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PowerShell is a powerful and versatile task automation tool. Unfortunately, it is also widely abused by cyber attackers. To bypass malware detection and hinder threat analysis, attackers often employ diverse techniques to obfuscate malicious PowerShell scripts. Existing deobfuscation tools suffer from the limitation of static analysis, which fails to simulate the real deobfuscation process accurately. In this paper, we propose PowerPeeler. To the best of our knowledge, it is the first dynamic PowerShell script deobfuscation approach at the instruction level. It utilizes expression-related Abstract Syntax Tree (AST) nodes to identify potential obfuscated script pieces. Then, PowerPeeler correlates the AST nodes with their corresponding instructions and monitors the script's entire execution process. Subsequently, PowerPeeler dynamically tracks the execution of these instructions and records their execution results. Finally, PowerPeeler stringifies these results to replace the corresponding obfuscated script pieces and reconstruct the deobfuscated script. To evaluate the effectiveness of PowerPeeler, we collect 1,736,669 real-world malicious PowerShell samples with diversity obfuscation methods. We compare PowerPeeler with five state-of-the-art deobfuscation tools and GPT-4. The evaluation results demonstrate that PowerPeeler can effectively handle all well-known obfuscation methods. Additionally, the deobfuscation correctness rate of PowerPeeler reaches 95%, significantly surpassing that of other tools. PowerPeeler not only recovers the highest amount of sensitive data but also maintains a semantic consistency over 97%, which is also the best. Moreover, PowerPeeler effectively obtains the largest quantity of valid deobfuscated results within a limited time frame. Furthermore, PowerPeeler is extendable and can be used as a helpful tool for other cyber security solutions., Comment: To appear in the ACM CCS 2024

The growing popular awareness of personal privacy raises the following quandary: what is the new paradigm for collecting and protecting the data produced by ever-increasing sensor devices. Most previous studies on co-design of data aggregation and privacy preservation assume that a trusted fusion center adheres to privacy regimes. Very recent work has taken steps towards relaxing the assumption by allowing data contributors to locally perturb their own data. Although these solutions withhold some data content to mitigate privacy risks, they have been shown to offer insufficient protection against disclosure attacks. Aiming at providing a more rigorous data safeguard for the Internet of Things (IoTs), this paper initiates the study of privacy-preserving data aggregation. We propose a novel paradigm (called RASE), which can be generalized into a 3-step sequential procedure, noise addition, followed by random permutation, and then parameter estimation. Specially, we design a differentially private randomizer, which carefully guides data contributors to obfuscate the truth. Then, a shuffler is employed to receive the noisy data from all data contributors. After that, it breaks the correct linkage between senders and receivers by applying a random permutation. The estimation phase involves using inaccurate data to calculate an approximate aggregate value. Extensive simulations are provided to explore the privacy-utility landscape of our RASE., Comment: 14 pages, 19 figures

3D Gaussian splatting (3DGS) has shown promising results in image rendering and surface reconstruction. However, its potential in volumetric reconstruction tasks, such as X-ray computed tomography, remains under-explored. This paper introduces R2-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction. By carefully deriving X-ray rasterization functions, we discover a previously unknown integration bias in the standard 3DGS formulation, which hampers accurate volume retrieval. To address this issue, we propose a novel rectification technique via refactoring the projection from 3D to 2D Gaussians. Our new method presents three key innovations: (1) introducing tailored Gaussian kernels, (2) extending rasterization to X-ray imaging, and (3) developing a CUDA-based differentiable voxelizer. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches by 0.93 dB in PSNR and 0.014 in SSIM. Crucially, it delivers high-quality results in 3 minutes, which is 12x faster than NeRF-based methods and on par with traditional algorithms. The superior performance and rapid convergence of our method highlight its practical value.

The task of chemical reaction predictions (CRPs) plays a pivotal role in advancing drug discovery and material science. However, its effectiveness is constrained by the vast and uncertain chemical reaction space and challenges in capturing reaction selectivity, particularly due to existing methods' limitations in exploiting the data's inherent knowledge. To address these challenges, we introduce a data-curated self-feedback knowledge elicitation approach. This method starts from iterative optimization of molecular representations and facilitates the extraction of knowledge on chemical reaction types (RTs). Then, we employ adaptive prompt learning to infuse the prior knowledge into the large language model (LLM). As a result, we achieve significant enhancements: a 14.2% increase in retrosynthesis prediction accuracy, a 74.2% rise in reagent prediction accuracy, and an expansion in the model's capability for handling multi-task chemical reactions. This research offers a novel paradigm for knowledge elicitation in scientific research and showcases the untapped potential of LLMs in CRPs.

