Search Region Research Articles

DETDO: An adaptive hybrid dandelion optimizer for engineering optimization

Dandelion Optimizer (DO) is a recently proposed swarm intelligence algorithm that coincides with the process of finding the best reproduction site for dandelion seeds. Compared with the classical Meta-heuristic algorithms, DO exhibits strong competitiveness, but it also has some drawbacks. In this paper, we proposed an adaptive hybrid dandelion optimizer called DETDO by combining three strategies of adaptive tent chaotic mapping, differential evolution (DE) strategy, and adaptive t-distribution perturbation to address the shortcomings of weak DO development, easy to fall into local optimum and slow convergence speed. Firstly, the adaptive tent chaos mapping is used in the initialization phase to obtain a uniformly distributed high-quality initial population, which helps the algorithm to enter the correct search region quickly. Secondly, the DE strategy is introduced to increase the diversity of dandelion populations to avoid algorithm stagnation, which improves the exploitation capability and the accuracy of the optimal solution. Finally, adaptive t-distribution perturbation around the elite solution successfully balances the exploration and exploitation phases while improving the convergence speed through a reasonable conversion from Cauchy to Gaussian distribution. The proposed DETDO is compared with classical or advanced optimization algorithms on CEC2017 and CEC2019 test sets, and the experimental results and statistical analysis demonstrate that the algorithm has better optimization accuracy and speed. In addition, DETDO has obtained the best results in solving six real-world engineering design problems. Finally, DETDO is applied to two bar topology optimization cases. Under a series of complex constraints, DETDO produces a lighter bar structure than the current scheme. It further illustrates the effectiveness and applicability of DETDO in practical problems. The above results mean that DETDO with strong competitiveness will become a preferred swarm intelligence algorithm to cope with optimization problems.

An Iterative Two-Stage Multifidelity Optimization Algorithm for Computationally Expensive Problems

Engineering design optimization often involves use of numerical simulations to assess the performance of candidate designs. The simulations for computing high-fidelity (HF) performance estimates, such as finite element analysis or computational fluid dynamics, are typically computationally expensive. In some cases, it may also be possible to run an alternate or cheaper version of the simulation (through, e.g., use of a coarse mesh) to yield a low-fidelity (LF) performance estimate. Multi-fidelity optimization refers to the class of methods that aim to manage LF and HF evaluations efficiently to optimize computationally expensive problems within a limited computing budget. Among the prominent existing multi-fidelity approaches, some of them depend on a sufficiently dense a priori sampling; while others use unidirectional information exchange from LF to HF; both of which lead to a possibility of spending evaluation budget on unpromising search regions. This paper proposes an improved multi-fidelity approach using an iterative, two-stage scheme (MFITS). It uses the collective information from previously evaluated designs to determine a sampling neighbourhood for LF evaluations. These samples are in-turn used for building a co-kriging surrogate model that is then searched globally to identify a good candidate for HF evaluation. By restricting the LF sampling neighbourhood, the computational budget can be used more efficiently, as the search is focused on regions that have historically produced good quality solutions. Numerical experiments and benchmarking are conducted on two suites of test problems and two practical design optimization problems to demonstrate the efficacy of MFITS.

Blind Search of the Solar Neighborhood Galactic Disk within 5 kpc: 1179 New Star Clusters Found in Gaia DR3

Studying open clusters (OCs) may help to provide a comprehensive understanding of the structure and evolution of the Milky Way. Many previous studies have systematically searched for OCs near the solar system within 1.2 kpc or 20° of Galactic latitude. However, few studies have searched for OCs at higher Galactic latitudes and deeper distances. In this study, based on a hybrid unsupervised clustering algorithm (friends-of-friends and pyUPMASK) and a binary classification algorithm (random forest), we extended the search region (i.e., Galactic latitude and performed a fine-grained blind search for Galactic clusters in Gaia DR3. After crossmatching, the newly discovered cluster candidates are fitted using isochrone fitting to estimate the main physical parameters (i.e., age and metallicity) of these clusters. These cluster candidates were then checked using manual visual inspection. Their statistical properties were also compared with previously exposed cluster catalogs. We found 1179 new clusters with considerable confidence within 5 kpc.

