October 2023 arXiv papers — page 32

Shih-Min Yang, Martin Magnusson, Johannes A. Stork, Todor Stoyanov

Many practically relevant robot grasping problems feature a target object for which all grasps are occluded, e.g., by the environment. Single-shot grasp planning invariably fails in such scenarios. Instead, it is necessary to first manipulate the object into a configuration that affords a grasp. We solve this problem by learning a sequence of actions that ut

Yuta Mizuno, Tamiki Komatsuzaki

We present a quantum algorithm that analyzes time series data simulated by a quantum differential equation solver. The proposed algorithm is a quantum version of the dynamic mode decomposition algorithm used in diverse fields such as fluid dynamics and epidemiology. Our quantum algorithm can also compute matrix eigenvalues and eigenvectors by analyzing the c

Jaime Oswaldo Gonzalez Maya, Fernando David Cúñez Benalcázar, Erick de Moraes Franklin

When a granular bed is sheared by a fluid that flows above a critical limit, it undergoes a complex motion that varies along time: it can contain fluid- (bedload) and solid-like (creep) regions, being prone to strain hardening and, in case of polydispersity, segregation. In this paper, we investigate experimentally the short- and long-time evolution of a bid

Soumen Koley, Jan Harms, Annalisa Allocca, Enrico Calloni

Terrestrial gravity perturbations caused by seismic fields produce the so-called Newtonian noise in gravitational-wave detectors, which is predicted to limit their sensitivity in the upcoming observing runs. In the past, this noise was seen as an infrastructural limitation, i.e., something that cannot be overcome without major investments to improve a detect

Zhe Bai, Abdelilah Essiari, Talita Perciano, Kristofer E. Bouchard

The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT

Muhammad Mohebujjaman, Julian Miranda, Md. Abdullah Al Mahbub, Mengying Xiao

We propose, analyze, and test a penalty projection-based efficient and accurate algorithm for the Uncertainty Quantification (UQ) of the time-dependent Magnetohydrodynamic (MHD) flow problems in convection-dominated regimes. The algorithm uses the Els\"asser variables formulation and discrete Hodge decomposition to decouple the stochastic MHD system into fou

Sergej Orlov, Alfonsas Juršėnas, Justas Baltrukonis, Vytautas Jukna

Bessel beams generated via axicons are widely used for various applications like optical tweezers or laser microfabrication of transparent materials. The specific intensity profile having high aspect ratio of beam width and length in turn generates high aspect ratio void that resembles a needle. In contrast to commonly generated Bessel beam that has a fixed

Onyekachi Emenike, Fred J. Hickernell, Peter Kritzer

A large literature specifies conditions under which the information complexity for a sequence of numerical problems defined for dimensions $1, 2, \ldots$ grows at a moderate rate, i.e., the sequence of problems is tractable. Here, we focus on the situation where the space of available information consists of all linear functionals and the problems are define

Sergej Orlov, Alfonsas Juršėnas, Ernestas Nacius

Bessel beams are known for their property of maintaining propagation-invariant transverse intensity distribution. The true Bessel beams has unlimited energy, however the experimental realization of limited energy Bessel-like beams provides almost diffraction-free beams over a certain distance. These beams are applicable to microfabrication, particle manipula

Efe Onaran, Omer Bobrowski, Robert J. Adler

We establish finite-dimensional central limit theorems for local, additive, interaction functions of temporally evolving point processes. The dynamics are those of a spatial Poisson process on the flat torus with points subject to a birth-death mechanism, and which move according to Brownian motion while alive. The results reveal the existence of a phase dia

Sathvik Nair, Philip Resnik

An important assumption that comes with using LLMs on psycholinguistic data has gone unverified. LLM-based predictions are based on subword tokenization, not decomposition of words into morphemes. Does that matter? We carefully test this by comparing surprisal estimates using orthographic, morphological, and BPE tokenization against reading time data. Our re

Tobias Hoek, Holger Caesar, Andreas Falkovén, Tommy Johansson

A scenario-based testing approach can reduce the time required to obtain statistically significant evidence of the safety of Automated Driving Systems (ADS). Identifying these scenarios in an automated manner is a challenging task. Most methods on scenario classification do not work for complex scenarios with diverse environments (highways, urban) and intera

Nathan Justin, Sina Aghaei, Andrés Gómez, Phebe Vayanos

We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data. This problem arises frequently in high stakes settings such as public health and social work where data is often collected using self-reported surveys which are highly sensitive to e.g., the framing of the questions, t

Nicholas Hurl, Farjana Siddiqua, Shuxian Xu

This paper analyzes a $\theta$-method and 3-point time filter. This approach adds one additional line of code to the existing source code of $\theta$-method. We prove the method's $0$-stability, accuracy, and $A$-stability for both constant time step and variable time step. Some numerical tests are performed to validate the theoretical results.

