Miracle-dz/UniFAR-FTUs huggingface dataset:
Centralized QoS-Aware Resource Allocation for D2D Communications with Multiple D2D Pairs in One Resource Block

Device-to-device (D2D) communications can result in spectral efficiency (SE) improvement of cellular networks when interference is addressed properly. For further improvement of overall network sum-rate, it can be assumed that multiple D2D pairs reuse the same resource block (RB) of a cellular user equipment (CUE). In this paper, we study a centralized joint power and spectrum resource allocation problem to maximize the overall sum-rate of the network while guaranteeing the quality-of-service (QoS) requirements of CUEs and admitted D2D pairs. the proposed method uses a distance based approach to select a CUE at each step with a distance and power based approach to increase the number of admitted D2D pairs. A power minimization approach when the number of admitted D2D pairs are increasing and a power maximization approach at the end of resource allocation for each RB is proposed. Numerical results show that the proposed algorithm significantly improves SE compared to the situation where at most one D2D pair can reuse the RB of a CUE. It also shows that the proposed algorithm has a slight lower performance compared to the optimal scheme but dramatically decreases the matching complexity and signaling overhead.
Performance of Weibull and Exponentiated Weibull Fading in Amplify-and-Forward Relaying Systems

Recent experiments have shown that Weibull fading can be a practical distribution to model weak to high signal levels at 800/900 MHz frequency spectrum in outdoor environments. In indoor, path loss can be distributed by Weibull fading. Moreover, exponentiated Weibull which is a special case of Weibull fading, is adopted in free space optical communication systems recently. Motivated by the wide usage of Weibull fading, we consider a dual-hop relay aided network with amplify-and-forward relaying and with the help of Monte Carlo simulations, we show the impact of Weibull and Exponentiated Weibull fading channels in terms of outage probability.
Prospective clinical biomarkers of caspase-mediated apoptosis associated with neuronal and neurovascular damage following stroke and other severe brain injuries: Implications for chronic neurodegeneration

Acute brain injuries, including ischemic and hemorrhagic stroke, as well as traumatic brain injury (TBI), are major worldwide health concerns with very limited options for effective diagnosis and treatment. Stroke and TBI pose an increased risk for the development of chronic neurodegenerative diseases, notably chronic traumatic encephalopathy, Alzheimer's disease, and Parkinson's disease. The existence of premorbid neurodegenerative diseases can exacerbate the severity and prognosis of acute brain injuries. Apoptosis involving caspase-3 is one of the most common mechanisms involved in the etiopathology of both acute and chronic neurological and neurodegenerative diseases, suggesting a relationship between these disorders. Over the past two decades, several clinical biomarkers of apoptosis have been identified in cerebrospinal fluid and peripheral blood following ischemic stroke, intracerebral and subarachnoid hemorrhage, and TBI. These biomarkers include selected caspases, notably caspase-3 and its specific cleavage products such as caspase-cleaved cytokeratin-18, caspase-cleaved tau, and a caspase-specific 120 kDa αII-spectrin breakdown product. The levels of these biomarkers might be a valuable tool for the identification of pathological pathways such as apoptosis and inflammation involved in injury progression, assessment of injury severity, and prediction of clinical outcomes. This review focuses on clinical studies involving biomarkers of caspase-3-mediated pathways, following stroke and TBI. The review further examines their prospective diagnostic utility, as well as clinical utility for improved personalized treatment of stroke and TBI patients and the development of prophylactic treatment chronic neurodegenerative disease.
Sparse-Based Medical Image Fusion Using a Novel Optimal Dictionary Strategy and Shearlet Transform

Combination of multi-scale transformations and sparse representation (SR) is one of the most useful techniques for fusion of multi modal images. Discrete Shearlet Transform (DST) is one of the directional transforms that is proper to merge MRI and PET scan images. Here, grey-level hit or miss transform (HMT) and matched filter are used to convert both modalities to image patches and form the dictionary of sparse coding. Finally, PCA is utilized to get more important atoms, because the number of atoms is one of important issues. The fusion rule for high frequency components is averaging and weighted averaging is considered for low frequency components. This fusion rule properly demonstrates fused image and induces to visual enhancement. The proposed dictionary has salient results in terms of: mutual information (MI), visual information fidelity (VIF) and correlation coefficients (SCC and PCC); compared to other dictionary learning strategies which are K-SVD and recursive least square (RLS) methods.
Efficiently Coupling the I-DLV Grounder with ASP Solvers

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$, a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).
An Experimental Evaluation and Analysis of Transient Voltage Fluctuations in FPGAs