Designing of 2d-IIR Filter Using a Fused ESMA-Pelican Optimization Algorithm (FEPOA)

Many Digital Signal Processing (DSP) applications and electronic gadgets today require digital filtering. Different optimization algorithms have been used to obtain fast and improved results. Several researchers have used Enhanced Slime Mould Algorithm for designing the 2D IIR filter. However, it is observed that the Enhanced Slime Mould Algorithm did not achieve a better solution structure and had a slower convergence rate. In order to overcome the issue a fused ESMA-pelican Optimization Algorithm (FEPOA) is utilized for designing the 2D IIR filter which incorporates the pelican Optimization Algorithm with the Enhanced slime Mould Algorithm (ESMA). At first, the Chaotic Approach is utilized to initialize the population which provides the high-quality population with excellent population diversity, after that the position of population members is to identify and correct the individual in the boundary search region. After that, by the pelican Tactical Approach is to examine the search space and exploration power of the FEPOA, then the Fitness is calculated randomly, and the best solution will be upgraded and then moved towards the iterations. It repeats the FEPOA phases until the execution completes. Then the best solution gives the optimal solution, which enhances the speed of convergence, convergence accuracy and the performances of FEPOA. The FEPOA is then implemented in the IIR filter to improve the overall filter design. The results provided by FEPOA accomplish the necessary fitness and best solution for 200 iterations, and the amplitude response will achieve the maximum value for =2,4,8 as well as the execution time of 3.0158s, which is much quicker than the other Genetic Algorithms often used for 2D IIR filters.

Big Data Cyber Security Using Machine Learning

It is well-known that cyber protection in the context of big data is a crucial issue and also presents a significant challenge to the research study field. The use of artificial intelligence algorithms has been suggested as a potential solution for complex information security issues. Support vector machines (SVMs), one of these techniques, have shown outstanding performance on several category problems. However, in order to construct an effective SVM, the user must first determine the correct SVM configuration, which is a difficult task that necessitates expert knowledge as well as a substantial amount of hands-on initiative for trial and error. In this study, precision and design intricacy are treated as two conflicting goals in our formulation of the SVM arrangement technique as a bi-objective optimisation problem. We propose a brand-new, issue-domain-independent hyper-heuristic framework for bi-objective optimisation. This is the first time a hyper-heuristic for this issue has been developed. The suggested hyper-heuristic framework consists of both high-level and low-level heuristics. The high-level strategy makes decisions on which low-level heuristics to employ in order to create a fresh SVM setup based on the search performance. Each of the low-level heuristics successfully locates the SVM layout search region using a distinct set of rules. The suggested structure adaptively integrates Pareto-based and disintegration-based strategy toughness to approximate the Pareto collection of SVM configurations in order to handle bi-objective optimisation. The effectiveness of the suggested structure has been evaluated in relation to two cyber security concerns: the detection of anomalous intrusions and Microsoft malware's large information category. The obtained results show that the suggested framework is quite effective, if not superior, when compared to its competitors and other formulas.

Hint for a TeV neutrino emission from the Galactic Ridge with ANTARES

Interactions of cosmic ray protons, atomic nuclei, and electrons in the interstellar medium in the inner part of the Milky Way produce a γ-ray flux from the Galactic Ridge. If the γ-ray emission is dominated by proton and nuclei interactions, a neutrino flux comparable to the γ-ray flux is expected from the same sky region.Data collected by the ANTARES neutrino telescope are used to constrain the neutrino flux from the Galactic Ridge in the 1-100 TeV energy range. Neutrino events reconstructed both as tracks and showers are considered in the analysis and the selection is optimized for the search of an excess in the region |l|<30°, |b|<2°. The expected background in the search region is estimated using an off-zone region with similar sky coverage. Neutrino signal originating from a power-law spectrum with spectral index ranging from Γν=1 to 4 is simulated in both channels. The observed energy distributions are fitted to constrain the neutrino emission from the Ridge.The energy distributions in the signal region are inconsistent with the background expectation at ∼96% confidence level. The mild excess over the background is consistent with a neutrino flux with a power law with a spectral index 2.45−0.34+0.22 and a flux normalization dNνdEν=4.0−2.0+2.7×10−16 GeV−1cm−2s−1sr−1 at 40 TeV reference energy. Such flux is consistent with the expected neutrino signal if the bulk of the observed γ-ray flux from the Galactic Ridge originates from interactions of cosmic ray protons and nuclei with a power-law spectrum extending well into the PeV energy range.

