October 2023 arXiv papers — page 138

Zhikai Li, Xiaoxuan Liu, Banghua Zhu, Zhen Dong

Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks. Fine-tuning these pretrained models on downstream datasets provides further significant performance gains; however, this process typically requires a large number of expensive, high-end GPUs. Although there have been efforts focused on

Zhiyu Chen, Yujie Lu, William Yang Wang

Mental illness remains one of the most critical public health issues of our time, due to the severe scarcity and accessibility limit of professionals. Psychotherapy requires high-level expertise to conduct deep, complex reasoning and analysis on the cognition modeling of the patients. In the era of Large Language Models, we believe it is the right time to de

A. Baha Balantekin, George M. Fuller, Anupam Ray, Anna M. Suliga

The neutrinos in the diffuse supernova neutrino background (DSNB) travel over cosmological distances and this provides them with an excellent opportunity to interact with dark relics. We show that a cosmologically-significant relic population of keV-mass sterile neutrinos with strong self-interactions could imprint their presence in the DSNB. The signatures

J. L. Qiu, H. Tong, H. G. Wang

The rotating vector model and radius-to-frequency mapping in the presence of multipole magnetic field in pulsars and magnetars are considered. An axisymmetric potential field is assumed. It is found that: (1) The radiation beam in the case of multipole field is wider than the dipole case. This may account the increasing pulse width at higher frequency of pul

Yunke Wang, Minjing Dong, Yukun Zhao, Bo Du

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing wi

Timo Breuer, Norbert Fuhr, Philipp Schaer

Evaluating retrieval performance without editorial relevance judgments is challenging, but instead, user interactions can be used as relevance signals. Living labs offer a way for small-scale platforms to validate information retrieval systems with real users. If enough user interaction data are available, click models can be parameterized from historical se

Yuankun Tang, Anjie Zhang, Miaowen Wen, Yu Huang

Affine frequency division multiplexing (AFDM) is an emerging multicarrier waveform that offers a potential solution for achieving reliable communications over time-varying channels. This paper proposes two maximum-likelihood (ML) estimators of symbol time offset and carrier frequency offset for AFDM systems. One is called joint ML estimator, which evaluates

Xiaoyan Zhu, Hui Li, Jing Meng, Xinwei Feng

A series of Fe$_x$Rh$_{100-x}$ ($30 \leq x \leq 57$) films were epitaxially grown using magnetron sputtering, and were systematically studied by magnetization-, electrical resistivity-, and Hall resistivity measurements. After optimizing the growth conditions, phase-pure Fe$_{x}$Rh$_{100-x}$ films were obtained, and their magnetic phase diagram was revisited

Sanker Timsina, Taha Hammadia, Sahar Gholami Milani, Filomeno S. de Aguiar Júnior

Raman scattering of photons into phonons gives rise to entangled photon pairs when the phonon emitted in a Stokes process is coherently absorbed in antiStokes scattering, forming the photonic analog of Cooper pairs. We present a nonperturbative theory for the time evolution of photonic Cooper pairs that treats interacting photons and phonons as a hybrid exci

Byeongjun Park, Sangmin Woo, Hyojun Go, Jin-Young Kim

Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplored area in designing neural architectures that explicitly incorporate MTL into the framework of diffusion mode

Zhongfen Deng, Wei-Te Chen, Lei Chen, Philip S. Yu

Product attribute value extraction plays an important role for many real-world applications in e-Commerce such as product search and recommendation. Previous methods treat it as a sequence labeling task that needs more annotation for position of values in the product text. This limits their application to real-world scenario in which only attribute values ar

Dar Gilboa, Hagay Michaeli, Daniel Soudry, Jarrod R. McClean

Training and inference with large machine learning models that far exceed the memory capacity of individual devices necessitates the design of distributed architectures, forcing one to contend with communication constraints. We present a framework for distributed computation over a quantum network in which data is encoded into specialized quantum states. We

Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle Ungar

Understanding how styles differ across languages is advantageous for training both humans and computers to generate culturally appropriate text. We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare styles across languages. Our framework (1) generates comprehensive style lexica in any language and (2) consol

