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We studied the possible presence of dark matter (DM) in neutron stars (NSs) and the structural properties of the DM admixed NSs (DMANSs) in one of our recent works \cite{Guha:2024pnn}. The feeble interaction between the fermionic DM ($\chi$) with the hadronic matter is introduced through a dark scalar ($\phi$) and a dark vector ($\xi$) boson as mediators. The allowed range of the mass of the fermionic DM ($m_{\chi}$), for a particular range of DM Fermi momentum ($k_F^{\chi}$), was obtained in the same work \cite{Guha:2024pnn} with respect to the various astrophysical constraints on the structural properties of compact stars viz. the mass, radius and tidal deformability. The present work is dedicated to the calculation and study of non-radial oscillation of the DMANSs using Cowling approximation. We particularly investigate the effect of presence of DM on the fundamental ($f$) mode oscillation frequencies of the DMANSs utilizing the previously obtained range of $m_{\chi}$ for four different hadronic models. In this work we thoroughly investigate how the individual and combined effects of $m_{\chi}$ and $k_F^{\chi}$ affect the $f$-mode oscillation frequency. Within the framework of our DMANS models, for a particular value of $k_F^{\chi}$, the range of $f_{max}^{DMANS}$ with respect to the allowed range of $m_{\chi}$, is also obtained in the present work for four different hadronic models. Since in the present era, the 1.4 and 2.01 $M_{\odot}$ NSs are of special interest after the detection of GW170817 and PSR J0740+6620, we particularly investigate, for the four hadronic models, the range of $f_{1.4}^{DMANS}$ and $f_{2.01}^{DMANS}$ with respect to the acceptable range of $m_{\chi}$ corresponding to the range of $k_F^{\chi}$., Comment: 18 Pages, 30 figures

Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in human-computer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES's highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within an event frame, across a wide range of eye movement patterns. EyeTrAES then applies lightweight image processing functions over accumulated event frames from just a single eye to perform pupil segmentation and tracking. We show that these methods boost pupil tracking fidelity by 6+%, achieving IoU~=92%, while incurring at least 3x lower latency than competing pure event-based eye tracking alternatives [38]. We additionally demonstrate that the microscopic pupillary motion captured by EyeTrAES exhibits distinctive variations across individuals and can thus serve as a biometric fingerprint. For robust user authentication, we train a lightweight per-user Random Forest classifier using a novel feature vector of short-term pupillary kinematics, comprising a sliding window of pupil (location, velocity, acceleration) triples. Experimental studies with two different datasets demonstrate that the EyeTrAES-based authentication technique can simultaneously achieve high authentication accuracy (~=0.82) and low processing latency (~=12ms), and significantly outperform multiple state-of-the-art competitive baselines., Comment: 32 pages,15 figures

Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of distractors for multiple-choice questions (MCQ) remains a challenging task. In this work, we present a simple, generic framework for distractor generation using readily available Pre-trained Language Models (PLMs). Unlike previous methods, our framework relies solely on pre-trained language models and does not require additional training on specific datasets. Building upon previous research, we introduce a two-stage framework consisting of candidate generation and candidate selection. Our proposed distractor generation framework outperforms previous methods without the need for training or fine-tuning. Human evaluations confirm that our approach produces more effective and engaging distractors. The related codebase is publicly available at https://github.com/obss/disgem.

We investigate microwave interference from a spin ensemble and its mirror image in a one-dimensional waveguide. Away from the mirror, the resonance frequencies of the Kittel mode (KM) inside a ferrimagnetic spin ensemble have sinusoidal shifts as the normalized distance between the spin ensemble and the mirror increases compared to the setup without the mirror. These shifts are a consequence of the KM's interaction with its own image. Furthermore, the variation of the magnon radiative decay into the waveguide shows a cosine squared oscillation and is enhanced twofold when the KM sits at the magnetic antinode of the corresponding eigenmode. We can finely tune the KM to achieve the maximum adsorption of the input photons at the critical coupling point. Moreover, by placing the KM in proximity to the node of the resonance field, its lifetime is extended to more than eight times compared to its positioning near the antinode.

This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management. The book focuses on simplifying the complex mathematical concepts behind deep learning, offering intuitive visualizations and practical case studies to help readers understand how neural networks and technologies like Convolutional Neural Networks (CNNs) work. It introduces several classic models and technologies such as Transformers, GPT, ResNet, BERT, and YOLO, highlighting their applications in fields like natural language processing, image recognition, and autonomous driving. The book also emphasizes the importance of pre-trained models and how they can enhance model performance and accuracy, with instructions on how to apply these models in various real-world scenarios. Additionally, it provides an overview of key big data management technologies like SQL and NoSQL databases, as well as distributed computing frameworks such as Apache Hadoop and Spark, explaining their importance in managing and processing vast amounts of data. Ultimately, the book underscores the value of mastering deep learning and big data management skills as critical tools for the future workforce, making it an essential resource for both beginners and experienced professionals., Comment: This book contains 93 pages and 60 figures

Discovery of polar skyrmions on the (PbTiO$_3$)$_n$/(SrTiO$_3$)$_n$ superlattice in the absence of any known interaction, which can orient electric polarizations at a point with respect to its neighboring points, is enigmatic. We here show that the coupling of the electric polarization and strain at each layer is responsible for orienting polarizations. We formulate coupled Euler equations for electric polarization and elastic displacement vectors, and solve to find skyrmion solution for a range of electric fields with unique topological number in each layer. The types of skyrmions vary from Neel to Bloch depending on the position of the layers in the superlattice, as observed in the experiments., Comment: 10 pages, 6 figures

We study a one-dimensional correlated-hopping model of spinless fermions with an East constraint. We first analytically unravel the fractured Hilbert space by labeling each fragment by a unique root configuration. This enables us to compute the total number of fragments and frozen states using transfer matrices. This method further allows us to analytically calculate how all the fragments grow with system size by mapping the transitions allowed by the East constraint to the combinatorics sequence of Dyck words with the help of the Catalan triangle. The energy level spacing statistics indicates that while the eigenstate thermalization hypothesis (ETH) does not hold within the full Hilbert space, the largest fragments obey a weaker version of the subspace-restricted thermalization. This weaker form of the ETH is supported by the presence of subspace-restricted quantum many-body scars. Our analysis further reveals that the filling fraction at which the model has the largest fragment shifts with increasing system sizes from half-filling. In order to stabilize the ground state at a particular filling fraction, we examine the effect of perturbations, namely, a nearest-neighbor density-density interaction with strength $V$. Within the largest fragment, the model undergoes a transition from a weakly ETH-violating phase to a statistical bubble localized phase with increasing $V$. The $V\to \infty$ limit reduces to an integrable model with inversion symmetry. The stabilization of the ground state at half-filling for finite $V$ abruptly changes exactly at $V\to\infty$ due to the emergence of a distinct fragmentation structure. We analytically uncover this new fractured Hilbert space, followed by the mapping of each fragment to a nearest-neighbor tight-binding model of non-interacting fermions. Finally, we propose an experimental setup to realize the infinite-$V$ model., Comment: 27 pages, 21 figures

