Miracle-dz/UniFAR-FTUs huggingface dataset:
Attack Graph Convolutional Networks by Adding Fake Nodes

In this paper, we study the robustness of graph convolutional networks (GCNs). Previous work have shown that GCNs are vulnerable to adversarial perturbation on adjacency or feature matrices of existing nodes; however, such attacks are usually unrealistic in real applications. For instance, in social network applications, the attacker will need to hack into either the client or server to change existing links or features. In this paper, we propose a new type of "fake node attacks" to attack GCNs by adding malicious fake nodes. This is much more realistic than previous attacks; in social network applications, the attacker only needs to register a set of fake accounts and link to existing ones. To conduct fake node attacks, a greedy algorithm is proposed to generate edges of malicious nodes and their corresponding features aiming to minimize the classification accuracy on the target nodes. In addition, we introduce a discriminator to classify malicious nodes from real nodes, and propose a Greedy-GAN attack to simultaneously update the discriminator and the attacker, to make malicious nodes indistinguishable from the real ones. Our non-targeted attack decreases the accuracy of GCN down to 0.03, and our targeted attack reaches a success rate of 78% on a group of 100 nodes, and 90% on average for attacking a single target node.
An Incremental Truth Inference Approach to Aggregate Crowdsourcing Contributions in Games with a Purpose

We introduce our approach for incremental truth inference over the contributions provided by players of Games with a Purpose: we motivate the need for such a method with the specificity of GWAP vs. traditional crowdsourcing; we explain and formalize the proposed process and we explain its positive consequences; finally, we illustrate the results of an experimental comparison with state-of-the-art approaches, performed on data collected through two different GWAPs, thus showing the properties of our proposed framework.
Reinforcement Learning-Based Predictive Control for Autonomous Electrified Vehicles

This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver’s demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver’s driving style and promote fuel economy.
Négociation bilatérale par concession : un état de l'art

Les conflits constituent une caracteristique intrinseque des systemes multi-agents qui peut etre resolu par la negociation. Cet article presente un etat de l'art de la negociation multi-agents en mettant l'accent sur sa variante la plus elementaire : la negociation bilaterale par concession. Nous fournissons ici une grille d'analyse qui distingue : i) l'environnement de negociation (objectifs, parties prenantes, protocole et strategies) ; ii) les proprietes attendues du resultat ; et iii) celles du processus.
Mechanism of Increased LDL (Low-Density Lipoprotein) and Decreased Triglycerides With SGLT2 (Sodium-Glucose Cotransporter 2) Inhibition

Objective— SGLT2 (sodium-glucose cotransporter 2) inhibition in humans leads to increased levels of LDL (low-density lipoprotein) cholesterol and decreased levels of plasma triglyceride. Recent studies, however, have shown this therapy to lower cardiovascular mortality. In this study, we aimed to determine how SGLT2 inhibition alters circulating lipoproteins. Approach and Results— We used a mouse model expressing human CETP (cholesteryl ester transfer protein) and human ApoB100 (apolipoprotein B100) to determine how SGLT2 inhibition alters plasma lipoprotein metabolism. The mice were fed a high-fat diet and then were made partially insulin deficient using streptozotocin. SGLT2 was inhibited using a specific antisense oligonucleotide or canagliflozin, a clinically available oral SGLT2 inhibitor. Inhibition of SGLT2 increased circulating levels of LDL cholesterol and reduced plasma triglyceride levels. SGLT2 inhibition was associated with increased LpL (lipoprotein lipase) activity in the postheparin plasma, decreased postprandial lipemia, and faster clearance of radiolabeled VLDL (very-LDL) from circulation. Additionally, SGLT2 inhibition delayed turnover of labeled LDL from circulation. Conclusions— Our studies in diabetic CETP-ApoB100 transgenic mice recapitulate many of the changes in circulating lipids found with SGLT2 inhibition therapy in humans and suggest that the increased LDL cholesterol found with this therapy is because of reduced clearance of LDL from the circulation and greater lipolysis of triglyceride-rich lipoproteins. Most prominent effects of SGLT2 inhibition in the current mouse model were seen with antisense oligonucleotides-mediated knockdown of SGLT2.
Using Boolean factors for the construction of an artificial neural networks

ABSTRACT We propose a novel approach to define Artificial Neural Network(ANN) architecture from Boolean factors. ANNs are a subfield of machine learning applicable to several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. Since it is difficult to look into the network and figure out exactly what it has learnt, the complexity of such a technique makes its interpretation more tedious. We propose in this paper to build feedforward ANNs using the optimal factors obtained from the Boolean context representing a data. Since optimal factors completely cover the data and therefore give an explanation to these data, We could give an interpretation to the neurons activation and justify the presence of a neuron in our proposed neural network. We show through experiments and comparisons on the use data sets that this approach provides relatively better results for some key performance measures.
Sirtuins and NAD+ in the Development and Treatment of Metabolic and Cardiovascular Diseases