Steph Sallum, Andrew Skemer, Deno Stelter, Ravinder Banyal

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES) is a $2-5~\mu$m, high-contrast integral field spectrograph (IFS) currently being built for Keck Observatory. With both low ($R\lesssim250$) and medium ($R\sim3500-7000$) spectral resolution IFS modes, SCALES will detect and characterize significantly colder exoplanets than those a

Shunke Ai, He Gao, Yong Yuan, Bing Zhang

The maximum mass of neutron stars ($M_{\rm TOV}$) plays a crucial role in understanding their equation of state (EoS). Previous studies have used the measurements for the compactness of massive pulsars and the tidal deformability of neutron stars in binary neutron star (BNS) mergers to constrain the EoS and thus the $M_{\rm TOV}$. The discovery of the most m

Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti, Kristjan Greenewald

We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical relative testing based on first and second order stochastic dominance of real random variables. We show that the second order statistics in this test are linked to mean-risk model

Phi Nguyen Van, Duc Tran Minh, Hieu Pham Huy, Long Tran Quoc

Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant achievements in various image and video generation tasks, including the domain of medical imaging. However, generating echocardiography videos based on semantic anatomical information remains an unexplored area of research. This is mostly due to the constraints imposed by the curr

Wenzhao Zhang, Cheng Guo, Yi Gao, Wei Dong

Structural Health Monitoring (SHM) is crucial for the safety and maintenance of various infrastructures. Due to the large amount of data generated by numerous sensors and the high real-time requirements of many applications, SHM poses significant challenges. Although the cloud-centric stream computing paradigm opens new opportunities for real-time data proce

Guangwen Li, Xiao Yu

This paper introduces three key initiatives in the pursuit of a hybrid decoding framework characterized by superior decoding performance, high throughput, low complexity, and independence from channel noise variance. Firstly, adopting a graphical neural network perspective, we propose a design methodology for a family of neural min-sum variants. Our explorat

Yuchao Huang, Junjie Wang, Zhe Liu, Yawen Wang

Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers need to reproduce the crash, which is an extremely time-consuming and tedious task. Existing studies conducted the automatic crash reproduction with the natural language described rep

Jingyu Shi, Rahul Jain, Hyungjun Doh, Ryo Suzuki

Generative AI (GenAI) has shown remarkable capabilities in generating diverse and realistic content across different formats like images, videos, and text. In Generative AI, human involvement is essential, thus HCI literature has investigated how to effectively create collaborations between humans and GenAI systems. However, the current literature lacks a co

Yvette Perrott

At temperatures above ~5 keV, the non-relativistic approximation used to derive the classical thermal Sunyaev-Zel'dovich effect spectrum begins to fail. When relativistic effects are included, the spectrum becomes temperature-dependent. This leads to both a problem and an opportunity: a problem, because when the temperature dependence is not accounted for th

Binke Xia, Jingzheng Huang, Hongjing Li, Zhongyuan Luo

The manipulation and metrology of light beams are pivotal for optical science and applications. In particular, achieving ultra-high precision in the measurement of light beam rotations has been a long-standing challenge. Instead of utilizing quantum probes like entangled photons, we address this challenge by incorporating a quantum strategy called "indefinit

Yuta Matsumoto

Age-period-cohort (APC) analysis is one of the fundamental time-series analyses used in the social sciences. This paper evaluates APC analysis via systematic simulation in term of how well the artificial parameters are recovered. We consider three models of Bayesian regularization using normal prior distributions: the random effects model with reference to m

Qitong Gao, Ge Gao, Juncheng Dong, Vahid Tarokh

Off-policy evaluation (OPE) is important for closing the gap between offline training and evaluation of reinforcement learning (RL), by estimating performance and/or rank of target (evaluation) policies using offline trajectories only. It can improve the safety and efficiency of data collection and policy testing procedures in situations where online deploym

Zhiqing Wei, Ling Zhang, Gaofeng Nie, Huici Wu

To accommodate Machine-type Communication (MTC) service, the wireless network needs to support low-delay and low-jitter data transmission, realizing delay deterministic wireless network. This paper analyzes the delay and jitter of the wireless network with and without spectrum sharing. When sharing the spectrum of the licensed network, the spectrum band of w

