Miracle-dz/UniFAR-FTUs huggingface dataset:
Hesperidin Induces ROS-Mediated Apoptosis along with Cell Cycle Arrest at G2/M Phase in Human Gall Bladder Carcinoma

Abstract A natural predominant flavonoid hesperidin rich in citrus fruits exhibits multifunctional medicinal properties. The anticancerous potential of hesperidin has been widely explored; however, the gall bladder carcinoma (GBC) still remains untouched due to the unavailability of efficient experimental model. The aim of our study was to identify the apoptotic and antiproliferative potential of hesperidin in GBC. The promising efficacy of hesperidin was assessed through the generation of reactive oxygen species (ROS), cellular apoptosis, and loss of mitochondrial membrane potential (MMP) in the primary cells generated from surgically removed cancerous gall bladder tissues. Moreover, cell cycle analysis and caspases-3 activity were performed to confirm the apoptosis inducing potential of hesperidin. Results revealed that hesperidin exposure for 24 h at a dose of 200 µM reduced the cell proliferation of GBC cells significantly. In addition, hesperidin treatment further resulted in an increased ROS generation and nuclear condensation at the same dose. Caspase-3 activation and cell cycle arrest at G2/M phase were also accelerated in a dose-dependent manner. Together, these results suggest that hesperidin can be considered as a potential anticancerous compound for the treatment of GBC. Furthermore, evaluation of the pharmacological aspects of hesperidin is desirable for drug development.
Soccer object motion recognition based on 3D convolutional neural networks

—Due to the development of video understanding and big data analysis research ﬁeld using deep learning technique, intelligent machines have replaced the tasks that people performed in the past in various ﬁelds such as trafﬁc, surveillance, and security area. In the sports ﬁeld, especially in soccer games, it is also attempting quantitative analysis of players and games through deep learning or big data analysis technique. However, because of the nature of soccer analysis, it is still difﬁcult to make sophisticated automatic analysis due to technical limitations. In this paper, we propose a deep learning based motion recognition technique which is the basis of high level automatic soccer analysis. For sophisticated motion recognition, we maximize recognition accuracy by sequentially processing the data in three steps: data acquisition, data augmentation, and 3D CNN based motion classiﬁer learning. As can be seen from the experimental results, the proposed method guarantees real-time speed performance and satisfactory accuracy performance.
EAQR: A Multiagent Q-Learning Algorithm for Coordination of Multiple Agents

We propose a cooperative multiagent Q-learning algorithm called exploring actions according to Q-value ratios (EAQR). Our aim is to design a multiagent reinforcement learning algorithm for cooperative tasks where multiple agents need to coordinate their behavior to achieve the best system performance. In EAQR, Q-value represents the probability of getting the maximal reward, while each action is selected according to the ratio of its Q-value to the sum of all actions’ Q-value and the exploration rate ε. Seven cooperative repeated games are used as cases to study the dynamics of EAQR. Theoretical analyses show that in some cases the optimal joint strategies correspond to the stable critical points of EAQR. Moreover, comparison experiments on stochastic games with finite steps are conducted. One is the box-pushing, and the other is the distributed sensor network problem. Experimental results show that EAQR outperforms the other algorithms in the box-pushing problem and achieves the theoretical optimal performance in the distributed sensor network problem.
News Articles Similarity for Automatic Media Bias Detection in Polish News Portals

Digital media have enormous impact on the public opinion. In the ideal world the news in public media should be presented in a fair and impartial way. In practice the information presented in digital media is often biased and may distort the opinion on a given entity/event or concept. It is important to work on tools that could support the detection and analysis of the information bias. One of the first steps is to study the methods of automatic detection of the articles reporting on the same topic, event or entity to further use them in comparative analysis or building a test or training set. In this paper we report on the experimental results concerning the problem of automatic detection of articles reporting on the same events or entities. We also report some experiments on detecting the source of information based on the content.
A low complexity LDPC-BCH concatenated decoder for NAND flash memory

: Low-density parity-check (LDPC) codes are widely used in NAND ﬂash memory as an advanced error correction method due to their excellent correcting capability. The major challenge is the error ﬂoor problem. Dispersed array LDPC (DA-LDPC) code is highly structured and provides implementation convenience due to its regularity. In this paper, it is shown that the constructed (18289 , 16384) DA-LDPC code suﬀers the error ﬂoor at BER of 10 − 9 , which is far from the demand of ﬂash memory error control. Carefully observing the error patterns in the error ﬂoor region, we propose a concatenation of BCH code to alleviate this issue. The error ﬂoor has been successfully brought down to BER of 10 − 14 by concatenating a BCH code with correcting capability of 14 bits. Compared to the standalone LDPC decoder, the concatenated decoder only consumes 7% extra hardware and the code rate penalty is less than 1%. Meanwhile, hardware implementation has shown that the throughput can achieve 3 . 52 Gbps with 6 iterations under a clock frequency of 200 MHz.
Applying artificial intelligence and machine learning techniques to create varying play style in artificial game opponents

