October 2023 arXiv papers — page 132

A. Bonafede, M. Gitti, N. La Bella, N. Biava

In this work, we analysed new LOw Frequency ARray observations of the mini halo in the cluster RBS797, together with archival Very Large Array observations and the recent Chandra results. This cluster is known to host a powerful active galactic nucleus (AGN) at its centre, with two pairs of jets propagating in orthogonal directions. Recent X-ray observations

Jacopo Lestingi, Enrico Cannizzaro, Paolo Pani

Even if globally neutral, in various scenarios compact objects can have a nonvanishing dipole moment. Examples include neutron stars with magnetic dipoles, black-hole microstates in the string-theory fuzzball scenario, and classical black holes in modified theories of gravity with spin-induced scalarization or Lorentz-violating terms. A fundamental dipole mo

Xiaofan Li, Yifu Zhang, Xiaoqing Ye

With the increasing popularity of autonomous driving based on the powerful and unified bird's-eye-view (BEV) representation, a demand for high-quality and large-scale multi-view video data with accurate annotation is urgently required. However, such large-scale multi-view data is hard to obtain due to expensive collection and annotation costs. To alleviate t

Gregory Bentsen, Phuc Nguyen, Brian Swingle

We discuss families of approximate quantum error correcting codes which arise as the nearly-degenerate ground states of certain quantum many-body Hamiltonians composed of non-commuting terms. For exact codes, the conditions for error correction can be formulated in terms of the vanishing of a two-sided mutual information in a low-temperature thermofield doub

V. M. Sabarish, Marcus Brüggen, Kai Schmidt-Hoberg, Moritz S. Fischer

Self-interacting dark matter (SIDM) has been proposed to solve small-scale problems in $\Lambda$CDM cosmology. In previous work, constraints on the self-interaction cross-section of dark matter have been derived assuming that the self-interaction cross-section is independent of velocity. However, a velocity-dependent cross-section is more natural in most the

Noga Bashan, Evyatar Tulipman, Jörg Schmalian, Erez Berg

We study a controlled large-$N$ theory of electrons coupled to dynamical two-level systems (TLSs) via spatially-random interactions. Such a physical situation arises when electrons scatter off low-energy excitations in a metallic glass, such as a charge or stripe glass. Our theory is governed by a non-Gaussian saddle point, which maps to the celebrated spin-

Nabendu Kumar Khan, Girish Kulkarni, James S. Bolton, Martin G. Haehnelt

Confronting measurements of the Lyman-$\alpha$ forest with cosmological hydrodynamical simulations has produced stringent constraints on models of particle dark matter and the thermal and ionization state of the intergalactic medium. We investigate the robustness of such models of the Lyman-$\alpha$ forest, focussing on the effect of particle initial conditi

Kaila Ronayne, Casey Papovich, Guang Yang, Lu Shen

We test the relationship between UV-derived star formation rates (SFRs) and the 7.7 ${\mu}$m polycyclic aromatic hydrocarbon (PAH) luminosities from the integrated emission of galaxies at z ~ 0 - 2. We utilize multi-band photometry covering 0.2 - 160 ${\mu}$m from HST, CFHT, JWST, Spitzer, and Herschel for galaxies in the Cosmic Evolution Early Release Scien

Hannah Day, Yonatan Kahn, Daniel A. Roberts

Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network. However, such networks still exhibit fluctuations that grow linearly with the depth of the network, which may impair the training of netwo

Ricardo P. Schiavon, Siân G. Phillips, Natalie Myers, Danny Horta

We introduce the SDSS/APOGEE Value Added Catalogue of Galactic Globular Cluster (GC) Stars. The catalogue is the result of a critical search of the APOGEE data release 17 (DR17) catalogue for candidate members of all known Galactic GCs. Candidate members are assigned to various GCs on the basis of position on the sky, proper motion, and radial velocity. The

Jan Boruch, Luca V. Iliesiu, Sameer Murthy, Gustavo J. Turiaci

The count of microstates for supersymmetric black holes is typically obtained from a supersymmetric index in weakly-coupled string theory. We find the saddles in the gravitational path integral corresponding to this index in a general theory of $N=2$ supergravity in asymptotically flat space. This saddle exhibits a new attractor mechanism which explains the

A. I. Yfantis, M. A. Mościbrodzka, M. Wielgus, J. T. Vos

Sagittarius A* exhibits frequent flaring activity across the electromagnetic spectrum. Signatures of an orbiting hot spot have been identified in the polarized millimeter wavelength light curves observed with ALMA in 2017 immediately after an X-ray flare. The nature of these hot spots remains uncertain. We expanded existing theoretical hot-spot models create

