December 2024 arXiv papers — page 83

Kun Zhang, Xiaoyan Yu, Pu Li, Hao Peng

SocialED is a comprehensive, open-source Python library designed to support social event detection (SED) tasks, integrating 19 detection algorithms and 14 diverse datasets. It provides a unified API with detailed documentation, offering researchers and practitioners a complete solution for event detection in social media. The library is designed with modular

Rui Zou, Mengqi Wei, Jintian Feng, Qian Wan

In recent years, large language models have shown exceptional performance in fulfilling diverse human needs. However, their training data can introduce harmful content, underscoring the necessity for robust value alignment. Mainstream methods, which depend on feedback learning and supervised training, are resource-intensive and may constrain the full potenti

Putian Li, Xianquan Yu, Seth Hew Peng Chew, Jinchao Mo

Ultracold gases of atoms from Main Group III (Group 13) of the Periodic Table, also known as "triel elements," have great potential for a new generation of quantum matter experiments. The first magneto-optical trap of a triel element (indium) was recently realized, but more progress is needed before a triel is ready for modern ultracold quantum science exper

Sanghyeon Lee, Jooyeol Yun, Jaegul Choo

Point-based interactive colorization techniques allow users to effortlessly colorize grayscale images using user-provided color hints. However, point-based methods often face challenges when different colors are given to semantically similar areas, leading to color intermingling and unsatisfactory results-an issue we refer to as color collapse. The fundament

Lijuan Han, Liugen Xue, Junshan Xie

This paper focuses on variable selection for a partially linear single-index varying-coefficient model. A regularized variable selection procedure by combining basis function approximations with SCAD penalty is proposed. It can simultaneously select significant variables in the parametric and nonparametric components and estimate the nonzero regression coeff

Xingyu Zhu, Xiapu Luo, Xuetao Wei

Recent advancements in text-to-3D generation can generate neural radiance fields (NeRFs) with score distillation sampling, enabling 3D asset creation without real-world data capture. With the rapid advancement in NeRF generation quality, protecting the copyright of the generated NeRF has become increasingly important. While prior works can watermark NeRFs in

Imam Nur Bani Yusuf, Lingxiao Jiang

Large language models have demonstrated promising performance across various software engineering tasks. While fine-tuning is a common practice to adapt these models for downstream tasks, it becomes challenging in resource-constrained environments due to increased memory requirements from growing trainable parameters in increasingly large language models. We

Xinrui Yu, Wenbin Pei, Bing Xue, Qiang Zhang

In federated learning, federated unlearning is a technique that provides clients with a rollback mechanism that allows them to withdraw their data contribution without training from scratch. However, existing research has not considered scenarios with skewed label distributions. Unfortunately, the unlearning of a client with skewed data usually results in bi

Jianchun Chu, Zihang Hao

We establish the diameter and global weighted volume comparison when the $N$-Bakry-Emery Ricci tensor has a positive lower bound in the spectrum sense.

Hina Binte Haq, Syed Taha Ali, Asad Salman, Patrick McCorry

The transaction pool plays a critical role in processing and disseminating transactions in cryptocurrency networks. However, increasing transaction loads strain the resources of full node deployments. We present Neonpool, an innovative transaction pool optimization using bloom filter variants, which reduces the memory footprint of the transaction pool to a f

Nan Wang, Yafei Liu, Chen Chen, Haonan Lu

Recent advancements in language modeling have enabled the translation of natural language into code, and the use of execution feedback to improve code generation. However, these methods often rely heavily on pre-existing test cases, which may not always be available or comprehensive. In this work, we propose a novel approach that concurrently trains a code g

Zixiao Wang, Junwu Weng, Mengyuan Liu, Bei Yu

Numerous well-annotated human key-point datasets are publicly available to date. However, annotating human poses for newly collected images is still a costly and time-consuming progress. Pose distributions from different datasets share similar pose hinge-structure priors with different geometric transformations, such as pivot orientation, joint rotation, and

Kazuki Shimada, Christian Simon, Takashi Shibuya, Shusuke Takahashi

This work addresses the lack of multimodal generative models capable of producing high-quality videos with spatially aligned audio. While recent advancements in generative models have been successful in video generation, they often overlook the spatial alignment between audio and visuals, which is essential for immersive experiences. To tackle this problem,

