Miracle-dz/UniFAR-FTUs huggingface dataset:
An online approach for joint task assignment and worker evaluation in crowd-sourcing

The paper tackles the problem of finding the correct solution to a set of binary choice questions or labeling tasks, by adaptively assigning them to workers in a crowdsourcing system. Such problem becomes quite challenging when we do not initially know neither workers' abilities, nor questions' difficulties (besides common a priori statistics), nor (of course) which is the correct answer. Indeed, such problem requires to jointly learn workers' abilities and questions' difficulties, while adaptively assigning questions to the most appropriate workers so as to maximize our chances to find which are the correct answers. To address such problem, we first cast it into a suitably constructed Bayesian framework which permits us to obtain an analytically tractable (closed form) single-question inference step, and then we address the more general framework via the Expectation Propagation algorithm, an approximated message-passing iterative technique. We then exploit the information gathered by the inference framework as adaptive weights for a maximum weight matching task assignment policy, proposing a computationally efficient algorithm which maximizes the entropy reduction for the questions assigned at each step.
Seasonal Time Series Forecasting Models based on Artificial Neural Network

Forecasting is the starting point for drawing good strategies facing the demand variability in the increasingly complex and competitive today's markets. This article discusses two methods of dealing with demand variability in seasonal time series using artificial neural networks (ANN). First a multilayer perceptron model for time series forecasting is proposed. Several learning rules used to adjust the ANN weights have been evaluated. Secondly a causal method based on artificial neural networks, using the components of decomposed time series as input variables, has been used. The results show that ANNs yield almost the same accuracy with or without decomposition of the original time series.
Hospital Social Work and Spirituality: Views of Medical Social Workers

This article is based on a study of 1,389 medical social workers in 108 hospitals across 12 countries, on their views on spirituality and spiritually sensitive interventions in hospital settings. Results of the logistic regression analyses and structural equation models showed that medical social workers from European countries, United States of America, Canada, and Australia, those had undergone spiritual training, and those who had higher self-reported spiritual experiences scale scores were more likely to have the view that spirituality in hospital settings is for facilitating integral healing and wellness of patients and were more likely to prefer spiritual packages of New Age movements as the form of spiritual program, understand spiritual assessment as assessing the patients' spiritual starting point, to then build on further interventions and were likely to attest the understanding of spiritual techniques as mindfulness techniques. Finally they were also likely to understand the spiritual goals of intervention in a holistic way, that is, as that of integral healing, growth of consciousness and promoting overall well-being of patients vis-à-vis only coping and coming to terms with health adversities. Results of the structural equation models also showed covariances between religion, spirituality training, and scores on the self-reported spiritual experiences scale, having thus a set of compounding effects on social workers' views on spiritual interventions in hospitals. The implications of the results for health care social work practice and curriculum are discussed.
Cardiac rehabilitation programme after transcatheter aortic valve implantation versus surgical aortic valve replacement: Systematic review and meta-analysis

Background Aortic stenosis is a valvular heart disease characterised by fixed obstruction of the left ventricular outflow. It can be managed by surgical aortic valve replacement (sAVR) or transcatheter aortic valve implantation (TAVI). This review aimed to describe the evidence supporting a cardiac rehabilitation programme on functional capacity and quality of life in aortic stenosis patients after sAVR or TAVI. Methods The search was conducted on multiple databases from January to March 2016. All studies were eligible that evaluated the effects of a post-interventional cardiac rehabilitation programme in aortic stenosis patients. The methodological quality was assessed using the PEDro scale. Meta-analysis was performed separately by procedure and between procedures. The walked distance during the six-minute walk test (6MWD) and Barthel index were evaluated. The analysis was conducted in Review Manager. Results Five studies were included (292 TAVI and 570 sAVR patients). The meta-analysis showed that a cardiac rehabilitation programme was associated with a significant improvement in 6MWD (0.69 (0.47, 0.91); P < 0.001) and Barthel index (0.80 (0.29, 1.30); P = 0.002) after TAVI and 6MWD (0.79 (0.43, 1.15); P < 0.001) and Barthel index (0.93 (0.67, 1.18); P < 0.001) after sAVR. In addition, the meta-analysis showed that the cardiac rehabilitation programme promoted a similar gain in 6MWD (4.28% (−12.73, 21.29); P = 0.62) and Barthel index (−1.52 points (−4.81, 1.76); P = 0.36) after sAVR or TAVI. Conclusions The cardiac rehabilitation programme improved the functional capacity and quality of life in aortic stenosis patients. Patients who underwent TAVI benefitted with a cardiac rehabilitation programme similar to sAVR patients.
Security and Virtualization: a Survey