The EHT has captured a series of images of black holes. These images could provide valuable information about the gravitational environment near the event horizon. However, accurate detection and parameter estimation for candidate black holes are necessary. This paper explores the potential for identifying black holes in the ultraviolet band using space telescopes. We establish a data pipeline for generating simulated observations and present an ensemble neural network model for black hole detection and parameter estimation. The model achieves mean average precision [0.5] values of 0.9176 even when reaching the imaging FWHM ($\theta_c$) and maintains the detection ability until $0.54\theta_c$. The parameter estimation is also accurate. These results indicate that our methodology enables super-resolution recognition. Moreover, the model successfully detects the shadow of M87* from background noise and other celestial bodies and estimates its inclination and positional angle. Our work demonstrates the feasibility of detecting black holes in the ultraviolet band and provides a new method for black hole detection and further parameter estimation., Comment: 20 pages, 17 figures, 9 tables. We propose a ensemble neural network which demonstrates the feasibility of detecting black holes in the UV band and provides a new method for the accurate and real-time detection of candidate black holes and further parameter estimation

Efficient molecular modeling and design are crucial for the discovery and exploration of novel molecules, and the incorporation of deep learning methods has revolutionized this field. In particular, large language models (LLMs) offer a fresh approach to tackle scientific problems from a natural language processing (NLP) perspective, introducing a research paradigm called scientific language modeling (SLM). However, two key issues remain: how to quantify the match between model and data modalities and how to identify the knowledge-learning preferences of models. To address these challenges, we propose a multi-modal benchmark, named ChEBI-20-MM, and perform 1263 experiments to assess the model's compatibility with data modalities and knowledge acquisition. Through the modal transition probability matrix, we provide insights into the most suitable modalities for tasks. Furthermore, we introduce a statistically interpretable approach to discover context-specific knowledge mapping by localized feature filtering. Our pioneering analysis offers an exploration of the learning mechanism and paves the way for advancing SLM in molecular science.

The gravitational lensing of supermassive black holes surrounded by dark matter halo has attracted a great number of interests in recent years. However, many studies employed simplified dark matter density models, which makes it very hard to give a precise prediction on the dark matter effects in real astrophysical galaxies. In this work, to more accurately describe the distribution of dark matter in real astrophysical galaxies, we study the gravitational lensing of black holes in astrophysical dark matter halo models (Beta, Burkert, Brownstein, and Moore). The deflection angle is obtained using a generalized Gibbons-Werner approach. The visual angular positions and the Einstein rings are also calculated by adopting the gravitational lens equation. Specifically, we choose the supermassive black holes in Milky Way Galaxy, Andromeda galaxy (M31), Virgo galaxy (M87), and ESO138-G014 galaxy as examples, including the corresponding fitted value of dark matter halos. The results suggest that the dark matter halo described by the Beta model has non-negligible influences on the gravitational deflection angle and gravitational lensing observations. However, the Burkert, Brownstein, and Moore models have relatively small influences on angular position of images and the Einstein ring., Comment: 30 pages, 9 figures, 3 appendices, V2: new references are included; V3 and V4: majar revision, two new appendices are added

In this paper, we investigate the thermodynamic properties of charged AdS Gauss-Bonnet black holes and the associations with the Lyapunov exponent. The chaotic features of the black holes and the isobaric heat capacity characterized by Lyapunov exponent are studied to reveal the stability of black hole phases. With the consideration of both timelike and null geodesic, we find the relationship between Lyapunov exponent and Hawking temperature can fully embody the feature of the Small/Large phase transition and the triple point even further. Then we briefly reveal the properties of Lyapunov exponent as an order parameter and explore the black hole shadow with it.

