Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-Temporal Attention-based Graph Convolution Networks for Traffic Prediction

Accurate traffic prediction is critical to the effectiveness of intelligent transportation systems. However, traffic data are highly nonlinear with complicated dynamic spatio-temporal correlations, accurate traffic forecasting, particularly long-term forecasting, remains a difficulty. Existing models generally learn a fixed graph to capture spatial correlations, which makes them difficult to effectively capture changing spatial dynamics over time, resulting in poor prediction outcomes. To tackle these challenges, we propose a new spatio-temporal graph convolution network model, named Spatio-Temporal Attention-based Graph Convolution Network (STAGCN), which jointly fuses dynamic evolving spatial correlations and long-term temporal correlations to improve the performance. First, we design a spatio-temporal multi-head self-attention module to capture both spatial heterogeneity and temporal correlations. Second, we propose an adaptive evolving graph convolution module that can learn a new graph at each time step and make STAGCN evolve dynamically. Meanwhile, self-attention is used to construct a dynamic adjacency matrix to further capture spatial correlations. In addition, we optimize the original Transformer by employing relation-aware attention mechanism to make it better suitable for time series prediction, thereby improving the long-term prediction performance of the model. Extensive experiments are conducted on two real-world datasets, demonstrating that our proposed model achieves state-of-the-art performance and consistently outperforms other baselines.
Calibration of the Mass-Temperature Relation for Clusters of Galaxies Using Weak Gravitational Lensing

The main uncertainty in current determinations of the power spectrum normalization, σ8, from abundances of X-ray-luminous galaxy clusters arises from the calibration of the mass-temperature relation. We use our weak-lensing mass determinations of 30 clusters from the hitherto largest sample of clusters with lensing masses, combined with X-ray temperature data from the literature, to calibrate the normalization of this relation at a temperature of 8 keV, M500c, 8 keV = (8.7 ± 1.6) h-1 1014 M☉. This normalization is consistent with previous lensing-based results based on smaller cluster samples, and with some predictions from numerical simulations, but higher than most normalizations based on X-ray-derived cluster masses. Assuming the theoretically expected slope α = 3/2 of the mass-temperature relation, we derive σ8 = 0.88 ± 0.09 for a spatially flat ΛCDM universe with Ωm = 0.3. The main systematic errors on the lensing masses result from extrapolating the cluster masses beyond the field of view used for the gravitational lensing measurements, and from the separation of cluster/background galaxies, contributing each with a scatter of 20%. Taking this into account, there is still significant intrinsic scatter in the mass-temperature relation indicating that this relation may not be very tight, at least at the high-mass end. Furthermore, we find that dynamically relaxed clusters are (75 ± 40)% hotter than nonrelaxed clusters.
Charge-transfer-induced x-ray spectra in collisions of Ne 10 + with He and Ne atoms

The x-ray spectra resulting from single-electron capture in charge-exchange collisions of Ne${}^{10+}$ with He and Ne atoms are investigated for the projectile energy of 4.54 keV/u using the two-center atomic-orbital close-coupling method. The calculated spectra of the Ne${}^{9+}$ ion produced in Ne${}^{10+}$ + He and Ne${}^{10+}$ + Ne electron-capture collisions have, generally speaking, the same level of agreement with experimental measurements as those calculated previously by the classical trajectory Monte Carlo method, indicating that multi-electron-capture processes, particularly in the case of the Ne${}^{10+}$ + Ne system, significantly affect the considered spectra. The effects of two-electron capture on the spectral line intensity have been demonstrated qualitatively by using the binomial statistics and independent particle model for the target outer-shell electrons. The role of multi-electron-capture processes in the Ne${}^{10+}$ + Ne collision system needs further attention.
Enhanced Thermal Confinement in Phase-Change Memory Targeting Current Reduction

In this work, we present the extensive electrical characterization of 4kb Phase-Change Memory (PCM) arrays based on “Wall” structure and Ge-rich GeSbTe (GST) material, integrating a SiC dielectric with low thermal conductivity surrounding the heater element for enhanced cell thermal efficiency. We investigate the effects of the introduction of such dielectrics on the electrical performances of the device and we provide a promising path to achieve energy-efficient PCM cells supporting our results by electro-thermal TCAD simulations.
Analysis of Research Value and Significance of ANXA2 in Pancreatic Cancer Based on Medical Database Mining