The study of oracle characters plays an important role in Chinese archaeology and philology. However, the difficulty of collecting and annotating real-world scanned oracle characters hinders the development of oracle character recognition. In this paper, we develop a novel unsupervised domain adaptation (UDA) method, i.e., unsupervised attention regularization net?work (UARN), to transfer recognition knowledge from labeled handprinted oracle characters to unlabeled scanned data. First, we experimentally prove that existing UDA methods are not always consistent with human priors and cannot achieve optimal performance on the target domain. For these oracle characters with flip-insensitivity and high inter-class similarity, model interpretations are not flip-consistent and class-separable. To tackle this challenge, we take into consideration visual perceptual plausibility when adapting. Specifically, our method enforces attention consistency between the original and flipped images to achieve the model robustness to flipping. Simultaneously, we constrain attention separability between the pseudo class and the most confusing class to improve the model discriminability. Extensive experiments demonstrate that UARN shows better interpretability and achieves state-of-the-art performance on Oracle-241 dataset, substantially outperforming the previously structure-texture separation network by 8.5%.

In this work, we consider solving optimization problems with a stochastic objective and deterministic equality constraints. We propose a Trust-Region Sequential Quadratic Programming method to find both first- and second-order stationary points. Our method utilizes a random model to represent the objective function, which is constructed from stochastic observations of the objective and is designed to satisfy proper adaptive accuracy conditions with a high but fixed probability. To converge to first-order stationary points, our method computes a gradient step in each iteration defined by minimizing a quadratic approximation of the objective subject to a (relaxed) linear approximation of the problem constraints and a trust-region constraint. To converge to second-order stationary points, our method additionally computes an eigen step to explore the negative curvature of the reduced Hessian matrix, as well as a second-order correction step to address the potential Maratos effect, which arises due to the nonlinearity of the problem constraints. Such an effect may impede the method from moving away from saddle points. Both gradient and eigen step computations leverage a novel parameter-free decomposition of the step and the trust-region radius, accounting for the proportions among the feasibility residual, optimality residual, and negative curvature. We establish global almost sure first- and second-order convergence guarantees for our method, and present computational results on CUTEst problems, regression problems, and saddle-point problems to demonstrate its superiority over existing line-search-based stochastic methods., Comment: 41 pages, 3 figures

The autoregressive world model exhibits robust generalization capabilities in vectorized scene understanding but encounters difficulties in deriving actions due to insufficient uncertainty modeling and self-delusion. In this paper, we explore the feasibility of deriving decisions from an autoregressive world model by addressing these challenges through the formulation of multiple probabilistic hypotheses. We propose LatentDriver, a framework models the environment's next states and the ego vehicle's possible actions as a mixture distribution, from which a deterministic control signal is then derived. By incorporating mixture modeling, the stochastic nature of decisionmaking is captured. Additionally, the self-delusion problem is mitigated by providing intermediate actions sampled from a distribution to the world model. Experimental results on the recently released close-loop benchmark Waymax demonstrate that LatentDriver surpasses state-of-the-art reinforcement learning and imitation learning methods, achieving expert-level performance. The code and models will be made available at https://github.com/Sephirex-X/LatentDriver.

Irregular time series, where data points are recorded at uneven intervals, are prevalent in healthcare settings, such as emergency wards where vital signs and laboratory results are captured at varying times. This variability, which reflects critical fluctuations in patient health, is essential for informed clinical decision-making. Existing self-supervised learning research on irregular time series often relies on generic pretext tasks like forecasting, which may not fully utilise the signal provided by irregular time series. There is a significant need for specialised pretext tasks designed for the characteristics of irregular time series to enhance model performance and robustness, especially in scenarios with limited data availability. This paper proposes a novel pretraining framework, EMIT, an event-based masking for irregular time series. EMIT focuses on masking-based reconstruction in the latent space, selecting masking points based on the rate of change in the data. This method preserves the natural variability and timing of measurements while enhancing the model's ability to process irregular intervals without losing essential information. Extensive experiments on the MIMIC-III and PhysioNet Challenge datasets demonstrate the superior performance of our event-based masking strategy. The code has been released at https://github.com/hrishi-ds/EMIT .

The emergence and merging of high-density coherent structures - plasma blobs - is a recurrent phenomenon in the Scrape-off layer (SOL) of a tokamak plasma that has a significant impact on the rate of convective transport in that region. We report on a model study of the merging of two electromagnetically interacting blobs in a high beta plasma. Our detailed numerical simulations show that the merging process is akin to the coalescence instability between two magnetic islands but with important differences due to the density perturbation. The blobs are found to rotate about each other during merging and the merging occurs with an acceleration in the poloidal direction that is directly proportional to the square of the current density of the blobs and inversely proportional to its density. The separation distance between two high current density blobs is also seen to oscillate indicating a sloshing behavior.

Astronomical research traditionally relies on extensive domain knowledge to interpret observations and narrow down hypotheses. We demonstrate that this process can be emulated using large language model-based agents to accelerate research workflows. We propose mephisto, a multi-agent collaboration framework that mimics human reasoning to interpret multi-band galaxy observations. mephisto interacts with the CIGALE codebase, which includes spectral energy distribution (SED) models to explain observations. In this open-world setting, mephisto learns from its self-play experience, performs tree search, and accumulates knowledge in a dynamically updated base. As a proof of concept, we apply mephisto to the latest data from the James Webb Space Telescope. mephisto attains near-human proficiency in reasoning about galaxies' physical scenarios, even when dealing with a recently discovered population of "Little Red Dot" galaxies. This represents the first demonstration of agentic research in astronomy, advancing towards end-to-end research via LLM agents and potentially expediting astronomical discoveries., Comment: Submitted. Comments are welcome!

