Miracle-dz/UniFAR-FTUs huggingface dataset:
Durum Wheat Stress Tolerance Induced by Endophyte Pantoea agglomerans with Genes Contributing to Plant Functions and Secondary Metabolite Arsenal

In the arid region Bou-Saâda at the South of Algeria, durum wheat Triticum durum L. cv Waha production is severely threatened by abiotic stresses, mainly drought and salinity. Plant growth-promoting rhizobacteria (PGPR) hold promising prospects towards sustainable and environmentally-friendly agriculture. Using habitat-adapted symbiosis strategy, the PGPR Pantoea agglomerans strain Pa was recovered from wheat roots sampled in Bou-Saâda, conferred alleviation of salt stress in durum wheat plants and allowed considerable growth in this unhostile environment. Strain Pa showed growth up to 35 °C temperature, 5–10 pH range, and up to 30% polyethylene glycol (PEG), as well as 1 M salt concentration tolerance. Pa strain displayed pertinent plant growth promotion (PGP) features (direct and indirect) such as hormone auxin biosynthesis, production of 1-aminocyclopropane-1-carboxylate (ACC) deaminase, and ammonia and phosphate solubilization. PGPR features were stable over wide salt concentrations (0–400 mM). Pa strain was also able to survive in seeds, in the non-sterile and sterile wheat rhizosphere, and was shown to have an endophytic life style. Phylogenomic analysis of strain Pa indicated that Pantoea genus suffers taxonomic imprecision which blurs species delimitation and may have impacted their practical use as biofertilizers. When applied to plants, strain Pa promoted considerable growth of wheat seedlings, high chlorophyll content, lower accumulation of proline, and favored K+ accumulation in the inoculated plants when compared to Na+ in control non-inoculated plants. Metabolomic profiling of strain Pa under one strain many compounds (OSMAC) conditions revealed a wide diversity of secondary metabolites (SM) with interesting salt stress alleviation and PGP activities. All these findings strongly promote the implementation of Pantoea agglomerans strain Pa as an efficient biofertilizer in wheat plants culture in arid and salinity-impacted regions.
Recent Advances in Pharmacological and Non-Pharmacological Strategies of Cardioprotection

Ischemic heart diseases (IHD) are the leading cause of death worldwide. Although the principal form of treatment of IHD is myocardial reperfusion, the recovery of coronary blood flow after ischemia can cause severe and fatal cardiac dysfunctions, mainly due to the abrupt entry of oxygen and ionic deregulation in cardiac cells. The ability of these cells to protect themselves against injury including ischemia and reperfusion (I/R), has been termed “cardioprotection”. This protective response can be stimulated by pharmacological agents (adenosine, catecholamines and others) and non-pharmacological procedures (conditioning, hypoxia and others). Several intracellular signaling pathways mediated by chemical messengers (enzymes, protein kinases, transcription factors and others) and cytoplasmic organelles (mitochondria, sarcoplasmic reticulum, nucleus and sarcolemma) are involved in cardioprotective responses. Therefore, advancement in understanding the cellular and molecular mechanisms involved in the cardioprotective response can lead to the development of new pharmacological and non-pharmacological strategies for cardioprotection, thus contributing to increasing the efficacy of IHD treatment. In this work, we analyze the recent advances in pharmacological and non-pharmacological strategies of cardioprotection.
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users' data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms' generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.
Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation

—This paper presents a learning framework to estimate an agent capability and task requirement model for multi-agent task allocation. With a set of team conﬁgurations and the corresponding task performances as the training data, linear task constraints can be learned to be embedded in many existing optimization-based task allocation frameworks. Comprehensive computational evaluations are conducted to test the scalability and prediction accuracy of the learning framework with a limited number of team conﬁgurations and performance pairs. A ROS and Gazebo-based simulation environment is developed to validate the proposed requirements learning and task allocation framework in practical multiagent exploration and manipulation tasks. Results show that the learning process for scenarios with 40 tasks and 6 types of agents uses around 12 seconds, ending up with prediction errors in the range of 0.5-2%.
Mitigation-Aware Bidding Strategies in Electricity Markets

