Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns of niche overlapping and richness among Geocoris species (Hemiptera: Geocoridae) in Iran

ABSTRACT Several big-eyed bugs, Geocoris species, are abundant predators in many important agricultural cropping systems. Despite their apparent importance and high visibility little is known about their environmental relationships, niche overlap and species richness. To determine these ecological characteristics of 5 Geocoris species for use in conservation efforts, an extensive sampling was done in 152 localities of Iran. A richness model was developed using a maximum entropy modelling approach (Maxent) and ArcGIS software for the five species based on collection records in conjunction with eight environmental variables. Maps for habitat overlap were created for paired species using ArcGIS 10.2 and ENMTools. The species displayed different overlapping niche ranges from 1.97% to 37.25% in pair-wise comparisons. For species richness three categories (habitats dominated by 0–1, 2–3, and 4–5 species) represented 38.44%, 46.07%, and 15.49% of the modelled landscape, respectively. A direct relationship was found between humidity and the number of species present in different locations. Understanding the degree of ecological overlap between the Geocoris species, and their effective predation of aphids and mites, is critical in designing biological control methods in agroecosystems.
Characterization of extragalactic point-sources on E- and B-mode maps of the CMB polarization

Although interesting in themselves, extragalactic sources emitting in the microwave range (mainly radio-loud active galactic nuclei and dusty galaxies) are also considered a contaminant from the point of view of Cosmic Microwave Background (CMB) experiments. These sources appear as unresolved point-like objects in CMB measurements because of the limited resolution of CMB experiments. Amongst other issues, point-like sources are known to obstruct the reconstruction of the lensing potential, and can hinder the detection of the Primordial Gravitational Wave Background for low values of r. Therefore, extragalactic point-source detection and subtraction is a fundamental part of the component separation process necessary to achieve some of the science goals set for the next generation of CMB experiments. As a previous step to their removal, in this work we present a new filter based on steerable wavelets that allows the characterization of the emission of these extragalactic sources. Instead of the usual approach of working in polarization maps of the Stokes' Q and U parameters, the proposed filter operates on E- and B-mode polarization maps. In this way, it benefits from the lower intensity that, both, the CMB, and the galactic foreground emission, present in B-modes to improve its performance. To demonstrate its potential, we have applied the filter to simulations of the future PICO satellite, and we predict that, for the regions of fainter galactic foreground emission in the 30 GHz and 155 GHz bands of PICO, our filter will be able to characterize sources down to a minimum polarization intensity of, respectively, 125 pK and 14 pK. Adopting a Π=0.02 polarization degree, these values correspond to 169 mJy and 288 mJy intensities.
Embedding-based Zero-shot Retrieval through Query Generation

Passage retrieval addresses the problem of locating relevant passages, usually from a large corpus, given a query. In practice, lexical term-matching algorithms like BM25 are popular choices for retrieval owing to their efficiency. However, term-based matching algorithms often miss relevant passages that have no lexical overlap with the query and cannot be finetuned to downstream datasets. In this work, we consider the embedding-based two-tower architecture as our neural retrieval model. Since labeled data can be scarce and because neural retrieval models require vast amounts of data to train, we propose a novel method for generating synthetic training data for retrieval. Our system produces remarkable results, significantly outperforming BM25 on 5 out of 6 datasets tested, by an average of 2.45 points for Recall@1. In some cases, our model trained on synthetic data can even outperform the same model trained on real data
Distributed deep reinforcement learning for simulation control

Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contain hyperparameters, which control solution fidelity and computational expense. The tuning of these parameters is non-trivial and the general approach is to manually ‘spot-check’ for good combinations. This is because optimal hyperparameter configuration search becomes intractable when the parameter space is large and when they may vary dynamically. To address this issue, we present a framework based on deep reinforcement learning (RL) to train a deep neural network agent that controls a model solve by varying parameters dynamically. First, we validate our RL framework for the problem of controlling chaos in chaotic systems by dynamically changing the parameters of the system. Subsequently, we illustrate the capabilities of our framework for accelerating the convergence of a steady-state computational fluid dynamics solver by automatically adjusting the relaxation factors of the discretized Navier–Stokes equations during run-time. The results indicate that the run-time control of the relaxation factors by the learned policy leads to a significant reduction in the number of iterations for convergence compared to the random selection of the relaxation factors. Our results point to potential benefits for learning adaptive hyperparameter learning strategies across different geometries and boundary conditions with implications for reduced computational campaign expenses 4 4 Data and codes available at https://github.com/Romit-Maulik/PAR-RL. Data and codes available at https://github.com/Romit-Maulik/PAR-RL.
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space. SNE considers Gaussian distribution for the probability in both the input and embedding spaces. However, t-SNE uses the Student-t and Gaussian distributions in these spaces, respectively. In this tutorial and survey paper, we explain SNE, symmetric SNE, t-SNE (or Cauchy-SNE), and t-SNE with general degrees of freedom. We also cover the out-of-sample extension and acceleration for these methods. Some simulations to visualize the embeddings are also provided.
Electronic properties of Bi2Se3 dopped by 3d transition metal (Mn, Fe, Co, or Ni) ions

