March 2024 arXiv papers — page 167

Sadegh Sadeghi Tabas, Vidya Samadi

Changes in demand, various hydrological inputs, and environmental stressors are among the issues that water managers and policymakers face on a regular basis. These concerns have sparked interest in applying different techniques to determine reservoir operation policy decisions. As the resolution of the analysis increases, it becomes more difficult to effect

Tao Zhou, Wenhan Luo, Qi Ye, Zhiguo Shi

Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images. These promptable models exhibit denoising abilities for imprecise prompt inputs, such as imprecise bounding boxes. In this paper, we explore the potential of applying SAM to track and segment obj

Zhiyin Qiu, Ding Zhou, Yahui Zhai, Bo Liu

Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on system or equipment. This paper aims to develop an open-set intrusion detection model to classify known attacks as well as inferring unknown ones. To achieve this, we employ OpenMax and variational autoencoder to propose a dual detection model, VAEMax. Fir

Lujunyu Wang, Jiaojiao Zhu, Haiyun Chen, Hui Wang

It has been theoretically predicted that perturbation of the Berry curvature by electromagnetic fields gives rise to intrinsic nonlinear anomalous Hall effects that are independent of scattering. Two types of nonlinear anomalous Hall effects are expected. The electric nonlinear Hall effect has recently begun to receive attention, while very few studies are c

Ki-Young Choi, Sung Hyun Kim, Yeong Gyun Kim, Kang Young Lee

We study the expected sensitivity to the mixing between sterile and tau neutrinos directly from the tau neutrino disappearance in the high-energy fixed target experiment. Here, the beam energy is large enough to produce tau neutrinos at the target with large luminosity. During their propagation to the detector, tau neutrinos may oscillate into sterile neutri

Xu Guo, Yiqiang Chen

The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This

Febin Sunny, Ebadollah Taheri, Mahdi Nikdast, Sudeep Pasricha

Modern machine learning (ML) applications are becoming increasingly complex and monolithic (single chip) accelerator architectures cannot keep up with their energy efficiency and throughput demands. Even though modern digital electronic accelerators are gradually adopting 2.5D architectures with multiple smaller chiplets to improve scalability, they face fun

R. E. Brophy, M. Kateb, I. Ghitiu, N. Filipoiu

One of the main degradation mechanisms of methylammonium lead iodine (MAPI), which is an important material for perovskite based solar cells, is the migration of iodide ions. It is believed that this phenomenon is in fact dominated by the diffusion of iodide vacancies. In this paper, we suggest that the addition of a small amount of OH$^-$ ions can help supp

Pouria Mistani, Venkatesh Mysore

We present a multi-objective binder design paradigm based on instruction fine-tuning and direct preference optimization (DPO) of autoregressive protein language models (pLMs). Multiple design objectives are encoded in the language model through direct optimization on expert curated preference sequence datasets comprising preferred and dispreferred distributi

Hideki Murahara, Tatsushi Tanaka, Noriko Wakabayashi

This paper is devoted to proving an infinite sequence of relations for rooted tree maps. On the way, we also give a basis for the space of rooted tree maps.

Gokce Basar, Jay Bhambure, Rajeev Singh, Derek Teaney

We study an approach to simulating the stochastic relativistic advection-diffusion equation based on the Metropolis algorithm. We show that the dissipative dynamics of the boosted fluctuating fluid can be simulated by making random transfers of charge between fluid cells, interspersed with ideal hydrodynamic time steps. The random charge transfers are accept

Bangde Du, Ziyi Ye, Monika Jankowska, Zhijing Wu

This paper explores the impact of Opinion Polarization (OP) in the increasingly prevalent context of short video browsing, a dominant medium in the contemporary digital landscape with significant influence on public opinion and social dynamics. We investigate the effects of OP on user perceptions and behaviors in short video consumption, and find that tradit

Yunhao Du, Zhicheng Zhao, Fei Su

Visible-infrared person re-identification (VI-ReID) is challenging due to considerable cross-modality discrepancies. Existing works mainly focus on learning modality-invariant features while suppressing modality-specific ones. However, retrieving visible images only depends on infrared samples is an extreme problem because of the absence of color information

Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix Yu

Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. Typically, one obtains outputs from an LLM via autoregressive sampling from the model's output distribution. We show that this inference strategy can be sub-optimal for common regression and scoring evaluation metrics. As a remedy, we bu

Wei Kou, Xurong Chen

We propose that the signal of dark photons can be found in the decay channel of $\eta$, and that a certain number of events of dark photon leptonic decay can be observed in the double-photon collision process of heavy-ion ultra-peripheral collisions (UPC). We estimate the total cross-section for producing dark photons in ultra-peripheral $PbPb$ collisions at

Tianfeng Wang, Gaojie Cui

An efficient customer service management system hinges on precise forecasting of service volume. In this scenario, where data non-stationarity is pronounced, successful forecasting heavily relies on identifying and leveraging similar historical data rather than merely summarizing periodic patterns. Existing models based on RNN or Transformer architectures ma

Feri Sulianta, Laksana Eka Angga, Thee Houw Liong

There are large amounts of transactional data which showed consumer shopping cart at a store that sells more than 150 types of products. In this case, the company is utilizing these data in making business action. In previous studies, the data that has a lot of attributes and record data reduction algorithms handled by the FSA Red (Feature Selection for Asso

Sai Akarsh, Vamshi Raghusimha, Anindita Mondal, Anil Vuppala

The language diversity in India's education sector poses a significant challenge, hindering inclusivity. Despite the democratization of knowledge through online educational content, the dominance of English, as the internet's lingua franca, limits accessibility, emphasizing the crucial need for translation into Indian languages. Despite existing Speech-to-Sp

Kazushi Ueda

We show that the Satake-Baily-Borel compactification of the moduli space of lattice polarized K3 surfaces parametrizing K3 surfaces of degree 2 with four rational double points of type $D_4$ is the projective 3-space. We also show that the corresponding graded ring of automorphic forms is generated by four elements of weight 2 and one element of weight 11 wi

Subin Kim, Ezekiel Horsley, Christie Nelson, Jacob Ruff

We report X-ray diffraction and resonant elastic X-ray scattering (REXS) studies on two $\alpha$-RuCl$_{3}$ crystals with distinct magnetic transition temperatures: T$_{N}$=7.3K and 6.5K. We find that the sample with T$_{N}$=6.5K exhibits a high degree of structural twinning at low temperature, whereas the T$_{N}$=7.3K sample primarily comprises a single dom

Hao Peng, Casey Moore, Debabrata Saha, Steve Jiang

PULSAR (personalized, ultra-fractionated stereotactic adaptive radiotherapy) is the adaptation of stereotactic ablative radiotherapy towards personalized cancer management. For the first time, we applied a transformer-based attention mechanism to investigate the underlying interactions between combined PULSAR and PD-L1 blockade immunotherapy based on a murin

Nathan A. Harper, Emily Y. Hwang, Philip A. Kocheril, Tze King Lam

Thin-film lithium niobate is a promising photonic platform for on-chip optical sensing because both nonlinear and linear components can be fabricated within one integrated device. To date, waveguided sample interactions for thin-film lithium niobate are not well explored. Compared to other integrated platforms, lithium niobate's high refractive index, birefr

Takahiro Shindo, Kein Yamada, Taiju Watanabe, Hiroshi Watanabe

Image Coding for Machines (ICM) is an image compression technique for image recognition. This technique is essential due to the growing demand for image recognition AI. In this paper, we propose a method for ICM that focuses on encoding and decoding only the edge information of object parts in an image, which we call SA-ICM. This is an Learned Image Compress

Quan Chen, Tingyu Wang, Zihao Yang, Haoran Li

Cross-view geo-localization aims to match images of the same target from different platforms, e.g., drone and satellite. It is a challenging task due to the changing appearance of targets and environmental content from different views. Most methods focus on obtaining more comprehensive information through feature map segmentation, while inevitably destroying

Haesol Im

This paper examines the feasible region of a standard conic program represented as the intersection of a closed convex cone and a set of linear equalities. It is recently shown that when Slater constraint qualification (strict feasibility) fails for the classes of linear and semidefinite programs, two key properties emerge within the feasible region; (a) eve

