March 2024 arXiv papers — page 50

U. Özdem

The magnetic moment yields an excellent framework to explore the inner structure of particles determined by the quark-gluon dynamics of QCD, as it is the leading-order response of a bound system to a weak external magnetic field. Motivated by this, in this study, the magnetic moments of possible axial-vector $T_{bc\bar u \bar u}$, $T_{bc\bar d \bar d}$, and

Francisco Mateus Rocha Filho, Thiago Alves Rocha, Reginaldo Pereira Fernandes Ribeiro, Ajalmar Rêgo da Rocha Neto

Support Vector Classifier (SVC) is a well-known Machine Learning (ML) model for linear classification problems. It can be used in conjunction with a reject option strategy to reject instances that are hard to correctly classify and delegate them to a specialist. This further increases the confidence of the model. Given this, obtaining an explanation of the c

Yangyang Niu, Zhiqing Wei, Lin Wang, Huici Wu

Emerging applications such as autonomous driving and Internet of things (IoT) services put forward the demand for simutaneous sensing and communication functions in the same system. Integrated sensing and communication (ISAC) has the potential to meet the demands of ubiquitous communication and high-precision sensing due to the advantages of spectrum and har

Zeyu Shangguan, Daniel Seita, Mohammad Rostami

Cross-modal feature extraction and integration have led to steady performance improvements in few-shot learning tasks due to generating richer features. However, existing multi-modal object detection (MM-OD) methods degrade when facing significant domain-shift and are sample insufficient. We hypothesize that rich text information could more effectively help

Zequan Liu, Jiawen Lyn, Wei Zhu, Xing Tian

Parameter-efficient fine-tuning (PEFT) is widely studied for its effectiveness and efficiency in the era of large language models. Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular and representative method. However, it is implemented with a fixed intrinsic rank that might not be the ideal setting for the downstream tasks. Reco

Yuqiang Heng, Yu Zhang, Ahmed Alkhateeb, Jeffrey G. Andrews

Beam alignment (BA) in modern millimeter wave standards such as 5G NR and WiGig (802.11ay) is based on exhaustive and/or hierarchical beam searches over pre-defined codebooks of wide and narrow beams. This approach is slow and bandwidth/power-intensive, and is a considerable hindrance to the wide deployment of millimeter wave bands. A new approach is needed

Seyed Keyarash Ghiasi, Marco Kaldenbach, Marco Zuniga

A recent technology known as transparent screens is transforming windows into displays. These smart windows are present in buses, airports and offices. They can remain transparent, as a normal window, or display relevant information that overlays their panoramic views. In this paper, we propose transforming these windows not only into screens but also into w

Yuxuan Wang, Xiaoyuan Liu

Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between objects. This complexity and diversity in SGG leads to underrepresentation, where parts of triplet labels are rare or even unseen during training, resulting in imprecise predictions. To tackle this, we propose

Yasir Ali

A study about the reflection and transmission of an electromagnetic pulse through a slab doped with four-level atomic system has been presented. The doped atoms are considered to be in N-configuration with a pump field and a weak probe field. An additional control field is also applied to manipulate the Raman gain of the atomic medium. The propagation of tra

Yifei Huang, Guo Chen, Jilan Xu, Mingfang Zhang

Being able to map the activities of others into one's own point of view is one fundamental human skill even from a very early age. Taking a step toward understanding this human ability, we introduce EgoExoLearn, a large-scale dataset that emulates the human demonstration following process, in which individuals record egocentric videos as they execute tasks g

Isaac Goldbring, Matthew Harrison-Trainor

We prove that if $G$ and $H$ are $\alpha$-back-and-forth equivalent groups (in the sense of computable structure theory) for some ordinal $\alpha \geq \omega$, then their group von Neumann algebras $L(G)$ and $L(H)$ are also $\alpha$-back-and-forth equivalent. In particular, if $G$ and $H$ are $\omega$-back-and-forth-equivalent groups, then $L(G)$ and $L(H)$

Sudip Saha, Amit Kumar Pal, Soumon Roy

In this short manuscript, we will put some light on the different outcomes when two non-constant meromorphic functions share a value with prescribed weight two.

