March 2024 arXiv papers — page 38

Ashwin Aravind, Mohammad Taha Toghani, César A. Uribe

We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems. We propose a Moreau Envelope-based surrogate LQR cost, built from a finite set of realizations of the uncertain system, to define a meta-policy efficiently adjustable to new realizations. Moreover, we design a

Nima Ebadi, Kellen Morgan, Adrian Tan, Billy Linares

Automatic Speech Recognition (ASR) technology is fundamental in transcribing spoken language into text, with considerable applications in the clinical realm, including streamlining medical transcription and integrating with Electronic Health Record (EHR) systems. Nevertheless, challenges persist, especially when transcriptions contain noise, leading to signi

Samuel J van Bohemen, Jeffrey M Rogers, Aleksandra Alavanja, Andrew Evans

This study assessed the safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients. The device captured electroencephalography (EEG) and electrocardiography (ECG) data to compute an ECG-based metric termed the Electrocardiography Brain Perfusion index (EBPi), which may function as a proxy for cerebral blood flow (CBF). Seve

Shirin Dabbaghi Varnosfaderani, Canasai Kruengkrai, Ramin Yahyapour, Junichi Yamagishi

FEVEROUS is a benchmark and research initiative focused on fact extraction and verification tasks involving unstructured text and structured tabular data. In FEVEROUS, existing works often rely on extensive preprocessing and utilize rule-based transformations of data, leading to potential context loss or misleading encodings. This paper introduces a simple y

Shehreen Azad, Yogesh Singh Rawat

In this work, we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of appearance biases such as clothing color and background. We propose ABNet, a novel framework which leverages disentanglement of biometric

Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu

We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of current QA applications: (i) unfaithful hallucination that is inconsistent with real-time or domain facts and (ii) weak reasoning performance over compositional information. Our key con

Paula Stocco, Suhas Chundi, Arec Jamgochian, Mykel J. Kochenderfer

Lagrangian-guided Monte Carlo tree search with global dual ascent has been applied to solve large constrained partially observable Markov decision processes (CPOMDPs) online. In this work, we demonstrate that these global dual parameters can lead to myopic action selection during exploration, ultimately leading to suboptimal decision making. To address this,

Xinglu Pan, Chenxiao Liu, Yanzhen Zou, Tao Xie

Code comments are important for developers in program comprehension. In scenarios of comprehending and reusing a method, developers expect code comments to provide supplementary information beyond the method signature. However, the extent of such supplementary information varies a lot in different code comments. In this paper, we raise the awareness of the s

Tsukasa Isoshima

We show that certain two kinds of trisection diagrams of the doubles of the Mazur type 4-manifolds introduced by Akbulut and Kirby are standard. One is constructed by doubling a certain relative trisection diagram of the Mazur type. The other is constructed by using an algorithm taking Kirby diagrams to trisection diagrams.

Andrey Zaikin, Artem Zaikin

This paper presents the experience of developing and utilizing computer simulations for student laboratory assignments in atomic physics, specifically focusing on emission atomic spectra. The fundamental component of each laboratory assignment is a computer simulator of a spectrograph, which displays atomic line spectra on the monitor screen. The simulator o

Zixuan Zhang, Mingyue Zhao, Li Ma, Guoke Li

Materials systems with spin chirality can provide ultra-high-density, ultra-fast, and ultralow-power information carriers for digital transformation. These material systems include magnetic skyrmions, chiral domain walls, spin reorientation,and so on. The topological Hall effect (THE) has been identified as the most convenient and effective tool for detectin

Beibei Zhang, Tian Xiang, Chentao Mao, Yuhua Zheng

Time-jerk optimal trajectory planning is crucial in advancing robotic arms' performance in dynamic tasks. Traditional methods rely on solving complex nonlinear programming problems, bringing significant delays in generating optimized trajectories. In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning. Firstly, we

Sofiane Bourouaine, Jean C. Perez, Benjamin D. G. Chandran, Vamsee K. Jagarlamudi

In this work we analyze plasma and magnetic field data provided by the Parker Solar Probe (\emph{PSP}) and Solar Orbiter (\emph{SO}) missions to investigate the radial evolution of the heating of Alfv\'enic slow wind (ASW) by imbalanced Alfv\'en-Wave (AW) turbulent fluctuations from 0.06 au to 1 au. in our analysis we focus on slow solar-wind intervals with