Yi Xiao, Xiangxin Zhou, Qiang Liu, Liang Wang

Artificial intelligence has demonstrated immense potential in scientific research. Within molecular science, it is revolutionizing the traditional computer-aided paradigm, ushering in a new era of deep learning. With recent progress in multimodal learning and natural language processing, an emerging trend has targeted at building multimodal frameworks to joi

Qi Zhang, Biao Wu

We demonstrate the superior capabilities of the recently proposed Lorentz quantum computer (LQC) compared to conventional quantum computers. We introduce an associated computational complexity class termed bounded-error Lorentz quantum polynomial-time (BLQP), demonstrating its equivalence to the complexity class ${\text P}^{\sharp \text{P}}$. We present LQC

Victor Nikhil Antony, Mengchi Li, Shu-Han Lin, Junxin Li

Poor sleep health is an increasingly concerning public healthcare crisis, especially when coupled with a dwindling number of health professionals qualified to combat it. However, there is a growing body of scientific literature on the use of digital technologies in supporting and sustaining individuals' healthy sleep habits. Social robots are a relatively re

Priyangshu Sen, Sherif Badran, Vitaly Petrov, Arjun Singh

Terahertz-band (100 GHz-10 THz) communication is a promising radio technology envisioned to enable ultra-high data rate, reliable and low-latency wireless connectivity in next-generation wireless systems. However, the low transmission power of THz transmitters, the need for high gain directional antennas, and the complex interaction of THz radiation with com

Purnaprajna Bangere, Francisco Javier Gallego, Jayan Mukherjee

In this article, we introduce a new approach to show the existence and smoothing of simple normal crossing varieties in a given projective space. Our approach relates the above to the existence of nowhere reduced schemes called ribbons and their smoothings via deformation theory of morphisms. As a consequence, we prove results on the existence and smoothing

Takamasa Hirai, Toshiaki Morita, Subrata Biswas, Jun Uzuhashi

Thermal conductivity, a fundamental parameter characterizing thermal transport in solids, is typically determined by electron and phonon transport. Although other transport properties including electrical conductivity and thermoelectric conversion coefficients have material-specific values, it is known that thermal conductivity can be modulated artificially

Fengchen Gong, Divya Raghunathan, Aarti Gupta, Maria Apostolaki

Fine-grained monitoring is crucial for multiple data-driven tasks such as debugging, provisioning, and securing networks. Yet, practical constraints in collecting, extracting, and storing data often force operators to use coarse-grained sampled monitoring, degrading the performance of the various tasks. In this work, we explore the feasibility of leveraging

Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao

Recently, 3D generative models have shown promising performances in structure-based drug design by learning to generate ligands given target binding sites. However, only modeling the target-ligand distribution can hardly fulfill one of the main goals in drug discovery -- designing novel ligands with desired properties, e.g., high binding affinity, easily syn

Jinfeng Wang, Sifan Song, Xinkun Wang, Yiyi Wang

With the proposal of the Segment Anything Model (SAM), fine-tuning SAM for medical image segmentation (MIS) has become popular. However, due to the large size of the SAM model and the significant domain gap between natural and medical images, fine-tuning-based strategies are costly with potential risk of instability, feature damage and catastrophic forgettin

Esther Xiaozhen Fu, Daniel Gottesman

We ask what is the general framework for a quantum error correcting code that is defined by a sequence of measurements. Recently, there has been much interest in Floquet codes and space-time codes. In this work, we define and study the distance of a dynamical code. This is a subtle concept and difficult to determine: At any given time, the system will be in

Ding Chen, Peixi Peng, Tiejun Huang, Yonghong Tian

As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies. Spiking neural networks (SNNs), due to their binary firing mechanism, have strong robustness to noise, making it difficult to realize efficient exploration with local disturbances. To solve this exploration problem, we propos

Yameng Peng, Andy Song, Haytham M. Fayek, Vic Ciesielski

Training-free metrics (a.k.a. zero-cost proxies) are widely used to avoid resource-intensive neural network training, especially in Neural Architecture Search (NAS). Recent studies show that existing training-free metrics have several limitations, such as limited correlation and poor generalisation across different search spaces and tasks. Hence, we propose