Xiufei Li, Miao Yang, Yuanxin Qi, Miao Zhang

A fuel cell system must output a steady voltage as a power source in practical use. A neural network (NN) based model predictive control (MPC) approach is developed in this work to regulate the fuel cell output voltage with safety constraints. The developed NN MPC controller stabilizes the polymer electrolyte fuel cell system's output voltage by controlling

Xin Liu, Chao Xu, Yasir Noori, Soon Xin Ng

New near-capacity continuous-variable quantum key distribution (CV-QKD) reconciliation schemes are proposed, where both the authenticated classical channel (ClC) and the quantum channel (QuC) for QKD are protected by separate forward error correction (FEC) coding schemes. More explicitly, a new codeword-based - rather than syndrome-based - QKD reconciliation

Muhammad Shoaib Farooq, Hina jabbar

In this digital era, our lives highly depend on the internet and worldwide technology. Wide usage of technology and platforms of communication makes our lives better and easier. But on the other side it carries out some security issues and cruel activities, phishing is one activity of these cruel activities. It is a type of cybercrime, which has the purpose

Koulik Khamaru

We consider the problem of stochastic convex optimization under convex constraints. We analyze the behavior of a natural variance reduced proximal gradient (VRPG) algorithm for this problem. Our main result is a non-asymptotic guarantee for VRPG algorithm. Contrary to minimax worst case guarantees, our result is instance-dependent in nature. This means that

Werner Treptow, Yichen Liu, Carlos A. Z. Bassetto, Bernardo I. Pinto

Many voltage-gated potassium (Kv) channels display a time-dependent phenomenon called C-type inactivation, whereby prolonged activation by voltage leads to the inhibition of ionic conduction, a process that involves a conformational change at the selectivity filter toward a non-conductive state. Recently, a high-resolution structure of a strongly inactivatin

Yechan Park, Yuya Sasaki

This paper addresses the challenge of estimating the Average Treatment Effect on the Treated Survivors (ATETS; Vikstrom et al., 2018) in the absence of long-term experimental data, utilizing available long-term observational data instead. We establish two theoretical results. First, it is impossible to obtain informative bounds for the ATETS with no model re

Rui Zheng, Yuhao Zhou, Zhiheng Xi, Tao Gui

Deep neural networks (DNNs) are notoriously vulnerable to adversarial attacks that place carefully crafted perturbations on normal examples to fool DNNs. To better understand such attacks, a characterization of the features carried by adversarial examples is needed. In this paper, we tackle this challenge by inspecting the subspaces of sample features throug

Arindam Majee, Avisek Gupta, Sourav Raha, Swagatam Das

Alzheimer's disease (AD), characterized by progressive cognitive decline and memory loss, presents a formidable global health challenge, underscoring the critical importance of early and precise diagnosis for timely interventions and enhanced patient outcomes. While MRI scans provide valuable insights into brain structures, traditional analysis methods often

Alexander Zlotnik, Timofey Lomonosov

We deal with an initial-boundary value problem for the multidimensional acoustic wave equation, with the variable speed of sound. For a three-level semi-explicit in time higher-order vector compact scheme, we prove stability and derive 4th order error bound in the enlarged energy norm. This scheme is three-point in each spatial direction, and it exploits add

Bouchenine Abderrahmen, Almaraj Ismail

This research investigates a multi-product, multi-echelon, and multi-period vaccine supply chain network model under uncertainty and quality inspection errors. The objective function seeks optimizing the total cost of the supply chain. Moreover, the proposed model is formulated as a mixed integer linear programming problem under multiple sources of uncertain

Yusuf Artan, Bensu Alkan Semiz

Latent fingerprints are one of the most widely used forensic evidence by law enforcement agencies. However, latent recognition performance is far from the exemplary performance of sensor fingerprint recognition due to deformations and artifacts within these images. In this study, we propose a fusion based local matching approach towards latent fingerprint re

Fabiano F. Santos, F. A. Brito

This work presents the application of the q-algebra in the glueballs spectrum. This algebra is implemented through Jackson derivatives in a Schr\"odinger-like equation resulting from the gravity fluctuations around the braneworld scenario in five dimensions. In our prescription, we consider a four-dimensional AdS$4$ brane, living in AdS$5$ bulk, that is also

