October 2023 arXiv papers — page 58

Rostom Mbarek, Alexander Philippov, Alexander Chernoglazov, Amir Levinson

We examine properties of accelerated protons potentially responsible for the neutrino excess observed in the direction of NGC 1068, using constraints from kinetic Particle-in-Cell (PIC) simulations. We find that i) coronal X-rays and Optical/Ultra-Violet light in the inner disk lead to efficient absorption of hadronic $\gamma$-rays within 100 Schwarzschild r

Alessandro Della Croce, Raffaele Pascale, Eric Giunchi, Carlo Nipoti

Globular clusters (GCs) were proposed as promising sites for discovering intermediate-mass black holes (IMBHs), possibly providing crucial insights into the formation and evolution of these elusive objects. The Galactic GC 47 Tucanae (also known as NGC 104) has been suggested as a potential IMBH host, but, previous studies have yielded conflicting results. W

Erik Wegner Hodt, Jacob Linder

Altermagnetism is a subclass of antiferromagnetism that features spin-polarized electron bands of a non-relativistic origin despite the absence of a net magnetiation in the material. We here theoretically study spin pumping from an altermagnetic insulator into a normal metal. The symmetry properties of the lattice and spin order of the altermagnet alters the

David Kipping

White dwarfs (WDs) have roughly Earth-sized radii - a fact long recognized to facilitate the potential discovery of sub-Earth sized planets via transits, as well atmospheric characterization including biosignatures. Despite this, the first (and still only) transiting planet discovered in 2020 was a roughly Jupiter-sized world, found using TESS photometry. Gi

Robert M. Yates, David Hendriks, Aswin P. Vijayan, Robert G. Izzard

We present detailed implementations of (a) binary stellar evolution (using binary_c) and (b) dust production and destruction into the cosmological semi-analytic galaxy evolution simulation, L-Galaxies. This new version of L-Galaxies is compared to a version assuming only single stars and to global and spatially-resolved observational data across a range of r

Thomas Hartman, Grégoire Mathys

We study applications of spectral positivity and the averaged null energy condition (ANEC) to renormalization group (RG) flows in two-dimensional quantum field theory. We find a succinct new proof of the Zamolodchikov $c$-theorem, and derive further independent constraints along the flow. In particular, we identify a natural $C$-function that is a completely

TaeHun Kim

A thermal interpretation of the stochastic formalism of a slow-rolling scalar field in de Sitter (dS) is given. We construct a correspondence between Hubble patches of dS and particles living in another space called an abstract space. By assuming a dual description of scalar fields and classical mechanics in the abstract space, we show that the stochastic ev

Poshak Gandhi

Motivated by efforts to return humanity to the Moon, three cases are reviewed for X-ray astronomy from the lunar surface: (1) Facilitation of ambitious engineering designs including high throughput telescopes, long focal length optics and X-ray interferometery; (2) Occultation studies and the gain they enable in astrometric precision; (3) Multimessenger time

M. Sten Delos

Prompt $\rho\propto r^{-1.5}$ density cusps are the densest and most abundant dark matter systems. If the dark matter is a weakly interacting massive particle (WIMP), recent studies have shown that prompt cusps dominate the aggregate dark matter annihilation rate. This article explores whether individual prompt cusps could be detected as gamma-ray sources. A

Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng

Depth estimation from monocular images is pivotal for real-world visual perception systems. While current learning-based depth estimation models train and test on meticulously curated data, they often overlook out-of-distribution (OoD) situations. Yet, in practical settings -- especially safety-critical ones like autonomous driving -- common corruptions can

Paul A Crowther, M J Barlow, P Royer, D J Hillier

We present Herschel PACS spectroscopy of the [O III] 88.4um fine-structure line in the nearby WC8+O binary system gamma Vel to determine its oxygen abundance. The critical density of this line corresponds to several 10^5 R* such that it is spatially extended in PACS observations at the 336 pc distance to gamma Vel. Two approaches are used, the first involvin

