December 2023 arXiv papers — page 26

Md Shafayat Hossain, Tyler A. Cochran, Yu-Xiao Jiang, Songbo Zhang

Correlated topological materials often maintain a delicate balance among physical symmetries: many topological orders are symmetry protected, while most correlated phenomena arise from spontaneous symmetry breaking. It is rare to find cases where symmetry breaking induces a non-trivial topological phase. Here, we present the discovery of such a phase in Ta2P

Sang-Heon Shim, Jiwoo Chung, Jae-Pil Heo

In this paper, we first investigate a visual quality degradation problem observed in recent high-resolution virtual try-on approach. The tendency is empirically found that the textures of clothes are squeezed at the sleeve, as visualized in the upper row of Fig.1(a). A main reason for the issue arises from a gradient conflict between two popular losses, the

Kwang-Il Seon, Hyung-Joe Kim, Hee-Gyeong Kim, Hyeon Jeong Youn

This paper investigates the number of scatterings a photon undergoes in random walks before escaping from a medium. The number of scatterings in random walk processes is commonly approximated as $\tau+\tau^2$ in the literature, where $\tau$ is the optical thickness measured from the center of the medium. However, it is found that this formula is not accurate

Leilei Zeng, Xuechen Li, Xinquan Yang, Linlin Shen

Due to the poor prognosis of Pancreatic cancer, accurate early detection and segmentation are critical for improving treatment outcomes. However, pancreatic segmentation is challenged by blurred boundaries, high shape variability, and class imbalance. To tackle these problems, we propose a multiscale attention network with shape context and prior constraint

Keivan Nalaie, Rong Zheng

In this paper, we introduce MVSparse, a novel and efficient framework for cooperative multi-person tracking across multiple synchronized cameras. The MVSparse system is comprised of a carefully orchestrated pipeline, combining edge server-based models with distributed lightweight Reinforcement Learning (RL) agents operating on individual cameras. These RL ag

Haibin Zhang, Yong Zhang, Xue Ding

Suppose $\left \{ X_{i,k}; 1\le i \le p, 1\le k \le n \right \} $ is an array of i.i.d.~real random variables. Let $\left \{ p=p_{n}; n \ge1 \right \} $ be positive integers. Consider the maximum interpoint distance $M_{n}=\max_{1\le i< j\le p} \left \| \boldsymbol{X}_{i}- \boldsymbol{X}_{j} \right \|_{2} $ where $\boldsymbol{X}_{i}$ and $\boldsymbol{X}_{j}$

Kaichen Zhou, Lanqing Hong, Xinhai Chang, Yingji Zhong

A key challenge in fine-grained 3D-based interactive editing is the absence of an efficient representation that balances diverse modifications with high-quality view synthesis under a given memory constraint. While 3D meshes provide robustness for various modifications, they often yield lower-quality view synthesis compared to 3D Gaussian Splatting, which, i

Yongheng Wang, Xinwei Shen, Yan Xu

This paper proposes a collaborative planning model for the active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers the vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on the physical characteristics of the problem, brea

Yingqi Lin, Xiaogang Xu, Jiafei Wu, Yan Han

Low-Light Enhancement (LLE) is aimed at improving the quality of photos/videos captured under low-light conditions. It is worth noting that most existing LLE methods do not take advantage of geometric modeling. We believe that incorporating geometric information can enhance LLE performance, as it provides insights into the physical structure of the scene tha

Olga Maryeva, Aynur Abdulkarimova, Sergey Karpov, Alexei Moiseev

The similarity in physical conditions in winds of low-mass post-asymptotic giant branch stars and evolved massive stars leads to the appearance of an interesting phenomenon of spectral mimicry. Due to that the discovery of every new star with Wolf--Rayet spectrum requires special study of its evolutionary status before it may be included in the list of Galac

Chenxi Sun, Hongyan Li, Moxian Song, Derun Cai

Time series widely exists in real-world applications and many deep learning models have performed well on it. Current research has shown the importance of learning strategy for models, suggesting that the benefit is the order and size of learning samples. However, no effective strategy has been proposed for time series due to its abstract and dynamic constru