Xiufei Li, Miao Zhang, Yuanxin Qi, Miao Yang

This study introduces a novel approach utilizing Gaussian process model predictive control (MPC) to stabilize the output voltage of a polymer electrolyte fuel cell (PEFC) system by simultaneously regulating hydrogen and airflow rates. Two Gaussian process models are developed to capture PEFC dynamics, taking into account constraints including hydrogen pressu

Jie Tian, Ran Ji, Lingxiao Yang, Suting Ni

Gaze plays a crucial role in revealing human attention and intention, particularly in hand-object interaction scenarios, where it guides and synchronizes complex tasks that require precise coordination between the brain, hand, and object. Motivated by this, we introduce a novel task: Gaze-Guided Hand-Object Interaction Synthesis, with potential applications

Ruyi Yang, Jingyu Hu, Zihao Li, Jianli Mu

Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as "black boxes" that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial i

Tuong Le, Shuge Ouyang, Leo Tao, Joseph Restivo

Schubert polynomials are polynomial representatives of Schubert classes in the cohomology of the complete flag variety and have a combinatorial formulation in terms of bumpless pipe dreams. Quantum double Schubert polynomials are polynomial representatives of Schubert classes in the torus-equivariant quantum cohomology of the complete flag variety, but no an

Minchan Kim, Minyeong Kim, Junik Bae, Suhwan Choi

Hallucinations in vision-language models pose a significant challenge to their reliability, particularly in the generation of long captions. Current methods fall short of accurately identifying and mitigating these hallucinations. To address this issue, we introduce ESREAL, a novel unsupervised learning framework designed to suppress the generation of halluc

Nick P. Petropoulos

Data are given, commentary is supplied and explanations are provided with regard to the technical, the organizational and, of course, the human history connected to the time of research, which resulted to the paper entitled "Soil sampling and Cs-137 analysis of the Chernobyl fallout in Greece", written by late Professor S.E. Simopoulos. This paper has been p

Dionysios Anninos, Tarek Anous, Alan Rios Fukelman

We consider the two-dimensional Schwinger model of a massless charged fermion coupled to an Abelian gauge field on a fixed de Sitter background. The theory admits an exact solution, first examined by Jayewardena, and can be analyzed efficiently using Euclidean methods. We calculate fully non-perturbative, gauge-invariant correlation functions of the electric

Torbjørn Cunis, Ilya Kolmanovsky

We show that Newton methods for generalized equations are input-to-state stable with respect to disturbances such as due to inexact computations. We then use this result to obtain convergence and robustness of a multistep Newton-type method for multivariate generalized equations. We demonstrate the usefulness of the results with other applications to nonline

Alexander A. Reshetnyak

We review the application of BRST and BRST-BV approaches to construct the generic off-shell local Lorentz covariant cubic interaction vertices for irreducible massless and massive higher integer spin fields (as the candidates for massive particles in the Dark Matter problem) on $d$-dimensional Minkowski spaces. It is shown that equivalence among two Lagrangi

Oren Wright, Yorie Nakahira, José M. F. Moura

Uncertainty quantification of neural networks is critical to measuring the reliability and robustness of deep learning systems. However, this often involves costly or inaccurate sampling methods and approximations. This paper presents a sample-free moment propagation technique that propagates mean vectors and covariance matrices across a network to accuratel

Lu Bai, Abhishek Gupta, Yew-Soon Ong

Multi-task learning solves multiple correlated tasks. However, conflicts may exist between them. In such circumstances, a single solution can rarely optimize all the tasks, leading to performance trade-offs. To arrive at a set of optimized yet well-distributed models that collectively embody different trade-offs in one algorithmic pass, this paper proposes t

Guillaume Thiry, Hao Tang, Radu Timofte, Luc Van Gool

Video inpainting tasks have seen significant improvements in recent years with the rise of deep neural networks and, in particular, vision transformers. Although these models show promising reconstruction quality and temporal consistency, they are still unsuitable for live videos, one of the last steps to make them completely convincing and usable. The main