SeongKu Kang, Shivam Agarwal, Bowen Jin, Dongha Lee

Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific applications for specialized areas or industries, due to unique terminologies, incomplete contexts of user queries, and specialized search intents. To capture the theme-specific info

Zhihui Li, Xin Liao, Dingding Yu

Each $x\in (0,1]$ can be uniquely expanded as a power-2-decaying Gauss-like expansion, in the form of \begin{equation*} x=\sum_{i=1}^{\infty}2^{-(d_1(x)+d_2(x)+\cdots+d_i(x))},\qquad d_i(x)\in \mathbb{N}. \end{equation*} Let $\phi:\mathbb{N}\to \mathbb{R}^{+}$ be an arbitrary positive function. We are interested in the size of the set $$F(\phi)=\{x\in (0,1]:

Ling Ge, Chunming Hu, Guanghui Ma, Jihong Liu

Multi-Source cross-lingual transfer learning deals with the transfer of task knowledge from multiple labelled source languages to an unlabeled target language under the language shift. Existing methods typically focus on weighting the predictions produced by language-specific classifiers of different sources that follow a shared encoder. However, all source

Melissa Lee, Kamilla Rekvényi

The intersection graph $\Delta_G$ of a finite group $G$ is a simple graph with vertices the non-trivial proper subgroups of $G$, and an edge between two vertices if their corresponding subgroups intersect non-trivially. These graphs were introduced by Cs\'ak\'any and Poll\'ak in 1969. In this paper we answer two long-standing open questions posed by Cs\'ak\'

Toshiki Sato, Kai Matsunaga, Ryo Sawada, Koh Takahashi

Simulations of pre-supernova evolution suggest that some intense shell burning can be so active that, in extreme cases, it can merge with the outer shell, changing the initial conditions for the supernova explosion. However, such violent activity in the interior of stars has been difficult to confirm from observations of stars. Here we propose that the eleme

Xinliang Huang, Yuhang Xiao, Rui Song, Ning Hao

In two-dimensional system, Rashba spin-orbit coupling can lift spin degeneracy and gives the opposite spin chirality of two split Fermi circles from two Rashba bands. Here, we propose a generic model which can produce unconventional Rashba bands. In such a case, the two Fermi circles from two bands have the same spin chirality. When various interactions are

Xiangxin Zhou, Liang Wang, Yichi Zhou

Considering generating samples with high rewards, we focus on optimizing deep neural networks parameterized stochastic differential equations (SDEs), the advanced generative models with high expressiveness, with policy gradient, the leading algorithm in reinforcement learning. Nevertheless, when applying policy gradients to SDEs, since the policy gradient is

Victor Nikhil Antony, Chien-Ming Huang

We present and evaluate a prototype social robot to encourage daily exercise among older adults in a home setting. Our prototype system, designed to lead users through exercise sessions with motivational feedback, was assessed through a case study with a 78-year-old participant for one week. Our case study highlighted preferences for greater user control ove

Vladimir Shemyakov

In analysis, it's often useful to know the value of a function at infinity, this operation possesses pleasant properties. However, even when the limit does not exist, some intuitive considerations may suggest that the function still assumes a specific value at infinity in a certain sense. In Nevanlinna theory, all objects are studied on average, i.e., their

Yuhu Bai, Jiangning Zhang, Zhaofeng Chen, Yuhang Dong

Few-shot anomaly detection (FSAD) plays a crucial role in industrial manufacturing. However, existing FSAD methods encounter difficulties leveraging a limited number of normal samples, frequently failing to detect and locate inconspicuous anomalies in the spatial domain. We have further discovered that these subtle anomalies would be more noticeable in the f

Yinchen Wang, Yu Duan, Yu-Qi Ye, Ren Wang

The simultaneous localization and recognition of subwavelength non-cooperative entities within complex multi-scattering environments using a simplified system continues to pose a substantial challenge. This letter addresses this challenge by synergistically integrating time reversal time-frequency phase prints (TRTFPPs) and neural networks. Initially, a time

Boyang Peng, Sanqing Qu, Yong Wu, Tianpei Zou

Deep learning has achieved remarkable progress in various applications, heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails not only authorizing usage but also ensuring the deployment of models in authorized data domains, i.e., making models exclusive to certain target domains. Previous methods necessi