Flow fields are often partitioned into data blocks for massively parallel computation and analysis based on blockwise relationships. However, most of the previous techniques only consider the first-order dependencies among blocks, which is insufficient in describing complex flow patterns. In this work, we present FlowHON, an approach to construct higher-order networks (HONs) from flow fields. FlowHON captures the inherent higher-order dependencies in flow fields as nodes and estimates the transitions among them as edges. We formulate the HON construction as an optimization problem with three linear transformations. The first two layers correspond to the node generation and the third one corresponds to edge estimation. Our formulation allows the node generation and edge estimation to be solved in a unified framework. With FlowHON, the rich set of traditional graph algorithms can be applied without any modification to analyze flow fields, while leveraging the higher-order information to understand the inherent structure and manage flow data for efficiency. We demonstrate the effectiveness of FlowHON using a series of downstream tasks, including estimating the density of particles during tracing, partitioning flow fields for data management, and understanding flow fields using the node-link diagram representation of networks., Comment: To be submitted to TVCG

In this paper, we study topological numbers for uncharged and charged static black holes obtained in Ho\v{r}ava-Lifshitz gravity theory in different ensembles. We first calculate the topological numbers for the uncharged black holes by changing the value of the dynamic coupling constant, and find that the black holes with spherical and flat horizons have the same topological number. When the black hole's horizon is hyperbolic, different values of the coupling constant generate different topological numbers, which can be $1$, $0$ or $-1$. This shows that the coupling constant plays an important role in the topological classification. Then, we study the topological numbers for the charged black holes in different ensembles. The black hole with a spherical horizon has the same topological number in canonical and grand canonical ensembles. When the horizons are flat or hyperbolic, they have different topological numbers in canonical and grand canonical ensembles. Therefore, the topological numbers for the uncharged black holes are parameter dependent, and those for the charged black holes are ensemble dependent., Comment: 17 pages, 13 figures

In this work, we investigate the microstructure of asymptotically flat black holes with Ruppeiner curvature. Specially, the cosmological constant is considered to have a fluctuation around 0. Under such consideration, both repulsive and attractive interactions are found in the Reissner-Nordstr\"om and Kerr black holes, while the Schwarzschild black hole has dominant attractive interaction. The result obtained is quite different from that of excluding the fluctuation of cosmological constant, where these black holes are found to be always characterised by repulsive interaction., Comment: 17 pages, 3 figures

We present a novel optimization-based Visual-Inertial SLAM system designed for multiple partially overlapped camera systems, named MAVIS. Our framework fully exploits the benefits of wide field-of-view from multi-camera systems, and the metric scale measurements provided by an inertial measurement unit (IMU). We introduce an improved IMU pre-integration formulation based on the exponential function of an automorphism of SE_2(3), which can effectively enhance tracking performance under fast rotational motion and extended integration time. Furthermore, we extend conventional front-end tracking and back-end optimization module designed for monocular or stereo setup towards multi-camera systems, and introduce implementation details that contribute to the performance of our system in challenging scenarios. The practical validity of our approach is supported by our experiments on public datasets. Our MAVIS won the first place in all the vision-IMU tracks (single and multi-session SLAM) on Hilti SLAM Challenge 2023 with 1.7 times the score compared to the second place., Comment: OpenMAVIS available at: https://github.com/MAVIS-SLAM/ORB_SLAM3_MULTI

Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing textual representations of molecules. However, most existing language models cannot capture the rich information with complex molecular structures or images. In this paper, we introduce GIT-Mol, a multi-modal large language model that integrates the Graph, Image, and Text information. To facilitate the integration of multi-modal molecular data, we propose GIT-Former, a novel architecture that is capable of aligning all modalities into a unified latent space. We achieve a 5%-10% accuracy increase in properties prediction and a 20.2% boost in molecule generation validity compared to the baselines. With the any-to-language molecular translation strategy, our model has the potential to perform more downstream tasks, such as compound name recognition and chemical reaction prediction., Comment: The article has been accepted by Computers in Biology and Medicine, with 14 pages and 4 figures

In this paper, we study topological numbers for five-, six- and seven-dimensional anti-de Sitter black holes in the ghost-free massive gravity. We find that when the black holes are charged, they have the same topological number. The topological numbers for the uncharged black holes are 0 or 1, and the specific values are determined by the values of the black holes' parameters. Since $k$ and $c_ 0^2c_2 m^2 $ appear together in the generalized free energy in the form of $k +c_ 0^2c_2 m^2 $, where $k$ characterizes the horizon curvature and $c_2 m^2 $ is the coefficient of the second term of massive potential associated with the graviton mass, this result is applicable to the black holes with the spherical, Ricci flat or hyperbolic horizons. This work shows that the parameters of the ghost-free massive gravity play an important role in topological classes of black holes., Comment: 21 pages, 13 figures

