Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Sector and Multi-Panel Performance in 5G mmWave Cellular Networks

The next generation of cellular networks (5G) will exploit the mmWave spectrum to increase the available capacity. Communication at such high frequencies, however, suffers from high path loss and blockage, therefore directional transmissions using antenna arrays and dense deployments are needed. Thus, when evaluating the performance of mmWave mobile networks, it is necessary to accurately model the complex channel, the directionality of the transmission, but also the interplay that these elements can have with the whole protocol stack, both in the radio access and in the higher layers. In this paper, we improve the channel model abstraction of the mmWave module for ns-3, by introducing the support of a more realistic antenna array model, compliant with 3GPP NR requirements, and of multiple antenna arrays at the base stations and mobile handsets. We then study the end-to-end performance of a mmWave cellular network by varying the channel and antenna array configurations, and show that increasing the number of antenna arrays and, consequently, the number of sectors is beneficial for both throughput and latency.
A Korean Knowledge Extraction System for Enriching a KBox

The increased demand for structured knowledge has created considerable interest in knowledge extraction from natural language sentences. This study presents a new Korean knowledge extraction system and web interface for enriching a KBox knowledge base that expands based on the Korean DBpedia. The aim is to create an endpoint where knowledge can be extracted and added to KBox anytime and anywhere.
Aspect-based summarization of pros and cons in unstructured product reviews

We developed three systems for generating pros and cons summaries of product reviews. Automating this task eases the writing of product reviews, and offers readers quick access to the most important information. We compared SynPat, a system based on syntactic phrases selected on the basis of valence scores, against a neural-network-based system trained to map bag-of-words representations of reviews directly to pros and cons, and the same neural system trained on clusters of word-embedding encodings of similar pros and cons. We evaluated the systems in two ways: first on held-out reviews with gold-standard pros and cons, and second by asking human annotators to rate the systems’ output on relevance and completeness. In the second evaluation, the gold-standard pros and cons were assessed along with the system output. We find that the human-generated summaries are not deemed as significantly more relevant or complete than the SynPat systems; the latter are scored higher than the human-generated summaries on a precision metric. The neural approaches yield a lower performance in the human assessment, and are outperformed by the baseline.
Learning What to Share: Leaky Multi-Task Network for Text Classification

Neural network based multi-task learning has achieved great success on many NLP problems, which focuses on sharing knowledge among tasks by linking some layers to enhance the performance. However, most existing approaches suffer from the interference between tasks because they lack of selection mechanism for feature sharing. In this way, the feature spaces of tasks may be easily contaminated by helpless features borrowed from others, which will confuse the models for making correct prediction. In this paper, we propose a multi-task convolutional neural network with the Leaky Unit, which has memory and forgetting mechanism to filter the feature flows between tasks. Experiments on five different datasets for text classification validate the benefits of our approach.
Atmospheric Pressure Plasma-Mediated Synthesis of Platinum Nanoparticles Stabilized by Poly(vinylpyrrolidone) with Application in Heat Management Systems for Internal Combustion Chambers

Poly(vinylpyrrolidone)-stabilized Pt nanoparticles (PVP-PtNPs) were produced in a continuous-flow reaction-discharge system by application of direct current atmospheric pressure glow discharge (dc-APGD) operated between the surface of a flowing liquid anode (FLA) and a pin-type tungsten cathode. Synthesized PVP-PtNPs exhibited absorption across the entire UV/Vis region. The morphology and elemental composition of PVP-PtNPs were determined with transmission electron microscopy (TEM) and energy dispersive X-ray scattering (EDX), respectively. As assessed by TEM, PVP-PtNPs were approximately spherical in shape, with an average size of 2.9 ± 0.6 nm. EDX proved the presence of Pt, C, and O. Dynamic light scattering (DLS) and attenuated total reflectance Fourier transform-infrared spectroscopy (ATR FT-IR) confirmed PtNPs functionalization with PVP. As determined by DLS, the average size of PtNPs stabilized by PVP was 111.4 ± 22.6 nm. A fluid containing resultant PVP-PtNPs was used as a heat conductive layer for a spiral radiator managing heat generated by a simulated internal combustion chamber. As compared to water, the use of PVP-PtNPs enhanced efficiency of the system, increasing the rate of heat transfer by 80% and 30% during heating and cooling, respectively.
Exercise-induced muscle damage: What is it, what causes it and what are the nutritional solutions?

