October 2023 arXiv papers — page 175

Manana Kachakhidze, Nino Kachakhidze-Murphy, Vaso Kukhianidze, Giorgi Ramishvili

it has been given scientifically proven suggestions for the classification of earthquake precursor, indicator, and triggering factors.

Yilue Qian, Peiyu Yu, Ying Nian Wu, Yao Su

Visual planning simulates how humans make decisions to achieve desired goals in the form of searching for visual causal transitions between an initial visual state and a final visual goal state. It has become increasingly important in egocentric vision with its advantages in guiding agents to perform daily tasks in complex environments. In this paper, we pro

Ethan Kou, Acshi Haggenmiller

Autonomous mobile robot competitions judge based on a robot's ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition robot. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark

Jie-Jing Shao, Jiang-Xin Shi, Xiao-Wen Yang, Lan-Zhe Guo

Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks. It is usually expected that satisfactory overall accuracy can be achieved across numerous domains through well-designed textual prompts. However, we found that their performance i

Alexandra Antoniouk, Anatoly Kochubei, Oleksii Nikitchenko

We develop a theory of generalized solutions of the nonlinear evolution equations for complex-valued functions of a real positive time variable and $p$-adic spatial variable, which can be seen as non-Archimedean counterparts of the fractional porous medium equation. In this case, we face the problem that a $p$-adic ball is simultaneously open and closed, thu

Rahul Chhimpa, Avinash Chand Yadav

We consider discrete models of kinetic rough interfaces that exhibit space-time scale-invariance in height-height correlation. A generic scaling theory implies that the dynamical structure factor of the height profile can uniquely characterize the underlying dynamics. We provide a finite-time scaling that systematically allows an estimation of the critical e

Meghana Pannikkote, Vaidehi S. Paliya, D. J. Saikia

The latest entry in the jetted active galactic nuclei (AGN) family is the Fanaroff-Riley type 0 (FR0) radio galaxies. They share several observational characteristics, e.g., nuclear emission and host galaxy morphology, with FR I sources; however, they lack extended, kiloparsec-scale radio structures, which are the defining features of canonical FR I and II s

Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis

Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks. However, FMs developed for biomedical domains have largely remained unimodal, i.e., independently trained and used for tasks on protein sequences alone, small molecule structures alone, or clinical data alone. To over

Mostafizur Rahaman Laskar, Kalyan Dasgputa, Amit Kumar Dutta, Atanu Bhattacharya

The time evolution operator plays a crucial role in the precise computation of chemical experiments on quantum computers and holds immense promise for advancing the fields of physical and computer sciences, with applications spanning quantum simulation and machine learning. However, the construction of large-scale quantum computers poses significant challeng

Yingfan Zong, Jing Bai, Xiaolin Chang, Fumio Machida

Metaverse as-a-Service (MaaS) enables Metaverse tenants to execute their APPlications (MetaAPP) by allocating Metaverse resources in the form of Metaverse service functions (MSF). Usually, each MSF is deployed in a virtual machine (VM) for better resiliency and security. However, these MSFs along with VMs and virtual machine monitors (VMM) running them may e

Heiko Dietrich, Melissa Lee, Tomasz Popiel

The classification of the maximal subgroups of the Monster $\mathbf{M}$ is believed to be complete subject to an unpublished result of Holmes and Wilson asserting that $\mathbf{M}$ has no maximal subgroups that are almost simple with socle isomorphic to $\text{PSL}_2(8)$, $\text{PSL}_2(16)$, or $\text{PSU}_3(4)$. We prove this result for $\text{PSL}_2(16)$,

Marian Apostol

In certain conditions a macroscopic quantum-mechanical scattering may occur, which may lead to a coherent cross-section on a macroscopic scale in a monocrystal. The conditions are satisfied by neutrinos, but not satisfied by other projectiles, with a higher cross-section. This may explain Weber-type experiments of neutrino detection by a perfect, stiff sapph

Aadi Kothari, Tony Tohme, Xiaotong Zhang, Kamal Youcef-Toumi

Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of neural network-based architecture or use regression models offline to fit hyper-parameters in the hope of capturing a model encompassing human motion. While these methods provide go