Artificial Intelligence is quickly becoming an integral part of the modern world, employed in almost every modern industry we interact with. Whether it be self-drive cars, integration with our web clients or the creation of actual intelligent companions such as Xiaoice1, artificial intelligence is now an integrated and critical part of our daily existence. The application of artificial intelligence to games has been explored for several decades, with many agents now competing at a high level in strategic games which prove challenging for human players (e.g. Go and Chess). With artificial intelligence now able to produce strong opponents for many games, we are more concerned with the style of play of artificial agents, rather than simply their strength. Our work here focusses on the modification of artificial game opponents to create varied playstyle in complex games. We explore several techniques of modifying Monte Carlo Tree Search in an attempt to create different styles of play, thus changing the experience for human opponents playing against them. We also explore improving artificial agent strength, both by investigating parallelization of MCTS and by using Association Rule Mining to predict opponent’s choices, thus improving our ability to play well against them.
Neural-network-based Impulse Noise Removal Using Group-based Weighted Couple Sparse Representation

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.
First molecular characterization of related cases of healthcare-associated infections involving multidrug-resistant Enterococcus faecium vanA in Algeria

Purpose Vancomycin-resistant Enterococcus (VRE) faecium (VREfm) are highly resistant bacteria emerging worldwide and rarely studied using molecular tools in Algeria since their first report in 2006. The aim of the study was to investigate healthcare-associated infections (HAIs) involving the first VRE in Batna University Hospital, Algeria, and characterize isolates using molecular tools. Patients and methods Medical charts were reviewed for patients with VREfm. van genes were detected by multiplex polymerase chain reaction (PCR), and strains were characterized by automated repetitive sequence-based PCR (rep-PCR), multiplex rep-PCR, pulsed-field gel electrophoresis (PFGE), and multilocus sequence typing (MLST). Results During a 6-month period, VREfm infections occurred in four patients hospitalized in three wards. The four isolates were E. faecium vanA belonging to the hospital-adapted clonal complex 17. PCR-based methods did not discriminate the isolates but MLST and PFGE delineated a subgroup of three VREfm of identical pulsotype and sequence type (ST) 80 (yet identified for five isolates in the international PubMLST database) while the fourth isolate was of ST789 (not previously identified for a VREfm) and displayed an unrelated pulsotype. The three genotypically related isolates were recovered in patients who underwent surgery in the same department, suggesting an outbreak for which the source and route of transmission remained unidentified. Conclusion This first molecular epidemiology study of VRE in Algeria was useful in delimiting an outbreak involving three of the four HAI cases and revealed rarely encountered genotypes. Considering the threat and burden of VRE infections worldwide, particularly in the USA, and the late emergence in Algeria, our study supports the urgent need for improved and early adequate infection control measures to avoid VRE spread in North African hospitals.
Simultaneous Identification of Model Structure and the Associated Parameters for Linear Systems Based on Particle Swarm Optimization

In this study, a novel easy-to-use meta-heuristic method for simultaneous identification of model structure and the associated parameters for linear systems is developed. This is achieved via a constrained multidimensional particle swarm optimization (PSO) mechanism developed by hybridizing two main methodologies: one for negating the limit for fixing the particle’s dimensions within the PSO process and another for enhancing the exploration ability of the particles by adopting a cyclic neighborhood topology of the swarm. This optimizer consecutively searches the dimensional optimum of particles and then the positional optimum in the search space, whose dimension is specified by the explored optimal dimension. The dimensional optimum provides the optimal model structure, while the positional optimum provides the optimal model parameters. Typical numerical examples are considered for evaluation purposes, which clearly demonstrate that the proposed PSO scheme provides novel and powerful impetus with remarkable reliability toward simultaneous identification of model structure and unknown model parameters. Furthermore, identification experiments are conducted on a magnetic levitation system and a robotic manipulator with joint flexibility to demonstrate the effectiveness of the proposed strategy in practical applications.
Stimulation of Tendon Healing With Delayed Dexamethasone Treatment Is Modified by the Microbiome

