December 2024 arXiv papers — page 106

David Brander, Shimpei Kobayashi, Peng Wang

In this note we discuss Gauss maps for M\"obius surfaces in the $n$-sphere, and their applications in the study of Willmore surfaces. One such ``Gauss map'', naturally associated to a Willmore surface that has a dual Willmore surface, is the Lorentzian $2$-plane bundle given by a lift of the suface and its dual. More generally, we define the concept of a Lor

Vladimir Pimanov, Lisandro Dalcin, Matteo Parsani

We present a novel technique for imposing non-linear entropy conservative and entropy stable wall boundary conditions for the resistive magnetohydrodynamic equations in the presence of an adiabatic wall or a wall with a prescribed heat entropy flow, addressing three scenarios: electrically insulating walls, thin walls with finite conductivity, and perfectly

Alexander B. Balakin, Kamil R. Valiullin

We establish an extended version of the kinetic theory of the relativistic axionically active multi-component plasma, which is based on the inclusion of a unit time-like vector field, associated with the velocity of dynamic aether, into the scheme of interactions. The proposed extension of the plasma theory can be indicated as semi-phenomenological. This ter

Giovanni Lodone

First idea is to compute a quantity like the angular momentum with respect to (0, 0), of an unitary mass of coordinates (<[Xi(s)], =[Xi(s)]) while =[s] is the time, and, <[s] = constant. If we impose that the derivative along <[s], at points <[s] = 1/2 is grater than zero, then, we find exactly a known RH equivalence statement about relative maxima and minim

Xin Li, Mengyue Wang, T. -P. Liang

Recommender systems are a critical component of e-commercewebsites. The rapid development of online social networking services provides an opportunity to explore social networks together with information used in traditional recommender systems, such as customer demographics, product characteristics, and transactions. It also provides more applications for re

K. H. Bohachov, A. A. Kordyuk

Disentangling coherent and incoherent effects in the photoemission spectra of strongly correlated materials is generally a challenging problem due to the involvement of numerous parameters. In this study, we employ machine learning techniques, specifically Convolutional Neural Networks (CNNs), to address the long-standing issue of the bilayer splitting in su

Xueliang Wang, Shu Chen

For an open quantum system described by the Lindblad equation, full characterization of its dynamics typically needs the knowledge of the Liouvillian spectrum and correlation functions. Solving the Liouvillian spectrum and correlation functions are usually formidable tasks, and most previous studies are constrained to simple models and lower-order correlatio

Haidong Zhang, Wancheng Ni, Xin Li, Yiping Yang

Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users' preferences often change over time, leading to studies on time-dependent recommender systems. However, most existing approaches that deal with time information remain primitive. In this paper, we extend existin

Shanqi Liu, Yaozhong Hu, Hongjun Gao

For stochastic Volterra equations driven by standard Brownian and with singular kernels $K(u)=u^{H-\frac{1}{2}}/\Gamma(H+1/2), H\in (0,1/2)$, it is known that the Milstein scheme has a convergence rate of $n^{-2H}$. In this paper, we show that this rate is optimal. Moreover, we show that the error normalized by $n^{-2H}$ converge stably in law to the (nonzer

Sijia Zhou, Xin Li

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, where the subjects, methods, and results a

Shengqiong Wu, Hao Fei, Liangming Pan, William Yang Wang

Recent advancements in multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing various vision-language tasks. However, MLLMs face significant challenges with hallucinations, and misleading outputs that do not align with the input data. While existing efforts are paid to combat MLLM hallucinations, several pivotal challenge

Runze Jiang, Pengjian Shang

Lempel-Ziv complexity (LZC) is a key measure for detecting the irregularity and complexity of nonlinear time series and has seen various improvements in recent decades. However, existing LZC-based metrics, such as Permutation Lempel-Ziv complexity (PLZC) and Dispersion-Entropy based Lempel-Ziv complexity (DELZC), focus mainly on patterns of independent embed

Bingchen Wang, Zhaoxuan Wu, Fusheng Liu, Bryan Kian Hsiang Low

Collaborative machine learning (CML) provides a promising paradigm for democratizing advanced technologies by enabling cost-sharing among participants. However, the potential for rent-seeking behaviors among parties can undermine such collaborations. Contract theory presents a viable solution by rewarding participants with models of varying accuracy based on

