Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation of glyoxalase II (gly II) and salt overly sensitive (SOS2) alleles from Egyptian sorghum and enhancing salt stress tolerance in yeast

Two salt stress related alleles namely, glyoxalase II (gly II) and salt overly sensitive (SOS2) were isolated by RT-PCR from Egyptian sorghum cv. R3. The sequencing results confirmed the isolation of Sbgly II and SbSOS2 alleles, respectively. Both alleles were cloned separately into the pYES2 expression vector for expression in Saccharomyces cerevisia. Yeast growth under different NaCl stress concentration, i.e. 0, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2 and 2.3 M. the results revealed significant growth differences between the transgenic yeast and control. proline accumulation increased significantly in all transgenic yeasts with gly II and SOS2. While, these values were significantly decreased in the controls by increasing NaCl concentration. The present results suggested that the Sbgly II and SbSOS2 could be mediated the improving the sat tolerance of different eukaryotic systems
TOWARDS HUMAN-SAFE NAVIGATION WITH PRO-ACTIVE COLLISION AVOIDANCE IN A SHARED WORKSPACE

This paper presents a human-safe navigation algorithm for shared human-robot workspaces, based on the velocity obstacles paradigm. By extending the velocity obstacle paradigm with different cost factors accounting for humans and robots, the approach allows human workers to use the same navigation space as robots. It does not rely on any external sensors and shows its feasibility even in densely packed environments. Experiments show that our approach leads to safer and smoother paths at a small cost of additional traveled time and distance.
Command Search for CAD System

In this paper, a Command Search Interface is proposed for contemporary CAD systems. The proposed Interface can help users quickly locate the desired commands by a list of key words and thus helping newbie users boost modeling efficiencies without the need of substantial familiarization of graphical user interfaces. In order to enhance the search intelligence, accuracy and robustness, we developed a novel search mechanism based on a proposed CAD Lexicon List, which is constructed from the help manuals of CAD systems and integrated with synonym word sets. This search engine has been initially designed to search CAD modeling commands from textual input, and further incorporated in our Voice-Enabled CAD systems. The definitions and structure of the CAD Lexicon List is presented in detail. The algorithms of the proposed search engine and its applications in our prototype system are also demonstrated.
CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE IN LIBYAN COMPANIES

This study aims to investigate the disclosures of corporate social responsibility CSR in the annual reports of the sixty one Libyan companies for six sectors, namely industry, services, investment, banking, insurance and oil. Study periods are three years; 2009, 2010 and 2012. The disclosures of the corporate social responsibility include four themes, namely environment, community, consumers and employees. Content analysis methodology was employed in order to study the disclosures of corporate social responsibility. Disclosure index was used to measure the corporate social responsibility disclosures. The findings revealed although many companies disclosed their corporate social responsibility activities in their annual reports, but the levels of disclosures are still low. Results of the study also indicated that there are slight differences in the disclosures of the corporate social responsibility between different sectors of Libyan companies. It is expected that usefulness of this study will be acknowledged by stakeholders, potential and present investors, policy makers, and regulatory bodies in Libya or by any other developing country.
New Convex Relaxations for MRF Inference With Unknown Graphs

Treating graph structures of Markov random fields as unknown and estimating them jointly with labels have been shown to be useful for modeling human activity recognition and other related tasks. We propose two novel relaxations for solving this problem. The first is a linear programming (LP) relaxation, which is provably tighter than the existing LP relaxation. The second is a non-convex quadratic programming (QP) relaxation, which admits an efficient concave-convex procedure (CCCP). The CCCP algorithm is initialized by solving a convex QP relaxation of the problem, which is obtained by modifying the diagonal of the matrix that specifies the non-convex QP relaxation. We show that our convex QP relaxation is optimal in the sense that it minimizes the L1 norm of the diagonal modification vector. While the convex QP relaxation is not as tight as the existing and the new LP relaxations, when used in conjunction with the CCCP algorithm for the non-convex QP relaxation, it provides accurate solutions. We demonstrate the efficacy of our new relaxations for both synthetic data and human activity recognition.
Exploring Ethereum’s Blockchain Anonymity Using Smart Contract Code Attribution

