Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Online social networks, World Wide Web, media, and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this article, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (Np-Glm), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train Np-Glm and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-à-vis competitive baselines.
Effects of environmental prompts on stair usage

Experts have advocated exercise with little success, and have turned to encouraging physical activity by incorporating it into daily activities such as taking the stairs over elevators. Much literature exists suggesting that environmental prompts can encourage the use of stairs and literature has established that some messages may be more effective than others. This study aimed to assess the effects of selected signage prompts on stair usage. Methods: Stair and elevator use were monitored in three, twostory buildings. One building served as a control, while a fitness message was placed in another building, and the final building received a weight control message. Observations took place twice per week for the seven weeks of the study. Results Predictors of stair usage included age (p<0.001), gender (p<0.001), and direction of stair usage (p<0.001). Stair rate usage in the three buildings was compared across three time points. Conclusions Though stair usage did not show significant change with the introduction of signs, a trend of increased use suggests that signs may influence stair usage. It was unexpected to find that the introduction of the signs didn’t impact use. Two explanations for this finding are a ceiling effect, and physical differences in building floor plans.
Test et diagnostic en ligne dans les systèmes embarqués. (Online testing and diagnosis in embedded systems)

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Test et diagnostic en ligne dans les systèmes embarqués Oum-El-Kheir Aktouf
Nonnegative Matrix Factorization With Basis Clustering Using Cepstral Distance Regularization

One successful approach for audio source separation involves applying nonnegative matrix factorization (NMF) to a magnitude spectrogram regarded as a nonnegative matrix. This can be interpreted as approximating the observed spectra at each time frame as the linear sum of the basis spectra scaled by time-varying amplitudes. This paper deals with the problem of the unsupervised instrument-wise source separation of polyphonic signals based on an extension of the NMF approach. We focus on the fact that each piece of music is typically played on a handful of musical instruments, which allows us to assume that the spectra of the underlying audio events in a polyphonic signal can be grouped into a reasonably small number of clusters in the mel-frequency cepstral coefficient (MFCC) domain. Based on this assumption, we propose formulating factorization of a magnitude spectrogram and clustering of the basis spectra in the MFCC domain as a joint optimization problem and derive a novel optimization algorithm based on the majorization–minimization principle. Experimental results revealed that our method was superior to a two-stage algorithm that consists of performing factorization followed by clustering the basis spectra, thus showing the advantage of the joint optimization approach.
Model Predictive Control for Lithium-Ion Battery Optimal Charging

Charging time and lifetime are important performances for lithium-ion (Li-ion) batteries, but are often competing objectives for charging operations. Model-based charging controls are challenging due to the complicated battery system structure that is composed of nonlinear partial differential equations and exhibits multiple time-scales. This paper proposes a new methodology for battery charging control enabling an optimal tradeoff between the charging time and battery state-of-health (SOH). Using recently developed model reduction approaches, a physics-based low-order battery model is first proposed and used to formulate a model-based charging strategy. The optimal fast charging problem is formulated in the framework of tracking model predictive control (MPC). This directly considers the tracking performance for provided state-of-charge and SOH references, and explicitly addresses constraints imposed on input current and battery internal state. The capability of this proposed charging strategy is demonstrated via simulations to be effective in tracking the desirable SOH trajectories. By comparing with the constant-current constant-voltage charging protocol, the MPC-based charging appears promising in terms of both the charging time and SOH. In addition, this obtained charging strategy is practical for real-time implementation.
A study of explanation generation in a rule-based system. (Une étude de la génération d'explication dans un système à base de règles)

The concept of “Business Rule Management System” (BRMS) has been introduced in order to facilitate the design, the management and the execution of company-specific business policies. Based on a symbolic approach, the main idea behind these tools is to enable the business users to manage the business rule changes in the system without requiring programming skills. It is therefore a question of providing them with tools that enable to formulate their business policies in a near natural language form and automate their processing. Nowadays, with the expansion of intelligent systems, we have to cope with more and more complex decision logic and large volumes of data. It is not straightforward to identify the causes leading to a decision. There is a growing need to justify and optimize automated decisions in a short time frame, which motivates the integration of advanced explanatory component into its systems. Thus, the main challenge of this research is to provide an industrializable approach for explaining the decision-making processes of business rules applications and more broadly rule-based systems. This approach should be able to provide the necessary information for enabling a general understanding of the decision, to serve as a justification for internal and external entities as well as to enable the improvement of existing rule engines. To this end, the focus will be on the generation of the explanations in themselves as well as on the manner and the form in which they will be delivered.
Guideline recommendations and antimicrobial resistance: the need for a change

