Miracle-dz/UniFAR-FTUs huggingface dataset:
A Relational Tucker Decomposition for Multi-Relational Link Prediction

We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patterns in its components. In contrast to these prior models, RT decouples the sizes of entity and relation embeddings, allows parameter sharing across relations, and does not make use of a predefined sparsity pattern. We use the RT decomposition as a tool to explore whether it is possible and beneficial to automatically learn sparsity patterns, and whether dense models can outperform sparse models (using the same number of parameters). Our experiments indicate that---depending on the dataset--both questions can be answered affirmatively.
Development and Validation of the Parent-Initiated Motivational Climate in Individual Sport Competition Questionnaire

This paper presents a series of studies that progresses the development and validation of the Parent-Initiated Motivational Climate in Individual Sport Competition Questionnaire (MCISCQ-Parent). Study 1 examined the face and content validity of an initial pool of 26 items based on the principles of achievement goal theory and prior research. In Study 2, data from an adolescent sample of individual sport athletes was subjected to an exploratory factor analysis (EFA) of items pertaining to the perceived task and ego involving characteristics of fathers and mothers in the competition setting. Study 3 tested the factor structure of the MCISCQ-Parent through confirmatory factor analysis (CFA) in a further youth athlete sample. Following appropriate CFA-related modifications, good goodness of fit indices emerged for the father- (three factor-model) and mother-related (two factor-model) dimensions of motivational climate. In Study 4, a further CFA was conducted and provided additional evidence for the revised factor structure of the MCISCQ-Parent, convergent and discriminant validity, and internal consistency. Finally, Study 5 provided support for the concurrent validity of the MCISCQ-Parent by demonstrating significant relationships between MCISCQ-Parent subscales and task and ego orientation, athlete engagement, and perceived social support. In sum, we present the MCISCQ-Parent as a measure with promising psychometric properties, and specifically to those applied researchers interested in assessing the quality of motivation-related parental involvement perceived by young athletes in the competition setting.
Assessment of Psychological Dimensions in Telemedicine Care for Gestational Diabetes Mellitus: A Systematic Review of Qualitative and Quantitative Studies

Background and Objective: Gestational Diabetes Mellitus (GDM) is a complex and wide spread problem and is considered one of the most frequent chronic metabolic conditions during pregnancy. According to a recent consensus conference held in Italy, new technologies can play a role in the so-called process of fertilization of the individual's ecosystem engagement, representing support for systemic collaboration among the main actors. The current systematic review aimed at providing an update of the literature about telemedicine for GDM, considering the role of psychological dimensions such as empowerment/self-efficacy, engagement and satisfaction. Methods: The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The data sources were PubMed, ScienceDirect, Cochrane, and Scopus databases. Results: Thirteen articles were identified as eligible and relevant for the final qualitative synthesis, but none was specific for the topic of engagement. The quality or research bias of the studies presents methodological limits. Most studies had clinical outcomes as a primary object. Concerning empowerment and self-efficacy, there were only preliminary findings reporting any improvements derived from using telemedicine approaches. Conversely, there were more consistent and positive results concerning the satisfaction of patients and clinicians. Conclusions: These results are not sufficient to state a conclusive evaluation of positive effects of telemedicine use for GDM care. A more in-depth investigation of engagement and empowerment dimensions is necessary, as some benefits for the management of chronic conditions were already detected. Further investigations will also be necessary concerning the acceptability and feasibility of telemedicine systems by clinicians.
River Regulation Causes Rapid Changes in Relationships Between Floodplain Oak Growth and Environmental Variables

The radial growth of pedunculate oak (Quercus robur), a species often ecologically dominating European deciduous forests, is closely tied up with local environmental variables. The oak tree-ring series usually contain a climatic and hydrologic signal that allows assessing the main drivers of tree growth in various ecosystems. Understanding the climate-growth relationship patterns in floodplains is important for providing insights into the species persistence and longevity in vulnerable riverine ecosystems experiencing human-induced hydrology alteration. Here, we use 139 years long instrumental records of local temperature, precipitation, and water levels in the Dnipro River in Kyiv to demonstrate that the implementation of river regulation has decoupled the established relationship between the radial growth of floodplain oak and local hydro-climatic conditions. Before the river flow has been altered by engineering modifications of 1965–1977, the water level in the Dnipro River was the key driver of oak radial growth, as reflected in the tree-ring width and earlywood width. The construction of two dams has altered the seasonal distribution of water level diminishing the positive effect of high water on oak growth and subsequently reversing this trend to negative, resulting from a seasonal ground water surplus. The decrease in the correlation between oak growth indices and the river’s water level in April–June was unprecedentedly rapid and clearly distinguishable among other changes in the growth-to-climate relationship. Our findings further demonstrate that trees growing in areas exposed to urban development are the most susceptible to downside effects of river regulation.
Resource Allocation in Green Dense Cellular Networks: Complexity and Algorithms