In this report, we investigate the security aspects and challenges about computer virtualization. In a few words, virtualization is what allows the execution of multiple operating systems on a single machine at the same time. A virtualization component can be viewed as a layer or a container making some kind of emulation, allowing to execute programs or operating systems on the virtualized layer, for example executing Microsoft Windows and Linux on one single machine at the same time. On one hand, as virtualization can provide a kind of isolation between users/applications/operating systems, it can address some security containment problems. But on the other hand, there exist today many security flaws and attacks focused on such systems, as the virtualization layer controls and monitors all virtualized applications/operating systems. The outline of this report is the following. We first review precisely all the various forms the virtualization layer and related components can take. And what technical aspects it involves. Then we survey more precisely all the vulnerabilities and exploits that currently exist or may appear in the near future. And then we study what are the proposed security protection by actual virtualization technologies. We conclude the report with what is remaining to be done and what are the forthcoming security challenges.
StreaMon: A Data-Plane Programming Abstraction for Software-Defined Stream Monitoring

The fast evolving nature of modern cyber threats and network monitoring needs calls for new, “software-defined”, approaches to simplify and quicken programming and deployment of online (stream-based) traffic analysis functions. StreaMon is a carefully designed data-plane abstraction devised to scalably decouple the “programming logic” of a traffic analysis application (tracked states, features, anomaly conditions, etc.) from elementary primitives (counting and metering, matching, events generation, etc), efficiently pre-implemented in the probes, and used as common instruction set for supporting the desired logic. Multi-stage multi-step real-time tracking and detection algorithms are supported via the ability to deploy custom states, relevant state transitions, and associated monitoring actions and triggering conditions. Such a separation entails platform-independent, portable, online traffic analysis tasks written in a high level language, without requiring developers to access the monitoring device internals and program their custom monitoring logic via low level compiled languages (e.g., C, assembly, VHDL). We validate our design by developing a prototype and a set of simple (but functionally demanding) use-case applications and by testing them over real traffic traces.
Forest Rescoring: Faster Decoding with Integrated Language Models

Efficient decoding has been a fundamental problem in machine translation, especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on k-best parsing algorithms and demonstrate their effectiveness on both phrase-based and syntax-based MT systems. In both cases, our methods achieve significant speed improvements, often by more than a factor of ten, over the conventional beam-search method at the same levels of search error and translation accuracy.
Imprints of deviations from the gravitational inverse-square law on the power spectrum of mass fluctuations

Deviations from the gravitational inverse-square law would imprint scale-dependent features on the power spectrum of mass density fluctuations. We model such deviations as a Yukawa-like contribution to the gravitational potential and discuss the growth function in a mixed dark matter model with adiabatic initial conditions. Evolution of perturbations is considered in general non-flat cosmological models with a cosmological constant, and an analytical approximation for the growth function is provided. The coupling between baryons and cold dark matter across recombination is negligibly affected by modified gravity physics if the proper cut-off length of the long-range Yukawa-like force is? 10 h -1 Mpc. Enhancement of gravity affects the subsequent evolution, boosting large-scale power in a way that resembles the effect of a lower matter density. This phenomenon is almost perfectly degenerate in power-spectrum shape with the effect of a background of massive neutrinos. Back-reaction on density growth from a modified cosmic expansion rate should, however, also affect the normalization of the power spectrum, with a shape distortion similar to the case of a non-modified background.
Mobile Telephone Text Messaging for Medication Adherence in Chronic Disease: A Meta-analysis.

