February 2024 arXiv papers — page 9

Nitin Jain, Hou-Man Chin, Adnan A. E. Hajomer, Dev Null

We demonstrate a proof-of-concept establishment of quantum-secure data transfer links in field trials at two locations in Denmark: on the campus of Technical University of Denmark in Lyngby and between power grid nodes owned and operated by Energinet in Odense. Several different links, implemented physically using optical ground wires, underground fibers as

Chi Zhang, Lei Zu, Hou-Zun Chen, Yue-Lin Sming Tsai

We investigate the elastic scattering cross section between dark matter and protons using the DES Year 3 weak lensing data. This scattering induces a dark acoustic oscillation structure in the matter power spectra. To address non-linear effects at low redshift, we utilize principal component analysis alongside a limited set of $N$-body simulations, improving

Jiaqi Cui, Yuanyuan Xu, Jianghong Xiao, Yuchen Fei

Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps. However, existing methods fail to derive the desirable radiotherapy parameters that can be directly input into the treatment planning system (TPS), impeding the full automation of radiotherapy. To enable more thorough automatic radiotherapy, in this pa

Melchi Chosenyah, Avulu Vinod Kumar, Rajadurai Chandrasekar

An unprecedented organic Fermat spiral optical waveguide (FSOW) self transducing green fluorescence is fabricated using a pseudo-plastic (E)-1-(((5-bromopyridin-2-yl)imino)methyl)naphthalene-2-ol crystal. A 1.618-millimeter-long crystal is initially bent into a hairpin-like bent waveguide. Later, a meticulous mechanophotonic strategy is employed to sculpt th

Yugo Murawaki

Bayesian approaches to reconstructing the evolutionary history of languages rely on the tree model, which assumes that these languages descended from a common ancestor and underwent modifications over time. However, this assumption can be violated to different extents due to contact and other factors. Understanding the degree to which this assumption is viol

M. Okuizumi, C. J. Auton, S. Endo, H. Fujioka

The enhancement of the parity-violating asymmetry in the vicinity of $p$-wave compound nuclear resonances was observed for a variety of medium-heavy nuclei. The enhanced parity-violating asymmetry can be understood using the $s$-$p$ mixing model. The $s$-$p$ mixing model predicts several neutron energy-dependent angular correlations between the neutron momen

Zhen Hao Wong, Hansi Yang, Xiaoyi Fu, Quanming Yao

Heterogeneous Graph Neural Networks (HGNNs) are a class of deep learning models designed specifically for heterogeneous graphs, which are graphs that contain different types of nodes and edges. This paper investigates the application of curriculum learning techniques to improve the performance and robustness of Heterogeneous Graph Neural Networks (GNNs). To

Akib Karim, Shaobo Zhang, Muhammad Usman

Virtual distillation (VD) using measurements of multiple copies of a quantum circuit have recently been proposed as a method of noise mitigation of expectation values. Circuit decompositions known as B gates were found only for single qubit expectation values however practical calculations require multi-qubit expectation values which cannot be corrected with

Fangwei Zhu, Peiyi Wang, Zhifang Sui

Entity abstract summarization aims to generate a coherent description of a given entity based on a set of relevant Internet documents. Pretrained language models (PLMs) have achieved significant success in this task, but they may suffer from hallucinations, i.e. generating non-factual information about the entity. To address this issue, we decompose the summ

Keita Owari

We consider a discrete-time robust utility maximisation with semistatic strategies, and the associated indifference prices of exotic options. For this purpose, we introduce a robust form of convex integral functionals on the space of bounded continuous functions on a Polish space, and establish some key regularity and representation results, in the spirit of

Ziyu Yue, Jiaxin Gao, Sihan Xie, Yang Liu

The visibility of real-world images is often limited by both low-light and low-resolution, however, these issues are only addressed in the literature through Low-Light Enhancement (LLE) and Super- Resolution (SR) methods. Admittedly, a simple cascade of these approaches cannot work harmoniously to cope well with the highly ill-posed problem for simultaneousl