This paper studies the problem of user association, deadline scheduling and channel allocation in dense cellular networks with energy harvesting base stations. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of single channel and single base station is solved by proposing polynomial-time optimal algorithms. The case of single channel and multiple base stations is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is solved by proposing efficient heuristic algorithms. Our theoretical analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms.
New Constellation Design and Bit Mapping for Dual Mode OFDM-IM

Dual mode orthogonal frequency division multiplexing with index modulation (DM-OFDM-IM) is recently proposed, which modulates all subcarriers to eliminate the limits of spectrum efficiency in OFDM with index modulation (OFDM-IM). In DM-OFDM-IM, the subcarriers within each subblock are divided into two groups, which are modulated by two distinguishable constellation alphabets drawn from the distinct two constellations. In this paper, a new constellation design for DM-OFDM-IM is proposed based on the pairwise error probability (PEP) analysis. In previous works, the design of the constellation pair is naive and induces much power. Also, a new bit mapping scheme for DM-OFDM-IM is proposed. The proposed bit mapping scheme can relieve the catastrophic effect of the index demodulation error incurring burst of errors, which is the endemic problem of the index demodulation systems. The simulation results show that the proposed constellation pair and the proposed bit mapping scheme substantially enhance the BER performance of the DM-OFDM-IM than the conventional DM-OFDM-IM.
Intelligent Traffic Signal Control: Using Reinforcement Learning with Partial Detection

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.
Impact of Physical Exercise on Growth and Progression of Cancer in Rodents—A Systematic Review and Meta-Analysis

Background: Physical exercise is suspected to reduce cancer risk and mortality. So far, little is known about the underlying mechanisms. Although limited, murine models represent a promising attempt in order to gain knowledge in this field. Objective: A systematic review and meta-analysis examining various treatment protocols was conducted in order to determine the impact of exercise on tumor growth in rodents. Methods: PubMed, Google scholar and System for information on Gray literature in Europe were screened from inception to October 2017. Risk of bias within individual studies was assessed using the Office of Health Assessment and Translation risk of bias rating tool for human and animal trials. The effect of exercise on tumor growth over and above non-exercise control was pooled using random-effects model. Subgroup analyses were conducted to identify potential moderators. Results: The quality of the included 17 articles ranged between “probably low” and “high risk of bias. ” A significant reduction in tumor growth in exercising animals compared to controls was detected (Hedges' g = −0.40; 95% CI −0.66 to −0.14, p < 0.01) with between-study heterogeneity (τ2 = 0.217, I2 = 70.28%, p < 0.001). The heterogeneity was partially explained by three moderators representing the in-between group differences of “maximum daily exercise” R2 = 33% (p < 0.01), “type of cancer administration” R2 = 28% (p < 0.05), and “training initiation” R2 = 27% (p < 0.05). Conclusion: This meta-analysis suggests that physical exercise leads to reduction of tumor size in rodents. Since “maximum daily exercise” was found to have at least modest impact on tumor growth, more clinical trials investigating dose-response relationships are needed.
Sound Event Detection Using Graph Laplacian Regularization Based on Event Co-occurrence