Jun Li, Minqing Zhang, Ke Niu, Yingnan Zhang

Video steganography has the potential to be used to convey illegal information, and video steganalysis is a vital tool to detect the presence of this illicit act. Currently, all the motion vector (MV)-based video steganalysis algorithms extract feature sets directly on the MVs, but ignoring the steganograhic operation may perturb the statistics distribution

Shobhit Gupta, Yizhong Huang, Shihan Liu, Yuxiang Pei

Optically active solid-state spin qubits thrive as an appealing technology for quantum interconnect and quantum networking, owing to their atomic size, scalable creation, long-lived coherence, and ability to coherently interface with flying qubits. Trivalent erbium dopants in particular emerge as a compelling candidate with their telecom C band emission and

Andrew Eberhardt, Alvaro Zamora, Michael Kopp, Tom Abel

The classical field approximation is widely used to better understand the predictions of ultra-light dark matter. Here, we use the truncated Wigner approximation method to test the classical field approximation of ultra-light dark matter. This method approximates a quantum state as an ensemble of independently evolving realizations drawn from its Wigner func

Ruta Jawale, Dakshita Khurana

A non-interactive ZK (NIZK) proof enables verification of NP statements without revealing secrets about them. However, an adversary that obtains a NIZK proof may be able to clone this proof and distribute arbitrarily many copies of it to various entities: this is inevitable for any proof that takes the form of a classical string. In this paper, we ask whethe

Mark Bishop, Yvette Perrott, Tulasi Parashar, Sean Oughton

Accurate estimation of galaxy cluster masses is a central problem in cosmology. Turbulence is believed to introduce significant deviations from the hydrostatic mass estimates. Estimation of turbulence properties is complicated by projection of the 3D cluster onto the 2D plane of the sky, and is commonly done in the form of indirect probes from fluctuations i

Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos

Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime. Traditional offline approaches for engineering compliance often involve modelling at a

Binke Xia, Jingzheng Huang, Hongjing Li, Han Wang

Achieving the ultimate precisions for multiple parameters simultaneously is an outstanding challenge in quantum physics, because the optimal measurements for incompatible parameters cannot be performed jointly due to the Heisenberg uncertainty principle. In this work, a criterion proposed for multiparameter estimation provides a possible way to beat this cur

Vinothkumar Latchoumanane, Murugan Varadhan

A graph $G$ with $p$ vertices and $q$ edges has an antimagic labelling if there is a bijection from the graph's edge set to the label set $\left\{1,2, \cdots, q \right\}$ such that $p$ vertices must have distinct vertex sums, where the vertex sums are determined by adding up all the edge labels incident to each vertex $v$ in $V(G)$. Hartsfield and Ringel \ci

Chengyang Shao

The spherical capillary water waves equation describes the motion of an almost spherical water droplet under zero gravity governed by water-air interface tension. Using para-differential calculus on compact Lie groups and homogeneous spaces developed by the author, the system is symmetrized into a quasi-linear dispersive para-differential equation of order 1

Zhihao Ge, Wenshuai Hu

In this paper, we develop a multiphysics finite element method for solving the quasi-static thermo-poroelasticity model with nonlinear permeability. The model involves multiple physical processes such as deformation, pressure, diffusion and heat transfer. To reveal the multi-physical processes of deformation, diffusion and heat transfer, we reformulate the o

Graham S. Kerr, Adam F. Kowalski, Joel C. Allred, Adrian N. Daw

The bulk of solar flare energy is deposited in the chromosphere. Flare ribbons and footpoints in the chromosphere therefore offer great diagnostic potential of flare energy release and transport processes. High quality observations from the IRIS spacecraft have transformed our view of the Sun's atmospheric response to flares. Since most of the chromospheric

Ye Li, Chen Wang

At the peak of the tech bubble, only 0.57% of market valuation comes from dividends in the next year. Taking the ratio of total market value to the value of one-year dividends, we obtain a valuation-based duration of 175 years. In contrast, at the height of the global financial crisis, more than 2.2% of market value is from dividends in the next year, implyi