Jingang Xiong

We study a family of nonlinear integral flows that involve Riesz potentials on Riemannian manifolds. In the Hardy-Littlewood-Sobolev (HLS) subcritical regime, we present a precise blow-up profile exhibited by the flows. In the HLS critical regime, by introducing a \textit{dual $Q$ curvature} we demonstrate the concentration-compactness phenomenon. If, in add

Zhuofu Li, Yonghong Zhang, Chengxia Wang, Shanshan Liu

Objective: This study aims to use artificial intelligence to realize the automatic planning of laminectomy, and verify the method. Methods: We propose a two-stage approach for automatic laminectomy cutting plane planning. The first stage was the identification of key points. 7 key points were manually marked on each CT image. The Spatial Pyramid Upsampling N

Zi-Feng Mai, Chang-Dong Wang, Zhongjie Zeng, Ya Li

Next-basket recommendation (NBR) aims to infer the items in the next basket given the corresponding basket sequence. Existing NBR methods are mainly based on either message passing in a plain graph or transition modelling in a basket sequence. However, these methods only consider point-to-point binary item relations while item dependencies in real world scen

Xin Yuan, Ning Li, Tuo Zhang, Muqing Li

Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is the key supporting technologies of B5G/6G, can achieve massive connections and high spectrum efficiency. Motivated by the benefits of NOMA, integrating NOMA with model split in MEC t

Chenxi Sun, Hongyan Li, Moxian Song, Derun Can

Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption. However, it is difficult to demonstrate their superiority in classification accuracy, because current efforts mainly focus on designing better network structures. In th

Xin Yuan, Ning Li, Jose Fernan Martinez

Mobile edge computing (MEC) can reduce the latency of cloud computing successfully. However, the edge server may fail due to the hardware of software issues. When the edge server failure happens, the users who offload tasks to this server will be affected. How to recover the services for these affected users quickly and effectively is challenging. Moreover,

Chengxin Chen, Pengyuan Zhang

As a vital aspect of affective computing, Multimodal Emotion Recognition has been an active research area in the multimedia community. Despite recent progress, this field still confronts two major challenges in real-world applications: 1) improving the efficiency of constructing joint representations from unaligned multimodal features, and 2) relieving the p

Chao Sun, Huiming Zhang, Bo Chen, Li Yu

This paper studies the distributed optimization problem under the influence of heavy-tailed gradient noises. Here, a heavy-tailed noise means that the noise does not necessarily satisfy the bounded variance assumption. Instead, it satisfies a more general assumption. The commonly-used bounded variance assumption is a special case of the considered noise assu

Yinfeng Ma, Xiaoling Cui

Shell-shaped Bose-Einstein condensate (BEC) is a typical quantum system in curved geometry. Here we propose a new type of shell-shaped BEC with self-bound character, thereby liberating it from stringent conditions such as microgravity or fine-tuned trap. Specifically, we consider a three-component (1,2,3) ultracold Bose gas where (1,2) and (2,3) both form qu

Haishan Ye, Xiangyu Chang

In this paper, we focus on the decentralized composite optimization for convex functions. Because of advantages such as robust to the network and no communication bottle-neck in the central server, the decentralized optimization has attracted much research attention in signal processing, control, and optimization communities. Many optimal algorithms have bee

Kanta Kaneda, Shunya Nagashima, Ryosuke Korekata, Motonari Kambara

Domestic service robots offer a solution to the increasing demand for daily care and support. A human-in-the-loop approach that combines automation and operator intervention is considered to be a realistic approach to their use in society. Therefore, we focus on the task of retrieving target objects from open-vocabulary user instructions in a human-in-the-lo

Bai Xue

This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs). The reachability probabilities considered in this paper encompass two aspects: the probability of reaching a set of specified states within a pred

Yida Chen, Yixian Gan, Sijia Li, Li Yao

Recent work found high mutual information between the learned representations of large language models (LLMs) and the geospatial property of its input, hinting an emergent internal model of space. However, whether this internal space model has any causal effects on the LLMs' behaviors was not answered by that work, led to criticism of these findings as mere

