May 2024 arXiv papers — page 80

Rowland G Seymour, Fabian Hernandez

Honour based abuse covers a wide range of family abuse including female genital mutilation and forced marriage. Safeguarding professionals need to identify where abuses are happening in their local community to best support those at risk of these crimes and take preventative action. However, there is little local data about these kinds of crime. To tackle th

Yuki Nishimura

Epistemic logic is known as a logic that captures the knowledge and beliefs of agents and has undergone various developments since Hintikka (1962). In this paper, we propose a new logic called agent-knowledge logic by taking the product of individual knowledge structures and the set of relationships among agents. This logic is based on the Facebook logic pro

Harish Prakash, Jia Cheng Shang, Ken M. Nsiempba, Yuhao Chen

Multi Object Tracking (MOT) in ice hockey pursues the combined task of localizing and associating players across a given sequence to maintain their identities. Tracking players from monocular broadcast feeds is an important computer vision problem offering various downstream analytics and enhanced viewership experience. However, existing trackers encounter s

Felix den Breejen, Sangmin Bae, Stephen Cha, Se-Young Yun

The recently introduced TabPFN pretrains an In-Context Learning (ICL) transformer on synthetic data to perform tabular data classification. In this work, we extend TabPFN to the fine-tuning setting, resulting in a significant performance boost. We also discover that fine-tuning enables ICL-transformers to create complex decision boundaries, a property regula

J. Maíz Apellániz, A. R. Youssef, M. S. El-Nawawy, W. H. Elsanhoury

(ABRIDGED) CONTEXT: The Villafranca project is combining Gaia data with ground-based surveys to analyze Galactic stellar groups with OB stars. AIMS: We want to analyze Stock 18 within the Villafranca project, a very young stellar cluster with a symmetrical and compact H II region around it. METHODS: We analyze the core, massive-star population, extinction, d

Catalin C. Mitelut

A central goal in neuroscience is to provide explanations for how animal nervous systems can generate actions and cognitive states such as consciousness while artificial intelligence (AI) and machine learning (ML) seek to provide models that are increasingly better at prediction. Despite many decades of research we have made limited progress on providing neu

Wonkeun Jo, Dongil Kim

Multivariate time-series forecasting (MTSF) stands as a compelling field within the machine learning community. Diverse neural network based methodologies deployed in MTSF applications have demonstrated commendable efficacy. Despite the advancements in model performance, comprehending the rationale behind the model's behavior remains an enigma. Our proposed

Sixin Zhang

We study min-max algorithms to solve zero-sum differential games on Riemannian manifold. Based on the notions of differential Stackelberg equilibrium and differential Nash equilibrium on Riemannian manifold, we analyze the local convergence of two representative deterministic simultaneous algorithms $\tau$-GDA and $\tau$-SGA to such equilibria. Sufficient co

David Wawrzyniak, Josef Winter, Steffen Schmidt, Thomas Indinger

We present a quantum algorithm for computational fluid dynamics based on the Lattice-Boltzmann method. Our approach involves a novel encoding strategy and a modified collision operator, assuming full relaxation to the local equilibrium within a single time step. Our quantum algorithm enables the computation of multiple time steps in the linearized case, spec

Yunzheng Lyu, Feng Bao

Kernel learning forward backward SDE filter is an iterative and adaptive meshfree approach to solve the nonlinear filtering problem. It builds from forward backward SDE for Fokker-Planker equation, which defines evolving density for the state variable, and employs KDE to approximate density. This algorithm has shown more superior performance than mainstream

Yunfan Lu, Yusheng Wang, Zipeng Wang, Pengteng Li

Continuous space-time video super-resolution (C-STVSR) aims to simultaneously enhance video resolution and frame rate at an arbitrary scale. Recently, implicit neural representation (INR) has been applied to video restoration, representing videos as implicit fields that can be decoded at an arbitrary scale. However, existing INR-based C-STVSR methods typical

Dingwen Zhang, Hao Li, Diqi He, Nian Liu

In recent times, following the paradigm of DETR (DEtection TRansformer), query-based end-to-end instance segmentation (QEIS) methods have exhibited superior performance compared to CNN-based models, particularly when trained on large-scale datasets. Nevertheless, the effectiveness of these QEIS methods diminishes significantly when confronted with limited tr