Wenbin Jiang, Yuhao Chen, Yongle Li

In this work, rate coefficients of four prototypical insertion reactions, X + H2 -- H + XH (X=C(1D), N(2D), O(1D), S(1D)) and associated isotope reactions are calculated based on ring polymer molecular dynamics (RPMD) with Cayley propagator (Cayley-RPMD). The associated kinetic isotope effects (KIEs) are systematically studied too. The Cayley propagator used

Siyu Zha, Yuehan Qiao, Qingyu Hu, Zhongsheng Li

Project-based learning (PBL) is an instructional method that is very helpful in nurturing students' creativity, but it requires significant time and energy from both students and teachers. Large language models (LLMs) have been proven to assist in creative tasks, yet much controversy exists regarding their role in fostering creativity. This paper explores th

Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang

Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP). Yet, there has not been an open-source medical NER dataset specifically for the Korean language. To address this, we utilized ChatGPT to assist in constructing the KBMC (Korean Bio-Medical Corpus), which we are now presenting to the public. With the KBMC dataset

Zhenyuan Lu, Wei Li, Burcu Ozek, Haozhou Zhou

The study of research trends is pivotal for understanding scientific development on specific topics. Traditionally, this involves keyword analysis within scholarly literature, yet comprehensive tools for such analysis are scarce, especially those capable of parsing large datasets with precision. pyKCN, a Python toolkit, addresses this gap by automating keywo

Xiaohan Yang, Ji Chu, Zechen Guo, Wenhui Huang

Superconducting qubits are a promising platform for building fault-tolerant quantum computers, with recent achievement showing the suppression of logical error with increasing code size. However, leakage into non-computational states, a common issue in practical quantum systems including superconducting circuits, introduces correlated errors that undermine Q

J. Piekarewicz

The historical detection of gravitational waves from the binary neutron star merger GW170817 is providing fundamental new insights into the astrophysical site for the creation of the heaviest elements in the cosmos and on the equation of state of neutron-rich matter. Shortly after this historical detection, electromagnetic observations of neutron stars toget

Yiwei Fu, Weizhong Yan

Accurate and reliable sensor measurements are critical for ensuring the safety and longevity of complex engineering systems such as wind turbines. In this paper, we propose a novel framework for sensor fault detection, isolation, and accommodation (FDIA) using masked models and self-supervised learning. Our proposed approach is a general time series modeling

Francesco Mori, Satya N. Majumdar, Pierpaolo Vivo

We consider a discrete-time random walk on a line starting at $x_0\geq 0$ where a cost is incurred at each jump. We obtain an exact analytical formula for the distribution of the total cost of a trajectory until the process crosses the origin for the first time. The formula is valid for arbitrary jump distribution and cost function (heavy- and light-tailed a

Albert Lu, Stephen Cranefield

The online community has increasingly been inundated by a toxic wave of harmful comments. In response to this growing challenge, we introduce a two-stage ultra-low-cost multimodal harmful behavior detection method designed to identify harmful comments and images with high precision and recall rates. We first utilize the CLIP-ViT model to transform tweets and

Hong Zhu, Alexander Venus, Erik Leitinger, Stefan Tertinek

This paper addresses the challenge of achieving reliable and robust positioning of a mobile agent, such as a radio device carried by a person, in scenarios where direct line-of-sight (LOS) links are obstructed or unavailable. The human body is considered as an extended object that scatters, attenuates and blocks the radio signals. We propose a novel particle

Yan Jia, Yuxin Song, Zihou Liu, Qingyin Tan

The Consumer Internet of Things (CIoT), a notable segment within the IoT domain, involves the integration of IoT technology into consumer electronics and devices, such as smart homes and smart wearables. Compared to traditional IoT fields, CIoT differs notably in target users, product types, and design approaches. While offering convenience to users, it also

Kouichi Taira

A local time decay estimate of fractional Schr\"odinger operators with slowly decaying positive potentials are studied. It is shown that its resolvent is smooth near zero and the time propagator has fast local time decay which is very different from very short-range cases. The key element of the proof is to establish a weaker Agmon estimate for a classically