IMPORTANCE Adherence to long-term therapies in chronic disease is poor. Traditional interventions to improve adherence are complex and not widely effective. Mobile telephone text messaging may be a scalable means to support medication adherence. OBJECTIVES To conduct a meta-analysis of randomized clinical trials to assess the effect of mobile telephone text messaging on medication adherence in chronic disease. DATA SOURCES MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, PsycINFO, and CINAHL (from database inception to January 15, 2015), as well as reference lists of the articles identified. The data were analyzed in March 2015. STUDY SELECTION Randomized clinical trials evaluating a mobile telephone text message intervention to promote medication adherence in adults with chronic disease. DATA EXTRACTION Two authors independently extracted information on study characteristics, text message characteristics, and outcome measures as per the predefined protocol. MAIN OUTCOMES AND MEASURES Odds ratios and pooled data were calculated using random-effects models. Risk of bias and study quality were assessed as per Cochrane guidelines. Disagreement was resolved by consensus. RESULTS Sixteen randomized clinical trials were included, with 5 of 16 using personalization, 8 of 16 using 2-way communication, and 8 of 16 using a daily text message frequency. The median intervention duration was 12 weeks, and self-report was the most commonly used method to assess medication adherence. In the pooled analysis of 2742 patients (median age, 39 years and 50.3% [1380 of 2742] female), text messaging significantly improved medication adherence (odds ratio, 2.11; 95% CI, 1.52-2.93; P < .001). The effect was not sensitive to study characteristics (intervention duration or type of disease) or text message characteristics (personalization, 2-way communication, or daily text message frequency). In a sensitivity analysis, our findings remained robust to change in inclusion criteria based on study quality (odds ratio, 1.67; 95% CI, 1.21-2.29; P = .002). There was moderate heterogeneity (I2 = 62%) across clinical trials. After adjustment for publication bias, the point estimate was reduced but remained positive for an intervention effect (odds ratio, 1.68; 95% CI, 1.18-2.39). CONCLUSIONS AND RELEVANCE Mobile phone text messaging approximately doubles the odds of medication adherence. This increase translates into adherence rates improving from 50% (assuming this baseline rate in patients with chronic disease) to 67.8%, or an absolute increase of 17.8%. While promising, these results should be interpreted with caution given the short duration of trials and reliance on self-reported medication adherence measures. Future studies need to determine the features of text message interventions that improve success, as well as appropriate patient populations, sustained effects, and influences on clinical outcomes.
Expression of Baculovirus Anti-Apoptotic Genes p35 and op-iap in Cotton (Gossypium hirsutum L.) Enhances Tolerance to Verticillium Wilt

Background Programmed cell death plays an important role in mediating plant adaptive responses to the environment such as the invasion of pathogens. Verticillium wilt, caused by the necrotrophic pathogen Verticillium dahliae, is a serious vascular disease responsible for great economic losses to cotton, but the molecular mechanisms of verticillium disease and effective, safe methods of resistance to verticillium wilt remain unexplored. Methodology/Principal Findings In this study, we introduced baculovirus apoptosis inhibitor genes p35 and op-iap into the genome of cotton via Agrobacterium-mediated transformation and analyzed the response of transgenic plants to verticillium wilt. Results showed that p35 and op-iap constructs were stably integrated into the cotton genome, expressed in the transgenic lines, and inherited through the T3 generation. The transgenic lines had significantly increased tolerance to verticillium wilt throughout the developmental stages. The disease index of T1–T3 generation was lower than 19, significantly (P<0.05) better than the negative control line z99668. After treatment with 250 mg/L VD-toxins for 36 hours, DNA from negative control leaves was fragmented, whereas fragmentation in the transgenic leaf DNA did not occur. The percentage of cell death in transgenic lines increased by 7.11% after 60 mg/L VD-toxin treatment, which was less than that of the negative control lines's 21.27%. This indicates that p35 and op-iap gene expression partially protects cells from VD-toxin induced programmed cell death (PCD). Conclusion/Significance Verticillium dahliae can trigger plant cells to die through induction of a PCD mechanism involved in pathogenesis. This paper provides a potential strategy for engineering broad-spectrum necrotrophic disease resistance in plants.
Reduced evoked gamma oscillations in the frontal cortex in schizophrenia patients: a TMS/EEG study.