Yilun Zheng, Jiahao Xu, Lihui Chen

Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance. Current studies on graph heterophily mainly focus on aggregation calibration or neighbor extension and address the heterophily issue by utilizing node features or structural

David Oranchak, Sam Blake, Jarl Van Eycke

The case of the Zodiac Killer is one of the most widely known unsolved serial killer cases in history. The unidentified killer murdered five known victims and terrorized the state of California. He also communicated extensively with the press and law enforcement. Besides his murders, Zodiac was known for his use of ciphers. The first Zodiac cipher was solved

Axel Kodat, Michael Shub

We show that for any sufficiently rich compact family $\mathcal{H}$ of $C^1$ diffeomorphisms of a closed Riemannanian manifold $M$, the average geometric intersection number over $h \in \mathcal{H}$ between $h(V)$ and $W$, for $V, W$ any complementary dimensional submanifolds of $M$, is approximately (i.e. up to a uniform multiplicative error depending only

Miroslav Bulíček, Tomáš Los, Josef Málek

Viscoelastic rate-type fluids are popular models of choice in many applications involving flows of fluid-like materials with complex micro-structure. A well-developed mathematical theory for the most of these classical fluid models is however missing. The main purpose of this study is to provide a complete proof of long-time and large-data existence of weak

Zezhong Sun, Shi Yan, Ning Jiang, Jiaen Zhou

Communication-sensing integration represents an up-and-coming area of research, enabling wireless networks to simultaneously perform communication and sensing tasks. However, in urban cellular networks, the blockage of buildings results in a complex signal propagation environment, affecting the performance analysis of integrated sensing and communication (IS

Chengyang Peng, Victor Paredes, Ayonga Hereid

Safe path and gait planning are essential for bipedal robots to navigate complex real-world environments. The prevailing approaches often plan the path and gait separately in a hierarchical fashion, potentially resulting in unsafe movements due to neglecting the physical constraints of walking robots. A safety-critical path must not only avoid obstacles but

Yufu Wang, Ziyun Wang, Lingjie Liu, Kostas Daniilidis

We propose TRAM, a two-stage method to reconstruct a human's global trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the presence of dynamic humans and uses the scene background to derive the motion scale. Using the recovered camera as a metric-scale reference frame, we introduce a video transformer model (

Xi Lu, Wojciech Górecki, Chiara Macchiavello, Lorenzo Maccone

We give two upper bounds to the mutual information in arbitrary quantum estimation strategies. The first is based on some simple Fourier properties of the estimation apparatus. The second is derived using the first but, interestingly, depends only on the Fisher information of the parameter, so it is valid even beyond quantum estimation. We illustrate the use

Zezhou Huang

Entity matching is a critical challenge in data integration and cleaning, central to tasks like fuzzy joins and deduplication. Traditional approaches have focused on overcoming fuzzy term representations through methods such as edit distance, Jaccard similarity, and more recently, embeddings and deep neural networks, including advancements from large languag

Zhixin Lai, Jing Wu, Suiyao Chen, Yucheng Zhou

In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data. The approach diverges from established methodologies by utilizing a frozen transformer block, extracted from pre-trained LLMs, as an innovative encoder la

Dian Chao, Xin Song, Shupeng Zhong, Boyuan Wang

In this paper, we propose a solution for improving the quality of captions generated for figures in papers. We adopt the approach of summarizing the textual content in the paper to generate image captions. Throughout our study, we encounter discrepancies in the OCR information provided in the official dataset. To rectify this, we employ the PaddleOCR toolkit

Zachary T. P. Fried, Samer J. El-Abd, Brian M. Hays, Gabi Wenzel

We use both chirped-pulse Fourier transform and frequency modulated absorption spectroscopy to study the rotational spectrum of 2-methoxyethanol in several frequency regions ranging from 8.7-500 GHz. The resulting rotational parameters permitted a search for this molecule in Atacama Large Millimeter/submillimeter Array (ALMA) observations toward the massive

