Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decentralized Privacy-Based Market Scheme for Responsive Demands

In this work, a market-based control scheme is proposed to minimize the billing costs of responsive demands (RDs) with the minimum impact on their privacy and satisfaction. For this purpose, the RDs are modeled as agents who bid to the day-ahead and real-time electricity markets through their energy management systems. In the model, the financial compensation provided by the market motivates the RDs to shift their load to off-peak periods. Since dissatisfaction is caused by the deviations from the reference consumptions, the RDs’ bids are dependent on the level of satisfaction that the consumers are willing to accept. In order to maintain the satisfaction and privacy of consumers in the demand response (DR) programs, a decentralized DR-based algorithm is developed considering the competition-based participation of consumers by a performance-based reward and recognition (PRR) strategy. Different numerical results verify the effectiveness of the proposed scheme. Keywords—Decentralized electricity market; Demand response; Multi-agent system; Performance-based reward and recognition; Privacy and satisfaction; Responsive demand. NOMENCLATURE A. Indices (Set) c (C) index (set) of customers d (D) index (set) of demand i (I) index (set) of demand response j (J) index (set) of suppliers
SER Analysis of Two-Hop Physical Layer Network Coding with 16QAM Modulator

The interference is the major factor disrupting the sending of information in wireless networks. To get better performance for these networks as well in the conventional case as in cooperative one, all the necessary ways must be used to eliminate network interference. This article deals with the concept of Physical Layer Network Coding (PLNC). It is a way to exploit the operation of Network Coding (NC) that occurs naturally in the superimposed electromagnetic waves (EM). It is a simple physical effect when several EM waves meet in the same physical space, they are mixed together. This mixture of EM waves is a form of NC produced by nature. Hence, the situation will be reversed and the interference will be a beneficial way to help the relay when sending information. This paper focuses on the Symbol Error Rate (SER) Analysis of PLNC in the case of 16QAM modulator. It will exploit in detail the concept of mapping (modulation/demodulation) and will demonstrate its contribution compared to NC and Traditional Network (TN). Keywords: Physical layer network coding, symbol error rate, 16QAM modulation, mapping;
Optimal experiment design for assessing plasma clearance of /sup 125/I-Iothalamate in peritoneal dialysis

Assessment of treatment adequacy in patients on peritoneal dialysis (PD) is important because the clearance rates obtained with this kind of therapy are small and are greatly affected by changes in residual renal function. The availability of a simple and accurate clinical test for measuring total clearance is a prerequisite for the efficacy of the follow-up of PD patients. In this study, the experiment design problem is considered for the plasma clearance approach, which is based on the analysis of concentration measurements of a test substance after impulsive injection of a known dose. In particular, the theoretical basis is given for determining the optimal time instants for collecting two blood samples for an accurate and precise assessment of total plasma clearance of /sup 125/I-Iothalamate.
Working Paper 2014-286 Investor Following and Volatility : A GARCH Approach

In this paper, we aim to investigate whether investor following is a determinant of the stock market volatility. To measure investor following, we use “Google Insights for search” freshly introduced to the financial literature. The latter records the online search traffic for any keyword submitted to Google since 2004. Thanks to an extensive database, we focus precisely on the French stock market unlike previous works, which have focused largely on the US stock market. Notably, our findings support strong significant effects of investor following as measured by online search behavior on the conditional volatility estimated from GARCH (1,1) Market model. Our results are robust to additional tests.
Redundancy Allocation Problem of a System with Three-state Components: A Genetic Algorithm

The redundancy allocation is one of the most important and useful problems in system optimization, especially in electrical and mechanical systems. The object of this problem is to maximize system reliability or availability within a minimum operation cost. Many works have been proposed in this area so far to draw the problem near to real-world situations. While in classic models the system components are assumed to have two states of working and failed, in this paper, parallel components of serial sub-systems are considered to work in three states, each with a certain performance rate. The component states are classified into two working states of working with full performance and working with half performance, and a failed state. Besides, technical and organizational activities are considered to improve the performance of the components as well as the sub-systems. As the problem belongs to the class of NP-Hard, a genetic algorithm is utilized to solve it.
Student Perceptions of Social Networking as a Supplemental Learning Tool in the Communication Disorders Classroom

ABSTRACT: Purpose: The purpose of this study was to examine students’ perceptions of benefits associated with the use of an education-based social networking site (SNS) for an undergraduate course in child language disorders. Method: A social networking page was created via the online platform, Ning in Education, and was used throughout the course. At the end of the semester, students rated their opinions of the use of the SNS and its contribution to their classroom experience. Fifty-nine out of 60 students responded (a 98% response rate). Results: The majority of respondents agreed or strongly agreed that through the use of the class social networking page, they were able to communicate with peers and colleagues outside of the traditional classroom setting and express their thoughts more clearly and openly than in face-to-face class T
Dissecting the CHES 2018 AES Challenge