Aditya K Surikuchi, Sandro Pezzelle, Raquel Fernández

A proper evaluation of stories generated for a sequence of images -- the task commonly referred to as visual storytelling -- must consider multiple aspects, such as coherence, grammatical correctness, and visual grounding. In this work, we focus on evaluating the degree of grounding, that is, the extent to which a story is about the entities shown in the ima

Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi

We explore the idea of aligning an AI assistant by inverting a model of users' (unknown) preferences from observed interactions. To validate our proposal, we run proof-of-concept simulations in the economic ultimatum game, formalizing user preferences as policies that guide the actions of simulated players. We find that the AI assistant accurately aligns its

Gyeongsik Moon, Shunsuke Saito, Weipeng Xu, Rohan Joshi

The two-hand interaction is one of the most challenging signals to analyze due to the self-similarity, complicated articulations, and occlusions of hands. Although several datasets have been proposed for the two-hand interaction analysis, all of them do not achieve 1) diverse and realistic image appearances and 2) diverse and large-scale groundtruth (GT) 3D

K. Pfnuer, J. Luedke, K. Hoffmann, F. Weickert

We present details on the current measurement setup at PTB used for high precision loss calibrations in the frequency range 50 Hz to 1 kHz. A combination of analog and digital feedback control is utilized in accordance with the standard. A detailed measurement uncertainty (MU) analysis based on a systematic model equation is presented and inter-dependencies

Wojciech Jamroga, Peter Y. A. Ryan, Steve Schneider, Carsten Schurmann

A voting system should not merely report the outcome: it should also provide sufficient evidence to convince reasonable observers that the reported outcome is correct. Many deployed systems, notably paperless DRE machines still in use in US elections, fail certainly the second, and quite possibly the first of these requirements. Rivest and Wack proposed the

Heather Johnston, Olja Panic, Beibei Liu

To understand giant planet formation, we need to focus on host stars close to $1.7\ \rm M_{\odot}$, where the occurrence rate of these planets is the highest. In this initial study, we carry out pebble-driven core accretion planet formation modelling to investigate the trends and optimal conditions for the formation of giant planets around host stars in the

Matthew J Heaton, Jacob A. Johnson

Gaussian processes (GPs) are a highly flexible, nonparametric statistical model that are commonly used to fit nonlinear relationships or account for correlation between observations. However, the computational load of fitting a Gaussian process is $\mathcal{O}(n^3)$ making them infeasible for use on large datasets. To make GPs more feasible for large dataset

Ganesh Narasimha, Saban Hus, Arpan Biswas, Rama Vasudevan

Scanning Tunneling microscopy (STM) is a widely used tool for atomic imaging of novel materials and its surface energetics. However, the optimization of the imaging conditions is a tedious process due to the extremely sensitive tip-surface interaction, and thus limits the throughput efficiency. Here we deploy a machine learning (ML) based framework to achiev

Reinhard Laubenbacher, Fred Adler, Gary An, Filippo Castiglione

Medical digital twins are computational models of human biology relevant to a given medical condition, which can be tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between

Vandan Gorade, Sparsh Mittal, Debesh Jha, Ulas Bagci

In recent years, continuous latent space (CLS) and discrete latent space (DLS) deep learning models have been proposed for medical image analysis for improved performance. However, these models encounter distinct challenges. CLS models capture intricate details but often lack interpretability in terms of structural representation and robustness due to their

Clay H. Batton, Grant M. Rotskoff

The fluctuations of a nonequilibrium bath enable dynamics inaccessible to any equilibrium system. Exploiting the driven dynamics of active matter in order to do useful work has become a topic of significant experimental and theoretical interest. Due to the unique modalities controlling self-assembly, the interplay between passive solutes and the particles in