Hanzhe Liang, Guoyang Xie, Chengbin Hou, Bingshu Wang

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look

S. Mironov, A. Shtennikova, M. Valencia-Villegas

In general the speed of Gravitational Waves (GWs) in Scalar-Tensor modifications of Einstein's gravity is different from the speed of Light. Nevertheless, it has been measured that their speeds are nearly the same. For the most general Scalar-Tensor theories classified to date that do propagate a graviton -- DHOST, {\it including Horndeski and Beyond Horndes

Biao Liu, Wenyi Fang, Xiaoyu Wu, Yang Zheng

Pre-trained Vision-Language (VL) models such as CLIP have demonstrated their excellent performance across numerous downstream tasks. A recent method, Context Optimization (CoOp), further improves the performance of VL models on downstream tasks by introducing prompt learning. CoOp optimizes a set of learnable vectors, aka prompt, and freezes the whole CLIP m

Hao He, Haoqin Yang, Philipp Burckhardt, Alexandros Kapravelos

GitHub, the de facto platform for open-source software development, provides a set of social-media-like features to signal high-quality repositories. Among them, the star count is the most widely used popularity signal, but it is also at risk of being artificially inflated (i.e., faked), decreasing its value as a decision-making signal and posing a security

Gaozheng Pei, Shaojie lyu, Ke Ma, Pinci Yang

Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model's structure, parameters, or training data. The adversary can only access the target model's predictions for generated samples. Once the substitute model closely approximates the behavior of the target model, attackers c

Jie Pan, Huanhuan Wang, Lin Zou, Haibo Xie

Breaking lattice symmetry is crucial for generating a nonzero Berry curvature. While manipulating twisting angles between adjacent layers has successfully broken lattice symmetry through strain field and generated nonzero Berry curvature, interlayer sliding in principle offers a promising alternative route. However, realizing uniform interlayer sliding faces

Ming Yang, Wenxuan Zhao, Dan Mu, Zhijian Shi

Massive Dirac fermions, which are essential for realizing novel topological phenomena, are expected to be generated from massless Dirac fermions by breaking the related symmetry, such as time-reversal symmetry (TRS) in topological insulators or crystal symmetry in topological crystalline insulators. Here, we report scanning tunneling microscopy and angle-res

Mahtab A. Khan, Naseem Ud Din, Dmitry Skachkov, Dirk R. Englund

Flat electronic bands strongly enhance Coulomb interactions and can stabilize unconventional insulating states. Motivated by the recent discovery of flat bands in breathing Kagome lattices, we use first-principles GW--Bethe--Salpeter theory to investigate the excitonic spectrum of single-layer Nb$_3$Cl$_8$. We find a dark spin-triplet Frenkel exciton whose s

Kaiwen Chen, Feng-Xiao Liu, Qiang Zhao, Xian-Hui Zhong

We derive helicity amplitudes for the fully charmed tetraquark states decays into vector meson pair under two types of models, where the one is from quark model and the other one is from diquark model. The decay angular distributions have been given by the cascade decays $T_{4c}\to J/\psi(D_{(s)}^*)+J/\psi(\bar{D}_{(s)}^*)$ along with $J/\psi\to \mu^++\mu^-$

Xiaoqi An, Lin Zhao, Chen Gong, Jun Li

With the rapid development of autonomous driving, LiDAR-based 3D Human Pose Estimation (3D HPE) is becoming a research focus. However, due to the noise and sparsity of LiDAR-captured point clouds, robust human pose estimation remains challenging. Most of the existing methods use temporal information, multi-modal fusion, or SMPL optimization to correct biased

Boris Beranger, Simone A. Padoan

From environmental sciences to finance, there is a growing demand for methods that can assess the risks of extreme events beyond those observed in available data. Extrapolating extreme events beyond the range of the data is not obvious. Risk assessments are often further complicated by the need to account for multiple variables simultaneously. Extreme value

Zijun Li, Zhipeng Cai, Bochun Yang, Xuelun Shen

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after de

Jannis N. Ahlers, Konstantin M. Pavlov, Marcus J. Kitchen, Stephanie A. Harker

X-ray dark-field imaging visualises scattering from sample microstructure, and has found application in medical and security contexts. While most X-ray dark-field imaging techniques rely on masks, gratings, or crystals, recent work on the Fokker--Planck model of diffusive imaging has enabled dark-field imaging in the propagation-based geometry. Images captur