Topological insulators are characterized by the existence of band inversion and the possibility of the realization of surface states. Doping with a magnetic atom, which is a source of the time-reversal symmetry breaking, can lead to realization of novel magneto-electronic properties of the system. In this paper, we study effects of substitution by the transition metal ions (Mn, Fe, Co and Ni) into Bi2Se3 on its electric properties. Using the ab inito supercell technique, we investigate the density of states and the projected band structure. Under such substitution the shift of the Fermi level is observed. We find the existence of nearly dispersionless bands around the Fermi level associated with substituted atoms, especially, in the case of the Co and Ni. Additionally, we discuss the modification of the electron localization function as well as charge and spin redistribution in the system. Our study shows a strong influence of the transition metal-Se bond on local modifications of the physical properties. The results are also discussed in the context of the interplay between energy levels of the magnetic impurities and topological surface states.
Attractants for management of the pine sawyer beetle Monochamus sutor, a potential vector of Bursaphelenchus xylophilus

Monochamus sutor (Linnaeus) (Coleoptera: Cerambycidae) is a secondary wood borer that has been hypothesized as capable of transmitting Bursaphelenchus xylophilus, the causal agent of pine wilt disease (PWD). This fact supposes a risk of spread of PWD over Europe and has created an urgent need for effective tools to detect and monitor both the nematode and the insect species that vectors it. Recent reporting of 2‐undecyloxy‐1‐ethanol as the M. sutor male‐produced aggregation pheromone has opened the possibility of developing an efficient lure for this species. It is known that some European bark beetle pheromone compounds and host volatiles kairomonally attract this species. Besides, smoke volatiles from burnt trees might play a role in M. sutor host location. In this work, field trapping experiments during 3 years in three countries (Spain, Sweden and Austria), aimed to develop an efficient pheromone‐kairomone lure operative for M. sutor management were carried out. Electroantennographic responses by M. sutor to Ips pheromones and to the Pityogenes chalcographus pheromone chalcogran were also studied. GC‐EAG recording showed that M. sutor males and females clearly responded to ipsenol and ipsdienol, and females also responded to 2‐methyl‐3‐buten‐2‐ol. Chalcogran elicited a response to M. sutor female antennae. In field tests, ipsenol was the most attractive kairomone to both sexes of M. sutor, whereas ipsdienol, cis‐verbenol and 2‐methyl‐3‐buten‐2‐ol were attractive and chalcogran was unattractive. When combined with the pheromone, most bark beetle kairomones increased catches of both sexes although chalcogran was completely ineffective. Thus, ipsenol was the strongest individual kairomone for M. sutor and the best single kairomone to be combined with the pheromone. Smoke volatile blends tested in Spain and Austria did not elicit responses, suggesting that these compounds are likely not involved in host finding by this species.
A Numerical Algorithm to Solve Supersonic Flow over a Wedge Shaped Airfoil

In this paper, a new algorithm is developed to solve a two dimensional supersonic flow around a wedge-shaped airfoil. The MacCormack predictor-corrector method is utilized to develop a solution. To further investigate the flow properties, a numerical algorithm written in C++ has been compiled so that it may be compared to commercial softwares. The developed algorithm is compiled and run with the initial conditions of a free stream Mach number of 2 while the wedge angle is set at 15o. The compiled program revealed that the flow velocities increased without bound. Adjustment of this condition was achieved by adding artificial dissipation. The addition of dissipation term into the code resulted stable output and the presence of a shock. Same case was also simulated with ANSYS Fluent and CFL3D softwares using second order discretization.
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning

Graph representation learning has attracted lots of attention recently. Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs. Thus, it remains a great challenge to capture rich information in large-scale graph data. Besides, these methods mainly focus on supervised learning and highly depend on node label information, which is expensive to obtain in the real world. As to unsupervised network embedding approaches, they overemphasize node proximity instead, whose learned representations can hardly be used in downstream application tasks directly. In recent years, emerging self-supervised learning provides a potential solution to address the aforementioned problems. However, existing self-supervised works also operate on the complete graph data and are biased to fit either global or very local (1-hop neighborhood) graph structures in defining the mutual information based loss terms. In this paper, a novel self-supervised representation learning method via Sub-graph Contrast, namely Subg-Con, is proposed by utilizing the strong correlation between central nodes and their sampled subgraphs to capture regional structure information. Instead of learning on the complete input graph data, with a novel data augmentation strategy, Subg-Con learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead. Compared with existing graph representation learning approaches, Subg-Con has prominent performance advantages in weaker supervision requirements, model learning scalability, and parallelization. Extensive experiments verify both the effectiveness and the efficiency of our work compared with both classic and state-of-the-art graph representation learning approaches on multiple realworld large-scale benchmark datasets from different domains.
TaskTorrent: a Lightweight Distributed Task-Based Runtime System in C++

We present TaskTorrent, a lightweight distributed task-based runtime in C++. TaskTorrent uses a parametrized task graph to express the task DAG, and one-sided active messages to trigger remote tasks asynchronously. As a result the task DAG is completely distributed and discovered in parallel. It is a C++14 library and only depends on MPI. We explain the API and the implementation. We perform a series of benchmarks against StarPU and ScaLAPACK. Micro benchmarks show it has a minimal overhead compared to other solutions. We then apply it to two large linear algebra problems. TaskTorrent scales very well to thousands of cores, exhibiting good weak and strong scalings.
Dynamic Multi-Agent Path Finding based on Conflict Resolution using Answer Set Programming

We study a dynamic version of multi-agent path finding problem (called D-MAPF) where existing agents may leave and new agents may join the team at different times. We introduce a new method to solve D-MAPF based on conflict-resolution. The idea is, when a set of new agents joins the team and there are conflicts, instead of replanning for the whole team, to replan only for a minimal subset of agents whose plans conflict with each other. We utilize answer set programming as part of our method for planning, replanning and identifying minimal set of conflicts.
Topology Optimization through Differentiable Finite Element Solver

In this paper, a topology optimization framework utilizing automatic differentiation is presented as an efficient way for solving 2D density-based topology optimization problem by calculating gradients through the fully differentiable finite element solver. The optimization framework with the differentiable physics solver is proposed and tested on several classical topology optimization examples. The differentiable solver is implemented in Julia programming language and can be automatically differentiated in reverse mode to provide the pullback functions of every single operation. The entire end-to-end gradient information can be then backed up by utilizing chain rule. This framework incorporates a generator built from convolutional layers with a set of learnable parameters to propose new designs for every iteration. Since the whole process is differentiable, the parameters of the generator can be updated using any optimization algorithm given the gradient information from automatic differentiation. The proposed optimization framework is demonstrated on designing a half MBB beam and compared to the results with the ones from the efficient 88-line code. By only changing the objective function and the boundary conditions, it can run an optimization for designing a compliant mechanism, e.g. a force inverter where the output displacement is in the opposite direction of the input.
An R package for Normality in Stationary Processes

Normality is the main assumption for analyzing dependent data in several time series models, and tests of normality have been widely studied in the literature, however, the implementations of these tests are limited. The \textbf{nortsTest} package performs the tests of \textit{Lobato and Velasco, Epps, Psaradakis and Vavra} and \textit{random projection} for normality of stationary processes. In addition, the package offers visual diagnostics for checking stationarity and normality assumptions for the most used time series models in several \R packages. The aim of this work is to show the functionality of the package, presenting each test performance with simulated examples, and the package utility for model diagnostic in time series analysis.
Enhancing Linear Algebraic Computation of Logic Programs Using Sparse Representation