The types of sound events that occur in a situation are limited, and some sound events are likely to co-occur; for instance, "dishes" and "glass jingling. " In this paper, we propose a technique of sound event detection utilizing graph Laplacian regularization taking the sound event co-occurrence into account. In the proposed method, sound event occurrences are represented as a graph whose nodes indicate the frequency of event occurrence and whose edges indicate the co-occurrence of sound events. This graph representation is then utilized for sound event modeling, which is optimized under an objective function with a regularization term considering the graph structure. Experimental results obtained using TUT Sound Events 2016 development, 2017 development, and TUT Acoustic Scenes 2016 development indicate that the proposed method improves the detection performance of sound events by 7.9 percentage points compared to that of the conventional CNN-BiGRU-based method in terms of the segment-based F1-score. Moreover, the results show that the proposed method can detect co-occurring sound events more accurately than the conventional method.
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc. However, an important expectation for commercial recommendation systems is to improve the final revenue/profit of the system. Traditional recommendation targets such as rating prediction and top-$k$ recommendation are not directly related to this goal. In this work, we blend the fundamental concepts in online advertising and micro-economics into personalized recommendation for profit maximization. Specifically, we propose value-aware recommendation based on reinforcement learning, which directly optimizes the economic value of candidate items to generate the recommendation list. In particular, we generalize the basic concept of click conversion rate (CVR) in computational advertising into the conversation rate of an arbitrary user action (XVR) in E-commerce, where the user actions can be clicking, adding to cart, adding to wishlist, etc. In this way, each type of user action is mapped to its monetized economic value. Economic values of different user actions are further integrated as the reward of a ranking list, and reinforcement learning is used to optimize the recommendation list for the maximum total value. Experimental results in both offline benchmarks and online commercial systems verified the improved performance of our framework, in terms of both traditional top-$k$ ranking tasks and the economic profits of the system.
Treating Chronic Migraine With Neuromodulation: The Role of Neurophysiological Abnormalities and Maladaptive Plasticity

Chronic migraine (CM) is the most disabling form of migraine, because pharmacological treatments have low efficacy and cumbersome side effects. New evidence has shown that migraine is primarily a disorder of brain plasticity and migraine chronification depends on a maladaptive process favoring the development of a brain state of hyperexcitability. Due to the ability to induce plastic changes in the brain, researchers started to look at Non-Invasive Brain Stimulation (NIBS) as a possible therapeutic option in migraine field. On one side, NIBS techniques induce changes of neural plasticity that outlast the period of the stimulation (a fundamental prerequisite of a prophylactic migraine treatment, concurrently they allow targeting neurophysiological abnormalities that contribute to the transition from episodic to CM. The action may thus influence not only the cortex but also brainstem and diencephalic structures. Plus, NIBS is not burdened by serious medication side effects and drug–drug interactions. Although the majority of the studies reported somewhat beneficial effects in migraine patients, no standard intervention has been defined. This may be due to methodological differences regarding the used techniques (e.g., transcranial magnetic stimulation, transcranial direct current stimulation), the brain regions chosen as targets, and the stimulation types (e.g., the use of inhibitory and excitatory stimulations on the basis of opposite rationales), and an intrinsic variability of stimulation effect. Hence, it is difficult to draw a conclusion on the real effect of neuromodulation in migraine. In this article, we first will review the definition and mechanisms of brain plasticity, some neurophysiological hallmarks of migraine, and migraine chronification-related (dys)plasticity. Secondly, we will review available results from therapeutic and physiological studies using neuromodulation in CM. Lastly we will discuss the results obtained in these preventive trials in the light of a possible effect on brain plasticity.
Lactobacillus plantarum PFM 105 Promotes Intestinal Development Through Modulation of Gut Microbiota in Weaning Piglets

Lactobacillus plantarum is a widespread bacterial species and is commonly used as a probiotic. L. plantarum PFM105 was isolated from the rectum of a healthy sow. Here we found that L. plantarum PFM105 showed probiotic effect on weaning piglets in which intestinal inflammation and unbalanced gut microbiota happened frequently. L. plantarum PFM105 was identified to improve the growth of weaning piglet and promote the development of small intestinal villi. Antibiotics are often used in weaning piglet to prevent intestinal infection and promote the growth of animal. We found that weaning piglets feeding with L. plantarum PFM105 showed similar growth promotion but decreased diarrhea incidence compared with those feeding with antibiotics. High-throughput sequencing was used to analyze the gut microbiota in weaning piglets treated with L. plantarum PFM105 or antibiotics. The relative abundance of beneficial microbes Prevotellaceae and Bifidobacteriaceae were increased in colon of weaning piglet feeding L. plantarum PFM105, while antibiotics increased the relative abundance of bacteria associated with pathogenicity, such as Spirochaeta and Campylobacteraceae. L. plantarum PFM 105 increased indicators of intestinal health including serum levels of IgM, IL-10, and TGF-β, and colonic levels of SCFAs. We found strong correlations between the alterations in gut microbiota composition caused by feeding antibiotics and probiotics and the measured growth and health parameters in weaning piglets. The addition of L. plantarum PFM105 could significantly increase the relative abundance of metabolic genes which may important to intestinal microbiota maturation. Altogether, we demonstrated here that L. plantarum PFM 105 could promote intestinal development through modulation of gut microbiota in weaning piglets.
Shear Stress Ameliorates Superoxide Impairment to Erythrocyte Deformability With Concurrent Nitric Oxide Synthase Activation