Yun Qu, Yuhang Jiang, Boyuan Wang, Yixiu Mao

Reinforcement learning (RL) often encounters delayed and sparse feedback in real-world applications, even with only episodic rewards. Previous approaches have made some progress in reward redistribution for credit assignment but still face challenges, including training difficulties due to redundancy and ambiguous attributions stemming from overlooking the m

Gazi Nazia Nur, Mohammad Ahnaf Sadat

In this paper, we investigate the impact of adversarial attacks on the explainability of deep learning models, which are commonly criticized for their black-box nature despite their capacity for autonomous feature extraction. This black-box nature can affect the perceived trustworthiness of these models. To address this, explainability techniques such as Gra

David J. W. Simpson

We prove that if a continuous piecewise-smooth map on $\mathbb{R}^n$ is comprised of two linear functions, has a bounded orbit, and satisfies a certain non-degeneracy condition, then it has a fixed point. The result has important consequences to the bifurcation theory of nonsmooth dynamical systems, yet the proof requires only elementary linear algebra.

Peng Jiang, Fucai Li, Jinkai Ni

In this paper, we study the global well-posedness and optimal time decay rates of strong solutions to the diffusion approximation model in radiation hydrodynamics in $\mathbb{R}^3$. This model consists of the full compressible Navier-Stokes equations and the radiative diffusion equation which describes the influence and interaction between thermal radiation

Gilles Callebaut, Jarne Van Mulders, Bert Cox, Benjamin J. B. Deutschmann

Wireless power transfer (WPT) technologies hold promise for enhancing device autonomy, particularly for energy-limited IoT systems. This paper presents experimental results on coherent and non-coherent transmit diversity approaches for WPT, tested in the near field using the Techtile testbed. We demonstrate that a fully synchronized beamfocusing system achie

Konstantin Y. Bliokh

We consider relativistic versions of the Ehrenfest relation between the expectation values of the coordinate and momentum of a quantum particle in free space: $d\langle {\bf r} \rangle /dt = \langle {\bf p} \rangle/m$. We find that the simple proportionality between the mean velocity and momentum holds true only for the simplest quadratic dispersion (i.e., d

David J. W. Simpson

For dynamical systems that switch between different modes of operation, parameter variation can cause periodic solutions to lose or acquire new switching events. When this causes the eigenvalues (stability multipliers) associated with the solution to change discontinuously, we show that if one eigenvalue remains continuous then all local invariant sets of th

Kairong Yu, Tianqing Zhang, Hongwei Wang, Qi Xu

Spiking Neural Networks (SNNs) are emerging as a promising alternative to Artificial Neural Networks (ANNs) due to their inherent energy efficiency. Owing to the inherent sparsity in spike generation within SNNs, the in-depth analysis and optimization of intermediate output spikes are often neglected. This oversight significantly restricts the inherent energ

Mridu Prabal Goswami

We consider an economic environment with one buyer and one seller. For a bundle $(t,q)\in [0,\infty[\times [0,1]=\mathbb{Z}$, $q$ refers to the winning probability of an object, and $t$ denotes the payment that the buyer makes. We consider continuous and monotone preferences on $\mathbb{Z}$ as the primitives of the buyer. These preferences can incorporate bo

Maohua Yan, Ruicheng Wang, Ke Liu

The trade-offs between different mechanical properties of materials pose fundamental challenges in engineering material design, such as balancing stiffness versus toughness, weight versus energy-absorbing capacity, and among the various elastic coefficients. Although gradient-based topology optimization approaches have been effective in finding specific desi

Zhi-Yan Wang, Xiao Wu, Xiao Xiong, Chen Yang

Integrated nonlinear photonics has emerged as a transformative platform, enabling nanoscale nonlinear optical processes with significant implications for sensing, computation, and metrology. Achieving efficient nonlinear frequency conversion in optical microresonators is paramount to fully unlocking this potential, yet the absolute conversion efficiency (ACE

Amin Soofiani

Let $K$ be a complete discretely valued field whose residue field has characteristic different from $2$. Let $(D,\sigma)$ be a $K-$division algebra with involution of the first kind, and $h$ be a $K-$anisotropic $\epsilon$-hermitian form over $(D,\sigma)$. By a theorem due to Larmour, there is a decomposition $h=h_0 \perp h_1$ such that the elements in a dia

