Miracle-dz/UniFAR-FTUs huggingface dataset:
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference

We study the hypothesis testing problem of inferring the existence of combinatorial structures in undirected graphical models. Although there exist extensive studies on the information-theoretic limits of this problem, it remains largely unexplored whether such limits can be attained by efficient algorithms. In this paper, we quantify the minimum computational complexity required to attain the information-theoretic limits based on an oracle computational model. We prove that, for testing common combinatorial structures, such as clique and nearest neighbor graph against an empty graph, or large clique against small clique, the information-theoretic limits are provably unachievable by tractable algorithms in general. More importantly, we define structural quantities called the weak and strong edge densities, which offer deep insight into the existence of such computational-statistical tradeoffs. To the best of our knowledge, our characterization is the first to identify and explain the fundamental tradeoffs between statistics and computation for combinatorial inference problems in undirected graphical models.
Tailoring Bariatric Surgery: Sleeve Gastrectomy, Roux-en-Y Gastric Bypass and Biliopancreatic Diversion with Duodenal Switch.

BACKGROUND A need exists to select the most appropriate bariatric operation for a particular patient. One-year data comparing sleeve gastrectomy (SG) to Roux-en-Y gastric bypass (RYGB) and biliopancreatic diversion with duodenal switch (BPD/DS) are sparse. METHODS The Bariatric Outcomes Longitudinal Database was queried from June 2007 to September 2011 for 30-day and 1-year adverse events, and 1-year weight loss and comorbidity resolution. Propensity scores with inverse probability weighting were used to match for age, gender, body mass index (BMI), ethnicity, and select comorbidities. Multivariate linear and logistic regressions estimated differences and odds ratios (ORs), respectively, for each pairwise bariatric operation comparison. RESULTS Among 73,702 subjects, 5942 patients underwent SG, 66,324 patients underwent RYGB, and 1436 patients underwent BPD/DS. Compared with SG, decrease in BMI units was greater by 5.3 for BPD/DS and by 2.2 U for RYGB at 1 year. Resolution of gastroesophageal reflux disease (GERD) was best for RYGB (OR = 1.88, 95% confidence interval [CI]: 1.73-2.03) and still good for BPD/DS (OR = 1.57, 95% CI: 1.29-1.90). Hypertension and diabetes mellitus (DM) resolution were better after BPD/DS (OR = 2.12, 95% CI: 1.83-1.64, and OR = 2.53, 95% CI: 2.13-3.00, respectively) and for RYGB were (OR = 1.54, 95% CI: 44-1.64 and OR = 1.63, 95% CI: 1.51-1.75, respectively). Odds of serious adverse events at 1 year were: RYGB, OR = 1.70, 95% CI: 1.45-2.00; BPD/DS, OR = 4.31, 95% CI: 3.06-6.07. CONCLUSIONS Using SG as reference, RYGB was associated with highest resolution of GERD, whereas BPD/DS was associated with highest resolution of DM and hypertension. These findings can guide decision making regarding choice of bariatric operation.
Novel Thermogravimetry Based Analytical Method for Cellulose Yield Prediction of Sri Lankan Rice Straw Varieties

Rice straw is the largest residue of Sri Lankan paddy cultivation which has not yet subjected to an effective value addition. But, rice straw can be converted to a high value product as it contains significant amount of Cellulose. Therefore, it is beneficial in many aspects to identify rice straw varieties which provide a sufficient cellulose yield. This research work focuses on introducing a novel, easy to use method based on Thermogravimetric Analysis to predict cellulose yield from most abundant traditional (Suwandel, Raththal) and technically modified (BG300, BG352) rice straw varieties in Sri Lanka. The above method was validated by comparing the results with the yields from established three-step wet chemical method. Proposed method showed a high level of accuracy as percentage variations were less than 5% for all four varieties. Highest cellulose yield was obtained from the rice straws of Suwandel variety. This study reveals a potentially effective course of value addition for Sri Lankan agricultural waste.
Differentially Altered NMDAR Dependent and Independent Long-Term Potentiation in the CA3 Subfield in a Model of Anti-NMDAR Encephalitis