Marc Kesseböhmer, Aljoscha Niemann

We investigate the possibility of defining meaningful upper and lower quantization dimensions for a compactly supported Borel probability measure of order $r$, including negative values of $r$. To this end, we use the concept of partition functions, which generalizes the idea of the $L^{q}$-spectrum and in this way naturally extends the work in [M. Kesseb\"o

360Zhinao Team

We present 360Zhinao models with 7B parameter size and context lengths spanning 4K, 32K and 360K, all available at https://github.com/Qihoo360/360zhinao. For rapid development in pretraining, we establish a stable and sensitive ablation environment to evaluate and compare experiment runs with minimal model size. Under such guidance, we perfect our data clean

Ponaki Das, Sainkupar Marwein Mawiong

We classify minimal finite models of the M\"{o}bius band and several wedge sums of spheres. In particular, we show that the minimal finite model of the M\"{o}bius band coincides with that of the circle $S^{1}$. Furthermore, we prove that both $S^{2}\vee S^{1}$ and $S^{2}\vee S^{2}$ admit minimal finite models on exactly seven points, and that each of $S^{1}\

Anjan Mukherjee, Biswanath Banerjee

This paper proposes an elastic-gap free strain gradient crystal plasticity model that addresses dissipation caused by plastic slip gradient and grain boundary (GB) Burger tensor. The model involves splitting plastic slip gradient and GB Burger tensor into energetic dissipative quantities. Unlike conventional models, the bulk and GB defect energy are consider

Jingyang Qiao, Zhizhong Zhang, Xin Tan, Yanyun Qu

Parameter-efficient tunings (PETs) have demonstrated impressive performance and promising perspectives in training large models, while they are still confronted with a common problem: the trade-off between learning new content and protecting old knowledge, leading to zero-shot generalization collapse, and cross-modal hallucination. In this paper, we reformul

Yongxin Guo, Jingyu Liu, Mingda Li, Dingxin Cheng

Video Temporal Grounding (VTG) strives to accurately pinpoint event timestamps in a specific video using linguistic queries, significantly impacting downstream tasks like video browsing and editing. Unlike traditional task-specific models, Video Large Language Models (video LLMs) can handle multiple tasks concurrently in a zero-shot manner. Consequently, exp

Chang Zhou, Yang Zhao, Jin Cao, Yi Shen

This paper explores the integration of strategic optimization methods in search advertising, focusing on ad ranking and bidding mechanisms within E-commerce platforms. By employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between adverti

Bin Wang, Tianjian Liu, Wenqi Wang, Yuan Weng

The rise of Web3 social ecosystems signifies the dawn of a new chapter in digital interaction, offering significant prospects for user engagement and financial advancement. Nonetheless, this progress is shadowed by potential privacy concessions, especially as these platforms frequently merge with existing Web2.0 social media accounts, amplifying data privacy

Ronald Cumbal, Birger Moell, Jose Lopes, Olof Engwall

The performance of Automatic Speech Recognition (ASR) systems has constantly increased in state-of-the-art development. However, performance tends to decrease considerably in more challenging conditions (e.g., background noise, multiple speaker social conversations) and with more atypical speakers (e.g., children, non-native speakers or people with speech di

Quyang Pan, Sheng Sun, Zhiyuan Wu, Yuwei Wang

Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model

Mostafa Jamshidian, Adam Wittek, Saeideh Sekhavat, Karol Miller

A search in Scopus within "Article title, Abstract, Keywords" unveils 2,444 documents focused on the biomechanics of Abdominal Aortic Aneurysm (AAA), mostly on AAA wall stress. Only 24 documents investigated AAA kinematics, an important topic that could potentially offer significant insights into the biomechanics of AAA. In this paper, we present an image-ba

Asaduz Zaman, Vanessa Kellermann, Alan Dorin

This study introduces markerless retro-identification of animals, a novel concept and practical technique to identify past occurrences of organisms in archived data, that complements traditional forward-looking chronological re-identification methods in longitudinal behavioural research. Identification of a key individual among multiple subjects may occur la

Neil G. Marchant, Benjamin I. P. Rubinstein

Reuse of data in adaptive workflows poses challenges regarding overfitting and the statistical validity of results. Previous work has demonstrated that interacting with data via differentially private algorithms can mitigate overfitting, achieving worst-case generalization guarantees with asymptotically optimal data requirements. However, such past work assu

