Miracle-dz/UniFAR-FTUs huggingface dataset:
HTLV-1 in Solid-Organ Transplantation: Current Challenges and Future Management Strategies

Human T-cell lymphotrophic virus (HTLV)-1 has been reported after solid-organ transplantation, with a related fatal outcome in less than five cases. The natural history of HTLV-1 transmission from donor to recipient is unknown in this setting, because available screening platforms are suboptimal in low-prevalence areas and there is a lack of long-term follow-up. Minimizing organ wastage due to false-positive screening and avoiding donor-derived HTLV-associated diseases remain the goal. To date, only six HTLV-naive organ recipients from four donors (only one had confirmed HTLV) have developed HTLV-associated disease after transplantation. All of these cases were described in countries or from donors from HTLV-endemic regions. To the best of our knowledge, there have been no reported cases of donor-derived HTLV-1–associated death after organ transplantation in the world. Based on data from low-prevalence countries (Europe and the United States) and the current shortage of donor organs, it appears plausible to authorize the decision to transplant an organ without the prior knowledge of the donor’s HTLV-1 status. Currently, it is not possible to exclude such transmission and recipients should be informed of the possible inadvertent transmission of this (and other) infections at the time of consent. In those cases where HTLV-1 transmission does occur, there may be a therapeutic window in which use of antiviral agents (i.e., zidovudine and raltegravir) may be of benefit. The development of national/international registries should allow a greater understanding of the extent and consequences of transmission risk and so allow a more evidence-based approach to management.
Investigating the neural basis of cooperative joint action. An EEG hyperscanning study

The aim of the present study is to investigate the neurophysiological basis of the cognitive functions underlying the execution of joint actions, by means of the recent technique called hyperscanning. Neuroelectrical hyperscanning is based on the simultaneous recording of brain activity from multiple subjects and includes the analysis of the functional relation between the brain activity of all the interacting individuals. We recorded simultaneous high density electroencephalography (hdEEG) from 16 pairs of subjects involved in a computerized joint action paradigm, with controlled levels of cooperation. Results of cortical connectivity analysis returned significant differences, in terms of inter-brain functional causal links, between the condition of cooperative joint action and a condition in which the subjects were told they were interacting with a PC, while actually interacting with another human subject. Such differences, described by selected brain connectivity indices, point toward an integration between the two subjects' brain activity in the cooperative condition, with respect to control conditions.
Comparative Study of Different Machine Learning Models for Customer Churn Analysis Using SMOTE and Feature Variation Along With Customer Segmentation

Customer churn is a major issue faced by the companies in both the online and offline markets, which adversely affects profit and revenue. Recently, Machine Learning (ML) is being used to analyze and predict customer churns. In this research the problem of churn prediction is studied with special focus on feature selection and unbalanced data sets. Also, churn analysis has mainly dealt with prediction and not methods to retain customers. In our study, we use a customer dataset from a US telecom company. We compare several classifiers for churn prediction including logistic regression, decision trees, SVM, random forest, k-NN and XgBoost. Besides, methods to retain the customers are discussed. The importance of feature selection is highlighted in this paper and a detailed experimental study of model performance on balanced and unbalanced datasets are explored. After comparing the F1 scores, AUC scores and precision-recall curve, it is seen that XgBoost outperformed all the other algorithms. On the other hand, retaining customers requires the careful study of their behavioral patterns. Customer segmentation is an effective way used by the marketing teams to identify the different groups of customers. In this paper, k-means, agglomerative clustering, gaussian mixture (GM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used for clustering the customers into segments. We evaluate the clustering results using silhouette analysis.
Dinucleotide Composition in Animal RNA Viruses Is Shaped More by Virus Family than by Host Species