Ehsan Latif, Luyang Fang, Ping Ma, Xiaoming Zhai

This study proposes a method for knowledge distillation (KD) of fine-tuned Large Language Models (LLMs) into smaller, more efficient, and accurate neural networks. We specifically target the challenge of deploying these models on resource-constrained devices. Our methodology involves training the smaller student model (Neural Network) using the prediction pr

Zifan Zhou, Ruoxi Zhu, Selim M. Shahriar

We show that the frequency of a Raman laser is highly correlated or anti-correlated with the frequency of the Raman pump laser, depending on whether the dispersion experienced by the Raman laser is positive or negative. For a subluminal laser, corresponding to a positive dispersion with a group index that is much larger than unity, the shift in its frequency

Zeqiang Wei, Kai Jin, Xiuzhuang Zhou

Cross-modal medical image-report retrieval task plays a significant role in clinical diagnosis and various medical generative tasks. Eliminating heterogeneity between different modalities to enhance semantic consistency is the key challenge of this task. The current Vision-Language Pretraining (VLP) models, with cross-modal contrastive learning and masked re

Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are qu

Shin'ichi Nojiri, Sergei D. Odintsov, Armen Sedrakian

We show analytically that there exist compact stellar objects akin to neutron stars whose radius is smaller than the Schwarzschild radius defined by Arnowitt-Deser-Misner (ADM) mass. The radius of the compact object is defined by the radius where the energy density and the pressure of ordinary matter vanish, while clouds of scalar(s) can extend beyond this r

Leighton Thompson, Seungmo Kim

Connected and autonomous vehicles are already right around the corner of our everyday life. One of the key technologies actualizing the connected vehicles is vehicle-to-everything communications (V2X), which has been enhanced along the lines of two technologies--i.e., dedicated short-range communications (DSRC) and cellular V2X (C-V2X). While the United Stat

Zefang Liu

In this paper, we introduce SecQA, a novel dataset tailored for evaluating the performance of Large Language Models (LLMs) in the domain of computer security. Utilizing multiple-choice questions generated by GPT-4 based on the "Computer Systems Security: Planning for Success" textbook, SecQA aims to assess LLMs' understanding and application of security prin

Zlatko Drmač, Igor Mezić

This paper introduces a new theoretical and computational framework for a data driven Koopman mode analysis of nonlinear dynamics. To alleviate the potential problem of ill-conditioned eigenvectors in the existing implementations of the Dynamic Mode Decomposition (DMD) and the Extended Dynamic Mode Decomposition (EDMD), the new method introduces a Koopman-Sc

Alex Kwiatkowski, Laurent J. Stephenson, Hannah M. Knaack, Alejandra L. Collopy

Randomized benchmarking (RB) is a widely used strategy to assess the quality of available quantum gates in a computational context. RB involves applying known random sequences of gates to an initial state and using the statistics of a final measurement step to determine an effective depolarizing error per step of the sequence, which is a metric of gate quali

Nils Barlaug

Blocking is a crucial step in large-scale entity matching but often requires significant manual engineering from an expert for each new dataset. Recent work has show that deep learning is state-of-the-art and has great potential for achieving hands-off and accurate blocking compared to classical methods. However, in practice, such deep learning methods are o

Vo Duc Thinh, Xiaolong Qin, Jen-Chih Yao

Establishing explicit formulas of coderivatives with respect to a set of the normal cone mapping to a polyhedron, the solution set of a variational inequalities system, is one of the main goals of this paper. By using our coderivative formulas, we provide a characteristic of the Aubin property with respect to a set of that normal cone mapping and thereby giv

Chenyang Zhong

Introduced by Mallows as a ranking model in statistics, Mallows permutation model is a class of non-uniform probability distributions on the symmetric group $S_n$. The model depends on a distance metric on $S_n$ and a scale parameter $\beta$. In this paper, we take the distance metric to be the $L^1$ distance (also known as Spearman's footrule in the statist

A. Flores, R. C. de Lamare, K. V. Mishra

Cell-free (CF) multiple-input multiple-output (MIMO) systems generally employ linear precoding techniques to mitigate the effects of multiuser interference. However, the power loss, efficiency, and precoding accuracy of linear precoders are usually improved by replacing them with nonlinear precoders that employ perturbation and modulo operation. In this work