Liyu Chen, Huaao Tang, Yi Wen, Hanting Chen

Recent semi-supervised object detection (SSOD) has achieved remarkable progress by leveraging unlabeled data for training. Mainstream SSOD methods rely on Consistency Regularization methods and Exponential Moving Average (EMA), which form a cyclic data flow. However, the EMA updating training approach leads to weight coupling between the teacher and student

Jonathan Barenboim, Andrei V. Frolov, Gabor Kunstatter

We numerically calculate the spacetime describing the formation and evaporation of a regular black hole in 2D dilaton gravity. The apparent horizons evaporate smoothly in finite time to form a compact trapped region. We nevertheless see rich dynamics; an anti-trapped region forms alongside the black hole, and additional compact trapped and anti-trapped regio

Shuai Wang, David W. Zhang, Jia-Hong Huang, Stevan Rudinac

Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intricate associations in data, though scalability is a notable challenge due to memory limitations. In this study,

Sepehr Assadi

Vizing's celebrated theorem states that every simple graph with maximum degree $\Delta$ admits a $(\Delta+1)$ edge coloring which can be found in $O(m \cdot n)$ time on $n$-vertex $m$-edge graphs. This is just one color more than the trivial lower bound of $\Delta$ colors needed in any proper edge coloring. After a series of simplifications and variations, t

Yasmeena Akhter, Rishabh Ranjan, Richa Singh, Mayank Vatsa

This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions, a common limitation in resource-constrained healthcare settings. High-resolution CXR imaging is crucial for identifying small but critical anomalies, such as nodules or opacities. However, when images are downsized for processing in Computer-Aided Diagnosis (CAD) sys

P. -C. Lai, Y. Wang, J. -X. Shi, Z. -B. Cui

Trapped atomic ions constitute one of the leading physical platforms for building the quantum repeater nodes to realize large-scale quantum networks. In a long-distance trapped-ion quantum network, it is essential to have crosstalk-free dual-type qubits: one type, called the communication qubit, to establish an entangling interface with telecom photons; and

Yuchao Chang, Hongli Wang, Wen Chen, Yonghui Li

Power saving is a main pillar in the operation of wireless communication systems. In this paper, we investigate cloud radio access network (C-RAN) capability to reduce power consumption based on the user equipment (UE) requirement. Aiming to save the long-term C-RAN energy consumption, an optimization problem is formulated to manage the downlink power withou

Søren Føns Nielsen, Francesco Da Ros, Mikkel N. Schmidt, Darko Zibar

We numerically demonstrate that joint optimization of FIR based pulse-shaper and receiver filter results in an improved system performance, and shorter filter lengths (lower complexity), for 4-PAM 100 GBd IM/DD systems.

Mostafa Ibrahim, Arsalan Ahmad, Sabit Ekin, Peter LoPresti

Radio frequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate pred

Zavareh Bozorgasl, Hao Chen

This paper presents a novel approach to enhance communication efficiency in federated learning through clipped uniform quantization. By leveraging optimal clipping thresholds and client-specific adaptive quantization schemes, the proposed method significantly reduces bandwidth and memory requirements for model weight transmission between clients and the serv

Krzysztof Jakubowski

Transparency rendering is problematic and can be considered an open problem in real-time graphics. There are many different algorithms currently available, but handling complex scenes and achieving accurate, glitch-free results is still costly. This paper describes LucidRaster: a software rasterizer running on a GPU which allows for efficient exact rendering

Myungho Choi, Hojin Chu, Suh-Ryung Kim

The competition-common enemy graph (CCE graph) of a digraph $D$ is the graph with the vertex set $V(D)$ and an edge $uv$ if and only if $u$ and $v$ have a common predator and a common prey in $D$. If each vertex of a digraph $D$ has indegree at most $i$ and outdegree at most $j$, then $D$ is called an $\langle i,j \rangle$ digraph. In this paper, we fully ch

Peng Xu, Qi Gao, Ying-Jie Wu

Feature extraction is a critical technology to realize the automatic transmission of feature information throughout product life cycles. As CAD models primarily capture the 3D geometry of products, feature extraction heavily relies on geometric information. However, existing feature extraction methods often yield inaccurate outcomes due to the diverse interp