Zeyong Li

In a recent breakthrough, Chen, Hirahara and Ren prove that $\mathsf{S_2E}/_1 \not\subset \mathsf{SIZE}[2^n/n]$ by giving a single-valued $\mathsf{FS_2P}$ algorithm for the Range Avoidance Problem ($\mathsf{Avoid}$) that works for infinitely many input size $n$. Building on their work, we present a simple single-valued $\mathsf{FS_2P}$ algorithm for $\mathsf

Yuyang Deng, Mohammad Mahdi Kamani, Pouria Mahdavinia, Mehrdad Mahdavi

This paper advocates a new paradigm Personalized Empirical Risk Minimization (PERM) to facilitate learning from heterogeneous data sources without imposing stringent constraints on computational resources shared by participating devices. In PERM, we aim to learn a distinct model for each client by learning who to learn with and personalizing the aggregation

Fangyijie Wang, Michael Salter-Townshend

Functional magnetic resonance imaging or functional MRI (fMRI) is a very popular tool used for differing brain regions by measuring brain activity. It is affected by physiological noise, such as head and brain movement in the scanner from breathing, heart beats, or the subject fidgeting. The purpose of this paper is to propose a novel approach to handling fM

Liang Zhang, Junchi Yang, Amin Karbasi, Niao He

Algorithmic reproducibility measures the deviation in outputs of machine learning algorithms upon minor changes in the training process. Previous work suggests that first-order methods would need to trade-off convergence rate (gradient complexity) for better reproducibility. In this work, we challenge this perception and demonstrate that both optimal reprodu

Ruoyu Wang

Given a tree $T$ of order $n,$ one can contract any edge and obtain a new tree $T^{*}$ of order $n-1.$ In 1983, Jamison made a conjecture that the mean subtree order, i.e., the average order of all subtrees, decreases at least $\frac{1}{3}$ in contracting an edge of a tree. In 2023, Luo, Xu, Wagner and Wang proved the case when the edge to be contracted is a

Houcine Ben Dali

In a recent work, Maciej Do\l{}e\k{}ga and the author have given a formula of the expansion of the Jack polynomial $J^{(\alpha)}_\lambda$ in the power-sum basis as a non-orientability generating series of bipartite maps whose edges are decorated with the boxes of the partition $\lambda$. We conjecture here a variant of this expansion in which we restrict the

Sarasadat Foroughipoor, Kimia Moradi, Hamidreza Bolhasani

The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis an

Archie F. A. Bott, Steven C. Cowley, Alexander A. Schekochihin

In this paper, we investigate the kinetic stability of classical, collisional plasma - that is, plasma in which the mean-free-path $\lambda$ of constituent particles is short compared to the length scale $L$ over which fields and bulk motions in the plasma vary macroscopically, and the collision time is short compared to the evolution time. Fluid equations a

Teng Guo, Jingjin Yu

At modern warehouses, mobile robots transport packages and drop them into collection bins/chutes based on shipping destinations grouped by, e.g., the ZIP code. System throughput, measured as the number of packages sorted per unit of time, determines the efficiency of the warehouse. This research develops a scalable, high-throughput multi-robot parcel sorting

Ligeng Zhu, Lanxiang Hu, Ji Lin, Wei-Chen Wang

On-device learning and efficient fine-tuning enable continuous and privacy-preserving customization (e.g., locally fine-tuning large language models on personalized data). However, existing training frameworks are designed for cloud servers with powerful accelerators (e.g., GPUs, TPUs) and lack the optimizations for learning on the edge, which faces challeng

Shang-Min Tsai, Vivien Parmentier, João M. Mendonça, Xianyu Tan

The atmospheric dynamics of tidally-locked hot Jupiters is characterized by strong equatorial winds. Understanding the interaction between global circulation and chemistry is crucial in atmospheric studies and interpreting observations. Two-dimensional (2D) photochemical transport models shed light on how the atmospheric composition depends on circulation. I

Ahmed Magooda, Alec Helyar, Kyle Jackson, David Sullivan

We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and sociotechnical expertise and leverages the capabilities of state-of-the-art LLMs, such as GPT-4. We use this fram

Lidiya Murakhovs'ka, Philippe Laban, Tian Xie, Caiming Xiong

Making big purchases requires consumers to research or consult a salesperson to gain domain expertise. However, existing conversational recommender systems (CRS) often overlook users' lack of background knowledge, focusing solely on gathering preferences. In this work, we define a new problem space for conversational agents that aim to provide both product r

Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni

Time series anomaly detection is a vital task in many domains, including patient monitoring in healthcare, forecasting in finance, and predictive maintenance in energy industries. This has led to a proliferation of anomaly detection methods, including deep learning-based methods. Benchmarks are essential for comparing the performances of these models as they

Andrej Novak, Carlos Granja, Andrea Sagatova, Jan Jakubek

The hybrid architecture of the Timepix (TPX) family of detectors enables the use of different semiconductor sensors, most commonly silicon (Si), as well as high-density materials such as Cadmium Telluride (CdTe) or Gallium Arsenide (GaAs). For this purpose, we explore the potential of a silicon carbide (SiC) sensor bump-bonded on a Timepix3 detector as a rad

Evan Ning, David Kaeli

Prime numbers are fundamental in number theory and play a significant role in various areas, from pure mathematics to practical applications, including cryptography. In this contribution, we introduce a multithreaded implementation of the Segmented Sieve algorithm. In our implementation, instead of handling large prime ranges in one iteration, the sieving pr

Irene Gonzalvez, Alfredo Miranda, Julio D. Rossi

In this paper we analyze iterations of the obstacle problem for two different operators. We solve iteratively the obstacle problem from above or below for two different differential operators with obstacles given by the previous functions in the iterative process. When we start the iterations with a super or a subsolution of one of the operators this procedu

Ryan Jacobs, Jian Liu, Harry Abernathy, Dane Morgan

The discovery and design of new materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode a

Hanqing Wang, Yajing Luo, Boya Xiong, Guanhua Chen

Stylistic headline generation is the task to generate a headline that not only summarizes the content of an article, but also reflects a desired style that attracts users. As style-specific article-headline pairs are scarce, previous researches focus on unsupervised approaches with a standard headline generation dataset and mono-style corpora. In this work,

Yi Hu, Kai Ye, Hyeonjin Kim, Ning Lu

Inspired by the success of the Transformer model in natural language processing and computer vision, this paper introduces BERT-PIN, a Bidirectional Encoder Representations from Transformers (BERT) powered Profile Inpainting Network. BERT-PIN recovers multiple missing data segments (MDSs) using load and temperature time-series profiles as inputs. To adopt a

Daohan Wang, Jin-Hwan Cho, Jinheung Kim, Soojin Lee

In this study, we explore the phenomenological signatures associated with a light fermiophobic Higgs boson, $h_{\rm f}$, within the type-I two-Higgs-doublet model at the HL-LHC. Our meticulous parameter scan illuminates an intriguing mass range for $m_{h_{\rm f}}$, spanning $[1,10]{\;{\rm GeV}}$. This mass range owes its viability to substantial parameter po

Yabo Li, Mikhail Litvinov, Tzu-Chieh Wei

Recent years have witnessed a surge of interest in performing measurements within topological phases of matter, e.g., symmetry-protected topological (SPT) phases and topological orders. Notably, measurements of certain SPT states have been known to be related to Kramers-Wannier duality and Jordan-Wigner transformations, giving rise to long-range entangled st

Ang Li, Alessandro Baroni, Ionel Stetcu, Travis S. Humble

Numerical simulation is an important method for verifying the quantum circuits used to simulate low-energy nuclear states. However, real-world applications of quantum computing for nuclear theory often generate deep quantum circuits that place demanding memory and processing requirements on conventional simulation methods. Here, we present advances in high-p

D. S. Zohrabyan, M. M. Glazov

We have developed a theory of the anomalous Hall effect in two-dimensional electron gas in the case where the time of electron-electron collisions is much smaller than the transport relaxation time. The transition between the diffusion transport regime, when the momentum relaxation length of electrons is much smaller than the channel width, and the hydrodyna

Mohammad Akbari, Saeed Ranjbar Alvar, Behnam Kamranian, Amin Banitalebi-Dehkordi

Building multi-modal language models has been a trend in the recent years, where additional modalities such as image, video, speech, etc. are jointly learned along with natural languages (i.e., textual information). Despite the success of these multi-modal language models with different modalities, there is no existing solution for neural network architectur

Benjamin Hinrichs, Marius Lemm, Oliver Siebert

We consider the quantum dynamics of a many-fermion system in $\mathbb R^d$ with an ultraviolet regularized pair interaction as previously studied in [M. Gebert, B. Nachtergaele, J. Reschke, and R. Sims, Ann. Henri Poincar\'e 21.11 (2020)]. We provide a Lieb-Robinson bound under substantially relaxed assumptions on the potentials. We also improve the associat