Vinod Chandra, Santosh K. Das

This article elucidates the pivotal role of b-mesons and bottomonium states in exploring the existence and properties of hot QCD matter (commonly known as quark-gluon-plasma (QGP) produced within the crucible heavy-ion collision experiments). Owing to the complex and confounding nature of strong interaction force the direct detection of probing the hot QCD m

Keshav Goyal, Han Mao Kiah

We revisit the well-known Gilbert-Varshamov (GV) bound for constrained systems. In 1991, Kolesnik and Krachkovsky showed that GV bound can be determined via the solution of some optimization problem. Later, Marcus and Roth (1992) modified the optimization problem and improved the GV bound in many instances. In this work, we provide explicit numerical procedu

Xueying Mai, Liyun Zhang, Yao Lu

High-fidelity detection of quantum states is indispensable for implementing quantum error correction, a prerequisite for fault-tolerant quantum computation. For promising trapped ion qubits, however, the detection fidelity is inherently limited by state leakage. Here, we propose an efficient approach to enhance the fidelity of detecting $^{171} \mathrm{Yb}^+

Huisheng Wang, Zhanjiang Chen, H. Vicky Zhao

Understanding the impact of messages on agents' opinions over social networks is important. However, to our best knowledge, there has been limited quantitative investigation into this phenomenon in the prior works. To address this gap, this paper proposes the Message-Enhanced DeGroot model. The Bounded Brownian Message model provides a quantitative descripti

Hany Hamed, Subin Kim, Dongyeong Kim, Jaesik Yoon

Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strategy of dreaming itself. Therefore, it is a question whether and how an agent can "dream better" in a more structured and strategic way. In th

Weijieying Ren, Xinlong Li, Lei Wang, Tianxiang Zhao

Existing research has shown that large language models (LLMs) exhibit remarkable performance in language understanding and generation. However, when LLMs are continuously fine-tuned on complex and diverse domain-specific downstream tasks, the inference performance on historical tasks decreases dramatically, which is known as a catastrophic forgetting problem

M. Zeeshan Gul, M. Sharif

In this paper, we use the embedding class-I technique to examine the effect of charge on traversable wormhole geometry in the context of $f(\mathcal{G})$ theory, where $\mathcal{G}$ is the Gauss-Bonnet term. For this purpose, we consider static spherical spacetime with anisotropic matter configuration to investigate the wormhole geometry. The Karmarkar condi

Bardia Azizian, Ivan V. Bajic

Privacy is a crucial concern in collaborative machine vision where a part of a Deep Neural network (DNN) model runs on the edge, and the rest is executed on the cloud. In such applications, the machine vision model does not need the exact visual content to perform its task. Taking advantage of this potential, private information could be removed from the dat

Lachlan Simpson, Kyle Millar, Adriel Cheng, Cheng-Chew Lim

Explainability models are now prevalent within machine learning to address the black-box nature of neural networks. The question now is which explainability model is most effective. Probabilistic Lipschitzness has demonstrated that the smoothness of a neural network is fundamentally linked to the quality of post hoc explanations. In this work, we prove theor

Zhihao Duan, Ming Lu, Justin Yang, Jiangpeng He

This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g., adapting to new data or target bitrates) without breaking backward compatibility, the ability to decode bitstreams encoded by the original model. We refer to this problem as continual learning of image compression. Our initial findings show that bas

Arkava Ganguly, Souradeep Roychowdhury, Ankur Gupta

The mobility of externally-driven phoretic propulsion of particles is evaluated by simultaneously solving the solute conservation equation, interaction potential equation, and the modified Stokes equation. While accurate, this approach is cumbersome, especially when the interaction potential decays slowly compared to the particle size. In contrast to externa

Kenta Kobayashi, Takuya Tsuchiya

In an error estimation of finite element solutions to the Poisson equation, we usually impose the shape regularity assumption on the meshes to be used. In this paper, we show that even if the shape regularity condition is violated, the standard error estimation can be obtained if "bad" elements elements that violate the shape regularity or maximum angle cond