Danial Ebrat, Eli Paradalis, Luis Rueda

Reinforcement learning (RL) recommender systems often rely on static datasets that fail to capture the fluid, ever changing nature of user preferences in real-world scenarios. Meanwhile, generative AI techniques have emerged as powerful tools for creating synthetic data, including user profiles and behaviors. Recognizing this potential, we introduce Lusifer,

Lingjun Zhu, Qijing Zheng, Yingqi Wang, Kerstin Krüger

To understand the recently observed mysterious non-adiabatic energy transfer for hyperthermal H atom scattering from a semiconductor surface, Ge(111)c(2*8), we present a mixed quantum-classical non-adiabatic molecular dynamics model based on time-dependent evolution of Kohn-Sham orbitals and a classical path approximation. Our results suggest that facile non

Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu

Latent generative models (e.g., Stable Diffusion) have become more and more popular, but concerns have arisen regarding potential misuse related to images generated by these models. It is, therefore, necessary to analyze the origin of images by inferring if a particular image was generated by a specific latent generative model. Most existing methods (e.g., i

Akshat Pandey

Acoustic spacetimes have been known to offer analogue models for black hole physics and cosmology. Within this context, aspects of analogue quantum field theories in curved spacetime are studied. In particular some new comments have been made on the analogue Hawking temperature including a quick derivation of the result. Further, analogue cosmology is explor

Alireza Ghaffari, Sharareh Younesian, Vahid Partovi Nia, Boxing Chen

The ever-growing computational complexity of Large Language Models (LLMs) necessitates efficient deployment strategies. The current state-of-the-art approaches for Post-training Quantization (PTQ) often require calibration to achieve the desired accuracy. This paper presents AdpQ, a novel zero-shot adaptive PTQ method for LLMs that achieves the state-of-the-

Elijah Miller, Thomas Dupont, Mingming Wang

This paper explores the innovative application of Stable Video Diffusion (SVD), a diffusion model that revolutionizes the creation of dynamic video content from static images. As digital media and design industries accelerate, SVD emerges as a powerful generative tool that enhances productivity and introduces novel creative possibilities. The paper examines

Nurullah Sevim, Mostafa Ibrahim, Sabit Ekin

The advent of Large Language Models (LLMs) has revolutionized language understanding and human-like text generation, drawing interest from many other fields with this question in mind: What else are the LLMs capable of? Despite their widespread adoption, ongoing research continues to explore new ways to integrate LLMs into diverse systems. This paper explore

Hua Wang, Kai Chang

We present the geodesic nature and quantization of geometric shift vector in quantum systems, with the parameter space defined by the Bloch momentum, using the Wilson loop approach. Our analysis extends to include bosonic phonon drag shift vectors with non-vertical transitions. We demonstrate that the gauge invariant shift vector can be quantized as integer

Subhra Bhattacharya

We consider interacting dark matter-dark energy models arising out of a general interaction term $Q=f(\rho_{m},\rho_{d},\dot{\rho}_{m},\dot{\rho}_{d}).$ Here $f$ is a functional relation connecting the energy densities $\rho_{m}$ and $\rho_{d}$ and their derivatives w.r.t. time $t.$ In our model we consider two interacting barotropic fluid with constant equa

Junhui He, Ying Yang, Jian Kang

In multivariate spline regression, the number and locations of knots influence the performance and interpretability significantly. However, due to non-differentiability and varying dimensions, there is no desirable frequentist method to make inference on knots. In this article, we propose a fully Bayesian approach for knot inference in multivariate spline re

Xiaoxin Yin

While LLMs have shown impressive capabilities in solving math or coding problems, the ability to make scientific discoveries remains a distinct challenge. This paper proposes a "Turing test for an AI scientist" to assess whether an AI agent can conduct scientific research independently, without relying on human-generated knowledge. Drawing inspiration from t

Poojan Shah, Ragesh Jaiswal

$D^2$-sampling is a fundamental component of sampling-based clustering algorithms such as $k$-means++. Given a dataset $V \subset \mathbb{R}^d$ with $N$ points and a center set $C \subset \mathbb{R}^d$, $D^2$-sampling refers to picking a point from $V$ where the sampling probability of a point is proportional to its squared distance from the nearest center i