Purpose: Autoantibodies against NMDA receptors (NMDAR) in the cerebrospinal fluid (CSF) from anti-NMDAR encephalitis patients have been suggested to be pathogenic since in previous studies using patient CSF, NMDAR-dependent processes such as long-term potentiation (LTP) were compromised. However, autoantibodies may represent a family of antibodies targeted against different epitopes, and CSF may contain further autoantibodies. Here, we tested the specificity of the autoantibody by comparing NMDAR-dependent and NMDAR-independent LTP within the same hippocampal subfield, CA3, using CSF samples from four anti-NMDAR encephalitis patients and three control patients. Methods: We performed a stereotactic injection of patient-derived cell-free CSF with proven presence or absence of NMDAR-antibodies into the rat hippocampus in vivo. Hippocampal brain slices were prepared 1–8 days after intrahippocampal injection, and NMDAR-dependent LTP at the associational-commissural (A/C) fiber-CA3 synapse was compared to NMDAR-independent LTP at the mossy fiber (MF)-CA3 synapse. Results: The LTP magnitude at A/C fiber-CA3 synapses in slices from control-CSF-treated animals (168 ± 8% n = 54) was significantly higher than LTP in slices from NMDAR-CSF-treated animals (139 ± 9%, n = 40; P = 0.015), although there was some variation between the individual CSF samples. We found residual LTP in NMDAR-CSF-treated tissue which could be abolished by the NMDAR inhibitor D-AP5. Moreover, the CA3 field excitatory postsynaptic potential (fEPSP) was followed by epileptiform afterpotentials in 5% of slices (4/78) from control-CSF-treated animals, but in 26% of slices (12/46) from NMDAR-CSF-treated animals (P = 0.002). Application of the LTP-inducing paradigm increased the proportion of slices with epileptiform afterpotentials, but D-AP5 significantly reduced the occurrence of epileptiform afterpotentials only in NMDAR-CSF-treated, but not in control tissue. At the MF synapse, no significant difference in LTP values of control-CSF and in NMDAR-CSF-treated tissue was observed indicating that NMDAR-independent MF-LTP is intact in NMDAR-CSF-treated tissue. Conclusion: These findings indicate that anti-NMDAR containing CSF impairs LTP at the A/C fiber-CA3 synapse, although there is substantial variation among CSF samples suggesting different epitopes among patient-derived antibodies. The differential inhibition of LTP at this synapse in contrast to the MF-CA3 synapse suggests the specificity and underlines the pathophysiological role of the NMDAR-antibody.
B2B E-Commerce Adoption in Iranian Manufacturing Companies: Analyzing the Moderating Role of Organizational Culture

ABSTRACT The objective of this research is to examine the factors affecting Business-to-Business Electronic Commerce (B2B EC) adoption within technology-organization-environment (TOE) framework, and to test how such effects are moderated by organizational culture. Using a survey questionnaire, 320 responses were received from managers and owners of manufacturing companies in Iran. PLS-SEM technique was used for analysis. B2B EC adoption in manufacturing companies was found to be affected by cost of adoption, top management support, competitive pressure, and government support; and organizational culture was found to negatively moderate the relationship between top management support and B2B EC adoption. Managers, owners, and policy makers can use these findings to facilitate the adoption of B2B EC. Previous research have not analyzed the moderating role of organizational culture; these findings contribute to the e-commerce literature by filling this gap. The results indicate that the TOE framework provides a strong base for the study of B2B EC in developing countries. The results also show that this framework is able to integrate moderating variable into the theoretical model.
The contribution of mitochondrial metagenomics to large-scale data mining and phylogenetic analysis of Coleoptera