David Zywina

For a non-CM elliptic curve $E$ defined over a number field $K$, the Galois action on its torsion points gives rise to a Galois representation $\rho_E: Gal(\overline{K}/K)\to GL_2(\widehat{\mathbb{Z}})$ that is unique up to isomorphism. A renowned theorem of Serre says that the image of $\rho_E$ is an open, and hence finite index, subgroup of $GL_2(\widehat{

Jiaming Zhang, Zhaomeng Zhang, Yihao Liu, Yaqian Chen

Realtime shape estimation of continuum objects and manipulators is essential for developing accurate planning and control paradigms. The existing methods that create dense point clouds from camera images, and/or use distinguishable markers on a deformable body have limitations in realtime tracking of large continuum objects/manipulators. The physical occlusi

Sean Dewar, Georg Grasegger, Anthony Nixon, Zvi Rosen

Consider a collection of points in the plane and the sets of slopes or directions of the lines between pairs of points. It is known that the algebraic matroid on the set of direction constraints between the points is equivalent to the algebraic matroid on the set of distances between the points. This is the well-studied generic 2-dimensional rigidity matroid

Diego A. de Aguiar, Hugo L. França, Cassio M. Oishi

Neural networks in fluid mechanics offer an efficient approach for exploring complex flows, including multiphase and free surface flows. The recurrent neural network, particularly the Long Short-Term Memory (LSTM) model, proves attractive for learning mappings from transient inputs to dynamic outputs. This study applies LSTM to predict transient and static o

Abhisek Ray, Gaurav Kumar, Maheshkumar H. Kolekar

Transformer-based models have revolutionized the field of image super-resolution (SR) by harnessing their inherent ability to capture complex contextual features. The overlapping rectangular shifted window technique used in transformer architecture nowadays is a common practice in super-resolution models to improve the quality and robustness of image upscali

Richard Johansson

We investigate the behavior of methods that use linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a method. A theoretical analysis and experiments on real-world and synthetic data show that these methods inject strong statistical dependencies

Takashi Otonari, Satoshi Ikehata, Kiyoharu Aizawa

Recent advancements in the study of Neural Radiance Fields (NeRF) for dynamic scenes often involve explicit modeling of scene dynamics. However, this approach faces challenges in modeling scene dynamics in urban environments, where moving objects of various categories and scales are present. In such settings, it becomes crucial to effectively eliminate movin

François Delarue, William R. P. Hammersley

This article provides a case study for a recently introduced diffusion in the space of probability measures over the reals, namely rearranged stochastic heat, which solves a stochastic partial differential equation valued in the set of symmetrised quantile functions over the unit circle. This contribution studies probability measure-valued flows perturbed by

Masahiro Kaneko, Timothy Baldwin

Large Language Models (LLMs) are trained on massive web-crawled corpora. This poses risks of leakage, including personal information, copyrighted texts, and benchmark datasets. Such leakage leads to undermining human trust in AI due to potential unauthorized generation of content or overestimation of performance. We establish the following three criteria con

Matthew J. Hayes, Jonathan C. Tan, Richard S. Ellis, Alice R. Young

We report first results from a deep near infrared campaign with the Hubble Space Telescope to obtain late-epoch images of the Hubble Ultra-Deep Field (HUDF), 10-15 years after the first epoch data were obtained. The main objectives are to search for faint active galactic nuclei (AGN) at high redshifts by virtue of their photometric variability, and measure (

Ziwen Zhao, Yixin Su, Yuhua Li, Yixiong Zou

Graph self-supervised learning (SSL) is now a go-to method for pre-training graph foundation models (GFMs). There is a wide variety of knowledge patterns embedded in the graph data, such as node properties and clusters, which are crucial to learning generalized representations for GFMs. However, existing surveys of GFMs have several shortcomings: they lack c

Jianglin Lan, Xianxian Zhao, Congcong Sun

This paper presents a new data-driven control for multi-input, multi-output nonlinear systems with partially unknown dynamics and bounded disturbances. Since exact nonlinearity cancellation is not feasible with unknown disturbances, we adapt sliding mode control (SMC) for system stability and robustness. The SMC features a data-driven robust controller to re