Corinne Aars, Lauren Adams, Xiaokan Tian, Zhaoyu Wang

This study presents the development and evaluation of a ByT5-based multilingual translation model tailored for translating the Bible into underrepresented languages. Utilizing the comprehensive Johns Hopkins University Bible Corpus, we trained the model to capture the intricate nuances of character-based and morphologically rich languages. Our results, measu

Guangda Sun, Tianyang Tao, Yanpei Guo, Michael Yiqing Hu

Logical clocks are a fundamental tool to establish causal ordering of events in a distributed system. They have been applied in weakly consistent storage systems, causally ordered broadcast, distributed snapshots, deadlock detection, and distributed system debugging. However, prior logical clock constructs fail to work in an open network with Byzantine parti

Pablo Rivas, Tomas Cerny, Alejandro Rodriguez Perez, Javier Turek

Our study addresses the challenges of building datasets to understand the risks associated with organized activities and human trafficking through commercial sex advertisements. These challenges include data scarcity, rapid obsolescence, and privacy concerns. Traditional approaches, which are not automated and are difficult to reproduce, fall short in addres

Abdullah M. Zyarah, Dhireesha Kudithipudi

Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems' ability to process information and learn locally on the device. Local processing and learning of time-series information typically demand intensive computations and massive storage as the process inv

William Hofgard, Jingruo Sun, Asaf Cohen

We establish the convergence of the deep Galerkin method (DGM), a deep learning-based scheme for solving high-dimensional nonlinear PDEs, for Hamilton-Jacobi-Bellman (HJB) equations that arise from the study of mean field control problems (MFCPs). Based on a recent characterization of the value function of the MFCP as the unique viscosity solution of an HJB

Sang-Hyun Lee, Daehyeok Kwon, Seung-Woo Seo

Recent reinforcement learning (RL) algorithms have demonstrated impressive results in simulated driving environments. However, autonomous vehicles trained in simulation often struggle to work well in the real world due to the fidelity gap between simulated and real-world environments. While directly training real-world autonomous vehicles with RL algorithms

Yui Sudo, Yosuke Fukumoto, Muhammad Shakeel, Yifan Peng

Deep biasing (DB) enhances the performance of end-to-end automatic speech recognition (E2E-ASR) models for rare words or contextual phrases using a bias list. However, most existing methods treat bias phrases as sequences of subwords in a predefined static vocabulary. This naive sequence decomposition produces unnatural token patterns, significantly lowering

Soh Kumabe, Yuichi Yoshida

In resource allocation, we often require that the output allocation of an algorithm is stable against input perturbation because frequent reallocation is costly and untrustworthy. Varma and Yoshida (SODA'21) formalized this requirement for algorithms as the notion of average sensitivity. Here, the average sensitivity of an algorithm on an input instance is,

Adamu Gaston Philipo, Doreen Sebastian Sarwatt, Jianguo Ding, Mahmoud Daneshmand

Cyberbullying has been a significant challenge in the digital era world, given the huge number of people, especially adolescents, who use social media platforms to communicate and share information. Some individuals exploit these platforms to embarrass others through direct messages, electronic mail, speech, and public posts. This behavior has direct psychol

Junhui He, Guoxuan Ma, Jian Kang, Ying Yang

We establish a scalable manifold learning method and theory, motivated by the problem of estimating fMRI activation manifolds in the Human Connectome Project (HCP). Our primary contribution is the development of an efficient estimation technique for heat kernel Gaussian processes in the exponential family model. This approach handles large sample sizes $n$,

Takeshi Kato, Yosuke Tanabe, Mohammad Rezoanul Hoque

Reducing wealth inequality and increasing utility are critical issues. This study reveals the effects of redistribution and consumption morals on wealth inequality and utility. To this end, we present a novel approach that couples the dynamic model of capital, consumption, and utility in macroeconomics with the interaction model of joint business and redistr

Qinian Jin, Duo Liu

Consider the linear ill-posed problems of the form $\sum_{i=1}^{b} A_i x_i =y$, where, for each $i$, $A_i$ is a bounded linear operator between two Hilbert spaces $X_i$ and ${\mathcal Y}$. When $b$ is huge, solving the problem by an iterative method using the full gradient at each iteration step is both time-consuming and memory insufficient. Although random