One challenge of the CHES 2018 side channel contest was to break a masked AES implementation. It was impressively won by Gohr et al. by applying ridge regression to obtain guesses for the hamming weights of the (unmasked) AES key schedule, and then using a SAT solver to brute force search the remaining key space. Template attacks are one of the most common approaches used to assess the leakage of a device in a security evaluation. Hence, this raises the question whether ridge regression is a more suitable choice for security evaluation, especially w.r. t. portability. We investigate the feasibility of template attacks to break the presented AES implementation, analyze the leakage of the device, and based on this mount a template attack on hamming weights of the key expansion. We then use classical key search algorithms to recover the AES key. By analyzing the leakage and applying dimension reduction techniques we are able to compress each trace from 650 000 points to only 30 points that are then used to create the templates. Our experimental results indicate that such classical templates achieve similar results compared to ridge regression, and in several cases even slightly outperforming it. According to the organizers, the CTF was aimed to evaluate the concepts of deep learning and classic profiling. Our final conclusion is that the challenge traces are not optimal to settle the question intended, as the leakage is very strong and local. Therefore it is very suitable to apply classical machine learning techniques such as template attacks or ridge regression, and the difficulty in recovering the key is more linked to the resulting key search problem than to the actual attack.
Working Group COFREND « Eddy Current NDT modeling » Benchmarks for validating and improving simulation codes acceptation

The working group « Eddy Current NDT modeling », hosted by the French NDT society “COFREND”, aims at proposing benchmark cases with associated experimental results, in order to support the validation and the acceptance of simulation programs, to put closer modeling tools and industrial needs, and finally to allow the NDT community to regularly exchange on the subjects related to Eddy Current simulation. Various types of members have joined the group and regularly participate to the task force: industrial users of NDT, services & engineering companies, universities and research centers. Several sectors are represented (power industry, aerospace, steel industry, etc.). This paper presents the latest works of the group performed within the last year. More precisely, two benchmarks have been addressed, inspired by inspection issues from the nuclear sector and the steel industry. Furthermore, new benchmarks are being proposed in order to be solved by candidate programs.
Into the mixed reality data sphere : mapping user ’ s movements to data exploration tools

In this paper, we propose to take advantage of user movements when exploring data in a virtual reality environment. As HumanComputer Interaction (HCI) relies on a fast and accurate binding between users actions and the system’s responses, we explore using the user location, as measured by the HoloLens head tracking, for data exploration, leaving the user hands free for data manipulation and selection. Since HoloLens has a very accurate measurement of user’s head movements, we investigated the available design space to efficiently map data exploration actions to these user head movements. To better investigate our design space, we implemented prototypes and explored new interaction techniques thanks to different data types and data exploration tasks.
CsPbBr3/CdS Core/Shell Structure Quantum Dots for Inverted Light-Emitting Diodes Application

Novel CsPbBr3/CdS core/shell structure quantum dots (QDs) were successfully synthesized using a facile hot-injection method. The corresponding CsPbBr3/CdS QDs based light-emitting diodes (QLEDs) were further prepared, which demonstrated the maximum luminance of 354 cd/m2 and an external quantum efficiency (EQE) of 0.4% with the current efficiency (CE) of 0.3 cd/A. Moreover, the optoelectronic performance of the CsPbBr3/CdS QDs based QLEDs exhibited a comparable enhancement in contrast to the pure CsPbBr3 QDs based QLEDs. Hypothetically, the novel CsPbBr3/CdS structure QDs introduced one new route for advanced light emission applications of perovskite materials.
Drug repositioning based on bounded nuclear norm regularization

Abstract Motivation Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug–disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug–disease associations. Results In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug–disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug–drug and disease–disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug–disease network, which integrates the drug–drug, drug–disease and disease–disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug–disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR. Availability and implementation The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR. Supplementary information Supplementary data are available at Bioinformatics online.
Acetylcholinesterase is not a generic marker of extracellular vesicles

ABSTRACT Acetylcholinesterase (AChE) activity is found in abundance in reticulocytes and neurons and was developed as a marker of reticulocyte EVs in the 1970s. Easily, quickly, and cheaply assayed, AChE activity has more recently been proposed as a generic marker for small extracellular vesicles (sEV) or exosomes, and as a negative marker of HIV-1 virions. To evaluate these proposed uses of AChE activity, we examined data from different EV and virus isolation methods using T-lymphocytic (H9, PM1 and Jurkat) and promonocytic (U937) cell lines grown in culture conditions that differed by serum content. When EVs were isolated by differential ultracentrifugation, no correlation between AChE activity and particle count was observed. AChE activity was detected in non-conditioned medium when serum was added, and most of this activity resided in soluble fractions and could not be pelleted by centrifugation. The serum-derived pelletable AChE protein was not completely eliminated from culture medium by overnight ultracentrifugation; however, a serum “extra-depletion” protocol, in which a portion of the supernatant was left undisturbed during harvesting, achieved near-complete depletion. In conditioned medium also, only small percentages of AChE activity could be pelleted together with particles. Furthermore, no consistent enrichment of AChE activity in sEV fractions was observed. Little if any AChE activity is produced by the cells we examined, and this activity was mainly present in non-vesicular structures, as shown by electron microscopy. Size-exclusion chromatography and iodixanol gradient separation showed that AChE activity overlaps only minimally with EV-enriched fractions. AChE activity likely betrays exposure to blood products and not EV abundance, echoing the MISEV 2014 and 2018 guidelines and other publications. Additional experiments may be merited to validate these results for other cell types and biological fluids other than blood.
A joint method for marker-free alignment of tilt series in electron tomography