Linyue Li, Zhijuan Du

In recent years, complementary recommendation has received extensive attention in the e-commerce domain. In this paper, we comprehensively summarize and compare 34 representative studies conducted between 2009 and 2024. Firstly, we compare the data and methods used for modeling complementary relationships between products, including simple complementarity an

CMS Collaboration

Specialized data-taking and data-processing techniques were introduced by the CMS experiment in Run 1 of the CERN LHC to enhance the sensitivity of searches for new physics and the precision of standard model measurements. These techniques, termed data scouting and data parking, extend the data-taking capabilities of CMS beyond the original design specificat

Zhuo Xu, Lixin Cui, Ming Li, Yue Wang

In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs. We individually employ the local graph convolution units as the local structure

Jianglin Lan, Siyuan Zhan, Ron Patton, Xianxian Zhao

There is an emerging trend in applying deep learning methods to control complex nonlinear systems. This paper considers enhancing the runtime safety of nonlinear systems controlled by neural networks in the presence of disturbance and measurement noise. A robustly stable interval observer is designed to generate sound and precise lower and upper bounds for t

Xiuquan Hou, Meiqin Liu, Senlin Zhang, Ping Wei

DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention, which is proven effective for improving performance but also introduces a heavy computational burden and high dependence on stable q

Feifei Qian, Lixin Cui, Ming Li, Yue Wang

In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between graphs and cannot provide an end-to-end learning mechanism for the classifier, the proposed AKBR approach aim

Miuru Abeysiriwardana, Deshan Sumanathilaka

This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomena such as polysemy and homonymy and their implications for computational models. Focusing extensively on Word Sense Disambiguation (WSD), it outlines diverse ap

Hui Lu, Hu Jian, Ronald Poppe, Albert Ali Salah

Owing to their ability to extract relevant spatio-temporal video embeddings, Vision Transformers (ViTs) are currently the best performing models in video action understanding. However, their generalization over domains or datasets is somewhat limited. In contrast, Visual Language Models (VLMs) have demonstrated exceptional generalization performance, but are

Wannaphong Phatthiyaphaibun, Surapon Nonesung, Patomporn Payoungkhamdee, Peerat Limkonchotiwat

This technical report describes the development of WangchanLion, an instruction fine-tuned model focusing on Machine Reading Comprehension (MRC) in the Thai language. Our model is based on SEA-LION and a collection of instruction following datasets. To promote open research and reproducibility, we publicly release all training data, code, and the final model

Guy F. de Teramond, Arpon Paul, Stanley J. Brodsky, Alexandre Deur

We use analytic continuation to extend the gauge/gravity duality nonperturbative description of the strong force coupling into the transition, near-perturbative, regime where perturbative effects become important. By excluding the unphysical region in coupling space from the flow of singularities in the complex plane, we derive a specific relation between th

Chunyu Xue, Weihao Cui, Quan Chen, Chen Chen

Efficiently training large-scale models (LMs) in GPU clusters involves two separate avenues: inter-job dynamic scheduling and intra-job adaptive parallelism (AP). However, existing dynamic schedulers struggle with large-model scheduling due to the mismatch between static parallelism (SP)-aware scheduling and AP-based execution, leading to cluster inefficienc

Bolin Ni, Hongbo Zhao, Chenghao Zhang, Ke Hu

Continual learning (CL) aims to empower models to learn new tasks without forgetting previously acquired knowledge. Most prior works concentrate on the techniques of architectures, replay data, regularization, \etc. However, the category name of each class is largely neglected. Existing methods commonly utilize the one-hot labels and randomly initialize the

Lei Shi, Zhiyuan Che, Yuri Kivshar

Optical Skyrmions, as an emergent cutting-edge topic in optics and photonics, extend the concept of non-singular topological defects to topological photonics, providing extra degrees of freedom for light-matter interaction manipulations, optical metrologies, optical communications, etc..