Guoan Yan, Zhengshuo Li

The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset. In this letter, a novel outlier-immune DD-LPF model construction method via mixed-integer programming is presented for automatically and optimally identifying outliers to form a more accurate LPF

Stefan M. Goetz, Ricardo Lizana F., Sebastian Rivera

There is currently a major trend to hairpin-winding motors for small and medium drives with increased power, specifically more torque density in the automotive industry. Practically all large players in the field either already use this winding technology or have announced doing so soon. However, hairpins, bar windings, and other segmented winding techniques

Xu Gao, Jianqi Liu, Yiyi Zhu

In this paper, we introduce a notion of $g$-twisted restricted conformal block on the three-pointed twisted projective line $\mathfrak{x}\colon\overline{C}\to\mathbb{P^1}$ associated with an untwisted module $M^1$ and the bottom levels of two $g$-twisted modules $M^2$ and $M^3$ over a vertex operator algebra $V$. We show that the space of twisted restricted

Julien Dirani, Liina Pylkkänen

The semantic knowledge stored in our brains can be accessed from different stimulus modalities. For example, a picture of a cat and the word &#34;cat&#34; both engage similar conceptual representations. While existing research has found evidence for modality-independent representations, their content remains unknown. Modality-independent representations coul

Ava Polzin, Yasmeen Asali, Sanah Bhimani, Madison Brady

This book was created as part of the SIRIUS B VERGE program to orient students to astrophysics as a broad field. The 2023-2024 VERGE program and the printing of this book is funded by the Women and Girls in Astronomy Program via the International Astronomical Union&#39;s North American Regional Office of Astronomy for Development and the Heising-Simons Found

Yi-Hsin Chen, Hong-Sheng Xie, Cheng-Wei Chen, Zong-Lin Gao

Conditional coding has lately emerged as the mainstream approach to learned video compression. However, a recent study shows that it may perform worse than residual coding when the information bottleneck arises. Conditional residual coding was thus proposed, creating a new school of thought to improve on conditional coding. Notably, conditional residual codi

K. S. Babu, Yongcheng Wu, Shiyuan Xu

The Yukawa and scalar sectors of a general $S_3$-symmetric three-Higgs doublet model (3HDM) are investigated. The Yukawa interactions are constructed in an $S_3$-invariant way, while the scalar potential contains $S_3$ soft-breaking terms. Global fits to the quark/lepton masses and CKM/PMNS matrices are performed. Excellent fits to all fermion mass and mixin

Nima Hoda, Jacek Świątkowski

We characterize those 1-ended word hyperbolic groups whose Gromov boundaries are homeomorphic to trees of graphs (i.e. to inverse limits of graphs that have particularly simple finitary descriptions). These are groups with the simplest connected Gromov boundaries of topological dimension 1. The characterization is expressed in terms of algebraic properties o

Lijian Chen, Wei Yuan, Tong Chen, Guanhua Ye

Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences. While the incorporation of visual information holds the promise of enhancing recommendation accuracy and alleviating the cold-start problem, it is essential to point out that the incl

Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt, Christian Ostalecki

This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cell-wise analysis. It assesses the effectiveness of gene expression profiles and Radiomic features, revealing that Radiomic features, particularly when combined with UMAP for dimensionality reduction, significantly enha

Ilyass Moummad, Romain Serizel, Nicolas Farrugia

Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost. This is pertinent in bioacoustics, where biologists routinely collect extensive sound datasets from the natural environment. In this study, we demonstrate that SSL is capable of

Reza Rezaie

A sequential detection and tracking (SDT) approach is proposed for detection and tracking of very low signal-to-noise (SNR) objects. The proposed approach is compared with two existing particle filter track-before-track (TBD) methods. It is shown that the former outperforms the latter. A conventional detection and tracking (CDT) approach, based on one-data-f

H. F. Y. Watson, A. Ruocco, M. Tiberi, J. E. Muench

Next-generation data networks need to support Tb/s rates. In-phase and quadrature (IQ) modulation combine phase and intensity information to increase the density of encoded data, reduce overall power consumption by minimising the number of channels, and increase noise tolerance. To reduce errors when decoding the received signal, intersymbol interference mus