We investigate the role of catalysts in energy extraction from quantum batteries. Two basic classes of catalysts are proposed in this regard, viz., energy- and state-invariant catalysts. Moreover, we separately consider cases when the catalysts are or are not correlated with the battery, in the final output. For energy extraction, we restrict to unitaries which jointly act on the battery and the catalyst. Total extraction via energy-invariant catalysis: We demonstrate that in presence of an energy-invariant catalyst, having the same dimension as the battery, all stored energy of the battery can always be extracted, transforming the battery into its ground state, when an appropriate joint unitary and catalyst state are employed. Additionally, we offer a necessary and sufficient condition of passivity for energy extraction from quantum batteries, considering arbitrary but fixed state of energy-invariant catalysts. Focusing on state-invariant catalysts, we first prove a no-go theorem, viz. there are no uncorrelated state-invariant catalysts that can provide more extraction of energy than ergotropy, and this is valid for an arbitrary-dimensional battery. Does there exist a corresponding no-go result for correlated state-invariant catalysts? Yes, we have found its existence for single-qubit batteries., Comment: 10 pages, 1 figure

Distributed computing, involving multiple servers collaborating on designated computations, faces a critical challenge in optimizing inter-server communication -- an issue central to the study of communication complexity. Quantum resources offer advantages over classical methods in addressing this challenge. In this work, we investigate a distributed computing scenario with multiple senders and a single receiver, establishing a scalable advantage of multipartite quantum entanglement in mitigating communication complexity. Specifically, we demonstrate that when the receiver and the senders share a multi-qubit Greenberger-Horne-Zeilinger (GHZ) state -- a quintessential form of genuine multipartite entanglement -- certain global functions of the distributed inputs can be computed with only one bit of classical communication from each sender. In contrast, without entanglement, two bits of communication are required from all but one sender. Consequently, quantum entanglement reduces communication overhead by (n-1) bits for n senders, allowing for arbitrary scaling with an increasing number of senders. We also show that the entanglement-based protocol exhibits significant robustness under white noise, thereby establishing the potential for experimental realization of this novel quantum advantage., Comment: 6 pages, 2 figures; Comments are welcome

Given a language expression, referring remote sensing image segmentation (RRSIS) aims to identify the ground objects and assign pixel-wise labels within the imagery. The one of key challenges for this task is to capture discriminative multi-modal features via text-image alignment. However, the existing RRSIS methods use one vanilla and coarse alignment, where the language expression is directly extracted to be fused with the visual features. In this paper, we argue that a "fine-grained image-text alignment" can improve the extraction of multi-modal information. To this point, we here proposed a new referring remote sensing image segmentation method, termed FIANet, that fully exploits the visual and linguistic representations. Specifically, the original referring expression is regarded as context text, which is further decoupled into ground object text and spatial position text. The proposed fine-grained image-text alignment module (FIAM) would simultaneously leverage the features of the input image and the corresponding texts and learn better discriminative multi-modal representation. Meanwhile, to handle the various scales of ground objects in remote sensing, we introduce a Text-aware Multi-scale Enhancement Module (TMEM) to adaptively perform cross-scale fusion and intersections. We evaluate the effectiveness of the proposed methods on two public referring remote sensing datasets including RefSegRS and RRSIS-D, and our method obtains superior performance over several state-of-the-art methods. The code will be publicly available.

This book focuses on the application of TensorFlow pre-trained models in deep learning, providing detailed guidance on effectively using these models for tasks such as image classification and object detection. It covers practical implementations of modern architectures like ResNet, MobileNet, and EfficientNet, demonstrating the power of transfer learning through real-world examples and experiments. The book compares linear probing and model fine-tuning, offering visualizations using techniques such as PCA, t-SNE, and UMAP to help readers intuitively understand the impact of different approaches. Designed for beginners to advanced users, this book includes complete example code and step-by-step instructions, enabling readers to quickly master how to leverage pre-trained models to improve performance in practical scenarios. By blending theoretical insights with hands-on practice, this book equips readers with the knowledge to confidently tackle various deep learning challenges., Comment: This book contains 148 pages and 7 figures

Massive communication is one of key scenarios of 6G where two magnitude higher connection density would be required to serve diverse services. As a promising direction, unsourced multiple access has been proved to outperform significantly over orthogonal multiple access (OMA) or slotted-ALOHA in massive connections. In this paper we describe a design framework of unsourced sparse multiple access (USMA) that consists of two key modules: compressed sensing for preamble generation, and sparse interleaver division multiple access (SIDMA) for main packet transmission. Simulation results of general design of USMA show that the theoretical bound can be approached within 1~1.5 dB by using simple channel codes like convolutional. To illustrate the scalability of USMA, a customized design for ambient Internet of Things (A-IoT) is proposed, so that much less memory and computation are required. Simulations results of Rayleigh fading and realistic channel estimation show that USMA based A-IoT solution can deliver nearly 4 times capacity and 6 times efficiency for random access over traditional radio frequency identification (RFID) technology., Comment: 7 pages, 5 figures and 1 table

Solving Singularly Perturbed Differential Equations (SPDEs) presents challenges due to the rapid change of their solutions at the boundary layer. In this manuscript, We propose Asymptotic Physics-Informed Neural Networks (ASPINN), a generalization of Physics-Informed Neural Networks (PINN) and General-Kindred Physics-Informed Neural Networks (GKPINN) approaches. This is a decomposition method based on the idea of asymptotic analysis. Compared to PINN, the ASPINN method has a strong fitting ability for solving SPDEs due to the placement of exponential layers at the boundary layer. Unlike GKPINN, ASPINN lessens the number of fully connected layers, thereby reducing the training cost more effectively. Moreover, ASPINN theoretically approximates the solution at the boundary layer more accurately, which accuracy is also improved compared to GKPINN. We demonstrate the effect of ASPINN by solving diverse classes of SPDEs, which clearly shows that the ASPINN method is promising in boundary layer problems. Furthermore, we introduce Chebyshev Kolmogorov-Arnold Networks (Chebyshev-KAN) instead of MLP, achieving better performance in various experiments.