A. Freese, D. Adamiak, I. Cloët, W. Melnitchouk

Generalized parton distributions (GPDs) characterize the 3-dimensional structure of hadrons, combining information about their internal quark and gluon longitudinal momentum distributions and transverse position within the hadron. The dependence of GPDs on the factorization scale $Q^2$ allows one to connect hard exclusive processes involving GPDs at disparat

Nengqun Li, Yuming Liu

In previous two papers, we defined fractional Brauer configuration algebras and developed their covering theory. In this paper, we study the representation theory of fractional Brauer graph algebras of type MS, a special class of fractional Brauer configuration algebras that properly generalizes Brauer graph algebras. We first introduce the notion of Brauer

Qiang Hou, Cong Yu

Fast type-I migration of (proto)planets poses a challenging problem for the core accretion formation scenario. We found that the dust-induced ``Streaming Torque (ST)'' may slow down or even reverse the planet migration in \cite{Hou2024}. But in realistic protoplanetary disks, dust diffusion induced by gas turbulence may have important influences on ST. We pe

Xiyuan Liu, Qingqing Wu, Rui Wang, Jun Wu

With the application of high-frequency communication and extremely large MIMO (XL-MIMO), the near-field effect has become increasingly apparent. The near-field channel estimation and position estimation problems both rely on the Angle of Arrival (AoA) and the Curvature of Arrival (CoA) estimation. However, in the near-field channel model, the coupling of AoA

Haywood Gelman, John D. Hastings, David Kenley, Eleanor Loiacono

Insider threats (InTs) within organizations are small in number but have a disproportionate ability to damage systems, information, and infrastructure. Existing InT research studies the problem from psychological, technical, and educational perspectives. Proposed theories include research on psychological indicators, machine learning, user behavioral log ana

Nengqun Li, Yuming Liu

In this paper, we develop a covering theory for the fractional Brauer configurations and connect it with the coverings of the associated quivers with relations in the sense of Mart\'inez-Villa and de la Pe\~na. Among the results, we show the following: (1) The universal cover of any fractional Brauer configuration is simply connected and we construct explici

Yadan Duan, Hechao Chen, Zhenyong Hou, Zheng Sun

Universal small-scale solar activity in quiet region are suggested to be a potential source of solar wind and the upper solar atmosphere. Here, with the high-resoltion 174 \AA~imaging observations from the Solar Orbiter/Extreme Ultraviolet Imager (EUI), we investigate 59 EUV upflow-like events observed in the quiet Sun. Their average apparent (plane-of-sky)

Daniel Feijoo, Juan C. Benito, Alvaro Garcia, Marcos V. Conde

Photography during night or in dark conditions typically suffers from noise, low light and blurring issues due to the dim environment and the common use of long exposure. Although Deblurring and Low-light Image Enhancement (LLIE) are related under these conditions, most approaches in image restoration solve these tasks separately. In this paper, we present a

Ruyue Liu, Rong Yin, Xiangzhen Bo, Xiaoshuai Hao

Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized server, thus maintaining privacy. However, graph data on clients are typically non-IID, posing a challenge for a single model to perform well across all clients. Another major bott

Zhuo Cao, Bingqing Zhang, Heming Du, Xin Yu

Text-guided Video Temporal Grounding (VTG) aims to localize relevant segments in untrimmed videos based on textual descriptions, encompassing two subtasks: Moment Retrieval (MR) and Highlight Detection (HD). Although previous typical methods have achieved commendable results, it is still challenging to retrieve short video moments. This is primarily due to t

Suvineetha Herath, Haywood Gelman, John Hastings, Yong Wang

The rapid growth of remote healthcare delivery has introduced significant security and privacy risks to protected health information (PHI). Analysis of a comprehensive healthcare security breach dataset covering 2009-2023 reveals their significant prevalence and impact. This study investigates the root causes of such security incidents and introduces the Att

Georgios Tertytchny, Georgios L. Stavrinides, Maria K. Michael

To address the challenges of imbalanced multi-class datasets typically used for rare event detection in critical cyber-physical systems, we propose an optimal, efficient, and adaptable mixed integer programming (MIP) ensemble weighting scheme. Our approach leverages the diverse capabilities of the classifier ensemble on a granular per class basis, while opti

Ali Saraeb

We propose a numerical method for approximating and discovering zeros of the Dirichlet L-function L(s, chi) corresponding to real Dirichlet characters chi.