Jonas Frey

Uniform preorders are a class of combinatory representations of Set-indexed preorders that generalize Pieter Hofstra's basic relational objects. An indexed preorder is representable by a uniform preorder if and only if it has as generic predicate. We study the $\exists$-completion of indexed preorders on the level of uniform preorders, and identify a combina

P. Algikar, P. Sharma, M. Netto, L. Mili

Sensor measurements are mission-critical for monitoring and controlling power systems because they provide real-time insight into the grid operating condition; however, confidence in these insights depends greatly on the quality of the sensor data. Uncertainty in sensor measurements is an intrinsic aspect of the measurement process. In this paper, we develop

Ehsan Sabouni, H. M. Sabbir Ahmad, Vittorio Giammarino, Christos G. Cassandras

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a perform

Xiaowei Yang, Haiqi Liu, Fanqin Meng, Xiaojing Shen

Directional motion towards a specified destination is a common occurrence in physical processes and human societal activities. Utilizing this prior information can significantly improve the control and predictive performance of system models. This paper primarily focuses on reconstructing linear dynamic system models based on destination constraints in the s

Yuki Tahara, Kakutaro Fukushi, Shunta Takahashi, Kayo Kinjo

The estimation of equations from data is of interest in physics. One of the famous methods is the sparse identification of nonlinear dynamics (SINDy), which utilizes sparse estimation techniques to estimate equations from data. Recently, a method based on the Koopman operator has been developed; the generator extended dynamic mode decomposition (gEDMD) estim

Zhiyuan Yu, Xiaogeng Liu, Shunning Liang, Zach Cameron

Recent advancements in generative AI have enabled ubiquitous access to large language models (LLMs). Empowered by their exceptional capabilities to understand and generate human-like text, these models are being increasingly integrated into our society. At the same time, there are also concerns on the potential misuse of this powerful technology, prompting d

Shoma F. Kamijima, Yutaka Ohira

We investigate the attainable maximum energy of particles accelerated in the core-collapse supernova remnant (SNR) shock propagating in the free wind region with the Parker-spiral magnetic field, current sheet, and the wind termination shock (WTS) by using test particle simulations. This work focuses on Wolf-Rayet stars as progenitors. The magnetic field amp

Ganlong Zhao, Guanbin Li, Weikai Chen, Yizhou Yu

Recent advances in Iterative Vision-and-Language Navigation (IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task, it poses more challenges on how to utilize the highly unstructured navigation memory with

Tahsin Alamgir Kheya, Mohamed Reda Bouadjenek, Sunil Aryal

Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as healthcare, education and employment. Since they are used in numerous sensitive environments and make decisions that can be life altering, potential biased outcomes are a pressing matter. Developers should ensure that such models don't manifest any unexpected discri

Tanmayee Samantaray, Jitender Saini, Pramod Kumar Pal, Bithiah Grace Jaganathan

Thresholding of networks has long posed a challenge in brain connectivity analysis. Weighted networks are typically binarized using threshold measures to facilitate network analysis. Previous studies on MRI-based brain networks have predominantly utilized density or sparsity-based thresholding techniques, optimized within specific ranges derived from network

Ashish Bastola, Hao Wang, Xiwen Chen, Abolfazl Razi

Many AI platforms, including traffic monitoring systems, use Federated Learning (FL) for decentralized sensor data processing for learning-based applications while preserving privacy and ensuring secured information transfer. On the other hand, applying supervised learning to large data samples, like high-resolution images requires intensive human labor to l

Jinrae Kim, Sunggoo Jung, Sung-Kyun Kim, Youdan Kim

A staircase localization method is proposed for robots to explore urban environments autonomously. The proposed method employs a modular design in the form of a cascade pipeline consisting of three modules of stair detection, line segment detection, and stair localization modules. The stair detection module utilizes an object detection algorithm based on dee

Junhoo Lee, Hyunho Lee, Kyomin Hwang, Nojun Kwak

Deep learning has achieved tremendous success. However, unlike SVMs, which provide direct decision criteria and can be trained with a small dataset, it still has significant weaknesses due to its requirement for massive datasets during training and the black-box characteristics on decision criteria. This paper addresses these issues by identifying support ve