SiamCCF: Siamese visual tracking via cross‐layer calibration fusion

AbstractSiamese networks have attracted wide attention in visual tracking due to their competitive accuracy and speed. However, the existing Siamese trackers usually leverage a fixed linear aggregation of feature maps, which does not effectively fuse the different layers of features with attention. Besides, most of Siamese trackers calculate the similarity between the template and the search region through a cross‐correlation operation between the features of the last blocks from the two branches, which might introduce the redundant noise information. In order to solve these problems, this study proposes a novel Siamese visual tracking method via cross‐layer calibration fusion, termed SiamCCF. An attention‐based feature fusion module is employed using local attention and non‐local attention to fuse the features from the deep and shallow layers, so as to capture both local details and high‐level semantic information. Moreover, a cross‐layer calibration module can use the fused features to calibrate the features of the last network blocks and build the cross‐layer long‐range spatial and inter‐channel dependencies around each spatial location. Extensive experiments demonstrate that the proposed method has achieved competitive tracking performance compared with state‐of‐the‐art trackers on challenging benchmarks, including OTB100, OTB2013, UAV123, UAV20L, and LaSOT.

Gaussian Process-based calculation of look-elsewhere trials factor

In high-energy physics it is a recurring challenge to efficiently and precisely (enough) calculate the global significance of, e.g., a potential new resonance. We propose a new method that models the significance in the search region as a Gaussian Process. The kernel of the Gaussian Process is approximated with a covariance matrix and is calculated with a carefully designed set of background-only data sets, comparable in number to the random background-only data sets used in a typical analysis that relies on the average upcrossings of the significance. The trials factor for both low and moderate significances can subsequently be calculated to the desired precision with a computationally inexpensive random sampling of the Gaussian Process. In addition, once the covariance of the Gaussian Process is determined, the average number of upcrossings can be computed analytically. In our work we give some highlights of the analytic calculation and also discuss some peculiarities of the trials factor estimation on a finite grid. We illustrate the method with studies of three complementary statistical models.

STFTrack: Spatio-Temporal-Focused Siamese Network for Infrared UAV Tracking

The rapid popularity of UAVs has encouraged the development of Anti-UAV technology. Infrared-detector-based visual tracking for UAVs provides an encouraging solution for Anti-UAVs. However, it still faces the problem of tracking instability caused by environmental thermal crossover and similar distractors. To address these issues, we propose a spatio-temporal-focused Siamese network for infrared UAV tracking, called STFTrack. This method employs a two-level target focusing strategy from global to local. First, a feature pyramid-based Siamese backbone is constructed to enhance the feature expression of infrared UAVs through cross-scale feature fusion. By combining template and motion features, we guide prior anchor boxes towards the suspicious region to enable adaptive search region selection, thus effectively suppressing background interference and generating high-quality candidates. Furthermore, we propose an instance-discriminative RCNN based on metric learning to focus on the target UAV among candidates. By measuring calculating the feature distance between the candidates and the template, it assists in discriminating the optimal target from the candidates, thus improving the discrimination of the proposed method to infrared UAV. Extensive experiments on the Anti-UAV dataset demonstrate that the proposed method achieves outstanding performance for infrared tracking, with 91.2% precision, 66.6% success rate, and 67.7% average overlap accuracy, and it exceeded the baseline algorithm by 2.3%, 2.7%, and 3.5%, respectively. The attribute-based evaluation demonstrates that the proposed method achieves robust tracking effects on challenging scenes such as fast motion, thermal crossover, and similar distractors. Evaluation on the LSOTB-TIR dataset shows that the proposed method reaches a precision of 77.2% and a success rate of 63.4%, outperforming other advanced trackers.

Real-time and robust visual tracking with scene-perceptual memory

Unmanned aerial vehicle (UAV) based aerial visual tracking is one of the research hotspots in computer vision. However, the mainstream trackers for UAV still have two shortcomings: (1) the accuracy of correlation filter tracker is greatly improved with more complex model, it impedes accuracy-speed trade-off. (2) object occlusion and camera motion in the aerial tracking scene also seriously restrict the application of aerial tracking. To address these problems, and inspired by AutoTrack tracker, we propose an aerial correlation filtering tracker based on scene-perceptual memory, Fast-AutoTrack. Firstly, to perceive and judge tracking anomalies, such as object occlusion and camera motion, inspired by the peak sidelobe ratio and AutoTrack, a confidence score is designed by perceiving and remembering the changing trend of the confidence and the local historical confidence. Secondly, after tracking anomaly occurring, several search regions are predicted based on the local object motion trend and the Spatio-temporal context information for object re-detection. Finally, to accelerate the model updating, the perceptual hashing algorithm (PHA) is used to obtain the similarity of the search regions between two adjacent frames. On typical aerial tracking datasets UAVDT, UAV123@10fps, and DTB70, Fast-AutoTrack run 71.4% faster than AutoTrack with almost equal accuracy and show favorable accuracy-speed trade-off.