Abstract Motivation Electron tomography (ET) is a widely used technology for 3D macro-molecular structure reconstruction. To obtain a satisfiable tomogram reconstruction, several key processes are involved, one of which is the calibration of projection parameters of the tilt series. Although fiducial marker-based alignment for tilt series has been well studied, marker-free alignment remains a challenge, which requires identifying and tracking the identical objects (landmarks) through different projections. However, the tracking of these landmarks is usually affected by the pixel density (intensity) change caused by the geometry difference in different views. The tracked landmarks will be used to determine the projection parameters. Meanwhile, different projection parameters will also affect the localization of landmarks. Currently, there is no alignment method that takes interrelationship between the projection parameters and the landmarks. Results Here, we propose a novel, joint method for marker-free alignment of tilt series in ET, by utilizing the information underlying the interrelationship between the projection model and the landmarks. The proposed method is the first joint solution that combines the extrinsic (track-based) alignment and the intrinsic (intensity-based) alignment, in which the localization of landmarks and projection parameters keep refining each other until convergence. This iterative approach makes our solution robust to different initial parameters and extreme geometric changes, which ensures a better reconstruction for marker-free ET. Comprehensive experimental results on three real datasets show that our new method achieved a significant improvement in alignment accuracy and reconstruction quality, compared to the state-of-the-art methods. Availability and implementation The main program is available at https://github.com/icthrm/joint-marker-free-alignment. Supplementary information Supplementary data are available at Bioinformatics online.
FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes

Abstract Motivation As the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive. Results To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. Availability and implementation The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. Supplementary information Supplementary data are available at Bioinformatics online.
Building large updatable colored de Bruijn graphs via merging

Abstract Motivation There exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed. Results We create a method for constructing the colored de Bruijn graph for large datasets that is based on partitioning the data into smaller datasets, building the colored de Bruijn graph using a FM-index based representation, and succinctly merging these representations to build a single graph. The last step, merging succinctly, is the algorithmic challenge which we solve in this article. We refer to the resulting method as VariMerge. This construction method also allows the graph to be updated with new data. We validate our approach and show it produces a three-fold reduction in working space when constructing a colored de Bruijn graph for 8000 strains. Lastly, we compare VariMerge to other competing methods—including Vari, Rainbowfish, Mantis, Bloom Filter Trie, the method of Almodaresi et al. and Multi-BRWT—and illustrate that VariMerge is the only method that is capable of building the colored de Bruijn graph for 16 000 strains in a manner that allows it to be updated. Competing methods either did not scale to this large of a dataset or do not allow for additions without reconstruction. Availability and implementation VariMerge is available at https://github.com/cosmo-team/cosmo/tree/VARI-merge under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.
Monoclonal antibody AC10364 inhibits cell proliferation in 5-fluorouracil resistant hepatocellular carcinoma via apoptotic pathways

Background This study was designed to investigate the antitumor activity of the mAb (AC10364) in vitro and elucidate the related mechanisms of inhibition to cell growth using bel/fu cells treated with AC10364. Methods The inhibitory effects of AC10364 on the proliferation of Bel/fu cells were examined using a cytotoxicity assay. Apoptosis of Bel/fu cells was detected using FITC annexin V and PI staining following treatment with AC10364 for 24 h. The factors regulating apoptosis were identified by Western blot using lysates of Bel/fu cells treated with AC10364 for 0, 12, 24, or 36 h. Genes associated with tumorigenesis or growth were analyzed by reverse transcription–quantitative polymerase chain reaction using Bel/fu cells treated for 12, 24, or 36 h with AC10364. Results The early apoptotic ratios of Bel/fu cells treated with AC10364 increased in a dose-dependent manner. The levels of caspases, including cleaved caspase-3, caspase-3 and caspase-9, were significantly high in Bel/fu cells treated with AC10364 (P<0.001). Compared with untreated cells, those exposed to AC10364 had showed significant downregulation of the expression of binding protein gene (G protein subunit α 15, GNA15) and other protein-coding genes, including fms-related tyrosine kinase 1(FLT1), nicotinamide phosphoribosyltransferase (NAMPT), netrin 4 (NTN4), platelet-derived growth factor subunit A (PDGFA), S100 calcium binding protein A11 (S100A11), tubulin β 3 class III (TUBB3), aldo-keto reductase family 1 member C3 (AKR1C3), endothelial PAS domain protein 1 (EPAS1), and interferon α-inducible protein 27 (IFI27) (P<0.001). Two other genes, AXL receptor tyrosine kinase (AXL) and carboxypeptidase A4 (CPA4), were significantly upregulated (P<0.001). Conclusion AC10364 inhibited cell viability and proliferation through aberrant expression of multiple genes associated with tumorigenesis or growth, which suggests that these genes may be promising therapeutic candidates for cancer therapy.