Xiao-Cheng Liao, Yi Mei, Mengjie Zhang

The control of traffic signals is crucial for improving transportation efficiency. Recently, learning-based methods, especially Deep Reinforcement Learning (DRL), garnered substantial success in the quest for more efficient traffic signal control strategies. However, the design of rewards in DRL highly demands domain knowledge to converge to an effective pol

Jeong-Yoon Kim, Seung-Ho Lee

In this paper, we propose a method to improve the accuracy of speech emotion recognition (SER) by using vision transformer (ViT) to attend to the correlation of frequency (y-axis) with time (x-axis) in spectrogram and transferring positional information between ViT through knowledge transfer. The proposed method has the following originality i) We use vertic

Ruirui Niu, Zhuoxian Li, Xiangyan Han, Qianling Liu

Interfacial ferroelectricity emerges in heterostructures consisting of nonpolar van der Waals (vdW) layers, greatly expanding the scope of two dimensional ferroelectrics. In particular, the unconventional moire ferroelectricity observed in bilayer graphene/boron nitride (BN) heterostructures, exhibits promising functionalities with topological current, super

Kristen M. Edwards, Brandon Man, Faez Ahmed

Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch into a diverse set of 2D images and 3D prototypes through sketch-to-text, text-to-image, and image-to-3D stages. This framework, shown across various sketches, rapidly generates text, image, and 3D modalities for enhanced early-stage design exploration. We show that using text as a

Dongdong Yan, Jianheng Guo, Kwang-il Seon, Manuel López-Puertas

This paper presents a hydrodynamic simulation that couples detailed non-local thermodynamic equilibrium (NLTE) calculations of the hydrogen and helium level populations to model the H$\alpha$ and He 10830 transmission spectra of the hot Jupiter HAT-P-32b. A Monte Carlo simulation is applied to calculate the number of Ly$\alpha$ resonance scatterings, which i

Junjie Ye, Lei Huang, Zhen Chen, Peichang Zhang

It is critical to design efficient beamforming in reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems for enhancing spectrum utilization. However, conventional methods often have limitations, either incurring high computational complexity due to iterative algorithms or sacrificing performance when using heuristi

Daniel G. Tiglea, Renato Candido, Magno T. M. Silva

In this paper, we analyze the effects of random sampling on adaptive diffusion networks. These networks consist in a collection of nodes that can measure and process data, and that can communicate with each other to pursue a common goal of estimating an unknown system. In particular, we consider in our theoretical analysis the diffusion least-mean-squares al

Yexin Li, Ping Jiang, Haochen Li

In this paper, we apply the coordinate increment discrete gradient (CIDG) method to solve the Lorentz force system which can be written as a non-canonical Hamiltonian system. Then we can obtain a new energy-preserving CIDG-I method for the system. The CIDG-I method can combine with its adjoint method CIDG-II which is also a energy-preserving method to form a

Mohamed A. Abba, Jonathan P. Williams, Brian J. Reich

Transfer learning (TL) has emerged as a powerful tool to supplement data collected for a target task with data collected for a related source task. The Bayesian framework is natural for TL because information from the source data can be incorporated in the prior distribution for the target data analysis. In this paper, we propose and study Bayesian TL method

Zhi Su, Xiaoyu Huang, Daniel Ordoñez-Apraez, Yunfei Li

Model-free reinforcement learning is a promising approach for autonomously solving challenging robotics control problems, but faces exploration difficulty without information of the robot's kinematics and dynamics morphology. The under-exploration of multiple modalities with symmetric states leads to behaviors that are often unnatural and sub-optimal. This i

Atsumoto Ohashi, Ryu Hirai, Shinya Iizuka, Ryuichiro Higashinaka

Dialogue datasets are crucial for deep learning-based task-oriented dialogue system research. While numerous English language multi-domain task-oriented dialogue datasets have been developed and contributed to significant advancements in task-oriented dialogue systems, such a dataset does not exist in Japanese, and research in this area is limited compared t

P. Algikar, P. Sharma, M. Netto, L. Mili

We apply random matrix theory to study the impact of measurement uncertainty on dynamic mode decomposition. Specifically, when the measurements follow a normal probability density function, we show how the moments of that density propagate through the dynamic mode decomposition. While we focus on the first and second moments, the analytical expressions we de

