Miracle-dz/UniFAR-FTUs huggingface dataset:
Reciprocal distance energy of complete multipartite graphs

In this paper, ﬁrst we compute the energy of a special partitioned matrix under some cases. As a consequence, we obtain the reciprocal distance energy of the complete multipartite graph and also we give various other energies of complete multipartite graphs. Next, we show that among all complete k -partite graphs on n vertices, the complete split graph CS ( n, k − 1) has minimum reciprocal distance energy and the reciprocal distance energy is maximum for the Turan graph T ( n, k ). At last, it is shown that the reciprocal distance energy of the complete bipartite graph K m,m decreases under deletion of an edge if 2 ≤ m ≤ 7, whereas the reciprocal distance energy increases if 8 ≤ m . Also, we show that the reciprocal distance energy of the complete tripartite graph does not increase under edge deletion.
Modular Control Plane Verification via Temporal Invariants

Monolithic control plane verification cannot scale to hyperscale network architectures with tens of thousands of nodes, heterogeneous network policies and thousands of network changes a day. Instead, modular verification offers improved scalability, reasoning over diverse behaviors, and robustness following policy updates. We introduce Timepiece, a new modular control plane verification system. While one class of verifiers, starting with Minesweeper, were based on analysis of stable paths, we show that such models, when deployed naïvely for modular verification, are unsound. To rectify the situation, we adopt a routing model based around a logical notion of time and develop a sound, expressive, and scalable verification engine. Our system requires that a user specifies interfaces between module components. We develop methods for defining these interfaces using predicates inspired by temporal logic, and show how to use those interfaces to verify a range of network-wide properties such as reachability or access control. Verifying a prefix-filtering policy using a non-modular verification engine times out on an 80-node fattree network after 2 hours. However, Timepiece verifies a 2,000-node fattree in 2.37 minutes on a 96-core virtual machine. Modular verification of individual routers is embarrassingly parallel and completes in seconds, which allows verification to scale beyond non-modular engines, while still allowing the full power of SMT-based symbolic reasoning.
Contour Generation with Realistic Inter-observer Variation

Contours are used in radiotherapy treatment planning to identify regions to be irradiated with high dose and regions to be spared. Therefore, any contouring uncertainty influences the whole treatment. Even though this is the biggest remaining source of uncertainty when daily IGRT or adaptation is used, it has not been accounted for quantitatively in treatment planning. Using probabilistic planning allows to directly account for contouring uncertainties in plan optimisation. The first step is to create an algorithm that can generate many realistic contours with variation matching actual inter-observer variation. We propose a methodology to generate random contours, based on measured spatial inter-observer variation, IOV, and a single parameter that controls its geometrical dependency: 𝜎 , the width of the 3D Gaussian used as point spread function (PSF). We used a level set formulation of the median shape, with the level set function defined as the signed distance transform. To create a new contour, we added the median level set and a noise map which was weighted with the IOV map and then convolved with the PSF. Thresholding the level set function reconstructs the newly generated contour. We used data from 18 patients from the golden atlas, consisting of five prostate delineations on T2-w MRI scans. To evaluate the similarity between the contours, we calculated the maximum distance to agreement to the median shape (maxDTA), and the minimum dose of the contours using an ideal dose distribution. We used the two-sample Kolmogorov-Smirnov test to compare the distributions for maxDTA and minDose between the generated and manually delineated contours. Only 𝜎 = 0.75 𝑐𝑚 produced maxDTA and minDose distributions that were not significantly different from the manually delineated structures. Accounting for the PSF is essential to correctly simulate inter-observer variation. The first step to incorporate contour variations directly into treatment optimisation has been taken.
SYK model with an extra diagonal perturbation: phase transition in the eigenvalue spectrum

We study the SYK model with an extra constant source, i.e. a constant matrix or equivalently a diagonal matrix with only one non-zero entry λ 1 . By using methods from analytic combinatorics [9], we ﬁnd exact expressions for the moments of this model. We further prove that the spectrum of this model can have a gap when λ 1 > λ c 1 , thus exhibiting a phase transition in λ 1 . In this case, a single isolated eigenvalue splits oﬀ from SYK’s eigenvalues distribution. We located this single eigenvalue by analyzing the singular behavior of a supercritical functional composition scheme. In certain limit our results recover the ones of random matrices with non-zero mean entries.
Dual Cluster Contrastive learning for Object Re-Identification