Haonan Qiu, Menghan Xia, Yong Zhang, Yingqing He

With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress. However, existing video generation models are typically trained on a limited number of frames, resulting in the inability to generate high-fidelity long videos during inference. Furthermore, these models on

Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu

The creation of photorealistic virtual worlds requires the accurate modeling of 3D surface geometry for a wide range of objects. For this, meshes are appealing since they 1) enable fast physics-based rendering with realistic material and lighting, 2) support physical simulation, and 3) are memory-efficient for modern graphics pipelines. Recent work on recons

Valerii K. Kozin, Dmitry Miserev, Daniel Loss, Jelena Klinovaja

We study double quantum dots coupled to a quasistatic cavity mode with high mode-volume compression allowing for strong light-matter coupling. Besides the cavity-mediated interaction, electrons in different double quantum dots interact with each other via dipole-dipole (Coulomb) interaction. For attractive dipolar interaction, a cavity-induced ferroelectric

Liangyu Chen, Bo Li, Sheng Shen, Jingkang Yang

Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsense reasoning ability in various domains. However, how to harness the collective power of these complementary VLMs is rarely explored. Existing methods like ensemble still struggle

Sara Pieri, Jose Renato Restom, Samuel Horvath, Hisham Cholakkal

Federated Learning (FL) is a promising research paradigm that enables the collaborative training of machine learning models among various parties without the need for sensitive information exchange. Nonetheless, retaining data in individual clients introduces fundamental challenges to achieving performance on par with centrally trained models. Our study prov

Theo X. Olausson, Alex Gu, Benjamin Lipkin, Cedegao E. Zhang

Logical reasoning, i.e., deductively inferring the truth value of a conclusion from a set of premises, is an important task for artificial intelligence with wide potential impacts on science, mathematics, and society. While many prompting-based strategies have been proposed to enable Large Language Models (LLMs) to do such reasoning more effectively, they st

Ahmed Telili, Wassim Hamidouche, Hadi Amirpour, Sid Ahmed Fezza

HTTP adaptive streaming (HAS) has emerged as a prevalent approach for over-the-top (OTT) video streaming services due to its ability to deliver a seamless user experience. A fundamental component of HAS is the bitrate ladder, which comprises a set of encoding parameters (e.g., bitrate-resolution pairs) used to encode the source video into multiple representa

Alison L. Coil, Serena Perrotta, David S. N. Rupke, Cassandra Lochhaas

A new class of extragalactic astronomical sources discovered in 2021, named Odd Radio Circles (ORCs, Norris et al. 2021), are large rings of faint, diffuse radio continuum emission spanning ~1 arcminute on the sky. Galaxies at the centers of several ORCs have photometric redshifts of z~0.3-0.6, implying physical scales of several 100 kiloparsecs in diameter

Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng

Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalities. This limitation restricts their broader clinical use. In this paper, we introduce SAM-Med3D for general-purpose segmentation on volumetric medical images. Given only a few 3D

Lihe Yang, Xiaogang Xu, Bingyi Kang, Yinghuan Shi

Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations to train, and the acquisition is laborious and unaffordable. Therefore, we present FreeMask in this work, which resorts to synthetic images from generative models to ease the burden

Zhong-Bo Kang, Kyle Lee, Ding Yu Shao, Fanyi Zhao

We study the azimuthal angle dependence of the energy-energy correlators $\langle \mathcal{E}(\hat{n}_1)\mathcal{E}(\hat{n}_2)\rangle$ in the back-to-back region for $e^+e^-$ annihilation and deep inelastic scattering (DIS) processes with general polarization of the proton beam. We demonstrate that the polarization information of the beam and the underlying

Gabriel Fabiano de Souza, Pieter Westera, Felipe Almeida-Fernandes, Guilherme Limberg

The Magellanic Clouds are the most massive and closest satellite galaxies of the Milky Way, with stars covering ages from a few Myr up to 13 Gyr. This makes them important for validating integrated light methods to study stellar populations and star-formation processes, which can be applied to more distant galaxies. We characterized a set of stellar clusters