Jason Crann, Rupert H. Levene, Ivan G. Todorov, Lyudmila Turowska

We develop a resource-theoretical approach that allows us to quantify values of two-player, one-round cooperative games with quantum inputs and outputs, as well as values of quantum probabilistic hypergraphs. We analyse the quantum game values arising from the type hierarchy of quantum no-signalling correlations, establishing tensor norm expressions for each

Haixia Chai, Michael Strube

Multilingual coreference resolution (MCR) has been a long-standing and challenging task. With the newly proposed multilingual coreference dataset, CorefUD (Nedoluzhko et al., 2022), we conduct an investigation into the task by using its harmonized universal morphosyntactic and coreference annotations. First, we study coreference by examining the ground truth

Jorge Martinez-Palomera, Christina Hedges, Jessie Dotson

NASA's \textit{Kepler} primary mission observed about 116 $deg^2$ in the sky for 3.5 consecutive years to discover Earth-like exoplanets. This mission recorded pixel cutouts, known as Target Pixel Files (TPFs), of over $200,000$ targets selected to maximize the scientific yield. The Kepler pipeline performed aperture photometry for these primary targets to c

Ramin Giahi, Reza Yousefi Maragheh, Nima Farrokhsiar, Jianpeng Xu

Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products. Despite the traditional machine learning models, Graph Neural Networks (GNNs), by design, can understand complex relations like similarity between products. However, in contrast to thei

Jordan Wilson-Gerow

Using a recently developed effective field theory formalism for extreme mass ratios [2308.14832], we present a calculation of charged black hole scattering at third post-Minkowskian order. The charges and masses are kept arbitrary, and the result interpolates from the scattering of Schwarzschild to extremal charged black holes, and beyond to charged particle

Yayi Fu

We introduce the notion of strongly $\binom{k}{2}$-free graphs, which contain dp-minimal graphs. We show that under some sparsity assumption, given a rainbow $\binom{k}{2}$-free blockade we can find a rainbow $\binom{k-1}{2}$-free blockade. This might serve as an intermediate step towards Erd\H os-Hajnal property for dp-minimal graphs.

Razib Hayat Khan, Jonayet Miah, S M Yasir Arafat, M M Mahbubul Syeed

This study delves into the application of graph neural networks in the realm of traffic forecasting, a crucial facet of intelligent transportation systems. Accurate traffic predictions are vital for functions like trip planning, traffic control, and vehicle routing in such systems. Three prominent GNN architectures Graph Convolutional Networks (Graph Sample

Kazi Ranjibul Islam, Andrey Chubukov

We analyze superconductivity in a multi-orbital fermionic system near the onset of a nematic order, using doped FeSe as an example. We associate the nematic order with spontaneous polarization between $d_{xz}$ and $d_{yz}$ orbitals. We derive the pairing interaction, mediated by soft nematic fluctuations, and show that it is attractive, and that its strength

Chaim Even-Zohar, Tsviqa Lakrec, Matteo Parisi, Ran Tessler

The amplituhedron $A_{n,k,m}(Z)$ is the image of the positive Grassmannian $Gr_{k,n}^{\geq 0}$ under the map ${Z}: Gr_{k,n}^{\geq 0} \to Gr_{k,k+m}$ induced by a positive linear map $Z:\mathbb{R}^n \to \mathbb{R}^{k+m}$. Motivated by a question of Hodges, Arkani-Hamed and Trnka introduced the amplituhedron in 2013 as a geometric object whose tilings conjectu

C. Ahdida, G. Arduini, K. Balazs, H. Bartosik

The Experimental Cavern North 3 (ECN3) is an underground experimental cavern on the CERN Pr\'evessin site. ECN3 currently hosts the NA62 experiment, with a physics programme devoted to rare kaon decays and searches of hidden particles approved until Long Shutdown 3 (LS3). Several options are proposed on the longer term in order to make best use of the worldw

Priya Hasan

The presence of gaps or regions of small numbers of stars in the main sequence of the Hertzsprung Russell Diagram (HRD) of star clusters has been reported in literature. This is interesting and significant as it could be related to star formation and/or rapid evolution or instabilities. In this paper, using Gaia DR3 photometry and confirmed membership data,