Brett Nasserden, Sasha Zotine

We study the dynamics of surjective endomorphisms of projective bundles on elliptic curves and relate their dynamical properties to the geometry of the bundle. As an application we prove the Kawaguchi--Silverman conjecture for projective bundles on elliptic curves, thereby completing the conjecture for all projective bundles on curves. Our approach is to use

Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang

Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attacks on both the graph structure and node attributes. Although empirical approaches have been proposed to enhance the robustness of GCL, the certifiable robustness of GCL is still rem

Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini

Variational dimensionality reduction methods are widely used for their accuracy, generative capabilities, and robustness. We introduce a unifying framework that generalizes both such as traditional and state-of-the-art methods. The framework is based on an interpretation of the multivariate information bottleneck, trading off the information preserved in an

Gaoqing Cao

Inspired from perturbative calculations, this work introduces imaginary ($\Omega_{\rm I}$) and real ($\Omega$) rotation effects to the pure $SU(3)$ gauge potentials simply through variable transformations: The empirical Polyakov loop (PL) potentials can be rewritten as functions of the imaginary chemical potentials of gluons and ghosts $(q_{\rm ij})$, and th

Junjie Liu, Shaotian Yan, Chen Shen, Zhengdong Xiao

Exploiting large language models (LLMs) to tackle reasoning has garnered growing attention. It still remains highly challenging to achieve satisfactory results in complex logical problems, characterized by plenty of premises within the context and requiring multi-hop reasoning. In particular, the reasoning capabilities of LLMs are brittle to disorder and dis

Jose Galarza, Tamer Oraby

In this paper, we show how convolutional neural networks (CNN) can be used in regression and classification learning problems of noisy and non-noisy functional data. The main idea is to transform the functional data into a 28 by 28 image. We use a specific but typical architecture of a convolutional neural network to perform all the regression exercises of p

Kyeongsoo Hong, Jae Woo Lee, Dong-Jin Kim, Jang-Ho Park

We present detailed analyses of updated eclipse timing diagrams for 32 contact binary merger candidates in the Galactic bulge. The photometric data was obtained from 2016 to 2021 using the Korea Microlensing Telescope Network (KMTNet) with the 1.6 m telescopes located at three southern sites (CTIO, SAAO, and SSO). The times of minimum lights were determined

Zihan Lin, Shota Abe, Zhe Chen, Surabhi Jaiswal

Zero-dimensional kinetic modeling of atmospheric pressure Ar-N2-H2 nonthermal plasma was carried out to gain mechanistic insights into ammonia formation during plasma-assisted catalysis of ammonia synthesis. The kinetic model was developed for a coaxial dielectric barrier discharge (DBD) quartz wool-packed bed reactor operating at near room temperature using

Christof Geiß, Daniel Labardini-Fragoso, Jon Wilson

We prove that for any possibly-punctured surface with non-empty boundary $\mathbf{\Sigma}=(\Sigma, \mathbb{M}, \mathbb{P})$, and any tagged triangulation $T$ of $\mathbf{\Sigma}$ in the sense of Fomin--Shapiro--Thurston, the coefficient-free bangle functions of Musiker--Schiffler--Williams coincide with the coefficient-free generic Caldero--Chapoton function

Eslam Abdelaleem, Ahmed Roman, K. Michael Martini, Ilya Nemenman

We explore two primary classes of approaches to dimensionality reduction (DR): Independent Dimensionality Reduction (IDR) and Simultaneous Dimensionality Reduction (SDR). In IDR methods, of which Principal Components Analysis is a paradigmatic example, each modality is compressed independently, striving to retain as much variation within each modality as pos

Yaochen Wu

We classify Harish-Chandra bimodules over the quantized flower quiver varieties with minimal support. We show that if the dimension vector is $n$, then there are $n!$ minimally supported simple Harish-Chandra bimodules for integral quantization parameters that are large enough or small enough, and there are none for other quantization parameters. The main to

Danqing Wang, Kevin Yang, Hanlin Zhu, Xiaomeng Yang

Recent research has increasingly focused on evaluating large language models' (LLMs) alignment with diverse human values and preferences, particularly for open-ended tasks like story generation. Traditional evaluation metrics rely heavily on lexical similarity with human-written references, often showing poor correlation with human judgments and failing to a