Xiaofan Cui, Muhammad Aadil Khan, Gabriele Pozzato, Surinder Singh

The reuse of retired electric vehicle batteries in grid energy storage offers environmental and economic benefits. This study concentrates on health monitoring algorithms for retired batteries deployed in grid storage. Over 15 months of testing, we collect, analyze, and publicize a dataset of second-life batteries, implementing a cycling protocol simulating

Shafiuddin Rehan Ahmed, Jon Z. Cai, Martha Palmer, James H. Martin

This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the e

Min Zhang, Runze Mao, Han Li, Zhenhua An

Within the scope of reacting flow simulations, the real-time direct integration (DI) of stiff ordinary differential equations (ODE) for the computation of chemical kinetics stands as the primary demand on computational resources. Meanwhile, as the number of transport equations that need to be solved increases, the computational cost grows more substantially,

Lena Ji, Fumiaki Suzuki

We study rationality questions for Fano schemes of linear spaces on smooth complete intersections of two quadrics, especially over non-closed fields. Our approach is to study hyperbolic reductions of the pencil of quadrics associated to $X$. We prove that the Fano schemes $F_r(X)$ of $r$-planes are birational to symmetric powers of hyperbolic reductions, gen

Lei Xie, Qingrun Zeng, Huajun Zhou, Guoqiang Xie

Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base leads to ambiguous spatial correspondence between diffusion directions and fiber geometry, and existing diffusion tractography methods of CNs identification are prone to producing er

Parth Kumar, Caleb M. Webb, Charles A. Stafford

A key question in the thermodynamics of open quantum systems is how to partition thermodynamic quantities such as entropy, work, and internal energy between the system and its environment. We show that the only partition under which entropy is nonsingular is based on a partition of Hilbert space, which assigns half the system-environment coupling to the syst

Archana Soam, Siddharth Maharana, B-G Andersson, A. N. Ramaprakash

We propose a spectropolarimeter a covering wavelength range of 3200--7000 {\AA} [3200{\AA} chosen as lower limit to go to the atmospheric cut-off. It's ``needed" for some Serkowski curves and would make the instrument even more unique] for a 2-4~m class telescope. In this article, we discuss the science cases which will be covered with this proposed instrume

Zhaorui Tan, Xi Yang, Kaizhu Huang

Multi-domain generalization (mDG) is universally aimed to minimize the discrepancy between training and testing distributions to enhance marginal-to-label distribution mapping. However, existing mDG literature lacks a general learning objective paradigm and often imposes constraints on static target marginal distributions. In this paper, we propose to levera

Yinuo Wang, Shan Wu, Cuicui Li, Zhenglu Duan

Vibrational resonance (VR) is a nonlinear phenomenon in which the system response to a weak signal can be resonantly enhanced by applying a high-frequency modulation signal with an appropriate amplitude. The majority of VR research has focused on amplifying the amplitude or intensity of the system response to a weak signal, whereas the study of the phase inf

Vrishabh Patil, Kara Hoppe, Yonatan Mintz

A key challenge in medical decision making is learning treatment policies for patients with limited observational data. This challenge is particularly evident in personalized healthcare decision-making, where models need to take into account the intricate relationships between patient characteristics, treatment options, and health outcomes. To address this,

Lei Chen, Yanpeng Zhu, Jiadong Zhu, Zhongyuan Ruan

This study introduces a comprehensive framework that situates information cascades within the domain of higher-order interactions, utilizing a double-threshold hypergraph model. We propose that individuals (nodes) gain awareness of information through each communication channel (hyperedge) once the number of information adopters surpasses a threshold $\phi_m

Mingyang Li, Maoqin Yuan, Luyao Li, Han Pengsihua

This study discusses a new method combining image steganography technology with Natural Language Processing (NLP) large models, aimed at improving the accuracy and robustness of extracting steganographic text. Traditional Least Significant Bit (LSB) steganography techniques face challenges in accuracy and robustness of information extraction when dealing wit

Hoon Kim, Minje Jang, Wonjun Yoon, Jisoo Lee

We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality li

Songjie Yang, Wanting Lyu, Boyu Ning, Zhongpei Zhang

This letter rethinks traditional precoding in multi-user wireless communications with movable antennas (MAs). Utilizing MAs for optimal antenna positioning, we introduce a sparse optimization (SO)-based approach focusing on regularized zero-forcing (RZF). This framework targets the optimization of antenna positions and the precoding matrix to minimize inter-