The cellular deformability of red blood cells (RBC) is exceptional among mammalian cells and facilitates nutrient delivery throughout the microcirculation; however, this physical property is negatively impacted by oxidative stress. It remains unresolved whether the molecular determinants of cellular deformability – which in the contemporary model of RBC are increasingly recognized – are sensitive to free radicals. Moreover, given cellular deformability has recently been demonstrated to increase following exposure to specific doses of mechanical stimulation, the potential for using shear “conditioning” as a novel method to reverse free-radical induced impairment of cell mechanics is of interest. We thus designed a series of experiments that explored the effects of intracellular superoxide (O2-) generation on the deformability of RBC and also activation of pivotal molecular pathways known to regulate cell mechanics – i.e., PI3K/Akt kinase and RBC nitric oxide synthase (NOS). In addition, RBC exposed to O2- were conditioned with specific shear stresses, prior to evaluation of cellular deformability and activation of PI3K/Akt kinase and RBC-NOS. Intracellular generation of O2- decreased phosphorylation of RBC-NOS at its primary activation site (Ser1177) (p < 0.001), while phosphorylation of Akt kinase at its active residue (Ser473) was also diminished (p < 0.001). Inactivation of these enzymes following O2- exposure occurred in tandem with decreased RBC deformability. Shear conditioning significantly improved cellular deformability, even in RBC previously exposed to O2-. The improvement in cellular deformability may have been the result of enhanced molecular signaling, given RBC-NOS phosphorylation in RBC exposed to O2- was restored following shear conditioning. Impaired RBC deformability induced by intracellular O2- may be due, in part, to impaired activation of PI3K/Akt, and downstream signaling with RBC-NOS. These findings may shed light on improved circulatory health with targeted promotion of blood flow (e.g., exercise training), and may prove fruitful in future development of blood-contacting devices.
NETopathic Inflammation in Chronic Obstructive Pulmonary Disease and Severe Asthma

Neutrophils play a central role in innate immunity, inflammation, and resolution. Unresolving neutrophilia features as a disrupted inflammatory process in the airways of patients with chronic obstructive pulmonary disease (COPD) and severe asthma. The extent to which this may be linked to disease pathobiology remains obscure and could be further confounded by indication of glucocorticoids or concomitant respiratory infections. The formation of neutrophil extracellular traps (NETs) represents a specialized host defense mechanism that entrap and eliminate invading microbes. NETs are web-like scaffolds of extracellular DNA in complex with histones and neutrophil granular proteins, such as myeloperoxidase and neutrophil elastase. Distinct from apoptosis, NET formation is an active form of cell death that could be triggered by various microbial, inflammatory, and endogenous or exogenous stimuli. NETs are reportedly enriched in neutrophil-dominant refractory lung diseases, such as COPD and severe asthma. Evidence for a pathogenic role for respiratory viruses (e.g., Rhinovirus), bacteria (e.g., Staphylococcus aureus) and fungi (e.g., Aspergillus fumigatus) in NET induction is emerging. Dysregulation of this process may exert localized NET burden and contribute to NETopathic lung inflammation. Disentangling the role of NETs in human health and disease offer unique opportunities for therapeutic modulation. The chemokine CXCR2 receptor regulates neutrophil activation and migration, and small molecule CXCR2 antagonists (e.g., AZD5069, danirixin) have been developed to selectively block neutrophilic inflammatory pathways. NET-stabilizing agents using CXCR2 antagonists are being investigated in proof-of-concept studies in patients with COPD to provide mechanistic insights. Clinical validation of this type could lead to novel therapeutics for multiple CXCR2-related NETopathologies. In this Review, we discuss the emerging role of NETs in the clinicopathobiology of COPD and severe asthma and provide an outlook on how novel NET-stabilizing therapies via CXCR2 blockade could be leveraged to disrupt NETopathic inflammation in disease-specific phenotypes.