Indrajit Sen, Stephon Alexander, Justin Dressel

Unitarity is a difficult concept to implement in canonical quantum gravity because of state non-normalizability and the problem of time. We take a realist approach based on pilot-wave theory to address this issue in the Ashtekar formulation of the Wheeler-DeWitt equation. We use the postulate of a definite configuration in the theory to define a global time

Hongshun Yao, Xia Liu, Chengkai Zhu, Xin Wang

Quantum broadcasting is central to quantum information processing and characterizes the correlations within quantum states. Nonetheless, traditional quantum broadcasting encounters inherent limitations dictated by the principles of quantum mechanics. In a previous study, Parzygnat et al. [Phys. Rev. Lett. 132, 110203 (2024)] introduced a canonical broadcasti

Dolapo Oyetunbi, Aaron Tikuisis

In this paper, we characterize $\ell$-open and $\ell$-closed $C^*$-algebras and deduce that $\ell$-open $C^*$-algebras are $\ell$-closed, as conjectured by Blackadar. Moreover, we show that a commutative unital $C^*$-algebra is $\ell$-open if and only if it is semiprojective.

Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, Neel Nanda

Sentiment is a pervasive feature in natural language text, yet it is an open question how sentiment is represented within Large Language Models (LLMs). In this study, we reveal that across a range of models, sentiment is represented linearly: a single direction in activation space mostly captures the feature across a range of tasks with one extreme for posit

Shaoxun Fan, Andrew L. Hitt, Ming Tang, Babak Sadigh

Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled compu

Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller

We report the presence of a simple neural mechanism that represents an input-output function as a vector within autoregressive transformer language models (LMs). Using causal mediation analysis on a diverse range of in-context-learning (ICL) tasks, we find that a small number attention heads transport a compact representation of the demonstrated task, which

Sarah Cannon, Wesley Pegden, Jamie Tucker-Foltz

We prove that a polynomial fraction of the set of $k$-component forests in the $m \times n$ grid graph have equal numbers of vertices in each component, for any constant $k$. This resolves a conjecture of Charikar, Liu, Liu, and Vuong, and establishes the first provably polynomial-time algorithm for (exactly or approximately) sampling balanced grid graph par

Sophie Hao, Tal Linzen

Deep architectures such as Transformers are sometimes criticized for having uninterpretable "black-box" representations. We use causal intervention analysis to show that, in fact, some linguistic features are represented in a linear, interpretable format. Specifically, we show that BERT's ability to conjugate verbs relies on a linear encoding of subject numb

David C. Epstein, Ishan Jain, Oliver Wang, Richard Zhang

With advancements in AI-generated images coming on a continuous basis, it is increasingly difficult to distinguish traditionally-sourced images (e.g., photos, artwork) from AI-generated ones. Previous detection methods study the generalization from a single generator to another in isolation. However, in reality, new generators are released on a streaming bas

Qi-Le Zhou, Han-Jia Ye, Le-Ye Wang, De-Chuan Zhan

Fine-tuning a pre-trained deep neural network has become a successful paradigm in various machine learning tasks. However, such a paradigm becomes particularly challenging with tabular data when there are discrepancies between the feature sets of pre-trained models and the target tasks. In this paper, we propose TabToken, a method aims at enhancing the quali

Leonardo K. Castelano, Iann Cunha, Fabricio S. Luiz, Marcelo V. de Souza Prado

Machine learning techniques are employed to perform the full characterization of a quantum system. The particular artificial intelligence technique used to learn the Hamiltonian is called physics informed neural network (PINN). The idea behind PINN is the universal approximation theorem, which claims that any function can be approximate by a neural network i

Fangyu Lei, Qian Liu, Yiming Huang, Shizhu He

The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning. However, as LLMs are able to process longer contexts, it becomes more challenging to evaluate whether they have acquired certain capabilities, since the length of text (e.g., 200K tokens) they can process far exc