Jiangxu Huang, Lei Wang, Zhenhua Chai, Baochang Shi

In this paper, a lattice Boltzmann model is proposed to simulate solid-liquid phase change phenomena in multiphase systems. The model couples the thermal properties of the solidification front with the dynamics of the liquid droplet interface, which enables the description of the complex interfacial changes during solid-liquid phase change process. The model

Tomoya Baba, Nakahiro Yoshida

It is of special importance in the clinical trial to compare survival times between the treatment group and the control group. Propensity score methods with a logistic regression model are often used to reduce the effects of confounders. However, the modeling of complex structures between the covariates, the treatment assignment and the survival time is diff

Lu Zhang

For the deformed complex Ginibre ensemble with a mean normal matrix, under certain assumptions on the mean matrix we prove that the same bulk statistics holds as in the complex Ginibre matrix bulk. This is the continuation of the previous joint papers ``Critical edge statistics for deformed GinUEs Preprint arXiv: 2311.13227v1'' and ``Repeated erfc statistics

Wenfeng Wu, Xiaocheng Bai, Xianlong Wang, Dayong Liu

Recently, the Kagome metal CsTi$_3$Bi$_5$ has exhibited several novel quantum properties similar to CsV$_3$Sb$_5$, such as nontrivial topology, double-dome superconductivity, and flat band features. However, CsTi$_3$Bi$_5$ lacks the charge-density wave (CDW) present in CsV$_3$Sb$_5$, making the study of its emergence of double-dome superconductivity a focus

Pranjal Sarmah, Umananda Dev Goswami

Considering the Universe as a dynamic system, the understanding of its evolution is an interesting aspect of study in cosmology. Here, we investigate the anisotropic locally rotationally symmetric (LRS) Bianchi type-I (LRS-BI) spacetime under the $f(Q)$ gravity of symmetric teleparallel theory equivalent to the GR (STEGR) as a dynamical system and try to und

Kilian Grage, Klaus Jansen, Björn Schumacher

In the Knapsack problem, one is given the task of packing a knapsack of a given size with items in order to gain a packing with a high profit value. An important connection to the $(\max,+)$-convolution problem has been established, where knapsack solutions can be combined by building the convolution of two sequences. This observation has been used in recent

Dongrui Liu, Daqi Liu, Xueqian Li, Sihao Lin

Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their success, the underlying reasons for their astonishing generalization capabilities remain unclear. Our research addresses this gap by examining the generalization capabilities of NSFP

Amrutha Muralidhar, Yathindra Lakkanna

Over the past years, e-commerce platforms have expanded substantially, providing customers with convenient shopping experiences. To enhance e-commerce websites, it's essential to grasp user engagement and factors affecting conversion rates. This optimization is achieved by aligning platforms with user expectations, thereby fostering successful online shoppin

Monica La Barbiera, Roya Moghimipor

Monomial ideals corresponding to strong quasi-n-partite graphs are considered. Some algebraic and combinatorial properties of generalized graph ideals of a strong quasi-n-partite graph are studied. Furthermore, we show that the edge ideal of a strong quasi-n-partite graph is Cohen-Macaulay.

András Biró

Let $\Gamma\subseteq PSL_2({\bf R})$ be a finite volume Fuchsian group. The hyperbolic circle problem is the estimation of the number of elements of the $\Gamma$-orbit of $z$ in a hyperbolic circle around $w$ of radius $R$, where $z$ and $w$ are given points of the upper half plane and $R$ is a large number. An estimate with error term $e^{{2\over 3}R}$ is k

Liangrui Pan, Zhenyu Zhao, Ying Lu, Kewei Tang

Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the de

Xiangpeng Yang, Linchao Zhu, Hehe Fan, Yi Yang

Current diffusion-based video editing primarily focuses on local editing (\textit{e.g.,} object/background editing) or global style editing by utilizing various dense correspondences. However, these methods often fail to accurately edit the foreground and background simultaneously while preserving the original layout. We find that the crux of the issue stems

Yuxing Han, Haoyu Wang, Lixiang Chen, Yifeng Dong

Cardinality estimation is a critical component and a longstanding challenge in modern data warehouses. ByteHouse, ByteDance's cloud-native engine for extensive data analysis in exabyte-scale environments, serves numerous internal decision-making business scenarios. With the increasing demand for ByteHouse, cardinality estimation becomes the bottleneck for ef