Zhiqiang Li, Xianghui Shi, Yiwei Zhang

Expanding Thurston maps were introduced by M. Bonk and D. Meyer with motivation from complex dynamics and Cannon's conjecture from geometric group theory via Sullivan's dictionary. In this paper, we introduce subsystems of expanding Thurston maps motivated via Sullivan's dictionary as analogs of some subgroups of Kleinian groups. We use thermodynamic formali

Apoorv Vyas, Bowen Shi, Matthew Le, Andros Tjandra

Audio is an essential part of our life, but creating it often requires expertise and is time-consuming. Research communities have made great progress over the past year advancing the performance of large scale audio generative models for a single modality (speech, sound, or music) through adopting more powerful generative models and scaling data. However, th

Qi Chen, Dileepa Pitawela, Chongyang Zhao, Gengze Zhou

Vision-and-Language Navigation (VLN) task aims to enable AI agents to accurately understand and follow natural language instructions to navigate through real-world environments, ultimately reaching specific target locations. We recognise a promising opportunity to extend VLN to a comparable navigation task that holds substantial significance in our daily liv

Ahad N. Zehmakan, Xiaotian Zhou, Zhongzhi Zhang

Consider a directed network where each node is either red (using the red product), blue (using the blue product), or uncolored (undecided). Then in each round, an uncolored node chooses red (resp. blue) with some probability proportional to the number of its red (resp. blue) out-neighbors. What is the best strategy to maximize the expected final number of re

Jayde Sylvie Massmann

We share both recent and older, well-known results regarding the notions of stable ordinals and shrewd cardinals. We then argue that $\Sigma_2$-nonprojectible ordinals may be considered as recursive analogues to subtle cardinals, a highly combinatorial type of cardinal related to Jensen's fine structure, due to the latter possessing a characterisation in ter

Hamed Haghighi, Mehrdad Dianati, Valentina Donzella, Kurt Debattista

Simulation models for perception sensors are integral components of automotive simulators used for the virtual Verification and Validation (V\&V) of Autonomous Driving Systems (ADS). These models also serve as powerful tools for generating synthetic datasets to train deep learning-based perception models. Lidar is a widely used sensor type among the percepti

Siheng Xiong, Yuan Yang, Ali Payani, James C Kerce

Conventional embedding-based models approach event time prediction in temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing essential temporal relationships such as order and distance. In this paper, we propose TEILP, a logical reasoning framework that naturally integrates such temporal elements into knowledge gra

Alvaro Corral

The total number of fatalities of an epidemic outbreak is a dramatic but extremely informative quantity. Knowledge of the statistics of this quantity allows the calculation of the mean total number of fatalities conditioned to the fact that the outbreak has surpassed a given number of fatalities, which is very relevant for risk assessment. However, the fact

Avik Ray, Yilin Shen, Hongxia Jin

State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack of model compositionality when trained on limited training data. In this paper, we study two types of compositionality: (a) novel slot combination, and (b) length generalization. W

Oliver Nagy, Manish Pandey, Georgios Exarchakos, Mark Bentum

Orbiting low frequency antennas for radio astronomy (OLFAR) that capture cosmic signals in the frequency range below 30MHz could provide valuable insights on our Universe. These wireless swarms of satellites form a connectivity graph that allows data exchange between most pairs of satellites. Since this swarm acts as an interferometer, the aim is to compute

Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas

Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0.1\% change can cause an overwhelming number of false positives. However, academic research is often restrained to public datasets on the order of ten thousand samples and is too small to detect

Michael Hochman

Let $(X,\mathcal{B},\mu,T)$ be a probability-preserving system with $X$ compact and $T$ a homeomorphism. We show that if every point in $X\times X$ is two-sided recurrent, then $h_{\mu}(T)=0$, resolving a problem of Benjamin Weiss, and that if $h_{\mu}(T)=\infty$ then every full-measure set in $X$ contains mean-asymptotic pairs (i.e. the associated process i

Mohamed Tharwat, Amr AlBarqawy, Adel Awad, Esraa Elkhateeb

We study phase structures of Lorentzian Dyonic Taub-NUT-AdS spacetimes for different horizon geometries, which are spherical, flat, and hyperbolic. We check the consistency of our extended thermodynamics approach through satisfying the first law, the Gibbs-Duhem relation, and the generalized Smarr's relation. Although we study the phase structure for the thr

Michael Hochman

We show that a topological Cantor set in the line has at most countably many real-analytic, onto self-maps.

