May 2024 arXiv papers — page 53

Yiteng Zhang, Lukas Gruenewald, Xin Cao, Doaa Abdelbarey

Strain-free GaAs/AlGaAs semiconductor quantum dots (QDs) grown by droplet etching and nanohole infilling (DENI) are highly promising candidates for the on-demand generation of indistinguishable and entangled photon sources. The spectroscopic fingerprint and quantum optical properties of QDs are significantly influenced by their morphology. The effects of nan

Seyed Arash Sheikholeslam, Andre Ivanov

In this paper, we introduce SynthAI, a new method for the automated creation of High-Level Synthesis (HLS) designs. SynthAI integrates ReAct agents, Chain-of-Thought (CoT) prompting, web search technologies, and the Retrieval-Augmented Generation (RAG) framework within a structured decision graph. This innovative approach enables the systematic decomposition

Yuzhong Zhao, Feng Liu, Yue Liu, Mingxiang Liao

One fundamental task of multimodal models is to translate referred image regions to human preferred language descriptions. Existing methods, however, ignore the resolution adaptability needs of different tasks, which hinders them to find out precise language descriptions. In this study, we propose a DynRefer approach, to pursue high-accuracy region-level ref

Arthur Hutsalyuk, Yunfeng Jiang, Balazs Pozsgay, Hefeng Xu

We calculate the correlation functions of strings of spin operators for integrable quantum circuits exactly. These observables can be used for calibration of quantum simulation platforms. We use algebraic Bethe Ansatz, in combination with computational algebraic geometry to obtain analytic results for medium-size (around 10-20 qubits) quantum circuits. The r

Fredrik D. Johansson

Evaluating observational estimators of causal effects demands information that is rarely available: unconfounded interventions and outcomes from the population of interest, created either by randomization or adjustment. As a result, it is customary to fall back on simulators when creating benchmark tasks. Simulators offer great control but are often too simp

Jeremy F. Alm, Matt Salomone

The first problem of the 2017 Putnam competition was to characterize a set of natural numbers closed under both the square-root map $n^2 \mapsto n$ and the "add 5 and square" map $ n \mapsto (n+5)^2$. We reframe this as a problem on an infinite directed graph, using this framing both to generalize the problem and its solution, as well as to determine the fir

Yu-Hang Dang, Shyam Dhamapurkar, Xiao-Long Zhu, Zheng-Yang Zhou

In quantum computing, the connectivity of qubits placed on two-dimensional chips limits the scalability and functionality of solid-state quantum computers. This paper presents two approaches to constructing complex quantum networks from simple qubit arrays, specifically grid lattices. The first approach utilizes a subset of qubits as tunable couplers, effect

R. Kanishka

In high energy physics experiments, the numerical estimation of primary ionization is crucial. An advance study on primary ionization can help in minimizing the effects such as electrical discharges that can damage the gaseous detectors used in high energy physics experiments. The simulation of primary ionization of electrons and positrons with the noble gas

Jing-Huan Li, Xu-Zhi Zhou, Zhi-Yang Liu, Shan Wang

Wave-particle resonance, a ubiquitous process in the plasma universe, occurs when resonant particles observe a constant wave phase to enable sustained energy transfer. Here, we present spacecraft observations of simultaneous Landau and anomalous resonances between oblique whistler waves and the same group of protons, which are evidenced, respectively, by pha

Kaituo Feng, Changsheng Li, Xiaolu Zhang, Jun Zhou

Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models. Previous methods typically require the student to mimic the step-by-step rationale produced by LLMs, often facing the following challenges: (i) Tokens within a rationale vary in significance, and treating the

Jiangnan Zhao, Dehui Du, Xing Yu, Hang Li

Advancements in Autonomous Driving Systems (ADS) have brought significant benefits, but also raised concerns regarding their safety. Virtual tests are common practices to ensure the safety of ADS because they are more efficient and safer compared to field operational tests. However, capturing the complex dynamics of real-world driving environments and effect

Zhiyong Feng, Yujia Zhao, Kan Yu, Dong Li

Physical layer security (PLS) technology based on the fixed-position antenna (FPA) has {attracted widespread attention}. Due to the fixed feature of the antennas, current FPA-based PLS schemes cannot fully utilize the spatial degree of freedom, and thus a weaken secure gain in the desired/undesired direction may exist. Different from the concept of FPA, mobi