Rafael Álvarez-García, Seung-Joo Lee, Timo Weigand

We study infinite-distance limits in the complex structure moduli space of elliptic Calabi-Yau threefolds. In F-theory compactifications to six dimensions, such limits include infinite-distance trajectories in the non-perturbative open string moduli space. The limits are described as degenerations of elliptic threefolds whose central elements exhibit non-min

Jonas F Karcher, Romain Vasseur, Sarang Gopalakrishnan

We consider the effects of quasiperiodic spatial modulation on the quantum Hall plateau transition, by analyzing the Chalker-Coddington network model for the integer quantum Hall transition with quasiperiodically modulated link phases. In the conventional case (uncorrelated random phases), there is a critical point separating topologically distinct integer q

Valentina Capalbo, Marco De Petris, Weiguang Cui, Antonio Ferragamo

The study of the morphology of 2D projected maps of galaxy clusters is a suitable approach to infer, from real data, the dynamical state of those systems. We recently developed a new method to recover the morphological features in galaxy cluster maps which consists of an analytical modelling through the Zernike polynomials. After the first validation of this

Alexander J. Dittmann, Geoffrey Ryan

Circumbinary accretion occurs throughout the universe, from the formation of stars and planets to the aftermath of major galactic mergers. We present an extensive investigation of circumbinary accretion disks, studying circular binaries with mass ratios ($q\equiv M_2/M_1$) from 0.01 to 1 and at each mass ratio probing the effects of disk thickness and viscos

J. R. Weaver, L. Zalesky, V. Kokorev, C. J. R. McPartland

While space-borne optical and near-infrared facilities have succeeded in delivering a precise and spatially resolved picture of our Universe, their small survey area is known to under-represent the true diversity of galaxy populations. Ground-based surveys have reached comparable depths but at lower spatial resolution, resulting in source confusion that hamp

Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu

Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. While augmentation-based SSRL algorithms push the boundaries of performance in computer vision and natural language processing, they are often not directly applicable to other data mod

S. Stieberger

We discuss relations between closed and open string amplitudes at one-loop. While at tree-level these relations are known as Kawai-Lewellen-Tye (KLT) and/or double copy relations, here we investigate how such relations are manifested at one-loop. While there exist examples of one-loop closed string amplitudes that can strikingly be written as sum over square

Stephanie O'Neil, Josh Borrow, Mark Vogelsberger, Hanzhang Zhao

We illuminate the altered evolution of galaxies in clusters compared to the field by tracking galaxies in the IllustrisTNG300 simulation as they enter isolated clusters of mass $10^{13} < M_{\rm 200, mean} / {\rm M}_\odot < 10^{15}$ (at $z=0$). We demonstrate significant trends in galaxy properties with residence time (time since first infall) and that there

Sam M. Ward, Suhail Dhawan, Kaisey S. Mandel, Matthew Grayling

To reduce systematic uncertainties in Type Ia supernova (SN Ia) cosmology, the host galaxy dust law shape parameter, $R_V$, must be accurately constrained. We thus develop a computationally-inexpensive pipeline, Bird-Snack, to rapidly infer dust population distributions from optical-near infrared SN colours at peak brightness, and determine which analysis ch

Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn

The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the C

Moritz S. Fischer, Lenard Kasselmann, Marcus Brüggen, Klaus Dolag

Dark matter self-interactions may have the capability to solve or at least mitigate small-scale problems of the cosmological standard model, Lambda Cold Dark Matter. There are a variety of self-interacting dark matter models that lead to distinguishable astrophysical predictions and hence varying success in explaining observations. Studies of dark matter (DM

Qiang Zhou, Weize Li, Lihan Jiang, Guoliang Wang

Object anomaly detection is an important problem in the field of machine vision and has seen remarkable progress recently. However, two significant challenges hinder its research and application. First, existing datasets lack comprehensive visual information from various pose angles. They usually have an unrealistic assumption that the anomaly-free training

Sireesh Gururaja, Amanda Bertsch, Clara Na, David Gray Widder

NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by better understanding our past. We study factors that shape NLP as a field, including culture, incentives, and infrastructure by conducting long-form interviews with 26 NLP research