Background: The immune system reflects the microbiome (microbiota). Modulation of the immune system during early tendon remodeling by dexamethasone treatment can improve rat Achilles tendon healing. The authors tested whether changes in the microbiota could influence the effect of dexamethasone treatment. Hypothesis: A change in microbiome would influence the response to dexamethasone on regenerate remodeling, specifically tendon material properties (peak stress). Study Design: Controlled laboratory study. Methods: Specific opportunist and pathogen-free female rats were housed separately (n = 41) or together with specific pathogen-free rats carrying opportunistic microbes such as Staphylococcus aureus (n = 41). After 6 weeks, all co-housed rats appeared healthy but now carried S aureus. Changes in the gut bacterial flora were tested by API and RapID biochemical tests. All rats (clean and contaminated) underwent Achilles tendon transection under aseptic conditions. Flow cytometry was performed 8 days postoperatively on tendon tissue. Sixty rats received subcutaneous dexamethasone or saline injections on days 5 through 9 after transection. The tendons were tested mechanically on day 12. The predetermined primary outcome was the interaction between contamination and dexamethasone regarding peak stress, tested by 2-way analysis of variance. Results: Dexamethasone increased peak stress in all groups but more in contaminated rats (105%) than in clean rats (53%) (interaction, P = .018). A similar interaction was found for an estimate of elastic modulus (P = .021). Furthermore, dexamethasone treatment reduced transverse area but had small effects on peak force and stiffness. In rats treated with saline only, contamination reduced peak stress by 16% (P = .04) and elastic modulus by 35% (P = .004). Contamination led to changes in the gut bacterial flora and higher levels of T cells (CD3+CD4+) in the healing tendon (P < .05). Conclusion: Changes in the microbiome influence tendon healing and enhance the positive effects of dexamethasone treatment during the early remodeling phase of tendon healing. Clinical Relevance: The positive effect of dexamethasone on early tendon remodeling in rats is strikingly strong. If similar effects could be shown in humans, immune modulation by a few days of systemic corticosteroids, or more specific compounds, could open new approaches to rehabilitation after tendon injury.
Simple mappings, expressive movement: a qualitative investigation into the end-user mapping design of experienced mid-air musicians

ABSTRACT In a New Interface for Musical Expression (NIME), the design of the relationship between a musician’s actions and the instrument’s sound response is critical in creating instruments that facilitate expressive music performance. A growing body of NIMEs expose this design task to the end performer themselves, leading to the possibility of new insights into NIME mapping design: what can be learned from the mapping design strategies of practicing musicians? This research contributes a qualitative study of four highly experienced users of an end-user mapping instrument to examine their mapping practice. The study reveals that the musicians focus on designing simple, robust mappings that minimize errors, embellishing these control gestures with theatrical ancillary gestures that express metaphors. However, musical expression is hindered by the unintentional triggering of musical events. From these findings, a series of heuristics are presented that can be applied in the future development of NIMEs.
A highly-integrated and efficient commercial distributed EV battery balancing system

This paper focuses on the imbalance problem of serial cells in lithium-ion batteries applied in electric vehicles (EVs). In order to meet more strict requirements of size, efficiency, cost, and reliability in commercial application, a highly-integrated and efficient distributed battery balancing system is developed. In the system, each balancing node (BN) and its controller, a designed specific integrated circuit (IC), are independent, distributed and integrated in an IC-packaged module. Besides, a 50% stateof-charge (SOC)-aligned balancing strategy is proposed and applied. The experiment and commercial application results show the designed balancing system performs excellently in improving battery capacity and extending cycle life.
What was the Question? a Systematization of Information Retrieval and NLP Problems

In this paper we suggest a novel systematization of Information Retrieval and Natural Language Processing problems. Using this rather general description of problems we are able to discuss and proof the equivalence of some problems. We provide reformulations of well-known problems like Named Entity Recognition using our novel description and discuss further research and the expected outcome. We will discuss the relation of two problems, cluster labeling and search query finding. With these results we are able to provide a novel optimization approach to both problems. This novel systematization approach provides a yet unknown view generating new classes of problems in NLP. It brings application and algorithmic approaches together and offers a better description with concepts of theoretical computer science.
Lecturers' Competences Configuration Model for the Timetabling Problem

The article presents the problem of academic teachers' competences configuration in the context of the university course timetabling problem (UCTP). Usually when solving UCTP, the set of available academic teachers and the competences they have is defined. The sets of lecture rooms, subjects (courses), student groups, time-slots, etc. are also known. Problems can emerge when it is not possible to find a satisfactory UTCP solution due to the missing sufficient number of specific academic teachers' (lecturers') competences, which is reflected in the possibility to teach specific classes (courses). In order to detect early such a situation and effectively manage the available and required lecturers' competences, a mathematical model of lecturers' competences configuration has been formulated in the form of a MILP (Mixed Integer Linear Programming) problem. Its solution has a direct impact on UTCP. The article also presents the implementation of the model in the LINGO solver environment and computational experiments.
Osteopontin, Cardiovascular Risk Factors and Carotid Intima-Media Thickness in Chronic Kidney Disease