Jintao Xue, Dongkun Zhang, Rong Xiong, Yue Wang

Multi-Agent Reinforcement Learning (MARL) has become a promising solution for constructing a multi-agent autonomous driving system (MADS) in complex and dense scenarios. But most methods consider agents acting selfishly, which leads to conflict behaviors. Some existing works incorporate the concept of social value orientation (SVO) to promote coordination, b

Qian Huang, Jian Vora, Percy Liang, Jure Leskovec

A central aspect of machine learning research is experimentation, the process of designing and running experiments, analyzing the results, and iterating towards some positive outcome (e.g., improving accuracy). Could agents driven by powerful language models perform machine learning experimentation effectively? To answer this question, we introduce MLAgentBe

Hyosoon Jang, Minsu Kim, Sungsoo Ahn

This paper studies generative flow networks (GFlowNets) to sample objects from the Boltzmann energy distribution via a sequence of actions. In particular, we focus on improving GFlowNet with partial inference: training flow functions with the evaluation of the intermediate states or transitions. To this end, the recently developed forward-looking GFlowNet re

Canming He, Xiaoyu Nie, Victor Avalos, Sofia Botsi

We report the creation of ultracold ground state $^{6}\textrm{Li}^{40}\textrm{K}$ polar molecules with high efficiency. Starting from weakly-bound molecules state, stimulated Raman adiabatic passage (STIRAP) is adopted to coherently transfer the molecules to their singlet ro-vibrational ground state $|\textrm{X}^{1}\Sigma^{+},v=0,J=0>$. By employing a single

Jingwen Zhang, Lauren M. Weiss, Daniel Huber, Eric L. N. Jensen

The effect of stellar multiplicity on planetary architecture and orbital dynamics provides an important context for exoplanet demographics. We present a volume-limited catalog up to 300 pc of 66 stars hosting planets and planet candidates from Kepler, K2 and TESS with significant Hipparcos-Gaia proper motion anomalies, which indicate the presence of companio

Yi-Ping Chen, Liwei Wang, Yigitcan Comlek, Wei Chen

Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate modeling and design optimization by incorporating data from various low-fidelity (LF) models. While most existing MF methods assume a fixed dataset, adaptive sampling methods that dynamically allocate resources among fidelity models can achieve higher efficiency in the exploring and e

Kehan Wu, Renqi Chen, Haiyu Wang, Chenqing Ji

Recent research has highlighted the detection of human respiration rate using commodity WiFi devices. Nevertheless, these devices encounter challenges in accurately discerning human respiration amidst the prevailing human motion interference encountered in daily life. To tackle this predicament, this paper introduces a passive sensing and communication syste

Vukan Levajac, Hristo Barakov, Grzegorz P. Mazur, Nick van Loo

We study the current-phase relation (CPR) of an InSb-Al nanowire Josephson junction in parallel magnetic fields up to $700$\,mT. At high magnetic fields and in narrow voltage intervals of a gate under the junction, the CPR exhibits $\pi$-shifts. The supercurrent declines within these gate intervals and shows asymmetric gate voltage dependence above and below

Yao Lu, Xuguang Chen, Yuchen Zhang, Jianyang Gu

Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training. Meanwhile, Pre-trained Models (PTMs) function as knowledge repositories, containing extensive information from the original dataset. This naturally raises a question: Can PTMs effectively tr

Dacheng Li, Rulin Shao, Anze Xie, Eric P. Xing

FlashAttention (Dao, 2023) effectively reduces the quadratic peak memory usage to linear in training transformer-based large language models (LLMs) on a single GPU. In this paper, we introduce DISTFLASHATTN, a distributed memory-efficient attention mechanism optimized for long-context LLMs training. We propose three key techniques: token-level workload balan

Siwei Wu, Xiangqing Shen, Rui Xia

Large Language Models (LLMs), such as ChatGPT, have recently been applied to various NLP tasks due to its open-domain generation capabilities. However, there are two issues with applying LLMs to dialogue tasks. 1. During the dialogue process, users may have implicit intentions that might be overlooked by LLMs. Consequently, generated responses couldn't align

Sakuna Harinda Jayasundara, Nalin Asanka Gamagedara Arachchilage, Giovanni Russello

Administrator-centered access control failures can cause data breaches, putting organizations at risk of financial loss and reputation damage. Existing graphical policy configuration tools and automated policy generation frameworks attempt to help administrators configure and generate access control policies by avoiding such failures. However, graphical poli