Objectives Antimicrobial resistance has become a global burden for which inappropriate antimicrobial use is an important contributing factor. Any decisions on the selection of antibiotics use should consider their effects on antimicrobial resistance. The objective of this study was to assess the extent to which antibiotic prescribing guidelines have considered resistance patterns when making recommendations for five highly prevalent infectious syndromes. Design We used Medline searches complemented with extensive use of Web engine to identify guidelines on empirical treatment of community-acquired pneumonia, urinary tract infections, acute otitis media, rhinosinusitis and pharyngitis. We collected data on microbiology and resistance patterns and identified discrete pattern categories. We assessed the extent to which recommendations considered resistance, in addition to efficacy and safety, when recommending antibiotics. Results We identified 135 guidelines, which reported a total of 251 recommendations. Most (103/135, 79%) were from developed countries. Community-acquired pneumonia was the syndrome mostly represented (51, 39%). In only 16 (6.4%) recommendations, selection of empirical antibiotic was discussed in relation to resistance and specific microbiological data. In a further 69 (27.5%) recommendations, references were made in relation to resistance, but the attempt was inconsistent. Across syndromes, 12 patterns of resistance with implications on recommendations were observed. 50% to 75% of recommendations did not attempt to set recommendation in the context of these patterns. Conclusion There is consistent evidence that guidelines on empirical antibiotic use did not routinely consider resistance in their recommendations. Decision-makers should analyse and report the extent of local resistance patterns to allow better decision-making.
Electrical and Thermal Co-Design for High-Power FPGA Packages

This paper describes a systematic approach to design field-programmable gate array (FPGA) package for high-power applications. As the power consumption is up to multihundred watts, not only the heat dissipation becomes a challenge, but the temperature-induced reliability concerns also mount up in the high current and noise-sensitive packages. Two most profound phenomena are substrate electromigration (EM) and package decoupling capacitor time-dependent dielectric breakdown (TDDB). The impact of EM is on maximum current-carrying capability, and impact of TDDB is on voltage noise suppression capability. However, we find both traditional static way of EM calculation and the legacy design considerations are oversimplified and insufficient for high-power products. In this paper, we report an enhanced methodology to derive substrate EM failure rate from composite current and temperature distributions. Moreover, we incorporate capacitor TDDB into the design flow to optimize capacitor lifetime for effective voltage noise suppression. In the end, we apply the electrical and thermal co-design to a 16-nm, 200-A, high-power FPGA meeting all current and dynamic noise specifications.
Functional food science and gastrointestinal physiology and function

Abstract The gut is an obvious target for the development of functional foods, acting as it does as the interface between diet and the metabolic events which sustain life. The key processes in digestive physiology which can be regulated by modifying diet are satiety, the rate and extent of macronutrient breakdown and absorption from the small bowel, sterol metabolism, the colonic microflora, fermentation, mucosal function and bowel habit, and the gut immune system. The intestinal microflora is the main focus of many current functional foods. Probiotics are foods which contain live bacteria which are beneficial to health whilst prebiotics, such as certain non-digestible oligosaccharides which selectively stimulate the growth of bifidobacteria in the colon, are already on the market. Their claimed benefits are to alleviate lactose maldigestion, increase resistance to invasion by pathogenic species of bacteria in the gut, stimulate the immune system and possibly protect against cancer. There are very few reports of well-designed human intervention studies with prebiotics as yet. Certain probiotic species have been shown to shorten the duration of rotavirus diarrhoea in children but much more work is needed on the mechanism of immunomodulation and of competitive exclusion and microflora modification. The develop-ment of functional foods for the gut is in its infancy and will be successful only if more fundamental research is done on digestive physiology, the gut microflora, immune system and mucosal function.
Cumulative Effective and Individual Organ Dose Levels in Paediatric Patients Undergoing Multiple Catheterisations for Congenital Heart Disease