Marcus Birch, Sabino Piazzolla, Preston Hooser, Francis Bennet

We report the first measurement of the atmospheric optical turbulence profile using the transmitted beam from a satellite laser communication terminal. A Ring Image Next Generation Scintillation Sensor (RINGSS) instrument for turbulence profiling, as described in Tokovinin (MNRAS, 502.1, 2021, 747-808), was deployed at the NASA/Jet Propulsion Laboratory's Ta

Zhiyu Jiang, Ryo Okamoto, Hideaki Obuse

Non-Hermitian systems exhibit richer topological properties compared to their Hermitian counterparts. It is well known that non-Hermitian systems have been classified based on either the symmetry relations for non-Hermitian Hamiltonians or the symmetry relations for non-unitary time-evolution operators in the context of Floquet topological phases. In this wo

Hong Lin, Lidan Shou, Ke Chen, Gang Chen

Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal federation where clients share homogeneous data distribution and learning behavior. However, FL may fail to function appropriately when the federation is not ideal, amid an unhealthy st

Leilei Jin, Jiajie Xu, Wenjie Fu, Hao Yan

With shrinking interconnect spacing in advanced technology nodes, existing timing predictions become less precise due to the challenging quantification of crosstalk-induced delay. During the routing, the crosstalk effect is typically modeled by predicting coupling capacitance with congestion information. However, the timing estimation tends to be overly pess

Md Sirajul Islam, Simin Javaherian, Fei Xu, Xu Yuan

Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in con

Lei Zheng, Rui Yang, Zengqi Peng, Wei Yan

Abrupt maneuvers by surrounding vehicles (SVs) can typically lead to safety concerns and affect the task efficiency of the ego vehicle (EV), especially with model uncertainties stemming from environmental disturbances. This paper presents a real-time fail-operational controller that ensures the asymptotic convergence of an uncertain EV to a safe state, while

Jingdong Zhang, Bo Zhang, Shuangjing Xu, Niu Liu

The Gaia celestial reference frame (Gaia-CRF) will benefit from a close assessment with independent methods, such as Very Long Baseline Interferometry (VLBI) measurements of radio stars at bright magnitudes. However, obtaining full astrometric parameters for each radio star through VLBI measurements demands a significant amount of observation time. This stud

Biqing Qi, Junqi Gao, Xingquan Chen, Dong Li

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escal

Jiuqiang Liu, Guihai Yu, Lihua Feng, Yongjiang Wu

The classical Erd\H{o}s-Ko-Rado theorem on the size of an intersecting family of $k$-subsets of the set $[n] = \{1, 2, \dots, n\}$ is one of the fundamental intersection theorems for set systems. After the establishment of the EKR theorem, many intersection theorems on set systems have appeared in the literature, such as the well-known Frankl-Wilson theorem,

Long-Fei Chen, Donghui Quan, Jiao He, Yao Wang

Abbreviation. Thermal diffusion is one of the basic processes for the mobility and formation of species on cosmic dust grains. Recent laboratory measurements have found that the diffusion pre-exponential factor can differ from that for desorption by several orders of magnitude. We aim to evaluate the effect of the newly experimentally measured diffusion pre-

Pavlos Motakis, Daniele Puglisi

For a Banach space $X$, let $\mathcal{L}(X)$ denote the algebra of all bounded linear operators on $X$ and let $\mathcal{K}(X)$ denote the compact operator ideal in $\mathcal{L}(X)$. The quotient algebra $\mathcal{L}(X)/\mathcal{K}(X)$ is called the Calkin algebra of $X$, and it is denoted $\mathcal{C}al(X)$. We prove that the unitization of $\mathcal{K}(c_0

Yunhuan Xiao, Jingda Wu, Jerry I Dadap, Kashif Masud Awan

Atomically thin cuprates exhibiting a superconducting phase transition temperature similar to bulk have recently been realized, although the device fabrication remains a challenge and limits the potential for many novel studies and applications. Here we use an optical pump-probe approach to noninvasively study the unconventional superconductivity in atomical

Yui Uehara

This paper presents a method of unsupervised learning of harmonic analysis based on a hidden semi-Markov model (HSMM). We introduce the chord quality templates, which specify the probability of pitch class emissions given a root note and a chord quality. Other probability distributions that comprise the HSMM are automatically learned via unsupervised learnin