Market power exercise in the electricity markets distorts the market and diminishes social welfare. Many markets have implemented market power mitigation processes to eliminate the impact of such behavior. In order to evaluate the effectiveness of the existing mitigation mechanisms, this paper proposes a mitigation-aware strategic bidding model and studies market participants’ bidding behavior under current practice. The proposed bidding model has a bilevel structure with the strategic participant’s profit maximization problem in the upper level and the dispatch problem for market operators in the lower level. In particular, the consideration of potential offer mitigation is incorporated as upper-level constraints based on the conduct and impact tests. This bilevel problem is reduced to a mixed-integer linear program using the KKT conditions, duality theory, and linearization. Numerical results illustrate how a strategic participant can exercise market power even under mitigation and the consequent social impact.
Plant model generation from event log using ProM for formal verification of CPS

This paper introduces the concept of plant model generation from the recorded traces of events using the process mining technique. The event logs are obtained by visually simulating a simple distributed manufacturing system using the OPC UA communication protocol. The process discovery alpha algorithm is used to extract the process model in Petri net format. The system behavior represented in terms of Petri net is then fed to construct the ECC of the basic function block in compliance with IEC 61499 standard using the proposed notation. Finally, the formal veriﬁcation of the closed-loop system is done with the help of a tool chain that consists of fb2smv converter, symbolic model checker NuSMV, and other tools for representing counter-examples.
Turbulent drag reduction by spanwise wall forcing. Part 1. Large-eddy simulations

Abstract Turbulent drag reduction (DR) through streamwise travelling waves of the spanwise wall oscillation is investigated over a wide range of Reynolds numbers. Here, in Part 1, wall-resolved large-eddy simulations in a channel flow are conducted to examine how the frequency and wavenumber of the travelling wave influence the DR at friction Reynolds numbers $Re_\tau = 951$ and $4000$. The actuation parameter space is restricted to the inner-scaled actuation (ISA) pathway, where DR is achieved through direct attenuation of the near-wall scales. The level of turbulence attenuation, hence DR, is found to change with the near-wall Stokes layer protrusion height $\ell _{0.01}$. A range of frequencies is identified where the Stokes layer attenuates turbulence, lifting up the cycle of turbulence generation and thickening the viscous sublayer; in this range, the DR increases as $\ell _{0.01}$ increases up to $30$ viscous units. Outside this range, the strong Stokes shear strain enhances near-wall turbulence generation leading to a drop in DR with increasing $\ell _{0.01}$. We further find that, within our parameter and Reynolds number space, the ISA pathway has a power cost that always exceeds any DR savings. This motivates the study of the outer-scaled actuation pathway in Part 2, where DR is achieved through actuating the outer-scaled motions.
Arabic Text Mining

The rapid growth of the internet has increased the number of online texts. This led to the rapid growth of the number of online texts in the Arabic language. The enormous amount of text must be organized into classes to make the analysis process and text retrieval easier. Text classification is, therefore, a key component of text mining. There are numerous systems and approaches for categorizing literature in English, European "French, German, Spanish", and Asian "Chinese, Japanese". In contrast, there are relatively few studies on categorizing Arabic literature due to the difficulty of the Arabic language. In this work, a brief explanation of key ideas relevant to Arabic text mining are introduced then a new classification system for the Arabic language is presented using light stemming and Classifier Naive Bayesian (CNB). Texts from two classes—politics and sports—are included in our corpus. Some texts are added to the system, and the system correctly classified them, demonstrating the system's effectiveness.
Mirror‐Coupled Plasmonic Bound States in the Continuum for Tunable Perfect Absorption

Tailoring critical light‐matter coupling is a fundamental challenge of nanophotonics, impacting fields from higher harmonic generation and energy conversion to surface‐enhanced spectroscopy. Plasmonic perfect absorbers (PAs), where resonant antennas couple to their mirror images in adjacent metal films, excel at obtaining different coupling regimes by tuning the antenna‐film gap size. However, practical PA applications require constant gap size, making it impossible to maintain critical coupling beyond singular wavelengths. Here, a new approach for plasmonic PAs is introduced by combining mirror‐coupled resonances with the unique loss engineering capabilities of plasmonic quasi‐bound states in the continuum. This novel combination allows to tailor the light–matter interaction within the under‐coupling, over‐coupling, and critical coupling regimes using flexible tuning knobs including asymmetry parameter, dielectric gap, and geometrical scaling factor. The study demonstrates a pixelated PA metasurface with optimal absorption over a broad range of mid‐infrared wavenumbers (950–2000 cm−1) using only a single gap size and applies it for multispectral surface‐enhanced molecular spectroscopy. Moreover, the asymmetry parameter enables convenient adjustment of the quality factor and resonance amplitude. This concept expands the capabilities and flexibility of traditional gap‐tuned PAs, opening new perspectives for miniaturized sensing platforms towards on‐chip and in situ detection.
Pseudo-Riemannian Graph Convolutional Networks

Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry. Recent works find that non-Euclidean Riemannian manifolds provide specific inductive biases for embedding hierarchical or spherical data. However, they cannot align well with data of mixed graph topologies. We consider a larger class of pseudo-Riemannian manifolds that generalize hyperboloid and sphere. We develop new geodesic tools that allow for extending neural network operations into geodesically disconnected pseudo-Riemannian manifolds. As a consequence, we derive a pseudo-Riemannian GCN that models data in pseudo-Riemannian manifolds of constant nonzero curvature in the context of graph neural networks. Our method provides a geometric inductive bias that is sufficiently flexible to model mixed heterogeneous topologies like hierarchical graphs with cycles. We demonstrate the representational capabilities of this method by applying it to the tasks of graph reconstruction, node classification and link prediction on a series of standard graphs with mixed topologies. Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
General theory of emergent elasticity for second-order topological phase transitions

The raise of the symmetry breaking mechanism by Landau[1] is a landmark in the studies of phase transitions. The Kosterlitz-Thouless phase transition[2-3] and the fractional quantum Hall effect[4], however, are believed to be induced by another mechanism: topology change. Despite rapid development of the theory of topological orders[5-7], a unified theoretical framework describing this new paradigm of phase transition and its relation to the Landau paradigm, is not seen. Here, we establish such a framework based on variational principle, and show that the critical condition for any second-order topological phase transitions is loss of positive-definiteness of a topologically protected second-order variation of the free energy. A topologically protected variation of a field solution is performed with respect to its “emergent displacements”, which is introduced by constructing an emergent elasticity problem for the solution. The Landau paradigm of phase transitions studies global property changes induced instability, while topological phase transitions study local property changes induced topological instability. The general effectiveness of this criterion is shown through analyzing the topological stability of several prototype solutions in both real space (two-kink solutions of the sine-Gordon model; an isolated skyrmion in chiral magnets) and reciprocal space (phonon spectrum of a monoatomic chain and a diatomic chain; band structure of a monoatomic chain within the Kronig-Penny model). Every field is emergent elastic with spatially modulated stiffness, and changes of topological property occur at its softened points. We anticipate our work to be a starting point for a general study of topological phase transitions in all field theories.
Indian IT and ICT Industry: A Performance Analysis Using Data Envelopment Analysis and Malmquist Index

The present study gives an account of IT and ICT infrastructure in India and points out the reasons for the boom in the IT exports and outsourcing business today and in times to come. The article analyzes the performances of the Indian IT industry by working out the technical efficiency of the software and telecommunication firms using the mathematical model of the Data Envelopment Analysis (DEA). The DEA is a non linear programming way of calculating technical efficiency of the decision making units. The study also examines the impact of various determinants on technical efficiency of software firms and net exports across the IT firms and further examines the determinants for new technology adoption by telecommunication industries, because the success of the software firms in terms of its exports is intertwined with the performance of telecommunication industry. The study uses a Malmquist index to estimate total factor productivity (TFP) change for the common set of software firms existing between 1996 and 2006 using a prowess data base. The total factor productivity is decomposed into efficiency change (catching up phenomena) and technical change (innovations) for the common set of software firms existing between 1996 and 2006 in India. At the end the study works out the performance of the Indian ICT sector in comparison with countries which are front runners using the DEA and global information technology report 2006 data base. The study will quantify the changes needed in the relatively good Indian ICT environment and ICT readiness to increase its ICT usage among individuals, business and government. The article confirms the improvements in productivity, efficiency, and technical change of the Indian Software industry from 1996 to 2006.
Evaluating Emerging CXL-enabled Memory Pooling for HPC Systems