This study examines the cumulative radiation dose levels received by a group of children who underwent multiple cardiac catheterisation procedures during the investigation and management of congenital heart disease (CHD). The purpose is to calculate cumulative doses, identify higher dose individuals, outline the inconsistencies with risk assessment and encourage the establishment of dose databases in order to facilitate the longitudinal research necessary to better understand health risks. A retrospective review of patient records for 117 paediatric patients who have undergone two or more cardiac catheterisations for the investigation of CHD was undertaken. This cohort consisted of patients who were catheterised over a period from September 2002 to August 2014. The age distribution was from newborn to 17 y. Archived kerma-area product (PKA) and fluoroscopy time (T) readings were retrieved and analysed. Cumulative effective and individual organ doses were determined. The cumulative PKA levels ranged from 1.8 to 651.2 Gycm2, whilst cumulative effective dose levels varied from 2 to 259 mSv. The cumulative fluoroscopy time was shown to vary from 8.1 to 193.5 min. Median cumulative organ doses ranged from 3 to 94 mGy. Cumulative effective dose levels are highly variable but may exceed 250 mSv. Individual organ and effective dose measurements remain useful for comparison purposes between institutions although current methodologies used for determining lifetime risks are inadequate.
Tree differences in primary and secondary growth drive convergent scaling in leaf area to sapwood area across Europe.

Trees scale leaf (AL ) and xylem (AX ) areas to couple leaf transpiration and carbon gain with xylem water transport. Some species are known to acclimate in AL : AX balance in response to climate conditions, but whether trees of different species acclimate in AL : AX in similar ways over their entire (continental) distributions is unknown. We analyzed the species and climate effects on the scaling of AL vs AX in branches of conifers (Pinus sylvestris, Picea abies) and broadleaved (Betula pendula, Populus tremula) sampled across a continental wide transect in Europe. Along the branch axis, AL and AX change in equal proportion (isometric scaling: b ˜ 1) as for trees. Branches of similar length converged in the scaling of AL vs AX with an exponent of b = 0.58 across European climates irrespective of species. Branches of slow-growing trees from Northern and Southern regions preferentially allocated into new leaf rather than xylem area, with older xylem rings contributing to maintaining total xylem conductivity. In conclusion, trees in contrasting climates adjust their functional balance between water transport and leaf transpiration by maintaining biomass allocation to leaves, and adjusting their growth rate and xylem production to maintain xylem conductance.
Adversarial Clustering: A Grid Based Clustering Algorithm Against Active Adversaries

Nowadays more and more data are gathered for detecting and preventing cyber attacks. In cyber security applications, data analytics techniques have to deal with active adversaries that try to deceive the data analytics models and avoid being detected. The existence of such adversarial behavior motivates the development of robust and resilient adversarial learning techniques for various tasks. Most of the previous work focused on adversarial classification techniques, which assumed the existence of a reasonably large amount of carefully labeled data instances. However, in practice, labeling the data instances often requires costly and time-consuming human expertise and becomes a significant bottleneck. Meanwhile, a large number of unlabeled instances can also be used to understand the adversaries' behavior. To address the above mentioned challenges, in this paper, we develop a novel grid based adversarial clustering algorithm. Our adversarial clustering algorithm is able to identify the core normal regions, and to draw defensive walls around the centers of the normal objects utilizing game theoretic ideas. Our algorithm also identifies sub-clusters of attack objects, the overlapping areas within clusters, and outliers which may be potential anomalies.
Catalyst for Gradient-based Nonconvex Optimization

We introduce a generic scheme to solve non-convex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. Even though these methods may originally require convexity to operate, the proposed approach allows one to use them without assuming any knowledge about the convexity of the objective. In general, the scheme is guaranteed to produce a stationary point with a worst-case efficiency typical of first-order methods, and when the objective turns out to be convex, it automatically accelerates in the sense of Nesterov and achieves near-optimal convergence rate in function values. We conclude the paper by showing promising experimental results obtained by applying our approach to incremental algorithms such as SVRG and SAGA for sparse matrix factorization and for learning neural networks.
Purification of Phospholipases A2 from American Snake Venoms

Snake venoms are a complex mixture of compounds with a wide range of biological and pharmacological activities, which more than 90% of their dry weight is composed by proteins, comprising a variety of enzymes, such as proteases (metalo and serine), phospholipases A2, L-aminoacid oxidases, esterases, and others [1-5]. A great number of proteins were purified and characterized from snake venoms [1, 2]. Some of these proteins exhibit enzymatic activity, while many others are non-enzymatic proteins and peptides. Based on their structures, they can be grouped into a small number of super-families based on remarkable similarities in their primary, secondary and tertiary structures, however showing distinct pharmacologic effects [3].
Expression and subcellular localization of AT motif binding factor 1 in colon tumours