Ding Wang, Danhao Wang, Samuel Yang, Zetian Mi

Polarization arising from non-centrosymmetric wurtzite lattice underpins the physics and functionality of gallium nitride (GaN)-the most produced semiconductor materials second only to silicon. However, recent direct experimental measurements unveiled remanent polarization of unexpectedly large magnitudes and opposite orientations to traditionally anticipate

Ke Zhu, Xin Li, Yuwen Chen, Yizhe Huang

Due to the permanent harm to human health and ecosystem balance, antibiotic pollution in water has become an important direction of current environmental governance. Spherical metal oxides (SMOs) have been frequently utilized as effective heterogeneous photocatalysts for the efficient degradation of antibiotics due to the unique properties (e.g., strong ligh

Yuya Murakami, Kaoru Sano, Kohei Takehira

We investigate the arithmetic properties of the multiplier polynomials for certain $1$-parameter families of polynomials. In particular, we prove integrality theorems of multiplier polynomials for $z^d+c$, $(z-c)z^d + c$ and $z^{d+1}+cz$. As a corollary, we obtain the uniform upper bound of the naive height of parabolic parameters of unicritical polynomials.

Masato Hagiwara, Joshua Tanner

Second language acquisition (SLA) is a complex and dynamic process. Many SLA studies that have attempted to record and analyze this process have typically focused on a single modality (e.g., textual output of learners), covered only a short period of time, and/or lacked control (e.g., failed to capture every aspect of the learning process). In Project MOSLA

Min Du, Hong-Chuan Ma, Wen-Yu Zhong, Luis C. Ho

Utilizing a kinematic decomposition of simulated galaxies, we focus on galaxies with tiny kinematically inferred stellar halos, indicative of weak external influences. We investigate the intricate interplay between internal (natural) and external (nurture) processes in shaping the scaling relationships of specific angular momentum ($j_\star$), stellar mass (

Youpeng Zhao, Di Wu, Jun Wang

The Transformer architecture has significantly advanced natural language processing (NLP) and has been foundational in developing large language models (LLMs) such as LLaMA and OPT, which have come to dominate a broad range of NLP tasks. Despite their superior accuracy, LLMs present unique challenges in practical inference, concerning the compute and memory-

Tainara Borges, Siddharth Mulherkar, Tongou Yang

This article is a study guide for ``On restricted projections to planes in $\mathbb R^3$" [arXiv:2207.13844] by Gan, Guo, Guth, Harris, Maldague and Wang. We first present the main problems and preliminaries related to restricted projections in $\mathbb R^3$. Then we introduce the high-low method and decoupling, which are the two central and novel ideas in t

Shiping Cao, Hua Qiu

We prove the uniqueness of self-similar $D_4$-symmetric resistance forms on unconstrained Sierpinski carpets ($\mathcal{USC}$'s). Moreover, on a sequence of $\mathcal{USC}$'s $K_n, n\geq 1$ converging in Hausdorff metric, we show that the associated diffusion processes converge in distribution if and only if the geodesic metrics on $K_n, n\geq 1$ are equicon

Siyuan Peng, Kate Ladenheim, Snehesh Shrestha, Cornelia Fermüller

This paper introduces the concept of a design tool for artistic performances based on attribute descriptions. To do so, we used a specific performance of falling actions. The platform integrates a novel machine-learning (ML) model with an interactive interface to generate and visualize artistic movements. Our approach's core is a cyclic Attribute-Conditioned

S. V. Koniakhin, O. I. Utesov, A. G. Yashenkin

The effect of point-like lattice impurities on nanoparticle Raman spectra (RS) is studied using both numerical and analytical methods. Particular cases of replacement atoms of various masses, vacancies, and disorder in interatomic bonds are considered. It is shown that the disorder leads not only to the broadening of optical phonon lines but also to the shif

Kenji Homma, Satoshi Morita, Naoki Kawashima

Using the nuclear norm regularization techniques on tensor network renormalization algorithm, we study the phase diagram, the critical behavior and the duality property of the antiferromagnetic 6-state clock model on the Union Jack lattice. We find that this model undergoes multiple phase transitions; there is the Berezinskii-Kosterlitz-Thouless, $Z_{6}$ sym