Due to recent technology scaling trends and increased circuit complexity, process and runtime variabilities are becoming major threats for correct circuit operation. Among these, transient voltage fluctuations appear to be the most critical issue, accounting for the biggest component of timing margin, at increased cost. As various design and workload parameters have an impact on voltage fluctuations, they need to be fully understood in order to design efficient countermeasures and margining. Field-programmable gate arrays are predestined for this analysis by allowing more control over such experiments at lower cost than application-specific integrated circuits. Furthermore, they highly suffer from the same issues, which are typically only handled by excessive and overpessimistic timing margining built into the mapping tools. In this paper, we implemented and calibrated sensors in configurable logic appropriate to observe delay changes caused by transient voltage fluctuations. We place them at multiple locations on the chip to evaluate temporal and spatial changes in a timing margin due to different workload characteristics. Moreover, we analyze the spatial and the temporal interdependence of various workloads and investigate their combined effect on a voltage drop. This analysis provides useful insights to designers for application mapping and workload scheduling.
On the Road to Making Science of "Art": Risk Bias in Market Scoring Rules

We study market scoring rule (MSR) prediction markets in the presence of risk-averse or risk-seeking agents that have unknown yet bounded risk preferences. It is well known that if agents can be prescreened, then MSRs can be corrected to elicit agents’ beliefs. However, agents cannot always be screened, and instead, an online MSR mechanism is needed. We show that agents’ submitted reports always deviate from their beliefs, unless their beliefs are identical to the current market estimate. This means, in most cases it is impossible for a MSR prediction market to elicit an individual agent’s exact belief. To analyze this issue, we introduce a measure to calculate the deviation between an agent’s reported belief and personal belief. We further derive the necessary and sufficient conditions for a MSR to yield a lower deviation relative to another MSR. We find that the deviation of a MSR prediction market is related to the liquidity provided in the MSR’s corresponding cost-function prediction market. We use th.
Implementation of distributed semaphores in IEC 61499 with consensus protocols

An approach is proposed for the implementation of distributed semaphores in the IEC 61499 architecture on the basis of protocols for solving consensus in a network of unreliable processes that allows developing function blockbased control applications with complex types of interactions using the resources sharing. The models of distributed semaphores (in the form of coloured Petri nets) based on Paxos and Raft protocols for solving consensus are developed, simulation experiments are performed in the CPN Tools, comparative analysis and recommendations are given.
BIST-Based Online Test Approach for SRAM-Based FPGAs

FPGA chips have wide applications in nowadays digital systems. Because of fault prone nature of FPGA chips, their testing is one of the major challengs for designers. Among various test methods, the Built-in Self- Test (BIST) based approaches have shown good performance. In this paper, an efficient online and non-concurrent method is proposed to test and detect faults in FPGA which preserves initial values of SRAM cell. Furthermore, the test process does not need additional reconfiguring and test of each configurable logic block has been done in 16 clock cycles. The proposed BIST architecture has been simulated in HSPICE based on 45-nm CMOS technology. Simulation results indicated 100% coverage for single stuck at faults along with 59% area overhead due to additional BIST hardware.
Zero-training Sentence Embedding via Orthogonal Basis

We propose a simple and robust training-free approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word's novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero training and zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Experimental results show that our model outperforms all existing zero-training alternatives in all the tasks and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.
TRM4 is essential for cellulose deposition in Arabidopsis seed mucilage by maintaining cortical microtubule organization and interacting with CESA3

Summary The differentiation of the seed coat epidermal (SCE) cells in Arabidopsis thaliana leads to the production of a large amount of pectin‐rich mucilage and a thick cellulosic secondary cell wall. The mechanisms by which cortical microtubules are involved in the formation of these pectinaceous and cellulosic cell walls are still largely unknown. Using a reverse genetic approach, we found that TONNEAU1 (TON1) recruiting motif 4 (TRM4) is implicated in cortical microtubule organization in SCE cells, and functions as a novel player in the establishment of mucilage structure. TRM4 is preferentially accumulated in the SCE cells at the stage of mucilage biosynthesis. The loss of TRM4 results in compact seed mucilage capsules, aberrant mucilage cellulosic structure, short cellulosic rays and disorganized cellulose microfibrils in mucilage. The defects could be rescued by transgene complementation of trm4 alleles. Probably, this is a consequence of a disrupted organization of cortical microtubules, observed using fluorescently tagged tubulin proteins in trm4 SCE cells. Furthermore, TRM4 proteins co‐aligned with microtubules and interacted directly with CELLULOSE SYNTHASE 3 in two independent assays. Together, the results indicate that TRM4 is essential for microtubule array organization and therefore correct cellulose orientation in the SCE cells, as well as the establishment of the subsequent mucilage architecture.
Ensemble Learning on Large Scale Financial Imbalanced Data