Haiyao Yu, Changyang She, Yunkai Hu, Geng Wang

Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this paper, we establish a zero-shot learning framework that does not need real-world measurements in a new commun

Azizbek Mamanazarov, Durvudkhan Suragan

In the present paper we study inverse problems related to determining the time-dependent coefficient and unknown source function of fractional heat equations. Our approach shows that having just one set of data at an observation point ensures the existence of a weak solution for the inverse problem. Furthermore, if there is an additional datum at the observa

Qihang Fan, Huaibo Huang, Mingrui Chen, Ran He

The Vision Transformer (ViT) has gained prominence for its superior relational modeling prowess. However, its global attention mechanism's quadratic complexity poses substantial computational burdens. A common remedy spatially groups tokens for self-attention, reducing computational requirements. Nonetheless, this strategy neglects semantic information in to

Qingrong Cheng, Xu Li, Xinghui Fu, Fei Xia

The automated synthesis of high-quality 3D gestures from speech is of significant value in virtual humans and gaming. Previous methods focus on synthesizing gestures that are synchronized with speech rhythm, yet they frequently overlook the inclusion of semantic gestures. These are sparse and follow a long-tailed distribution across the gesture sequence, mak

Yuguang Zhang, Qihang Fan, Huaibo Huang

In recent years, Transformers have achieved remarkable progress in computer vision tasks. However, their global modeling often comes with substantial computational overhead, in stark contrast to the human eye's efficient information processing. Inspired by the human eye's sparse scanning mechanism, we propose a \textbf{S}parse \textbf{S}can \textbf{S}elf-\te

Sanghyeon Song, Yoojin Kang, Jungho Im

Continuous aerosol monitoring in East Asia is essential due to the massive aerosol emissions from natural and anthropogenic sources. Geostationary satellites enable continuous aerosol monitoring; however, the observation is limited to the daytime. This study proposed machine learning-based models to estimate daytime and nighttime aerosol optical depth (AOD)

Behrooz Farkiani, Raj Jain

The scalability and flexibility of microservice architecture have led to major changes in cloud-native application architectures. However, the complexity of managing thousands of small services written in different languages and handling the exchange of data between them have caused significant management challenges. Service mesh is a promising solution that

N. N. Achasov, G. N. Shestakov

It is shown that the values of the ratios $\mathcal{B}(D^0\to a_0(980)^+\pi^-)/\mathcal{B} (D^0\to a_0(980)^-\pi^+ )$ and $\mathcal{B}(D^+\to a_0(980)^+\pi^0)/\mathcal{B} (D^+\to a_0(980)^0 \pi^+)$, recently measured by the BESIII Collaboration, are naturally explained in the four-quark model of the $a_0(980) $ resonance.

Md. Toukir Ahmed, Arthur Villordon, Mohammed Kamruzzaman

Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspec

Eliot Heinrich, Ayano Nakamura, Shinichi Nishihaya, Eugen Weschke

We investigate the magnetic order in single crystalline EuCd$_2$Sb$_2$ thin films using a combined theoretical and experimental approach. Resonant elastic x-ray scattering experiments reveal a sharp magnetic peak at $q = (0, 0, \frac{1}{2})$ below $T_N = 7.2$ K, indicative of interlayer antiferromagnetic ordering. Additionally, we observe a weak diffuse magn

Nithin Parthasarathy, James Soetedjo, Saarang Panchavati, Nitya Parthasarathy

Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300 speller brain-computer interface (BCI) offers an alternative communication method by interpreting a subject's EEG response to characters presented on a grid interface. This paper

Xinyue Ming, Tao Feng, Menglong Zhang

A $(v,k,\lambda)$-BIBD $(X,\mathcal B)$ can be nested if there is a mapping $\phi:\mathcal B\rightarrow X$ such that $(X,\{B\cup\{\phi(B)\}\mid B\in\mathcal B\})$ is a $(v,k+1,\lambda+1)$-packing. A $(v,k,\lambda)$-BIBD has a (perfect) nesting if and only if its incidence graph has a harmonious (exact) coloring with $v$ colors. This paper shows that given an