We observe a superfluid $\lambda$ transition in the $P-V$ criticality of charged AdS black holes, within a holographic extended thermodynamics that considers the variation of Newton's constant $G$. We calculate the critical exponents and find that they coincide with those of a superfluid transition in liquid $^4\text{He}$ and the Bose-Einstein condensation of hard-sphere Bose gas. Moreover, the independence of entropy and thermodynamic volume in the holographic framework allows us to construct a well-defined Ruppeiner metric. The associated scalar curvature suggests that the black holes show similar microscopic interactions with the hard-sphere Bose gas, where the superfluid (condensed) phase is dominated by repulsive interactions, while the normal (gas) phase is dominated by attractive interactions. These findings might provide us with new insights into the quantum aspect of charged AdS black holes., Comment: 8 pages, 5 figures, minor improvements, accepted in Science China Physics, Mechanics & Astronomy

In this paper, we characterize the phase transitons of Born-Infeld AdS black holes in terms of Lyapunov exponents. We calculate the Lyapunov exponents for both null and timelike geodesics. It is found that black hole phase transitions can be described by multiple-valued Lyapunov exponents. And its phase diagram can be characterized by Lyapunov exponents and Hawking temperature. Besides, the change of Lyapunov exponents can be considered as order parameter, and exists a critical exponent $1/2$ near critical point., Comment: 22 pages, 21 figures

The Euler-Heisenberg black holes with quantum electrodynamics (QED) correction are embraced by a cavity in this paper, which serves as a boundary of the black hole spacetime and contributes to the equilibrium of the system. We explore the thermodynamic properties of the black hole, including the phase transitions and phase structures. The small/large black hole phase transition occurs for a negative QED parameter, while the reentrant phase transition can be observed for a small positive QED parameter. Then the thermodynamic geometry is investigated to diagnose microscopic interactions of black hole thermodynamic systems. For the reentrant phase transition, the small black holes are dominated by repulsion for the first-order coexistence curve, while the interaction between the small black hole molecules could be attractive or repulsive for the small/large black hole phase transition., Comment: 26 pages, 10 figures

Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion. The availability of individual customer-level path-to-purchase data and the increasing number of online marketing channels and types of touchpoints bring new challenges to this fundamental problem. We aim to tackle the attribution problem with finer granularity by conducting attribution at the path level. To this end, we develop a novel graphical point process framework to study the direct conversion effects and the full relational structure among numerous types of touchpoints simultaneously. Utilizing the temporal point process of conversion and the graphical structure, we further propose graphical attribution methods to allocate proper path-level conversion credit, called the attribution score, to individual touchpoints or corresponding channels for each customer's path to purchase. Our proposed attribution methods consider the attribution score as the removal effect, and we use the rigorous probabilistic definition to derive two types of removal effects. We examine the performance of our proposed methods in extensive simulation studies and compare their performance with commonly used attribution models. We also demonstrate the performance of the proposed methods in a real-world attribution application., Comment: 38 pages, 10 figures

In this paper, we study the phase tranisions in the extended holographic thermodynamics of Born-Infeld AdS black holes. The extended gravitational thermodynamics of black holes is related to the thermodynamics of the dual field theory by varying both cosmological constant and Newton's constant. With topological analysis and numerical calculation, we find that the reentrant large-small-large black hole phase transition still exists in $D=4$. Interestingly, a similar reentrant phase transition is also observed in the dual field theory, between the high-, low- and high-entropy thermal states. For higher dimension $D>4$, only the van der Waals like phase transition is observed, both in the bulk and the dual field theory., Comment: 23 pages, 8 figures. The calculation of central charge is added

In this paper, we investigate the observational appearances of magnetically charged black holes in Born-Infeld (BI) electrodynamics. We examine the effects of the magnetic charge and the BI parameter on the geodesics with different impact parameters. Using the backward ray tracing method, we investigate how spherically symmetric accretions interact with black hole shadows and photon spheres. The shadows of infalling accretion are darker than that of static ones. And the radius of the photon sphere is an intrinsic property of the spacetime that is independent of accretions. We then study how the thin disk models affect the black hole shadows. After obtaining the transfer functions, we divide photons emitted from the thin disk into three categories: direct emission, lens ring, and photon ring. Applying three emission models, we find that the width of the shadow is dominated by the direct emission, the photon ring and the lens ring can hardly be identified by changing the emission models., Comment: 37 pages, 18 figures, 5 tables. v3: EPJC publication version

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning.