Sam K. Miller

We strengthen the results of Boltje and Yilmaz regarding the Galois descent of equivalences of blocks of $p$-nilpotent groups and a result of Kessar and Linckelmann regarding Galois descent of splendid Rickard equivalences for blocks with compatible Galois stabilizers. A more general descent criteria for chain complexes is proven along the way, which require

Kan Yu, Fuze Zhu, Xiaowu Liu, Zhiyong Feng

The task offloading technology plays a crucial vital role in the Internet of Vehicle (IoV) with the demands of delay minimum, by jointly optimizing the heterogeneous computing resources supported by the vehicles, roadside units (RSUs), and macro base stations (MBSs). In previous works, on the one hand, they ignored the wireless interference among the exchang

Yusuke Kawamoto, Kentaro Kobayashi, Kohei Suenaga

Statistical methods have been widely misused and misinterpreted in various scientific fields, raising significant concerns about the integrity of scientific research. To mitigate this problem, we propose a tool-assisted method for formally specifying and automatically verifying the correctness of statistical programs. In this method, programmers are required

Zizhuo Meng, Ke Wan, Yadong Huang, Zhidong Li

Social networks represent complex ecosystems where the interactions between users or groups play a pivotal role in information dissemination, opinion formation, and social interactions. Effectively harnessing event sequence data within social networks to unearth interactions among users or groups has persistently posed a challenging frontier within the realm

Yifan Wang, Xianghui Cao, Shi Jin, Mo-Yuen Chow

Federated learning (FL) has been widely regarded as a promising paradigm for privacy preservation of raw data in machine learning. Although, the data privacy in FL is locally protected to some extent, it is still a desideratum to enhance privacy and alleviate communication overhead caused by repetitively transmitting model parameters. Typically, these challe

Xudong Lu, Aojun Zhou, Yuhui Xu, Renrui Zhang

Large Language Models (LLMs) have become pivotal in advancing the field of artificial intelligence, yet their immense sizes pose significant challenges for both fine-tuning and deployment. Current post-training pruning methods, while reducing the sizes of LLMs, often fail to maintain their original performance. To address these challenges, this paper introdu

Wenfei Liang, Yanan Zhao, Rui She, Yiming Li

Personalized subgraph Federated Learning (FL) is a task that customizes Graph Neural Networks (GNNs) to individual client needs, accommodating diverse data distributions. However, applying hypernetworks in FL, while aiming to facilitate model personalization, often encounters challenges due to inadequate representation of client-specific characteristics. To

Hoang-Son Do, Duc-Viet Vu

We generalize several known stability estimates for complex Monge-Amp\`ere equations to the setting of low (or high) energy potentials. We apply our estimates to obtain, among other things, a quantitative domination principle, and metric properties of the space of potentials of finite energy. Further applications will be given in subsequent papers.

Conor Hassan, Joshua J Bon, Elizaveta Semenova, Antonietta Mira

Federated learning (FL) allows for collaborative model training across decentralized clients while preserving privacy by avoiding data sharing. However, current FL methods assume conditional independence between client models, limiting the use of priors that capture dependence, such as Gaussian processes (GPs). We introduce the Structured Independence via de

Yao Yu, Hai-Bing Fu, Han Zhang, Bai-Cian Ke

The measurements in $b\to s$ penguin-dominated decays are widely recognized as a powerful test for searching for New Physics by studying the deviation from theoretical estimations within the Standard Model. We examine the final-state rescattering effects on the decay $B_s\to K^{*0}\bar{K}^{*0}$ and provide estimations of the branching ratio and longitudinal

Hyunin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi

Real-time inference is a challenge of real-world reinforcement learning due to temporal differences in time-varying environments: the system collects data from the past, updates the decision model in the present, and deploys it in the future. We tackle a common belief that continually updating the decision is optimal to minimize the temporal gap. We propose

Anish Rai, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices.