The pleiotropic cytokine osteopontin (OPN) is found to be involved in the pathogenesis of both kidney and cardiovascular disease (CVD). We evaluated the relationship between OPN, other cardiovascular risk factors and carotid intima-media thickness (CIMT) in chronic kidney disease (CKD) (predialysis) patients. This is a 2-year cross-sectional prospective study involving 75 patients with CKD from stage 1 to stage 5 attending the nephrology outpatient department and 25 healthy controls. Routine biochemical parameters were analyzed on clinical chemistry Autoanalyzer Beckman Coulter DXC 600 Synchron, USA. OPN was estimated by ELISA method. Carotid intima-media wall thickness was estimated by Doppler of carotid vessels. Serum OPN and other nontraditional cardiovascular risk factors such as CIMT, lipoprotein (a) Lp(a), fibrinogen, and homocysteine were significantly increased in patients of CKD compared to controls. OPN, Lp(a), fibrinogen, CIMT, parathyroid hormone, and homocysteine progressively increased from early stages of CKD and increased further with progression of the disease, but nitric oxide (NO) level progressively decreased with progression of CKD. OPN showed a positive correlation with CIMT, Lp(a), fibrinogen, and homocysteine and negative correlation with estimated glomerular filtration rate and NO. There was a close direct association between circulating levels of OPN and the presence of atherosclerotic plaques in carotid arteries of patients with CKD. Osteopontin and nontraditional CVD risk factors are altered in early stages of CKD and might predict adverse outcomes in these patients.
Application specific cache design using STT-RAM based block-RAM for FPGA-based soft processors

: As many emerging applications use FPGAs for acceleration (e.g. deep learning, data mining), designing highly-optimized application-speci ﬁ c soft processors on FPGAs gets much attention. Cache is an important component of the soft processor, which is built from Block-RAMs (BRAMs) in FPGAs. SRAM based BRAMs su ﬀ er from high static power consumption and area penalty, which prevents implementing large caches with high associativity. STT-RAM based BRAM may be a good solution to these issues. However, existing cache design with SRAM-based BRAMs for soft processors or SRAM and STT-RAM hybrid cache design in conventional processors is not suitable for the cache with STT-RAM based BRAMs. In this paper, we propose a BRAM allocation method that can e ﬀ ectively implement highly set-associative caches whereas reducing the impact of long delays and power consumption of write operations in STT-RAM. Using our framework, we show that the optimal size of STT-RAM based BRAM is 1 KB with 64-bit IO width for soft-processor cache and the proposed cache structure reduces power and area on average by 55.3% and 76.9%, and reduces runtime by up to 15.6%. Supporting diverse sizes and associativity enables application speci ﬁ c optimization of a cache. In addition, we show that a hybrid cache with SRAM and STT-RAM is not recommended for the soft processor.
A dual-band circularly polarized antenna with wide HPBWs for CNSS applications

A dual-band circularly polarized (CP) antenna with wide half power beamwidths (HPBWs) for compass navigation satellite system (CNSS) applications is proposed in this paper. The CP radiation is realized by feeding four sequentially rotated U-shape patches shorted to the ground at both ends of them with a 90° phase difference feeding network. Loading four parasitic arc-shape patches with one end shorted to the ground provides the dual-band CP operation. Experimental results show that the proposed antenna exhibits two overlapped impedance and axial ratio relative bandwidth of 2.8% and 3.3%. Wide HPBWs of over 122° at center frequencies (1.268 and 1.561GHz) of the CNSS-2 B3 and B1 bands are obtained. With these good performances, the antenna can be a good candidate for CNSS applications.
Re-evaluation of illuminant estimation algorithms in terms of reproduction results and failure cases

Illuminant estimation algorithms are usually evaluated by measuring the recovery angular error, the angle between the RGB vectors of the estimated and ground-truth illuminants. However, this metric reports a wide range of errors for an algorithm-scene pair viewed under multiple lights. In this thesis, a new metric, “Reproduction Angular Error”, is introduced which is an improvement over the old metric and enables us to evaluate the performance of the algorithms based on the reproduced white surface by the estimated illuminant rather than the estimated illuminant itself. Adopting new reproduction error is shown to both effect the overall ranking of algorithms as well as the choice of optimal parameters for particular approaches. A psychovisual image preference experiment is carried out to investigate whether human observers prefer colour balanced images predicted by, respectively, the reproduction or recovery error metric. Human observers rank algorithms mostly according to the reproduction angular error in comparison with the recovery angular error. Whether recovery or reproduction error is used, the common approach to measuring algorithm performance is to calculate accurate summary statistics over a dataset. Mean, median and percentile summary errors are often employed. However, these aggregate statistics, by definition, make it hard to predict performance for individual images or to discover whether there are certain “hard images” where some illuminant estimation algorithms commonly fail. Not only do we find that such hard images exist, based only on the outputs of simple algorithms we provide an algorithm for identifying these hard images (which can then be assessed using more computationally complex advanced algorithms).