Recently, cluster contrastive learning has been proven effective for object ReID by computing the contrastive loss between the individual features and the cluster memory. However, existing methods that use the individual features to momentum update the cluster memory will ﬂuctuate over the training examples, especially for the outlier samples. Unlike the individual-based updating mechanism, the centroid-based updating mechanism that applies the mean feature of each cluster to update the cluster memory can reduce the impact of individual samples. Therefore, we for-mulate the individual-based updating and centroid-based updating mechanisms in a uniﬁed cluster contrastive framework, named Dual Cluster Contrastive framework (DCC), which maintains two types of memory banks: individual and centroid cluster memory banks. Signiﬁcantly, the individual cluster memory considers just one individual at a time to take a single step for updating. The centroid cluster memory applies the mean feature of each cluster to update the corresponding cluster memory. During optimization, besides the vallina contrastive loss of each memory, a cross-view consistency constraint is applied to exchange the beneﬁts of two memories for generating a discriminative description for the object ReID. Note that DCC can be easily applied for unsupervised or supervised object ReID by using ground-truth labels or the generated pseudo-labels. Exten-sive experiments on three benchmarks, e.g., Market-1501, MSMT17, and VeRi-776, under supervised Object ReID and unsupervised Object ReID demonstrate the superior-ity of the proposed DCC.
Signature of Supersonic Turbulence in Galaxy Clusters Revealed by AGN-driven Hα Filaments

The hot intracluster medium (ICM) is thought to be quiescent with low observed velocity dispersions. Surface brightness fluctuations of the ICM also suggest that its turbulence is subsonic with a Kolmogorov scaling relation, indicating that the viscosity is suppressed and the kinetic energy cascades to small scales unscathed. However, recent observations of the cold gas filaments in galaxy clusters find that the scaling relations are steeper than that of the hot plasma, signaling kinetic energy losses and the presence of supersonic flows. In this work we use high-resolution simulations to explore the turbulent velocity structure of the cold filaments at the cores of galaxy clusters. Our results indicate that supersonic turbulent structures can be “frozen” in the cold gas that cools and fragments out of a fast, ∼107 K outflow driven by the central active galactic nucleus (AGN), when the radiative cooling time is shorter than the dynamical sound-crossing time. After the cold gas formation, however, the slope of the velocity structure function (VSF) flattens significantly over short, ∼10 Myr timescales. The lack of flattened VSF in observations of Hα filaments indicates that the Hα-emitting phase is short-lived for the cold gas in galaxy clusters. On the other hand, the ubiquity of supersonic turbulence revealed by observed filaments strongly suggests that supersonic outflows are an integral part of AGN–ICM interaction, and that AGN activity plays a crucial role at driving turbulence in galaxy clusters.
A COMPREHENSIVE REVIEW OF BAT ALGORITHMS AND THEIR HYBRIDIZATION

Swarm intelligence is a modern and efficient mechanism for solving hard problems in computer science, engineering, mathematics, economics, medicine and optimization. Swarm intelligence is the collective behavior of decentralized and self-organized systems. This research area is a branch of artificial intelligence and could be viewed as some kind of family relationship with evolutionary computation because both communities share a lot of common characteristics. To date, a lot of swarm intelligence algorithms have been developed and applied to several real-world problems. The main focus of this thesis is devoted to the bat algorithm which is a member of the swarm intelligence community, as developed recently. In line with this, a comprehensive analysis of papers was performed tackling this algorithm. Some hybridizations of the original algorithm were proposed because the preliminary results of this algorithm regarding the optimization of benchmark functions with higher dimensions had not too promising. Extensive experiments showed that the hybridizing the original bat algorithm has beneficial effects on the results of the original bat algorithm. Finally, an experimental study was performed during which we researched for the dependence of an applied randomized method on the results of the original bat algorithm. The results of this study showed that selecting the randomized method had a crucial impact on the results of the original bat algorithm and that the bat algorithm using Levy flights is also suitable for solving the harder optimization problems.
Spatiotemporal Patterns of Snake Captures and Activity in Upland Pine Forests