Lei Zhang, Jiacheng Pei, Kaixin Bai, Zhaopeng Chen

Traditional visual servoing methods suffer from serving between scenes from multiple perspectives, which humans can complete with visual signals alone. In this paper, we investigated how multi-perspective visual servoing could be solved under robot-specific constraints, including self-collision, singularity problems. We presented a novel learning-based multi

Yu Zhang, Yanmin Gong, Lei Fan, Yu Wang

In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high alti

Akshayveer, Federico C Buroni, Roderick Melnik, Luis Rodriguez-Tembleque

In recent times, there have been notable advancements in haptic technology, particularly in screens found on mobile phones, laptops, LED screens, and control panels. However, it is essential to note that the progress in high-temperature haptic applications is still in the developmental phase. Due to its complex phase and domain structures, lead-free piezoele

Congzao Dong, Alexander Iksanov, Andrey Pilipenko

Let $d$ be a positive integer and $A$ a set in $\mathbb{Z}^d$, which contains finitely many points with integer coordinates. We consider $X$ a standard random walk perturbed on the set $A$, that is, a Markov chain whose transition probabilities from the points outside $A$ coincide with those of a standard random walk on $\mathbb{Z}^d$, whereas the transition

Zhuangyu Han, Abhronil Sengupta

Neuromorphic computing systems, where information is transmitted through action potentials in a bio-plausible fashion, is gaining increasing interest due to its promise of low-power event-driven computing. Application of neuromorphic computing in robotic locomotion research have largely focused on Central Pattern Generators (CPGs) for bionics robotic control

A. V. Bednyakov

In this Letter we consider renormalization of a class of scalar operators with fixed hypercharge $Q$ within the Standard Model. We carry out explicit computation of the corresponding anomalous dimensions up to the three-loop order. In spite of the fact that our result is gauge-dependent, in the Landau gauge and in the limit of vanishing weak isospin coupling

Olivier Schiffmann, Eric Vasserot

We construct an isomorphism between the preprojective cohomological Hall algebra of an arbitrary quiver and a positive half of the corresponding Maulik-Okounkov Yangian, which intertwines the respective actions on the cohomology of the Nakajima quiver varieties. We use this to prove a conjecture of Okounkov relating the character of the Maulik-Okounkov Lie a

Xi Yang, Victor Smaluk, Timur Shaftan

To optimize electron energy toward in-situ imaging large bio-samples up to 10-um thickness with nanoscale resolution, we implemented an analytical model based on elastic and inelastic characteristic angles [1]. This model can be used to predict the transverse beam size broadening as a function of electron energy while the probe beam traverses through the sam

Soham Banerjee, Joseph R. Smith, Chris Orban

Particle-in-Cell (PIC) codes are a popular tool to model laser-plasma interactions. Many different PIC codes already exist, and many new PIC codes are being developed constantly. It is therefore important to compare different PIC codes to ascertain which code is best suited for a particular kind of physical problem. In a paper by Smith et al. (2021) they com

Jeremy A. Riousset, Manasvi Lingam

The applicability of advanced classical mechanics (viz., the Lagrangian and/or Hamiltonian approaches) to real-world problems may not always seem straightforward, despite the mathematical rigor and elegance of this field. Here, we present a proof of the Jacobi integral using the Lagrangian formulation as a viable alternative to the usual demonstration using

Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov

Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods heavily rely on data exchangeability, a condition often violated in practice. Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond th

Valentin D. Ivanov

For most writers the science is either an exotic setting or a source of thrilling conflict that would drive the story forward. For a communicator it is the other way around - the science is neatly wrapped in a package of literary tools that make it "invisible" while it remains tangible and most importantly - it can be conveyed to the reader in understandable

Jerryman A. Gyamfi, Thomas-C. Jagau

Complex absorbing potentials (CAPs) are artificial potentials added to electronic Hamiltonians to make the wave function of metastable electronic states square-integrable. This makes electronic-structure theory of resonances comparable to that of bound states, thus reducing the complexity of the problem. However, the most often used box and Voronoi CAPs depe

