Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Interplay Between Communication and Computation in Green C-RAN With Limited Fronthaul and Computation Capacity

Supporting cooperative radio among remote radio heads (RRHs) and elastic cloud service in the baseband unit (BBU) pool, cloud radio access network (C-RAN) is perceived as a promising solution for the next mobile network. In C-RAN, cooperative radio can enhance power saving at RRHs, along with impact on computation effort and power saving in the BBU pool. In turn, power saving at RRHs, benefited from cooperative radio is restricted by the constrained computation capacity provisioned by processors in the BBU pool. Besides, limited fronthauls, which support baseband signal transfer between the BBU pool and RRHs, affect the cooperative radio design and power consumption at RRH as well. By jointly optimizing transmit beamforming among RRHs and processor sleeping in the BBU pool, we exploit such interplay between communication and computation for system power minimization in C-RAN with limited fronthaul and computation capacity. Specifically, we formulate this problem as a mixed-integer non-linear programming problem (MINLP), and then leverage the special structure of the MINLP to make a near optimal decision on the set of active processors and transmit beamforming vectors with high efficiency. Finally, extensive numerical results demonstrate that our proposed algorithms can enforce processor sleeping and reduce system power consumption significantly.
A General 3-D Non-Stationary 5G Wireless Channel Model

A novel unified framework of geometry-based stochastic models for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output, high-speed train, vehicle-to-vehicle, and millimeter wave communications. It is a 3-D non-stationary channel model based on the WINNER II and Saleh-Valenzuela channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.
WordNet-Based Information Retrieval Using Common Hypernyms and Combined Features

Text search based on lexical matching of keywords is not satisfactory due to polysemous and synonymous words. Semantic search that exploits word meanings, in general, improves search performance. In this paper, we survey WordNet-based information retrieval systems, which employ a word sense disambiguation method to process queries and documents. The problem is that in many cases a word has more than one possible direct sense, and picking only one of them may give a wrong sense for the word. Moreover, the previous systems use only word forms to represent word senses and their hypernyms. We propose a novel approach that uses the most specific common hypernym of the remaining undisambiguated multi-senses of a word, as well as combined WordNet features to represent word meanings. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 17.7% better than the lexical search, and at least 9.4% better than all surveyed search systems using WordNet. Keywords Ontology, word sense disambiguation, semantic annotation, semantic search.
Definiteness and Maximality in French Language Acquisition, More Adult-Like Than You Would Expect

This study considers the mastery of maximality, or domain restrictions, in a group of 47 children acquiring French (aged 4;06 to 8;09), as well as a control group of young adults. Definite (le ‘the’) and indefinite (un ‘a/one’) singular and plural (des ‘some’, les ‘the’) as well as explicitly maximal contexts (tous les ‘all the’) were provided to participants. Animals were arranged in groups of three. Participants were asked to select one or more animals from these groups and give them to the experimenter (similar to Munn et al., 2006). Following Munn, we expected children to make maximality errors on the singular definite items. However, we did not observe this pattern. On the contrary we observed more errors on plurals generally. Further, the developmental patterns show that participants become less maximal in their responses to indefinite plurals (an adult-like pattern, also found in Caponigro et al., 2012) with no important changes on definite types: no strong age effects are observed on maximality patterns. These point to the importance of cross-linguistic data for the understanding of child language acquisition and error patterns for psycholinguistic theory.
Lightweight verification of array indexing

In languages like C, out-of-bounds array accesses lead to security vulnerabilities and crashes. Even in managed languages like Java, which check array bounds at run time, out-of-bounds accesses cause exceptions that terminate the program. We present a lightweight type system that certifies, at compile time, that array accesses in the program are in-bounds. The type system consists of several cooperating hierarchies of dependent types, specialized to the domain of array bounds-checking. Programmers write type annotations at procedure boundaries, allowing modular verification at a cost that scales linearly with program size. We implemented our type system for Java in a tool called the Index Checker. We evaluated the Index Checker on over 100,000 lines of open-source code and discovered array access errors even in well-tested, industrial projects such as Google Guava.
Personalized Health Monitoring System of Elderly Wellness at the Community Level in Hong Kong