Felipe González-Pizarro, Giuseppe Carenini

Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This paper presents the first systematic and comprehensive evaluation of multimodal topic modeling of documents containing both text and images. In the process, we propose two novel topi

He Zhu, Junran Wu, Ruomei Liu, Yue Hou

Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information. In this paper, we tend to inv

Anku Rani, Vipula Rawte, Harshad Sharma, Neeraj Anand

The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI. In recent times, considerable research has focused on detecting and mitigating hallucination in Large Language Models (LLMs). However, it's worth noting that hallucination is also quite prevalent in Vision-Language models (VLMs). In this

Ronan Herry, Baptiste Huguet

Motivated by a problem from incompressible fluid mechanics of Brenier (JAMS 1989), and its recent entropic relaxation by Arnaudo, Cruizero, L\'eonard & Zambrini (AIHP PS 2020), we study a problem of entropic minimization on the path space when the reference measure is a generic Feller semimartingale. We show that, under some regularity condition, our problem

Norberto Romanelli, Christopher M. Fowler, Gina A. DiBraccio, Jared R. Espley

The solar wind upstream of Mars's bow shock can be described in terms of Alfv\'enic turbulence, with an incompressible energy cascade rate of $10^{-17}$ J m$^{-3}$ s$^{-1}$ at magnetohydrodynamics (MHD) scales. The solar wind has more Alfv\'en waves propagating outwards from the Sun (than inwards) and a median Alfv\'en ratio of $\sim0.33$. Newly ionized plan

Jianqing Liu, Na Gong, Hritom Das

The software-based implementation of differential privacy mechanisms has been shown to be neither friendly for lightweight devices nor secure against side-channel attacks. In this work, we aim to develop a hardware-based technique to achieve differential privacy by design. In contrary to the conventional software-based noise generation and injection process,

Jean-Lou De Carufel, Marie Rose Jerade

So Long Sucker is a strategy board game that requires 4 players, each with $c$ chips of their designated color, and a board made of $k$ empty piles. With a clear set-up comes intricate rules, such as: players taking turns but not in a fixed order, agreements made between some players broken at any time, or a player winning the game without any chips in hand.

Junhao Zheng, Chenhao Lin, Jiahao Sun, Zhengyu Zhao

Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patches, so they only affect a small, localized region in the MDE map but fail under various viewpoints. To address these limitations, we propose

Leonardo Ribeiro da Cunha, Leonardo Oliveira Mendes, Renio dos Santos Mendes

Tasks of different nature and difficulty levels are a part of people's lives. In this context, there is a scientific interest in the relationship between the difficulty of the task and the persistence need to accomplish it. Despite the generality of this problem, some tasks can be simulated in the form of games. In this way, we employ data from a large onlin

Shigeo Yamauchi, Azusa Inaba, Yumiko Anraku

An emission line at ~6.7 keV is attributable to a He-like iron K-shell transition, which indicates existence of a thin thermal plasma with a temperature of several keV. Using Suzaku archival data, we searched for the iron K-line from the spiral galaxy NGC 6946, and found the iron K-line at 6.68+/-0.07 keV at the 3.1 sigma level in the central r<2.'5 region.

Linyang He, Peili Chen, Ercong Nie, Yuanning Li

Inspired by cognitive neuroscience studies, we introduce a novel `decoding probing' method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the `brain' and its representations as `neural activations', we decode grammaticality labels of minimal pa

Nathan D. Rock, Haobo Yang, Brian Eisner, Aviva Levin

Diffusion of native defects such as vacancies and their interactions with impurities are fundamental in semiconductor crystal growth, device processing, and long-term aging of equilibration and transient diffusion of vacancies are rarely investigated. We used aluminum-gallium oxide/gallium oxide superlattices (SLs) to detect and analyze transient diffusion o

Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dime

Hamza Saleem, Amir Ziashahabi, Muhammad Naveed, Salman Avestimehr

Training machine learning models on data from multiple entities without direct data sharing can unlock applications otherwise hindered by business, legal, or ethical constraints. In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models. We adopt a two-server model where data own