Juan Pablo Mesa, Alejandro Montoya, Raul Ramos-Pollán, Mauricio Toro

The Multi-Objective Vehicle Routing Problem (MOVRP) is a complex optimization problem in the transportation and logistics industry. This paper proposes a novel approach to the MOVRP that aims to create routes that consider drivers' and operators' decisions and preferences. We evaluate two approaches to address this objective: visually attractive route planni

Roy Long

The game theoretic concepts of rationalizability and iterated dominance are closely related and provide characterizations of each other. Indeed, the equivalence between them implies that in a two player finite game, the remaining set of actions available to players after iterated elimination of strictly dominated strategies coincides with the rationalizable

Ayushi Suman, Sarika Jalan

Finite-size systems of Kuramoto model display intricate dynamics, especially in the presence of multi-stability where both coherent and incoherent states coexist. We investigate such scenario in globally coupled populations of Kuramoto phase oscillators with higher-order interactions, and observe that fluctuations inherent to finite-size systems drives the t

Rajen Kumar, Prashant Kumar Srivastava, Sudhan Majhi

We propose new constructions for a two-dimensional ($2$D) perfect array, complete complementary code (CCC), and multiple CCCs as an optimal symmetrical $Z$-complementary code set (ZCCS). We propose a method to generate a two-dimensional perfect array and CCC. By utilising mutually orthogonal sequences, we developed a method to extend the length of a CCC with

Lintao Li, Anders E. Kalør, Petar Popovski, Wei Chen

Goal-oriented communication has become one of the focal concepts in sixth-generation communication systems owing to its potential to provide intelligent, immersive, and real-time mobile services. The emerging paradigms of goal-oriented communication constitute closed loops integrating communication, computation, and sensing. However, challenges arise for clo

Kan Chen, Ting Ye, Dylan S. Small

The case$^2$ study, also referred to as the case-case study design, is a valuable approach for conducting inference for treatment effects. Unlike traditional case-control studies, the case$^2$ design compares treatment in two types of cases with the same disease. A key quantity of interest is the attributable effect, which is the number of cases of disease a

Pricila S. Barbosa, Manuel Villanueva-Pesqueira

In this paper we analyze the limit behavior of a family of solutions of the Laplace operator with homogeneous Neumann boundary conditions, set in a two-dimensional thin domain which presents weak oscillations on both boundaries and with terms concentrated in a narrow oscillating neighborhood of the top boundary. The aim of this problem is to study the behavi

Jingyu Liu, Yao Yao

In the strong exciton-cavity coupling regime, exciton polaritons play a pivotal role in governing exciton transport. However, the specific contributions of the two branches of polaritons to exciton current remain unexplored. Here, we decompose the exciton current operator into components coherent with the upper and lower polaritons. Subsequently, we employ t

Hunter Lang, David Sontag, Aravindan Vijayaraghavan

Strong student models can learn from weaker teachers: when trained on the predictions of a weaker model, a strong pretrained student can learn to correct the weak model's errors and generalize to examples where the teacher is not confident, even when these examples are excluded from training. This enables learning from cheap, incomplete, and possibly incorre

Ziao Yang, Han Yue, Jian Chen, Hongfu Liu

Influence functions serve as crucial tools for assessing sample influence in model interpretation, subset training set selection, noisy label detection, and more. By employing the first-order Taylor extension, influence functions can estimate sample influence without the need for expensive model retraining. However, applying influence functions directly to d

Andrew Li, Xianle Feng, Siddhant Narang, Austin Peng

When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in psycholinguistic experiments using online measures such as reading times and offline measures such as comprehension questions. Here, we investigate the processing of garden-path sent

Zhenzhong Wang, Zehui Lin, Wanyu Lin, Ming Yang

Providing explainable molecular property predictions is critical for many scientific domains, such as drug discovery and material science. Though transformer-based language models have shown great potential in accurate molecular property prediction, they neither provide chemically meaningful explanations nor faithfully reveal the molecular structure-property

Ziao Yang, Han Yue, Jian Chen, Hongfu Liu

Shapley value-based data valuation methods, originating from cooperative game theory, quantify the usefulness of each individual sample by considering its contribution to all possible training subsets. Despite their extensive applications, these methods encounter the challenge of value inflation - while samples with negative Shapley values are detrimental, s

Shilong Hu, Hao Liu, Dong Wang

In this paper, we introduce two iterative methods for longest minimal length partition problem, which asks whether the disc (ball) is the set maximizing the total perimeter of the shortest partition that divides the total region into sub-regions with given volume proportions, under a volume constraint. The objective functional is approximated by a short-time

Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber, Christopher Potts

Previous work on Universal Transformers (UTs) has demonstrated the importance of parameter sharing across layers. By allowing recurrence in depth, UTs have advantages over standard Transformers in learning compositional generalizations, but layer-sharing comes with a practical limitation of parameter-compute ratio: it drastically reduces the parameter count

Xue Zhang, Si-Yuan Cao, Fang Wang, Runmin Zhang

Most recent multispectral object detectors employ a two-branch structure to extract features from RGB and thermal images. While the two-branch structure achieves better performance than a single-branch structure, it overlooks inference efficiency. This conflict is increasingly aggressive, as recent works solely pursue higher performance rather than both perf

Song Gao, Shu-Min Zhao, Ming-Yue Liu, Xing-Yu Han

In the $U(1)_X$SSM, we delve into the flavor violation of $h \rightarrow bs$, where $h$ is identified with the SM-like Higgs boson discovered at the LHC. As the U(1) extension of the minimal supersymmetric standard model (MSSM), the U(1)XSSM has new super fields such as right-handed neutrinos and three Higgs singlets. We conduct a thorough analysis of the un

Jieren Deng, Hanbin Hong, Aaron Palmer, Xin Zhou

Randomized smoothing has become a leading method for achieving certified robustness in deep classifiers against l_{p}-norm adversarial perturbations. Current approaches for achieving certified robustness, such as data augmentation with Gaussian noise and adversarial training, require expensive training procedures that tune large models for different Gaussian

Michael Maxfield, Jingcheng Xu, Cécile Ané

Semidirected networks have received interest in evolutionary biology as the appropriate generalization of unrooted trees to networks, in which some but not all edges are directed. Yet these networks lack proper theoretical study. We define here a general class of semidirected phylogenetic networks, with a stable set of leaves, tree nodes and hybrid nodes. We

Xiangyu Chen, Zhenzhen Liu, Katie Z Luo, Siddhartha Datta

Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups or geographic locations, often resulting in poor localization accuracy due to domain shift. To overcome this ch

Qiaolin Qin, Heng Li, Ettore Merlo

Data quality is vital for user experience in products reliant on data. As solutions for data quality problems, researchers have developed various taxonomies for different types of issues. However, although some of the existing taxonomies are near-comprehensive, the over-complexity has limited their actionability in data issue solution development. Hence, rec

Patricio Pérez-Piña

We propose a p-adic version of Duke's Theorem on the equidistribution of closed geodesics on modular curves. Our approach concerns quadratic fields split at p as well as a p-adic covering of the modular curve. We also prove an equidistribution result of Heegner points in the p-adic space attached to Shimura curves.

Jianbo Lu, Yongxin Guo, G. Y. Chee

In this paper, it is argued that in gravity theories the local Lorentz group can not be considered as a gauge group in the sense of Yang-Mills theories, the Lorentz connection is not a gauge potential but an artificial force, the inertial force. A genuine gravity theory should be a translation gauge theory, though a unnormal gauge theory. All the three theor

Chenjia Bai, Rushuai Yang, Qiaosheng Zhang, Kang Xu

Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning useful behaviors via reward-free per-training. Existing methods for unsupervised RL mainly conduct empowerment-driven skill discovery or entropy-based exploration. However, empowerment often leads to static skills, and pure exploration only maximizes the state coverage rather

Huaiguang Cai, Zhi Zhou, Qianyi Huang

With edge intelligence, AI models are increasingly pushed to the edge to serve ubiquitous users. However, due to the drift of model, data, and task, AI model deployed at the edge suffers from degraded accuracy in the inference serving phase. Model retraining handles such drifts by periodically retraining the model with newly arrived data. When colocating mod

Wen-Hao Wu, Ze-Zheng Li, Wen-Xu Wang

Complex spatiotemporal patterns in nature significantly challenge reductionism-based modern science. The lack of a paradigm beyond reductionism hinders our understanding of the emergence of complexity. The diversity of countless patterns undermines any notion of universal mechanisms. Here, however, we show that breaking the symmetry of three simple and self-