Jonathan Brande, Ian J. M. Crossfield, Laura Kreidberg, Caroline V. Morley

Over the last decade, precise exoplanet transmission spectroscopy has revealed the atmospheres of dozens of exoplanets, driven largely by observatories like the Hubble Space Telescope. One major discovery has been the ubiquity of atmospheric aerosols, often blocking access to exoplanet chemical inventories. Tentative trends have been identified, showing that

Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu

Pretraining auto-regressive large language models~(LLMs) with retrieval demonstrates better perplexity and factual accuracy by leveraging external databases. However, the size of existing pretrained retrieval-augmented LLM is still limited (e.g., Retro has 7.5B parameters), which limits the effectiveness of instruction tuning and zero-shot generalization. In

Raphael Tang, Xinyu Zhang, Xueguang Ma, Jimmy Lin

Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking. To address this, we propose permutation self-consistency, a form of self-consistency over ranking list outputs of black-box LLMs. Our key idea is to marginalize out different list orders in the prompt to produce an order-independent ra

Jie An, Zhengyuan Yang, Linjie Li, Jianfeng Wang

This work investigates a challenging task named open-domain interleaved image-text generation, which generates interleaved texts and images following an input query. We propose a new interleaved generation framework based on prompting large-language models (LLMs) and pre-trained text-to-image (T2I) models, namely OpenLEAF. In OpenLEAF, the LLM generates text

Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark

Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional modularity, in which neurons can be clustered by activity similarity and participation in shared computational subtasks. Unlike brains, these RNNs do not exhibit anatomical modularity, in which functional clustering is correlated with strong recurrent coupling and spatial

Yihan Wu, Zhengmian Hu, Junfeng Guo, Hongyang Zhang

Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models. A challenge in the domain lies in preserving the distribution of original generated content after watermarking. Our research extends and improves upon existing watermarking framework, placing e

Christina Konstantopoulou, Annalisa De Cia, Cédric Ledoux, Jens-Kristian Krogager

The evolution of the cosmic dust content and the cycle between metals and dust in the interstellar medium (ISM) play a fundamental role in galaxy evolution. The chemical enrichment of the Universe can be traced through the evolution of the dust-to-metals ratio (DTM) and the dust-to-gas ratio (DTG) with metallicity. We use a novel method to determine mass est

Alberto Castellano, Alvaro Herráez, Luis E. Ibáñez

The concept of the species scale as the quantum gravity cut-off has been recently emphasised in the context of the Swampland program. Along these lines, we continue the quest for a precise understanding of its role within effective field theories of gravity as well as a global definition of the latter in case there is enough supersymmetry preserved. To do so

Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers

Foundation models are applied in a broad spectrum of settings with different inference constraints, from massive multi-accelerator clusters to resource-constrained standalone mobile devices. However, the substantial costs associated with training these models often limit the number of unique model sizes that can be offered. Consequently, practitioners are co

Sascha Rosbach, Stefan M. Leupold, Simon Großjohann, Stefan Roth

Automated vehicles operating in urban environments have to reliably interact with other traffic participants. Planning algorithms often utilize separate prediction modules forecasting probabilistic, multi-modal, and interactive behaviors of objects. Designing prediction and planning as two separate modules introduces significant challenges, particularly due

N. O. Vambold, G. A. Sazhaev, I. V. Leonov

We report a theoretical study of the effects of electron correlations and structural confinement on the electronic properties and magnetic state of LaNiO3 (LNO) thin films epitaxially deposited on the (001) LaAlO3 (LAO) substrate. Using the DFT+U method we compute the electronic band structure, magnetic properties, and phase stability of the 1.5 unit-cell-th

Haoxuan You, Haotian Zhang, Zhe Gan, Xianzhi Du

We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of understanding spatial referring of any shape or granularity within an image and accurately grounding open-vocabulary descriptions. To unify referring and grounding in the LLM paradigm, Ferret employs a novel and powerful hybrid region representation that integrates discrete coordina

Sriram Bharadwaj, Eric D'Hoker

The Seiberg-Witten solution to four-dimensional $\mathcal{N}=2$ super-Yang-Mills theory with gauge group $\text{SU}(N)$ and without hypermultiplets is used to investigate the neighborhood of the maximal Argyres-Douglas points of type $(\mathfrak{a}_1,\mathfrak{a}_{N-1})$. A convergent series expansion for the Seiberg-Witten periods near the Argyres-Douglas p