Qinglin Qi, Yun Luo, Yijia Xu, Wenbo Guo

Large Language Models (LLMs) are increasingly capable, aiding in tasks such as content generation, yet they also pose risks, particularly in generating harmful spear-phishing emails. These emails, crafted to entice clicks on malicious URLs, threaten personal information security. This paper proposes an adversarial framework, SpearBot, which utilizes LLMs to

Chicago Y. Park, Yuyang Hu, Michael T. McCann, Cristina Garcia-Cardona

Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-based diffusion models (SBMs) have recently emerged as a powerful framework for image generation by training deep denoisers to represent the score of the image prior. While both PnP

Xiao-Long Liu, Cong-Yuan Yue, Jun Nian, Wenni Zheng

Confinement is a well-known phenomenon in the infrared regime of (supersymmetric) Yang-Mills theory. While both experimental observations and numerical simulations have robustly confirmed its existence, the underlying physical mechanism remains elusive. Unraveling the theoretical origin of confinement continues to be a profound and longstanding challenge in

Bing Xiong, Yue Peng, RanRan Zhang, Fuqiang Chen

Virtual staining leverages computer-aided techniques to transfer the style of histochemically stained tissue samples to other staining types. In virtual staining of pathological images, maintaining strict structural consistency is crucial, as these images emphasize structural integrity more than natural images. Even slight structural alterations can lead to

Zhe Yang, Tiantian Liang

Session-based recommendation focuses on predicting the next item a user will interact with based on sequences of anonymous user sessions. A significant challenge in this field is data sparsity due to the typically short-term interactions. Most existing methods rely heavily on users' current interactions, overlooking the wealth of auxiliary information availa

Taehun Cha, Donghun Lee

In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a policy minimizing an estimation error on

Narges Bagherifard

In this paper, we introduce a function which counts minimal tori in a Riemann manifold $(M, g)$ with $\mathrm{dim}\, M \ge 6$. Moreover, we show that this count function is invariant under perturbations of the metric.

Ximing Xing, Juncheng Hu, Guotao Liang, Jing Zhang

The unprecedented advancements in Large Language Models (LLMs) have profoundly impacted natural language processing but have yet to fully embrace the realm of scalable vector graphics (SVG) generation. While LLMs encode partial knowledge of SVG data from web pages during training, recent findings suggest that semantically ambiguous and tokenized representati

Keisuke Taga, Akihiko Toda, Yoshihiro Yamazaki

Spatiotemporal patterns, which are of interest in statistical physics and nonlinear dynamics, form on the tape-peeling trace. Recently, we have proposed a mathematical model to describe these pattern formation in the tape-peeling trace. In this paper, we further investigate the tape-peeling model from the perspective of its universality class. We confirm tha

Jinxiu Liu, Shaoheng Lin, Yinxiao Li, Ming-Hsuan Yang

The increasing demand for immersive AR/VR applications and spatial intelligence has heightened the need to generate high-quality scene-level and 360${\deg}$ panoramic video. However, most video diffusion models are constrained by limited resolution and aspect ratio, which restricts their applicability to scene-level dynamic content synthesis. In this work, w

Mikhail Bochkarev, Nikolay Solodovchenko, Kirill Samusev, Mikhail Limonov

Fano profiles are observed across various fields of wave physics. They emerge from interference phenomena and are quantified by the asymmetry parameter q. In optics, q is usually considered as a phenomenological coefficient obtained by fitting experimental or numerical data. In this work, we introduce an ab initio Maxwellian approach using quasinormal modes

Zhinan Hou, Keyou You

In a wide range of applications, we are required to rapidly solve a sequence of convex multiparametric quadratic programs (mp-QPs) on resource-limited hardwares. This is a nontrivial task and has been an active topic for decades in control and optimization communities. Observe that the main computational cost of existing solution algorithms lies in addressin

Hisanori Oshima, Ken Mochizuki, Ryusuke Hamazaki, Yohei Fuji

Competition among repetitive measurements of noncommuting observables and unitary dynamics can give rise to a wide variety of entanglement phases. Here, we propose a general framework based on Lyapunov analysis to characterize topological properties in monitored quantum systems through their spectrum and many-body topological invariants. We illustrate this f