This study focused on evaluating the performance of ensemble learning on handling imbalanced data. Imbalanced data is a special problem in classification task where the class distribution is not uniformed. Resampling (SMOTE and ENN) is employed to improve the classifier performance. Four metrics is applied for performance evaluation i.e., precision, recall, specificity, and F-1 score. Based on the experiments, Bagging has a superior performance compared to baseline classifiers (Naïve Bayes and Log Regression) and other ensemble learnings (Boosting and Random Forest). In addition, the combination of SMOTE and ENN successfully increase the classification performance and avoiding biased to the majority class.
Centroid Selection in Kernel Extreme Learning Machine using K-means

Kernel Extreme Learning Machine (KELM) is used for classification, regression, clustering and feature selection with the help of kernel functions. Conventional KELM uses all training instances as centroids for classification problem while reduced KELM uses randomly choosen training instances as centroids. Furthermore, reduced KELM is used for reducing the computational complexity of conventional KELM. To further improve the computational complexity of KELM, K-means clustering algorithm for centroid selection in KELM is proposed in this paper. In this proposed approach, number of centroids are selected as 1/10 or 5/10 of the total number of training instances and then centroids are computed by using K-means algorithm. Experiments have been carried out by using 15 data sets to illustrate the effectiveness of the proposed method. The results obtained show the reduction in computational time and increment in G-mean which verify the proposed method as an efficient approach in comparison to earlier works.
Noncanonical features and modifications on the 5′‐end of bacterial sRNAs and mRNAs

Although many eukaryotic transcripts contain cap structures, it has been long thought that bacterial RNAs do not carry any special modifications on their 5′‐ends. In bacteria, primary transcripts are produced by transcription initiated with a nucleoside triphosphate and are therefore triphosphorylated on 5′‐ends. Some transcripts are then processed by nucleases that yield monophosphorylated RNAs for specific cellular activities. Many primary transcripts are also converted to monophosphorylated species by removal of the terminal pyrophosphate for 5′‐end‐dependent degradation. Recent studies surprisingly revealed an expanded repertoire of chemical groups on 5′‐ends of bacterial RNAs. In addition to mono‐ and triphosphorylated moieties, some mRNAs and sRNAs contain cap‐like structures and diphosphates on their 5′‐ends. Although incorporation and removal of these groups have become better understood in recent years, the physiological significance of these modifications remain obscure. This review highlights recent studies aimed at identification and elucidation of novel modifications on the 5′‐ends of bacterial RNAs and discusses possible physiological applications of the modified RNAs.
A Pipeline Supporting a Smart Access to Historical Documents based on a Rich Semantic Representation of Their Content: A Case Study on Time Expressions

: This work is part of two ongoing projects whose main goal is to demonstrate how semantic technologies can support an effective access to historical archives. In this paper we present a full pipeline, from rough texts up to the final user interface, aimed at creating and exploiting such representations. The pipeline is structured in three modules - handling information extraction, semantic representations, and queries - and offers external applications the possibility of accessing, and thus re-using, the output of each module, by providing a tagged text, a SPARQL endpoint, and a RESTful web service. In the paper, we describe the details of a proof-of-concept implementation of the pipeline architecture that focuses on time expressions. Moreover, we present an example application that exploits the pipeline to enable users to access historical documents by searching and browsing events and time specifications, thus demonstrating the effectiveness of an access to historical texts based on a rich semantic representation of their content.
Pre-Coder Design to Improve Secure Capacity in the MISO Wiretap Fading Channel with Spatial Correlations

In This paper, improving secure capacity using the appropriate pre-coder is investigated. Both main and wiretap channels assumed to be multiple-input single-output (MISO) wiretap channels. Also, the correlation between the Transmitting antennas is considered and broadcasting environment between the transmitter and each receiver is supposed to be Rayleigh fading. Secure capacity has been defined by the difference between the capacity of the main and the Eavesdropping channel. In the definition of secure capacity, channel correlation matrix is employed. Pre-coder is designed for the cases that the main receiver channel is superior and equal to the Eavesdropping channel. It is shown that that the proposed method has a superior performance regarding to secure capacity in comparison with similar pre-coding design algorithms.
Pre-Emptive Detection and Localization of Failures Towards Marginless Operations of Optical Networks