Blockchain users are identified by addresses (public keys), which cannot be easily linked back to them without out-of-network information. This provides pseudo-anonymity, which is amplified when the user generates a new address for each transaction. Since all transaction history is visible to all users in public blockchains, finding affiliation between related addresses can hurt pseudo-anonymity. Such affiliation information can be used to discriminate against addresses that were found to be related to a specific group, or can even lead to the de-anonymization of all addresses in the associated group, if out-of-network information is available on a few addresses in that group. In this work we propose to leverage a stylometry approach on Ethereum’s deployed smart contracts’ bytecode and high level source code, which is publicly available by third party platforms. We explore the extent to which a deployed smart contract’s source code can contribute to the affiliation of addresses. To address this, we prepare a dataset of real-world Ethereum smart contracts data, which we make publicly available; design and implement feature selection, extraction techniques, data refinement heuristics, and examine their effect on attribution accuracy. We further use these techniques to test the classification of real-world scammers data.
Variability in Polyphenols, Antioxidants and Mineral Composition in Different Genotypes of Pigeonpea (Cajanus Cajan) Grown in India

Pigeonpea [Cajanus cajan (L.) Millsp] have diverse biological activities such as antioxidant, anti-aging, anti-cancer, anti-inflammation, anti-atherosclerosis, cardiovascular protection, improvement in endothelial function, as well as inhibition of angiogenesis and cell proliferation activity for disease prevention and health promotion [1-3]. It is widely accepted that significant antioxidant activity of food is related to high total phenolic content. It contains a large variety of phenolic derivatives, including simple phenols, phytosterols, saponins, phenyl propanoids, benzoic acid derivatives, flavonoids, stilbenes, tannins, lignans and lignins [4]. Natural polyphenols exert their beneûcial health effects by their antioxidant activity, these compounds are capable of removing free radicals, chelate metal catalysts, activate antioxidant enzymes, reduce α-tocopherol radicals, and inhibit oxidases. Numerous studies have reported the bioactive component such as vanillic acid, caffeic acid, cajaninstilbene acid, pinostrobin and vitexin from different parts such as leaves, roots, stem and seeds which have biological and pharmacological activities [1-3]. Pigeonpea seeds are used for treating kidney ailments, measles, hepatitis and sickle cell anemia [5-7]. Pigeonpea seeds are consumed as a grain crop and also used as a fodder for animals. This fodder is a rich source of fibers and protein. In the perspective of nutritional and medicinal attributes of Cajanus cajan, characterization and compositional analysis of pigeonpea seeds are of great importance. The phytochemical analysis depends both on genetic and environment. The present investigation was undertaken to develop on efficient protocol for determining total phenolic content, antioxidants and mineral composition in seeds of pigeonpea.
Antioxidant and Mutagenic Activity of Herbal Tea Prepared from Cosmos caudatus Leaves at Different Maturity Stages

Different maturity stages of Cosmos caudatus leaves have been used to prepare herbal tea were investigated for their effect on antioxidant activity and mutagenic activity. The analyses carried out were total phenolic content (TPC), total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), 2-2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, β-carotene bleaching assay, oxygen radical absorbance capacity (ORAC) and Ames Salmonella mutagenicity. The results demonstrated that, C. caudatus herbal tea prepared from young leaves showed significantly highest antioxidant activity for all assays tested, followed by mixed leaves, mature leaves, old leaves and the lowest was in C. caudatus herbal tea from a commercial brand. Pearson’s correlation coefficient also demonstrated that TPC and TFC displayed a strong correlation with all antioxidant activity assays, showing that these compounds were the major contributors to the antioxidant activity in C. caudatus herbal tea. However, all studied C. caudatus herbal tea showed no mutagenic effects against Salmonella typhimurium tester strain TA98 and TA100 with and without S9 metabolic activation. Hence, it can be concluded that, different maturity stages could affect the antioxidant activity in C. caudatus herbal tea as it reduced the antioxidant activity as maturity increased, but did not give any effect on the mutagenic activity.
Mining Anomalies using Static and Dynamic Graphs