Tian-Cai Peng, Zi-Yue Bai, Jun-Zhang Wang, Xiang Liu

A puzzling phenomenon, where the measured mass of the $\psi(4160)$ is pushed higher, presents a challenge to current theoretical models of hadron spectroscopy. This study suggests that the issue arises from analyses based on the outdated quenched charmonium spectrum. In the past two decades, the discovery of new hadronic states has emphasized the importance

Nooshin Yousefzadeh, Rahul Sengupta, Sanjay Ranka

Effective congestion management along signalized corridors is essential for improving productivity and reducing costs, with arterial travel time serving as a key performance metric. Traditional approaches, such as Coordinated Signal Timing and Adaptive Traffic Control Systems, often lack scalability and generalizability across diverse urban layouts. We propo

Xuanxuan Zhao

This paper concerns the Onsager-type problem for general 2-dimensional active scalar equations of the form: $\partial_t \theta+u\cdot\nabla \theta= 0$, with $u=T[\theta]$ being a divergence-free velocity field and $T$ being a Fourier multiplier operator with symbol $m$. It is shown that if $m$ is a odd and homogeneous symbol of order $\delta$: $m(\lambda\xi)

M. K. Volkov, A. A. Pivovarov, K. Nurlan

In the $U(3) \times U(3)$ quark NJL model, $\tau$ lepton decays with the production of scalar mesons $f_0(\pi,K])$ and neutrinos are studied, where $f_0=f_0(500), f_0(980)$. It is shown that these decays mainly occur via contact channels and channels with axial-vector mesons $a_1$, $K_1(1270)$ and $K_1(1400)$. All mesons are considered as quark-antiquark sta

N. Li, M. T. Xie, Q. Huang, Z. W. Zhuo

We study the ultralow-temperature thermodynamics and thermal conductivity ($\kappa$) of the single-crystal rare-earth chalcogenides NaYbS$_2$ and NaYbSe$_2$, which have an ideal triangular lattice of the Yb$^{3+}$ ions and have been proposed to be quantum spin liquid candidates. The magnetic specific heat divided by temperature $C_{\rm{mag}}/T$ is nearly con

Abhishek Sarkar, Bikash Kumar Dey

We study message identification over the noisy permutation channel. For discrete memoryless channels (DMCs), the number of identifiable messages grows doubly exponentially, and the maximum second-order exponent is same as the Shannon capacity of the DMC. We consider a $q$-ary noisy permutation channel where the transmitted vector is first permuted by a permu

Wonchan Kim, Michelle Meehyun Kim

Hangeul was created as a phonetic alphabet and is known to have the best 1:1 correspondence between letters and pronunciation among existing alphabets. In this paper, we examine the possibility of modifying the basic form of Hangeul and using it as a kind of phonetic symbol. The core concept of this approach is to preserve the basic form of the alphabet, mod

Xiuting Tang

In this paper, I mainly prove the following results. For every energy value below the minimum of the first, second and third critical value, each bounded component of the regularized energy hypersurface of the Lagrange problem under some ranges of the parameters in the Hamiltonian function arises as the boundary of a strictly monotone toric domain, which is

Sebastian Gutierrez, Irene Hou, Jihye Lee, Kenneth Angelikas

Recent advancements in generative AI systems have raised concerns about academic integrity among educators. Beyond excelling at solving programming problems and text-based multiple-choice questions, recent research has also found that large multimodal models (LMMs) can solve Parsons problems based only on an image. However, such problems are still inherently

Zelong Sun, Dong Jing, Guoxing Yang, Nanyi Fei

Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes the user intent. Recent studies attempt to utilize Vision-Language Pre-training Models (VLPMs) with various fusion strategies for addressing the task.However, these methods typica

Abdulhady Abas Abdullah, Srwa Hasan Abdulla, Dalia Mohammad Toufiq, Halgurd S. Maghdid

Nowadays, Natural Language Processing (NLP) is an important tool for most people's daily life routines, ranging from understanding speech, translation, named entity recognition (NER), and text categorization, to generative text models such as ChatGPT. Due to the existence of big data and consequently large corpora for widely used languages like English, Span