The sirtuin family of nicotinamide adenine dinucleotide–dependent deacylases (SIRT1–7) are thought to be responsible, in large part, for the cardiometabolic benefits of lean diets and exercise and when upregulated can delay key aspects of aging. SIRT1, for example, protects against a decline in vascular endothelial function, metabolic syndrome, ischemia-reperfusion injury, obesity, and cardiomyopathy, and SIRT3 is protective against dyslipidemia and ischemia-reperfusion injury. With increasing age, however, nicotinamide adenine dinucleotide levels and sirtuin activity steadily decrease, and the decline is further exacerbated by obesity and sedentary lifestyles. Activation of sirtuins or nicotinamide adenine dinucleotide repletion induces angiogenesis, insulin sensitivity, and other health benefits in a wide range of age-related cardiovascular and metabolic disease models. Human clinical trials testing agents that activate SIRT1 or boost nicotinamide adenine dinucleotide levels are in progress and show promise in their ability to improve the health of cardiovascular and metabolic disease patients.
Is Human Auditory Cortex Organization Compatible With the Monkey Model? Contrary Evidence From Ultra-High-Field Functional and Structural MRI

Abstract It is commonly assumed that the human auditory cortex is organized similarly to that of macaque monkeys, where the primary region, or “core,” is elongated parallel to the tonotopic axis (main direction of tonotopic gradients), and subdivided across this axis into up to 3 distinct areas (A1, R, and RT), with separate, mirror-symmetric tonotopic gradients. This assumption, however, has not been tested until now. Here, we used high-resolution ultra-high-field (7 T) magnetic resonance imaging (MRI) to delineate the human core and map tonotopy in 24 individual hemispheres. In each hemisphere, we assessed tonotopic gradients using principled, quantitative analysis methods, and delineated the core using 2 independent (functional and structural) MRI criteria. Our results indicate that, contrary to macaques, the human core is elongated perpendicular rather than parallel to the main tonotopic axis, and that this axis contains no more than 2 mirror-reversed gradients within the core region. Previously suggested homologies between these gradients and areas A1 and R in macaques were not supported. Our findings suggest fundamental differences in auditory cortex organization between humans and macaques.
The Elusive Internet Flattening: 10 Years of IXP Growth

Over the past decade, Internet eXchange Points (IXPs) have been playing a key role in enabling interdomain connectivity. Their traffic volumes have quickly become similar to those of Tier-1 ASes and their physical presence has spread across the world. While the relevance of IXPs is undeniable, their contribution to the shaping of the current Internet is not fully understood yet, especially on the so-called "flattening" phenomenon. In this paper, we disentangle "flattening" from "path-shortening": we show that the impact of IXPs on path length is limited. Surprisingly, Internet path-lengths have globally barely decreased over the last decade, regardless of whether they cross IXPs or not. Instead of a "path-shortening", we observe a diversion of the routes away from the central Tier-1 ASes, supported by IXPs. This diversion has however not fundamentally changed the existence of a hierarchy, but rather replaced the central Tier-1s with a new set of players.
Global transcriptional activity dynamics reveal functional enhancer RNAs

Active enhancers of the human genome generate long noncoding transcripts known as enhancer RNAs (eRNAs). How dynamic transcriptional changes of eRNAs are physically and functionally linked with target gene transcription remains unclear. To investigate the dynamic functional relationships among eRNAs and target promoters, we obtained a dense time series of GRO-seq and ChIP-seq data to generate a time-resolved enhancer activity map of a cell undergoing an innate antiviral immune response. Dynamic changes in eRNA and pre-mRNA transcription activities suggest distinct regulatory roles of enhancers. Using a criterion based on proximity and transcriptional inducibility, we identified 123 highly confident pairs of virus-inducible enhancers and their target genes. These enhancers interact with their target promoters transiently and concurrently at the peak of gene activation. Accordingly, their physical disassociation from the promoters is likely involved in post-induction repression. Functional assessments further establish that these eRNAs are necessary for full induction of the target genes and that a complement of inducible eRNAs functions together to achieve full activation. Lastly, we demonstrate the potential for eRNA-targeted transcriptional reprogramming through targeted reduction of eRNAs for a clinically relevant gene, TNFSF10, resulting in a selective control of interferon-induced apoptosis.
High‐throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations

High‐throughput identification technologies provide efficient tools for understanding the ecology and functioning of microorganisms. Yet, these methods have been only rarely used for monitoring and testing ecological hypotheses in plant pathogens and pests in spite of their immense importance in agriculture, forestry and plant community dynamics. The main objectives of this manuscript are the following: (a) to provide a comprehensive overview about the state‐of‐the‐art high‐throughput quantification and molecular identification methods used to address population dynamics, community ecology and host associations of microorganisms, with a specific focus on antagonists such as pathogens, viruses and pests; (b) to compile available information and provide recommendations about specific protocols and workable primers for bacteria, fungi, oomycetes and insect pests; and (c) to provide examples of novel methods used in other microbiological disciplines that are of great potential use for testing specific biological hypotheses related to pathology. Finally, we evaluate the overall perspectives of the state‐of‐the‐art and still evolving methods for diagnostics and population‐ and community‐level ecological research of pathogens and pests.
Association between PCOS and autoimmune thyroid disease: a systematic review and meta-analysis

Polycystic ovary syndrome (PCOS) is the most prevalent endocrine disorder affecting women of reproductive age. PCOS has been associated with distinct metabolic and cardiovascular diseases and with autoimmune conditions, predominantly autoimmune thyroid disease (AITD). AITD has been reported in 18–40% of PCOS women, depending on PCOS diagnostic criteria and ethnicity. The aim of this systematic review and meta-analysis was to summarize the available evidence regarding the likelihood of women with PCOS also having AITD in comparison to a reference group of non-PCOS women. We systematically searched EMBASE and MEDLINE for non-interventional case control, cross-sectional or cohort studies published until August 2017. The Ottawa–Newcastle Scale was used to assess the methodological quality of studies. Statistical meta-analysis was performed with R. Thirteen studies were selected for the present analysis, including 1210 women diagnosed with PCOS and 987 healthy controls. AITD was observed in 26.03 and 9.72% of PCOS and control groups respectively. A significant association was detected between PCOS and chance of AITD (OR = 3.27, 95% CI 2.32–4.63). Notably, after geographical stratification, the higher risk of AITD in PCOS women persisted for Asians (OR = 4.56, 95% CI 2.47–8.43), Europeans (OR = 3.27, 95% CI 2.07–5.15) and South Americans (OR = 1.86, 95% CI 1.05–3.29). AIDT is a frequent condition in PCOS patients and might affect thyroid function. Thus, screening for thyroid function and thyroid-specific autoantibodies should be considered in patients with PCOS even in the absence of overt symptoms. This systematic review and meta-analysis is registered in PROSPERO under number CRD42017079676.
A Comparison of the Declarative Modelling Languages B, Dash, and TLA+

Declarative behavioural modelling is a powerful modelling paradigm that enables users to model system functionality abstractly and concisely. We compare two well-used formal declarative modelling languages, B and TLA+, with a new modelling language called Dash. Dash is an extension of Alloy with explicit syntactic constructs for modelling transition systems, and it includes control state hierarchy and events. Particular topics that we cover in our comparison are: differences in the datatypes and type systems; how the transitions/operations can be described; how the transition relation is a combination of the transitions; and the default choice each language makes regarding permitted variable changes in a transition. Our goal is to discuss the interesting differentiating characteristics of each language to aid users in determining which language is the most suitable for their system.
Altered Binding of Tumor Antigenic Peptides to MHC Class I Affects CD8+ T Cell–Effector Responses

Influence of T-cell priming on effector antitumor responses was evaluated. Loss of secondary contacts between a peptide non-anchor residue and MHC skews effector functions to favor cytotoxicity over cytokine production in T cells specific for a melanoma self-antigen. T-cell priming occurs when a naïve T cell recognizes cognate peptide–MHC complexes on an activated antigen-presenting cell. The circumstances of this initial priming have ramifications on the fate of the newly primed T cell. Newly primed CD8+ T cells can embark onto different trajectories, with some becoming short-lived effector cells and others adopting a tissue resident or memory cell fate. To determine whether T-cell priming influences the quality of the effector T-cell response to tumors, we used transnuclear CD8+ T cells that recognize the melanoma antigen TRP1 using TRP1high or TRP1low TCRs that differ in both affinity and fine specificity. From a series of altered peptide ligands, we identified a point mutation (K8) in a nonanchor residue that, when analyzed crystallographically and biophysically, destabilized the peptide interaction with the MHC binding groove. In vitro, the K8 peptide induced robust proliferation of both TRP1high and TRP1low CD8+ T cells but did not induce expression of PD-1. Cytokine production from K8-stimulated TRP1 cells was minimal, whereas cytotoxicity was increased. Upon transfer into B16 tumor–bearing mice, the reference peptide (TRP1-M9)- and K8-stimulated TRP1 cells were equally effective at controlling tumor growth but accomplished this through different mechanisms. TRP1-M9–stimulated cells produced more IFNγ, whereas K8-stimulated cells accumulated to higher numbers and were more cytotoxic. We, therefore, conclude that TCR recognition of weakly binding peptides during priming can skew the effector function of tumor-specific CD8+ T cells.
Prominent role of histone lysine demethylases in cancer epigenetics and therapy