ABSTRACT Viruses use the cellular machinery of their hosts for replication. It has therefore been proposed that the nucleotide and dinucleotide compositions of viruses should match those of their host species. If this is upheld, it may then be possible to use dinucleotide composition to predict the true host species of viruses sampled in metagenomic surveys. However, it is also clear that different taxonomic groups of viruses tend to have distinctive patterns of dinucleotide composition that may be independent of host species. To determine the relative strength of the effect of host versus virus family in shaping dinucleotide composition, we performed a comparative analysis of 20 RNA virus families from 15 host groupings, spanning two animal phyla and more than 900 virus species. In particular, we determined the odds ratios for the 16 possible dinucleotides and performed a discriminant analysis to evaluate the capability of virus dinucleotide composition to predict the correct virus family or host taxon from which it was isolated. Notably, while 81% of the data analyzed here were predicted to the correct virus family, only 62% of these data were predicted to their correct subphylum/class host and a mere 32% to their correct mammalian order. Similarly, dinucleotide composition has a weak predictive power for different hosts within individual virus families. We therefore conclude that dinucleotide composition is generally uniform within a virus family but less well reflects that of its host species. This has obvious implications for attempts to accurately predict host species from virus genome sequences alone. IMPORTANCE Determining the processes that shape virus genomes is central to understanding virus evolution and emergence. One question of particular importance is why nucleotide and dinucleotide frequencies differ so markedly between viruses. In particular, it is currently unclear whether host species or virus family has the biggest impact on dinucleotide frequencies and whether dinucleotide composition can be used to accurately predict host species. Using a comparative analysis, we show that dinucleotide composition has a strong phylogenetic association across different RNA virus families, such that dinucleotide composition can predict the family from which a virus sequence has been isolated. Conversely, dinucleotide composition has a poorer predictive power for the different host species within a virus family and across different virus families, indicating that the host has a relatively small impact on the dinucleotide composition of a virus genome.
Two androdioecious and one dioecious new species of pristionchus (nematoda: diplogastridae): new reference points for the evolution of reproductive mode.

Rhabditid nematodes are one of a few animal taxa in which androdioecious reproduction, involving hermaphrodites and males, is found. In the genus Pristionchus, several cases of androdioecy are known, including the model species P. pacificus. A comprehensive understanding of the evolution of reproductive mode depends on dense taxon sampling and careful morphological and phylogenetic reconstruction. In this article, two new androdioecious species, P. boliviae n. sp. and P. mayeri n. sp. , and one gonochoristic outgroup, P. atlanticus n. sp. , are described on morphological, molecular, and biological evidence. Their phylogenetic relationships are inferred from 26 ribosomal protein genes and a partial SSU rRNA gene. Based on current representation, the new androdioecious species are sister taxa, indicating either speciation from an androdioecious ancestor or rapid convergent evolution in closely related species. Male sexual characters distinguish the new species, and new characters for six closely related Pristionchus species are presented. Male papillae are unusually variable in P. boliviae n. sp. and P. mayeri n. sp. , consistent with the predictions of "selfing syndrome. " Description and phylogeny of new androdioecious species, supported by fuller outgroup representation, establish new reference points for mechanistic studies in the Pristionchus system by expanding its comparative context.
You are where you eat: Foursquare checkins as indicators of human mobility and behaviour

Location-sharing services such as Foursquare provide a rich source of information about the visits of users to locations. In the case of Foursquare, users voluntarily `check in' to places they visit using a mobile application. An analysis of these data may reveal differences in users personality in terms of their mobility habits, preferred places, and action and location patterns. This knowledge about user behaviour can be used, in addition to information about their preferences, to improve current recommendation systems for mobile platforms.
Optimal selection theory for superconcurrency

This paper describes a mathematical programming approach to finding an optimal, heterogeneous suite of processors to solve supercomputing problems. This technique, called superconcurrency, works best when the computational requirements are diverse and significant portions of the code are not tightly-coupled. It is also dependent on new methods of benchmarking and code profiling, as well as eventual use of AI techniques for intelligent management of the selected superconcurrent suite.
Towards Imperceptible Adversarial Image Generation: Minimizing Perceptual Difference

Currently most deep learning models in computer vision are being implemented for real-time inference. Thorough examination of these models in adversarial settings, especially those involving security-critical matters, is highly significant. Adversarial samples are specifically crafted to fool the deep learning models. Adversarial attacks can be categorized as either black box or white box attacks. Gradient based adversarial attacks like FGSM, BIM, Deepfool, and CW are some of the common white box attacks. However, no previous gradient-based attack has specifically prioritized the visual quality of the images it generates. We propose a novel method for generating adversarial images perceptually close to the original images. To apply our methodology, we incorporated SSIM and L1 loss functions in addition to cross entropy loss. The experimental findings demonstrate that the Xception model, which was pretrained on the Imagenet dataset, exhibits superior visual metrics such as SSIM, VFP, and PSNR when compared to previous state-of-the-art methods including FGSM, BIM, and Deepfool. To the best of the author’s knowledge, there has been no previous demonstration of the visual quality of generated adversarial images in gradient-based attacks.
Operating systems - concepts, policies and mechanisms