Yingpeng Du, Zhu Sun, Ziyan Wang, Haoyan Chua

Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillation (KD)-based methods can alleviate these issues by transferring the knowledge to a small student, which trains a student based on the predictions of a cumbersome teacher. However

Zihao Guo, Qingyun Sun, Haonan Yuan, Xingcheng Fu

Real-world graphs have inherently complex and diverse topological patterns, known as topological heterogeneity. Most existing works learn graph representation in a single constant curvature space that is insufficient to match the complex geometric shapes, resulting in low-quality embeddings with high distortion. This also constitutes a critical challenge for

Tiancheng Gao, Graham W. Taylor

DNA barcodes are crucial in biodiversity analysis for building automatic identification systems that recognize known species and discover unseen species. Unlike human genome modeling, barcode-based invertebrate identification poses challenges in the vast diversity of species and taxonomic complexity. Among Transformer-based foundation models, BarcodeBERT exc

Severino V. Gervacio

A graph operator is a function $\Gamma$ defined on some set of graphs such that whenever two graphs $G$ and $H$ are isomorphic, written $G\simeq H$, then $\Gamma(G)\simeq \Gamma(H)$. For a graph $G$ not in the domain of $\Gamma$, we put $\Gamma(G)=\emptyset$. Also, let us define $\Gamma^0(G)=G$, and for any integr $k\ge1$, $\Gamma^k(G)=\Gamma(\Gamma^{k-1}(G)

Qingwen Tian, Yuxin Xu, Yixuan Yang, Zhen Wang

Molecular 3D conformations play a key role in determining how molecules interact with other molecules or protein surfaces. Recent deep learning advancements have improved conformation prediction, but slow training speeds and difficulties in utilizing high-degree features limit performance. We propose EquiFlow, an equivariant conditional flow matching model w

Peize Lin, Yuyang Ji, Lixin He, Xinguo Ren

We present an efficient linear-scaling algorithm for evaluating the analytical force and stress contributions derived from the exact-exchange energy, a key component in hybrid functional calculations. The algorithm, working equally well for molecular and periodic systems, is formulated within the framework of numerical atomic orbital (NAO) basis sets and tak

Wengang Guo, Wei Ye

Spectral clustering is a popular clustering method. It first maps data into the spectral embedding space and then uses Kmeans to find clusters. However, the two decoupled steps prohibit joint optimization for the optimal solution. In addition, it needs to construct the similarity graph for samples, which suffers from the curse of dimensionality when the data

Chengyu Sun, Jinyu Hu, Hong Jiang

Unbalanced optimal transport (UOT) has been widely used as a fundamental tool in many application domains, where it often dominates the application running time. While many researchers have proposed various optimizations for UOT, few have attempted to optimize it from a computer architecture's perspective. In this paper, we first study the performance bottle

Marcio Gameiro, Tomáš Gedeon, Hiroshi Kokubu, Konstantin Mischaikow

We introduce a combinatorial topological framework for characterizing the global dynamics of ordinary differential equations (ODEs). The approach is motivated by the study of gene regulatory networks, which are often modeled by ODEs that are not explicitly derived from first principles. The proposed method involves constructing a combinatorial model from a s

Yuanmin Tang, Xiaoting Qin, Jue Zhang, Jing Yu

Composed Image Retrieval (CIR) aims to retrieve target images that closely resemble a reference image while integrating user-specified textual modifications, thereby capturing user intent more precisely. Existing training-free zero-shot CIR (ZS-CIR) methods often employ a two-stage process: they first generate a caption for the reference image and then use L

Zhiwei Yang, Yucong Meng, Kexue Fu, Shuo Wang

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling such attention, we observe that the Locali

Yujun Li, Hongyuan Zhang, Yuan Yuan

Graph contrastive learning (GCL) aims to learn representations from unlabeled graph data in a self-supervised manner and has developed rapidly in recent years. However, edgelevel contrasts are not well explored by most existing GCL methods. Most studies in GCL only regard edges as auxiliary information while updating node features. One of the primary obstacl

