, and also for any proof system that allows any formula with small communication complexity, and any set of sound rules of inference.
A large-scale candidate gene analysis of mood disorders: evidence of neurotrophic tyrosine kinase receptor and opioid receptor signaling dysfunction

Background Despite proven heritability, little is known about the genetic architecture of mood disorders. Although a number of family and case–control studies have examined the genetics of mood disorders, none have carried out joint linkage-association studies and sought to validate the results with gene expression analyses in an independent cohort. Methods We present findings from a large candidate gene study that combines linkage and association analyses using families and singletons, providing a systematic candidate gene investigation of mood disorder. For this study, 876 individuals were recruited, including 83 families with 313 individuals and 563 singletons. This large-scale candidate gene analysis included 130 candidate genes implicated in addictive and other psychiatric disorders. These data showed significant genetic associations for 28 of these candidate genes, although none remained significant after correction for multiple testing. To evaluate the functional significance of these 28 candidate genes in mood disorders, we examined the transcriptional profiles of these genes within the dorsolateral prefrontal cortex and anterior cingulate for 21 cases with mood disorders and 25 nonpsychiatric controls, and carried out a pathway analysis to identify points of high connectivity suggestive of particular molecular pathways that may be dysregulated. Results Two primary gene candidates were supported by the linkage-association, gene expression profiling, and network analysis: neurotrophic tyrosine kinase receptor, type 2 (NTRK2), and the opioid receptor, &kgr;1 (OPRK1). Conclusion This study supports a role for NTRK2 and OPRK1 signaling in the pathophysiology of mood disorder. The unique approach incorporating evidence from multiple experimental and computational modalities enhances confidence in these findings.