Abstract Exercise-induced muscle damage (EIMD) is characterized by symptoms that present both immediately and for up to 14 days after the initial exercise bout. The main consequence of EIMD for the athlete is the loss of skeletal muscle function and soreness. As such, numerous nutrients and functional foods have been examined for their potential to ameliorate the effects of EIMD and accelerate recovery, which is the purpose of many nutritional strategies for the athlete. However, the trade-off between recovery and adaptation is rarely considered. For example, many nutritional interventions described in this review target oxidative stress and inflammation, both thought to contribute to EIMD but are also crucial for the recovery and adaptation process. This calls into question whether long term administration of supplements and functional foods used to target EIMD is indeed best practice. This rapidly growing area of sports nutrition will benefit from careful consideration of the potential hormetic effect of long term use of nutritional aids that ameliorate muscle damage. This review provides a concise overview of what EIMD is, its causes and consequences and critically evaluates potential nutritional strategies to ameliorate EIMD. We present a pragmatic practical summary that can be adopted by practitioners and direct future research, with the purpose of pushing the field to better consider the fine balance between recovery and adaptation and the potential that nutritional interventions have in modulating this balance.
Frame-validity games and lower bounds on the complexity of modal axioms

We introduce frame-equivalence games tailored for reasoning about the size, modal depth, number of occurrences of symbols and number of different propositional variables of modal formulae defining a given frame property. Using these games, we prove lower bounds on the above measures for a number of well-known modal axioms; what is more, for some of the axioms, we show that they are optimal among the formulae defining the respective class of frames.
Direct Medical Cost of Hospitalization for Acute Stroke in Lebanon: A Prospective Incidence-Based Multicenter Cost-of-Illness Study

Stroke is a major social and health problem posing heavy burden on national economies. We provided detailed financial data on the direct in-hospital cost of acute stroke care in Lebanon and evaluated its drivers. This was an observational, quantitative, prospective, multicenter, incidence-based, bottom-up cost-of-illness study. Medical and billing records of stroke patients admitted to 8 hospitals in Beirut over 1 year were analyzed. Direct medical costs were calculated, and cost drivers were assessed using a multivariable linear regression analysis. In total, 203 stroke patients were included (male: 58%; mean age: 68.8 ± 12.9 years). The direct in-hospital cost for all cases was US$1 413 069 for 2626 days (US$538 per in-hospital day). The average in-hospital cost per stroke patient was US$6961 ± 15 663. Hemorrhagic strokes were the most costly, transient ischemic attack being the least costly. Cost drivers were hospital length of stay, intensive care unit length of stay, type of stroke, stroke severity, modified Rankin Scale, third party payer, surgery, and infectious complications. Direct medical cost of acute stroke care represents high financial burden to Lebanese health system. Development of targeted public health policies and primary prevention activities need to take priority to minimize stroke admission in future and to contain this cost.
Motion Investigation of a Snake Robot with Different Scale Geometry and Coefficient of Friction

Most snakes in nature have scales at their ventral sides. The anisotropic frictional coefficient of the ventral side of the snakes, as well as snake robots, is considered to be responsible for their serpentine kind of locomotion. However, little work has been done on snake scales so far to make any guidelines for designing snake robots. This paper presents an experimental investigation on the effects of artificial scale geometry on the motion of snake robots that move in a serpentine manner. The motion of a snake robot equipped with artificial scales with different geometries was recorded using a Kinect camera under different speeds of the actuating motors attached to the links of the robot. The results of the investigation showed that the portion of the scales along the central line of the robot did not contributed to the locomotion of the robot, rather, it is the parts of the scales along the lateral edges of the robot that contributed to the motion. It was also found that the lower frictional ratio at low slithering speeds made the snake robot motion unpredictable. The scales with ridges along the direction of the snake body gave better and more stable motion. However, to get the peg effect, the scales needed to have a very high lateral to forward friction ratio, otherwise, significant side slipping occurred, resulting in unpredictable motion.
Latest advances in diagnosing and predicting DILI: what was new in 2017?

ABSTRACT Introduction: Drug-induced liver injury (DILI) remains an increasingly recognized cause of hepatotoxicity and liver failure worldwide. In 2017, we continued to learn about predicting, diagnosing, and prognosticating drug hepatotoxicity. Areas covered: In this review, we selected from over 1200 articles from 2017 to synopsize updates in DILI. There were new HLA haplotypes associated with medications including HLA-C0401 and HLA-B*14. There has been continued work with quantitative systems pharmacology, particularly with the DILIsym® initiative, which employs mathematical representations of DILI mechanisms to predict hepatotoxicity in simulated populations. Additionally, knowledge regarding microRNAs (miRNAs) continues to expand. Some new miRNAs this past year include miRNA-223 and miRNA-605. Aside from miRNAs, other biomarkers for diagnosis, prognosis, and even prediction of DILI were explored. Studies on K18, OPN, and MCSFR have correlated DILI and liver-associated death within 6 months. Conversely, a new prognostic panel using apolipoportein-A1 and haptoglobin has been proposed to predict recovery. Further study of CDH5 has also provided researchers a possible new biomarker for prediction and susceptibility to DILI. Expert commentary: Although research on DILI remains quite promising, there is yet to be a reliable, simple method to predict, diagnose, and risk assess this form of hepatotoxicity.
Generalization of Equilibrium Propagation to Vector Field Dynamics