Ting Bai, Yue Yu, Le Huang, Zenan Xu

The sparse Mixture-of-Experts (MoE) architecture of large language models (LLMs) confronts an inherent issue of load imbalance arising from the simplistic linear router strategy, which ultimately causes the instability and inefficient learning of LLMs. To address this challenge, we introduce a novel MoE graph-based framework $\textbf{GMoE}$, aimed at enhanci

Wenchao Xu, Jinyu Chen, Peirong Zheng, Xiaoquan Yi

Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI). To achieve high reliability and scalability in deploying these agent services, it is essential to collaboratively optimize computational and communication resources,

Takuya Kawada, Kei Yamamoto, Masashi Kawaguchi, Hiroki Matsumoto

Surface acoustic waves (SAWs), coherent vibrational modes localized at solid surfaces, have been employed to manipulate and detect electronic and magnetic states in condensed-matter systems via strain. SAWs are commonly excited in a piezoelectric material, often the substrate. In such systems, SAWs not only generate strain but also electric field at the surf

Yunyi Cai, Weiyi Wang, Lanlan Yu, Ruixiang Wang

In the early stage of an infectious disease outbreak, public health strategies tend to gravitate towards non-pharmaceutical interventions (NPIs) given the time required to develop targeted treatments and vaccines. One of the most common NPIs is Test-Trace-Isolate (TTI). One of the factors determining the effectiveness of TTI is the ability to identify contac

Mason Sawtell, Tula Masterman, Sandi Besen, Jim Brown

In this paper, we introduce a novel technique for content safety and prompt injection classification for Large Language Models. Our technique, Layer Enhanced Classification (LEC), trains a Penalized Logistic Regression (PLR) classifier on the hidden state of an LLM's optimal intermediate transformer layer. By combining the computational efficiency of a strea

Wenrui Huang, Benoît Pausader, Masahiro Suzuki

We consider the Vlasov--Poisson system in a $C^3$ convex domain $D$ with a perfectly conducting wall. We introduce the asymptotic domain $D_{\infty}$ for the domain $D$. Then under acceptable assumptions on $D$, we show that for localized initial data, the velocity of particles is asymptotically supported in the (closure of the) asymptotic domain $\overline{

Haole Jiao, Minjie Wang, Jiajin Lu, Can Sun

The non-reciprocal (unidirectional) atom-photon couplings are crucial for modern photonics ranging from chiral quantum networks to cold-atom many-body physics. In the presented experiment, we demonstrated unidirectional Raman emission of Stokes photons from 87Rb atoms in a ring cavity. A bias magnetic field B0 is applied along z-direction on the atoms to def

Qidong Liu, Xiangyu Zhao, Yuhao Wang, Yejing Wang

Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known

Itsuki Maeda, Yasuhiro Inoue

While logic puzzles have engaged individuals through problem-solving and critical thinking, the creation of new puzzle rules has largely relied on ad-hoc processes. Pencil puzzles, such as Slitherlink and Sudoku, represent a prominent subset of these games, celebrated for their intellectual challenges rooted in combinatorial logic and spatial reasoning. Desp

Aroj Subedi

Camera traps have become integral tools in wildlife conservation, providing non-intrusive means to monitor and study wildlife in their natural habitats. The utilization of object detection algorithms to automate species identification from Camera Trap images is of huge importance for research and conservation purposes. However, the generalization issue, wher

Tiancheng Ma, Decheng An, Zihan Zhang, Shuting Wu

After the decade-long exhaustive study of binary high-Tc superconducting hydrides, the frontier of this stimulating research field has recently shifted to ternary hydrides with much expanded conformational space in search of coveted room-temperature superconductors. This task, however, presents a formidable challenge due to enormous demands on computational