Abstract. Patterns of species' occurrences across space and time are fundamental components to understanding their ecology, as this variation often reflects responses to local environmental gradients. We built species-specific models to understand the spatial and temporal factors predicting captures and activity of five snake species in upland pine forests: copperhead (Agkistrodon contortrix), racer (Coluber constrictor), coachwhip (Masticophis flagellum), western ratsnake (Pantherophis obsoletus), and western ribbonsnake (Thamnophis proximus). From mid-May to mid-July across 3 y (2018, 2019, 2020), we monitored boxtraps in two upland pine forests experiencing different management regimes: (1) subjected to frequent thinning and prescribed burning, and (2) subjected to infrequent thinning and prescribed burning. Significantly more copperheads and western ribbonsnakes were captured at the infrequently thinned and burned forest, whereas significantly more racers were captured at forest subjected to frequent thinning and burning. As the summer progressed, captures decreased each subsequent month for both racers and western ratsnakes, with the fewest captures in July. Western ratsnakes were the only species to exhibit a response to the weather in that activity decreased with increasing rainfall. No variables were significant predictors of coachwhip captures. The variation in captures across space may be attributed to the physiological tolerances of each species based on their habitat preferences or differences in prey availability at each forest. Interactions between the physiological tolerances, foraging behaviors, or their reproductive phenology may be underlying the temporal variation in activity patterns.
Influence of Vegetation and Soil on Relative Density of Baird's Pocket Gopher (Geomys breviceps) in Louisiana

Abstract. Baird's pocket gopher (Geomys breviceps) is the only species of pocket gopher in Louisiana. Interest in this species' natural history has increased due to its close association with the Louisiana pine snake (Pituophis ruthveni), which recently was listed as threatened under the Endangered Species Act. Approximately 75% of P. ruthveni's estimated prey biomass consists of G. breviceps, whose burrow systems also provide shelter and hibernacula. Therefore, increasing densities of G. breviceps may assist in the recovery and management of P. ruthveni. Previous studies have identified specific habitat characteristics that are preferred by G. breviceps, but none have assessed how different habitats affect pocket gopher density. Live trapping was conducted at sixteen sites throughout Louisiana to determine relative density of G. breviceps. Vegetation and soil data were collected, and a multi-model selection approach was used to determine which variable(s) best explained variation in relative density. Relative densities of G. breviceps ranged from 0 to 12.5 gophers/ha with an average of 3.44 gophers/ha. Canopy cover best predicted numbers of G. breviceps across the study area. Soil data was not a good predictor; however, all sampling plots possessed sandy loam or loamy sand soils, which is preferred by Geomys spp. Management strategies aimed at increasing density of G. breviceps should focus on continued restoration of longleaf pine forest and supplemented by opening patches of forest, especially in areas with preferred soil textures.
A Comparison of Mechanical Versus Chemical Control of the Invasive Species Myrtle Spurge (Euphorbia myrsinites)

ABSTRACT Myrtle spurge (Euphorbia myrsinites) is an invasive plant in North America that was introduced from Eurasia as a drought-tolerant ornamental. It has escaped cultivation and is widespread in western North America. Infestations can be reduced by either mechanical or chemical means, but the two approaches have not been compared directly. One study recommended mechanical treatment in the spring and chemical treatment in the fall, but neither recommendation was supported by data. To test the effectiveness of mechanical versus chemical treatment in the fall, we located six sites in the foothills of the Wasatch Mountains in Utah that were uniformly infested with myrtle spurge. At each of the sites we randomly assigned a 2.25 m2 quadrat to one of three treatments (i.e., mechanical, chemical, or control). The mechanical treatment entailed hand pulling and the chemical treatment involved applying the herbicide Roundup to each myrtle spurge. The controls were unmanipulated. We photographed each quadrat pretreatment and one year post-treatment. Both mechanical and chemical approaches significantly reduced the percent cover and stalk count of myrtle spurge compared to the controls. Additionally, the effects of the two treatments did not differ from each other. These results indicate that land managers can control myrtle spurge in the fall with either treatment and do not have to wait until the spring to employ mechanical methods. Mechanical removal of myrtle spurge is better suited to events that involve members of the general public such as “Purge Your Spurge” activities because they reduce the public's exposure to herbicide.
Target Detection in Mainlobe Jamming Using Convolutional Neural Network

The traditional array adaptive beamforming methods yield serious target loss in the presence of mainlobe jamming. Super-resolution methods based on sparse recovery can separate the mainlobe jamming from the target in the space domain, but it cannot reconstruct the target information when the angle between the jamming and the target is too small or the signal-to-noise ratio is too low. In order to solve above problems, we propose a novel super-resolution method based on the convolutional neural network. The proposed method can effectively separate the mainlobe jamming and the weak target even within the half-power mainlobe width, thus can achieve the target detection in the mainlobe jamming scenario. Simulation experiments verify the effectiveness of the proposed method.
A Support Worker Perspective on Use of New Technologies by People with Intellectual Disabilities