Xueqi Yang, Mariusz Jakubowski, Li Kang, Haojie Yu

As software projects rapidly evolve, software artifacts become more complex and defects behind get harder to identify. The emerging Transformer-based approaches, though achieving remarkable performance, struggle with long code sequences due to their self-attention mechanism, which scales quadratically with the sequence length. This paper introduces SparseCod

Gang Cui, Kai Jiang, Tiejun Zhou

In this work, we propose an efficient nullspace-preserving saddle search (NPSS) method for a class of phase transitions involving translational invariance, where the critical states are often degenerate. The NPSS method includes two stages, escaping from the basin and searching for the index-1 generalized saddle point. The NPSS method climbs upward from the

Rong Jiang, Keming Yu

Extremile (Daouia, Gijbels and Stupfler,2019) is a novel and coherent measure of risk, determined by weighted expectations rather than tail probabilities. It finds application in risk management, and, in contrast to quantiles, it fulfills the axioms of consistency, taking into account the severity of tail losses. However, existing studies (Daouia, Gijbels an

Ariel Goldstein, Eric Ham, Mariano Schain, Samuel Nastase

Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholinguistic models, DLMs use layered sequences of continuous numerical vectors to represent words and context, allowing a plethora of emerging applications such as human-like text gener

Andreea Iorga

In this paper, we prove, under a technical assumption, that any semi-direct product of a $p$-group $G$ with a group $\Phi$ of order prime to $p$ can appear as the Galois group of a tower of extensions $H/K/F$ with the property that $H$ is the maximal pro-$p$ extension of $K$ that is unramified everywhere, and $\operatorname{Gal}(H/K) = G$. A consequence of t

Jingyuan Zhang, Xian'an Jin, Weigen Yan, Qinghai Liu

As a variant of the Ulam's vertex reconstruction conjecture and the Harary's edge reconstruction conjecture, Cvetkovi\'c and Schwenk posed independently the following problem: Can the characteristic polynomial of a simple graph $G$ with vertex set $V$ be reconstructed from the characteristic polynomials of all subgraphs in $\{G-v|v\in V\}$ for $|V|\geq 3$? T

Sakshi Mishra, Roohallah Khatami, Yu Christine Chen

For more than a century, the grid has operated in a centralized top-down fashion. However, as distributed energy resources (DERs) penetration grows, the grid edge is increasingly infused with intelligent computing and communication capabilities. Thus, the bottom-up approach to grid operations inclined toward decentralizing energy systems will likely gain mom

Vadim M. Uritsky, James A. Klimchuk

We report results of an in-depth numerical investigation of three-dimensional projection effects which could influence the observed loop-like structures in an optically thin solar corona. Several archetypal emitting geometries are tested, including collections of luminous structures with circular cross-sections of fixed and random size, light-emitting struct

Heedong Do, Namyoon Lee, Robert W. Heath, Angel Lozano

With increasing frequencies, bandwidths, and array apertures, the phenomenon of beam squint arises as a serious impairment to beamforming. Fully digital arrays with true time delay per antenna element are a potential solution, but they require downconversion at each element. This paper shows that hybrid arrays can perform essentially as well as digital array

Yixin Liu, Chenrui Fan, Xun Chen, Pan Zhou

As Graph Neural Networks (GNNs) become increasingly prevalent in a variety of fields, from social network analysis to protein-protein interaction studies, growing concerns have emerged regarding the unauthorized utilization of personal data. Recent studies have shown that imperceptible poisoning attacks are an effective method of protecting image data from s

Jaesuk Hwang

Radiative cooling, taking advantage of the coldness of the sky, has a potential to be a sustainable alternative to meet cooling needs. The performance of a radiative cooling device is fundamentally limited by the emissivity of the sky, therefore depends heavily on the regional weather conditions. Although the sky emissivity is known to increase with the dew