Valentin D. Ivanov

The target auditory of scientific outreach efforts is often limited to the small enthusiastic subset of the society that value science and actively seeks knowledge. However, the vast majority is usually indifferent or in some cases may even be opposed to sciences. To bring these people around to support sciences, we have to double and triple our efforts. I d

Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R. Andersson

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather, to planning renewable energy use. Here, we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecas

Timothé Albouy, Davide Frey, Ran Gelles, Carmit Hazay

We address the problem of Reliable Broadcast in asynchronous message-passing systems with $n$ nodes, of which up to $t$ are malicious (faulty), in addition to a message adversary that can drop some of the messages sent by correct (non-faulty) nodes. We present a Message-Adversary-Tolerant Byzantine Reliable Broadcast (MBRB) algorithm that communicates ${\cal

D. Barba-González, C. Albertus, M. Ángeles Pérez-García

We present microscopic Molecular Dynamics simulations including the efficient Ewald sum procedure to study warm and dense stellar plasmas consisting of finite-size ion charges immerse in a relativistic neutralizing electron gas. For densities typical of Supernova matter and crust in a proto-neutron star, we select a representative single ion composition and

Assaf Marron, Smadar Szekely, Irun R. Cohen, David Harel

Commonly recognized evolutionarily relevant effects of sexual reproduction include increased diversity, accelerated adaptation, and constrained accumulation of deleterious mutations, along with a secondary effect of species genotype homogenization. Still, strong published arguments prioritize the contribution of biological mechanisms underlying bi-parental r

Cihan Okay

Simplicial distributions provide a framework for studying quantum contextuality, a generalization of Bell's non-locality. Understanding extremal simplicial distributions is of fundamental importance with applications to quantum computing. We introduce a rank formula for twisted simplicial distributions defined for $2$-dimensional measurement spaces and provi

Franz J. Brandenburg

It is well-known that every 3-connected planar graph has a unique planar embedding on the sphere. We study the extension to triangulated 1-planar graphs, T1P graphs for short, which admit an embedding in which each edge is crossed at most once and each face is a triangle, and obtain an algorithmic solution by a cubic time recognition algorithm that also coun

Rena A. Lee, Eric Gaidos, Jennifer van Saders, Gregory A. Feiden

Determining the precise ages of young (tens to a few hundred Myr) kinematic (``moving") groups is important for placing star, protoplanetary disk, and planet observations on an evolutionary timeline. The nearby $\sim$25 Myr-old $\beta$ Pictoris Moving Group (BPMG) is an important benchmark for studying stars and planetary systems at the end of the primordial

Kosuke Ito, Keisuke Fujii

We introduce SantaQlaus, a resource-efficient optimization algorithm tailored for variational quantum algorithms (VQAs), including applications in the variational quantum eigensolver (VQE) and quantum machine learning (QML). Classical optimization strategies for VQAs are often hindered by the complex landscapes of local minima and saddle points. Although som

Cameron Beetar, Nitin Gupta, S. Shajidul Haque, Jeff Murugan

Krylov complexity is a measure of operator growth in quantum systems, based on the number of orthogonal basis vectors needed to approximate the time evolution of an operator. In this paper, we study the Krylov complexity of a $\mathsf{PT}$-symmetric system of oscillators, which exhibits two phase transitions that separate a dissipative state, a Rabi-oscillat

Manuel Eduardo Hernández-García, Jorge Velázquez-Castro

The Hill function is relevant for describing enzyme binding and other processes in gene regulatory networks. Despite its theoretical foundation, it is often empirically used as a useful fitting function. Theoretical predictions suggest that the Hill coefficient should be an integer. However, it is often assigned a decimal value. The deterministic approximati

Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro

Deep unrolling, or unfolding, is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. However, the convergence guarantees and generalizability of the unrolled networks are still open theoretical problems. To tackle these problems, we provide deep unrolled architectures with a stocha

Sujoy Chakraborty, Souradeep Majumder

Let $D$ be an effective divisor on a smooth projective variety $X$ over an algebraically closed field $k$ of characteristic $0$. We show that there is a one-to-one correspondence between the class of orthogonal (respectively, symplectic) parabolic vector bundles on $X$ with parabolic structure along $D$ and having rational weights and the class of orthogonal