People with intellectual disability access innovative technologies in disability community centres in Australia, under the guidance of support workers. This article investigates the perspectives of 15 support workers and 5 managers across four community centres on the introduction and use of technology like tablets, video games, 3D printing, virtual reality, and social robots. They had diverse views on who is responsible for facilitating, embedding, and shaping technology for learning life skills and socializing. We found technology use to be driven by facilitator's knowledge (pre-existing, observed, or trained), interests, the value they place on the interactions afforded by the technology, and organization values. We discuss how future designs can emphasise communities of users while empowering individuals to achieve their goals. We suggest co-design strategies for assistive technology that involve support workers in the dual roles of proxy and co-user. We finally discuss how broader organisational factors can influence appropriation and use.
Study on Discharge Characteristics of Air Gap Defects at Low Temperature of Contact Cable Termination in Traction Substation

Ethylene Propylene Rubber (EPR) cable termination air gap defects will lead to the occurrence of partial discharge phenomenon in the cold area caused by frequent termination breakdown failure. To study the characteristics of cable termination air gap discharge at low temperature, a low-temperature partial discharge test system is designed to simulate the environment in the severe cold region. The partial discharge process of termination air gap defects during discharge is monitored. According to the characteristics of termination air gap discharge and the variation rule, the whole PD process of contact cable termination is divided into three main stages at low temperatures. And the local discharge characteristics of each stage are also analyzed. The results show that the discharge voltage in the air gap is significantly reduced and the discharge pulse is concentrated in the negative half cycle. The discharge will go through a period of "stagnation". However, with the prolonging of the test time, the discharge is developing rapidly and the discharge phase gradually expands. The amplitude of the half-week discharge increases, the positive and negative half-cycle discharge tends to be symmetrical, before the breakdown occurs, the discharge volume changes are not obvious, but the phase distribution extends to the whole cycle. The study of the discharge characteristics under the conditions of low temperature and high-pressure operation can provide the basis for assessing the serious situation of termination defects.
A Methodology to Design and Evaluate HRI Teaming Tasks in Robotic Competitions

As social robots become more prominent in our lives, their interaction with humans takes an increasing role, and new collaborative scenarios emerge. This development brings the need to realize robust test methods enabling the design and evaluation of Human-Robot Interaction (HRI) teaming tasks to prove functionality and promote adoption. In this article, we present a general-purpose and repeatable methodology for conducting studies in collaborative HRI in the range of robotic competitions. The methodology includes a step-by-step approach to design HRI teaming tasks tailored to be enacted in a robotic competition and to evaluate the performance of social robots to execute the designed tasks, exploring the relationship between robots’ performance and user perceptions based on the feedback of the users participating to such tasks. We assess the feasibility of the methodology to design and evaluate an HRI teaming task in the context of “Smart CIties RObotics Challenges” (SciRoc) competition, which targets at investigating the impact of social of robots in smart cities.
Attention based Bi-LSTM for Power Line Partial Discharge Fault Detection

Partial discharge is one of the faults in power distribution networks, especially in overhead lines with covered conductor of the medium voltage distribution networks, which may damage equipment and stop it’s functioning entirely. However, it is more difficult to detect partial discharge faults because of the different physical and chemical reactions and the same characterization caused by partial discharge faults, and the noise interference of the environment itself. By deploying distributed high-frequency sampling sensors to collecting three-phase voltage signal and using machine learning algorithms to detect the presence of partial discharge fault, can not only not interfere with the normal work of transmission lines to realize on-line detection of partial discharge fault, also can pass the upgrade in fault detection fault detection algorithm which can improve accuracy, therefore, it has become one of the main methods of partial discharge fault detection. In this paper, an attention mechanism based bidirectional long short term memory (Attention-LSTM) fault detection model is proposed, which can take full advantage of the bidirectional long neural network (Bi-LSTM) and attention mechanism, hence, no feature engineering expert is needed to solve the power line partial discharge fault detection issues. Experimental results show that the proposed model outperforms the existing methods in all selected performance metrics.
Factors that impact the acceptance and usage of e-assessment by academics in Saudi universities

As assessment is one of the important pillars of the learning process, and E-assessment has become an essential part of education systems. E-assessment developed to address some of the limitations and problems of a paper-test. In last 10 years, E-assessment has improved in developed countries such as UK. In contrast, in Saudi Arabia, one of the developing countries, less attention is still paid to the usage of E-assessment and research which discusses E-assessment issues in Saudi Arabia is limited. Consequently, this paper will investigate the factors that impact on academic’s use of E-assessment in Saudi universities. In order to examine these factors, this paper adopts the Decomposed Theory of Planned Behaviour model (DTPB) with slight modification. Age and gender are added to the proposed model as moderating factors that affect attitude, subjective norms and perceived behavioural control. IT support is also added as a factor under perceived behavioural control and technology facilitating conditions are included under resources facilitating conditions