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

A Chern-Simons matrix model was proposed by Dorey, Tong, and Turner to describe non-Abelian fractional quantum Hall effect. In this paper we study the Hilbert space of the Chern-Simons matrix model from a geometric quantization point of view. We show that the Hilbert space of the Chern-Simons matrix model can be identified with the space of sections of a line bundle on the quiver variety associated to a framed Jordan quiver. We compute the character of the Hilbert space using localization technique. Using a natural isomorphism between vortex moduli space and a Beilinson-Drinfeld Schubert variety, we prove that the ground states wave functions are flat sections of a bundle of conformal blocks associated to a WZW model. In particular they solve a Knizhnik-Zamolodchikov equation. We show that there exists a natural action of the deformed double current algebra (DDCA) on the Hilbert space, moreover the action is irreducible. We define and study the conformal limit of the Chern-Simons matrix model. We show that the conformal limit of the Hilbert space is an irreducible integrable module of $\widehat{\mathfrak{gl}}(n)$ with level identified with the matrix model level. Moreover, we prove that $\widehat{\mathfrak{gl}}(n)$ generators can be obtained from scaling limits of matrix model operators, which settles a conjecture of Dorey-Tong-Turner. The key to the proof is the construction of a Yangian $Y(\mathfrak{gl}_n)$ action on the conformal limit of the Hilbert space, which we expect to be equivalent to the $Y(\mathfrak{gl}_n)$ action on the integrable $\widehat{\mathfrak{gl}}(n)$ modules constructed by Uglov. We also characterize eigenvectors and eigenvalues of the matrix model Hilbert space with respect to a maximal commutative subalgebra of Yangian., Comment: 80+9 pages. Comments are welcome

Faraday rotation is an important probe of the magnetic fields and magnetized plasma around active galactic nuclei (AGN) jets. We present a Faraday rotation measure image of the M87 jet between 85.2 GHz and 101.3 GHz with a resolution of ~2" with the Atacama Large Millimeter/submillimeter Array (ALMA). We found that the rotation measure (RM) of the M87 core is $\rm (4.5\pm 0.4)\times10^{4}\ rad\ m^{-2}$ with a low linear polarization fraction of $\rm (0.88\pm 0.08)\%$. The spatial RM gradient in the M87 jet spans a wide range from $\sim -2\times10^4\rm~rad\ m^{-2}$ to $\sim 3\times10^4\rm~rad\ m^{-2}$ with a typical uncertainty of $0.3\times10^4\rm~rad\ m^{-2}$. A comparison with previous RM measurements of the core suggests that the Faraday rotation of the core may originate very close to the super massive black hole (SMBH). Both an internal origin and an external screen with a rapidly varying emitting source could be possible. As for the jet, the RM gradient indicates a helical configuration of the magnetic field that persists up to kpc scale. Combined with the kpc-scale RM measurements at lower frequencies, we found that RM is frequency-dependent in the jet. One possible scenario to explain this dependence is that the kpc-scale jet has a trumpet-like shape and the jet coil unwinds near its end., Comment: 11pages, 5 figures. Accepted for publication in ApJ. Comments are welcome

We present a spectro-timing analysis of the black hole X-ray transient GX 339-4 using simultaneous observations from Astrosat and NICER during the 2021 outburst period. The combined spectrum obtained from NICER, LAXPC, and SXT data is effectively described by a model comprising a thermal disk component, hard Comptonization component, and reflection component with an edge. Our analysis of the Astrosat and NICER spectra indicates the source to be in a low/hard state, with a photon index of ~1.64. The Power Density Spectra (PDS) obtained from both Astrosat and NICER observations exhibit two prominent broad features at 0.22 Hz and 2.94 Hz. We generated energy-dependent time lag and fractional root mean square (frms) at both frequencies in a broad energy range of 0.5-30 keV and found the presence of hard lags along with a decrease in variability at higher energy levels. Additionally, we discovered that the correlated variations in accretion rate, inner disc radius, coronal heating rate, and the scattering fraction, along with a delay between them, can explain the observed frms and lag spectra for both features., Comment: 5 Figures, 4 Tables, Accepted for publication in ApJ

We investigate the thermodynamics of a Schwarzschild black hole, surrounded by the quintessence energy-matter in the linear and quadratic generalized uncertainty principle framework. Considering the variance in the position to be of the order of the event horizon radius and equating the variance in the momentum to the Hawking temperature of the black hole, we substitute these variances in the deformed algebra. From there we obtained the generalized uncertainty principle-modified black hole temperature and eventually the specific heat of the black hole. Then we calculate the critical as well as the remnant mass and obtain the entropy relation. We observe that the entropy relation includes the usual leading order ``\textit{area divided by four}" term, sub-leading logarithmic term, and higher order inverse of the area corrections. Finally, calculating the energy output as a function of time, we obtain the evaporation time of the black hole. The results show the dependence of the quintessence parameter on the thermodynamic quantities in the framework of linear and quadratic generalized uncertainty principle., Comment: 17 pages LATEX; Comments are welcome

Study of phase transitions provide insights into how a many-body system behaves under different conditions, enabling us to understand the symmetry breaking, critical phenomena, and topological properties. Strong long-range interactions in highly excited Rydberg atoms create a versatile platform for exploring exotic emergent topological phases. Here, we report the experimental observation of dynamical topological phase transitions in cold Rydberg atomic gases under a microwave field driving. By measuring the system transmission curves while varying the probe intensity, we observe complex hysteresis trajectories characterized by distinct winding numbers as they cross the critical point. At the transition state, where the winding number flips, the topology of these hysteresis trajectories evolves into more non-trivial structures. The topological trajectories are shown to be robust against noise, confirming their rigidity in dynamic conditions. These findings contribute to the insights of emergence of complex dynamical topological phases in many-body systems.

Physics-Informed Neural Networks (PINNs) represent a significant advancement in Scientific Machine Learning (SciML), which integrate physical domain knowledge into an empirical loss function as soft constraints and apply existing machine learning methods to train the model. However, recent research has noted that PINNs may fail to learn relatively complex Partial Differential Equations (PDEs). This paper addresses the failure modes of PINNs by introducing a novel, hard-constrained deep learning method -- trust-region Sequential Quadratic Programming (trSQP-PINN). In contrast to directly training the penalized soft-constrained loss as in PINNs, our method performs a linear-quadratic approximation of the hard-constrained loss, while leveraging the soft-constrained loss to adaptively adjust the trust-region radius. We only trust our model approximations and make updates within the trust region, and such an updating manner can overcome the ill-conditioning issue of PINNs. We also address the computational bottleneck of second-order SQP methods by employing quasi-Newton updates for second-order information, and importantly, we introduce a simple pretraining step to further enhance training efficiency of our method. We demonstrate the effectiveness of trSQP-PINN through extensive experiments. Compared to existing hard-constrained methods for PINNs, such as penalty methods and augmented Lagrangian methods, trSQP-PINN significantly improves the accuracy of the learned PDE solutions, achieving up to 1-3 orders of magnitude lower errors. Additionally, our pretraining step is generally effective for other hard-constrained methods, and experiments have shown the robustness of our method against both problem-specific parameters and algorithm tuning parameters., Comment: 20 pages, 9 figures, 3 tables