Ruijia Niu, Dongxia Wu, Kai Kim, Yi-An Ma

Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian processes can hardly scale to high-dimensional data. Deep learning approaches utilize neural network based encoders and decoders to improve scalability. These approaches share enc

Subash Chandra Behera, Shiv Parsad

Let $S_g$ be a closed orientable surface of genus $g \geq 2$, and let $\mathcal{T}_g$ be the Teichm\"uller space of $S_g$. Let $\mathcal{H}_g$ denotes the space of all hyperelliptic surfaces of genus $g$. For $g\geq 3$, we have proved that $\mathcal{T}_g$ can be parametrized by $6g-5$ angle parameters. We also prove that for $g\geq 2$, $\mathcal{H}_g$ can be

Yang Liu, Changzhen Qiu, Zhiyong Zhang

3D human pose estimation and mesh recovery have attracted widespread research interest in many areas, such as computer vision, autonomous driving, and robotics. Deep learning on 3D human pose estimation and mesh recovery has recently thrived, with numerous methods proposed to address different problems in this area. In this paper, to stimulate future researc

Ricardo Torres, Manuel Pinto

In this work, we give a variation of parameters formula for nonautonomous linear impulsive differential equations with piecewise constant arguments of generalized type. We cover several cases of differential equations with deviated arguments investigated before as particular cases. We also give some examples showing the applicability of our results.

Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani

Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet, the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this, we introduce ViewFusion, a novel, training-free algorithm that can be se

Ricardo Torres

We give some sufficient conditions that ensure oscillations and nonoscillations for nonautonomous impulsive differential equations with piecewise constant arguments of generalized type. We cover several cases of differential equations with deviated arguments investigated before as particular cases.

Muhammad Yarahmadi, Amin Salehi, Kazuharu Bamba, Hosein Farajollahi

We conduct a thorough examination of cosmological parameters within the context of $f(R)$ gravity coupled with neutrinos, leveraging a diverse array of observational datasets, including Cosmic Microwave Background (CMB), Cosmic Chronometers (CC), Baryon Acoustic Oscillations (BAO), and Pantheon supernova data. Our analysis unveils compelling constraints on p

Archana Soam, Chakali Eswaraiah, Amit Seta, Lokesh Dewangan

Molecular clouds are prime locations to study the process of star formation. These clouds contain filamentary structures and cores, which are crucial sites for the formation of young stars. The star-formation process has been investigated using various techniques, including polarimetry for tracing magnetic fields. In this small review-cum-short report, we pu

Noboru Isobe, Masanori Koyama, Jinzhe Zhang, Kohei Hayashi

The task of conditional generation is one of the most important applications of generative models, and numerous methods have been developed to date based on the celebrated flow-based models. However, many flow-based models in use today are not built to allow one to introduce an explicit inductive bias to how the conditional distribution to be generated chang

Xuanda Chen, Timothy O'Donnell, Siva Reddy

Language models (LMs) may appear insensitive to word order changes in natural language understanding (NLU) tasks. In this paper, we propose that linguistic redundancy can explain this phenomenon, whereby word order and other linguistic cues such as case markers provide overlapping and thus redundant information. Our hypothesis is that models exhibit insensit

Seong-Hoon Jang, Yukitoshi Motome

The Kitaev honeycomb model plays a pivotal role in the quest for quantum spin liquids, in which fractional quasiparticles would provide applications in decoherence-free topological quantum computing. The key ingredient is the bond-dependent Ising-type interactions, dubbed the Kitaev interactions, which require strong entanglement between spin and orbital deg

Jun-En Ding, Shihao Yang, Anna Zilverstand, Kaustubh R. Kulkarni

The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marijuana addiction, coupled with an analysis of localized brain network communities exhibiting abnormal activities