Differential evolution with nearest density clustering for multimodal optimization problems

Multimodal optimization problems (MMOPs) refer to problems with multiple optimal solutions in a given search region. Evolutionary algorithms (EAs) are widely used to search for optimal solutions. To address the multimodal problem, we propose differential evolution with nearest density clustering (NDC-DE), which combines a density clustering-based technique and a differential evolutionary algorithm. NDC-DE includes three mechanisms. First, we use nearest density clustering (NDC) to divide the initial population into multiple subpopulations and identify the best individuals in each ecological niche. Combining niche techniques with evolutionary algorithms significantly improve their ability to solve MMOPs. Clustering algorithms are useful niche techniques divide the population into subpopulations based on different characteristics. Second, we propose two new mutation operators based on nbest. Last, an adaptive species redistribution mechanism based on the opposition-based learning (OBL) mechanism is proposed during the evolutionary iteration of NDC-DE to enhance the diversity of niches. We compare NDC-DE with 15 other algorithms on the CEC2013 benchmark function and show that it outperforms these algorithms in handling MMOPs.

Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks.

The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the gradient descent method is used to find the optimal values of weights and biases. The gradient descent method suffers from the local optimal trap and slow convergence. Therefore, stochastic approximation methods such as metaheuristics are invited. Coronavirus herd immunity optimizer (CHIO) is a recent metaheuristic human-based algorithm stemmed from the herd immunity mechanism as a way to treat the spread of the coronavirus pandemic. In this paper, an external archive strategy is proposed and applied to direct the population closer to more promising search regions. The external archive is implemented during the algorithm evolution, and it saves the best solutions to be used later. This enhanced version of CHIO is called ACHIO. The algorithm is utilized in the training process of MLP to find its optimal controlling parameters thus empowering their classification accuracy. The proposed approach is evaluated using 15 classification datasets with classes ranging between 2 to 10. The performance of ACHIO is compared against six well-known swarm intelligence algorithms and the original CHIO in terms of classification accuracy. Interestingly, ACHIO is able to produce accurate results that excel other comparative methods in ten out of the fifteen classification datasets and very competitive results for others.

Transformer tracking with multi-scale dual-attention

Transformer-based trackers greatly improve tracking success rate and precision rate. Attention mechanism in Transformer can fully explore the context information across successive frames. Nevertheless, it ignores the equally important local information and structured spatial information. And irrelevant regions may also affect the template features and search region features. In this work, a multi-scale feature fusion network is designed with box attention and instance attention in Encoder–Decoder architecture based on Transformer. After extracting features, the local information and structured spatial information is learnt by multi-scale box attention, and the global context information is explored by instance attention. Box attention samples grid features from the region of interest. Therefore, it effectively focuses on the region of interest (ROI) and avoids the influence of irrelevant regions in feature extraction. At the same time, instance attention can also pay attention to the context information across successive frames, and avoid falling into local optimum. The long-range feature dependencies are learned in this stage. Extensive experiments are conducted on six challenging tracking datasets to demonstrate the superiority of the proposed tracker MDTT, including UAV123, GOT-10k, LaSOT, VOT2018, TrackingNet, and NfS. In particular, the proposed tracker achieves AUC score of 64.7 % on LaSOT, 78.1 % on TrackingNet and precision score of 89.2 % on UAV123, which outperforms the baseline and most recent advanced trackers.

Auto-segmentation of thoracic brachial plexuses for radiation therapy planning.

Recently, deep learning networks have achieved considerable success in segmenting organs in medical images. Several methods have used volumetric information with deep networks to achieve segmentation accuracy. However, these networks suffer from interference, risk of overfitting, and low accuracy as a result of artifacts, in the case of very challenging objects like the brachial plexuses. In this paper, to address these issues, we synergize the strengths of high-level human knowledge (i.e., natural intelligence (NI)) with deep learning (i.e., artificial intelligence (AI)) for recognition and delineation of the thoracic brachial plexuses (BPs) in computed tomography (CT) images. We formulate an anatomy-guided deep learning hybrid intelligence approach for segmenting thoracic right and left brachial plexuses consisting of 2 key stages. In the first stage (AAR-R), objects are recognized based on a previously created fuzzy anatomy model of the body region with its key organs relevant for the task at hand wherein high-level human anatomic knowledge is precisely codified. The second stage (DL-D) uses information from AAR-R to limit the search region to just where each object is most likely to reside and performs encoder-decoder delineation in slices. The proposed method is tested on a dataset that consists of 125 images of the thorax acquired for radiation therapy planning of tumors in the thorax and achieves a Dice coefficient of 0.659.