The ageing intensity function is a powerful analytical tool that provides valuable insights into the ageing process across diverse domains such as reliability engineering, actuarial science, and healthcare. Its applications continue to expand as researchers delve deeper into understanding the complex dynamics of ageing and its implications for society. One common approach to defining the ageing intensity function is through the hazard rate or failure rate function, extensively explored in scholarly literature. Equally significant to the hazard rate function is the mean residual life function, which plays a crucial role in analyzing the ageing patterns exhibited by units or components. This article introduces the mean residual life ageing intensity (MRLAI) function to delve into component ageing behaviours across various distributions. Additionally, we scrutinize the closure properties of the MRLAI function across different reliability operations. Furthermore, a new order termed the mean residual life ageing intensity order is defined to analyze the ageing behaviour of a system, and the closure property of this order under various reliability operations is discussed.

A system of interacting walkers on a two-dimensional space where the dynamics of each walker are governed by the opinions of agents of a three-state opinion dynamics model are considered. Such walks, inspired by Ising-like models and opinions dynamics models, are usually considered in one-dimensional virtual spaces. Here, the mapping is done in such a way that the walk is directed along the $y$ axis while it can move either way along the $x$ axis. We explore the properties of such walks as the parameter representing the noise in the opinion dynamics model, responsible for a continuous phase transition, is varied. The walk features show marked differences as the system crosses the critical point. The bivariate distribution of the displacements below the critical point is a modified biased Gaussian function of x and y which is symmetric about the X axis. The marginal probability distributions can be extracted and the scaling forms of different quantities, showing power law behaviour, are obtained. The directed nature of the walk is reflected in the marginal distributions as well as in the exponents.

We investigate a dynamical model of opinion formation in which an individual's opinion is influenced by interactions with a group of other agents. We introduce a bias towards one of the opinions in a manner not considered earlier to the best of our knowledge. When the bias is neutral, the model is reduced to a mean-field voter model. We analyze the behavior and steady states of the system, identifying three distinct regimes based on the bias level: one favoring negative opinions, one favoring positive opinions, and a neutral case. In large systems, the equilibrium properties become independent of the size of the group, indicating that only the bias influences the final outcome. However, for small groups, the time to reach equilibrium depends on the size of the group. Our results show that even a small initial bias leads to a consensus where all agents eventually share the same opinion when the bias is not neutral. The system exhibits universal behavior, with critical slowing down occurring near the neutral bias point, marking it as a critical dynamical threshold. The time required to reach consensus scales logarithmically when the bias is non-neutral and linearly when it is neutral. Although short-term dynamics depends on group size for small groups, long-term behavior is governed solely by the bias., Comment: 34 pages, 10 figures

The transition metal dichalcogenides (TMDCs) with a 1T' structural phase are predicted to be two-dimensional topological insulators at zero temperature. Although the quantized edge conductance of 1T'-WTe$_2$ has been confirmed to survive up to 100 K, this temperature is still relatively low for industrial applications. Addressing the limited studies on temperature effects in 1T'-TMDCs, our research focuses on the electronic and crystal properties of the epitaxial 1T'-WSe$_2$ monolayers grown on bilayer graphene (BLG) and SrTiO$_3$(100) substrates at various temperatures. For the 1T'-WSe$_2$ grown on BLG, we observed a significant thermal expansion effect on its band structures with a thermal expansion coefficient of $\sim$60$\times$10$^{-6}$ K$^{-1}$. In contrast, the 1T'-WSe$_2$ grown on SrTiO$_3$(100) exhibits minimal changes with varied temperatures due to the enhanced strain exerted by the substrate. Besides, A significant Coulomb gap (CG) was observed pinned at the Fermi level in the angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling spectroscopy (STS). The CG was founded to decrease with increasing temperatures, and can persist up to 200 K for 1T'-WSe$_2$/BLG, consistent with our Monte Carlo simulations. The robustness of the CG and the positive fundamental gap endow the epitaxial 1T'-WSe$_2$ monolayers with huge potential for realizing the quantum spin Hall devices.

In this study, we examine the role of circumgalactic medium (CGM) angular momentum ($j_{\rm CGM}$) on star formation in galaxies, whose influence is currently not well understood. The analysis utilises central galaxies from two hydrodynamical simulations, SIMBA and IllustrisTNG. We observe a substantial divergence in how star formation rates correlate with CGM angular momentum between the two simulations. Specifically, quenched galaxies in IllustrisTNG show high $j_{\rm CGM}$, while in SIMBA, quenched galaxies have low $j_{\rm CGM}$. This difference is attributed to the distinct active galactic nucleus (AGN) feedback mechanisms active in each simulation. Moreover, both simulations demonstrate similar correlations between $j_{\rm CGM}$ and environmental angular momentum ($j_{\rm Env}$) in star-forming galaxies, but these correlations change notably when kinetic AGN feedback is present. In IllustrisTNG, quenched galaxies consistently show higher $j_{\rm CGM}$ compared to their star-forming counterparts with the same $j_{\rm Env}$, a trend not seen in SIMBA. Examining different AGN feedback models in SIMBA, we further confirm that AGN feedback significantly influences the CGM gas distribution, although the relationship between the cold gas fraction and the star formation rate (SFR) remains largely stable across different feedback scenarios., Comment: Submitted to A&A

We present an analysis of lepton-jet azimuthal decorrelation in deep-inelastic scattering (DIS) at next-to-next-to-next-to-leading logarithmic (N$^{3}$LL) accuracy, combined with fixed-order corrections at $\mathcal{O}(\alpha_s^2)$. In this study, jets are defined in the lab frame using the anti-$k_T$ clustering algorithm and the winner-take-all recombination scheme. The N$^{3}$LL resummation results are derived from the transverse-momentum dependent factorization formula within the soft-collinear effective theory, while the $\mathcal{O}(\alpha_s^2)$ fixed-order matching distribution is calculated using the {\tt NLOJET++} event generator. The azimuthal decorrelation between the jet and electron serves as a critical probe of the three-dimensional structure of the nucleon. Our numerical predictions provide a robust framework for precision studies of QCD and the nucleon's internal structure through jet observables in DIS. These results are particularly significant for analyses involving jets in HERA data and the forthcoming electron-ion collider experiments., Comment: 27 pages, 5 figures