Erhan Can Ozcan, Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics mode

Sachita Nishal, Nicholas Diakopoulos

This article considers the increasing use of algorithmic decision-support systems and synthetic media in the newsroom, and explores how generative models can help reporters and editors across a range of tasks from the conception of a news story to its distribution. Specifically, we draw from a taxonomy of tasks associated with news production, and discuss wh

Yu Lu, Guo-Hui Hu

Thermophoresis is an effective method to drive the motion of nanoparticles in fluids. The transport of nanoparticles in polymer networks has significant fundamental and applied importance in biology and medicine, and can be described as Brownian particles crossing entropic barriers. This study proposes a novel extension of dissipative particle dynamics (DPD)

Zhenyu Sun, Zhaopeng Guo, Dayu Yan, Peng Cheng

Two-dimensional topological insulators (2DTIs) or quantum spin Hall insulators are attracting increasing attention due to their potential applications in next-generation spintronic devices. Despite their promising prospects, realizable 2DTIs are still limited. Recently, Ta2Pd3Te5, a semiconducting van der Waals material, has shown spectroscopic evidence of q

Xiwu Yang

Let $x_1,...,x_n$ be a list of real numbers, let $s :=\sum_{i=1}^{n}x_i$ and let $h:\mathbb{N} \rightarrow \mathbb{R}$ be a function. We gave a necessary and sufficient condition for $s>h(n)$ (respectively, $s<h(n)$). Let $G=(V,E)$ be a graph, let $\{H_1,...,H_n\}$ and $\{V_1,...,V_n\}$ be a decomposition and a partition of $G$, respectively. Let $H_{i,j}$ a

Gao-Chan Yong

Using the Li\`{e}ge intranuclear-cascade model together with the ablation model ABLA, an investigation is conducted into the effects of $\Lambda$ potential in $\Lambda$-nucleus and $\Lambda$-hypernucleus-nucleus collisions across various beam energies. The findings show that the angle and transverse-momentum distributions of scattered $\Lambda$ hyperon, the

Dongchen Huang, Junde Liu, Tian Qian, Hongming Weng

De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here

Ruyu Yang, Xiaoming Sun, Hongyi Zhou

Shadow estimation is a method for deducing numerous properties of an unknown quantum state through a limited set of measurements, which suffers from noises in quantum devices. In this paper, we introduce an error-mitigated shadow estimation approach based on virtual distillation, tailored for applications in near-term quantum devices. Our methodology leverag

Wei-Jie Huang, Yu-Biao Wu, Guang-Can Guo, Wu-Ming Liu

We investigate the quantum dynamics of the 1D spinless Fermi-Hubbard model with a linear-tilted potential. Surprisingly in a strong resonance regime, we show that the model can be described by the kinetically constrained effective Hamiltonian, and it can be spontaneously divided into two commuting parts dubbed Hamiltonian dimerization, which consist of a sum

Ruiyu Song, Kwan Chuen Chan, Haojie Xu, Weilun Zheng

We use a galaxy sample derived from the DECaLS DR9 to measure the Baryonic Acoustic Oscillations (BAO). The magnitude-limited sample consists of 10.6 million galaxies in an area of 4974 deg$^2$ over the redshift range of [0.6, 1]. A key novelty of this work is that the true redshift distribution of the photo-$z$ sample is derived from the self calibration me

Baihan Lin

What is the true heart of mental healthcare -- innovation or humanity? Can virtual therapy ever replicate the profound human bonds where healing arises? As artificial intelligence and immersive technologies promise expanded access, safeguards must ensure technologies remain supplementary tools guided by providers' wisdom. Implementation requires nuance balan

Zihan Wang, Peiyi Wang, Houfeng Wang

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure. Nearly all recent HTC works focus on how the labels are structured but ignore the sub-structure of ground-truth labels according to each input text which contains fruitful label co-occurrence information. In this work, we intr

Xi Wang, Laurence Aitchison

We propose a batch size invariant version of Adam, for use in large-scale, distributed settings, in which the mini-batch is divided into micro-batches which are distributed among worker nodes. For the v term, standard Adam first computes the average over micro-batch gradients, then squares, while in the batch size invariant Adam proposed here, we first squar

Corneel Casert, Stephen Whitelam

We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles. By encoding the protocol in the form of a neural network we use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little

Jung-Chao Ban, Wen-Guei Hu, Guan-Yu Lai, Lingmin Liao

We calculate the Minkowski and Hausdorff dimensions of affine multiplicative subshifts on $\mathbb{N}$.