Franz J. Brandenburg

Every planar graph has a 4-page book embedding and this bound is tight. We show that every 1-planar graph, which is a graph that admits a drawing with at most one crossing per edge, has a 10-page book embedding. In addition, four pages are sometimes necessary and always sufficient if the planar skeleton, obtained from a 1-planar drawing by removing all cross

V. I. Kopylov, M. Yu. Gryaznov, S. V. Shotin, A. V. Nokhrin

Hot rolled commercial metastable austenitic steel 321 with strongly elongated thin delta-ferrite particles in its microstructure was the object of investigations. Ultrafine-grained (UFG) microstructure in steel 321 was formed by Equal Channel Angular Pressing (ECAP) at 150 oC and 450 oC. When heating the UFG steel specimens, the nucleation of sigma-phase par

Boshko Koloski, Nada Lavrač, Bojan Cestnik, Senja Pollak

In an era marked by a rapid increase in scientific publications, researchers grapple with the challenge of keeping pace with field-specific advances. We present the `AHAM' methodology and a metric that guides the domain-specific \textbf{adapt}ation of the BERTopic topic modeling framework to improve scientific text analysis. By utilizing the LLaMa2 generativ

Krzysztof Bartosz, Paweł Szafraniec, Jing Zhao

In this paper we deal with a first order evolution inclusion involving a multivalued term generated by a Clarke subdifferential of a locally Lipschitz potential. For this problem we construct a double step time-semidiscrete approximation, known as the Rothe scheme. We study a sequence of solutions of the semidiscrete approximate problems and provide its weak

Ming Yuan, Alireza Seif, Andrew Lingenfelter, David I. Schuster

Resonators with weak single-photon self-Kerr nonlinearities can theoretically be used to prepare Fock states in the presence of a loss much larger than their nonlinearities. Two necessary ingredients are large displacements and a two-photon (parametric) drive. Here, we find that these systems can be controlled to achieve any desired gate operation in a finit

Rakesh Pawar, Husney Parvez Sarwar

In this paper, we discuss the cancellation and splitting of the symplectic modules. The symplectic cancellation result presented here can be thought of as an analog of the Projective module cancellation result of Fasel. The symplectic splitting is similar to Murthy's splitting theorem. To prove the cancellation and splitting, we carefully analyze the Postnik

A. Bekker, A. Kheyri, M. Arashi

This article explores the estimation of precision matrices in high-dimensional Gaussian graphical models. We address the challenge of improving the accuracy of maximum likelihood-based precision estimation through penalization. Specifically, we consider an elastic net penalty, which incorporates both L1 and Frobenius norm penalties while accounting for the t

Shengmin Zhang, Zhencai Shen

Let $p$ be a prime, $S$ be a $p$-group and $\mathcal{F}$ be a saturated fusion system over $S$. Then $\mathcal{F}$ is said to be supersolvable, if there exists a series of $S$, namely $1 = S_0 \leq S_1 \leq \cdots \leq S_n = S$, such that $S_{i+1}/S_i$ is cyclic, $i=0,1,\cdots, n-1$, $S_i$ is strongly $\mathcal{F}$-closed, $i=0,1,\cdots,n$. In this paper, we

Shengmin Zhang, Zhencai Shen

A subgroup $H$ of a finite group $G$ is said to be an NC-subgroup of $G$, if $ H^G N_G (H) =G$, where $H^G$ denotes the normal closure of $H$ in $G$. A finite group $G$ is called a PNC-group, if any subgroup of $G$ is an NC-subgroup of $G$, and $G$ is said to be an ON-group, if for any subgroup $H$ of $G$, either $N_G (H)=H,\,H^G=G$, or $H \unlhd G$. In this

Ulugbek Salaev, Elmurod Kuriyozov, Gayrat Matlatipov

The accurate syllabification of words plays a vital role in various Natural Language Processing applications. Syllabification is a versatile linguistic tool with applications in linguistic research, language technology, education, and various fields where understanding and processing language is essential. In this paper, we present a comprehensive approach t