Baocai Yin, Ji Zhao, Huajie Jiang, Ningning Hou

Continual learning (CL) enables models to adapt to evolving data streams. A major challenge of CL is catastrophic forgetting, where new knowledge will overwrite previously acquired knowledge. Traditional methods usually retain the past data for replay or add additional branches in the model to learn new knowledge, which has high memory requirements. In this

Deyi Xing, Weicong Chen, Curtis Tatsuoka, Xiaoyi Lu

The proliferation of heterogeneous configurations in distributed systems presents significant challenges in ensuring stability and efficiency. Misconfigurations, driven by complex parameter interdependencies, can lead to critical failures. Group Testing (GT) has been leveraged to expedite troubleshooting by reducing the number of tests, as demonstrated by me

Yixuan Zhang, Zhidong Li, Yang Wang, Fang Chen

Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale d

Shang-Ru Yang, Yung-Han Liao, Chih-Ching Chien, Hao-Hsiang Wu

Cs\'{a}ji, Jungers, and Blondel prove that while a PageRank optimization problem with edge selection constraints is NP-hard, it can be solved optimally in polynomial time for the unconstrained case. This theoretical result is accompanied by several observations, which we leverage to develop valid inequalities in polynomial time for this class of NP-hard prob

Tengfei Liu, Jiapu Wang, Yongli Hu, Mingjie Li

Radiology report generation (RRG) models typically focus on individual exams, often overlooking the integration of historical visual or textual data, which is crucial for patient follow-ups. Traditional methods usually struggle with long sequence dependencies when incorporating historical information, but large language models (LLMs) excel at in-context lear

T. Makoto Minamide, Yoshio Tanigawa, Nigel Watt

Let $\Delta_{k}(x)$ be the error term in the classical asymptotic formula for the sum $\sum_{n\leq x}d_{k}(n)$, where $d_{k}(n)$ is the number of ways $n$ can be written as a product of $k$ factors. We study the analytic properties of the Dirichlet series $\sum_{n=1}^{\infty}\Delta_{k}(n)n^{-s}$ and use Perron's formula to estimate the sums $\sum_{n\leq x}\D

Zhuo Wu, Qinglin Jia, Chuhan Wu, Zhaocheng Du

Evaluating the quality of recommender systems is critical for algorithm design and optimization. Most evaluation methods are computed based on offline metrics for quick algorithm evolution, since online experiments are usually risky and time-consuming. However, offline evaluation usually cannot fully reflect users' preference for the outcome of different rec

Liyuan Cui, Xiaogang Xu, Wenqi Dong, Zesong Yang

Human video synthesis aims to create lifelike characters in various environments, with wide applications in VR, storytelling, and content creation. While 2D diffusion-based methods have made significant progress, they struggle to generalize to complex 3D poses and varying scene backgrounds. To address these limitations, we introduce CFSynthesis, a novel fram

Binghui Zhang, Sayedeh Leila Noorbakhsh, Yun Dong, Yuan Hong

Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to mitigate both the security and privacy attacks, and maintain task utility as well. Particularly, we propose an information-theoretic framework to achieve the goals through the lens of

Haixu Wang, Jiguo Cao, Jian Pei

This study presents a novel representation learning model tailored for dynamic networks, which describes the continuously evolving relationships among individuals within a population. The problem is encapsulated in the dimension reduction topic of functional data analysis. With dynamic networks represented as matrix-valued functions, our objective is to map

Philip J. Armitage

The standard model for planet formation is a bottom-up process in which the origin of rocky and gaseous planets can be traced back to the collision of micron-sized dust grains within the gas-rich environment of protoplanetary disks. Key milestones along the way include disk formation, grain growth, planetesimal formation, core growth, gas accretion, and plan

William Watson, Nicole Cho, Nishan Srishankar, Zhen Zeng

Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is challenging for an off-the-shelf Large Language Model (LLM) to ingest these contracts due to the lengthy unstructured streams of text, limited LLM context windows, and complex legal j

Yeonwook Jung, Chun-Kit Lai, Yuveshen Mooroogen

Erd\H{o}s similarity conjecture was proposed by P. Erd\H{o}s in 1974. The conjecture remains open for exponentially decaying sequences as well as Cantor sets that have both Newhouse thickness and Hausdorff dimension zero. In this article, written after 50 years of the conjecture being proposed, we review progress on some new variants of the original problem:

Rahul Nair, Gabriel Tseng, Esther Rolf, Bhanu Tokas

Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier work studied general-purpose image datasets (e.g., ImageNet) and simple tasks like image recognition, we investigated geo-biases in real-world driving datasets on a more complex task: instance segmentation. We exami

Bhanu Tokas, Rahul Nair, Hannah Kerner

Most ML datasets today contain biases. When we train models on these datasets, they often not only learn these biases but can worsen them -- a phenomenon known as bias amplification. Several co-occurrence-based metrics have been proposed to measure bias amplification in classification datasets. They measure bias amplification between a protected attribute (e

Sk. Safique Ahmad, Neha Bhadala

This study focuses on addressing the challenge of solving the reduced biquaternion equality constrained least squares (RBLSE) problem. We develop algebraic techniques to derive real and complex solutions for the RBLSE problem by utilizing the real and complex forms of reduced biquaternion matrices. Furthermore, we propose algorithms and provide a detailed an

Zhaoyang Sun, Shengwu Xiong, Yaxiong Chen, Fei Du

This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence of paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. Additionally, different makeup styles gen

Xin Wang, Feilong Wang, Xuegang Ban

This paper proposes a sensitivity analysis framework based on set valued mapping for deep neural networks (DNN) to understand and compute how the solutions (model weights) of DNN respond to perturbations in the training data. As a DNN may not exhibit a unique solution (minima) and the algorithm of solving a DNN may lead to different solutions with minor pert

Deepak Gupta, Dina Demner-Fushman

One of the key goals of artificial intelligence (AI) is the development of a multimodal system that facilitates communication with the visual world (image and video) using a natural language query. Earlier works on medical question answering primarily focused on textual and visual (image) modalities, which may be inefficient in answering questions requiring

Dekui Peng, Gao Zhang

Let $\tau$ be an uncountable cardinal. The notion of a \emph{$\tau$-fine} topological group was introduced in 2021. More recently, H. Zhang et al. generalized this concept by defining pseudo-$\tau$-fine topological groups to study certain factorization properties of continuous functions on topological groups. It is known that $\tau$-fineness cannot coexist w

Brett C. Johnson, Mitchell O. de Vries, Alexander J. Healey, Marco Capelli

Photoluminescent defects in diamond, like the nitrogen-vacancy (NV) color center, are at the forefront of emerging optical quantum technologies. Most emit in the visible and near-infrared spectral region below 1000 nm (NIR-I), limiting their applications in photonics, fiber communications, and biology. Here, we show that the nitrogen-vacancy-nitrogen ($N_2V$

Anthony Fei, Mohamed S. Abdelfattah

Large Language Models (LLMs) have become essential tools in natural language processing, finding large usage in chatbots such as ChatGPT and Gemini, and are a central area of research. A particular area of interest includes designing hardware specialized for these AI applications, with one such example being the neural processing unit (NPU). In 2023, Intel r

Alexey Sharapov, David Shcherbatov, Evgeny Skvortsov

We address the issue of electromagnetic interaction for massive higher-spin fields in $3d$ Minkowski space. We show that consistent field equations can be obtained through the dimensional reduction of the higher-spin extension of self-dual Yang-Mills theory, which itself is a truncation of chiral higher-spin gravity in four dimensions. The resulting electrom

Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong

Multimodal large language models (MLLMs) are effective at capturing the semantics of short video content; however, they often fail to attend to the policy-specific details required for reliable content moderation. To address this limitation, we introduce IPS, a novel framework that integrates In-prompt Process Supervision into MLLMs by introducing sequential

Jacob F. Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho

We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach that uses a generative model to learn a joint distribut

Yujin Wang, Quanfeng Liu, Jiaqi Fan, Jinlong Hong

Understanding and addressing corner cases is essential for ensuring the safety and reliability of autonomous driving systems. Vision-language models (VLMs) play a crucial role in enhancing scenario comprehension, yet they face significant challenges, such as hallucination and insufficient real-world grounding, which compromise their performance in critical d

Feiyue Sun

We study the distributed facility location games with candidate locations, where agents on a line are partitioned into groups. Both desirable and obnoxious facility location settings are discussed. In distributed location problems, distortion can serve as a standard for quantifying performance, measuring the degree of difference between the actual location p