Rapid advances in information and sensor technology have led to the development of tools and methods for personalized health monitoring. These techniques support timely and efficient healthcare services by tracking the vital signs, detecting physiological changes and predicting health risks. In this paper, we propose an integrated system to monitor the wellness condition of elderly. This system is conceptualized to provide a computer-aided decision support for clinicians and community nurses, by means of which they can easily monitor and analyze an elderly’s overall activity and vital signs using a wearable wellness tracker and an all-in-one satiation-based monitoring device, offering an efficient solution with a reduction in time cost and human error. We design a data-preparing scheme for acquiring data and processing data from multiple monitoring devices, and propose a personalized scheme for forecasting the elderly’s one-day-ahead wellness condition via data integration and statistical learning. We conduct a pilot study at a nursing home in Hong Kong to demonstrate the implementation of the proposed system. The proposed forecasting scheme is validated by the collected data.
Metabolomic analysis of serum from rats following long-term intake of Chinese sausage

Introduction Owing to the contamination of chemical pollutants, especially nitrosamines and their precursors, in Chinese sausage, long-term intake of Chinese sausage may have potential health effects. Objection This study investigated the effects of long-term intake of Chinese sausage with different contaminations of N-nitrosodimethylamine (NDMA) on rat liver and the potential biomarkers in the serum. Methods Serum metabolomic analysis was performed by gas chromatography–mass spectrometry at weeks 7, 17, 25, and 33; simultaneously, liver histopathological examination was conducted and its relationship with the serum metabolomics was also investigated. Results In the study, long-term intake of Chinese sausage with different NDMA contents induced significant changes in serum metabolites and liver histopathology in rats. Metabonomic analysis showed that seven metabolites – β-alanine, 3-aminoisobutyric acid, aminooxyacetic acid, D-alanyl-D-alanine, pelargonic acid, palmitic acid (PA), and linoleic acid (LA) – in three sausage diet groups were significantly decreased at four time points, where three other metabolites were notably increased, which included putrescine, ethanolamine phosphate, and taurine. Among the various treatments, the NDMA (sausage-free) group demonstrated the most remarkable changes. Phenylalanine was decreased followed by an increase, and tyrosine persistently declined, both of which were elevated in the NDMA group. In addition, the histopathological result was consistent with that of the serum metabolomic analysis, and the changes in serum metabolites in each sausage diet group and the NDMA group were consistently associated with disorders of lipids, amino acid, and energy metabolism. Conclusion This work indicates that excessive NDMA content in sausage may cause liver damage.
Optimizing Dynamic Multi-Agent Performance in E-Learning Environment

The main objective of e-learning systems is to improve the student learning performance and satisfaction. This can be achieved by providing a personalized learning experience that identifies and satisfies the individual learner’s requirements and abilities. The performance of the e-learning systems can be significantly improved by exploiting dynamic self-learning capabilities that rapidly adapts to prior user interactions within the system and the continuous changes in the environment. In this paper, a dynamic multi-agent system using particle swarm optimization for the e-learning systems is proposed. The system incorporates five agents that take into consideration the variations in the capabilities among the different users. First, the project clustering agent is used to cluster a set of learning resources/projects into similar groups. Second, the student clustering agent (SCA) groups students according to their preferences and abilities. Third, the student-project matching agent is used to map each learner’s group to a suitable project or particular learning resources according to specific design criteria. Fourth, the student-student matching agent is designed to perform the efficient mapping between different students. Finally, the dynamic SCA (DSCA) is employed to continuously track and analyze the student’s behavior within the system such as changes in knowledge and skill levels. Consequently, the DSCA adapts the e-learning environments to accommodate these variations. Experimental results demonstrate the effectiveness of the proposed system in providing near-optimal solutions in considerably less computational time.
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags

Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator’s performance is shown to be competitive.
Sparse Channel Estimation for MIMO-OFDM Systems in High-Mobility Situations

A new approach of channel estimation for multiantenna systems is put forward in this paper, which can be adopted in high-mobility situations such as high speed trains. The channel impulse response is abstracted as three domains to improve the modeling accuracy. Both the time-domain preamble and the frequency-domain pilot are adopted in the orthogonal frequency division multiplexing (OFDM) frame. First, the training in time domain is exploited to obtain the partial common support of the channel. Then, the pilot location optimized by the genetic algorithm is employed to build the framework of structured compressive sensing and recover the channel. A novel compressive recovery algorithm called adaptive support-aware block orthogonal matching pursuit for multiple input multiple output-OFDM systems is proposed to solve the problem. It is manifested in the simulation that the scheme in this paper outperforms the traditional ones in both recovery probability, mean square error, and bit error rate over the doubly selective channel with low computational complexity.
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone

We present the design of a competitive artificial intelligence for Scopone, a popular Italian card game. We compare rule-based players using the most established strategies (one for beginners and two for advanced players) against players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS) with different reward functions and simulation strategies. MCTS requires complete information about the game state and thus implements a cheating player, whereas ISMCTS can deal with incomplete information and thus implements a fair player. Our results show that, as expected, the cheating MCTS outperforms all the other strategies; ISMCTS is stronger than all the rule-based players implementing well-known and most advanced strategies and it also turns out to be a challenging opponent for human players.
Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation

The rapid growth of structural information for G-protein-coupled receptors (GPCRs) has led to a greater understanding of their structure, function, selectivity, and ligand binding. Although novel ligands have been identified using methods such as virtual screening, computationally driven lead optimization has been possible only in isolated cases because of challenges associated with predicting binding free energies for related compounds. Here, we provide a systematic characterization of the performance of free-energy perturbation (FEP) calculations to predict relative binding free energies of congeneric ligands binding to GPCR targets using a consistent protocol and no adjustable parameters. Using the FEP+ package, first we validated the protocol, which includes a full lipid bilayer and explicit solvent, by predicting the binding affinity for a total of 45 different ligands across four different GPCRs (adenosine A2AAR, β1 adrenergic, CXCR4 chemokine, and δ opioid receptors). Comparison with experimental binding affinity measurements revealed a highly predictive ranking correlation (average spearman ρ = 0.55) and low root-mean-square error (0.80 kcal/mol). Next, we applied FEP+ in a prospective project, where we predicted the affinity of novel, potent adenosine A2A receptor (A2AR) antagonists. Four novel compounds were synthesized and tested in a radioligand displacement assay, yielding affinity values in the nanomolar range. The affinity of two out of the four novel ligands (plus three previously reported compounds) was correctly predicted (within 1 kcal/mol), including one compound with approximately a tenfold increase in affinity compared to the starting compound. Detailed analyses of the simulations underlying the predictions provided insights into the structural basis for the two cases where the affinity was overpredicted. Taken together, these results establish a protocol for systematically applying FEP+ to GPCRs and provide guidelines for identifying potent molecules in drug discovery lead optimization projects.
Postexercise hypotension and autonomic modulation response after full versus split body resistance exercise in trained men

To investigate the effects of full versus split body resistance exercise on postexercise hypotension and autonomic modulation in trained men. Sixteen resistance recreationally trained males (age, 24.9±5.3 years) performed three randomized trials: upper body (UB), lower body (LB), and full body (FB) conditions. Blood pressure and heart rate variability were collected at rest, immediately postexercise, post-10, and post-30 min during recovery. For systolic blood pressure, delta (30 min minus rest) was lower for the FB condition compared to the UB (−10.1±7.4 mmHg [FB] vs. −3.3±12.6 mmHg [LB] vs. −1.9±8.1 mmHg [UB], P=0.004). For diastolic blood pressure (−8.2±10.9 mmHg [FB] vs. −1.5±9.8 mmHg [LB] vs. −8.7±11.4 mmHg [UB], P=0.038) and mean blood pressure delta during recovery (−11.7±14 mmHg [FB] vs. −2.2±10.6 mmHg [LB] vs. −5.2±6.8 mmHg [UB], P=0.045), there were statistically significant lower values in the FB condition in relation to the LB condition. Regarding heart rate variability, there were no significant differences between conditions, however, the square root of the mean squared difference between adjacent RR intervals presented an increase until post-30 during recovery (P<0.001), and there was a higher values of low frequency and lower values of high frequency across time (P<0.001). Postexercise hypotension was influenced by the amount of muscle mass involved in a bout of resistance exercise, with the FB condition inducing lower systolic blood pressure in relation to the UB after exercise, as well as a great decrease in postexercise diastolic and mean blood pressure compared with the LB. The autonomic modulation response was similar between conditions during recovery.
CiD: automating the detection of API-related compatibility issues in Android apps

The Android Application Programming Interface provides the necessary building blocks for app developers to harness the functionalities of the Android devices, including for interacting with services and accessing hardware. This API thus evolves rapidly to meet new requirements for security, performance and advanced features, creating a race for developers to update apps. Unfortunately, given the extent of the API and the lack of automated alerts on important changes, Android apps are suffered from API-related compatibility issues. These issues can manifest themselves as runtime crashes creating a poor user experience. We propose in this paper an automated approach named CiD for systematically modelling the lifecycle of the Android APIs and analysing app bytecode to flag usages that can lead to potential compatibility issues. We demonstrate the usefulness of CiD by helping developers repair their apps, and we validate that our tool outperforms the state-of-the-art on benchmark apps that take into account several challenges for automatic detection.
Inflammasome Priming Mediated via Toll-Like Receptors 2 and 4, Induces Th1-Like Regulatory T Cells in De Novo Autoimmune Hepatitis