Yiren Jian, Tingkai Liu, Yunzhe Tao, Chunhui Zhang

In this paper, we introduce $\text{EVL}_{\text{Gen}}$, a streamlined framework designed for the pre-training of visually conditioned language generation models with high computational demands, utilizing frozen pre-trained large language models (LLMs). The conventional approach in vision-language pre-training (VLP) typically involves a two-stage optimization

Tianchen Yu, Junyi Yang, Zhongguo Li, Xingzhi Wu

In this study, using only a single pulse, pump-probe measurement with a large time window of more than 100 ps is implemented. A commercial grating is used to encode a time window of ~ 56 ps in a single pulse; therefore, there is no need for machining customization. In addition, in this technique, the grating surface is accurately imaged, eliminating the imag

Brooks A. Butler, Philip E. Paré

As modern systems become ever more connected with complex dynamic coupling relationships, developing safe control methods becomes paramount. In this paper, we discuss the relationship of node-level safety definitions for individual agents with local neighborhood dynamics. We define a collaborative control barrier function (CCBF) and provide conditions under

Zherui Li, Raye Chen-Hua Yeow

Real-time intelligent detection and prediction of subjects' behavior particularly their movements or actions is critical in the ward. This approach offers the advantage of reducing in-hospital care costs and improving the efficiency of healthcare workers, which is especially true for scenarios at night or during peak admission periods. Therefore, in this wor

Siqi Li, Yansong Zhu, Benjamin A. Spencer, Guobao Wang

Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-ray CT scan. The recent PET-enabled DECT method allows DECT imaging on PET/CT without requiring a second X-ray

Samuel T. Bieberich, Ketan C. Maheshwari, Sean R. Wilkinson, Prasanna Date

Quantum Computers offer an intriguing challenge in modern Computer Science. With the inevitable physical limitations to Moore's Law, quantum hardware provides avenues to solve grander problems faster by utilizing Quantum Mechanical properties at subatomic scales. These futuristic devices will likely never replace traditional HPC, but rather work alongside th

Ahmed Abusnaina, Yizhen Wang, Sunpreet Arora, Ke Wang

Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversarial malware examples. Additionally, we uncover the sensitivity of volatile features within the detection engines and exhibit their exploitability. Highlighting volatile informatio

Zeyu Lyu, Kohei Ichikawa, Yongchao Cheng, Hisashi Hayakawa

Historical weather records from Europe indicate that the Earth experienced substantial climate variability, which caused, for instance, the Little Ice Age and the global crisis in the period between the 14th and 19th centuries. However, it is still unclear how global this climate variability was because of the scarce meteorological data availability in other

Ke Shen, Mayank Kejriwal

Large Language Models (LLMs), such as ChatGPT, have achieved impressive milestones in natural language processing (NLP). Despite their impressive performance, the models are known to pose important risks. As these models are deployed in real-world applications, a systematic understanding of different risks posed by these models on tasks such as natural langu

Henan Wu, Wenting Zhang

Each $\frac{1}{2}$-derivation of the planar Galilean conformal algebra is proven to be a scalar. As a corollary, all transposed Poisson structures on the planar Galilean conformal algebra are trivial.

Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang

The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5' UTR, which we refer to as the UTR-L

Dmitry Prokhorov, Shiu-Hang Lee, Shigehiro Nagataki, Jacco Vink

Modern imaging atmospheric Cherenkov telescopes have extensively observed young nearby supernova remnants (SNRs), with ages of about 1000 years or less, in the very-high-energy (VHE) gamma-ray band. These efforts resulted in the detection of VHE emission from three young SNRs - Cassiopeia A, Tycho, and SN 1006 - and provided significant evidence for emission

Long Nguyen, Aiden Nibali, Joshua Millward, Zhen He

Recently there have been many algorithms proposed for the classification of very high resolution whole slide images (WSIs). These new algorithms are mostly focused on finding novel ways to combine the information from small local patches extracted from the slide, with an emphasis on effectively aggregating more global information for the final predictor. In

Muhammad Kamran Saeed, Ahmed E. Kamal, Ashfaq Khokhar

Massive MIMO is expected to play an important role in the development of 5G networks. This paper addresses the issue of pilot contamination and scalability in massive MIMO systems. The current practice of reusing orthogonal pilot sequences in adjacent cells leads to difficulty in differentiating incoming inter- and intra-cell pilot sequences. One possible so