Mathieu Luisier, Cedric Klinkert, Sara Fiore, Jonathan Backman

Two-dimensional (2D) materials are particularly attractive to build the channel of next-generation field-effect transistors (FETs) with gate lengths below 10-15 nm. Because the 2D technology has not yet reached the same level of maturity as its Silicon counterpart, device simulation can be of great help to predict the ultimate performance of 2D FETs and prov

Zhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu

Quantization techniques are pivotal in reducing the memory and computational demands of deep neural network inference. Existing solutions, such as ZeroQuant, offer dynamic quantization for models like BERT and GPT but overlook crucial memory-bounded operators and the complexities of per-token quantization. Addressing these gaps, we present a novel, fully har

Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure

We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text instructions and visual egocentric observations and output actions directly in the environment. Using reinforcem

Alex Kim, Maximilian Muhn, Valeri Nikolaev

We explore the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk. We develop and validate firm-level measures of risk exposure to political, climate, and AI-related risks. Using the GPT 3.5 model to generate risk summaries and assessments from the context provided by earnings call transcripts, we show th

Jonayet Miah, Duc M Cao, Md Abu Sayed3, Md Siam Taluckder

Brain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. Detecting brain tumors at an early stage is crucial for effective treatment and patient outcomes. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using

M. Aleixo, C. H. Lenzi, W. de Paula, R. da Rocha

This work investigates static and dynamical quark star properties within a $D_3-D_7$ holographic model. We solve the Tolman-Oppenheimer-Volkoff equations for the quark matter equation of state obtained from the brane configuration. We determine the mass-radius diagram for a range of model parameters and compare with recent NICER observational data for the pu

Maike Gremmel, Simon Fichtner

This paper investigates wake-up and imprint in ferroelectric AlScN films. The study employs a series of I-V and P-E measurements with varying electric field amplitudes and voltage cycles as well as structural investigation via Scanning Electron Microscopy to understand the origin and underlying principle of wake-up and imprint as well as their relation. It i

Blake Ledger, Toshiki Saito, Daisuke Iono, Christine D. Wilson

We present an archival Atacama Large Millimeter/submillimeter Array (ALMA) study of the CN N = 1 - 0 / CO J = 1 - 0 intensity ratio in nearby (z < 0.05) Ultra Luminous and Luminous Infrared Galaxies (U/LIRGs). We identify sixteen U/LIRGs that have been observed in both CN and CO lines at $\sim$ 500 pc resolution based on sixteen different ALMA projects. We m

Alexander Nietner

Kearns' statistical query (SQ) oracle (STOC'93) lends a unifying perspective for most classical machine learning algorithms. This ceases to be true in quantum learning, where many settings do not admit, neither an SQ analog nor a quantum statistical query (QSQ) analog. In this work, we take inspiration from Kearns' SQ oracle and Valiant's weak evaluation ora

William Rudman, Catherine Chen, Carsten Eickhoff

Representations from large language models (LLMs) are known to be dominated by a small subset of dimensions with exceedingly high variance. Previous works have argued that although ablating these outlier dimensions in LLM representations hurts downstream performance, outlier dimensions are detrimental to the representational quality of embeddings. In this st

Pawan Kumar Rajpoot, Ankur Parikh

Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation classes, caused by language complexity and data sparsity. Further, these approaches and models are largely inaccessible to users who d

Salvatore Tringali, Weihao Yan

Let $S$ be a numerical monoid, i.e., a submonoid of the additive monoid $(\mathbb N, +)$ of non-negative integers such that $\mathbb N \setminus S$ is finite. Endowed with the operation of set addition, the family of all finite subsets of $S$ containing $0$ is itself a monoid, which we denote by $\mathcal P_{{\rm fin}, 0}(S)$. We show that, if $S_1$ and $S_2

Andrew Davison, S. Carlyle Morgan, Owen G. Ward

Embedding the nodes of a large network into an Euclidean space is a common objective in modern machine learning, with a variety of tools available. These embeddings can then be used as features for tasks such as community detection/node clustering or link prediction, where they achieve state of the art performance. With the exception of spectral clustering m

Yi-Li Hsu, Shih-Chieh Dai, Aiping Xiong, Lun-Wei Ku

With advancements in natural language processing (NLP) models, automatic explanation generation has been proposed to mitigate misinformation on social media platforms in addition to adding warning labels to identified fake news. While many researchers have focused on generating good explanations, how these explanations can really help humans combat fake news