Guoyi Chen

Designing and developing Artificial Intelligence controllers on separately dedicated chips have many advantages. This report reviews the development of a real-time fuzzy logic controller for optimizing locomotion control of a two-wheeled differential drive platform using an Arduino Uno board. Based on the Raspberry Pi board, fuzzy sets are used to optimize c

Yingqing He, Shaoshu Yang, Haoxin Chen, Xiaodong Cun

In this work, we investigate the capability of generating images from pre-trained diffusion models at much higher resolutions than the training image sizes. In addition, the generated images should have arbitrary image aspect ratios. When generating images directly at a higher resolution, 1024 x 1024, with the pre-trained Stable Diffusion using training imag

L. J. Janssen, P. Woitke, O. Herbort, M. Min

The first JWST observations of hot Jupiters showed an unexpected detection of SO2 in their hydrogen-rich atmospheres. We investigate how much sulphur can be expected in the atmospheres of rocky exoplanets and which sulphur molecules can be expected to be most abundant and detectable by transmission spectroscopy. We run thermo-chemical equilibrium models at t

Mengzhao Jia, Qianglong Chen, Liqiang Jing, Dawei Fu

The prevalence of mental disorders has become a significant issue, leading to the increased focus on Emotional Support Conversation as an effective supplement for mental health support. Existing methods have achieved compelling results, however, they still face three challenges: 1) variability of emotions, 2) practicality of the response, and 3) intricate st

Zhengfeng Lai, Haotian Zhang, Bowen Zhang, Wentao Wu

Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-crawled AltTexts pose challenges in achieving precise image-text alignment. Existing methods utilizing large language models (LLMs) for caption rewriting have shown promise on smal

Bo Pan, Zhenke Liu, Yifei Zhang, Liang Zhao

Explainable AI seeks to bring light to the decision-making processes of black-box models. Traditional saliency-based methods, while highlighting influential data segments, often lack semantic understanding. Recent advancements, such as Concept Activation Vectors (CAVs) and Concept Bottleneck Models (CBMs), offer concept-based explanations but necessitate hum

Bo Peng, Xinyuan Chen, Yaohui Wang, Chaochao Lu

Recent works have successfully extended large-scale text-to-image models to the video domain, producing promising results but at a high computational cost and requiring a large amount of video data. In this work, we introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text, by levera

Lu Chen, Léna Le Roux, Gaël Grissonnanche, Marie-Eve Boulanger

A surprising "planar" thermal Hall effect, whereby the field is parallel to the current, has recently been observed in a few magnetic insulators, and this has been attributed to exotic excitations such as Majorana fermions or chiral magnons. Here we investigate the possibility of a planar thermal Hall effect in three different cuprate materials, in which the

Anowar J. Shajib

Galaxy-scale strong lensing is a powerful tool in Astrophysics and Cosmology, enabling studies of massive galaxies' internal structure, their formation and evolution, stellar initial mass function, and cosmological parameters. In this conference proceeding, we highlight key findings from the past decade in astrophysical applications of strong lensing at the

Jarrod T. Reilly, Simon B. Jäger, John Drew Wilson, John Cooper

We present a simple and effective method to create highly entangled spin states on a faster timescale than that of the commonly employed one-axis twisting (OAT) model. We demonstrate that by periodically driving the Dicke Hamiltonian at a resonance frequency, the system effectively becomes a two-axis countertwisting Hamiltonian which is known to quickly crea

Łukasz Mazurkiewicz, Szymon Żeberski

The aim of this paper is to give natural examples of $\mathbf{\Sigma}_1^1$-complete and $\mathbf{\Pi}_1^1$-complete sets. In the first part, we consider ideals on $\omega$. In particular, we show that the Hindman ideal $\mathcal{H}$ is $\mathbf{\Pi}_1^1$-complete and consider a number of ideals generated in the similar fashion. Moreover, we show that the ide

Jun-Chen Wang, Zi-Yan Yuwen, Yu-Shi Hao, Shao-Jiang Wang

The gravitational-wave energy-density spectra from cosmological first-order phase transitions crucially depend on the terminal wall velocity of asymptotic bubble expansion when the driving force from the effective potential difference is gradually balanced by the backreaction force from the thermal plasma. Much attention has previously focused on the backrea

Hao Wang, Jianqi Hu, Andrea Morandi, Alfonso Nardi

Neural networks find widespread use in scientific and technological applications, yet their implementations in conventional computers have encountered bottlenecks due to ever-expanding computational needs. Photonic neuromorphic hardware, which manipulates information and represents data continuously in the optical domain, is one of the promising platforms wi