Maria S. Kirsanova, Alexei V. Moiseev, Paul A. Boley

The S254-258 star-forming complex is a place of massive star formation where five OB-stars have created HII regions, visible as optical nebulae, and disrupted the parental molecular gas. In this work, we study the 3D structure of these \HII regions using optical spectroscopy and tunable-filter photometry with the 6-m and 1-m telescopes of the Special Astroph

Yutaka Hosotani, Shuichiro Funatsu, Hisaki Hatanaka, Yuta Orikasa

The $W$ boson mass $m_W$ in the GUT inspired $SO(5) \times U(1) \times SU(3)$ gauge-Higgs unification in the Randall-Sundrum (RS) warped space is evaluated. The muon decay $\mu^- \rightarrow e^- \bar{\nu}_e \nu_\mu$ proceeds by the exchange of not only the zero mode of the $W$ boson $(W^{(0)}$) but also Kaluza-Klein (KK) excited modes $W^{(n)}$ and $W_R^{(n)

Baolin Chong, Hancheng Lu, Langtian Qin, Chenwu Zhang

With the advancement of video sensors in the Internet of Things, Internet of Video Things (IoVT) systems, capable of delivering abundant and diverse information, have been increasingly deployed for various applications. However, the extensive transmission of video data in IoVT poses challenges in terms of delay and power consumption. Intelligent reconfigurab

Kha-Dinh Luong, Ambuj Singh

Property prediction on molecular graphs is an important application of Graph Neural Networks. Recently, unlabeled molecular data has become abundant, which facilitates the rapid development of self-supervised learning for GNNs in the chemical domain. In this work, we propose pretraining GNNs at the fragment level, a promising middle ground to overcome the li

Takayuki Komatsu, Yoshiyuki Ohmura, Yasuo Kuniyoshi

Multi-object representation learning aims to represent complex real-world visual input using the composition of multiple objects. Representation learning methods have often used unsupervised learning to segment an input image into individual objects and encode these objects into each latent vector. However, it is not clear how previous methods have achieved

Jiashu He, Charilaos I. Kanatsoulis, Alejandro Ribeiro

Network alignment is the task of establishing one-to-one correspondences between the nodes of different graphs. Although finding a plethora of applications in high-impact domains, this task is known to be NP-hard in its general form. Existing optimization algorithms do not scale up as the size of the graphs increases. While being able to reduce the matching

Hiroaki Ishizuka, Masahiro Sato

Steady current in metals induces a thermal gradient, a phenomenon known as the Peltier effect. The Peltier effect is one of the fundamental phenomena in the thermoelectric properties of materials and is also used in applications such as refrigerators. In this work, we show that an analogous phenomenon occurs by phonons in a material subject to linearly-polar

Yefei He, Jing Liu, Weijia Wu, Hong Zhou

Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for real-world applications is constrained by substantial computational costs and latency issues. Quantization is a dominant way to compress and accelerate diffusion models, where post-training quantization (PTQ) and qu

Neil Daniel

A common practice in the medical industry is the use of clinical notes, which consist of detailed patient observations. However, electronic health record systems frequently do not contain these observations in a structured format, rendering patient information challenging to assess and evaluate automatically. Using computational systems for the extraction of

Sheik Murad Hassan Anik, Xinghua Gao, Na Meng

The paper describes a dataset comprising indoor environmental factors such as temperature, humidity, air quality, and noise levels. The data was collected from 10 sensing devices installed in various locations within three single-family houses in Virginia, USA. The objective of the data collection was to study the indoor environmental conditions of the house

Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin

Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence

Baolin Chong, Fengqian Guo, Hancheng Lu, Langtian Qin

Cell-free (CF) massive multiple-input multiple-output (mMIMO) has been considered as a potential technology for Beyond 5G communication systems. However, the performance of CF mMIMO systems has not been well studied. Most existing analytical work on CF mMIMO systems is based on the expected signal-to-interference-plus-noise ratio (SINR). The statistical char

Stephanie Striegel, Eduardo Ibarra-García-Padilla, Ehsan Khatami

Recent proposals have suggested the use of supervised learning with convolutional neural networks to shed light on some of the less well known phases of the Fermi-Hubbard model through the classification of snapshots from the quantum gas microscopy of ultracold atoms in optical lattices. However, there have been challenges in the interpretability of networks