Daniel Echeverri, Jerry W. Xuan, John D. Monnier, Jacques-Robert Delorme

Vortex fiber nulling (VFN) is a technique for detecting and characterizing faint companions at small separations from their host star. A near-infrared ($\sim2.3 \mu$m) VFN demonstrator mode was deployed on the Keck Planet Imager and Characterizer (KPIC) instrument at the Keck Observatory and presented earlier. In this paper, we present the first VFN companio

Benjamin Horowitz, Roger de Belsunce, Zarija Lukic

We present a novel maximum a posteriori estimator to jointly estimate band-powers and the covariance of the three-dimensional power spectrum (P3D) of Lyman-alpha forest flux fluctuations, called MAPLE. Our Wiener-filter based algorithm reconstructs a window-deconvolved P3D in the presence of complex survey geometries typical for Lyman-alpha surveys that are

Salim Sazzed

Understanding the structures of biological macromolecules is highly important as they are closely associated with cellular functionalities. Comprehending the precise organization actin filaments is crucial because they form the dynamic cytoskeleton, which offers structural support to cells and connects the cell's interior with its surroundings. However, dete

Rachid Chergui

We consist of first presenting Zeckendorf Theorem with these two versions Fibonacci and Luca. In this document we obtain results on the generalized of the Zeckendorf theorem for Fibonacci numbers (multibonacci). Such results find applications in coding theory.

Jason Fulman, Daniele Garzoni, Robert M. Guralnick

We prove that if G is a sufficiently large finite almost simple group of Lie type, then given a fixed nontrivial element x in G and a coset of G modulo its socle, the probability that x and a random element of the coset generate a subgroup containing the socle is uniformly bounded away from 0 (and goes to 1 if the field size goes to infinity). This is new ev

Ramin Javadi, Meysam Miralaei

Wu in 1999 conjectured that if $H$ is a subgraph of the complete graph $K_{2n+1}$ with $n$ edges, then there is a Hamiltonian cycle decomposition of $K_{2n+1}$ such that each edge of $H$ is in a separate Hamiltonian cycle. The conjecture was partially settled by Liu and Chen (2023) in cases that $|V(H)|\leq n+1$, $H$ is a linear forest, or $n\leq 5$. In this

Justin Swain, Giordano Tierra

In this work, we present three linear numerical schemes to model nematic liquid crystals using the Landau-de Gennes $\textbf{Q}$-tensor theory. The first scheme is based on using a truncation procedure of the energy, which allows for an unconditionally energy stable first order accurate decoupled scheme. The second scheme uses a modified second order accurat

Yuan Zhou, Lun Quan, Chao Xu, Guangtong Xu

The formation trajectory planning using complete graphs to model collaborative constraints becomes computationally intractable as the number of drones increases due to the curse of dimensionality. To tackle this issue, this paper presents a sparse graph construction method for formation planning to realize better efficiency-performance trade-off. Firstly, a

Gustav A. Baumgart, Jaemin Shin, Ali Payani, Myungjin Lee

Federated Learning (FL) emerged as a practical approach to training a model from decentralized data. The proliferation of FL led to the development of numerous FL algorithms and mechanisms. Many prior efforts have given their primary focus on accuracy of those approaches, but there exists little understanding of other aspects such as computational overheads,

Ben Wang, Jiqun Liu

When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude changes of users searching online, our study examined how cognitively biased users interact with algorithmicall

Qianglin Wen, Chengchun Shi, Ying Yang, Niansheng Tang

A/B testing has become the gold standard for policy evaluation in modern technological industries. Motivated by the widespread use of switchback experiments in A/B testing, this paper conducts a comprehensive comparative analysis of various switchback designs in Markovian environments. Unlike many existing works which derive the optimal design based on speci

Ibrahim Khebour, Kenneth Lai, Mariah Bradford, Yifan Zhu

Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on ``dialogue state tracking'' (DST), which is the ability to update the representations of the speaker's needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is ``c

Demi E. Moed, Michael S. Dimitriyev, Benjamin R. Greenvall, Gregory M. Grason

Inspired by adhesive bio-filamentous structure, such as bacterial pili, this work details the methods used to fabricate and characterize a surface-anchored array of thin, flexible and shape-responsive mesoscale polymer ribbons with a length-to-thickness aspect ratio of up to 100,000. The resulting structures exhibit geometrically complex and dynamic morpholo