Ethan Kroll Gordon, Rajat Kumar Jenamani, Amal Nanavati, Ziang Liu

We demonstrate a robot-assisted feeding system that enables people with mobility impairments to feed themselves. Our system design embodies Safety, Portability, and User Control, with comprehensive full-stack safety checks, the ability to be mounted on and powered by any powered wheelchair, and a custom web-app allowing care-recipients to leverage their own

Napat Karnchanachari, Dimitris Geromichalos, Kok Seang Tan, Nanxiang Li

Machine Learning (ML) has replaced traditional handcrafted methods for perception and prediction in autonomous vehicles. Yet for the equally important planning task, the adoption of ML-based techniques is slow. We present nuPlan, the world's first real-world autonomous driving dataset, and benchmark. The benchmark is designed to test the ability of ML-based

Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos

Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwise comparison approach and leverages inpu

Jiawei Fu

Understanding causal mechanisms is crucial for explaining and generalizing empirical phenomena. Causal mediation analysis offers statistical techniques to quantify the mediation effects. Although numerous methods have been developed for causal inference more broadly, the methodological toolkit for causal mediation analysis remains limited. Current methods of

Al Amin, Kamrul Hasan, Saleh Zein-Sabatto, Deo Chimba

The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems. With the increasing complexity of Artificial Intelligence (AI) models, the need for Explainable Artificial Intelligence (XAI) techniques become paramount, particularly

Margaret Bayer, Amanda Burcroff, Tyrrell B. McAllister, Leilani Pai

A magic labeling of a graph is a labeling of the edges by nonnegative integers such that the label sum over the edges incident to every vertex is the same. This common label sum is known as the index. We count magic labelings by maximum edge label, rather than index, using an Ehrhart-theoretic approach. In contrast to Stanley's 1973 work showing that the fun

Zezheng Feng, Yifan Jiang, Hongjun Wang, Zipei Fan

Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows. Such predictions are beneficial for understanding the situation and making decisions in traffic control. However, most state-of-the-art DL models are considered "black boxes" with little to no transparency for end users with respect to the underlying mechanisms.

Ryoichi Tanaka, Yuta Mizuno, Takuro Tsutsumi, Mikito Toda

Transition state theory is a standard framework for predicting the rate of a chemical reaction. Although the transition state theory has been successfully applied to numerous chemical reaction analyses, many experimental and theoretical studies have reported chemical reactions with a reactivity which cannot be explained by the transition state theory due to

Kazuya Yonekura

We discuss some topological constraints on Type IIB string theory that cannot be described by elementary characteristic classes. Anomaly cancellation of the worldvolume theory of a D3-brane requires a shifted Dirac quantization condition of the Ramond-Ramond 5-form flux. However, the required shift is sometimes inconsistent depending on the topology of space

Samuel J. Elman, Jason Gavriel, Ryan L. Mann

We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graphs. We define the spatial cost of a measurement schedule based on the number of simultaneously active qubits and prove that an optimal measurement schedule corresponds to a path dec

Devanshu Brahmbhatt, Yilun Xu, Neel Vora, Larry Chen

Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource

Evelyn J. Mannix, Liam Hodgkinson, Howard Bondell

Interpretable computer vision models explain their classifications through comparing the distances between the local embeddings of an image and a set of prototypes that represent the training data. However, these approaches introduce additional hyper-parameters that need to be tuned to apply to new datasets, scale poorly, and are more computationally intensi

Tiejin Chen, Longchao Da, Huixue Zhou, Pingzhi Li

The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT. Differential Privacy (DP) techniques are explored in existing work to mitigate their privacy risks at the cost of generalization degradation. Our paper reveals that the flatness of DP-trained models' loss landscape pl

Devjeet Roy, Xuchao Zhang, Rashi Bhave, Chetan Bansal

The growing complexity of cloud based software systems has resulted in incident management becoming an integral part of the software development lifecycle. Root cause analysis (RCA), a critical part of the incident management process, is a demanding task for on-call engineers, requiring deep domain knowledge and extensive experience with a team's specific se

Patrick G. Staudt, James S. Bullock, Michael Boylan-Kolchin, David Kirkby

We use FIRE-2 zoom simulations of Milky Way size disk galaxies to derive easy-to-use relationships between the observed circular speed of the Galaxy at the Solar location, $v_\mathrm{c}$, and dark matter properties of relevance for direct detection experiments: the dark matter density, the dark matter velocity dispersion, and the speed distribution of dark m

Subbarao Kambhampati

While humans sometimes do show the capability of correcting their own erroneous guesses with self-critiquing, there seems to be no basis for that assumption in the case of LLMs.