Operating optical networks much closer to their physical capacity is very tempting but necessarily requires much improvement on the way failures are handled. In this paper, we experimentally demonstrate the ability of the ORCHESTRA solution for early detection and localization of failures, to proactively mitigate their impact and thus guarantee smooth operation without traffic interruption.
Indian Art Form Recognition Using Convolutional Neural Networks

Indian Culture is one of the richest cultures of the world. Art forms are a key component of the Indian culture that reveal a great deal about the various traditions, customs and practices of ancient as well as modern India. The paper proposes a framework classify Indian art forms into 8 different categories viz. Kalamkari, Kangra, Madhubani, Mural, Pattachitra, Portrait, Tanjore and Warli depending upon the style of art form. The proposed framework relies on the fusion of several state of the art deep convolutional neural networks for feature extraction from the dataset. Further, the experiments were carried out on a newly introduced dataset of over two thousand digital images of Indian paintings. The data-set is publically available for further experimentations. The model is able to achieve an accuracy of 86.56% outperforming other models.
Technique Development for Probing RNA Structure In Vivo and Genome-Wide.

SUMMARYHow organisms perceive and respond to their surroundings is one of the great questions in biology. It is clear that RNA plays key roles in sensing. Cellular and environmental cues that RNA responds to include temperature, ions, metabolites, and biopolymers. Recent advances in next-generation sequencing and in vivo chemical probing have provided unprecedented insights into RNA folding in vivo and genome-wide. Patterns of chemical reactivity have implicated control of gene expression by RNA and aided prediction of RNA structure. Central to these advances has been development of molecular biological and chemical techniques. Key advances are improvements in the quality, cost, and throughput of library preparation; availability of a wider array of chemicals for probing RNA structure in vivo; and robustness and user friendliness of data analysis. Insights from probing transcriptomes and future directions are provided.
Engineering Components of the Lactobacillus S-Layer for Biotherapeutic Applications

Lactic acid bacteria (LAB) are frequently harnessed for the delivery of biomolecules to mucosal tissues. Several species of Lactobacillus are commonly employed for this task, of which a subset are known to possess surface-layers (S-layers). S-layers are two-dimensional crystalline arrays of repeating proteinaceous subunits that form the outermost coating of many prokaryotic cell envelopes. Their periodicity and abundance have made them a target for numerous biotechnological applications. In the following review, we examine the multi-faceted S-layer protein (Slp), and its use in both heterologous protein expression systems and mucosal vaccine delivery frameworks, through its diverse genetic components: the strong native promoter, capable of synthesizing as many as 500 Slp subunits per second; the signal peptide that stimulates robust secretion of recombinant proteins; and the structural domains, which can be harnessed for both cell surface display of foreign peptides or adhesion enhancement of a host bacterium. Although numerous studies have established vaccine platforms based on one or more components of the Lactobacillus S-layer, this area of research still remains largely in its infancy, thus this review is meant to not only highlight past works, but also advocate for the future usage of Slps in biotherapeutic research.
Identifying the Relative Importance of Customer Issues on Product Ratings through Machine Learning

Millions of customer reviews for products are available online across hundreds of different websites. These reviews have a tremendous influence on the purchase decision of new customers and in creating a positive brand image. Understanding which of the product issues are critical in determining the product ratings is crucial for marketing teams. We have developed a solution which can derive deep insights from customer reviews which goes significantly beyond keyword based analysis. Our solution can identify key customer issues voiced in the reviews and the impact of each of these on the final rating that a customer gives the product. This insight is very actionable as it helps identify which customer concerns are responsible for bad ratings of products.
Robust Lane Detection for Complicated Road Environment Based on Normal Map

Detection of road or lane is indispensable for the environmental perception of advanced driver assistance systems. It has been an active field of research with a wide application prospect. However, due to the complex illumination and interferences, such as vehicles and shadows in the real driving environment, lane detection is still a challenging task today. To address these issues, a robust method for road segmentation and lane detection based on a normal map is proposed. The first step of this approach is to generate the normal map by using the depth information and then extract a segmented road pavement without vehicles and buildings based on the normal map. Second, we improve an adaptive threshold segmentation method and denoising operations to enhance the lane markings. Third, the combination of Hough transform and vanishing point makes it more accurate to determine the starting points of host lanes, and then the lanes in the following image sequence can be detected in the adaptive region of interest. Compared with the state-of-the-art methods, the experimental results on the data sets in two countries demonstrate that our approach produces more credible performance under various light conditions or dense traffic.