Daniel Kosmas, Özlem Ergun

Drug shortages have been a persistent problem in American healthcare for decades, and the resulting lack of access to necessary drugs has been disastrous to patient health. A majority of these shortages were caused by quality issues related to problems in the manufacturing process. More frequent inspections can reduce quality concerns, but deciding when to i

Jingyun Yang, Max Sobol Mark, Brandon Vu, Archit Sharma

The pre-train and fine-tune paradigm in machine learning has had dramatic success in a wide range of domains because the use of existing data or pre-trained models on the internet enables quick and easy learning of new tasks. We aim to enable this paradigm in robotic reinforcement learning, allowing a robot to learn a new task with little human effort by lev

Kevin Lin, Zhengyuan Yang, Linjie Li, Jianfeng Wang

We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailored for visual design scenarios. Recent T2I models like DALL-E 3 and others, have demonstrated remarkable capabilities in generating photorealistic images that align closely with textual inputs. While the allure of creating visually captivating images is undeniable, our emphasis extend

Franco Cerino, Andrés Diaz-Pace, Emmanuel Tassone, Manuel Tiglio

In a previous work we introduced, in the context of gravitational wave science, an initial study on an automated domain-decomposition approach for reduced basis through hp-greedy refinement. The approach constructs local reduced bases of lower dimensionality than global ones, with the same or higher accuracy. These ``light'' local bases should imply both fas

Zachary Bradshaw, Misha Chernobai, Tai-Peng Tsai

We construct global weak solutions of the three dimensional incompressible Navier-Stokes equations in intermediate spaces between the space of uniformly locally square integrable functions and Herz-type spaces which involve weighted integrals centered at the origin. Our results bridge the existence theorems of Lemari\'e-Rieusset and of Bradshaw, Kukavica and

Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami

Autoregressive sampling from large language models has led to state-of-the-art results in several natural language tasks. However, autoregressive sampling generates tokens one at a time making it slow, and even prohibitive in certain tasks. One way to speed up sampling is $\textit{speculative decoding}$: use a small model to sample a $\textit{draft}$ (block

Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu

Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks. Recent studies suggest that defending against these attacks is possible: adversarial attacks generate unlimited but unreadable gibberish prompts, detectable by perplexity-based filters; manual jailbreak attacks craft readable

Alexandru Chirvasitu

We prove a number of results linking properties of actions by compact groups (both quantum and classical) on Banach spaces, such as uniform continuity, spectrum finiteness and extensibility of the actions across several constructions. Examples include: (a) a unitary representation of a compact quantum group induces a continuous action on the $C^*$-algebra of

Kaiming Fu, Peng Wei, Juan Villacres, Zhaodan Kong

Fruit distribution is pivotal in shaping the future of both agriculture and agricultural robotics, paving the way for a streamlined supply chain. This study introduces an innovative methodology that harnesses the synergy of RGB imagery, LiDAR, and IMU data, to achieve intricate tree reconstructions and the pinpoint localization of fruits. Such integration no

Andreas S. Kronfeld

A method, known as ``minimal renormalon subtraction'' [Phys. Rev. D 97 (2018) 034503, JHEP 2017 (2017) 62], relates the factorial growth of a perturbative series (in QCD) to the power~$p$ of a power correction $\Lambda^p/Q^p$. ($\Lambda$ is the QCD scale, $Q$ some hard scale.) Here, the derivation is simplified and generalized to any~$p$, more than one such

Alakabha Datta, Danny Marfatia, Lopamudra Mukherjee

Belle II has reported the first evidence for $B^+ \to K^+\nu\bar\nu$ with a branching ratio $2.7 \sigma$ higher than the standard model expectation. We explain this, and the MiniBooNE and muon anomalous magnetic moment anomalies in a model with a dark scalar that couples to a slightly heavier sterile Dirac neutrino and that communicates with the visible sect