A phylogenetic tree at the species level is still far off for highly diverse insect orders, including the Coleoptera, but the taxonomic breadth of public sequence databases is growing. In addition, new types of data may contribute to increasing taxon coverage, such as metagenomic shotgun sequencing for assembly of mitogenomes from bulk specimen samples. The current study explores the application of these techniques for large-scale efforts to build the tree of Coleoptera. We used shotgun data from 17 different ecological and taxonomic datasets (5 unpublished) to assemble a total of 1942 mitogenome contigs of >3000 bp. These sequences were combined into a single dataset together with all mitochondrial data available at GenBank, in addition to nuclear markers widely used in molecular phylogenetics. The resulting matrix of nearly 16000 species with two or more loci produced trees (RAxML) showing overall congruence with the Linnaean taxonomy at hierarchical levels from suborders to genera. We tested the role of full-length mitogenomes in stabilizing the tree from GenBank data, as mitogenomes might link terminals with non-overlapping gene representation. However, the mitogenome data were only partly useful in this respect, presumably because of the purely automated approach to assembly and gene delimitation, but improvements in future may be possible by using multiple assemblers and manual curation. In conclusion, the combination of data mining and metagenomic sequencing of bulk samples provided the largest phylogenetic tree of Coleoptera to date, which represents a summary of existing phylogenetic knowledge and a defensible tree of great utility, in particular for studies at the intra-familial level, despite some shortcomings for resolving basal nodes.
Linking Transformational Leadership and Knowledge Sharing: The Mediating Roles of Perceived Team Goal Commitment and Perceived Team Identification

It is widely assumed that transformational leadership can effectively facilitate the sharing of knowledge among followers, but most investigations of the underlying mechanisms were based on the social exchange perspective. Based on a sensegiving theory perspective, this article attempts to uncover the mechanisms by which transformational leadership has its impact on employee knowledge sharing behavior by proposing two team-directed mediating mechanisms: perceived team goal commitment and perceived team identification. Results of multi-source and time-lagged data from 186 leader–follower pairs supported the proposed mediating effects. Implications and limitations are discussed.
Design of scalable agent-based reconfigurable manufacturing systems with Petri nets

ABSTRACT Reconfigurable manufacturing systems (RMS) provide a flexible paradigm to deal with frequently changing demand and technologies. Scalability is an important property that determines whether the capacity of RMS can accommodate fluctuation in product demand by adjusting/reconfiguring machines and/or structure of production processes. Although there are several studies on scalability of RMS, there are relatively few studies that provide a framework that supports the development of scalable RMS based on multi-agent systems (MAS), from modelling, analysis to design. Motivated by this urgent need, this paper attempts to bridge the gap between theoretical development and design of scalable agent-based RMS. Although MAS and agents’ characteristics of autonomy provide a suitable architecture to model and capture interactions of entities in agent-based manufacturing systems, the original MAS lack process models for agents. By adopting Petri nets as the process models of agents, the capacity scalability problem (CSP) in agent-based RMS can be described by Petri nets in MAS architecture. Due to the lack of optimisation theory developed for Petri nets, the CSP is transformed into an optimisation problem that can be solved by applying classical Lagrange relaxation optimisation theory to relevant agents in the problem-solving processes. Therefore, the methodology proposed to develop scalable agent-based RMS is based on MAS, Petri net models and Lagrange relaxation optimisation theory. The effectiveness of agent-based RMS is illustrated by examples which show that fewer configurable resources are required for agent-based RMS to meet order requirements.
Development and Validation of a Phenotypic High-Content Imaging Assay for Assessing the Antiviral Activity of Small-Molecule Inhibitors Targeting Zika Virus