Data generated in a multitude of diverse contexts today have relational and temporal characteristics, with multiple entities interacting with each other and also evolving over time. Examples range from e-commerce logs to online social networks to the internet-of-things. Our thesis addresses the problem of detecting and predicting anomalies–deviations from usual patterns–in such settings. Anomalies often encode suspicious, fraudulent or malicious behavior. They do not just influence users into making sub-optimal decisions but also steadily erode their trust in businesses. As such, algorithms to detect ongoing anomalies and warn against upcoming anomalies have high impact for businesses and end-users alike. In the first part of the thesis, we focus on the case where only static connectivity information is known, and the goal is to infer labels for vertices, e.g., whether a user account is honest or fraudulent, from limited labeled data. Our completed work broadens the scope of literature by handling heterogeneous graphs, and leveraging label uncertainty for more accurate vertex labeling. In the second part of the thesis, we mine anomalies from data where the connectivity evolves over time. Our primary focus here is on real-time detection and early warning so as to enable timely corrective or preventive measures against anomalies. Our completed work can detect anomalous dense subgraphs and edges in near real-time, by only storing a small synopsis of the graph seen so far and requiring no supervision. We also show how to early warn against user-labeled anomalies in the presence of confounding interventions. As part of ongoing and future work, we will continue to push on both fronts by investigating the importance of higher-order structures for vertex labeling and characterizing the anomalousness of any given graph substructure or motif.
Relation Parsing Neural Network for Human-Object Interaction Detection

Human-Object Interaction Detection devotes to infer a triplet < human, verb, object > between human and objects. In this paper, we propose a novel model, i.e., Relation Parsing Neural Network (RPNN), to detect human-object interactions. Specifically, the network is represented by two graphs, i.e., Object-Bodypart Graph and Human-Bodypart Graph. Here, the Object-Bodypart Graph dynamically captures the relationship between body parts and the surrounding objects. The Human-Bodypart Graph infers the relationship between human and body parts, and assembles body part contexts to predict actions. These two graphs are associated through an action passing mechanism. The proposed RPNN model is able to implicitly parse a pairwise relation in two graphs without supervised labels. Experiments conducted on V-COCO and HICO-DET datasets confirm the effectiveness of the proposed RPNN network which significantly outperforms state-of-the-art methods.
Using SASISTAT® Software for Nonparametric 2-Way Analysis of Variance: A Biomedical Device Process Validation Problem

In analyzing data from a medical device, the researcher is often faced with data across the range of a biological analyte that may not satisfy the usual assumption for analysis of variance. Friedman's 5 Statistic is a useful nonparametric statistic when conSidering a randomized complete block design (2-way layout) which may be used when the usual assumptions required for analysis of variance (ANOYA) are not met However, calculation of Friedman's 5 is tedious when applied to large data sets. This paper presents an example ofthree manufacturing material sources (pilot lots) to prove manufacturing process capability whens patient values are used to determine analyte response. The model under . consideration is the usual 2-way ANOYA witlliot as a main effect (~), and patient as a blocking variable. The hypotheses to be tested are Ho: ~ = \ \f i, j l,j=1, . ,k versus H. : at least one t, . \ ,hoj. This document also presents an automated met~od for converting a data set from continuous to ranked response using Proc RANK, 2way ANOYA on the ranks using Proc ANOYA, and finally calculation of Friedman's S from results obtained within SAS. Operating Systems (all).
Memory Access Optimization