Juexiao Feng, Yuhong Yang, Yanchun Xie, Yaqian Li

In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignoring a large number of potential objects whose categories are not defined in the training set. These objects are often identified as background or incorrectly classified as pre-defi

Kai Chen, Pavan Hosur

The standard paradigm of topological phases posits that two phases with identical symmetries are separated by a bulk phase transition, while symmetry breaking provides a path in parameter space that allows adiabatic connection between the phases. Typically, if symmetry is broken only at the boundary, topological surface states become gapped, and single-parti

Ziqian Lin, Kangwook Lee

In-context learning (ICL) exhibits dual operating modes: task learning, i.e., acquiring a new skill from in-context samples, and task retrieval, i.e., locating and activating a relevant pretrained skill. Recent theoretical work investigates various mathematical models to analyze ICL, but existing models explain only one operating mode at a time. We introduce

Hao Wang, Zeyu Gao, Chao Zhang, Mingyang Sun

Binary code similarity detection (BCSD) is a fundamental technique for various application. Many BCSD solutions have been proposed recently, which mostly are embedding-based, but have shown limited accuracy and efficiency especially when the volume of target binaries to search is large. To address this issue, we propose a cost-effective BCSD framework, CEBin

Binh M. Le, Simon S. Woo

Recent advancements in domain generalization (DG) for face anti-spoofing (FAS) have garnered considerable attention. Traditional methods have focused on designing learning objectives and additional modules to isolate domain-specific features while retaining domain-invariant characteristics in their representations. However, such approaches often lack guarant

Wenbo Sun, Adrian E. Rubio López, Zubin Jacob

Pure dephasing and spontaneous emission are two non-unitary processes of atoms or spins interacting with fluctuating electromagnetic (EM) modes. The dissipative collective emission processes (e.g., superradiance) originate from interactions with EM modes in resonance with atoms and have received considerable attention. Meanwhile, the analogous non-dissipativ

Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi

Large language models (LLMs) have demonstrated impressive capabilities across diverse languages. This study explores how LLMs handle multilingualism. Based on observed language ratio shifts among layers and the relationships between network structures and certain capabilities, we hypothesize the LLM's multilingual workflow ($\texttt{MWork}$): LLMs initially

Eduardo Brandani da Silva, Evandro Mazetto Brizola

In this work, we present new constructions for topological subsystem codes using semi-regular Euclidean and hyperbolic tessellations. They give us new families of codes, and we also provide a new family of codes obtained through an already existing construction, due to Sarvepalli and Brown. We also prove new results that allow us to obtain the parameters of

Ziqi Gao, Xiangguo Sun, Zijing Liu, Yu Li

Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the multimer structure prediction~(MSP) can be well handled in a step-wise assembly fashion using provided dimer structures and predicted protein-protein interactions~(PPIs). However, due

Xiao-Jun Wu, Ye-Fei Yuan, Yan Luo, Wenbin Lin

In the centers of galaxies, stars that orbit supermassive black hole binaries (SMBHBs) can undergo tidal disruptions due to the Lidov-Kozai mechanism. Nevertheless, most previous researches have predominantly focused on full tidal disruption events (FTDEs). In this study, we employ N-body simulations to investigate partial tidal disruption events (PTDEs) ind

Guojing Ge, Qi Song, Guibo Zhu, Yuting Zhang

Blind face restoration is a challenging task due to the unknown and complex degradation. Although face prior-based methods and reference-based methods have recently demonstrated high-quality results, the restored images tend to contain over-smoothed results and lose identity-preserved details when the degradation is severe. It is observed that this is attrib