Pancreatic cancer is a malignant tumor with a high mortality rate, so the efficient search for its potential molecular targets is of great practical importance for clinical treatment. In this study, we explore the expression of the ANXA2 molecule in pancreatic cancer tissues and the effect of its expression alteration on patient survival based on the database of medical information mining and determine the research value and significance of this molecule in pancreatic cancer for predicting potential molecular targets for medical diagnosis. We use medical databases such as Oncomine, GEPIA, and TCGA to mine and analyze ANXA2 expression changes in a variety of tumor tissues and Kaplan Meier-Plotter to analyze the survival differences of ANXA2 molecules in pancreatic cancer patients. Through literature review and by mining medical databases, it is revealed that the ANXA2 gene is closely related to pancreatic cancer progression and is worthy of further study as a new molecular target. This research method also makes full use of publicly available medical information and provides convenience for the study of pancreatic cancer and other diseases.
Detailed chemical composition of classical Cepheids in the LMC cluster NGC 1866 and in the field of the SMC

Context: Cepheids are excellent tracers of young stellar populations. They play a crucial role in astrophysics as standard candles. The chemistry of classical Cepheids in the Milky Way is now quite well-known. Despite a much larger sample, the chemical composition of Magellanic Cepheids has been only scarcely investigated. Aims: For the first time, we study the chemical composition of several Cepheids located in the same populous cluster: NGC 1866, in the Large Magellanic Cloud (LMC). To also investigate the chemical composition of Cepheids at lower metallicity, four targets are located in the Small Magellanic Cloud (SMC). Our sample allows us to increase the number of Cepheids with known metallicities in the LMC/SMC by 20%/25% and the number of Cepheids with detailed chemical composition in the LMC/SMC by 46%/50%. Methods: We use canonical spectroscopic analysis to determine the chemical composition of Cepheids and provide abundances for a good number of $\alpha$, iron-peak and neutron-capture elements. Results: We find that six Cepheids in the LMC cluster NGC 1866 have a very homogeneous chemical composition, also consistent with red giant branch (RGB) stars in the cluster. Period--age relations that include no or average rotation indicate that all the Cepheids in NGC 1866 have a similar age and therefore belong to the same stellar population. Our results are in good agreement with theoretical models accounting for luminosity and radial velocity variations. Using distances based on period-luminosity relations in the near- or mid-infrared, we investigate for the first time the metallicity distribution of the young population in the SMC in the depth direction. Preliminary results show no metallicity gradient along the SMC main body, but our sample is small and does not contain Cepheids in the inner few degrees of the SMC.
A numerical nonlinear analysis of two-dimensional ventilating entry of surface-piercing hydrofoils with effects of gravity

The presence of the free surface adds an element of difficulty to the development of numerical and theoretical methods for the performance prediction of surface-piercing hydrofoils. Existing methods of analysis for two-dimensional surface-piercing hydrofoils or blade sections of a surface-piercing propeller solve either a linear problem, assuming a thin section and ventilated surface along with linear free-surface boundary conditions, or a nonlinear problem in a self-similar setting. Both these approaches cannot be used when the effects of gravity are important, which is the case when a craft is operating at low speeds. A two-dimensional boundary-element-method-based numerical scheme is presented here that overcomes these drawbacks by solving the fully ventilated flow past a surface-piercing hydrofoil of finite dimensions and includes the whole gamut of nonlinear free-surface interactions. The unique aspect of the numerical scheme is that fully nonlinear boundary conditions are applied on the free surface which allows for the accurate modelling of the jet generated on the wetted boundary and the ventilated surface formed on the suction side as a result of the passage of the hydrofoil through the free surface. Moreover, the effects of gravity can be considered to take into account the influence of the Froude number. Ventilated-surface shapes predicted by the present scheme are compared with existing experimental results and are shown to be in good agreement.
Multi-level Operation of FeFETs Memristors: the Crucial Role of Three Dimensional Effects

This paper investigates and compares through a comprehensive TCAD analysis 2D and 3D simulations for ferro-electric based FETs. We provide clear evidence that the multiple read conductance values experimentally observed in FeFETs stem from source to drain percolation current paths, which are governed by the polarization patterns in the ferroelectric domains. Such a physical picture makes 3D simulations indispensable to capture even the qualitative features of the device behaviour, not to mention the quantitative aspects.
Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters

We use 1169 Pan-STARRS supernovae (SNe) and 195 low-z (z < 0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper we demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation. Combining SNe with cosmic microwave background (CMB) constraints from Planck, we measure the dark energy equation-of-state parameter w to be −0.989 ± 0.057 (stat+sys). If w evolves with redshift as w(a) = w0 + wa(1 − a), we find w0 = −0.912 ± 0.149 and wa = −0.513 ± 0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample. We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, , is the third-smallest source of systematic uncertainty in this work. We find limited (1.6σ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high-z analyses. Our data provide one of the best current constraints on w, demonstrating that samples with ∼5% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework.
Respiratory Modulation of Cortical Rhythms - Testing The Phase Transition Hypothesis