AT motif binding factor 1 (ATBF1) is a transcriptional regulator that functions as a tumour suppressor to negatively affect cancer cell growth. In the present study four specific polyclonal antibodies against ATBF1 were generated, and the expression and intracellular localization of ATBF1 in colonic mucosae, polyps, adenoma and adenocarcinoma tissue samples were investigated. The four polyclonal antibodies produced were as follows: MB34 and MB49, which recognize the N- and C-terminal fragments of ATBF1, respectively; and D1-120 and MB44, which recognize the middle fragments of ATBF1 that contain three nuclear localization signals (NLS). In total, 191 colon samples were examined by immunohistochemical analysis. In addition, colon cancer cells were transfected with four ATBF1 expression vectors, and the subcellular localization of each fragment was examined. Normal colon mucosal cells were not observed to express ATBF1. However, a small number of hyperplastic polyps, serrated adenomas and tubular adenomas expressed ATBF1. Colon cancer cells were observed to express D1-120- and MB44-reactive middle fragments of ATBF1 in their cell nuclei. However, the N- and C-terminal fragments of ATBF1 did not translocate to the nucleus. Transfection of ATBF1 fragments revealed cleavage of the ATBF1 protein and nuclear translocation of the cleaved middle portion containing the NLS. A positive correlation between the cytoplasmic localization of the N- and C-termini of ATBF1, nuclear localization of the middle portion of ATBF1 and malignant cancer cell invasion was observed. In conclusion, the results of the present study suggest that alterations in the expression and subcellular localization of ATBF1, as a result of post-transcriptional modifications, are associated with malignant features of colon tumours.
Parallelization of a series of extreme learning machine algorithms based on spark

With the development of the Internet, traditional big data computing platform gradually lose its competitive advantages as a result of high latency. On the contrary, a fast, easy to use and generic big data computing frame called Spark draws more and more attentions. At the same time the integrated solution which is based on RDD (Resilient Distributed Datasets) and is offered by Spark makes the applications of Spark in actual projects broader and broader. Non-iterative ELM (Extreme Learning Machine) algorithm which generates hidden layer weights randomly determines the output layer weights by analyzing. Using this method to reduce learning time as more as possible can bring much convenience to many time-sensitive applications. In this article we put forward a kind of Feedforward Neural Network Parallel Algorithm which is based on Spark platform, establish VMware vSphere platform, experiments on vSphere VMware experiment platform. Our experiment results show that this algorithm can increase the analysis speed of ELM algorithm.
Prospects for cannabinoid therapies in basal ganglia disorders

Cannabinoids are promising medicines to slow down disease progression in neurodegenerative disorders including Parkinson's disease (PD) and Huntington's disease (HD), two of the most important disorders affecting the basal ganglia. Two pharmacological profiles have been proposed for cannabinoids being effective in these disorders. On the one hand, cannabinoids like Δ9‐tetrahydrocannabinol or cannabidiol protect nigral or striatal neurons in experimental models of both disorders, in which oxidative injury is a prominent cytotoxic mechanism. This effect could be exerted, at least in part, through mechanisms independent of CB1 and CB2 receptors and involving the control of endogenous antioxidant defences. On the other hand, the activation of CB2 receptors leads to a slower progression of neurodegeneration in both disorders. This effect would be exerted by limiting the toxicity of microglial cells for neurons and, in particular, by reducing the generation of proinflammatory factors. It is important to mention that CB2 receptors have been identified in the healthy brain, mainly in glial elements and, to a lesser extent, in certain subpopulations of neurons, and that they are dramatically up‐regulated in response to damaging stimuli, which supports the idea that the cannabinoid system behaves as an endogenous neuroprotective system. This CB2 receptor up‐regulation has been found in many neurodegenerative disorders including HD and PD, which supports the beneficial effects found for CB2 receptor agonists in both disorders. In conclusion, the evidence reported so far supports that those cannabinoids having antioxidant properties and/or capability to activate CB2 receptors may represent promising therapeutic agents in HD and PD, thus deserving a prompt clinical evaluation.