Significant work has been done on computing the ``average'' optimal solution value for various $\mathsf{NP}$-complete problems using the Erd\"{o}s-R\'{e}nyi model to establish \emph{critical thresholds}. Critical thresholds define narrow bounds for the optimal solution of a problem instance such that the probability that the solution value lies outside these bounds vanishes as the instance size approaches infinity. In this paper, we extend the Erd\"{o}s-R\'{e}nyi model to general hypergraphs on $n$ vertices and $M$ hyperedges. We consider the problem of determining critical thresholds for the largest cardinality matching, and we show that for $M=o(1.155^n)$ the size of the maximum cardinality matching is almost surely 1. On the other hand, if $M=\Theta(2^n)$ then the size of the maximum cardinality matching is $\Omega(n^{\frac12-\gamma})$ for an arbitrary $\gamma >0$. Lastly, we address the gap where $\Omega(1.155^n)=M=o(2^n)$ empirically through computer simulations.

Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is an stringent requirement for several wireless systems like sonar, radar, communications, and dual-function radar communication (DFRC). Due to the use of high carrier frequency and bandwidth, most of these systems are designed with multiple antennae and subcarriers. Although the resolution is high in the large array regime, the DoA-ToA estimation accuracy of the practical on-grid estimation methods still suffers from estimation inaccuracy due to the spectral leakage effect. In this article, we propose DoA-ToA estimation methods for multi-antenna multi-carrier systems with an orthogonal frequency division multiplexing (OFDM) signal. In the first method, we apply discrete Fourier transform (DFT) based coarse signature estimation and propose a low complexity multistage fine-tuning for extreme enhancement in the estimation accuracy. The second method is based on compressed sensing, where we achieve the super-resolution by taking a 2D-overcomplete angle-delay dictionary than the actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP method, we apply the low complexity 2D-OMP method on the matrix data model that makes the use of CS methods practical in the context of large array regimes. Through numerical simulations, we show that our proposed methods achieve the similar performance as that of the subspace-based 2D-MUSIC method with a significant reduction in computational complexity., Comment: 5 pages, 4 figures, 1 table

We develop a method to transform a collection of higher-dimensional spin systems from the thermal state with a very high temperature of a local spin-s Hamiltonian to a low-lying energy eigenstate of the same. The procedure utilizes an auxiliary system, interactions between all systems, and appropriate projective measurements of arbitrary rank performed on the auxiliary system. We refer to this process as subspace cooling. The performance of the protocol is assessed by determining the fidelity of the target state with the output one and the success probability of achieving the resulting state. For this analysis, spin-s XXZ and bilinear biquadratic models are employed as the evolving Hamiltonian. We demonstrate that in both scenarios, unit fidelity can be attained after a reasonable number of repeated measurements and a finite amount of evolution time when all the systems are aligned in an open chain, but it fails when the interactions between the spin follow the star configuration. We report that the success probability increases with the rank of the projectors in the measurement for a fixed dimension and that for each dimension, there exists a range of interaction strength and evolution period for which the fidelity gets maximized. Even when some subsystems are in contact with the thermal bath, the method proves to be resistant to decoherence., Comment: 12 pages, 10 figures

Loop current order has been suggested as a promising candidate for the spontaneous time-reversal symmetry breaking $2a_0 \times 2a_0$ charge density wave (CDW) revealed in vanadium-based kagom\'e metals \avs\ ($A$ = K, Rb, Cs) near van Hove filling $n_\text{vH} = 5/12$. Weak-coupling analyses and mean field calculations have demonstrated that nearest-neighbor Coulomb repulsion $V_1$ and next-nearest-neighbor Coulomb repulsion $V_2$ drives, respectively, real and imaginary bond-ordered CDW, with the latter corresponding to time-reversal symmetry breaking loop current CDW. It is important to understand the inter-layer correlation of these bond-ordered CDWs and its consequences in the bulk kagom\'e materials. To provide physical insights, we investigate in this paper the $c$-axis stacking of them, loop current CDW in particular, on the minimal bilayer kagom\'e lattice. The bare susceptibilities for stacking of real and imaginary bond orders are calculated for the free electrons on the bilayer kagom\'e lattice with inter-layer coupling $t_\perp=0.2t$, which splits the van Hove filling to $n_{+\text{vH}}=4.64/12$ and $n_{-\text{vH}}=5.44/12$. While real and imaginary bond-ordered CDWs are still favored, respectively, by $V_1$ and $V_2$, their inter-layer coupling is sensitive to band filling $n$. They tend to stack symmetrically near $n_{\pm\text{vH}}$ with identical bond orders in the two layers and give rise to a $2a_0 \times 2a_0 \times 1c_0$ CDW. On the other hand, they prefer to stack antisymmetrically around $n_\text{vH}$ with opposite bond orders in the two layers and lead to a $2a_0 \times 2a_0 \times 2c_0$ CDW. The concrete bilayer $t$-$t_\perp$-$V_1$-V$_2$ model is then studied. We obtain the mean-field ground states and determine the inter-layer coupling as a function of band filling at various interactions. The nontrivial topological properties of loop current CDWs are studied ..., Comment: 12 pages, 8 figures, 2 tables

The demand for innovation in product design necessitates a prolific ideation phase. Conversational AI (CAI) systems that use Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) have been shown to be fruitful in augmenting human creativity, providing numerous novel and diverse ideas. Despite the success in ideation quantity, the qualitative assessment of these ideas remains challenging and traditionally reliant on expert human evaluation. This method suffers from limitations such as human judgment errors, bias, and oversight. Addressing this gap, our study introduces a comprehensive mathematical framework for automated analysis to objectively evaluate the plethora of ideas generated by CAI systems and/or humans. This framework is particularly advantageous for novice designers who lack experience in selecting promising ideas. By converting the ideas into higher dimensional vectors and quantitatively measuring the diversity between them using tools such as UMAP, DBSCAN and PCA, the proposed method provides a reliable and objective way of selecting the most promising ideas, thereby enhancing the efficiency of the ideation phase., Comment: 20 pages, 12 figures, 5 tables

Billions of dollars have been lost due to vulnerabilities in smart contracts. To counteract this, researchers have proposed attack frontrunning protections designed to preempt malicious transactions by inserting "whitehat" transactions ahead of them to protect the assets. In this paper, we demonstrate that existing frontrunning protections have become ineffective in real-world scenarios. Specifically, we collected 158 recent real-world attack transactions and discovered that 141 of them can bypass state-of-the-art frontrunning protections. We systematically analyze these attacks and show how inherent limitations of existing frontrunning techniques hinder them from protecting valuable assets in the real world. We then propose a new approach involving 1) preemptive hijack, and 2) attack backrunning, which circumvent the existing limitations and can help protect assets before and after an attack. Our approach adapts the exploit used in the attack to the same or similar contracts before and after the attack to safeguard the assets. We conceptualize adapting exploits as a program repair problem and apply established techniques to implement our approach into a full-fledged framework, BACKRUNNER. Running on previous attacks in 2023, BACKRUNNER can successfully rescue more than \$410M. In the real world, it has helped rescue over \$11.2M worth of assets in 28 separate incidents within two months.