Xiwang Xiang, Minda Ma

The building sector is the largest emitter globally and as such is at the forefront of the net-zero emissions pathway. This study is the first to present a bottom-up assessment framework integrated with the decomposing structural decomposition method to evaluate the emission patterns and decarbonization process of global residential building operations and c

Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao

Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primarily relies on teacher models or human intervention to generate and refine the instructions and responses for training, which are costly, non-sustainable, and may lack diversity. In

Wenxuan Wang, Gengdong Zhou, Wenlu Lin, Zuo Feng

Electrons residing in flat-band system can play a vital role in triggering spectacular phenomenology due to relatively large interactions and spontaneous breaking of different degeneracies. In this work we demonstrate chirally twisted triple bilayer graphene, a new moir\'e structure formed by three pieces of helically stacked Bernal bilayer graphene, as a hi

Guanghui Wang, Dexi Liu, Jian-Yun Nie, Qizhi Wan

Recent advancements in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference, such as retrieved instances and event templates. These methods face two challenges: (1) the retrieval results may be irrelevant and (2) templates are developed independently for each event without considering th

Shayan Mohajer Hamidi, Linfeng Ye

Adversarial training (AT) is a popular method for training robust deep neural networks (DNNs) against adversarial attacks. Yet, AT suffers from two shortcomings: (i) the robustness of DNNs trained by AT is highly intertwined with the size of the DNNs, posing challenges in achieving robustness in smaller models; and (ii) the adversarial samples employed durin

Taekyunj Kim, Dae San Kim

Assume that Y is a random variable whose moment generating function exists in a neighborhood of the origin. We study the probabilistic degenerate r-Stirling numbers of the second kind associated with Y and the probabilistic degenerate r-Bell polynomials associated with Y. They are respectively probabilistic extensions of the degenerate r-Stirling numbers of

Chi Hin Chan, Magdalena Czubak

We revisit the questions of density of smooth functions, and differential forms, in Sobolev spaces on Riemannian manifolds. We carefully show equivalence of weak covariant derivatives to weak partial derivatives.

Junpeng Jiao

We study the structures of klt Calabi--Yau pairs. We show that the discrepancies of log centers of all klt Calabi--Yau varieties with fixed dimension are in a finite set. As a corollary, we show that the index of 4-dimensional non-canonical Calabi--Yau variety is bounded.

Yun Fan, Yue Leng

In this paper, we investigate the existence and asymptotic property of self-dual $2$-quasi negacyclic codes of length $2n$ over a finite field of cardinality $q$. When $n$ is odd, we show that the $q$-ary self-dual $2$-quasi negacyclic codes exist if and only if $q\,{\not\equiv}-\!1~({\rm mod}~4)$. When $n$ is even, we prove that the $q$-ary self-dual $2$-qu

Denys Katerenchuk, Rivka Levitan

Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we a

Masafumi Endo, Kohei Honda, Genya Ishigami

As robotic navigation techniques in perception and planning advance, mobile robots increasingly venture into off-road environments involving complex traversability. However, selecting suitable planning methods remains a challenge due to their algorithmic diversity, as each offers unique benefits. To aid in algorithm design, we introduce BenchNav, an open-sou

Sheng-Jun Lin, Shih-Ping Lai, Laurent Pagani, Charlène Lefèvre

Molecular deuteration is commonly seen in starless cores and is expected to occur on a timescale comparable to that of the core contraction. Thus, the deuteration serves as a chemical clock, allowing us to investigate dynamical theories of core formation. We aim to provide a 3D cloud description for the starless core L 1498 located in the nearby low-mass sta

Aritro Pathak

We extend known methods to establish upper bounds on the least character non-residues contingent on different zero-free regions within the critical strip, in particular on bounded rectangles within the critical strip along the $\sigma=1$ line at arbitrary heights. This relates to earlier conditional results on least character non-residues, and recent results

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(27.12\pm 0.14)\times10^{8}$ $\psi(3686)$ events collected with the BESIII detector, we present the first observation of the decays $\chi_{cJ}\to\Lambda\bar{\Lambda}\omega$, where $J=0, 1, 2$, with statistical significances of $11.7 \sigma, 11.2 \sigma$, and $11.8 \sigma$. The branching fractions of these decays are determined to be $\mathcal{B}(\chi_