Lu Tan, Huei Zhou, Yinxiang Huang, Zeming Zheng

With the availability of large pre-trained models, a modern workflow for building real-world machine learning solutions is to fine-tune such models on a downstream task with a relatively small domain-specific dataset. In such applications, one major challenge is that the small fine-tuning dataset does not have sufficient coverage of the distribution encounte

Chi-Fang Chen, Jorge Garza-Vargas, Joel A. Tropp, Ramon van Handel

A family of random matrices $\boldsymbol{X}^N=(X_1^N,\ldots,X_d^N)$ is said to converge strongly to a family of bounded operators $\boldsymbol{x}=(x_1,\ldots,x_d)$ when $\|P(\boldsymbol{X}^N,\boldsymbol{X}^{N*})\|\to\|P(\boldsymbol{x}, \boldsymbol{x}^*)\|$ for every noncommutative polynomial $P$. This phenomenon plays a key role in several recent breakthroug

Daniel Jost, Eder G. Lomeli, Ta Tang, Joshua J. Kas

X-ray spectroscopy has been a key method to determine ground and excited state properties of quantum materials with atomic specificity. Now, new x-ray facilities are opening the door to the study of pump-probe x-ray spectroscopy - specifically time-resolved x-ray absorption (trXAS) and time-resolved resonant inelastic x-ray scattering (trRIXS). In this paper

Gabriel P. Martins, Oleg Berman, Godfrey Gumbs, Yurii E. Lozovik

We studied the effects arising from a coherent source of photons on the entanglement between excitons in a strained graphene monolayer. The graphene layer was considered to be embedded in an imperfect optical microcavity. In our investigation, we have studied the entanglement dynamics of systems consisting of up to five excitons, which are treated as atomic-

Lei Zhang

This paper presents a novel approach for signal reconstruction using Spiking Neural Networks (SNN) based on the principles of Cognitive Informatics and Cognitive Computing. The proposed SNN leverages the Discrete Fourier Transform (DFT) to represent and reconstruct arbitrary time series signals. By employing N spiking neurons, the SNN captures the frequency

Handan Kose, Burcu Ungor, Abdullah Harmanci

Let $R$ be a ring, $e$ an idempotent of $R$ and $\delta(R)$ denote the intersection of all essential maximal right ideals of $R$ which is called Zhou radical. In this paper, the Zhou radical of a ring is applied to the $e$-reduced property of rings. We call the ring $R$ {\it Zhou right} (resp. {\it left}) {\it $e$-reduced} if for any nilpotent $a$ in $R$, we

Michael Ahn, Montserrat Gonzalez Arenas, Matthew Bennice, Noah Brown

Robots today can exploit the rich world knowledge of large language models to chain simple behavioral skills into long-horizon tasks. However, robots often get interrupted during long-horizon tasks due to primitive skill failures and dynamic environments. We propose VADER, a plan, execute, detect framework with seeking help as a new skill that enables robots

Liangzu Peng, Wotao Yin

Block coordinate descent is a powerful algorithmic template suitable for big data optimization. This template admits a lot of variants including block gradient descent (BGD), which performs gradient descent on a selected block of variables, while keeping other variables fixed. For a very long time, the stepsize for each block has tacitly been set to one divi

Joel E. Cohen

Based on the primes less than $4 \times 10^{18}$, Oliveira e Silva et al. (2014) conjectured an asymptotic formula for the sum of the $k$th power of the gaps between consecutive primes less than a large number $x$. We show that the conjecture of Oliveira e Silva holds if and only if the $k$th moment of the first $n$ gaps is asymptotic to the $k$th moment of

Qi Chai, Wen Yang

Identifying quantum resources for quantum sensing is of paramount importance. Up to date, two quantum resources has been widely recognized: the number $N$ of entangled quantum probes and the coherent evolution time $T$. Here we identify the spin quantum number $S$ of high-spin systems as another quantum resource, which can improve the sensing precision of ma

Juanca Carrasco-Martinez

In this essay, we argue that certain aspects of the measurement require revision in Quantum Gravity. Using entropic arguments, we propose that the number of measurement outcomes and the accuracy (or the range) of the measurement are limited by the entropy of the black hole associated with the observer scale. This also implies the necessity of modifying the a