Zika virus (ZIKV) has been linked to the development of microcephaly in newborns, as well as Guillain-Barré syndrome. There are currently no drugs available to treat ZIKV infection, and accordingly, there is an unmet medical need for the discovery of new therapies. ABSTRACT Zika virus (ZIKV) has been linked to the development of microcephaly in newborns, as well as Guillain-Barré syndrome. There are currently no drugs available to treat ZIKV infection, and accordingly, there is an unmet medical need for the discovery of new therapies. High-throughput drug screening efforts focusing on indirect readouts of cell viability are prone to a higher frequency of false positives in cases where the virus is viable in the cell but the cytopathic effect (CPE) is reduced or delayed. Here, we describe a fast and label-free phenotypic high-content imaging assay to detect cells affected by the virus-induced CPE using automated imaging and analysis. Protection from the CPE correlates with a decrease in viral antigen production, as observed by immunofluorescence. We trained our assay using a collection of nucleoside analogues with activity against ZIKV; the previously reported antiviral activities of 2′-C-methylribonucleosides and ribavirin against the Zika virus in Vero cells were confirmed using our developed method. To validate the ability of our assay to reveal new anti-ZIKV compounds, we profiled a novel library of 24 natural product derivatives and found compound 1 to be an inhibitor of the ZIKV-induced cytopathic effect; the activity of the compound was confirmed in human fetal neural stem cells (NSCs). The described technique can be easily leveraged as a primary screening assay for profiling of the activities of large compound libraries against ZIKV and can be expanded to other ZIKV strains and other cell lines displaying morphological changes upon ZIKV infection.
Epistemology in the Era of Fake News

Fake news has recently garnered increased attention across the world. Digital collaboration technologies now enable individuals to share information at unprecedented rates to advance their own ideologies. Much of this sharing occurs via social networking sites (SNSs), whose members may choose to share information without consideration for its authenticity. This research advances our understanding of information verification behaviors among SNS users in the context of fake news. Grounded in literature on the epistemology of testimony and theoretical perspectives on trust, we develop a news verification behavior research model and test six hypotheses with a survey of active SNS users. The empirical results confirm the significance of all proposed hypotheses. Perceptions of news sharers' network (perceived cognitive homogeneity, social tie variety, and trust), perceptions of news authors (fake news awareness and perceived media credibility), and innate intentions to share all influence information verification behaviors among SNS members. Theoretical implications, as well as implications for SNS users and designers, are presented in the light of these findings.
Global Processing Styles Facilitate the Discovery of Structural Similarity

This study examined whether global processing style facilitates the discovery of structural similarity. In the two experiments, the participants were presented with three stories after being primed with global or local processing through a Navon task. The first story was the base story, and the other two stories shared either surface similarity or structural similarity with the base story. The results showed that, compared with the participants of the local processing and control groups, a substantially greater number of participants of the global processing group selected the story with structural similarity to the base story. This finding indicated that the global processing style can facilitate the discovery of structural similarity.
CpG islands' clustering uncovers early development genes in the human genome

We address the problem of the annotation of CpG islands (CGIs) clusters in the human genome. Upon analyzing gene content within CGIs clusters, piRNA, tRNA, and miRNA-encoding genes were found as well as CpG-rich homeobox genes reported previously. Chromosome-wide CGI density is positively correlated with replication timing, confirming that CGIs may serve as open chromatin markers. Early embryonic stage expressed KRAB-ZNF genes abundant at chromosome 19 were found to be interlinked with CGI clusters. We detected that a number of long CGIs and CGI clusters are, in fact, tandem copies with multiple annotated macrosatellites and paralogous genes. This finding implies that tandem expansion of CGIs may serve as a substrate for nonhomologous recombination events.
Detecting Offensive Tweets in Hindi-English Code-Switched Language

The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort to ease communication. The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script. The paper introduces a novel tweet dataset, titled Hindi-English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: non-offensive, abusive and hate-speech. Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English followed by retraining on Hinglish tweets.
Soil Microbial Resources for Improving Fertilizers Efficiency in an Integrated Plant Nutrient Management System