Ruiyu Wang, Zifeng Wang, Jimeng Sun

Tabular data prediction is a fundamental machine learning task for many applications. Existing methods predominantly employ discriminative modeling and operate under the assumption of a fixed target column, necessitating re-training for every new predictive task. Inspired by the generative power of large language models (LLMs), this paper exploits the idea o

Xu Chen, Zhiyong Feng, J. Andrew Zhang, Zhaohui Yang

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC function

Shoichiro Tsutsui, Keita Kanno

We explore the feasibility of fault-tolerant quantum computation using the bit-flip repetition code in a biased noise channel where only the bit-flip error can occur. While several logic gates can potentially produce phase-flip errors even in such a channel, we propose bias-preserving implementation of $S$, $H$, $\mathrm{CZ}$, and $R_z$ gates. We demonstrate

E. Contini, S. Jeon, J. Rhee, S. Han

We investigate on the role of the halo concentration in the formation of the intra-cluster light (ICL) in galaxy groups and clusters, as predicted by a state-of-art semi-analytic model of galaxy formation, coupled with a set of high-resolution dark matter only simulations. The analysis focuses on how the fraction of ICL correlates with halo mass, concentrati

Shengding Hu, Xin Liu, Xu Han, Xinrong Zhang

The scientific scale-up of large language models (LLMs) necessitates a comprehensive understanding of their scaling properties. However, the existing literature on the scaling properties only yields an incomplete answer: optimization loss decreases predictably as the model size increases, in line with established scaling law; yet no scaling law for task has

Tanushkina Daria, Shevchenko Valeriy, Lukashevich Aleksander, Bulkin Aleksandr

Agriculture is crucial in sustaining human life and civilization that relies heavily on natural resources. This industry faces new challenges, such as climate change, a growing global population, and new models for managing food security and water resources. Through a machine learning framework, we estimate the future productivity of croplands based on CMIP5

Kecheng Chen, Kenichi Soga, Patrick Dobson, Peter Nico

The current plan divides the UC Berkeley (UCB) campus energy system into five nodes, where the Business and Law node was studied because of an open field site for borehole installation. The Pacific Northwest National Laboratory's Commercial Prototype Building Models were used to estimate heating and cooling load requirements for UCB campus building types by

Haoyu Fan, Carlos M. R. Rocha, Martin Cordiner, Harold Linnartz

We carried out a sensitive survey of C$_2$ and C$_3$ using the EDIBLES data set. We also expanded our searches to C$_4$, C$_5$, and $^{13}$C$^{12}$C isotopologue in the most molecule-rich sightlines. We fit synthetic spectra generated following a physical excitation model to the C$_2$ (2-0) Phillips band to obtain the C$_2$ column density ($N$) as well as th

Song Wei, Xiangrui Kong, Alinson Santos Xavier, Shixiang Zhu

Energy justice is a growing area of interest in interdisciplinary energy research. However, identifying systematic biases in the energy sector remains challenging due to confounding variables, intricate heterogeneity in counterfactual effects, and limited data availability. First, this paper demonstrates how one can evaluate counterfactual unfairness in a po

Ryan Malthaner

In this paper we present new proofs of the non-embeddability of countably branching trees into Banach spaces satisfying property $(\beta_p)$ and of countably branching diamonds into Banach spaces which are $p$-AMUC for $p > 1$. These proofs are entirely metric in nature and are inspired by previous work of Ji\v{r}\'i Matou\v{s}ek. In addition, using this met

Abdelhak Bentaleb, May Lim, Mehmet N. Akcay, Ali C. Begen

This survey presents the evolution of live media streaming and the technological developments behind today's IP-based low-latency live streaming systems. Live streaming primarily involves capturing, encoding, packaging and delivering real-time events such as live sports, live news, personal broadcasts and surveillance videos. Live streaming also involves con

Hantaek Bae, Hyunwoo Kwon, Jaeyong Shin

In this paper, we investigate a Stokes-Magneto system with fractional diffusions. We first deal with the non-resistive case in $\mathbb{T}^{d}$ and establish the local and global well-posedness with initial magnetic field $\mathbf{b}_0\in H^{s}(\mathbb{T}^d)$. We also show the existence of a unique mild solution of the resistive case with initial data $\math