Yusuke Akamatsu, Terumi Umematsu, Hitoshi Imaoka, Shizuko Gomi

Daily monitoring of intra-personal facial changes associated with health and emotional conditions has great potential to be useful for medical, healthcare, and emotion recognition fields. However, the approach for capturing intra-personal facial changes is relatively unexplored due to the difficulty of collecting temporally changing face images. In this pape

Michael J. Larsen

Let $K$ be an algebraically closed field of characteristic $2$, $G$ be the algebraic group $\mathrm{SL}_2$ over $K$, and $V$ be the natural representation of $G$. Let $b_k^{G,V}$ denote the number of $G$-indecomposable factors of $V^{\otimes k}$, counted with multiplicity, and let $\delta = \frac 32 - \frac{\log 3}{2\log 2}$. Then there exists a smooth multi

Santiago R. Simanca

We identify the smooth metrics $\mc{M}(M)$ on a manifold $M^n$ with the smooth isometric embeddings $f_g: (M,g) \rightarrow (\mb{S}^{\tn}, \tg)$ into a standard sphere of large dimension $\tn=\tn(n)$, and their Palais isotopic deformations, and the space $\mc{C}(M)$ of conformal classes with the space of classes of metrics whose embeddings are isotopic to ea

Steven An, Sanjoy Dasgupta

Labeling data via rules-of-thumb and minimal label supervision is central to Weak Supervision, a paradigm subsuming subareas of machine learning such as crowdsourced learning and semi-supervised ensemble learning. By using this labeled data to train modern machine learning methods, the cost of acquiring large amounts of hand labeled data can be ameliorated.

Hyosoon Jang, Yunhui Jang, Minsu Kim, Jinkyoo Park

This paper studies Generative Flow Networks (GFlowNets), which learn to sample objects proportionally to a given reward function through the trajectory of state transitions. In this work, we observe that GFlowNets tend to under-exploit the high-reward objects due to training on insufficient number of trajectories, which may lead to a large gap between the es

Shuaishuai Guo, Yanhu Wang, Jia Ye, Anbang Zhang

Semantic communications, a promising approach for agent-human and agent-agent interactions, typically operate at a feature level, lacking true semantic understanding. This paper explores understanding-level semantic communications (ULSC), transforming visual data into human-intelligible semantic content. We employ an image caption neural network (ICNN) to de

Phill Schultz

The lattice of ideals of the torsion ideal of the endomorphism ring of an abelian p-group is classified by a system of cardinal invariants.

Rui Qian, Xiaoyi Dong, Pan Zhang, Yuhang Zang

This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for video understanding, that capably understands arbitrary-length video with a constant number of video tokens streamingly encoded and adaptively selected. The challenge of video understanding in the vision language area mainly lies in the significant computational burden cau

Hakim Ikebayashi, Norihiko Kawai

Augmented reality (AR) using camera images in mobile devices is becoming popular for tourism promotion. However, obstructions such as tourists appearing in the camera images may cause the camera pose estimation error, resulting in CG misalignment and reduced visibility of the contents. To avoid this problem, Indirect AR (IAR), which does not use real-time ca

Liangze Mao, Jizhe Cui, Rong Yu

There exist two groups of electron microscopy methods that are capable of providing three-dimensional (3D) structural information of an object, i.e., electron tomography and depth sectioning. Electron tomography is capable of resolving atoms in all three dimensions, but the accuracy in atomic positions is low and the object size that can be reconstructed is

Jose Rafael Espinosa Mena

The Sinkhorn algorithm has emerged as a powerful tool for solving optimal transport problems, finding applications in various domains such as machine learning, image processing, and computational biology. Despite its widespread use, the intricate structure and scaling properties of the coupling matrices generated by the Sinkhorn algorithm remain largely unex

Junyi Wu, Haoxuan Wang, Yuzhang Shang, Mubarak Shah

The recent introduction of Diffusion Transformers (DiTs) has demonstrated exceptional capabilities in image generation by using a different backbone architecture, departing from traditional U-Nets and embracing the scalable nature of transformers. Despite their advanced capabilities, the wide deployment of DiTs, particularly for real-time applications, is cu