The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections. We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues. In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism. Our learning method generalizes Equilibrium Propagation to vector field dynamics, relaxing the requirement of an energy function. As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function, but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights. We show experimentally that our algorithm optimizes the objective function.
Privacy policy disclosures of behavioural tracking on consumer health Websites

Many Internet users are seeking health information online, encountering significant privacy risks in the process. Historically, these risks are associated with personally identifiable information, but behavioural tracking presents a new and increasing threat to privacy. In this paper, we analyze the disclosure, in a set of website privacy policies, of the collection of non-personally identifiable information by consumer health information websites. The websites all engage in first and third party behavioural tracking using cookies and web beacons, and are among the sites recommended by consumer health sections of the Medical Library Association or the Canadian Health Libraries Association (see Burkell and Fortier, 2012, 2013). Our analysis reveals that while the majority of these sites disclose both first party (6/7) and third party (5/7) behavioural tracking, the language used in these disclosures is difficult to understand, tending to minimize behavioural tracking and obfuscate agency in the tracking process. These results suggest that consumer health information website privacy policies do not provide optimal disclosure of behavioural tracking practices. Library and information science professionals should work with users to ensure they are aware of the behavioural tracking practices of the websites they visit, assisting them to interpret the disclosures provided in website privacy policies.
Information horizons of college students: Source preferences and source referrals in academic contexts

An information horizon (IH) is a mental map where individuals position various sources according to their preferences in specific contexts. IH not only highlights the roles of contexts, situations, and social networks in individuals' information behavior, but also emphasizes the importance of examining the relationships among sources used by individuals. This study investigates undergraduate students' source use preferences in academic contexts---course-related, program-related, and coursework-related moral-support situations. The study also investigates the referral networks among sources used by students. Interviews were conducted with twenty undergraduate students. Each participant was also asked to draw their IH maps. Based on existent IH literature, the study used innovative methods to analyze and present the data. Results show that students had different source preferences in different situations in academic contexts. While peers were consulted across all situations, most of the other sources were typically consulted only in certain situations. As to source referrals, interpersonal sources (e.g., peers) were more likely to direct students to other sources than to receive referrals from other sources, and experts tended to direct students to a wide range of sources, especially to official sources. The findings suggest that universities should facilitate students' use of both official sources and experts as academic sources.
A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages

This paper presents a flexible and open source framework for deep semantic role labeling. We aim at facilitating easy exploration of model structures for multiple languages with different characteristics. It provides flexibility in its model construction in terms of word representation, sequence representation, output modeling, and inference styles and comes with clear output visualization. The framework is available under the Apache 2.0 license.
Temporal Phase Shifts in SCADA Networks

In Industrial Control Systems (ICS/SCADA), machine to machine data traffic is highly periodic. Previous work showed that in many cases, it is possible to create an automata-based model of the traffic between each individual Programmable Logic Controller (PLC) and the SCADA server, and to use the model to detect anomalies in the traffic. When testing the validity of previous models, we noticed that overall, the models have difficulty in dealing with communication patterns that change over time. In this paper we show that in many cases the traffic exhibits phases in time, where each phase has a unique pattern, and the transition between the different phases is rather sharp. We suggest a method to automatically detect traffic phase shifts, and a new anomaly detection model that incorporates multiple phases of the traffic. Furthermore we present a new sampling mechanism for training set assembly, which enables the model to learn all phases during the training stage with lower complexity. The model presented has similar accuracy and much less permissiveness compared to the previous general Deterministic Finite Automata (DFA) model. Moreover, the model can provide the operator with information about the state of the controlled process at any given time, as seen in the traffic phases.
Lexicon Adaptation for Spanish Emotion Mining

Emotion mining is an emerging task that is still at a first stage of research. Most of the existing works and resources focus on English, but there are other languages, such as Spanish, whose presence on the Internet is greater every day. In WASSA-2017 Shared Task on Emotion Intensity, it was found that the best systems included features from affect lexicons. This fact combined with the scarcity of resources in Spanish, led us to build a new Spanish lexicon that has been tested over the dataset released at SemEval 2018 Task 1. Moreover, it has been compared with the unique emotion intensity lexicon existing in Spanish, SEL lexicon, and it has shown the difficulty of the task and the importance of continuing working on the development of resources.
Bisphenol A vascular toxicity: Protective effect of Vitis vinifera (grape) seed extract and resveratrol