Protein methylation has an important role in the regulation of chromatin, gene expression and regulation. The protein methyl transferases are genetically altered in various human cancers. The enzymes that remove histone methylation have led to increased awareness of protein interactions as potential drug targets. Specifically, Lysine Specific Demethylases (LSD) removes methylated histone H3 lysine 4 (H3K4) and H3 lysine 9 (H3K9) through formaldehyde-generating oxidation. It has been reported that LSD1 and its downstream targets are involved in tumor-cell growth and metastasis. Functional studies of LSD1 indicate that it regulates activation and inhibition of gene transcription in the nucleus. Here we made a discussion about the summary of histone lysine demethylase and their functions in various human cancers.
Learning to Listen to Hunger and Fullness Cues

Have you ever stopped to think about how many decisions around food and eating we make each day? According to Brian Wansink, researcher at Cornell University, we make over 200 food related decisions every day and often food choices are made without our awareness (Wansink & Sobal, 2007). Each day, we are inundated with external eating cues, or environmental triggers, that can influence what we eat and how much we eat. For instance, portion sizes may influence the amount of food we believe is appropriate to eat and eating while distracted may influence how much we consume (Wansink, Payne, & Chandon, 2007).
Identifying Similar Eye Movement Patterns with t-SNE

In this paper we describe an approach based on the t-distributed stochastic neighbor embedding (t-SNE) focusing on projecting high-dimensional eye movement data to two dimensions. The lower-dimensional data is then represented as scatterplots reﬂecting the local structure of the high-dimensional eye movement data and hence, providing a strategy to identify similar eye movement patterns. The scatterplots can be used as means to interact with and to further annotate and analyze the data for additional properties focusing on space, time, or participants. Since t-SNE oftentimes produces groups of data points mapped to and overplotted in small scatterplot regions, we additionally support the modiﬁcation of data point groups by a force-directed placement as a post processing in addition to t-SNE that can be run after the initial t-SNE algorithm is stopped. This spatial modiﬁcation can be applied to each identiﬁed data point group independently which is difﬁcult to integrate into a standard t-SNE approach. We illustrate the usefulness of our technique by applying it to formerly conducted eye tracking studies investigating the readability of public transport maps and map annotations.
Sensor Fusion of Intensity and Depth Cues using the ChiNet for Semantic Segmentation of Road Scenes

Vision-based environment perception is an important research topic for autonomous driving and advanced driver assistance systems. Vision sensors, such as the monocular camera and stereo camera, are widely used for environment perception. The monocular camera provides the appearance information like intensity, and the stereo camera provides the depth information. The appearance and depth information are complementary, and their effective fusion would result in robust environment perception. Consequently, in this paper, we propose a novel deep learning framework, termed as the ChiNet, for the effective sensor fusion of the appearance and depth information for free space and road object estimation. The ChiNet has two input branches and two output branches. The ChiNet input branches contains separate branches for the intensity and depth information. For the output branches, the ChiNet contains separate branches for the free space and road object semantic segmentation. A comparative of the proposed framework with state-of-the-art baseline algorithms is performed using an acquired dataset. Moreover, a detailed parameter analysis is performed to validate the ChiNet architecture as well as the advantages of sensor fusion. The experimental results show that the ChiNet is better than baseline algorithms. We also show that the proposed ChiNet architecture is better than other variations of the ChiNet architecture.
Bartonella quintana, past, present, and future of the scourge of World War I

During World War I, a mysterious new disease affected soldiers on both sides of battle field. The first reports described a relapsing fever of unknown origin with body lice being suggested as the vector. The outbreak affected >1 000 000 people, mostly soldiers fighting in front‐line trenches. Shortly afterward, the illness was known as Trench fever, of which the causal infectious agent is currently classified as Bartonella quintana.
Trajectory Prediction of Turning Vehicles based on Intersection Geometry and Observed Velocities

Urban intersections are known to be a hotspot for traffic accidents, and understanding of the dynamic traffic situations at intersections can help to prevent injuries. When an ego-vehicle crosses an intersection, predicting the trajectory of oncoming other vehicles is a requirement for Advanced Driver Assistant Systems. In this paper, we propose a method of trajectory prediction of turning vehicles at urban intersections. Trajectory prediction of turn maneuver vehicles is more difficult than straight-maneuver vehicle because a turning vehicle slows down as it approaches the intersections and speeds up as it leaves the intersections. Furthermore, the variation in velocities depends on factors such as an intersection angle, a corner radius. Our method generates a novel desired velocity model for trajectory prediction that takes into account intersection geometry and observed velocities of other vehicles. Specifically, we assume that the velocity becomes minimum at around the crosswalk, and calculate the velocity model by fitting past sequential velocities and estimated minimum velocity to the cubic function. Our method has the advantage of being able to predict the trajectory at any intersection and from any position. The prediction performance of our method in the real traffic scenarios is better than one of other methods.