Yu Fu

Inspired by the work of Ellenberg, Elsholtz, Hall, and Kowalski, we investigate how the property of the generic fiber of a one-parameter family of abelian varieties being geometrically simple extends to other fibers. In \cite{EEHK09}, the authors studied a special case involving specific one-parameter families of Jacobians of curves using analytic methods. W

Chenhui Deng, Yunsheng Bai, Haoxing Ren

Recent advancements in large language models (LLMs) have expanded their application across various domains, including chip design, where domain-adapted chip models like ChipNeMo have emerged. However, these models often struggle with instruction alignment, a crucial capability for LLMs that involves following explicit human directives. This limitation impede

Peng Zhou, Dylan R. Muir

Deploying Spiking Neural Networks (SNNs) on the Xylo neuromorphic chip via the Rockpool framework represents a significant advancement in achieving ultra-low-power consumption and high computational efficiency for edge applications. This paper details a novel deployment pipeline, emphasizing the integration of Rockpool's capabilities with Xylo's architecture

Nazanin Tabatabaei Anaraki, Taneisha Ng, Gaurav Verma, Yu Fu

Team projects in Computer Science (CS) help students build collaboration skills, apply theory, and prepare for real-world software development. Online classes present unique opportunities to transform the accessibility of CS education at scale. Still, the geographical distribution of students and staff adds complexity to forming effective teams, providing co

Huijun Han, Congyi Zhang, Lifeng Zhu, Pradeep Singh

Orthognathic surgery consultation is essential to help patients understand the changes to their facial appearance after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. To overcome these challenges, this study aims to develop a fully automated

Zhengyu Fang, Zhimeng Jiang, Huiyuan Chen, Xiao Li

Tabular data generation has attracted significant research interest in recent years, with the tabular diffusion models greatly improving the quality of synthetic data. However, while memorization, where models inadvertently replicate exact or near-identical training data, has been thoroughly investigated in image and text generation, its effects on tabular d

Minhao Bai, Jinshuai Yang, Kaiyi Pang, Yongfeng Huang

In the era of Large Language Models (LLMs), generative linguistic steganography has become a prevalent technique for hiding information within model-generated texts. However, traditional steganography methods struggle to effectively align steganographic texts with original model-generated texts due to the lower entropy of the predicted probability distributi

Zhongrui Wang, Nan Chen, Di Qi

State estimation in multi-layer turbulent flow fields with only a single layer of partial observation remains a challenging yet practically important task. Applications include inferring the state of the deep ocean by exploiting surface observations. Directly implementing an ensemble Kalman filter based on the full forecast model is usually expensive. One wi

Di Wu, Xin Lu, Yanyan Zhao, Bing Qin

Although large language models (LLMs) achieve effective safety alignment at the time of release, they still face various safety challenges. A key issue is that fine-tuning often compromises the safety alignment of LLMs. To address this issue, we propose a method named IRR (Identify, Remove, and Recalibrate for Safety Realignment) that performs safety realign

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

An amplitude analysis of the Cabibbo-favored decay $D^+ \to K^-\pi^+\pi^+\pi^0$ is performed, using 7.93 $\rm{fb}^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector at the center-of-mass energy of 3.773 GeV. The branching fractions of the intermediate processes are measured, with the dominant contribution $D^+ \to \bar{K}^{*}(892)^0\rho(770)

Minghui Zhang, Chenyu Li, Fangfang Xie, Yaoyu Liu

Accurate anatomical labeling and analysis of the pulmonary structure and its surrounding anatomy from thoracic CT is getting increasingly important for understanding the etilogy of abnormalities or supporting targetted therapy and early interventions. Whilst lung and airway cell atlases have been attempted, there is a lack of fine-grained morphological atlas

Zhi-Gang Wang

In this work, we study the mass spectrum of the hidden-charm hybrid states with the $J^{PC}=0^{-+}$, $0^{++}$, $0^{--}$, $1^{++}$, $1^{+-}$, $1^{-+}$, $1^{--}$, $2^{-+}$ and $2^{++}$ via the QCD sum rules in a consistent way. We calculate the vacuum condensates up to dimensions-6 by taking account of both the leading order and next-to-leading order contribut