Kuanren Qian, Genesis Omana Suarez, Toshihiko Nambara, Takahisa Kanekiyo

Neurodevelopmental disorders (NDDs) cover a variety of conditions, including autism spectrum disorder, attention-deficit/hyperactivity disorder, and epilepsy, which impair the central and peripheral nervous systems. Their high comorbidity and complex etiologies present significant challenges for accurate diagnosis and effective treatments. Conventional clini

Yuewen Hou, Yun Li, Longjie Xie

In this paper, we develop a novel argument, the non-autonomous approximation method, to seek the asymptotic limits of the fully coupled multi-scale McKean-Vlasov stochastic systems with irregular coefficients, which, as summarized in [3,Section 7], remains an open problem in the field. We provide an explicit characterization for the averaged limit of the non

Sergey Masaev, Andrey Minkin, Evgeniy Troyak, Andrey Khrulkevich

A review of scientific literature showed the relevance of the issue of assessing the training of an engineering specialist. Engineering includes a variety of works that relate to production issues. To assess the training of an engineering specialist, the digital twin of the enterprise is used. The digital twin of an enterprise includes all major pre-producti

Wenhao Li, Qiang Wang, Huaifeng Bao, Xiao-Yu Zhang

Network access detection plays a crucial role in global network management, enabling efficient network monitoring and topology measurement by identifying unauthorized network access and gathering detailed information about mobile devices. Existing methods for endpoint-based detection primarily rely on deploying monitoring software to recognize network connec

Jun Jason Luo, Lin Mao, Jing-Cheng Liu

For \(m\geq 2\), let \(D_m=\{0,1,\ldots,m-1\}\). For each \(k\geq 1\), consider the family of contractions \[ Φ_k=\{ϕ_{k,d}:d\in D_{n_k}\}, \qquad ϕ_{k,d}(x)=(-1)^d b_k^{-1}(x+d), \] where \(b_k\) and \(n_k\) are integers satisfying \(b_k\geq n_k\geq 2\). We construct the canonical pullback attractor generated by \(\{Φ_k\}_{k\geq 1}\) and the associated equa

Zhifeng Jiang, Zhihua Jin, Guoliang He

System prompts are widely used to guide the outputs of large language models (LLMs). These prompts often contain business logic and sensitive information, making their protection essential. However, adversarial and even regular user queries can exploit LLM vulnerabilities to expose these hidden prompts. To address this issue, we propose PromptKeeper, a defen

Federico Franceschini, Ovidiu Savin

We give a simple proof of the fact that - in all dimensions - there are no homogeneous solutions to the thin obstacle problem with frequency $\lambda$ belonging to intervals of the form $(2k,2k+1)$, $k \in \mathbb{N}$. In particular, there are no frequencies in the interval $(2,3)$.

Hideo Kojima, Takanori Nagamine, Riko Sasagawa

Let $A$ be a retract of the polynomial ring in three variables over a field $k$. It is known that if ${\rm char}\: (k) = 0$ or ${\rm tr.deg}\:_k A \not= 2$ then $A$ is a polynomial ring. In this paper, we give some sufficient conditions for $A$ to be the polynomial ring in two variables over $k$ when ${\rm char}\: (k) > 0$ and ${\rm tr.deg}\:_k A = 2$.

Sandro D. P. Vitenti, Fernando de Simoni, Mariana Penna-Lima, Eduardo J. Barroso

In astrophysical and cosmological analyses, the increasing quality and volume of astronomical data demand efficient and precise computational tools. This work introduces a novel adaptive algorithm for automatic knots (AutoKnots) allocation in spline interpolation, designed to meet user-defined precision requirements. Unlike traditional methods that rely on m

Kang-il Lee, Hyukhun Koh, Dongryeol Lee, Seunghyun Yoon

Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best explains the observations. However, due to th

Enming Luo, Wei Qiao, Katie Warren, Jingxiang Li

We present a scalable and agile approach for ads image content moderation at Google, addressing the challenges of moderating massive volumes of ads with diverse content and evolving policies. The proposed method utilizes human-curated textual descriptions and cross-modal text-image co-embeddings to enable zero-shot classification of policy violating ads imag

Yupei Li, Qiyang Sun, Hanqian Li, Lucia Specia

Machine-generated music (MGM) has become a groundbreaking innovation with wide-ranging applications, such as music therapy, personalised editing, and creative inspiration within the music industry. However, the unregulated proliferation of MGM presents considerable challenges to the entertainment, education, and arts sectors by potentially undermining the va