Anjali Kantharuban, Ivan Vulić, Anna Korhonen

Historically, researchers and consumers have noticed a decrease in quality when applying NLP tools to minority variants of languages (i.e. Puerto Rican Spanish or Swiss German), but studies exploring this have been limited to a select few languages. Additionally, past studies have mainly been conducted in a monolingual context, so cross-linguistic trends hav

Antonio Palasciano

Fragmentation functions are one of the key components of the factorisation theorem used to calculate heavy-flavour hadron production cross sections. The non-perturbative nature of fragmentation functions necessitates that they are constrained through experimental measurements, commonly performed in the clean environments of $\mathrm{e}^{+}\mathrm{e}^{-}$ and

Hamza El-Kebir, Richard Berlin, Joseph Bentsman, Melkior Ornik

In this work, we solve the problem of quantifying and mitigating control authority degradation in real time. Here, our target systems are controlled nonlinear affine-in-control evolution equations with finite control input and finite- or infinite-dimensional state. We consider two cases of control input degradation: finitely many affine maps acting on unknow

Eben Kenah

Here, we explain and illustrate a geometric perspective on causal inference in cohort studies that can help epidemiologists understand the role of standardization in causal inference as well as the distinctions between confounding, effect modification, and noncollapsibility. For simplicity, we focus on a binary exposure X, a binary outcome D, and a binary co

Byeongjoo Ahn, Karren Yang, Brian Hamilton, Jonathan Sheaffer

We investigate the benefit of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis. Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sources, we estimate the sound anywhere in the scene. We identify the main challenges of novel-view acoustic synth

Nicholas Collin Suwono, Justin Chih-Yao Chen, Tun Min Hung, Ting-Hao Kenneth Huang

This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that

Maximilian Krahn, Michele Sasdelli, Fengyi Yang, Vladislav Golyanik

We present, QP-SBGD, a novel layer-wise stochastic optimiser tailored towards training neural networks with binary weights, known as binary neural networks (BNNs), on quantum hardware. BNNs reduce the computational requirements and energy consumption of deep learning models with minimal loss in accuracy. However, training them in practice remains to be an op

Gabriel Sarch, Yue Wu, Michael J. Tarr, Katerina Fragkiadaki

Pre-trained and frozen large language models (LLMs) can effectively map simple scene rearrangement instructions to programs over a robot's visuomotor functions through appropriate few-shot example prompting. To parse open-domain natural language and adapt to a user's idiosyncratic procedures, not known during prompt engineering time, fixed prompts fall short

Yigitcan Comlek, Liwei Wang, Wei Chen

Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with

Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal

Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into account multiple aspects and criteria. However, their performance can fall short, due to the model's lack of coherence and inability to plan and decompose the problem. We propose Branch-

S. Shekarabi, M. A. Zare Pour, H. Su, W. Zhang

III-V semiconductor-based photoelectrochemical (PEC) devices show the highest solar-to-electricity or solar-to-fuel conversion efficiencies. GaInP is a relevant top photoabsorber layer or a charge-selective contact in PEC for integrated and direct solar fuel production, due to its tunable lattice constant, electronic band structure, and favorable optical pro

A. Soshnikov, R. K. Lindsey, A. Kulkarni, N. Goldman

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and computationally expensive nature of the quantum-based methods generally required to study such processes. In this regard, we have develop

Jacques Audibert, Michael Zshornack

A consequence of Rapinchuk et al. is that for $S$ a closed surface of genus $g\geq 2$, the set of Hitchin representations of $\pi_1(S)$ with image in $\mathrm{SL}(n,\mathbb{Q})$ is dense in the Hitchin component. We give a dynamical proof of this fact provided that $g\geq 3$. Moreover, we extend it to some other $\mathbb{Q}$-groups such as $\mathrm{Sp}(2k,\m

Yicui Kang, Simão M. João, Rui Lin, Li Zhu

In the realm of plasmonic catalytic systems, much attention has been devoted to the plasmon-derived mechanisms, yet the influence of nanoparticles' crystal facets in this type of processes has been sparsely investigated. In this work, we study the plasmon-assisted electrocatalytic CO2 reduction reaction using three different shapes of plasmonic Au nanopartic