Cevdet Akyel, Slobodan Babic

This book aims to familiarize Electrical Engineering students with exercises and problems related to electromagnetic wave transmission. It includes a brief theoretical introduction to classical electromagnetism formulas to bridge the gap between theory and practical applications. The book focuses on free space, coaxial cables, and waveguides, emphasizing the

Miao Zhang, Ziming Wang, Runtian Xing, Kui Xiao

Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students' proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it's hardly feasible to completely cover al

Minhak Song, Kwangjun Ahn, Chulhee Yun

Understanding the training dynamics of deep neural networks is challenging due to their high-dimensional nature and intricate loss landscapes. Recent studies have revealed that, along the training trajectory, the gradient approximately aligns with a low-rank top eigenspace of the training loss Hessian, referred to as the dominant subspace. Given this alignme

J. X. Dai, Y. X. Zhao

Chiral symmetry on bipartite lattices with different numbers of $A$-sites and $B$-sites is exceptional in condensed matter, as it gives rise to zero-energy flat bands. Crystalline systems featuring chiral symmetry with non-equal sublattices include Lieb lattices, dice lattices, and particularly Moir\'e systems, where interaction converts the flat bands into

Sanjay Natesan, Homayoon Beigi

Large scale machine learning-based Raga identification continues to be a nontrivial issue in the computational aspects behind Carnatic music. Each raga consists of many unique and intrinsic melodic patterns that can be used to easily identify them from others. These ragas can also then be used to cluster songs within the same raga, as well as identify songs

Naoki Seto

The proposed space gravitational wave (GW) detector LISA has potential to detect stellar-mass black hole binaries (BBHs). The majority of the detected BBHs are expected to emit nearly monochromatic GWs, whose frequency evolution will be efficiently described by Taylor expansions. We study the measurability of the associated time derivative coefficients of th

Anirban Biswas, Arpan Kar, Bum-Hoon Lee, Hocheol Lee

We provide a transparent discussion of the high temperature asymptotic behaviour of Cosmology in a dilaton-Einstein-Gauss-Bonnet (dEGB) scenario of modified gravity with vanishing scalar potential. In particular, we show that it has a clear interpretation in terms of only three attractors (stable critical points) of a set of autonomous differential equations

Leonardo Santos, Brady Moon, Sebastian Scherer, Hoa Van Nguyen

Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up t

Kazutaka Kiuchi, Shimpei Imamura, Norihiko Kawai

Recent studies have shown that visually impaired people have desires to take selfies in the same way as sighted people do to record their photos and share them with others. Although support applications using sound and vibration have been developed to help visually impaired people take selfies using smartphone cameras, it is still difficult to capture everyo

Peiyao Wang, Yuewei Lin, Erik Blasch, Jie Wei

Although the performance of Temporal Action Segmentation (TAS) has improved in recent years, achieving promising results often comes with a high computational cost due to dense inputs, complex model structures, and resource-intensive post-processing requirements. To improve the efficiency while keeping the performance, we present a novel perspective centered

Junlin Wu, Huan Zhang, Yevgeniy Vorobeychik

Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy. However, training a controller that can be formally verified to be safe remains a major challenge. We introduce a novel approach for learning verified safe control policies in nonlinear neural dynamical systems while maximizing overall performance. Our approach aim

Alberto Fossà, Roberto Armellin, Emmanuel Delande, Francesco Sanfedino

A multifidelity method for the nonlinear propagation of uncertainties in the presence of stochastic accelerations is presented. The proposed algorithm treats the uncertainty propagation (UP) problem by separating the propagation of the initial uncertainty from that of the process noise. The initial uncertainty is propagated using an adaptive Gaussian mixture

Nikola B. Kovachki, Samuel Lanthaler, Hrushikesh Mhaskar

Operator learning has emerged as a new paradigm for the data-driven approximation of nonlinear operators. Despite its empirical success, the theoretical underpinnings governing the conditions for efficient operator learning remain incomplete. The present work develops theory to study the data complexity of operator learning, complementing existing research o

Xuesong Wang, He Zhao, Edwin V. Bonilla

Neural Processes (NPs) are deep probabilistic models that represent stochastic processes by conditioning their prior distributions on a set of context points. Despite their advantages in uncertainty estimation for complex distributions, NPs enforce parameterization coupling between the conditional prior model and the posterior model. We show that this coupli