Hyun Min Lee, Adriana G. Menkara, Myeong-Jung Seong, Jun-Ho Song

We propose a minimal extension of the Standard Model with the Peccei-Quinn (PQ) scalar field and explain the relic density of the QCD axion through the kinetic misalignment with a relatively small axion decay constant. To this purpose, we consider a slow-roll inflation from the radial component of the PQ field with the PQ conserving potential near the pole o

Yu Hin Chan, Adam Jacob

The line bundle mean curvature flow is a complex analogue of the mean curvature flow for Lagrangian graphs, with fixed points solving the deformed Hermitian-Yang-Mills equation. In this paper we construct two distinct examples of singularities along the flow. First, we find a finite time singularity, ruling out long time existence of the flow in general. Nex

Jochem Kip, Zhongyi Zhang

The $U(1)_{L_\mu-L_\tau}$ extended Standard Model (SM) is anomaly free, and contains a massive $Z^\prime$ boson. The associated Higgs, which generates the $Z'$'s mass via spontaneous symmetry breaking (SSB) can mix with the SM Higgs. The new parameters relating to extra Higgs cannot be probed at the LHC with final states containing no more than $4$ leptons.

Carlos Jurado, Smadar Naoz, Casey Y. Lam, Bao-Minh Hoang

Most galaxies, including the Milky Way, harbor a central supermassive black hole (SMBH) weighing millions to billions of solar masses. Surrounding these SMBHs are dense regions of stars and stellar remnants, such as neutron stars and black holes. Neutron stars and possibly black holes receive large natal kicks at birth on the order of hundreds of km s$^{-1}$

Luhang Yang, Thomas P. Devereaux, Hong-Chen Jiang

A lightly doped single-band Hubbard model on a two leg ladder exhibits a Luther-Emery phase, while the three-band Hubbard ladder behaves as a Luttinger liquid upon hole doping. In order to understand this discrepancy, we present a systematic density-matrix renormalization group study of the three-band Hubbard model on two-leg cylinders with further-neighbor

Runze Cheng, Yao Sun, Dusit Niyato, Lan Zhang

With the significant advances in AI-generated content (AIGC) and the proliferation of mobile devices, providing high-quality AIGC services via wireless networks is becoming the future direction. However, the primary challenges of AIGC services provisioning in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed co

So Chigusa, Asuka Ito, Kazunori Nakayama, Volodymyr Takhistov

Magnetic materials are particularly favorable targets for detecting axions interacting with electrons because the collective excitation of electron spins, the magnon, can be excited through the axion-magnon conversion process. It is often assumed that only the zero-momentum uniformly precessing magnetostatic (Kittel) mode of the magnon is excited. This is ju

Shan Chen, Marco Guevara, Shalini Moningi, Frank Hoebers

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical r

Misha Chernobai

We obtain a global existence result for the three-dimensional Navier-Stokes equations with a large class of initial data allowing growth at spatial infinity. Our work is a continuation of the results by T.-P. Tsai, Z. Bradshaw, I. Kukavica and proves global existence of suitable weak solutions with initial data in different weighted spaces as well as eventua

A. M Matthews, D. D. Kelson, A. B. Newman, F. Camilo

We present the initial sample of redshifts for 3,839 galaxies in the MeerKAT DEEP2 field -- the deepest $\sim$1.4\,GHz radio field yet observed. Using a spectrophotometric technique combining coarse optical spectra with broadband photometry, we obtain redshifts with $\sigma_z \leq 0.01(1+z)$. The resulting radio luminosity functions between $0.2<z<1.3$ from

Georgios Itsios, Konstantinos Sfetsos, Konstantinos Siampos

We provide the first supersymmetric embedding of an integrable $\lambda$-deformation to type-II supergravity. Specifically, that of the near horizon of the NS1-NS5 brane intersection, geometrically corresponding to $AdS_3 \times S^3 \times T^4$. We show that the deformed background preserves 1/4 of the maximal supersymmetry. In the Penrose limit we show that

Konstantinos Tanidis, Federico R. Urban, Stefano Camera

The chemical composition of the highest-energy cosmic rays, namely the atomic number $Z$ of rays with energies $E\gtrsim40~\mathrm{EeV}$, remains to date largely unknown. Some information on the composition can be inferred from the deflections that charged ultra-high-energy cosmic rays experience while they traverse intervening magnetic fields. Indeed, such

Jorge Chávez-Carlos, Miguel A. Prado Reynoso, Ignacio García-Mata, Victor S. Batista