Shunian Xiang, Patrick J. Lawrence, Bo Peng, ChienWei Chiang

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively ide

Mallory Harris, Diana Dragomir, Ismael Mireles, Karen A. Collins

We report the discovery of two TESS sub-Neptunes orbiting the early M dwarf TOI-904 (TIC 261257684). Both exoplanets, TOI-904 b and c, were initially observed in TESS Sector 12 with twin sizes of ${2.426}_{-0.157}^{+0.163}$ and ${2.167}_{-0.118}^{+0.130}$ R$_\oplus$, respectively. Through observations in five additional sectors in the TESS primary mission an

Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu

Large Language Models (LLMs) have been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variable pair. However, such experts, including human domain experts, cannot distinguish between direct and indirect effects given a pairwise prompt. Therefore, instead of the graph, we

Adam Doliwa

We review recent results on the connection between Hermite-Pad\'e approximation problem, multiple orthogonal polynomials, and multidimensional Toda equations in continuous and discrete time. In order to motivate interest in the subject we first present a pedagogical introduction to the classical, by now, relation between the Pad\'e approximation problem, ort

Weihao Lin, Tao Chen, Chong Yu

Semi-supervised video object segmentation (Semi-VOS), which requires only annotating the first frame of a video to segment future frames, has received increased attention recently. Among existing pipelines, the memory-matching-based one is becoming the main research stream, as it can fully utilize the temporal sequence information to obtain high-quality segm

Marco Gaido, Dennis Fucci, Matteo Negri, Luisa Bentivogli

When translating from notional gender languages (e.g., English) into grammatical gender languages (e.g., Italian), the generated translation requires explicit gender assignments for various words, including those referring to the speaker. When the source sentence does not convey the speaker's gender, speech translation (ST) models either rely on the possibly

Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai

Large language models (LLMs) have recently reached an impressive level of linguistic capability, prompting comparisons with human language skills. However, there have been relatively few systematic inquiries into the linguistic capabilities of the latest generation of LLMs, and those studies that do exist (i) ignore the remarkable ability of humans to genera

Qingxiao Zheng, Zhuoer Chen, Yun Huang

This study examines the impact of AI-generated digital clones with self-images on enhancing perceptions and skills in online presentations. A mixed-design experiment with 44 international students compared self-recording videos (self-recording group) to AI-clone videos (AI-clone group) for online English presentation practice. AI-clone videos were generated

Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Josh Susskind

Diffusion models are the de facto approach for generating high-quality images and videos, but learning high-dimensional models remains a formidable task due to computational and optimization challenges. Existing methods often resort to training cascaded models in pixel space or using a downsampled latent space of a separately trained auto-encoder. In this pa

Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, we develop various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ ex

Yichuan Li, Kaize Ding, Kyumin Lee

Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately. However, existing methods either struggle to capture the full extent of structural context information or rely on task-specific training labels, w

Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender

Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, particularly those where observations are not independently and identically distributed, resampling using simple random data divisions may lead to biased GE estimates. This paper strive

Kurt Walsen, Paula Jofré, Sven Buder, Keaghan Yaxley

Stellar chemical abundances have proved themselves a key source of information for understanding the evolution of the Milky Way, and the scale of major stellar surveys such as GALAH have massively increased the amount of chemical data available. However, progress is hampered by the level of precision in chemical abundance data as well as the visualization me

Daniel Romero, Tien Ngoc Ha, Raju Shrestha, Massimo Franceschetti

Radio maps provide radio frequency metrics, such as the received signal strength, at every location of a geographic area. These maps, which are estimated using a set of measurements collected at multiple positions, find a wide range of applications in wireless communications, including the prediction of coverage holes, network planning, resource allocation,