Sirui Li, Roy Dong, Cathy Wu

Autonomous vehicles (AVs) hold vast potential to enhance transportation systems by reducing congestion, improving safety, and lowering emissions. AV controls lead to emergent traffic phenomena; one such intriguing phenomenon is traffic breaks (rolling roadblocks), where a single AV efficiently stabilizes multiple lanes through frequent lane switching, simila

Florian Kranhold

For a manifold $W$ and an $E_d$-algebra $A$, the factorisation homology $\int_W A$ can be seen as a generalisation of the classical configuration space of labelled particles in $W$. It carries an action by the diffeomorphism group $\mathrm{Diff}_\partial(W)$, and for the generalised surfaces $W_{g,1}:=(\#^g S^n\times S^n)\setminus\mathring D{}^{2n}$, we have

Aviad Levis, Andrew A. Chael, Katherine L. Bouman, Maciek Wielgus

The interaction between the supermassive black hole at the center of the Milky Way, Sagittarius A*, and its accretion disk occasionally produces high-energy flares seen in X-ray, infrared, and radio. One proposed mechanism that produces flares is the formation of compact, bright regions that appear within the accretion disk and close to the event horizon. Un

Yinbin Ma, Daniela Tuninetti

This paper presents a new achievable scheme for coded caching systems with $\mathsf{N}$ files, $\mathsf{K}=\mathsf{N}$ users, and cache size $\mathsf{M}=1/(\mathsf{N}-1)$. The scheme employs linear coding during the cache placement phase, and a three-stage transmissions designed to eliminate interference in the delivery phase. The achievable load meets a kno

Arka Adhikari, Izumi Okada

In this paper, we find a natural four dimensional analog of the moderate deviation results for the capacity of the random walk, which corresponds to Bass, Chen and Rosen \cite{BCR} concerning the volume of the random walk range for $d=2$. We find that the deviation statistics of the capacity of the random walk can be related to the following constant of gene

Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernardez, Olga Zaghen

Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to overshadowing specific characteristics of hypergraphs. This paper attempts to confront some pending questions in that regard: Q1 Can the concept of homophily play a crucial role in Hyp

Bo Pan, Muran Qin, Shiyu Wang, Yifei Zhang

Deep generative models have been widely used for their ability to generate realistic data samples in various areas, such as images, molecules, text, and speech. One major goal of data generation is controllability, namely to generate new data with desired properties. Despite growing interest in the area of controllable generation, significant challenges stil

Kshitij Ingale, Sun Hae Hong, Josh S. K. Bell, Abbas Rizvi

MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC) and is the subject of active drug development. Challenges in identifying patients for these therapies include lack of access to validated testing, such as standardized immunohistochemistry (IHC) assessment, and consumption of valuable tissue for a single gene/protein assay

Nina Zubrilina

We establish a case of the surprising correlation phenomenon observed in the recent works of He, Lee, Oliver, Pozdnyakov, and Sutherland between Fourier coefficients of families of modular forms and their root numbers.

Takuo Matsubara

This paper advances theoretical understanding of infinite-dimensional geometrical properties associated with Bayesian inference. First, we introduce a novel class of infinite-dimensional Hamiltonian systems for saddle Hamiltonian functions whose domains are metric spaces. A flow of this system is generated by a Hamiltonian arc field, an analogue of Hamiltoni

Francis A. Bayocboc, Jacek Dziarmaga, Wojciech H. Zurek

A quantum phase transition from the miscible to the immiscible phase of a quasi-one-dimensional binary Bose-Einstein condensate is driven by ramping down the coupling amplitude of its two hyperfine states. It results in a random pattern of spatial domains where the symmetry is broken separated by defects. In distinction to previous studies [J. Sabbatini et a

Ioannis E. Livieris, Emmanuel Pintelas, Niki Kiriakidou, Panagiotis Pintelas

With the proliferation of image-based applications in various domains, the need for accurate and interpretable image similarity measures has become increasingly critical. Existing image similarity models often lack transparency, making it challenging to understand the reasons why two images are considered similar. In this paper, we propose the concept of exp

Natalia Stepanova, Marie Turcicova

We study the problem of adaptive variable selection in a Gaussian white noise model of intensity $\varepsilon$ under certain sparsity and regularity conditions on an unknown regression function $f$. The $d$-variate regression function $f$ is assumed to be a sum of functions each depending on a smaller number $k$ of variables ($1 \leq k \leq d$). These functi