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Accurate Traffic Classification

The analysis of network traffic can provide important information for network operators and administrators. One of the main purposes of traffic analysis is to identify the traffic mixture the network carries. A couple of different approaches have been proposed in the literature, but none of them performs well for all different application traffic types present in the Internet. Thus, a combined method that includes the advantages of different approaches is needed, in order to provide a high level of classification completeness and accuracy. According to our best knowledge, this study is the first attempt where the currently known traffic classification methods are benchmarked on network traces captured in operational mobile networks. The pros and cons of the classification methods are analyzed, based on the experienced accuracy for different types of applications. Using the gained knowledge about the strengths and weaknesses of the existing approaches, a novel traffic classification method is proposed. The novel method is based on a complex decision mechanism, in order to provide an appropriate identification mode for each different application type. As a consequence, the ratio of the unclassified traffic becomes significantly lower. Further, the reliability of the classification improves, as the various methods validate the results of each other. The novel method is tested on several network traces, and it is shown that the proposed solution improves both the completeness and the accuracy of the traffic classification, when compared to existing methods.
Les troubles sémantiques catégoriels reflètent l'organisation catégorielle de la mémoire sémantique

There are now numerous published case-reports of brain-damaged patients presenting with a category-specific conceptual deficit. The most frequently reported pattern is the one in which patients have a selective or disproportionate impairment in processing the concepts of living things, but the reverse pattern, with the nonliving concepts being more impaired, has also been reported. The existence of such patterns of dissociation suggests that the conceptual system is organized on a categorical basis. However, according to the most widely accepted account for category-specific semantic deficits, these patterns in fact emerge as an accidental consequence of another organizing principle of semantic knowledge, namely, the kind of properties (sensorial or functional) being represented. This "sensori-functional" account of category-specific semantic deficits presented itself as superior to a categorical account on the basis of the following points: (1) the patterns of dissociation between impaired versus spared categories do not follow the living versus nonliving distinction; (2) these patterns can be accounted for by reference to the kind of properties (visual versus functional) that are relevant for a given class of concepts; (3) the patients with a selective deficit for living things also present with a selective deficit in processing the visual properties of concepts. Here we show that none of these points stand up to analysis of the empirical facts, which in addition are more consistent with the hypothesis of a categorical organization of the conceptual system.
Real-time Bézier Trajectory Deformation for Potential Fields planning methods

This article presents a new technique for obtaining a flexible trajectory based on the deformation of a Bézier curve through a field of vectors. This new technique is called Bézier Trajectory Deformation (BTD). The trajectory deformation is computed with a constrained optimization method (Lagrange Multipliers Theorem). A linear system is solved to achieve the result. As a consequence, the deformed trajectory is computed in a few milliseconds. In addition, the linear system can be solved offline if the Bézier curve order is maintained constant during the movement of the robot, which is the common case. This technique can be combined with any collision avoidance algorithm that produces a field of vectors. In particular, it has been developed for artificial potential field methods. BTD is combined with a recently proposed PF method, the Potential Field Projection method (PFP). The resulting technique is tested in dynamic environment showing its real-time performance and its efficacy for obstacle avoidance.
Image and Video Abstraction by Coherence‐Enhancing Filtering

In this work, we present a non‐photorealistic rendering technique to create stylized abstractions from color images and videos. Our approach is based on adaptive line integral convolution in combination with directional shock filtering. The smoothing process regularizes directional image features while the shock filter provides a sharpening effect. Both operations are guided by a flow field derived from the structure tensor. To obtain a high‐quality flow field, we present a novel smoothing scheme for the structure tensor based on Poisson's equation. Our approach effectively regularizes anisotropic image regions while preserving the overall image structure and achieving a consistent level of abstraction. Moreover, it is suitable for per‐frame filtering of video and can be efficiently implemented to process content in real‐time.
Searching the library and asking the peers: learning to use Java APIs on demand