Martin Larsson, Aaditya Ramdas, Johannes Ruf

We consider testing a composite null hypothesis $\mathcal{P}$ against a point alternative $\mathsf{Q}$ using e-variables, which are nonnegative random variables $X$ such that $\mathbb{E}_\mathsf{P}[X] \leq 1$ for every $\mathsf{P} \in \mathcal{P}$. This paper establishes a fundamental result: under no conditions whatsoever on $\mathcal{P}$ or $\mathsf{Q}$, t

Changhun Oh, Senrui Chen, Yat Wong, Sisi Zhou

We show that quantum entanglement can provide an exponential advantage in learning properties of a bosonic continuous-variable (CV) system. The task we consider is estimating a probabilistic mixture of displacement operators acting on $n$ bosonic modes, called a random displacement channel. We prove that if the $n$ modes are not entangled with an ancillary q

Masaru Kuwabara, Ryota Kanai

In individuals afflicted with conditions such as paralysis, the implementation of Brain-Computer-Interface (BCI) has begun to significantly impact their quality of life. Furthermore, even in healthy individuals, the anticipated advantages of brain-to-brain communication and brain-to-computer interaction hold considerable promise for the future. This is attri

Chenglei Shen, Guofu Xie, Xiao Zhang, Jun Xu

Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts. When interacting in real-world scenarios, the decision-making abilities of a role significantly shape its behavioral patterns. In this paper, we concentrate on evaluating the decision-

Ketevan Arabuli, Andria Rogava

Dynamics of Langmuir modes - Langmuir waves (LW) and shear Langmuir vortices (SLV) - in kinematically complex astrophysical plasma flows is studied. It is found that they exhibit a number of peculiar, velocity shear induced, asymptotically persistent phenomena: efficient energy exchange with the background flow, various kinds of instabilities, leading to the

Guillaume Braun, Masashi Sugiyama

Social networks are often associated with rich side information, such as texts and images. While numerous methods have been developed to identify communities from pairwise interactions, they usually ignore such side information. In this work, we study an extension of the Stochastic Block Model (SBM), a widely used statistical framework for community detectio

Allen H Boozer

Stellarator plasmas are externally controlled to a degree unparalleled by any other fusion concept, magnetic or inertial. This control is largely through the magnetic fields produced by external coils. The development of fusion energy could be expedited by carrying out remarkably straight-forward computations to define strategies for exploiting this external

Cyrus Cousins, I. Elizabeth Kumar, Suresh Venkatasubramanian

In fair machine learning, one source of performance disparities between groups is over-fitting to groups with relatively few training samples. We derive group-specific bounds on the generalization error of welfare-centric fair machine learning that benefit from the larger sample size of the majority group. We do this by considering group-specific Rademacher

Ming-Yuan Gao, Yin-Hai Li, Yan Li, Zhenghe Zhou

A high-quality narrowband polarization-entangled source in the telecom band is preferred to avoid frequency dispersion for long-distance transmission in optical fibers and to efficiently couple with telecom band quantum memories. Here, we report narrowband, telecom-band, polarization-entangled photon pair generation based on the superposition of single-longi

Dylan Wright, Zahra Ebrahim Nataj, Erick Guzman, Jake Polster

We report the results of the investigation of the acoustic and optical phonons in quasi-two-dimensional antiferromagnetic semiconductors of the transition metal phosphorus trisulfide family with Mn, Fe, Co, Ni, and Cd as metal atoms. The Brillouin-Mandelstam and Raman light scattering spectroscopies were conducted at room temperature to measure the acoustic

Qiao Han, Mingqian Li, Yao Yang, Yiteng Zhai

Block-wise missing data poses significant challenges in real-world data imputation tasks. Compared to scattered missing data, block-wise gaps exacerbate adverse effects on subsequent analytic and machine learning tasks, as the lack of local neighboring elements significantly reduces the interpolation capability and predictive power. However, this issue has n

Jingwen Chen, Pedro Gaspar

In this short note, we present new observations and examples concerning the existence and rigidity of solutions to the Allen-Cahn equation with degenerate minimal hypersurfaces as their limit interfaces.