Solving the Single-Row Facility Layout Problem by K-Medoids Memetic Permutation Group

The single-row facility layout problem (SRFLP) is concerned with arranging facilities along a straight line so as to minimize the sum of the products of the flow costs and distances among all facility pairs. SRFLP has rich practical applications and is however NP-hard. In this article, we first investigate a dedicated symmetry-breaking approach based on the permutation group theory for reducing the solution space of SRFLP. Relevant symmetry properties are identified through the alternating group of the original solution space or the corresponding coordinate rotation space. Then, a memetic algorithm is proposed to explore promising search regions regarding the reduced solution space. The memetic algorithm employs a problem-specific crossover operator guided by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -medoids clustering technique to produce meaningful offspring solutions. The algorithm additionally uses a simulated annealing procedure to intensively exploit a given search region and a distance-and-quality-based population management strategy to ensure a reasonable diversity of the population. Experimental results on commonly used benchmark instances and newly introduced large-scale instances with sizes up to 2000 facilities show that the proposed algorithm competes favorably with state-of-the-art SRFLP algorithms. It attains all but one previous best known upper bounds (BKS) and discovers new upper bounds for 33 instances out of the 93 popular benchmark instances.

Adaptive Nonlinear Least Squares Framework for Contactless Vital Sign Monitoring

The respiratory and heart rates are critical physiological parameters, and conventional contact-based monitoring techniques may cause discomfort and epidermal damage, being therefore inadequate for long-term monitoring. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios, especially in relation to heart rate estimation. In this work, we propose a comprehensive framework for vital sign processing in frequency-modulated continuous-wave radar systems and evaluate its performance with real data imitating common working conditions in an office environment. First, to improve the signal-to-noise ratio before estimation, we propose a novel slow-time phase correlation processing, which allows early integration of the vital sign energy at nearby range bins. Subsequently, we present an adaptive nonlinear least squares framework that explores the harmonic structure existing in the recovered displacement signal. An additional Kalman filter stage is designed to select among multiple estimates from different search regions, thus conferring adaptivity and robustness against harmonic interference and noise. This approach largely provides estimates within the predefined error intervals, being capable of tracking the true breathing and heart rate values even during continuous small body movements. The final accuracy and root mean square error values have shown enhanced estimation, outperforming conventional spectral estimation and other recently proposed methods in almost all scenarios.

Automated Reservoir Model Calibration Applied to a Complex Multizone Heavy Oil Field

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 210185, “Automated Reservoir Model Calibration for Field-Development-Plan Evaluation Under Subsurface Uncertainty Applied to a Complex Multizone Heavy Oil Field,” by Luis D. Mendoza, Atahualpa J. Villarroel, SPE, and Maria F. Hurtado, Schlumberger, et al. The paper has not been peer reviewed. _ Fully integrated reservoir modeling for field-development optimization under subsurface uncertainty has been a major challenge so far for Rubiales, a major heavy oil field in Colombia with original oil in place of greater than 5 billion STB. An automated reservoir characterization work flow was developed to generate multiple history-matched models on the field and well level. The developed methodology and work flow successfully delivered field-development evaluation under subsurface uncertainty. The work-flow design is applicable for other fields with similar characteristics and delivery objectives. Introduction The Rubiales field is in the southeast of Puerto Gaitán, approximately 310 km from Bogotá. It is the most important oil field in Colombia in terms of extension, original volumes, and production but also is one of the country’s most complex fields, with a variety of technical challenges. Lithologically, the field is an unconsolidated sandstone reservoir with stratigraphic complexity that includes a high degree of vertical and lateral heterogeneity. Additionally, it has an infinite aquifer with tilted oil/water contact (OWC) in combination with high permeability. The produced fluid is a heavy oil (15 °API) with high water cut (approximately 95%). The current field-development plan (FDP) is based on horizontal wells as infill drilling to maintain oil production levels. A solution was designed to be tested in a specific area of the field implementing digital integration technologies. This solution included the capture of geological heterogeneities in terms of lateral and vertical continuity of the sand bodies. This digital methodology allows understanding seal and OWC effects on water production using a high-resolution model combined with an automated calibration process based on actual production as evidence. Work-Flow Design History Matching. In this project, a typical assisted history-matching process was run initially that included sensitivity and uncertainty analyses followed by an optimization algorithm. Once the sensitivity analysis was complete, the most-influential parameters were identified. Then, some variables were disabled and the uncertainty process was run with a Monte Carlo sampling considering only those parameters considered significant for the objective response. Through this process, a case ensemble with a wide-spectrum result was obtained. The next step was to focus on the optimization that was designed to improve the objective function value that should be minimized by tuning the set of input parameters remaining as active influencers. An evolutionary algorithm was implemented for the optimization that operated with three phases. In the first phase, simulation cases were distributed randomly in the search region, providing an initial ensemble. During the second phase, the better simulation cases were retained and the average objective function value was reduced. In the final phase, only the best simulation cases survived. Even when a modern algorithm was applied, the matching results were not sufficient at the well level. However, this work flow provided a good base case and insights regarding reservoir response, which are the inputs for the next stage of history matching. The further development of the history-matching methodology is detailed in the complete paper.