Athanasios Angelakis, Andrey Rass

Data augmentation (DA) enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly. Our study extends this inquiry, examining DA's class-specific bias across various datasets, including those distinct from ImageNet, through random cropping. We evaluated this phenomenon with ResNet50, EfficientNetV2S, and SWIN

Takeo Okazaki

Let (pi,V) be a generic irreducible representation of a general linear group over a p-adic field. Jacquet, Piatetski-Shapiro, and Shalika gave an open compact subgroup K, so that the subspace V^K consisting of v in V fixed by K is one-dimensional. If pi has a Shalika model Lambda, then we call vectors in Lambda(V) the Shalika forms of pi, and those in Lambda

Amin Abyaneh, Mariana Sosa Guzmán, Hsiu-Chin Lin

Imitation learning presents an effective approach to alleviate the resource-intensive and time-consuming nature of policy learning from scratch in the solution space. Even though the resulting policy can mimic expert demonstrations reliably, it often lacks predictability in unexplored regions of the state-space, giving rise to significant safety concerns in

Yuanhao Cai, Yixun Liang, Jiahao Wang, Angtian Wang

X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on NeRF suffer from long training time and slow inference speed. In this paper, we propose a 3D Gaussian splatting-based framework, namely X-Gaussian, for X-ray novel view synthesis. F

Ge Yan, Yueh-Hua Wu, Xiaolong Wang

This paper presents DNAct, a language-conditioned multi-task policy framework that integrates neural rendering pre-training and diffusion training to enforce multi-modality learning in action sequence spaces. To learn a generalizable multi-task policy with few demonstrations, the pre-training phase of DNAct leverages neural rendering to distill 2D semantic f

Nikhil Mishra, Maximilian Sieb, Pieter Abbeel, Xi Chen

Deep learning methods for perception are the cornerstone of many robotic systems. Despite their potential for impressive performance, obtaining real-world training data is expensive, and can be impractically difficult for some tasks. Sim-to-real transfer with domain randomization offers a potential workaround, but often requires extensive manual tuning and r

Gobinda Chakraborty, Subhadeep Chakraborty, Tanmoy Basu, Manas Mukherjee

We investigate the local thermal transport in a quantum trimer of harmonic oscillators connected to two thermal baths. The coupling between them are augmented by complex phases which leads to the quantum control of the local atypical heat current between two oscillators connected to the same heat bath. Our study reveals that this atypical heat current is a c

Mojtaba Mazoochi, Leila Rabiei, Farzaneh Rahmani, Zeinab Rajabi

Introduction: Microblogging websites have massed rich data sources for sentiment analysis and opinion mining. In this regard, sentiment classification has frequently proven inefficient because microblog posts typically lack syntactically consistent terms and representatives since users on these social networks do not like to write lengthy statements. Also, t

Aly S. Abdalla, Joshua Moore, Nisha Adhikari, Vuk Marojevic

The open radio access network (O-RAN) offers new degrees of freedom for building and operating advanced cellular networks. Emphasizing on RAN disaggregation, open interfaces, multi-vendor support, and RAN intelligent controllers (RICs), O-RAN facilitates adaptation to new applications and technology trends. Yet, this architecture introduces new security chal

Riccardo Pieroni, Simone Specchia, Matteo Corno, Sergio Matteo Savaresi

This paper presents a novel multi-modal Multi-Object Tracking (MOT) algorithm for self-driving cars that combines camera and LiDAR data. Camera frames are processed with a state-of-the-art 3D object detector, whereas classical clustering techniques are used to process LiDAR observations. The proposed MOT algorithm comprises a three-step association process,