Alfredo Guevara, Yangrui Hu, Sabrina Pasterski

We start by defining two-particle operators that appear in celestial CFT. We then show how to compute their OPE coefficients with the known single-particle operators at tree level from multiparticle factorization channels, focusing on the leading contribution involving the two-particle states. These factorization channels only give us single-particle exchang

Xukun Liu, Zhiyuan Peng, Xiaoyuan Yi, Xing Xie

While achieving remarkable progress in a broad range of tasks, large language models (LLMs) remain significantly limited in properly using massive external tools. Existing in-context learning approaches simply format tools into a list of plain text descriptions and input them to LLMs, from which, LLMs generate a sequence of tool calls to solve problems step

Timothy J. Healey, Roberto Paroni, Phoebus Rosakis

We derive sharp-interface models for one-dimensional brittle fracture via the inverse-deformation approach. Methods of Gamma-convergence are employed to obtain the singular limits of previously proposed models. The latter feature a local, non-convex stored energy of inverse strain, augmented by small interfacial energy, formulated in terms of the inverse-str

Guande Wu, Jing Qian, Sonia Castelo, Shaoyu Chen

Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models

Huaxiaoyue Wang, Kushal Kedia, Juntao Ren, Rahma Abdullah

We present MOSAIC, a modular architecture for coordinating multiple robots to (a) interact with users using natural language and (b) manipulate an open vocabulary of everyday objects. MOSAIC employs modularity at several levels: it leverages multiple large-scale pre-trained models for high-level tasks like language and image recognition, while using streamli

Wei Lv, Wei-Kun Chen, Yi-Long Chen, Yu-Hong Dai

In this paper, we investigate the probabilistic set covering problem (PSCP) in which the right-hand side is a binary random vector and the covering constraint is required to be satisfied with a prespecified probability. We consider the case with a finite discrete distribution of the random vector, which usually arises in the context of the sample average app

Abigail J. Lee, Andrew J. Monson, Wendy L. Freedman, Barry F. Madore

We present near-infrared JHK photometry for the resolved stellar populations in 13 nearby galaxies: NGC 6822, IC 1613, NGC 3109, Sextans B, Sextans A, NGC 300, NGC 55, NGC 7793, NGC 247, NGC 5253, Cen A, NGC 1313, and M83, acquired from the 6.5m Baade-Magellan telescope. We measure distances to each galaxy using the J-region asymptotic giant branch (JAGB) me

Lin Li, Yangyi Ye, Huiyuan Li

This paper presents an innovative approach, the Adaptive Orthogonal Basis Method, tailored for computing multiple solutions to differential equations characterized by polynomial nonlinearities. Departing from conventional practices of predefining candidate basis pools, our novel method adaptively computes bases, considering the equation's nature and structur

Fangyuan Zhang, Huichi Zhou, Shuangjiao Li, Hongtao Wang

Deep neural networks have been proven to be vulnerable to adversarial examples and various methods have been proposed to defend against adversarial attacks for natural language processing tasks. However, previous defense methods have limitations in maintaining effective defense while ensuring the performance of the original task. In this paper, we propose a

Krishnakanta Bhattacharya, Kazuharu Bamba, Douglas Singleton

Topological arguments are currently being used as a novel scheme to discern the properties of black holes while ignoring their detailed structure and specific field equations. Among various avenues of black hole physics, where this novel approach is being utilized, the phase transition in black hole thermodynamics lies at the forefront. There are several typ

Fernando Granha Jeronimo, Pei Wu

Quantum entanglement is a fundamental property of quantum mechanics and plays a crucial role in quantum computation and information. We study entanglement via the lens of computational complexity by considering quantum generalizations of the class NP with multiple unentangled quantum proofs, the so-called QMA(2) and its variants. The complexity of QMA(2) is

Gabriele Oliaro, Xupeng Miao, Xinhao Cheng, Vineeth Kada

Finetuning large language models (LLMs) is essential for task adaptation, yet today's serving stacks isolate inference and finetuning on separate GPU clusters -- wasting resources and under-utilizing hardware. We introduce FlexLLM, the first system to co-serve LLM inference and PEFT-based finetuning on shared GPUs by fusing computation at the token level. Fl

Ho Fai Po, Akke Mats Houben, Anna-Christina Haeb, David Rhys Jenkins

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a networ