Kun Song, Huimin Ma, Bochao Zou, Huishuai Zhang

Due to the limited availability of data, existing few-shot learning methods trained from scratch fail to achieve satisfactory performance. In contrast, large-scale pre-trained models such as CLIP demonstrate remarkable few-shot and zero-shot capabilities. To enhance the performance of pre-trained models for downstream tasks, fine-tuning the model on downstre

Walker Melton, Atul Sharma, Andrew Strominger

The general form of a 2D conformal field theory (CFT) correlator on a Euclidean Riemann surface, Lorentzian plane or Lorentzian cylinder is well-known. This paper describes the general form of 2- and 3-point CFT correlators on the Lorentzian torus $\mathcal{LT}^2$ which arises as the conformal boundary of the group manifold $\mathrm{SL}(2,\mathbb{R})$ $\sime

Tobias Herrig, Christina Koliofoti, Jedediah H. Pixley, Elio J. König

Topological bandstructures interfering with moir\'e superstructures give rise to a plethora of emergent phenomena, which are pivotal for correlated insulating and superconducting states of twisttronics materials. While quasiperiodicity was up to now a notion mostly reserved for solid-state materials and cold atoms, we here demonstrate the capacity of convent

A. Fronzetti Colladon, F. Grippa, C. Broccatelli, C. Mauren

Purpose: This study investigates the dynamics of knowledge sharing in healthcare, exploring some of the factors that are more likely to influence the evolution of idea sharing and advice seeking in healthcare. Design/methodology/approach: We engaged 50 pediatricians representing many subspecialties at a mid-size US children's hospital using a social network

Sara Zamani, J. Naji, R. Jafari, A. Langari

The nonequilibrium dynamics of a periodically driven extended XY model, in the presence of linear time dependent magnetic filed, is investigated using the notion of dynamical quantum phase transitions (DQPTs). Along the similar lines to the equilibrium phase transition, the main purpose of this work is to search the fundamental concepts such as scaling and u

Shih-Chieh Dai, Aiping Xiong, Lun-Wei Ku

Thematic analysis (TA) has been widely used for analyzing qualitative data in many disciplines and fields. To ensure reliable analysis, the same piece of data is typically assigned to at least two human coders. Moreover, to produce meaningful and useful analysis, human coders develop and deepen their data interpretation and coding over multiple iterations, m

Matheus del-Valle, Emerson Soares Bernardes, Denise Maria Zezell

Breast cancer molecular subtypes classification plays an import role to sort patients with divergent prognosis. The biomarkers used are Estrogen Receptor (ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNB

Yu-Cheng Chou, Bowen Li, Deng-Ping Fan, Alan Yuille

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors

Zachary McBride Lazri, Ivan Brugere, Xin Tian, Dana Dachman-Soled

Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demographic groups to be treated fairly. However, algorithms that aim to satisfy inter-group fairness (also called gr

Marco Alberto Javarone, Shaurya Pratap Singh

Commonly, the strategy revision phase in evolutionary games relies on payoff comparison. Namely, agents compare their payoff with the opponent, assessing whether changing strategy can be potentially convenient. Even tiny payoff differences can be crucial in this decision process. In this work, we study the dynamics of cooperation in the Public Goods Game, in

H. Rivera-Rodríguez, R. Jáuregui

An operator that generates an approximate symmetry of long-range Rydberg molecules (LRRMs) formed by two alkali atoms, one in a Rydberg state and the other in the ground state, is identified. This is first done by evaluating the natural orbitals associated to a variational calculation of the binding wave function within the Born-Oppenheimer description of th

Matheus del-Valle, Emerson Soares Bernardes, Denise Maria Zezell

Breast cancer treatment still remains a challenge, where molecular subtypes classification plays a crucial role in selecting appropriate and specific therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is the gold-standard evaluation, although interobserver variations are

J. Karthein, C. M. Ricketts, R. F. Garcia Ruiz, J. Billowes

Our understanding of nuclear properties in the vicinity of $^{100}$Sn, suggested to be the heaviest doubly magic nucleus with equal numbers of protons (Z=50) and neutrons (N=50), has been a long-standing challenge for experimental and theoretical nuclear physics. Contradictory experimental evidence exists on the role of nuclear collectivity in this region of