Yejin Jeon, Gary Geunbae Lee

This paper explores the task of language-agnostic speaker replication, a novel endeavor that seeks to replicate a speaker's voice irrespective of the language they are speaking. Towards this end, we introduce a multi-level attention aggregation approach that systematically probes and amplifies various speaker-specific attributes in a hierarchical manner. Thr

George Brooks, Fadekemi Osaye, Anna Schenfisch, Zhiyu Wang

In this paper, we show that all simple outerplanar graphs $G$ with minimum degree at least $2$ and positive Lin-Lu-Yau Ricci curvature on every edge have maximum degree at most $9$. Furthermore, if $G$ is maximally outerplanar, then $G$ has at most $10$ vertices. Both upper bounds are sharp.

Nathaniel Dennler, Stefanos Nikolaidis, Maja Matarić

Adaptive training programs are crucial for recovery post stroke. However, developing programs that automatically adapt depends on quantifying how difficult a task is for a specific individual at a particular stage of their recovery. In this work, we propose a method that automatically generates regions of different task difficulty levels based on an individu

Rupert Li, Elchanan Mossel, Benjamin Weiss

We consider non-homogeneous random walks on the two-dimensional positive quadrant $\mathbb{N}^2$ and the one-dimensional slab $\{0,1,\dots,k\}\times\mathbb{N}$. In the 1960's the following question was asked for $\mathbb{N}^2$: is it true if such a random walk $X$ is recurrent and $Y$ is another random walk that at every point is more likely to go down and m

Ende Pan, Ce Xu

Recently, Mneimneh proved the remarkable identity \begin{align*} \sum_{k=0}^n H_k\binom{n}{k} p^k(1-p)^{n-k}=\sum_{i=1}^n \frac{1-(1-p)^i}{i}\quad (p\in [0,1]) \end{align*} as the main result of a 2023 \emph{Discrete Mathematics} paper, where $H_k:=\sum\nolimits_{i=1}^k 1/i$ is the classical $k$-th harmonic number. Thereafter, Campbell provided several other

Elsa Lawrence, Adham El-Shazly, Srijit Seal, Chaitanya K Joshi

Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation

Lekang Jiang, Stephan Goetz

Patents, which encapsulate crucial technical and legal information in text form and referenced drawings, present a rich domain for natural language processing (NLP) applications. As NLP technologies evolve, large language models (LLMs) have demonstrated outstanding capabilities in general text processing and generation tasks. However, the application of LLMs

Bo Li

Empirical business cycle studies using cross-country data usually cannot achieve causal relationships while within-country studies mostly focus on the bust period. We provide the first causal investigation into the boom period of the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research design, we show that credit expansion in private-labe

Vincenzo Calabrese, Amy Q. Shen, Simon J. Haward

Measurements of the capillary-driven thinning and breakup of fluid filaments are widely used to extract extensional rheological properties of complex materials. For viscoelastic (e.g., polymeric) fluids, the determination of the longest relaxation time depends on several assumptions concerning the polymeric response to the flow that are derived from constitu

Shin-ichi Ohta, Kohei Suzuki

We prove the integral Varadhan short-time formula for non-linear heat flow on measured Finsler manifolds. To the best of the authors' knowledge, this is the first result establishing a Varadhan-type formula for non-linear semigroups. We do not assume the reversibility of the metric, and the distance function can be asymmetric. In this generality, we reveal t

Julius Ross, Kuang-Yu Wu

To any Schur polynomial $s_{\lambda}$ one can associated its derived polynomials $s_{\lambda}{(i)}$ $i=0,\ldots,|\lambda|$ by the rule $$s_{\lambda}(x_1+t,\ldots,x_n+t) = \sum_i s_{\lambda}^{(i)}(x_1,\ldots,x_n) t^i.$$ We conjecture that $$(s_{\lambda}^{(i)})^2 - s_{\lambda}^{(i-1)} s_{\lambda}^{(i+1)}$$ is always Schur positive and prove this when $i=1$ for

Tuyen Pham, Hubert Wagner

With the growing availability of efficient tools, persistent homology is becoming a useful methodology in a variety of applications. Significant work has been devoted to implementing tools for persistent homology diagrams; however, computing representative cycles corresponding to each point in the diagram can still be inefficient. To circumvent this problem,