The existence of large API libraries contributes significantly to the programming productivity and quality of Java programmers. The vast number of available library APIs, however, presents a learning challenge for Java programmers. Most Java programmers do not know all the APIs. Whenever their programming task requires API methods they do not yet know, they have to be able to find what they need and learn how to use them on demand. This paper describes a tool called STeP_IN_Java (a <u>S</u>ocio-<u>Te</u>chnical <u>P</u>latform for <u>I</u>n situ <u>N</u>etworking of <u>Java</u> programmers) that helps Java programmers find APIs, and learn from both examples and experts how to use them on demand. STeP_IN_Java features a sophisticated yet easy-to-use search interface that enables programmers to conduct a personalized search and to progressively refine their search by limiting search scopes. Example programs are provided and embedded to assist programmers in using APIs. Furthermore, if a programmer still has questions about a particular API method, he or she can ask peer programmers. The STeP_IN_Java system automatically routes the question to a group of experts who are chosen based on two criteria: they have high expertise on the particular API method and they have a good social relationship with the programmer who is requesting the information.
Association of Beta Blocker Exposure with Outcomes in Heart Failure Differs Between African American and White Patients Lanfear

Background—Beta blockers (BB) are a mainstay of heart failure (HF) treatment, yet there is inconclusive data regarding their efficacy in African American individuals. Methods and Results—We performed a retrospective study of insured patients who received care from a large health system who were hospitalized for HF between January, 2000 and June, 2008, and had a documented ejection fraction <50%. BB exposure was estimated over six-month rolling windows using pharmacy claims data. Proportional hazards regression was used to test the association between BB exposure and all-cause hospitalization or death with adjustment for baseline covariates and other HF medication exposure. We performed analyses stratified by race, and overall with a BB exposure*Race interaction term. A total of 1,094 patients met inclusion criteria (476 white and 618 African American individuals). Median follow up was 2.1 years. In adjusted models BB exposure was associated with lower risk of death or hospitalization in both groups, but more so in white individuals (HR 0.40, 95%CI 0.27, 0.60, p<0.001) compared with African American individuals (HR 0.67, 95%CI 0.48, 0.94, p=0.024). A formal test for interaction indicated that the protection association for BB exposure differed by race (p=0.098, =0.40). Reanalysis restricted to BBs approved for HF, or HFspecific hospitalizations did not substantively alter the findings. Conclusions—BB appears to be 40-50% less effective in preventing death or hospitalization among African American patients with HF as compared with white individuals. Further study is needed to better understand BB effectiveness in African Americans with HF.
Multilevel communication modeling for Multiprocessor System-on-Chip

The high complexity of current multi-processor system on chip (MPSoC) impels the designers to model and simulate the system components and their interaction in the early design stages. High level modeling usually requires less modeling effort and executes faster. In this paper we propose high level communication models that allow early MPSoC design, performance estimation and evaluation of the application's communication requirements. We applied the proposed modeling methods to analyze the impact on performance for different communication architectures for the H.264 Encoder running on a complex MPSoC platform.
Non-reflective electrode cell for SAW with quarter-wavelength electrodes

Non-reflective interdigital transducers (IDTs) for surface acoustic waves (SAWs) are important elements for development of SAW devices with precise frequency responses. Until now a common way to have a non-reflective periodic cell of IDT at the resonance frequency is to use split electrodes with periodicity P/4 and finger width P/8, where P is the IDT period equal to the SAW wavelength. But for 2.45 GHz (ISM frequency band), standard lithography allows electrode (and gap) widths only to be about quarter-wavelength. Thus, a new structure solution for non-reflective IDT is required for high frequencies. A novel solution for non-reflective cell with two quarter-wavelength electrodes per one-wavelength period is based on 2D-periodic structure consisting of alternating active IDT tracks and passive reflector tracks. If tracks have equal apertures and if electrodes in adjacent tracks are shifted to each other by P/4, SAWs reflected from electrodes in adjacent tracks have equal amplitudes but with phases shifted by a half wavelength. So the reflected waves cancel each other and the total reflection from the structure should be equal to zero.
How dia-shows turn into video flows: Adapting scalable video communication to heterogeneous network conditions in real-time

Video conferencing over IP (VCoIP) is a major trend in current Internet communication and has particularly spread to the mobile realm. In this environment, users face the problem of heterogeneous and fluctuating network conditions. A promising solution to this issue is the scalable video coding (SVC). It allows an adaptation of the video stream to the available bandwidth, but requires a reliable bandwidth estimation. Adaptation times for conversational video at fluctuating network conditions are critical, and a fast strategy for bandwidth estimation is needed to avoid congestion. In this work, we analyse the capabilities of the sender and the receiver to adapt the video coding to changing network conditions. We derive an early congestion indicator at the sender side based on the jitter variation. For receivers, we use sustained goodput to extract a feasible scaling. In thorough evaluations that include real-world 3G networks, we reveal a faster congestion detection at the sender that are also more robust but less accurate than probing at the receiver.