Jin-Hui Rao, Shu-Xu Yi, Lian Tao, Qing-Wen Tang

Theories of modified gravity suggest that the propagation speed of gravitational wave (GW) $v_g$ may deviate from the speed of light $c$. A constraint can be placed on the difference between $c$ and $v_g$ with a simple method that uses the arrival time delay between GW and electromagnetic (EM) wave simultaneously emitted from a burst event. We simulated the

Aritro Pathak

We show an example of a sequence of elliptic operators in the unit ball with drifts that diverge as the inverse distance to the boundary, for which we do not get uniform upper estimates for the Green's function with the pole at the origin. Such drifts have been considered in the literature in the study of the $L^{p}$ Dirichlet problem for both the parabolic

T. Ssettumba, Z. Shao, L. Landau, R. C. de Lamare

In this paper, we propose low-complexity local detectors and log-likelihood ratio (LLR) refinement techniques for a coded cell-free massive multiple input multiple output (CF- mMIMO) systems, where an iterative detection and decoding (IDD) scheme is applied using parallel interference cancellation (PIC) and access point (AP) selection. In particular, we prop

Yongliang Song, Jiangtao Su, Qingmin Zhang, Mei Zhang

Filament eruptions often result in flares and coronal mass ejections (CMEs). Most studies attribute the filament eruptions to their instabilities or magnetic reconnection. In this study, we report a unique observation of a filament eruption whose initiation process has not been reported before. This large-scale filament, with a length of about 360 Mm crossin

Ellis Fenske, Aaron Johnson

Fully Encrypted Protocols (FEPs) have arisen in practice as a technique to avoid network censorship. Such protocols are designed to produce messages that appear completely random. This design hides communications metadata, such as version and length fields, and makes it difficult to even determine what protocol is being used. Moreover, these protocols freque

Hibiki Takegami, Takao Morinari

The Kitaev model, defined on a honeycomb lattice, features an exactly solvable ground state with fractionalized Majorana fermion excitations, which can potentially form non-Abelian anyons crucial for fault-tolerant topological quantum computing. Although Majorana fermions are essential for obtaining the exact ground state, their physical interpretation in te

Tobias Diez, Tudor S. Ratiu

We initiate the study of the norm-squared of the momentum map as a rigorous tool in infinite dimensions. In particular, we calculate the Hessian at a critical point, show that it is positive semi-definite along the complexified orbit, and determine a decomposition of the stabilizer under the complexified action. We apply these results to the action of the gr

Max Peter Ronecker, Xavier Diaz, Michael Karner, Daniel Watzenig

This paper introduces a novel hybrid architecture that enhances radar-based Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep learning for state-classification. Traditional radar-based DOGM often faces challenges in accurately distinguishing between static and dynamic objects. Our approach addresses this limitation by introducin

Yuan Zhang, Na Wang, Chuntao Lin, Kazuhito Ohmi

Both horizontal and vertical coherent beam-beam instability are important issues at CEPC. The horizontal instability (X-Z instability) could be induced by beam-beam itself. In this paper we try to study the effect of chromaticity and resistive feedback by analysis and simulation. The vertical instability may be induced due to the combined effect of beam-beam

Jan-Peter George, Mari Rusanen, Egbert Beuker, Leena Yrjänä

Ash dieback (ADB) is threatening populations of European ash (Fraxinus excelsior & F. angustifolia) for more than three decades. Although much knowledge has been gathered in the recent past, practical conservation measures have been mostly implemented at local scale. Since range contraction in both ash species will be exacerbated in the near future by westwa

Muhammad Ansab Butt, Absaar Ul Jabbar

Brain tumor segmentation is a critical task in medical image analysis, aiding in the diagnosis and treatment planning of brain tumor patients. The importance of automated and accurate brain tumor segmentation cannot be overstated. It enables medical professionals to precisely delineate tumor regions, assess tumor growth or regression, and plan targeted treat

Renyuan Wang, Sudhanshu Tiwari, Yiyang Feng, Sen Dai

Yttrium Iron Garnet based tunable magnetostatic wave and spin wave devices are poised to revolutionize the fields of Magnonics, Spintronics, Microwave devices, and quantum information science. The magnetic bias required for operating and tuning these devices is traditionally achieved through large power-hungry electromagnets, which significantly restraints t