Algebraic characterization of logic programs has received increasing attention in recent years. Researchers attempt to exploit connections between linear algebraic computation and symbolic computation to perform logical inference in large-scale knowledge bases. In this paper, we analyze the complexity of the linear algebraic methods for logic programs and propose further improvement by using sparse matrices to embed logic programs in vector spaces. We show its great power of computation in reaching the fixed point of the immediate consequence operator. In particular, performance for computing the least models of definite programs is dramatically improved using the sparse matrix representation. We also apply the method to the computation of stable models of normal programs, in which the guesses are associated with initial matrices, and verify its effect when there are small numbers of negation. These results show good enhancement in terms of performance for computing consequences of programs and depict the potential power of tensorized logic programs.
A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge towards the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.
The application of the adiabatic compression scenario to the radio relic in the galaxy cluster Abell 3411−3412

Although radio relics are understood to originate in intracluster shock waves resulting from merger shocks, the most widely used model for describing this (re-)acceleration process at shock fronts, the diffusive shock acceleration (DSA) model, has several challenges, including the fact that it is inefficient at low shock Mach numbers. In light of these challenges, it is worthwhile to consider alternative mechanisms. One possibility is the adiabatic compression by a shock wave of a residual fossil electron population which has been left over from a radio galaxy jet. This paper applies this model to the relic hosted in the merging galaxy cluster Abell 3411−3412, where a radio bridge between the relic and a radio galaxy has been observed, with the aim to reproduce the spatial structure of the spectral index of the relic. Four scenarios are presented, in which different effects are investigated, such as effects behind the shock front and different shock strengths. The results show that the adiabatic compression model can reproduce the observed spectral indices across the relic for a shock Mach number that is lower than the value required by the DSA-type modelling of this relic and is in accordance with the values derived from X-ray observations, if other mechanisms, such as an expansion phase or post-shock turbulence, are effective behind the shock front.
Building a Virtual Reality Fire Training with Unity and HTC Vive

Simulations in virtual environments for educational purpose are well-known in many domains to achieve an interactive learning experience. This paper addresses the development of a virtual reality application for training non-professionals in using a ﬁre extinguisher. Virtual ﬁre-ﬁghting is advantageous compared to ﬁre training in a real environment in terms of security, practicality and the selection of training scenarios. Additionally, it is a cost-effective alternative while offering a more engaging training experience. The state-of-the-art virtual reality device HTC Vive, paired with the high-level game development framework Unity, is used to develop a 360-degree ﬁre simulation and therefore allows trainees to interact with a ﬁre in a safe setting. The scenario can be controlled from an external supervisor for professional learning feedback. Through visual effects, realistic lighting, suitable shading methods and a physical approximation of ﬁre growth, we achieved to develop a realistic and an immersive learning environment which is also maintainable for further development.
Secure Search on Encrypted Data : Faster & Post-Processing Free

Outsourcing storage and computations to the cloud, despite its many celebrated advantages, raises serious privacy concerns. Encrypting the data with Fully Homomorphic Encryption (FHE) prior to uploading to the cloud makes it possible for the client to securely outsource computations to the server. This is because FHE allows processing the underlying clear-text data while it still remains encrypted (and without giving away the secret key). While theoretically feasible, the practical run-time overhead of FHE is notoriously high, with the primary FHE bottlenecks emanating from the degree and overall number of multiplications in the polynomial specifying the desired computation. The challenge therefore is to specify the desired computation via a polynomial of low degree and low number of multiplications. In this work we focus on the problem of Secure Search on FHE encrypted data, which is useful in numerous data analysis and retrieval tasks. Specifically, we focus on settings where the server holds an unsorted array of previously uploaded encrypted elements [[x]] = ( [[x[1]]], . . . , [[x[n]]] ) , the client sends to the server an encrypted query [[q]], and the server returns to the client the encrypted index and element pair [[y]] = ( [[i∗]], [[x[i∗]]] ) for the first element satisfying the agreed matching condition. We present a novel secure search solution for these settings, improving the prior state-ofthe-art (Akavia, Feldman, Shaul, CCS’2018) in being: • The first secure search solution that simultaneously achieves: (1) full security; (2) efficient server; (3) efficient client; (4) efficient communication; (5) unrestricted search functionality; (6) retrieval of both index and element; (7) post-processing free client; (8) negligible error probability. In contrast, all prior works achieved only a strict subset of these desired properties. • Faster than the prior-state-of-the-art, attaining: (a) optimal client time (log n/ log log n speedup), and (b) considerably improved server time (degree reduced from cubic to linear in log n, and overall multiplications reduce by up to log n factor). Moreover, (c) our solution requires only computa-