Nadja Willenborg, Martino Borello, Anna-Lena Horlemann, Habibul Islam

We establish dihedral quantum codes of short block length, a class of CSS codes obtained by the lifted product construction. We present the code construction and give a formula for the code dimension, depending on the two classical codes that the CSS code is based on. We also give a lower bound on the code distance and construct an example of short dihedral

Marco Ballarin, Giovanni Cataldi, Giuseppe Magnifico, Daniel Jaschke

We numerically analyze the feasibility of a platform-neutral, general strategy to perform quantum simulations of fermionic lattice field theories under open boundary conditions. The digital quantum simulator requires solely one- and two-qubit gates and is scalable since integrating each Hamiltonian term requires a finite (non-scaling) cost. The exact local f

Ovidiu Cristinel Stoica

The quantum world is described by a unit vector in the Hilbert space and the Hamiltonian. Do these abstract basis-independent objects give a complete description of the physical world, or should we include observables like positions and momenta and the decomposition into subsystems? According to "Hilbert-space fundamentalism" they give a complete description

Elyana Crowder, Lance Lampert, Grihith Manchanda, Brian Shoffeitt

Despite recent advances in quantum sciences, a quantum master equation that accurately and simply characterizes open quantum dynamics across extremely long timescales and in dispersive environments is still needed. In this study, we optimize the computation of the fourth-order time-convolutionless master equation to meet this need. Early versions of this mas

Yining Cao, Weisheng Niu, Xiaoming Wang

We establish the existence of global weak solution in 2D and 3D, as well as the uniqueness of weak solution in 2D, for the Darcy-Boussinesq model for convection in layered porous media with square integrable initial data. We also derived tangential regularity in the 2D case. In addition, we obtain the existence and uniqueness of regular solution in a novel p

Marcin Łyczak

Atomism is the view that everything is composed of atoms. The view within the framework of the contemporary formal approach is expressed on the ground of mereology with the use of the primitive notion of being a part as every object has at least one atomic part [2, 48], [3, 145], [17, 42], or using mereological fusion [16, 24] which is defined by being a par

Zhengping Gui

We construct a trace map on the chiral homology of chiral Weyl algebra for any smooth Riemann surface. Our trace map can be viewed as a chiral version of the deformed HKR quasi-isomorphism. This also provides a mathematical rigorous construction of correlation function for symplectic bosons in physics. We calculate some examples of trace maps with one insert

Erwin Quiring, Andreas Müller, Konrad Rieck

Image scaling is an integral part of machine learning and computer vision systems. Unfortunately, this preprocessing step is vulnerable to so-called image-scaling attacks where an attacker makes unnoticeable changes to an image so that it becomes a new image after scaling. This opens up new ways for attackers to control the prediction or to improve poisoning

Tyler Wang, Huan-Hsin Tseng, Shinjae Yoo

A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties. Ideas of using the quantum internet to address this issue have been previously proposed, which would enable fast and complete

Saswat Mishra, Sharmila Karumuri, Vincent Mika, Collin Scott

The area-normalized change of mass ($\Delta$m/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the im

Michele Vodret

Time irreversibility in neuronal dynamics has recently been demonstrated to correlate with various indicators of cognitive effort in living systems. Using Landauer's principle, which posits that time-irreversible information processing consumes energy, we establish a thermodynamically consistent measure of cognitive energy cost associated with belief dynamic

Maomao Li, Ge Yuan, Cairong Wang, Zhian Liu

This paper proposes a novel approach to face swapping from the perspective of fine-grained facial editing, dubbed "editing for swapping" (E4S). The traditional face swapping methods rely on global feature extraction and fail to preserve the detailed source identity. In contrast, we propose a Regional GAN Inversion (RGI) method, which allows the explicit dise

Tianshi Che, Ji Liu, Yang Zhou, Jiaxiang Ren

Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the nu