The underlying mechanism of Bisphenol A (BPA)‐induced vascular toxicity and the protective role of grape seed extract (GSE) and resveratrol were investigated in vitro and in vivo. Human umbilical vein endothelial cells (HUVECs) were exposed to different concentrations of GSE and resveratrol. Then, BPA was added to the cells and cell viability and effects on the protein level of cell adhesion molecules were measured through MTT and western blotting. Animals were randomly divided into control, GSE (3 and 12 mg·kg−1·day−1 ip), resveratrol (100 mg·kg−1·day−1 ip), BPA (35 mg·kg−1·day−1, gavage), BPA plus GSE (3, 6, and 12 mg·kg−1·day−1 ip), BPA plus resveratrol (25, 50, and 100 mg·kg−1·day−1 ip), and BPA plus vitamin E (200 IU/kg per every other day ip). After 2 months, contractile and relaxant responses were evaluated on the isolated aorta. BPA increased the level of aorta malondialdehyde (p < 0.001) and decreased vascular responses to KCl (p < 0.01), phenylephrine (p < 0.001), and acetylcholine (p < 0.01). In HUVECs, BPA (IC50: 220 μM) increased protein level of vascular cell adhesion molecule (p < 0.05) and cleaved capase3 (p < 0.001). GSE, resveratrol, and vitamin E cotreatment restored toxic effects of BPA in some levels. BPA vascular toxicity was attributed to lipid peroxidation and endothelial dysfunction. The protective role of GSE and resveratrol against BPA‐endothelial dysfunction could be attributed to their potent antioxidant properties.
A Spectrum Sharing Solution for the Efficient Use of mmWave Bands in 5G Cellular Scenarios

Regulators all around the world have started identifying the portions of the spectrum that will be used for the next generation of cellular networks. A band in the mmWave spectrum will be exploited to increase the available capacity. In response to the very high expected traffic demand, a sharing mechanism may make it possible to use the spectrum more efficiently. In this work, moving within the European and Italian regulatory conditions, we propose the use of Licensed Spectrum Access (LSA) to coordinate sharing among cellular operators. Additionally, we show some preliminary results on our research activities which are focused on a dynamic spectrum sharing approach applied in simulated 5G cellular scenarios.
To Lead or to Be Liked: When Prestige-Oriented Leaders Prioritize Popularity Over Performance

Leaders often are faced with making difficult decisions for their group, such as when a course of action preferred by group members conflicts with one that is likely to optimize group success. Across 5 experiments (N = 1110), we provide evidence that a psychological orientation toward prestige (but not dominance) causes leaders to adhere publicly to group members’ desires at the expense of group task outcomes—to prioritize popularity over performance. Experiments 1–3 demonstrated that, in private, prestige-oriented leaders chose what they saw as best for group performance but that, in public, they chose whichever option was preferred by members of their group. In private, prestige-oriented leaders’ tendency to choose the performance-enhancing option was mediated by group performance motives; in public, their adherence to group preferences was mediated by social approval motives. Experiments 4 and 5 advanced the investigation by using experimental manipulations to prime an orientation toward prestige. Findings replicated those from the earlier studies: participants primed with a prestige orientation prioritized popularity over performance. Results illuminate the conditions under which “good” leaders might make poor decisions.
Successful Treatment of Massive Pulmonary Thromboembolism with Reteplase: Case Series

Massive pulmonary thromboembolism (PTE) has an increased risk of mortality. Thrombolytic therapy is the accepted treatment. Reteplase, a variant of tissue plasminogen activator, has been used in acute myocardial infarction with acceptable safety and efficacy, but studies in massive PTE are rare. In this study we report five cases of successful treatment of massive PTE with reteplase.
Multi-User Communication Networks: A Coordinated Multi-Armed Bandit Approach

Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other users while keeping coordination overhead to a minimum. The proposed solution combines Multi-Armed Bandit learning with a lightweight signalling-based coordination scheme, and ensures convergence to a stable allocation of resources. Our work considers single-user level algorithms for two scenarios: an unknown fixed number of users, and a dynamic number of users. Analytic performance guarantees, proving convergence to stable marriage configurations, are presented for both setups. The algorithms are designed based on a system-wide perspective, rather than focusing on single user welfare. Thus, maximal resource utilization is ensured. An extensive experimental analysis covers convergence to a stable configuration as well as reward maximization. Experiments are carried out over a wide range of setups, demonstrating the advantages of our approach over existing state-of-the-art methods.