Tomorrow’s agriculture, challenged by increasing global demand for food, scarcity of arable lands, and resources alongside multiple environment pressures, needs to be managed smartly through sustainable and eco-efficient approaches. Modern agriculture has to be more productive, sustainable, and environmentally friendly. While macronutrients such as nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) supplied by mineral fertilizers are vital to crop production, agriculturally beneficial microorganisms may also contribute directly (i.e., biological N2 fixation, P solubilization, and phytohormone production, etc.) or indirectly (i.e., antimicrobial compounds biosynthesis and elicitation of induced systemic resistance, etc.) to crop improvement and fertilizers efficiency. Microbial-based bioformulations that increase plant performance are greatly needed, and in particular bioformulations that exhibit complementary and synergistic effects with mineral fertilization. Such an integrated soil fertility management strategy has been demonstrated through several controlled and non-controlled experiments, but more efforts have to be made in order to thoroughly understand the multiple functions of beneficial microorganisms within the soil microbial community itself and in interaction with plants and mineral resources. In fact, the combined usage of microbial [i.e., beneficial microorganisms: N2-fixing (NF), P-solubilizing, and P mobilizing, etc.] and mineral resources is an emerging research area that aims to design and develop efficient microbial formulations which are highly compatible with mineral inputs, with positive impacts on both crops and environment. This novel approach is likely to be of a global interest, especially in most N- and P-deficient agro-ecosystems. In this review, we report on the importance of NF bacteria and P solubilizing/mobilizing microbes as well as their interactions with mineral P fertilization in improving crop productivity and fertilizers efficiency. In addition, we shed light on the interactive and synergistic effects that may occur within multi-trophic interactions involving those two microbial groups and positive consequences on plant mineral uptake, crop productivity, and resiliency to environmental constraints. Improving use of mineral nutrients is a must to securing higher yield and productivity in a sustainable manner, therefore continuously designing, developing and testing innovative integrated plant nutrient management systems based on relevant biological resources (crops and microorganisms) is highly required.
A Framework for Human Recognition and Counting in Restricted Area for Video Surveillance

We introduce a multi-process framework for human counting and recognition that exploits the combination of multiple deep neural networks. Deep networks have advanced the state of the art in many fields and play an essential role in computer vision for detection and recognition. However, very deep networks are still slow at inference time, and they require a substantial amount of hardware to perform complex operations. Real-time recognition from video source is still an issue due to complexity of scenario and the amount of data to process. In this paper, we propose an approach that combines multiple neural networks, that is fast and accurate.
Modified progressive random walk with chaotic PRNG

Abstract In this paper, two modifications are proposed to the Progressive Random Walk (PRW) algorithm in order to address its potentially insufficient search space coverage. The first modification replaces the Pseudo-Random Number Generator (PRNG) with the uniform distribution by the chaotic map based PRNG for generating of the offset values and the second modification is called direction switching and is based on experiment observation. The modifications are implemented into the PRW and the resulting algorithm is called modified Progressive Random Walk. The search space coverage of the two algorithms is compared. Both algorithms are used in macro ruggedness estimation of the CEC2015 benchmark set and the results are discussed. Search space coverage of progressive random walk improved by chaotic sequences used as pseudo-random number generators for step size.
Exploring the effects of web-mediated socially-shared regulation of learning and experience-based learning on improving students’ learning

ABSTRACT The application of online learning and educational technologies in higher education has changed teaching methods, the channels of delivering learning materials, and modes of communication between teachers and students. This study is aimed to improve learning effects and investigate, via quasi-experiments, the effects of web-mediated socially-shared regulation of learning (SSRL) and experience-based learning (ExBL) on improving students’ learning results. The experimental design in this study was a 2 (SSRL vs. non-SSRL) × 2 (ExBL vs. non-ExBL) factorial pretest/post-test design. Four classes in a one-semester course titled “Applied Information Technology: Data Processing” at university level were chosen for this study. Based on the analysis carried out in this study, students who received the treatments of web-mediated SSRL and/or ExBL did not have significantly better computing skills in using Excel. The reasons for these insignificances and implications thereof are discussed in this paper. Nevertheless, students in the SSRL and non-ExBL class had significant increases in scores for their learning motivation at the end of semester compared with the beginning.