Accurate channel estimation is critical to the performance of orthogonal frequency-division multiplexing (OFDM) underwater acoustic (UWA) communications, especially under multiple-input multiple-output (MIMO) scenarios. In this paper, we explore Vector Approximate Message Passing (VAMP) coupled with Expected Maximum (EM) to obtain channel estimation (CE) for MIMO OFDM UWA communications. The EM-VAMP-CE scheme is developed by employing a Bernoulli-Gaussian (BG) prior distribution for the channel impulse response, and hyperparameters of the BG prior distribution are learned via the EM algorithm. Performance of the EM-VAMP-CE is evaluated through both synthesized data and real data collected in two at-sea UWA communication experiments. It is shown the EM-VAMP-CE achieves better performance-complexity tradeoff compared with existing channel estimation methods., Comment: Journal:IEEE Journal of Oceanic Engineering(Date of Submission:2022-06-25)

In this paper, we investigate the thermodynamics of D-dimensional charged Gauss-Bonnet black holes in anti-de Sitter spacetime. Varying the cosmological constant, Newton constant and Gauss-Bonnet coupling constant in the bulk, one can rewrite the first law of thermodynamics for black holes. Furthermore, we introduce the central charge and study the critical behaviors, which show the apparent discrepancy from other black holes. Based on this approach, we disclose the phase transition structures in $D=4, 5$, and $6$. Besides, a triple point where the small/intermediate/large black holes can coexist is found in $D=6$.

The maximum correntropy criterion (MCC) has been employed to design outlier-robust adaptive filtering algorithms, among which the recursive MCC (RMCC) algorithm is a typical one. Motivated by the success of our recently proposed proportionate recursive least squares (PRLS) algorithm for sparse system identification, we propose to introduce the proportionate updating (PU) mechanism into the RMCC, leading to two sparsity-aware RMCC algorithms: the proportionate recursive MCC (PRMCC) algorithm and the combinational PRMCC (CPRMCC) algorithm. The CPRMCC is implemented as an adaptive convex combination of two PRMCC filters. For PRMCC, its stability condition and mean-square performance were analyzed. Based on the analysis, optimal parameter selection in nonstationary environments was obtained. Performance study of CPRMCC was also provided and showed that the CPRMCC performs at least as well as the better component PRMCC filter in steady state. Numerical simulations of sparse system identification corroborate the advantage of proposed algorithms as well as the validity of theoretical analysis.

We investigate the thermodynamic chaos of RN-AdS black holes immersed in Perfect Fluid Dark Matter by considering the dynamical equations of the fluid system evolved in the spinodal region. Based on the Melnikov method, it is shown that there exists a critical amplitude that affects the temporal chaos. And the influence of black holes charge and state parameter on the critical amplitude is investigated with specific initial temperature. Then, for inevitable spatial chaos, three different types of portraits are discssued according to the difference between the phase transition pressure and the ambient pressure. Additionally, we check the local equilibrium near saddle points which shows that spatial chaos always exists regardless of the perturbation intensity.

In this work, we present a convenient method to perform the topological analysis of black hole thermodynamics. Utilizing the spinodal curve, thermodynamic critical points of a black hole are endowed with a topological quantity, Brouwer degree, which reflects intrinsic properties of the system under smooth deformations. Specially, in our setup, it can be easily calculated without exact solution of critical points. This enables us to conveniently investigate the topological transition between different thermodynamic systems, and give a topological classification for them. In this framework, topology of Lovelock AdS black holes with spherical horizon geometry is explored. Results show that charged black holes in arbitrary dimensions can be classified into the same topology class, whereas the $d=7$ and $d \geq 8$ uncharged black holes are in different topology classes. Moreover, we revisit the relation between different phase structures of these black holes from the viewpoint of topology. Some general topological properties of critical points are also discussed., Comment: 11 pages, 7 figures