The presence of respiration-locked sensory cortical activity has been documented in various mammalian species in recent years, including cognitively-relevant gamma oscillations (30-80 Hz). This work builds on previous evidence suggesting that respiration has a direct influence on oscillatory activity in human sensory, motor and association cortical areas. The entrainment of cortical activity patterns by respiratory phase suggests a direct influence of respiration on cognitive processing, which represents a possible neuronal mechanism behind the well-documented but unexplained effects of respiratory exercises on emotional and cognitive functions. We explore possible interpretation of those findings in the context of the cinematic view of cognition, in particular cortical phase transitions. In addition to traditional Fourier-based correlational analysis of cortical signals, we introduce Hilbert analysis, which allows to monitor rapid phase synchronization-desynchronization transitions. Our results support the hypothesis that respiration-locked cortical activity is linked to phase transitions in the cortex, measured by discontinuities of the instantaneous phase of the analytic signal determined by the Hilbert transform. Taken together, these findings suggest that respiration acts as master clock exerting a subtle but unfailing synchronizing influence on the temporal organization of dynamic cortical activity patterns and the cognitive processes they control.
Cryptographic Strength Study of the Pseudorandom Sequences Generator Based on the Blender Algorithm

The paper deals with the cryptographic strength characteristics of Boolean functions used in the blender algorithm. This algorithm is used as a part of a pseudorandom sequences generator. The general features of the blender Boolean functions are formulated. To assess their cryptographic strength, a computational study of compliance with characteristics such as balance, algebraic degree, the absence of fictitious variables, high nonlinearity etc. was performed. A comparison is made with similar results for some known cryptographic algorithms. The ability to adjust the blender dimension for obtaining blocks of the desired size that form the output sequence, allows increasing the values of some characteristics of Boolean functions cryptographic strength. It is discovered that some cryptographic strength characteristics have asymptotic regularities of behavior. The results of the study make it possible to consider the level of cryptographic strength of the blender Boolean functions to be acceptable for use in cryptographic protection systems.
MultiEvasion: Evasion Attacks Against Multiple Malware Detectors

End-to-end malware detection analyzes raw bytes of programs with deep neural networks. It is considered as a new promising approach to simplify feature selection in static analysis but still provide accurate detection. Unfortunately, recent studies show that evasion attacks can modify raw bytes of malware and force a well-trained detector to predict the crafted malware as benign. In this paper, we propose a new evasion attack and validate the vulnerability of end-to-end malware detection in the context of multiple detectors, where our evasion attack MultiEvasion can defeat two (or even three) classifiers simultaneously without affecting functionalities of malware. This raises emerging concerns to end-to-end malware detection as running multiple classifiers was considered as one of the major countermeasures against evasion attacks. Specifically, our experimental results over real-world datasets show that our proposed attack can achieve 99.5 % evasion rate against two classifiers and 18.3 % evasion rate against three classifiers. Our findings suggest that the security of end-to-end malware detection need to be carefully examined before being applied in the real world.
A Comprehensive Study of Pulse Profile Evolution in SGR 1806-20&SGR 1900+14 with the RXTE PCA

Soft Gamma Repeaters undergo pulse proﬁle changes in connection with their burst activity. Here we present a comprehensive pulse proﬁle history of SGR 1806-20 and SGR 1900+14 in three energy bands using Rossi X-ray Timing Ex-plorer/Proportional Counter Array observations performed between 1996 and 2001. Using the Fourier harmonic powers of pulse proﬁles, we quantify the pulse shape evolution. Moreover, we determined the RMS pulsed count rates (PCRs) of each proﬁle. We show that the pulse proﬁles of SGR 1806 − 20 remain single pulsed showing only modest changes for most of our observing span, while those of SGR 1900+14 change remarkably in all energy bands. Highly signiﬁcant pulsations from SGR 1900+14 following the 1998 August 27 and 2001 April 18 bursts enabled us to study not only the decay of PCRs in diﬀerent energy bands but also their correlations with each other.
Particle Swarm Optimised Controller for Solid-State Transfer Switch Towards Fast Power Transfer and PQ Mitigation