User equipment geolocation depended on long-term evolution signal-level measurements and timing advance

&lt;p&gt;A new approach is described for investigating the accuracy of positioning active long-term evolution (LTE) users. The explored approach is a network-based method and depends on signal level measurements as well as the coverage of the serving cell. In a two-dimensional coordinate system, the algorithm simultaneously applies LTE measured data with a combination of a basic prediction model to locate the mobile device’s user. Furthermore, we introduce a unique method that combines timing advance (TA) and the measured signal level to narrow the search region and improve accuracy. The developed method is assessed by comparing the predicted results from the proposed algorithm with satellite measurements from the global positioning system (GPS) in various scenarios calculated via the number of cells that user equipment concurrently reports. This work separates seven different cases starting from a single reported cell to five reported cells from up to 3 sites. For analysis, the root mean square error (RMSE) is computed to obtain the validation for the proposed approach. The study case demonstrates location accuracy based on the numbers of registered cells with the mean RMSE improved using TA to approximately 70-191 m for the range of scenarios.&lt;/p&gt;

Cross-Correlation Fusion Graph Convolution-Based Object Tracking

Most popular graph attention networks treat pixels of a feature map as individual nodes, which makes the feature embedding extracted by the graph convolution lack the integrity of the object. Moreover, matching between a template graph and a search graph using only part-level information usually causes tracking errors, especially in occlusion and similarity situations. To address these problems, we propose a novel end-to-end graph attention tracking framework that has high symmetry, combining traditional cross-correlation operations directly. By utilizing cross-correlation operations, we effectively compensate for the dispersion of graph nodes and enhance the representation of features. Additionally, our graph attention fusion model performs both part-to-part matching and global matching, allowing for more accurate information embedding in the template and search regions. Furthermore, we optimize the information embedding between the template and search branches to achieve better single-object tracking results, particularly in occlusion and similarity scenarios. The flexibility of graph nodes and the comprehensiveness of information embedding have brought significant performance improvements in our framework. Extensive experiments on three challenging public datasets (LaSOT, GOT-10k, and VOT2016) show that our tracker outperforms other state-of-the-art trackers.

Azimuthal asymmetry in cosmic-ray boosted dark matter flux

Light halo dark matter (DM) particles up-scattered by high-energy cosmic rays (referred to as CRDM) can be energetic and become detectable at conventional DM and neutrino experiments. We show that the CRDM flux has a novel and detectable morphological feature. Unlike most of the recently proposed boosted DM (BDM) models which predict azimuthally symmetric DM fluxes around the Galactic Center, the CRDM flux breaks the azimuthal symmetry significantly. Using cosmic-ray electron distribution in the whole Galaxy and optimized search region in the sky according to the morphology of the CRDM flux, we derive so far the most stringent constraints on the DM-electron scattering cross section from the Super-Kamiokande (SK) IV data, which improves the previous constraints from the SK-IV full-sky data by more than an order of magnitude. Based on the improved constraints, we predict that the azimuthal symmetry-breaking effect can be observed in the future Hyper-Kamiokande experiment at $\sim 3\sigma$ level.