In this paper, we extend Joule-Thomson expansion to the low-dimensional regime in rainbow gravity by considering the rainbow rotating BTZ metric in the (2+1)-dimensional spacetime. After the metric of the black hole is obtained, we get the Joule-Thomson expansion of the black hole, including the Joule-Thomson coefficient, inversion curves, and isenthalpic curves. We find that a rainbow rotating BTZ black hole does not have $P-V$ critical behavior. The effects of rainbow gravity are to slow down the trend of the increase of the Joule-Thomson coefficient and make its zero point larger. Moreover, the rainbow gravity slows down the inverse temperature of the black hole, meaning that a rainbow rotating BTZ black hole tends to change its heating or cooling action at a lower temperature, which can be attributed to the topology of the black hole., Comment: 14 pages, 7 figures, 2 tables

Being placed in a cavity is an effective way of reaching thermodynamic equilibrium for black holes. We investigate a Kerr-Newman black hole in a cavity as well as compare it with two reduced cases, i.e., a RN black hole in a cavity and a Kerr black hole in a cavity. We derive the quasi-local energy from the Hamiltonian, and construct the first law of thermodynamics accordingly. In a canonical ensemble, these black holes could undergo a van der Waals-like phase transition, which is very similar to that in AdS space. We further investigate the black holes' thermodynamic geometry, which is a powerful tool to diagnose microscopic interactions of a thermodynamic system. Our results show that in a cavity, although phase structures of these black holes are similar, their thermodynamic geometry show strong dissimilarities, implying that the microstructure of a black hole is sensitive to its states., Comment: 25 pages, 8 figures

Doubly special relativity (DSR) is an effective model for encoding quantum gravity in flat spacetime. To incorporate DSR into general relativity, one could use gravity's rainbow, where the spacetime background felt by a test particle would depend on its energy. In this paper, we investigate the thermodynamics of rainbow Kerr black hole in the scenario with the stationary(ST) orthonormal frame and free-fall(FF) orthonormal frame. After the rainbow metric in ST frame and FF frame is deduced, the Hamilton-Jacobi method is used to acquire the modified Hawking temperature, specific heat and corresponding the modified entropy to each scenario, then the thermodynamic properties are discussed. We find that the effects of rainbow gravity on Kerr black holes are quite model-dependent. In other words, the value of parameter $\eta$ and $n$ with Amelino-Camelia's proposal are crucially important and worth discussing. Specificly, with most widly accepted choice ($n=2,\eta >0$), the effects of rainbow gravity tend to decrease the Hawking temperature but increase the black hole entropy in ST frame, and increase the Hawking temperature but decrease the black hole entropy in FF frame conversely., Comment: 15 pages, 8 figures, 2 tables. arXiv admin note: text overlap with arXiv:2205.02712

In this work, we study the shadow of Born-Infeld (BI) black holes with magnetic monopoles and Schwarzschild black holes immersed in the BI uniform magnetic field. Illuminated by a celestial sphere, black hole images are obtained by using the backward ray-tracing method. For magnetically charged BI black holes, we find that the shadow radius increases with the increase of nonlinear electromagnetics effects. For Schwarzschild black holes immersed in the BI uniform magnetic field, photons tend to move towards the axis of symmetric, resulting in stretched shadows along the equatorial plane., Comment: 19 pages, 19 figures

In this paper, we investigate the thermodynamic properties of the RN-AdS black hole with cloud of strings and quintessence in rainbow gravity with the stationary (ST) orthonormal frame and the free-fall (FF) orthonormal frame. After the SF and the FF rainbow metric is obtained, we get the Hawking temperature and the entropy, and their physical meanings are discussed. We find that, for the ST rainbow RN-AdS black hole with cloud of strings and quintessence, the effect of rainbow gravity is to increase the Hawking temperature but decrease the entropy of the black hole. However, for the FF rainbow case, rainbow gravity turns out to decrease the Hawking temperature but increase the entropy of the black hole, which seems that the effects rainbow gravity has are quite model-dependent., Comment: 14 pages, 5 figures, 1 table