Benjamin Kereopa-Yorke

In a digital epoch where cyberspace is the emerging nexus of geopolitical contention, the melding of information operations and Large Language Models (LLMs) heralds a paradigm shift, replete with immense opportunities and intricate challenges. As tools like the Mistral 7B LLM (Mistral, 2023) democratise access to LLM capabilities (Jin et al., 2023), a vast s

Maoqi Liu, Xun Zhang, Hailei Gong, Changchun Liu

The share-of-choice (SOC) problem is a widely studied problem for product line design (PLD) where representative customers are sampled from a target population and the percentage of the ones who choose the offered products over outside options over the sample is maximized. The utility maximization framework captures individual choices. A significant challeng

Gui-Qiang G. Chen

This article is a survey of Cathleen Morawetz's contributions to the mathematical theory of transonic flows, shock waves, and partial differential equations of mixed elliptic-hyperbolic type. The main focus is on Morawetz's fundamental work on the non-existence of continuous transonic flows past profiles, Morawetz's program regarding the construction of glob

Shawn Tan, Yikang Shen, Zhenfang Chen, Aaron Courville

The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers. Empirical evidence shows that UTs have better compositional generalization than Vanilla Transformers (VTs) in formal language tasks. The parameter-sharing also affords it better parameter efficiency than VTs. Despite its many advantages, scaling UT parame

XueGuang Zhang

In the manuscript, effects of Tidal Disruption Events (TDEs) are estimated on long-term AGN variability, to provide interesting clues to detect probable hidden TDEs in normal broad line AGN with apparent intrinsic variability which overwhelm the TDEs expected variability features, after considering the unique TDEs expected variability patterns. Based on theo

Jiho Shin, Clark Tang, Tahmineh Mohati, Maleknaz Nayebi

The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prompt engineering and fine-tuning. Prompt engineering involves applying different strategies to query LLMs, like ChatGPT, while fine-tuning further adapts pre-trained models, such as

Kota Ido, Takahiro Misawa

The realization of topological insulators induced by correlation effects is one of the main issues of modern condensed matter physics. An intriguing example of the correlated topological insulators is a magnetic Chern insulator induced by a noncoplanar multiple-Q magnetic order. Although the realization of the magnetic Chern insulator has been studied in the

Arushi Sharma, Abhibha Gupta, Maneesh Bilalpur

To advance argumentative stance prediction as a multimodal problem, the First Shared Task in Multimodal Argument Mining hosted stance prediction in crucial social topics of gun control and abortion. Our exploratory study attempts to evaluate the necessity of images for stance prediction in tweets and compare out-of-the-box text-based large-language models (L

Sameer Pokhrel, Sameh A. Eisa

This paper provides a rigorous derivation for what is known in the literature as the Lie bracket approximation of control-affine systems in a more general and sequential framework for higher-orders. In fact, by using chronological calculus, we show that said Lie bracket approximations can be derived, and considered, as higher-order averaging terms. Hence, th

Jieting Long, Zewei Shi, Penghao Jiang, Yidong Gan

Document-based Visual Question Answering poses a challenging task between linguistic sense disambiguation and fine-grained multimodal retrieval. Although there has been encouraging progress in document-based question answering due to the utilization of large language and open-world prior models\cite{1}, several challenges persist, including prolonged respons

K. Whitley, A. Kuznetsova, K. Gültekin, M. Ruszkowski

We investigate the time-varying electromagnetic emission of a low-mass-ratio supermassive black hole binary (SMBHB) embedded in a circumprimary disk, with a particular interest in variability of shocks driven by the binary. We perform a 2D, locally isothermal hydrodynamics simulation of a SMBHB with mass ratio $q=0.01$ and separation $a=100\;R_g$, using a ph

Carlo da Cunha, Nobuyuki Aoki, David Ferry, Kevin Vora

In the realm of quantum-effect devices and materials, two-dimensional electron gases (2DEGs) stand as fundamental structures that promise transformative technologies. However, the presence of impurities and defects in 2DEGs poses substantial challenges, impacting carrier mobility, conductivity, and quantum coherence time. To address this, we harness the powe