De novo autoimmune hepatitis (DAIH) is an important cause of late allograft dysfunction following liver transplantation, but its cause and underlying pathogenesis remains unclear. We sought to identify specific innate and adaptive immune mechanisms driving the pro-inflammatory cytokine secreting regulatory T cell (Treg) phenotype in DAIH and determine if modulation of these pathways could resolve the inflammatory milieu observed in the livers of patients with DAIH. Here, we demonstrate toll-like receptors (TLRs) 2- and 4-mediated inflammasome activation in CD14++ monocytes, a finding that is key to maintaining dysfunctional Tregs in patients with DAIH. Furthermore, silencing of TLR 2 and 4 in CD14++ monocytes prevented activation of the inflammasome and significantly decreased IFN-γ production by FOXP3+ Tregs. We also observed significantly increase in expression of tumor necrosis factor α-induced protein 3 (TNFAIP3), a negative regulator of the NLRP3 Inflammasome, in monocytes/macrophages of liver transplant subjects who have normal allograft function and do not have DAIH. TNFAIP3 expression was virtually absent in monocytes/macrophages of patients with DAIH. Our findings suggest that autoimmunity in DAIH is promoted by CD14++ monocytes predominantly through activation of inflammatory signaling pathways.
Glycoprotein Ibα Kozak polymorphism in patients presenting with early-onset acute coronary syndrome

Introduction Glycoprotein Ibα (GPIbα) receptor is the chief molecule responsible for initial platelet adhesion to the subendothelium. A thymidine to cytosine single nucleotide substitution at position –5 from the ATG start codon characterizes the Kozak sequence polymorphism. The Kozak sequence polymorphism may increase the surface expression of GPIbα and contribute to thrombogenesis. We evaluated the allele frequencies of GPIbα Kozak sequence polymorphism in the Turkish population and examined the relationship between GPIbα Kozak sequence polymorphism and early-onset acute coronary syndrome (ACS). Material and methods This study enrolled 200 patients (122 male, 78 female, mean age: 39 ±5 years) and 200 healthy control subjects (110 male, 90 female, 41 ±4 years). The patient group was composed of patients admitted to our coronary care unit with early-onset ACS and patients who attended to our cardiology outpatient clinic after hospital discharge with a diagnosis of early-onset ACS. Results Kozak polymorphism frequencies in patients and control subjects did not differ significantly (23% versus 22.5%, p = 0.812, respectively). In patients who presented with non-ST elevation myocardial infarction (NSTEMI), the frequency of GPIbα Kozak polymorphism was borderline significantly higher when compared with patients who presented with ST elevation myocardial infarction (STEMI) (35% vs. 20%, p = 0.05, respectively). Allele frequencies of T and C were calculated to be 0.873 and 0.128. Conclusions Although the frequency of GPIbα Kozak polymorphism did not differ significantly in early-onset ACS patients versus control subjects, Kozak polymorphism frequency was borderline significantly higher in patients who presented with NSTEMI when compared to patients with STEMI.
The Combination Of Weak Expression Of PRDX4 And Very High MIB-1 Labelling Index Independently Predicts Shorter Disease-free Survival In Stage I Lung Adenocarcinoma

Background:Oxidative stress plays pivotal roles in the progression of lung adenocarcinoma (LUAD) through cell signaling related closely to cancer growth. We previously reported that peroxiredoxin 4 (PRDX4), a secretory-type antioxidant enzyme, can protect against the development of various diseases, including potential malignancies. Since many patients with early-stage LUAD develop recurrence, even after curative complete resection, we investigated the association of the PRDX4 expression with the clinicopathological features and recurrence/prognosis using post-surgical samples of stage I-LUAD. Methods: The expression of PRDX4 and MIB-1, a widely accepted Ki67 protein, was immunohistochemically analysed in 206 paraffin-embedded tumour specimens of patients with stage I-LUAD. The PRDX4 expression was considered to be weak when less than 25% of the adenocarcinoma cells showed positive staining. Results: A weak PRDX4+ expression demonstrated a significantly close relationship with pathologically poor differentiation, highly invasive characteristics and recurrence. The decrease in PRDX4-positivity potentially induced cell growth in LUAD, which was correlated significantly with a very high MIB-1 labelling index (≥17.3%). Univariate/multivariate analyses revealed that the subjects with both weak PRDX4+ expression and a very high MIB-1 index had significantly worse disease-free survival rates than other subjects. Conclusions: The combination of weak PRDX4 expression and a very high MIB-1 index can predict high proliferating activity and recurrence with a potential poor prognosis, especially in post-operative stage I-LUAD patients.