Boyu Qiao, Kun Li, Wei Zhou, Shilong Li

Social media platforms like X(Twitter) and Reddit are vital to global communication. However, advancements in Large Language Model (LLM) technology give rise to social media bots with unprecedented intelligence. These bots adeptly simulate human profiles, conversations, and interactions, disseminating large amounts of false information and posing significant

Chandra Raskoti, Weizi Li

Accurate vehicle trajectory prediction is crucial for ensuring safe and efficient autonomous driving. This work explores the integration of Transformer based model with Long Short-Term Memory (LSTM) based technique to enhance spatial and temporal feature learning in vehicle trajectory prediction. Here, a hybrid model that combines LSTMs for temporal encoding

Jed A. Rowland, Chris Perrella, Rachel F. Offer, Andre N. Luiten

We investigate near-infrared to telecommunications frequency conversion via a diamond four-wave mixing scheme in rubidium vapor contained within a hollow-core photonic-crystal fiber. The strong light-atom interaction in the fiber results in lower pump power requirements and higher conversion efficiency than can be achieved under equivalent conditions in a ru

Ye Wu, Qing Zhang, Yishi Wang, Yu Hu

Davemaoite, as the third most abundant mineral in the lower mantle, constitutes significant amounts in pyrolite and mid-ocean ridge basalts. Due to its unquenchable nature, measurements by static compression techniques on physical properties of davemaoite at lower mantle conditions are rare and technically challenging, and those are essential to constrain co

Daniel Otero

In this short article, we present a solution to one of the probability puzzles that Daniel Litt, a mathematician at the University of Toronto, posted on his X account earlier this year. The main goal of this note is to show how some of the typical concepts taught in an undergraduate probability course can be used to solve these types of probability problems,

Stav Haldar, Rachel L. McDonald, Sage Ducoing, Ivan Agullo

Establishing reliable quantum links between a network of satellites and ground stations is a crucial step towards realizing a wide range of satellite-based quantum protocols, including global quantum networks, distributed sensing, quantum key distribution, and quantum clock synchronization. In this article, we envision a network of satellites and ground stat

Emmit K. Pert, Clay H. Batton, Sherry Li, Steven Dunne

Multi-component polymer mixtures are ubiquitous in biological self-organization but are notoriously difficult to study computationally. Plagued by both slow single molecule relaxation times and slow equilibration within dense mixtures, molecular dynamics simulations are typically infeasible at the spatial scales required to study the stability of mesophase s

Jiangnan Xia, Yu Yang, Jiaxing Shen, Senzhang Wang

Traffic prediction plays a crucial role in intelligent transportation systems. Existing approaches primarily focus on improving overall accuracy, often neglecting a critical issue: whether predictive models lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors across urban areas results in imbalanced da

Hongbing Qiu

By carrying out refined point-wise estimates for the mean curvature, we prove better rigidity theorems of Lagrangian and symplectic translating solitons.

Daniel Thompson, Gianluca Pescaroli, Maham Furqan

This study examines historical relationships between Public Safety Power Shutoffs (PSPS) events enacted by California's investor-owned utilities (IOUs), at the system and sub-system levels, along with other disruptions to macro electricity systems. This study contributes to understanding the balance between system-wide resilience goals, such as wildfire haza

Sacip Toker, Mahir Akgun

This study investigates whether assessments fostering higher-order thinking skills can reduce plagiarism involving generative AI tools. Participants completed three tasks of varying complexity in four groups: control, e-textbook, Google, and ChatGPT. Findings show that AI plagiarism decreases as task complexity increases, with higher-order tasks resulting in

Likai Yang, Jiacheng Xie, Hong X. Tang

Converting phonons to photons with optomechanical interaction provides a pathway to realize single phonon counting, which is instrumental in the quantum applications of mechanical systems such as entanglement generation, thermometry, and study of macroscopic quantum phenomenon. In this process, the key requirement is high-extinction, narrowbandwidth, and sta