Design of programmable processors and embedded applications requires instruction-set simulators for early exploration and validation of candidate architectures. Interpretive simulators are widely used in embedded systems design. One of the key performance bottlenecks in interpretive simulation is the instruction and data memory access translation between host and target machines. The simulators must maintain and update the status of the simulated processor including memory and register values. A major challenge in the simulation is to efficiently map the target address space to the host address space. This paper presents two optimization techniques that aggressively utilize the spatial locality of the instruction and data accesses in interpretive simulation: signature based address mapping for optimizing general memory accesses; and incremental instruction fetch for optimizing instruction accesses. To demonstrate the utility of this approach we applied these techniques on SimpleScalar simulator, and obtained up to 30% performance improvement. Our techniques complement the recently proposed optimizations (JIT-CCS [1] and IS-CS [2]) and further improve the performance (up to 89%) on ARM7 and Sparc processors.
Dynamical Mean-Field Theory of Strongly Correlated Electron Systems

Dynamical Mean-Field Theory (DMFT) has opened new perspectives for the investigation of strongly correlated electron systems and greatly improved our understanding of correlation effects in models and materials. In contrast to Hartree-Fock-type approximations the mean field of DMFT is dynamical, whereby local quantum fluctuations are fully taken into account. DMFT becomes exact in the limit of high spatial dimensions or coordination number. Using DMFT the dynamics of correlated electron systems can be investigated non-perturbatively at all interaction strengths, electron densities and temperatures. By merging density functional theory with DMFT a powerful method for the calculation of the properties of correlated electron materials has become available, which is applicable to bulk systems and heterostructures, including topological states of matter. The inclusion of non-local correlations into DMFT makes it possible to explore unconventional superconductivity and the critical behavior at thermal or quantum phase transitions. By generalizing DMFT to non-equilibrium states also the real-time dynamics of correlated systems can be investigated. In this brief review the foundations and current status of DMFT are discussed along with characteristic physical insights obtained with this approach.
Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection

Insider threat is one of the most damaging cyber security attacks to companies and organizations. In this paper, we explore different techniques to leverage spatial and temporal characteristics of user behaviours for insider threat detection. In particular, feature normalization (scaling) techniques and a scheme for representing explicit temporal information are explored to improve the performance of the machine learning based insider threat detection. The results show that these data characteristics have different effects on different classifiers, where Standard Scaler with Random Forest classifier produces the best performance.
Weighted Matrix Completion From Non-Random, Non-Uniform Sampling Patterns

We study the matrix completion problem when the observation pattern is deterministic and possibly non-uniform. We propose a simple and efficient debiased projection scheme for recovery from noisy observations and analyze the error under a suitable weighted metric. We introduce a simple function of the weight matrix and the sampling pattern that governs the accuracy of the recovered matrix. We derive theoretical guarantees that upper bound the recovery error and nearly matching lower bounds that showcase optimality in several regimes. Our numerical experiments demonstrate the computational efficiency and accuracy of our approach, and show that debiasing is essential when using non-uniform sampling patterns.
Diffusion Monte Carlo study of a spin-imbalanced two-dimensional Fermi gas with attractive interactions

We probe the superconducting gap in the zero temperature ground state of an attractively interacting spin-imbalanced two-dimensional Fermi gas with Diffusion Monte Carlo. A condensate fraction at nonzero pair momentum evidences a spatially non-uniform superconducting order parameter. Comparison with exact diagonalisation studies confirms that the nonzero condensate fraction across a range of nonzero fermion pair momenta is consistent with non-exclusive pairing between majority and minority fermions, an extension beyond FFLO theory.
Group Centrality Maximization for Large-scale Graphs