Ranjita Naik, Varun Chandrasekaran, Mert Yuksekgonul, Hamid Palangi

Large language models (LLMs) are documented to struggle in settings that require complex reasoning. Nevertheless, instructing the model to break down the problem into smaller reasoning steps, or ensembling various generations through modifying decoding steps boosts performance. However, these methods assume that the input prompt is fixed and expect the decod

Mohini Pachchigar, Yvette Perrott, Tulasi Parashar

Mass is the most fundamental property of galaxy clusters. However, measuring it is still a challenge. Calibrating mass from intracluster medium observables such as the Sunyaev-Zel'dovich (SZ) effect is subject to uncertainty and biases because of the hydrostatic equilibrium assumption. On the other hand, merging cluster systems have been shown to exhibit rad

Adway Das, Abhishek Kumar Prajapati, Pengxiang Zhang, Mukund Srinath

Traditional methods of collecting user feedback through transit surveys are often time-consuming, resource intensive, and costly. In this paper, we propose a novel NLP-based framework that harnesses the vast, abundant, and inexpensive data available on social media platforms like Twitter to understand users' perceptions of various service issues. Twitter, be

Hao Song, Han Ma, Catherine Kallin, Sung-Sik Lee

Metals can undergo geometric quantum phase transitions where the local curvature of the Fermi surface changes sign without a change in symmetry or topology. At the inflection points on the Fermi surface, the local curvature vanishes, leading to an anomalous dynamics of quasiparticles. In this paper, we study geometric quantum critical metals that support inf

Pieter Belmans, Sergey Galkin, Swarnava Mukhopadhyay

We introduce graph potentials, which are Laurent polynomials associated to (colored) trivalent graphs. We show that the birational type of the graph potential only depends on the homotopy type of the colored graph, and use this to define a topological quantum field theory. A similar construction was recently introduced independently by Kontsevich--Odesskii u

Aureo de Paula, Imran Rasul, Pedro Souza

Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model,

Marius Arvinte, Cory Cornelius, Jason Martin, Nageen Himayat

Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the training data distribution. In this work, we investigate the robustness of density estimation using the probability flow (PF) neural ordinary differential equation (ODE) model agains

Anh Xuan Do, Joshua Flynn, Nguyen Lam, Guozhen Lu

We establish a general identity (Theorem 1.2) that implies both the $L^{p}$-Hardy identities and the $L^{p}$-Caffarelli-Kohn-Nirenberg identities (Theorems 1.3 and 1.4) and $L^{p}$-Hardy inequalities and the $L^{p}$-Caffarelli-Kohn-Nirenberg inequalities (Theorems 1.5 and 1.6)). Weighted $L^{p}$-Caffarelli-Kohn-Nirenberg inequalities with nonradial weights a

Raul A. Monsalve, Christian H. Bye, Jonathan L. Sievers, Vadym Bidula

The Mapper of the IGM Spin Temperature (MIST) is a new ground-based, single-antenna, radio experiment attempting to detect the global 21 cm signal from the Dark Ages and Cosmic Dawn. A significant challenge in this measurement is the frequency-dependence, or chromaticity, of the antenna beam directivity. MIST observes with the antenna above the soil and with

Ilias Mitrai, Prodromos Daoutidis

The repeated solution of large-scale optimization problems arises frequently in process systems engineering tasks. Decomposition-based solution methods have been widely used to reduce the corresponding computational time, yet their implementation has multiple steps that are difficult to configure. We propose a machine learning approach to learn the optimal i

Emmy Liu, Aditi Chaudhary, Graham Neubig

Idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. Despite significant advances, machine translation systems still struggle to translate idiomatic expressions. We provide a simple characterization of idiomatic translation and related issues. This allows us t

Xiaoxue Zhang, Yifan Hua, Chen Qian

Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To avoid the single point of failure problem in FL, decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation, which has been co

Luis H. Gallardo, Joshua Zelinsky

In this note, we fix a gap in a proof of the first author that 28 is the only even perfect number which is the sum of two perfect cubes. We also discuss the situation for higher powers.