OBJECTIVE Transcranial magnetic stimulation (TMS) combined with high-density electroencephalography (EEG) can be used to directly examine the properties of thalamocortical circuits in the brain without engaging an individual in cognitive or motor tasks. The authors investigated EEG responses in schizophrenia patients and healthy comparison subjects following the application of TMS to the premotor cortex. METHOD Sixteen schizophrenia patients and 14 healthy comparison subjects were recruited to participate in the study. Participants underwent three to five TMS/high-density EEG sessions at various TMS doses. The following three aspects of TMS-evoked responses were analyzed: amplitude, synchronization, and source localization. RESULTS Relative to healthy comparison subjects, schizophrenia patients had a marked decrease in evoked gamma oscillations that occurred within the first 100 msec after TMS, particularly in a cluster of electrodes located in a fronto-central region. These oscillations were significantly reduced in amplitude (calculated using global-mean field power and event-related spectral perturbation analysis) and synchronization (measured using intertrial coherence). Furthermore, source modeling analysis revealed that the TMS-evoked brain activation underlying these gamma oscillations in patients with schizophrenia did not propagate (as it did in healthy comparison subjects) and was mostly confined to the stimulated brain region. CONCLUSIONS Schizophrenia patients showed a decrease in EEG-evoked responses in the gamma band when TMS was applied to directly stimulate the frontal cortex while these responses were recorded. Since EEG responses to direct cortical stimulation are not affected by an individual's motivation, attention, or cognitive capacity and are not relayed through peripheral afferent pathways, these findings suggest that there might be an intrinsic dysfunction in frontal thalamocortical circuits in individuals with schizophrenia.
Using Deep Convolutional Neural Networks to Predict Goal-scoring Opportunities in Soccer

Deep learning approaches have successfully been applied to several image recognition tasks, such as face, object, animal and plant classification. However, almost no research has examined on how to use the field of machine learning to predict goal-scoring opportunities in soccer from position data. In this paper, we propose the use of deep convolutional neural networks (DCNNs) for the above stated problem. This aim is actualized using the following steps: 1) development of novel algorithms for finding goal-scoring opportunities and ball possession which are used to obtain positive and negative examples. The dataset consists of position data from 29 matches played by a German Bundlesliga team. 2) These examples are used to create original and enhanced images (which contain object trails of soccer positions) with a resolution size of $256 \times 256$ pixels. 3) Both the original and enhanced images are fed independently as input to two DCNN methods: instances of both GoogLeNet and a 3-layered CNN architecture. A K-nearest neighbor classifier was trained and evaluated on ball positions as a baseline experiment. The results show that the GoogLeNet architecture outperforms all other methods with an accuracy of 67.1%.
Di-copper metallodrugs promote NCI-60 chemotherapy via singlet oxygen and superoxide production with tandem TA/TA and AT/AT oligonucleotide discrimination