In this work, the microstructure of charged AdS black holes under minimal length effects is investigated. We study the thermodynamics of black holes in the extended phase space, where the cosmological constant is regarded as the thermodynamic pressure. The modified Hawking temperature and phase transition are obtained based on the generalized uncertainty principle (GUP). Then, using thermodynamic geometry, we show that the microstructure of black holes can be determined by the ratio of GUP parameter to charge. For a small ratio, the black hole exhibits the typical RN-AdS microstructure with van der Waals phase transition and repulsive/attractive interactions. As the ratio increases, the reentrant phase transition takes place, and both the repulsion-attraction coexisted black hole and the attraction dominated black hole can be found in this case. For a large ratio, the black hole behaves like a Schwarzchild-AdS black hole in which neither phase transition nor repulsive interaction exists. These results suggest that the GUP effect will reduce the repulsive interaction presented by the charged AdS black hole, which can also be qualitatively understood from the perspective of black hole molecules., Comment: 29 pages, 10 figures

Many researches from both the theoretical and experimental sides have been performed to search for a new Higgs Boson lighter than the 125 $GeV$ Higgs boson which was discovered at the LHC in 2012. In this paper we explore the possibility of constraining a lighter neutral custodial fiveplet scalar $H_{5}^{0}$ in the Georgi-Machacek (GM) model by the latest results of the search for a lighter Higgs boson decaying into two photons from LHC data. The custodial-singlet mass eigenstate $h$ or $H$ is considered to be the LHC observed 125 $GeV$ Higgs boson. A new set of constrained parameters that is favoured by low-mass $H_{5}^{0}$, is proposed to generate events efficiently. The production of $H_{5}^{0}$ from the scan based on the constrained parameters is compared to the latest results of the search for a lighter Higgs boson decaying into two photons by CMS Collaboration, after the theoretical constraints from GM model and the constraints from all existing relevant experimental measurements including the recent results of the Higgs boson searches from the LHC. The numerical analyses of the surviving GM parameter space are performed. The tendencies and correlations of the GM input parameters from the phenomenological studies are summarized. In addition the discovery potential of other interesting decay channels of this low-mass neutral custodial fiveplet scalar are discussed., Comment: 8 pages, 5 figures, submitted to Chinese Physics C

Comprehensively evaluating and comparing researchers' academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this paper, we present an interactive visualization framework, SD2, to enable flexible data partition and composition to support various analysis requirements within a single system. SD2 features the hierarchical histogram, a novel visual representation for flexibly slicing and dicing the data, allowing different aspects of scholarly performance to be studied and compared. We also leverage the state-of-the-art set visualization technique to select individual researchers or combine multiple scholars for comprehensive visual comparison. We conduct multiple rounds of expert evaluation to study the effectiveness and usability of SD2 and revise the design and system implementation accordingly. The effectiveness of SD2 is demonstrated via multiple usage scenarios with each aiming to answer a specific, commonly raised question.

We firstly simulated disease dynamics by KAN (Kolmogorov-Arnold Networks) nearly 4 years ago, but the kernel functions in the edge include the exponential number of infected and discharged people and is also in line with the Kolmogorov-Arnold representation theorem, and the shared weights in the edge are the infection rate and cure rate, and used activation function by tanh at the node of edge. And this Arxiv preprint version 1 of March 2022 is an upgraded version of KAN, considering the invariant coarse-grained which calculated by residual or gradient of MSE loss. The improved KAN is PNN (Plasticity Neural Networks) or ELKAN (Edge Learning KNN), in addition to edge learning, it also considered the trimming of the edge. We not inspired by the Kolmogorov-Arnold representation theorem but inspired by the brain science. The ELKAN to explain brain, the variables correspond to different types of neurons, the learning edge can be explained by rebalance of synaptic strength and glial cells phagocytose synapses, and the kernel function means the discharge of neurons and synapses, different neurons and edges mean brain regions. Through testing by cosine, the ELKAN or ORPNN (Optimized Range PNN) is better than the KAN or CRPNN (Constant Range PNN).The ELKAN is more general to explore brain, such as mechanism of consciousness, interactions of natural frequencies in brain regions, synaptic and neuronal discharge frequencies, and data signal frequencies; mechanism of Alzheimer's disease, the Alzheimer's patients has more high frequencies in the upstream brain regions; long short-term relatively good and inferior memory which means gradient of architecture and architecture; turbulent energy flow in different brain regions, turbulence critical conditions need to be met; heart-brain of the quantum entanglement may occur between the emotions of heartbeat and the synaptic strength of brain potentials.