Kexun Zhang, Hongqiao Chen, Lei Li, William Wang

Instruction-tuned large language models (LLMs) excel at many tasks but often fail to use external tools due to complicated and unfamiliar syntax constraints. While extensive fine-tuning and prompting can mitigate the issue, these approaches are expensive and hard to generalize. Furthermore, because syntax constraints are only learned implicitly during fine-t

Shuang Liang, Renata Turkeš, Jiayi Li, Nina Otter

Persistent homology (PH) is a method for generating topology-inspired representations of data. Empirical studies that investigate the properties of PH, such as its sensitivity to perturbations or ability to detect a feature of interest, commonly rely on training and testing an additional model on the basis of the PH representation. To gain more intrinsic ins

Kial Stewart, William Palm, Callum Shakespeare, Noa Kraitzman

Pound for pound, sea ice is the most important component of Earth's climate system. The changing conditions in which sea ice forms and exists are likely to affect the properties of sea ice itself, and potential climate feedbacks need to be identified and understood to improve future projections. Here we perform a set of idealised experiments which model sea

Md Ishat-E-Rabban, Pratap Tokekar

Recently, a number of learning-based models have been proposed for multi-robot navigation. However, these models lack memory and only rely on the current observations of the robot to plan their actions. They are unable to leverage past observations to plan better paths, especially in complex environments. In this work, we propose a fully differentiable and d

Ibrahim Habiballah, Wasiu Sulaimon, Fahad Al-Ismail

This letter presents a novel non-iterative power flow solution for radial distribution systems. In the pursuit of a linear power flow solution that seamlessly integrates into other power system operations, an approximate solution via complex linearization of non-holomorphic functions, making no assumptions about the network's parameters was developed. This a

Ilias Mitrai, Prodromos Daoutidis

In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition-based solution method. In this approach, an optimization problem is represented as a graph that captures the structural and functional coupling among the variables and constraints of the problem via an appropriate set of feat

Sabbir Ahmed Chowdhury, Md Aminul Islam, Mostafa Azad Kamal

Big Data Analytics has gained tremendous momentum in many sectors worldwide. Big Data has substantial influence in the field of Learning Analytics that may allow academic institutions to better understand the learners needs and proactively address them. Hence, it is essential to understand Big Data and its application. With the capability of Big Data to find

Blake J. Boudreaux, Rasul Shafikov

We consider generalizations of rational convexity to Stein manifolds and prove related results

Raymond Ehlers

Jet substructure observables provide unique probes of the properties of the Quark-Gluon Plasma (QGP). In these proceedings we report new measurements of groomed jet substructure in central Pb-Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$ TeV. We present the first application of dynamical grooming in heavy-ion collisions to search for excess $k_{\mathrm{T,g}}$

Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou

When prompted with a few examples and intermediate steps, large language models (LLMs) have demonstrated impressive performance in various reasoning tasks. However, prompting methods that rely on implicit knowledge in an LLM often generate incorrect answers when the implicit knowledge is wrong or inconsistent with the task. To tackle this problem, we present

Austin Cummings

The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2) experiment is a pathfinder mission for future space-based instruments targeting the fluxes of Ultra-High Energy Cosmic Rays (UHECR), with energies exceeding 1EeV and very high energy diffuse and transient neutrinos, with energies exceeding 1PeV. Using two telescope designs: the

Zhihong Lei, Mingbin Xu, Shiyi Han, Leo Liu

Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level. The E2E systems implicitly model all conventional ASR components, such as the acoustic model (AM) and the language model (LM), in a single network trained on audio-text pairs. Despite this simpler syste

He Zhang, Chuhao Wu, Jingyi Xie, ChanMin Kim

Qualitative research delves deeply into individual complex perspectives on technology and various phenomena. However, a meticulous analysis of qualitative data often requires a significant amount of time, especially during the crucial coding stage. Although there is software specifically designed for qualitative evaluation, many of these platforms fall short

Prantik Deb, Lalith Bharadwaj Baru, Kamalaker Dadi, Bapi Raju S

Brain stroke has become a significant burden on global health and thus we need remedies and prevention strategies to overcome this challenge. For this, the immediate identification of stroke and risk stratification is the primary task for clinicians. To aid expert clinicians, automated segmentation models are crucial. In this work, we consider the publicly a