Abstract In order to expand the current repertoire of cancer treatments and to help circumvent limitations associated with resistance, the identification of new metallodrugs with high potency and novel mechanisms of action is of significant importance. Here we present a class of di-copper(II) complex based on the synthetic chemical nuclease [Cu(Phen)2]+ (where Phen = 1,10-phenanthroline) that is selective against solid epithelial cancer cells from the National Cancer Institute’s 60 human cell line panel (NCI-60). Two metallodrug leads are studied and in each case two [Cu(Phen)2]+ units are bridged by a dicarboxylate linker but the length and rigidity of the linkers differ distinctly. Both agents catalyze intracellular superoxide (O2•−) and singlet oxygen (1O2) formation with radical species mediating oxidative damage within nuclear DNA in the form of double strand breaks and to the mitochondria in terms of membrane depolarization. The complexes are effective DNA binders and can discriminate AT/AT from TA/TA steps of duplex DNA through induction of distinctive Z-like DNA or by intercalative interactions.
Achievable Rates of MIMO Systems With Linear Precoding and Iterative LMMSE Detection

We establish area theorems for iterative detection and decoding (or simply, iterative detection) over coded linear systems, including multiple-input multiple-output channels, intersymbol interference channels, and orthogonal frequency-division multiplexing systems. We propose a linear precoding technique that asymptotically ensures the Gaussianness of the messages passed in iterative detection, as the transmission block length tends to infinity. Area theorems are established to characterize the behavior of the iterative receiver. We show that, for unconstrained signaling, the proposed single-code scheme with linear precoding and iterative linear minimum mean-square error (LMMSE) detection is potentially information lossless, under various assumptions on the availability of the channel state information at the transmitter. We further show that, for constrained signaling, our proposed single-code scheme considerably outperforms the conventional multicode parallel transmission scheme based on singular value decomposition and water-filling power allocation. Numerical results are provided to verify our analysis.
NEW HIGH PERFORMANCE P/M ALLOYS FOR REPLACING DUCTILE CAST IRONS

Improvements in atomizing technology and binder treatment technology have allowed Hoeganaes to develop unique alloy systems with high strength, ductility, and impact energy. These systems, material MDD (currently experimental) and Ancorloy ® MDA (commercial product), have been shown to be extremely competitive with numerous ductile cast iron grades. This paper presents the powder, green, and sintered properties of the new materials and compares their performance with existing material systems.
Is Clinical Practice Concordant with the Changes in Guidelines for Antiretroviral Therapy Initiation during Primary and Chronic HIV-1 Infection? The ANRS PRIMO and COPANA Cohorts

Objective Guidelines for initiating HIV treatment are regularly revised. We explored how physicians in France have applied these evolving guidelines for ART initiation over the last decade in two different situations: chronic (CHI) and primary HIV-1 infection (PHI), since specific recommendations for PHI are also provided in France. Methods Data came from the ANRS PRIMO (1267 patients enrolled during PHI in 1996–2010) and COPANA (800 subjects enrolled at HIV diagnosis in 2004–2008) cohorts. We defined as guidelines-inconsistent during PHI and CHI, patients meeting criteria for ART initiation and not treated in the following month and during the next 6 months, respectively. Results ART initiation during PHI dramatically decreased from 91% of patients in 1996–99 to 22% in 2007 and increased to 60% in 2010, following changes in recommendations. In 2007, however, after the CD4 count threshold was raised to 350 cells/mm3 in 2006, only 55% of the patients with CD4≤350 were treated and 66% in 2008. During CHI, ART was more frequently initiated in patients who met the criteria at entry (96%) than during follow-up: 83% when recommendation to treat was 200 and 73% when it was 350 cells/mm3. Independent risk factors for not being treated during CHI despite meeting the criteria were lower viral load, lower educational level, and poorer living conditions. Conclusion HIV ART initiation guidelines are largely followed by practitioners in France. What can still be improved, however, is time to treat when CD4 cell counts reach the threshold to treat. Risk factors for lack of timely treatment highlight the need to understand better how patients’ living conditions and physicians’ perceptions influence the decision to initiate treatment.