The study of vertex centrality measures is a key aspect of network analysis. Naturally, such centrality measures have been generalized to groups of vertices; for popular measures it was shown that the problem of finding the most central group is $\mathcal{NP}$-hard. As a result, approximation algorithms to maximize group centralities were introduced recently. Despite a nearly-linear running time, approximation algorithms for group betweenness and (to a lesser extent) group closeness are rather slow on large networks due to high constant overheads. That is why we introduce GED-Walk centrality, a new submodular group centrality measure inspired by Katz centrality. In contrast to closeness and betweenness, it considers walks of any length rather than shortest paths, with shorter walks having a higher contribution. We define algorithms that (i) efficiently approximate the GED-Walk score of a given group and (ii) efficiently approximate the (proved to be $\mathcal{NP}$-hard) problem of finding a group with highest GED-Walk score. Experiments on several real-world datasets show that scores obtained by GED-Walk improve performance on common graph mining tasks such as collective classification and graph-level classification. An evaluation of empirical running times demonstrates that maximizing GED-Walk (in approximation) is two orders of magnitude faster compared to group betweenness approximation and for group sizes $\leq 100$ one to two orders faster than group closeness approximation. For graphs with tens of millions of edges, approximate GED-Walk maximization typically needs less than one minute. Furthermore, our experiments suggest that the maximization algorithms scale linearly with the size of the input graph and the size of the group.
Motion-Nets: 6D Tracking of Unknown Objects in Unseen Environments using RGB

In this work, we bridge the gap between recent pose estimation and tracking work to develop a powerful method for robots to track objects in their surroundings. Motion-Nets use a segmentation model to segment the scene, and separate translation and rotation models to identify the relative 6D motion of an object between two consecutive frames. We train our method with generated data of floating objects, and then test on several prediction tasks, including one with a real PR2 robot, and a toy control task with a simulated PR2 robot never seen during training. Motion-Nets are able to track the pose of objects with some quantitative accuracy for about 30-60 frames including occlusions and distractors. Additionally, the single step prediction errors remain low even after 100 frames. We also investigate an iterative correction procedure to improve performance for control tasks.
Not even wrong: Reply to Loreau and Hector

The Loreau and Hector (2019) Comment on our paper (Pillai and Gouhier, 2019) failed to address the two core elements of our critique, both the circularity of the BEF research program, in general, and the mathematical flaws of the Loreau-Hector partitioning scheme, in particular. Loreau and Hector avoided dealing with the first part of our critique by arguing against a non-existent claim that all biodiversity effects could be reduced to coexistence, while the mathematical flaws in the Loreau-Hector partitioning method that we described in the second part of our critique were ignored altogether. Here, we address these misconceptions and demonstrate that all of the claims that were made in our original paper hold. We conclude that (i) BEF studies need to adopt baselines that account for coexistence in order to avoid overestimating the effects of biodiversity and (ii) the LH partitioning method should not be used unless the linearity of the abundance-ecosystem functioning relationship in monocultures can be verified for all species.
Large-scale Dynamics of Winds Originating from Black Hole Accretion Flows. I. Hydrodynamics

Winds from black hole accretion flows are ubiquitous. Previous works mainly focus on the launching of wind on the accretion flow scale. It still remains unclear how far the winds can propagate outward and what their large-scale dynamics is. As the first paper of this series, we study the large-scale dynamics of thermal wind beyond accretion scales via analytical and numerical methods. Boundary conditions, which are crucial to our problem, are analyzed and presented based on small-scale simulations combined with observations of winds. Both black hole and galaxy potential are taken into account. For winds originating from hot accretion flows, we find that the wind can reach large scales. The radial profiles of velocity, density, and temperature can be approximated by and , where vr0, ρ0, and T0 are the velocity, density, and temperature of winds at the boundary , and γ is the polytropic index. During the outward propagation, the enthalpy and rotational energy compensate for the increase of gravitational potential. For thin disks, we find that because the Bernoulli parameter is smaller, winds cannot propagate as far as the hot winds, but stop at a certain radius where the Bernoulli parameter is equal to the potential energy. Before the winds stop, the profiles of dynamical quantities can also be approximated by the above relations. In this case, the rotational energy alone compensates for the increase in potential energy.