Unchitta Kan, Jericho McLeod, Eduardo López

Changes in U.S. migration during the COVID-19 pandemic show that many moved to less populated cities from larger cities, deviating from previous trends. In this study, building on prior work in the literature showing that the abundance of family ties is inversely related to population size, we analyze these migration changes with a focus on the crucial, yet

Deqian Kong, Yuhao Huang, Jianwen Xie, Ying Nian Wu

This paper proposes a latent prompt Transformer model for solving challenging optimization problems such as molecule design, where the goal is to find molecules with optimal values of a target chemical or biological property that can be computed by an existing software. Our proposed model consists of three components. (1) A latent vector whose prior distribu

Edgar Pacheco

About thirty years ago, when the Internet started to be commercialised, access to the medium became a topic of research and debate. Up-to-date evidence about key predictors, such age, is crucial because of the Internet's ever-changing nature and the challenges associated with gaining access to it. This paper aims to give an overview of New Zealand's Internet

Sumit Bam Shrestha, Jonathan Timcheck, Paxon Frady, Leobardo Campos-Macias

Loihi 2 is an asynchronous, brain-inspired research processor that generalizes several fundamental elements of neuromorphic architecture, such as stateful neuron models communicating with event-driven spikes, in order to address limitations of the first generation Loihi. Here we explore and characterize some of these generalizations, such as sigma-delta enca

Emiko Arahata, Tetsuro Nikuni

We develop a simulation method for evaporative cooling of trapped Bose-Einstein condensate at finite temperatures using Zaremba-Nikuni-Griffin (ZNG) formalism. ZNG formalism includes the generalized GP equation and a semiclassical kinetic equation for the thermal cloud, which treats the excitations semiclassically within the Hartree Fock approximation. The g

Mohamed Aghzal, Erion Plaku, Ziyu Yao

Large language models (LLMs) have achieved remarkable success across a wide spectrum of tasks; however, they still face limitations in scenarios that demand long-term planning and spatial reasoning. To facilitate this line of research, in this work, we propose a new benchmark, termed $\textbf{P}$ath $\textbf{P}$lanning from $\textbf{N}$atural $\textbf{L}$ang

Siavash Hosseini

In today's modern world, software plays a pivotal role. Software development is a highly complex and time-consuming process, demanding multidimensional efforts. Companies continually adapt their requirements to align with the evolving environment, with a specific emphasis on rapid delivery and the acceptance of changing requirements. Traditional models, such

Min Zhang, Tong Zhang, Xiaoping Xie

This paper develops a weak Galerkin (WG) finite element method of arbitrary order for the steady incompressible Magnetohydrodynamics equations. The WG scheme uses piecewise polynomials of degrees $k(k\geq 1),k,k-1$, and $k-1$ respectively for the approximations of the velocity, the magnetic field, the pressure, and the magnetic pseudo-pressure in the interio

Ewerton R. Vieira, Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados

Estimating the region of attraction (${\tt RoA}$) for a robot controller is essential for safe application and controller composition. Many existing methods require a closed-form expression that limit applicability to data-driven controllers. Methods that operate only over trajectory rollouts tend to be data-hungry. In prior work, we have demonstrated that t

Qiao Xue, Jacob L. Bean, Michael Zhang, Luis Welbanks

We present the transmission spectrum of the original transiting hot Jupiter HD\,209458b from 2.3 -- 5.1 $\mu$m as observed with the NIRCam instrument on the James Webb Space Telescope (JWST). Previous studies of HD 209458b's atmosphere have given conflicting results on the abundance of H$_2$O and the presence of carbon- and nitrogen-bearing species, which ha

Jongho Park, Keiichi Asada, Do-Young Byun

We present a new method of time-dependent instrumental polarization calibration for Very Long Baseline Interferometry (VLBI). This method has been implemented in the recently developed polarization calibration pipeline GPCAL. Instrumental polarization, also known as polarimetric leakage, is a direction-dependent effect, and it is not constant across the beam

Mingxuan Zhang, Yan Sun, Faming Liang

Sparse deep learning has become a popular technique for improving the performance of deep neural networks in areas such as uncertainty quantification, variable selection, and large-scale network compression. However, most existing research has focused on problems where the observations are independent and identically distributed (i.i.d.), and there has been