Artem Agafonov, Petr Ostroukhov, Roman Mozhaev, Konstantin Yakovlev

Variational inequalities represent a broad class of problems, including minimization and min-max problems, commonly found in machine learning. Existing second-order and high-order methods for variational inequalities require precise computation of derivatives, often resulting in prohibitively high iteration costs. In this work, we study the impact of Jacobia

Uche Ochuba

This paper addresses the critical issue of deforestation by exploring the application of vision transformers (ViTs) for classifying the drivers of deforestation using satellite imagery from Indonesian forests. Motivated by the urgency of this problem, I propose an approach that leverages ViTs and machine learning techniques. The input to my algorithm is a 33

David Lindsay

The Transductive Confidence Machine Nearest Neighbours (TCMNN) algorithm and a supporting, simple user interface was developed. Different settings of the TCMNN algorithms' parameters were tested on medical data sets, in addition to the use of different Minkowski metrics and polynomial kernels. The effect of increasing the number of nearest neighbours and mar

Andy Skumanich, Han Kyul Kim

This paper highlights the developing need for quantitative modes for capturing and monitoring malicious communication in social media. There has been a deliberate "weaponization" of messaging through the use of social networks including by politically oriented entities both state sponsored and privately run. The article identifies a use of AI/ML characteriza

Fernando C. Marques, André Neves

In this paper, we describe the intersection between geodesic and conformal currents on closed hyperbolic three-manifolds. We use this to prove some sharp bounds which involve the Liouville entropy of a negatively curved metric, the minimal surface entropy, and the area ratio. Using these ideas we also give a new proof of the Mostow Rigidity Theorem in the th

Javad A. Asadzade, Nazim I. Mahmudov

In this manuscript, we investigate a fractional stochastic neutral differential equation with time delay, which includes both deterministic and stochastic components. Our primary objective is to rigorously prove the existence of a unique solution that satisfies given initial conditions. Furthermore, we extend our research to investigate the finite-time stabi

Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant M. Rotskoff

Diffusion models have become a leading method for generative modeling of both image and scientific data. As these models are costly to train and \emph{evaluate}, reducing the inference cost for diffusion models remains a major goal. Inspired by the recent empirical success in accelerating diffusion models via the parallel sampling technique~\cite{shih2024par

Robert Wolfe, Tanushree Mitra

Generative AI appears poised to transform white collar professions, with more than 90% of Fortune 500 companies using OpenAI's flagship GPT models, which have been characterized as "general purpose technologies" capable of effecting epochal changes in the economy. But how will such technologies impact organizations whose job is to verify and report factual i

Simon Yu, Jie He, Pasquale Minervini, Jeff Z. Pan

With the emergence of large language models, such as LLaMA and OpenAI GPT-3, In-Context Learning (ICL) gained significant attention due to its effectiveness and efficiency. However, ICL is very sensitive to the choice, order, and verbaliser used to encode the demonstrations in the prompt. Retrieval-Augmented ICL methods try to address this problem by leverag

Hossein Jowhari

In this paper, we introduce a local search algorithm for hierarchical clustering. For the local step, we consider a tree re-arrangement operation, known as the {\em interchange}, which involves swapping two closely positioned sub-trees within a tree hierarchy. The interchange operation has been previously used in the context of phylogenetic trees. As the obj

Emily Jensen, Sriram Sankaranarayanan, Bradley Hayes

The workforce will need to continually upskill in order to meet the evolving demands of industry, especially working with robotic and autonomous systems. Current training methods are not scalable and do not adapt to the skills that learners already possess. In this work, we develop a system that automatically assesses learner skill in a quadrotor teleoperati

Jingnan Hu, Aiyun Luo, Zhijun Wang, Jingyu Zou

Topological superconductors have garnered significant attention due to their potential for realizing topological quantum computation. However, a universal computational tool based on first-principles calculations for predicting topological superconductivity has not yet been fully developed, posing substantial challenges in identifying topological superconduc

Peng Gao, Liangyi Zhao

We evaluate the twisted first moment of central values of the family of primitive quadratic Dirichlet $L$-functions using the method of double Dirichlet series together with a recursive argument. Our main result is an asymptotic formula with an error term of size that is the square root of that of the main term.