Conservation of energy under thermal operations, \textbf{TO}, is ensured by commutation of the unitary generating such operations with the total Hamiltonian. However in realistic scenarios, perturbations or disturbances in the system are unavoidable, which in turn may alter the commutation relation and hence in succession may affect the physical processes governed by \textbf{TO}. We call the altered set of operations as approximate thermal operations, \textbf{TO}$_\epsilon$, where $\epsilon$ denotes a degree of disturbance. We provide state transformation conditions under such operations, providing what can be referred to as approximate second laws. We show that in presence of feeble perturbations in the system's Hamiltonian, the states transform in such a way that diagonal elements of the system states start talking not only with each other but also with the off-diagonal elements. In parallel, the off-diagonal elements transform in a way such that they start connecting with diagonal elements and other off-diagonal elements. Such cross-talk is disallowed in the unperturbed second laws. As an application, we show that approximate thermal operations may lead to finite ergotropy extraction from quantum batteries, something that the exact ones are unable to., Comment: 10 pages, 1 figure

This paper presents a novel conversational AI-enabled active ideation interface as a creative idea-generation tool to assist novice designers in mitigating the initial latency and ideation bottlenecks that are commonly observed. It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI) to produce multiple statements of potential ideas for different design problems. Integrating such AI models with ideation creates what we refer to as an Active Ideation scenario, which helps foster continuous dialogue-based interaction, context-sensitive conversation, and prolific idea generation. A pilot study was conducted with thirty novice designers to generate ideas for given problems using traditional methods and the new CAI-based interface. The key parameters of fluency, novelty, and variety were used to compare the outcomes qualitatively by a panel of experts. The findings demonstrated the effectiveness of the proposed tool for generating prolific, diverse and novel ideas. The interface was enhanced by incorporating a prompt-engineered structured dialogue style for each ideation stage to make it uniform and more convenient for the designers. The resulting responses of such a structured CAI interface were found to be more succinct and aligned towards the subsequent design stage, namely conceptualization. The paper thus established the rich potential of using Generative AI (Gen-AI) for the early ill-structured phase of the creative product design process., Comment: 21 pages, 16 figures, AIEDAM Journal Article

An \textit{$(n,m)$-graph} $G$ is a graph having both arcs and edges, and its arcs (resp., edges) are labeled using one of the $n$ (resp., $m$) different symbols. An \textit{$(n,m)$-complete graph} $G$ is an $(n,m)$-graph without loops or multiple edges in its underlying graph such that identifying any pair of vertices results in a loop or parallel adjacencies with distinct labels. We show that a planar $(n,m)$-complete graph cannot have more than $3(2n+m)^2+(2n+m)+1$ vertices, for all $(n,m) \neq (0,1)$ and the bound is tight. This answers a naturally fundamental extremal question in the domain of homomorphisms of $(n,m)$-graphs and positively settles a recent conjecture by Bensmail \textit{et al.}~[Graphs and Combinatorics 2017]. Essentially, our result finds the clique number for planar $(n,m)$-graphs, which is a difficult problem except when $(n,m)=(0,1)$, answering a sub-question to finding the chromatic number for the family of planar $(n,m)$-graphs., Comment: 12 pages

Recent research has seen significant interest in methods for concept removal and targeted forgetting in diffusion models. In this paper, we conduct a comprehensive white-box analysis to expose significant vulnerabilities in existing diffusion model unlearning methods. We show that the objective functions used for unlearning in the existing methods lead to decoupling of the targeted concepts (meant to be forgotten) for the corresponding prompts. This is concealment and not actual unlearning, which was the original goal. The ineffectiveness of current methods stems primarily from their narrow focus on reducing generation probabilities for specific prompt sets, neglecting the diverse modalities of intermediate guidance employed during the inference process. The paper presents a rigorous theoretical and empirical examination of four commonly used techniques for unlearning in diffusion models. We introduce two new evaluation metrics: Concept Retrieval Score (CRS) and Concept Confidence Score (CCS). These metrics are based on a successful adversarial attack setup that can recover forgotten concepts from unlearned diffusion models. The CRS measures the similarity between the latent representations of the unlearned and fully trained models after unlearning. It reports the extent of retrieval of the forgotten concepts with increasing amount of guidance. The CCS quantifies the confidence of the model in assigning the target concept to the manipulated data. It reports the probability of the unlearned model's generations to be aligned with the original domain knowledge with increasing amount of guidance. Evaluating existing unlearning methods with our proposed stringent metrics for diffusion models reveals significant shortcomings in their ability to truly unlearn concepts. Source Code: https://respailab.github.io/unlearning-or-concealment

Despite significant progress in multilingual information retrieval, the lack of models capable of effectively supporting multiple languages, particularly low-resource like Indic languages, remains a critical challenge. This paper presents NLLB-E5: A Scalable Multilingual Retrieval Model. NLLB-E5 leverages the in-built multilingual capabilities in the NLLB encoder for translation tasks. It proposes a distillation approach from multilingual retriever E5 to provide a zero-shot retrieval approach handling multiple languages, including all major Indic languages, without requiring multilingual training data. We evaluate the model on a comprehensive suite of existing benchmarks, including Hindi-BEIR, highlighting its robust performance across diverse languages and tasks. Our findings uncover task and domain-specific challenges, providing valuable insights into the retrieval performance, especially for low-resource languages. NLLB-E5 addresses the urgent need for an inclusive, scalable, and language-agnostic text retrieval model, advancing the field of multilingual information access and promoting digital inclusivity for millions of users globally.