Jongho Park, Keiichi Asada, Do-Young Byun

In this series of papers, we present new methods of frequency- and time-dependent instrumental polarization calibration for Very Long Baseline Interferometry (VLBI). In most existing calibration tools and pipelines, it has been assumed that instrumental polarization is constant over frequency within the instrument bandwidth and over time. The assumption is n

Alon Duvall, Eduardo Sontag

It is often of interest to know which systems will approach a periodic trajectory when given a periodic input. Results are available for certain classes of systems, such as contracting systems, showing that they always entrain to periodic inputs. In contrast to this, we demonstrate that there exist systems which are globally exponentially stable yet do not e

Awni Altabaa, John Lafferty

An evolving area of research in deep learning is the study of architectures and inductive biases that support the learning of relational feature representations. In this paper, we address the challenge of learning representations of hierarchical relations--that is, higher-order relational patterns among groups of objects. We introduce "relational convolution

Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Noah R. Carver

This paper aims to improve the computational efficiency of motion planning for mobile robots with non-trivial dynamics through the use of learned controllers. Offline, a system-specific controller is first trained in an empty environment. Then, for the target environment, the approach constructs a data structure, a "Roadmap with Gaps," to approximately learn

Linsen Zhang, Jianjun Tao

Isolated turbulent bands observed in transitional channel flows have downstream heads and inclined bulks at a characteristic angle. In the large-scale mean flow, a $\nu$-shape vortex found at the head elongates into the bulk part, forming a pair of counter-rotating vortex tube structures. It is revealed numerically and theoretically that the head and the bul

Hayyu Imanda, Kasper Rasmussen

In this paper we propose a protocol that can be used to covertly send a distress signal through a seemingly normal webserver, even if the adversary is monitoring both the network and the user's device. This allows a user to call for help even when they are in the same physical space as their adversaries. We model such a scenario by introducing a strong adver

Shifeng Huang, Ning Jiang, Rong-Feng Shen, Tinggui Wang

ASASSN-14ko was identified as an abnormal periodic nuclear transient with a potential decreasing period. Its outbursts in the optical and UV bands have displayed a consistent and smooth "fast-rise and slow-decay" pattern since its discovery, which has recently experienced an unexpected alteration in the last two epochs, as revealed by our proposed high-caden

Nerissa Nance, Andrew Mertens, Thomas Gerds, Zeyi Wang

The causal roadmap is a formal framework for causal and statistical inference that supports clear specification of the causal question, interpretable and transparent statement of required causal assumptions, robust inference, and optimal precision. The roadmap is thus particularly well-suited to evaluating longitudinal causal effects using large scale regist

Quanqi Hu, Dixian Zhu, Tianbao Yang

This paper investigates new families of compositional optimization problems, called $\underline{\bf n}$on-$\underline{\bf s}$mooth $\underline{\bf w}$eakly-$\underline{\bf c}$onvex $\underline{\bf f}$inite-sum $\underline{\bf c}$oupled $\underline{\bf c}$ompositional $\underline{\bf o}$ptimization (NSWC FCCO). There has been a growing interest in FCCO due to

Tianji Cong, Madelon Hulsebos, Zhenjie Sun, Paul Groth

Language models and specialized table embedding models have recently demonstrated strong performance on many tasks over tabular data. Researchers and practitioners are keen to leverage these models in many new application contexts; but limited understanding of the strengths and weaknesses of these models, and the table representations they generate, makes th

CMS Collaboration

The azimuthal anisotropy of $\Upsilon$(1S) mesons in high-multiplicity proton-lead collisions is studied using data collected by the CMS experiment at a nucleon-nucleon center-of-mass energy of 8.16 TeV. The $\Upsilon$(1S) mesons are reconstructed using their dimuon decay channel. The anisotropy is characterized by the second Fourier harmonic coefficients, f

Xinyang Ren, Hannah A Burkhardt, Patricia A Areán, Thomas D Hull

Prior work has shown that analyzing the use of first-person singular pronouns can provide insight into individuals' mental status, especially depression symptom severity. These findings were generated by counting frequencies of first-person singular pronouns in text data. However, counting doesn't capture how these pronouns are used. Recent advances in neura