Xueren Ge, Satpathy Abhishek, Ronald Dean Williams, John A. Stankovic

Multi-label text classification (MLTC) tasks in the medical domain often face the long-tail label distribution problem. Prior works have explored hierarchical label structures to find relevant information for few-shot classes, but mostly neglected to incorporate external knowledge from medical guidelines. This paper presents DKEC, Domain Knowledge Enhanced C

Allison L. Carter, Jameson O'Reilly, George Toh, Sagnik Saha

Photonic interconnects between quantum systems will play a central role in both scalable quantum computing and quantum networking. Entanglement of remote qubits via photons has been demonstrated in many platforms; however, improving the rate of entanglement generation will be instrumental for integrating photonic links into modular quantum computers. We pres

Daniel Killough, Amy Pavel

People watch livestreams to connect with others and learn about their hobbies. Livestreams feature multiple visual streams including the main video, webcams, on-screen overlays, and chat, all of which are inaccessible to livestream viewers with visual impairments. While prior work explores creating audio descriptions for recorded videos, live videos present

B. Hawashin, J. Sirker, G. S. Uhrig

We investigate how topological Chern numbers can be defined when single-particle states hybridize with continua. We do so exemplarily in a bosonic Haldane model at zero temperature with an additional on-site decay of one boson into two and the conjugate fusion of two bosons into one. Restricting the Hilbert space to two bosons at maximum, the exact self-ener

Matthew J. Baumstark, Giuseppe Vinci

The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it cannot keep up with the massive influx of data from emerging sky surveys. A variety of approaches have been proposed to classify large numbers of

Chengyang Zhao, Yikang Shen, Zhenfang Chen, Mingyu Ding

Panoptic Scene Graph has recently been proposed for comprehensive scene understanding. However, previous works adopt a fully-supervised learning manner, requiring large amounts of pixel-wise densely-annotated data, which is always tedious and expensive to obtain. To address this limitation, we study a new problem of Panoptic Scene Graph Generation from Purel

Jose Avila

We present a common framework to study varieties in great generality from a categorical point of view. The main application of this study is in the setting of algebraic categories, where we introduce Birkhoff varieties which are essentially subvarieties of algebraic categories, and we get a generalization of Birkhoff's variety theorem. In particular, we show

Ayaka Usui, Anna Sanpera, María García Díaz

Local Hamiltonians, $H_k$, describe non-trivial $k$-body interactions in quantum many-body systems. Here, we address the dynamical simulatability of a $k$-local Hamiltonian by a simpler one, $H_{k'}$, with $k'<k$, under the realistic constraint that both Hamiltonians act on the same Hilbert space. When it comes to exact simulation, we build upon known method

Juan R. Chamorro, Julia L. Zuo, Euan N. Bassey, Aurland K. Watkins

${\lambda}$-RhO$_2$, a prototype 4d transition metal oxide, has been prepared by oxidative delithiation of spinel LiRh$_2$O$_4$ using ceric ammonium nitrate. Average-structure studies of this RhO$_2$ polytype, including synchrotron powder X-ray diffraction and electron diffraction, indicate the room temperature structure to be tetragonal, in the space group

John R. Birge

Optimization software enables the solution of problems with millions of variables and associated parameters. These parameters are, however, often uncertain and represented with an analytical description of the parameter's distribution or with some form of sample. With large numbers of such parameters, optimization of the resulting model is often driven by mi

John D Boom, Matthew Greenig, Pietro Sormanni, Pietro Liò

Score-based generative models (SGMs) have proven to be powerful tools for designing new proteins. Designing proteins that bind a pre-specified target is highly relevant to a range of medical and industrial applications. Despite the flurry of new SGMs in the last year, there has been little systematic exploration of the impact of design choices in SGMs for pr

Marvin Fritz

We study the Cahn-Hilliard-Biot model with respect to its mathematical well-posedness. The system models flow through deformable porous media in which the solid material has two phases with distinct material properties. The two phases of the porous material evolve according to a generalized Ginzburg-Landau energy functional, with additional influence from bo