Monica La Barbiera, Roya Moghimipor

Combinatorial properties of some ideals related to strong quasi-n-partites graphs are examined. We prove that the edge ideal of a strong quasi-n-partite graph is not integrally closed and we give an expression for its integral closure. Moreover, we are able to determine the structure of the ideals of vertex covers for the edge ideals associated to a strong q

Oscar Llorente, Jose Portela

This paper presents a novel approach to electricity price forecasting (EPF) using a pure Transformer model. As opposed to other alternatives, no other recurrent network is used in combination to the attention mechanism. Hence, showing that the attention layer is enough for capturing the temporal patterns. The paper also provides fair comparison of the models

Jingze Tian, Yingna Wang, Keye Yu, Liyi Xu

Recent research proposed gaze-assisted gestures to enhance interaction within virtual reality (VR), providing opportunities for people with motor impairments to experience VR. Compared to people with other motor impairments, those with Spinal Muscular Atrophy (SMA) exhibit enhanced distal limb mobility, providing them with more design space. However, it rema

Sergey Ostapchenko

The hadronization procedure of the QGSJET-III Monte Carlo (MC) generator of high energy hadronic interactions is discussed. Selected results of the model, regarding production spectra of secondary particles, are presented in comparison to experimental data and to the corresponding predictions of the QGSJET-II-04 MC generator. The model is applied to calculat

Chandan Shakya, Jasper van der Gucht, Joshua A. Dijksman

Granular materials are ubiquitous in nature and industry; their mechanical behavior has been of academic and engineering interest for centuries. One of the reasons for their rather complex mechanical behavior is that stresses exerted on a granular material propagate only through contacts between the grains. These contacts can change as the packing evolves. T

Grégoire Sergeant-Perthuis

In this document, we aim to gather various results related to a compositional/categorical approach to rigorous Statistical Mechanics. Rigorous Statistical Mechanics is centered on the mathematical study of statistical systems. Central concepts in this field have a natural expression in terms of diagrams in a category that couples measurable maps and Markov k

Ville J. Härkönen

We develop a non-relativistic quantum field theory of electrons and nuclei based on the Coulomb Hamiltonian. We derive the exact equations of motion and write these equations in the form of Hedin's equations for all species of identical particles involved. Theory derived allows the computation of exact observables and provides a rigorous starting point to de

Boris Okun, Kevin Schreve

In 2018, Kielak gave a virtual fibering criterion for RFRS groups. In this paper, we present a simpler proof of this.

Yuya Sasaki, Sohei Tokuno, Haruka Maeda, Kazuki Nakajima

Which fairness metrics are appropriately applicable in your contexts? There may be instances of discordance regarding the perception of fairness, even when the outcomes comply with established fairness metrics. Several questionnaire-based surveys have been conducted to evaluate fairness metrics with human perceptions of fairness. However, these surveys were

Louise A. Dennis, Michael Fisher

We consider the question of what properties a Machine Ethics system should have. This question is complicated by the existence of ethical dilemmas with no agreed upon solution. We provide an example to motivate why we do not believe falling back on the elicitation of values from stakeholders is sufficient to guarantee correctness of such systems. We go on to

Benjamin Icard, François Maine, Morgane Casanova, Géraud Faye

We present a corpus of 100 documents, OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic of fake news, and compare them to the pre

Monica La Barbiera, Roya Moghimipor

Let L be the generalized mixed product ideal induced by a monomial ideal I. In this paper, we study the polymatroidal property of generalized mixed product ideals. Furthermore, some algebraic invariants of L are computed.

Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu

Transformer-based large language models (LLMs) have demonstrated significant potential in addressing logic problems. capitalizing on the great capabilities of LLMs for code-related activities, several frameworks leveraging logical solvers for logic reasoning have been proposed recently. While existing research predominantly focuses on viewing LLMs as natural

Krishnendu Gongopadhyay, Lokenath Kundu

In this note, we initiate the concept of Dehn functions for a family of finite groups. We investigate the Dehn function for some specific families of finite polycyclic groups. We also consider related notions of spherical Dehn function and mean Dehn function for family of finite simple cyclic groups.