Victoria Gould, Tim Stokes

We give an algebraic characterisation of ordered groupoids, namely, we show that there is a categorical isomophism between the category of ordered groupoids and the category of $D$-inverse constellations. Here constellations are partial algebras in the sense that they possess a partial product, and a unary operation $D$. We consider constellations in which e

Hang Li, Tianlong Xu, Jiliang Tang, Qingsong Wen

Knowledge concept tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization. Traditionally, these annotations have been conducted manually with help from pedagogical experts, as the task requires not only a strong s

Shota Kikuchi

We study quark and lepton flavor structures on magnetized $T^2/\mathbb{Z}_2$ twisted orbifold model. There are 6,460 number of flavor models but most of them cannot lead to realistic flavor observables because of the difficulties on realizing mass hierarchies and small (large) mixing angles of quarks (leptons). We find that certain zero point patterns of zer

Ryosuke Nishide, Shuji Ishihara

Pattern dynamics on curved surfaces are ubiquitous. Although the effect of surface topography on pattern dynamics has gained much interest, there is a limited understanding of the roles of surface geometry and topology in pattern dynamics. Recently, we reported that a static pattern on a flat plane can become a propagating pattern on a curved surface [Nishid

Ryosuke Nishide, Shuji Ishihara

Patterns on curved surfaces are ubiquitous, yet the influence of surface geometry on pattern dynamics remains elusive. We recently reported a new mechanism of pattern propagation in which a static pattern on a flat plane becomes a propagating pattern on a curved surface [Nishide and Ishihara, Phys. Rev. Lett. 2022]. Here, we address whether surface curvature

Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias

We study the problem of approximating a matrix $\mathbf{A}$ with a matrix that has a fixed sparsity pattern (e.g., diagonal, banded, etc.), when $\mathbf{A}$ is accessed only by matrix-vector products. We describe a simple randomized algorithm that returns an approximation with the given sparsity pattern with Frobenius-norm error at most $(1+\varepsilon)$ ti

Daniel M. Markiewitz, Zachary A. H. Goodwin, Michael McEldrew, J. Pedro de Souza

Ionic liquids (ILs) are an extremely exciting class of electrolytes for energy storage applications because of their unique combination of properties. Upon dissolving alkali metal salts, such as Li or Na based salts, with the same anion as the IL, an intrinsically asymmetric electrolyte can be created for use in batteries, known as a salt-in-ionic liquid (Si

Kevin Patrick Griffin, Hilary Egan, Marc T. Henry de Frahan, Juliane Mueller

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and man

Meng Sun, Seth Gossage, Emily M. Leiner, Aaron M. Geller

Motivated by measurements of the rotation speed of accretor stars in post-mass-transfer (post-MT) systems, we investigate how magnetic braking affects the spin-down of individual stars during binary evolution with the MESAbinary module. Unlike the conventional assumption of tidal synchronization coupled with magnetic braking in binaries, we first calculate w

Júlio C. Fabris, Richard Kerner

Unimodular gravity became an object of increasing interest in the late $80$-ties and was recently used in primordial Universe modeling with cosmological constant, in the context of the Brans-Dicke gravity including scalar field. In the present article we investigate the possibility of imposing the unimodular condition within the $5$-dimensional Kaluza-Klein

Xieyang Xu, Yifei Yuan, Zachary Kincaid, Arvind Krishnamurthy

Relational network verification is a new approach to validating network changes. In contrast to traditional network verification, which analyzes specifications for a single network snapshot, relational network verification analyzes specifications concerning two network snapshots (e.g., pre- and post-change snapshots) and captures their similarities and diffe

Meriem Behiri, Marika Giulietti, Vincenzo Galluzzi, Andrea Lapi

In this paper, we investigate the benefits of teaming up data from the radio to the far- 1 infrared (FIR) regime for the characterization of Dusty Star-Forming Galaxies (DSFGs). These galaxies 2 are thought to be the star-forming progenitors of local massive quiescent galaxies, and play a pivotal 3 role in the reconstruction of the cosmic star formation rate