Our understanding of the assembly timeline of the Milky Way has been transforming along with the dramatic increase in astrometric and spectroscopic data available over the past several years. Many substructures in chemo-dynamical space have been discovered and identified as the remnants of various galactic mergers. To investigate the timeline of these mergers we select main sequence turn off & subgiant stars (MSTOs) from the H3 survey, finding members in seven metal poor components of the halo: GSE, the Helmi Streams, Thamnos, Sequoia, Wukong/LMS-1, Arjuna, and I'itoi. We also select out the metal poor in situ disk to facilitate comparison to the evolution of the Milky Way itself at these early epochs. We fit individual isochrone ages to the MSTOs in each of these substructures and use the resulting age distributions to infer simple star formation histories. For GSE we resolve an extended star formation history that truncates $\approx10$ Gyr ago, as well as a clear age -- metallicity relation. From this age distribution and measured star formation history we infer that GSE merged with the Milky Way at a time $9.5-10.2$ Gyr ago, in agreement with previous estimates. We infer that the other mergers occurred at various times ranging from $9-13$ Gyr ago, and that the metal poor component of the disk built up within only a few billion years. These results reinforce the emerging picture that both the disk and halo of the Milky Way experienced a rapid assembly., Comment: 27 pages, 17 figures

We develop a notion of quantum channels that can make states useless for universal quantum computation by destroying their magic (non-stabilizerness) - we refer to them as magic-breaking channels. We establish the properties of these channels in arbitrary dimensions. We prove the necessary and sufficient criteria for qubit channels to be magic-breaking and present an algorithm for determining the same. Moreover, we provide compact criteria in terms of the parameters for several classes of qubit channels to be magic-breaking under various post-processing operations. Further, we investigate the necessary and sufficient conditions for the tensor product of multiple qubit channels to be magic-breaking. We establish implications of the same for the dynamical resource theory of magic preservability., Comment: 14 pages

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Coronal jets are the captivating eruptions which are often found in the solar atmosphere, and primarily formed due to magnetic reconnection. Despite their short-lived nature and lower energy compared to many other eruptive events, e.g. flares and coronal mass ejections, they play an important role in heating the corona and accelerating charged particles. However, their generation in the ambience of non-standard flare regime is not fully understood, and warrant a deeper investigation, in terms of their onset, growth, eruption processes, and thermodynamic evolution. Toward this goal, this paper reports the results of a data-constrained three-dimensional (3D) magnetohydrodynamics (MHD) simulation of an eruptive jet; initialized with a Non-Force-Free-Field (NFFF) extrapolation and carried out in the spirit of Implicit Large Eddy Simulation (ILES). The simulation focuses on the magnetic and dynamical properties of the jet during its onset, and eruption phases, that occurred on February 5, 2015 in an active region NOAA AR12280, associated with a seemingly three-ribbon structure. In order to correlate its thermal evolution with computed energetics, the simulation results are compared with differential emission measurement (DEM) analysis in the vicinity of the jet. Importantly, this combined approach provides an insight to the onset of reconnection in transients in terms of emission and the corresponding electric current profiles from MHD evolutions. The presented study captures the intricate topological dynamics, finds a close correspondence between the magnetic and thermal evolution in and around the jet location. Overall, it enriches the understanding of the thermal evolution due to MHD processes, which is one of the broader aspects to reveal the coronal heating problem., Comment: 26 pages, 16 figures, accepted for publication in the Astrophysical Journal. Animations associated with Figure 1, Figure 7, and Figure 8 are available upon reasonable request from the corresponding authors

Motivated by the recent discovery of charge density wave (CDW) order in the magnetic kagom\'e metal FeGe, we study the single-orbital $t$-$U$-$V_1$-$V_2$ model on the kagom\'e lattice, where $U$, $V_1$, and $V_2$ are the onsite, nearest neighbor, and next-nearest-neighbor Coulomb repulsions, respectively. When the Fermi level lies in the flat band, the instability toward ferromagnetic (FM) order gives rise to a FM half-metal at sufficiently large onsite $U$. Intriguingly, at band filling $n=17/24$, the Fermi level crosses the van Hove singularity of the spin-minority bands of the half-metal. We show that, due to the unique geometry and sublattice interference on the kagom\'e lattice at van Hove singularity, the intersite Coulomb interactions $V_1$ and $V_2$ drive a real and an imaginary bond-ordered $2a_0 \times 2a_0$ CDW instability, respectively. The FM loop current CDW with complex bond orders is a spin-polarized Chern insulator exhibiting the quantum anomalous Hall effect. The bond fluctuations are found to be substantially enhanced compared to the corresponding nonmagnetic kagom\'e metals at van Hove filling, providing a concrete model realization of the bond-ordered CDWs, including the FM loop current CDW, over the onsite charge density ordered states. When the spins are partially polarized, we find that the formation of bond-ordered CDWs enhances substantially the ordered magnetic moments. These findings provide physical insights for the emergence of loop-current and bond-ordered CDW and their interplay with magnetism on the kagom\'e lattice, with possible connections to the magnetic kagom\'e metal FeGe.

Developers usually use TPLs to facilitate the development of the projects to avoid reinventing the wheels, however, the vulnerable TPLs indeed cause severe security threats. The majority of existing research only considered whether projects used vulnerable TPLs but neglected whether the vulnerable code of the TPLs was indeed used by the projects, which inevitably results in false positives and further requires additional patching efforts and maintenance costs. To address this, we propose VAScanner, which can effectively identify vulnerable root methods causing vulnerabilities in TPLs and further identify all vulnerable APIs of TPLs used by Java projects. Specifically, we first collect the initial patch methods from the patch commits and extract accurate patch methods by employing a patch-unrelated sifting mechanism, then we further identify the vulnerable root methods for each vulnerability by employing an augmentation mechanism. Based on them, we leverage backward call graph analysis to identify all vulnerable APIs for each vulnerable TPL version and construct a database consisting of 90,749 (2,410,779 with library versions) vulnerable APIs with 1.45% false positive proportion with a 95% CI of [1.31%, 1.59%] from 362 TPLs with 14,775 versions. Our experiments show VAScanner eliminates 5.78% false positives and 2.16% false negatives owing to the proposed sifting and augmentation mechanisms. Besides, it outperforms the state-of-the-art method-level tool in analyzing direct dependencies, Eclipse Steady, achieving more effective detection of vulnerable APIs. Furthermore, in a large-scale analysis of 3,147 projects using vulnerable TPLs, we find only 21.51% of projects (with 1.83% false positive proportion and a 95% CI of [0.71%, 4.61%]) were threatened through vulnerable APIs by vulnerable TPLs, demonstrating that VAScanner can potentially reduce false positives significantly., Comment: 15 pages, 4 figures