Tommy Nguyen, Mehmet Ergezer, Christian Green

The increasing deployment of AI models in critical applications has exposed them to significant risks from adversarial attacks. While adversarial vulnerabilities in 2D vision models have been extensively studied, the threat landscape for 3D generative models, such as Neural Radiance Fields (NeRF), remains underexplored. This work introduces \textit{AdvIRL},

Dezhi Song, Fuyang Huang, Yu Gao, Jiamin Yao

On the spin-valve-like ferromagnet/spin glass/ferromagnet (FM/SG/FM) structure, the tunneling current is dominated by resistance switch (RS) instead of the local density of states according to the conventional tunneling theory. Here we show lattice-site dependent RS behaviors in one-quintuple-layer Bi2Te3 deposited on single MnBi2Te4 septuple layer, which co

Hengyuan Guo, Hui Liu, Jarah Evslin

In a general (2+1)-dimensional scalar model, we consider the scattering of a single quantum of radiation off a domain wall string, which excites or de-excites the wall's internal shape mode. We refer to these two process as Stokes and anti-Stokes scattering. We calculate the probability densities for these processes to first order in quantum field theory, as

Jinyu Zhang, Zhongying Zhao, Chao Li, Yanwei Yu

Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous studies on SSR struggle to capture the fine-grained associations between interactions and different latent users within the shared account's hybrid sequences. Moreover, most existing S

Raghav G. Jha, Anosh Joseph, David Schaich

We investigate the thermal phase structure of the Berenstein--Maldacena--Nastase (BMN) matrix model using non-perturbative lattice Monte Carlo calculations. Our main analyses span three orders of magnitude in the coupling, involving systems with sizes up to $N_{\tau} = 24$ lattice sites and SU($N$) gauge groups with $8 \leq N \leq 16$. In addition, we carry

Yicheng Wang, Jixiang Jing, Yumeng Luo, Linjie Ma

Diamond exhibits superb performance across a wide range of applications due to its enormous outstanding properties in electronic, photonic and quantum fields. Yet heterogeneous integration of diamond for on-chip functionalities, like 2D materials, remains challenging due to the hard acquisition of scalable, transferable and ultrathin diamond samples. Recentl

Mohammad Hossein Jarrahi, Stanley Ahalt

This article explores human-horse interactions as a metaphor for understanding and designing effective human-AI partnerships. Drawing on the long history of human collaboration with horses, we propose that AI, like horses, should complement rather than replace human capabilities. We move beyond traditional benchmarks such as the Turing test, which emphasize

Simon Allzén

In recent years, the hope to confirm the existence of dark matter by experimentally detecting it has diminished significantly. After more than 30 years of experimental searches, many of the most promising candidates have since been ruled out, leaving the epistemic and scientific condition of dark matter in a state of suspension. In efforts to improve the epi

Youxin Pang, Ruizhi Shao, Jiajun Zhang, Hanzhang Tu

In this paper, we introduce ManiVideo, a novel method for generating consistent and temporally coherent bimanual hand-object manipulation videos from given motion sequences of hands and objects. The core idea of ManiVideo is the construction of a multi-layer occlusion (MLO) representation that learns 3D occlusion relationships from occlusion-free normal maps

Alan Williams, Christopher Leon, Alexander Scheinker

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for integrating physics-based constraints and data to address forward and inverse problems in machine learning. Despite their potential, the implementation of PINNs are hampered by several challenges, including issues related to convergence, stability, and the design of neural networks

Desen Sun, Henry Tian, Tim Lu, Sihang Liu

Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from substantial computational complexity, often requiring several minutes to generate a single video. While prior research has

Jia Yao, Ivan Maliyov, David J. Gardner, Carol S. Woodward

Electronic structure calculations in the time domain provide a deeper understanding of nonequilibrium dynamics in materials. The real-time Boltzmann equation (rt-BTE), used in conjunction with accurate interactions computed from first principles, has enabled reliable predictions of coupled electron and lattice dynamics. However, the timescales and system siz

Joshua Cho, Sara Aghajanzadeh, Zhen Zhu, D. A. Forsyth

In this paper, we present a simple yet highly effective "free lunch" solution for low-light image enhancement (LLIE), which aims to restore low-light images as if acquired in well-illuminated environments. Our method necessitates no optimization, training, fine-tuning, text conditioning, or hyperparameter adjustments, yet it consistently reconstructs low-lig