Kerr parametric oscillators are potential building blocks for fault-tolerant quantum computers. They can stabilize Kerr-cat qubits, which offer advantages toward the encoding and manipulation of error-protected quantum information. The recent realization of Kerr-cat qubits made use of the nonlinearity of the SNAIL transmon superconducting circuit and a squee

Pei-Kai Tsai, Yue Wu, Shruti Puri

An important outstanding challenge that must be overcome in order to fully utilize the XY surface code for correcting biased Pauli noise is the phenomena of fragile temporal boundaries that arise during the standard logical state preparation and measurement protocols. To address this challenge we propose a new logical state preparation protocol based on loca

Tao Han, Matthew Low, Tong Arthur Wu

Quantum entanglement is a fundamental property of quantum mechanics. Recently, studies have explored entanglement in the $t\bar{t}$ system at the Large Hadron Collider (LHC) when both the top quark and anti-top quark decay leptonically. Entanglement is detected via correlations between the polarizations of the top and anti-top and these polarizations are mea

Daniel Junghans

The DGKT-CFI construction of AdS flux vacua in type IIA string theory has interesting features such as classical moduli stabilization and a parametric scale separation between the Hubble scale and the Kaluza-Klein scale. A possible worry regarding the consistency of these vacua is that pathologies could arise due to intersections of the O6-planes, which are

Sumit K. Sarbadhicary, Jordan Wagner, Eric W. Koch, Ness Mayker Chen

The effect of supernovae (SNe) on star-formation in the interstellar medium (ISM) depends sensitively on where SNe explode with respect to ISM clouds. Observationally, SN ISM environments characterized by spatially-resolved gas maps can empirically guide the placement of SNe in subgrid models, but unfortunately such measurements remain scarce, as SNe are rar

V. A. Cúneo, J. Casares, M. Armas Padilla, J. Sánchez-Sierras

Among the sample of Galactic transient X-ray binaries (SXTs) discovered to date, about 70 have been proposed as likely candidates to host a black hole. Yet, only 19 have been dynamically confirmed. Such a reliable confirmation requires phase-resolved spectroscopy of their companion stars, which is generally feasible when the system is in a quiescent state. H

Kung-Yi Su, Greg L. Bryan, Christopher C. Hayward, Rachel S. Somerville

In the absence of supplementary heat, the radiative cooling of halo gas around massive galaxies (Milky Way mass and above) leads to an excess of cold gas or stars beyond observed levels. AGN jet-induced heating is likely essential, but the specific properties of the jets remain unclear. Our previous work (Su et al. 2021) concludes from simulations of a halo

Fangze Liu, Zhaoyu Han

We investigate the large Kondo coupling limit of the Kondo-Heisenberg model on one- and two-dimensional lattices. Focusing on the possible superconducting states when slightly doping the Kondo insulator state, we identify different pairing modes to be most stable in different parameter regimes. Possibilities include uniform $s$-wave, pair-density-wave with m

Marco Farina, Duccio Pappadopulo

Sample contrastive methods, typically referred to simply as contrastive are the foundation of most unsupervised methods to learn text and sentence embeddings. On the other hand, a different class of self-supervised loss functions and methods have been considered in the computer vision community and referred to as dimension contrastive. In this paper, we thor

Martin Hoferichter, Bai-Long Hoid, Jacobo Ruiz de Elvira

We present a comprehensive calculation of the $K_L\to\gamma^*\gamma^*$ form factor in dispersion theory, using input from the leptonic decays $K_L\to\ell^+\ell^-\gamma$, $K_L\to \ell_1^+\ell_1^-\ell_2^+\ell_2^-$, the hadronic mode $K_L\to \pi^+\pi^-\gamma$, the normalization $K_L\to\gamma\gamma$, and the matching to asymptotic constraints. As key result we o

Karsten Roth, Lukas Thede, Almut Sophia Koepke, Oriol Vinyals

Training deep networks requires various design decisions regarding for instance their architecture, data augmentation, or optimization. In this work, we find these training variations to result in networks learning unique feature sets from the data. Using public model libraries comprising thousands of models trained on canonical datasets like ImageNet, we ob

Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie

We study the problem of making predictions of an adversarially chosen high-dimensional state that are unbiased subject to an arbitrary collection of conditioning events, with the goal of tailoring these events to downstream decision makers. We give efficient algorithms for solving this problem, as well as a number of applications that stem from choosing an a