This study proposes a solid-state transfer switch (SSTS) using fuzzy logic controller (FLC) design approach for enhancing the productivity and effectiveness of FLCs under harmonics conditions of a non-linear load using particle swarm optimization (PSO). A PSO-based FLC (PSOF) fitness function is also used to optimise and reduce the MSE to enhance the load transfer performance in a short period of time. The PSOF approach eliminates the time-consuming conventional trial-and-error method of deriving membership functions (MFs). Based on the fitness function evaluation findings, the created adaptive MFs are incorporated into voltage error and rate of change of voltage error for input and output. A harmonic filter is used to remove unwanted harmonic components induced by linear and nonlinear loads. To make sure the proposed control system works, the results are looked at both with and without PSO. The obtained transfer times were reduced by about 2ms, 4.35ms, 3.68ms and 3.56ms for 100%, 50%, 25% and 10% respectively, by optimising the fuzzy based system with PSO. Optimisation resulted in a total transfer time of 0.5ms, 8.72ms, 7.88ms and 7.32ms for 100%, 50%, 25% and 10% voltage sag, respectively. The design procedure and accuracy of the developed FLC are illustrated and investigated via simulation tests for the SSTS system. Results show that the optimised fuzzy controller is better than those obtained without the PSO algorithm in all tested cases in terms of transfer time and detection time and harmonic reduction.
Multi-Patches Cooperative Point Cloud Denoising Algorithm Based on Locally Linear Embedding

Point clouds are an increasingly important type of geometric data, but they are often corrupted by noise, which affects some downstream tasks such as registration, reconstruction, and rendering. However, limited attention has been paid to the geometric structure among points when denoising point clouds. In this paper, a novel algorithm for denoising point clouds is proposed that utilizes the structure of the underlying manifold. Specifically, the locally linear embedding method is employed to construct the several local structure patches and build a non-local similarity tensor that preserves the geometric structure information of the underlying manifolds. Based on the general assumption that similar structural patches contain redundant information, the noisy point clouds are smoothed by the low-rank tensor recovery model for the global tensor. Experiments demonstrate that the proposed algorithm can smooth noisy point clouds better than related algorithms.
Dynamic and Preemptive Task Offloading in Edge-cloud Computing Systems

The edge-cloud computing systems are widely used to support various computation services. In this paper, we consider a dynamic task offloading problem in the edge-cloud computing system with multiple independent and stochastic arriving tasks. The system periodically schedules and offloads tasks to heterogenous resources in consideration of the required transmission delays and computation times. Our goal is to minimize the sum of weighted response times of all the tasks. A greedy local search based online offloading framework is proposed for the problem under study, which dynamically assigns tasks to the appropriate destination (edge servers or cloud servers) and preemptively allocates computing resources to each task according to its latency-sensitivity. Evaluation experiments are delicately designed on a number of testing instances with various parameter settings. Experimental results indicate that the proposal algorithm is more effective than the compared algorithms.
Sentiment-Aware Fake News Detection on Social Media with Hypergraph Attention Networks

The rapid development of social media makes it easy for people to acquire information while also providing a platform for publishing and spreading fake news. Fake news brings plenty of explicit and implicit risks to social stability, making fake news detection an issue that deserves attention. Recent methods based on graph neural networks (GNN) achieve impressive results in fake news detection, but their performance is still limited in practice due to the absence of high-order relations between nodes. In this paper, we propose a Sentiment-Aware Hypergraph Attention Network (SA-HyperGAT) for fake news detection. SA-HyperGAT can better leverage different kinds of information from news contents and user comments with hypergraphs, which can capture higher-order dependency between words and sentences compared with general graphs. Specifically, we first construct two hypergraphs with distinct types of nodes and hyperedges to utilize structural information of news contents and sentimental information of user comments. Then we adopt a hypergraph attention network with a dual attention mechanism to learn the composed representations of two hypergraphs for the final prediction. Our proposed SA-HyperGAT outperforms competitive baselines on two real-world datasets. Extensive experimental results prove the effectiveness of each component in SA-HyperGAT.
Weight-Specific-Decoder Attention Model to Solve Multiobjective Combinatorial Optimization Problems

The multiobjective combinatorial optimization problems (MOCOPs) have a wide range of real-world applications. Designing an effective algorithm has an important and practical significance. Due to the huge search space and limited time, it is generally difficult to obtain the optimal solution of this kind of problem by traditional exact and heuristic algorithms. Recently, learning-based algorithms have achieved good results in solving MOCOPs, but the quality and diversity of found solutions can be further improved. In this paper, we propose a Weight-Specific-Decoder Attention Model (WSDAM) to better approximate the whole Pareto set. It embeds a weight-adaptive layer into the decoder to concentrate on the information of different weight vectors. During the model training, the weight vector is sampled from the Dirichlet distribution, which can further strengthen the learning of boundary solutions. We evaluate our method on two classic MOCOPs, i.e., the multiobjective traveling salesman problem (MOTSP) and multiobjective capacitated vehicle routing problem (MOCVRP). The experimental results show that our proposed method outperforms current state-of-the-art learning-based methods in both solution quality and generalization ability.