V. I. Tselyaev

A new version of the modified theory of gravity is formulated in which two physical metrics are constructed out of two vierbeins connected with each other by the duality condition including the flat metric of the prior geometry. The duality condition plays a crucial role in this theoretical scheme, and thus gives the name to the whole approach: the theory of

Yue Zhang, Xinyang Che, Yuanbang Wei, Rui Tian

Uncertainty principle is one of the fundamental principles of quantum mechanics. Exploring such uncertainty relations in pre- and postselected (PPS) systems, where weak measurements on post-selected states have been used as a powerful tool for exploring the foundation of quantum mechanics, has so far eluded experimental effort. In this work, we experimentall

Joseph W. Cutler, Alex Collins, Bin Fan, Mahesh Ravishankar

PyPM is a Python-based domain specific language (DSL) for building rewrite-based optimization passes on machine learning computation graphs. Users define individual optimizations by writing (a) patterns that match subgraphs of a computation graph and (b) corresponding rules which replace a matched subgraph with an optimized kernel. PyPM is distinguished from

Rajesh Gopakumar, Rishabh Kaushik, Shota Komatsu, Edward A. Mazenc

For correlators in $\mathcal{N}=4$ Super Yang-Mills preserving half the supersymmetry, we manifestly recast the gauge theory Feynman diagram expansion as a sum over dual closed strings. Each individual Feynman diagram maps on to a Riemann surface with specific moduli. The Feynman diagrams thus correspond to discrete lattice points on string moduli space, rat

Lorna Gregory

Let $R,S$ be rings, $\mathcal{X}\subseteq \text{mod}$-$R$ a covariantly finite subcategory, $\mathcal{C}$ the smallest definable subcategory of $\text{Mod}$-$R$ containing $\mathcal{X}$ and $\mathcal{D}$ a definable subcategory of $\text{Mod}$-$S$. We show that if $I:\mathcal{C}\rightarrow \mathcal{D}$ is an interpretation functor such that $I\mathcal{X}\sub

Jie Cao, Abhijit Suresh, Jennifer Jacobs, Charis Clevenger

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves - a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensi

Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson, Gilles Hacheme

Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from in-distribution (ID) data. However, existing methods either assume access to OO

Manuel Cebrian, Petter Holme, Niccolo Pescetelli

Advancements in multimodal Large Language Models (LLMs), such as OpenAI's GPT-4o, offer significant potential for mediating human interactions across various contexts. However, their use in areas such as persuasion, influence, and recruitment raises ethical and security concerns. To evaluate these models ethically in public influence and persuasion scenarios

Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Mayur Jagdishbhai Patel, Hongfei Xue

Reconstructing a 3D hand mesh from a single RGB image is challenging due to complex articulations, self-occlusions, and depth ambiguities. Traditional discriminative methods, which learn a deterministic mapping from a 2D image to a single 3D mesh, often struggle with the inherent ambiguities in 2D-to-3D mapping. To address this challenge, we propose MaskHand

Alice Lacaze-Masmonteil

We affirm most open cases of a conjecture that first appeared in Alspach et al. (1987) which stipulates that the wreath (lexicographic) product of two hamiltonian decomposable directed graphs is also hamiltonian decomposable. Specifically, we show that the wreath product of a hamiltonian decomposable directed graph $G$, such that $|V(G)|$ is even and $|V(G)|

David Damanik, Íris Emilsdóttir, Jake Fillman

We discuss gap labelling for operators generated by the full shift over a compact subset of the real line. The set of Johnson--Schwartzman gap labels is the algebra generated by weights of clopen subsets of the support of the single-site distribution. Due to the presence of a dense set of periodic orbits, it is impossible to find a sampling function for whic

Luke Woolcock, Robert Schmid

The stability of interconnected linear time-invariant systems using singular values and the small gain theorem has been studied for many decades. The methods of mu-analysis and synthesis has been extensively developed to provide robustness guarantees for a plant subject to structured perturbations, with components in the structured perturbation satisfying a

Massimiliano Viola, Kevin Qu, Nando Metzger, Bingxin Ke

Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings and tend to struggle when applied to images outside the training domain or when the available depth measurements are sparse, irregularly distributed, or of varying de