Current HPC systems provide memory resources that are statically configured and tightly coupled with compute nodes. However, workloads on HPC systems are evolving. Diverse workloads lead to a need for configurable memory resources to achieve high performance and utilization. In this study, we evaluate a memory subsystem design leveraging CXL-enabled memory pooling. Two promising use cases of composable memory subsystems are studied – fine-grained capacity provisioning and scalable bandwidth provisioning. We developed an emulator to explore the performance impact of various memory compositions. We also provide a profiler to identify the memory usage patterns in applications and their optimization opportunities. Seven scientific and six graph applications are evaluated on various emulated memory configurations. Three out of seven scientific applications had less than 10% performance impact when the pooled memory backed 75% of their memory footprint. The results also show that a dynamically configured high-bandwidth system can effectively support bandwidth-intensive unstructured mesh-based applications like OpenFOAM. Finally, we identify interference through shared memory pools as a practical challenge for adoption on HPC systems.
Simple Set Sketching

Imagine handling collisions in a hash table by storing, in each cell, the bit-wise exclusive-or of the set of keys hashing there. This appears to be a terrible idea: For $\alpha n$ keys and $n$ buckets, where $\alpha$ is constant, we expect that a constant fraction of the keys will be unrecoverable due to collisions. We show that if this collision resolution strategy is repeated three times independently the situation reverses: If $\alpha$ is below a threshold of $\approx 0.81$ then we can recover the set of all inserted keys in linear time with high probability. Even though the description of our data structure is simple, its analysis is nontrivial. Our approach can be seen as a variant of the Invertible Bloom Filter (IBF) of Eppstein and Goodrich. While IBFs involve an explicit checksum per bucket to decide whether the bucket stores a single key, we exploit the idea of quotienting, namely that some bits of the key are implicit in the location where it is stored. We let those serve as an implicit checksum. These bits are not quite enough to ensure that no errors occur and the main technical challenge is to show that decoding can recover from these errors.
A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. In video surveillance based face recognition, face images are typically captured over multiple frames in uncontrolled conditions; where head pose, illumination, shadowing, motion blur and focus change over the sequence. We can generalize that the three fundamental operations involved in the facial recognition tasks: face detection, face alignment and face recognition. This study presents comparative benchmark tables for the state-of-art face recognition methods by testing them with same backbone architecture in order to focus only on the face recognition solution instead of network architecture. For this purpose, we constructed a video surveillance dataset of face IDs that has high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses.
The Causes and Consequences of Adopting a Works Council

Summary This paper reports on the results of a study on the determinants of the introduction of a works council and its consequences. We consider the effects of expectations concerning the economic conditions of a firm on the probability of adopting a works council. This is done, on the one hand, by use of employees' expectations on employment security and, on the other hand, by including forecasts of the management concerning employment growth and business conditions. Secondly, the effects of the introduction of works councils on wages and overtime working are investigated. Works councils are introduced when employees are concerned about job security. After the introduction of a works council the concerns about job security are less pronounced.
Detecting Invalid Map Merges in Lifelong SLAM

For Lifelong SLAM, one has to deal with temporary localization failures, e.g., induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e.g., due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place using a dataset that consists of multiple flat and office environments, as well as the public MIT Stata Center dataset. We show that methods based on a change detection algorithm and on comparison of gridmaps perform well in both environments and can be run in real-time with a reasonable computational cost.
Spin-induced scalarization of Kerr-Newman black hole in extended scalar-tensor theory

With certain non-minimal coupling between a massless scalar field and the Gauss-Bonnet curvature invariant in the extended scalar-tensor-Gauss-Bonnet (ESTGB) field theory, tachyonic instability of Kerr-Newman (KN) black hole is promoted. Critical spin and charge for the onset of the spontaneous scalarization phenomenon for the KN black hole are calculated in the infinitely large coupling limit. Then analytical formula for the coupling-strength-dependent critical existence line is obtained for the black-hole-scalar-field configuration in a finite large coupling regime. Moreover, numerical methods are used to perform threshold curves which are boundaries between bald KN black holes and their hairy counterparts. Intriguingly, we get to know KN black hole can be scalarized in the vanishing spin limit in ESTGB theory.