Hai Huang, Zhengyu Zhao, Michael Backes, Yun Shen

Large language models (LLMs) have demonstrated superior performance compared to previous methods on various tasks, and often serve as the foundation models for many researches and services. However, the untrustworthy third-party LLMs may covertly introduce vulnerabilities for downstream tasks. In this paper, we explore the vulnerability of LLMs through the l

Manuel Estrada, Michael Ruderman, Leonid Fridman

This paper provides a novel surface design and experimental evaluation of a super-twisting algorithm (STA) based control for hydraulic cylinder actuators. The proposed integral sliding surface allows to track a sufficiently smooth reference without using the velocity state which is hardly accessible in the noisy hydraulic systems. A design methodology based

Wangjun Jiang, Zhiqing Wei, Zhiyong Feng, Xu Chen

This paper studies the sensing base station (SBS) that has great potential to improve the safety of vehicles and pedestrians on roads. It can detect the targets on the road with communication signals using the integrated sensing and communication (ISAC) technique. Compared with vehicle-mounted radar, SBS has a better sensing field due to its higher deploymen

Walaa M. Moursi, Viktor Pavlovic, Stephen A. Vavasis

The $O(1/k^2)$ convergence rate in function value of accelerated gradient descent is optimal, but there are many modifications that have been used to speed up convergence in practice. Among these modifications are restarts, that is, starting the algorithm with the current iteration being considered as the initial point. We focus on the adaptive restart techn

Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar

Learning controllers with offline data in decision-making systems is an essential area of research due to its potential to reduce the risk of applications in real-world systems. However, in responsibility-sensitive settings such as healthcare, decision accountability is of paramount importance, yet has not been adequately addressed by the literature. This pa

M Romero-Bastida, Brandon Armando Martínez-Torres

In this work we perform a systematic analysis of various structural parameters that have influence on the thermal rectification effect, i.e. asymmetrical heat flow, and the negative differential thermal resistance -- reduction of the heat flux as the applied thermal bias is increased -- present in a one-dimensional, segmented mass-graded system consisting of

Jeremy Goldwasser, Giles Hooker

Shapley values are among the most popular tools for explaining predictions of blackbox machine learning models. However, their high computational cost motivates the use of sampling approximations, inducing a considerable degree of uncertainty. To stabilize these model explanations, we propose ControlSHAP, an approach based on the Monte Carlo technique of con

Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira, Carine Ribeiro dos Santos

Artificial intelligence holds promise to improve materials discovery. GFlowNets are an emerging deep learning algorithm with many applications in AI-assisted discovery. By using GFlowNets, we generate porous reticular materials, such as metal organic frameworks and covalent organic frameworks, for applications in carbon dioxide capture. We introduce a new Py

Manuel Friedrich, Camille Labourie, Kerrek Stinson

We present regularity results for the crack set of a minimizer for the Griffith fracture energy, arising in the variational modeling of brittle materials. In the planar setting, we prove an epsilon-regularity theorem showing that the crack is locally a $C^{1,1/2}$ curve outside of a singular set of zero Hausdorff measure. The main novelty is that, in contras

Rick Groenendijk, Leo Dorst, Theo Gevers

Signals from different modalities each have their own combination algebra which affects their sampling processing. RGB is mostly linear; depth is a geometric signal following the operations of mathematical morphology. If a network obtaining RGB-D input has both kinds of operators available in its layers, it should be able to give effective output with fewer

Makan Kananian, Fatima Badiei, S. AmirAli Gh. Ghahramani

The proliferation of social media platforms such as Twitter, Instagram, and Weibo has significantly enhanced the dissemination of false information. This phenomenon grants both individuals and governmental entities the ability to shape public opinions, highlighting the need for deploying effective detection methods. In this paper, we propose GraMuFeN, a mode

Drake Brown, Trevor Garrity, Kaden Parker, Jason Oliphant

We analyze the performance of graph neural network (GNN) architectures from the perspective of random graph theory. Our approach promises to complement existing lenses on GNN analysis, such as combinatorial expressive power and worst-case adversarial analysis, by connecting the performance of GNNs to typical-case properties of the training data. First, we th

David Alfaya, Indranil Biswas, Tomás L. Gómez, Swarnava Mukhopadhyay

Given any irreducible smooth complex projective curve $X$, of genus at least $2$, consider the moduli stack of vector bundles on $X$ of fixed rank and determinant. It is proved that the isomorphism class of the stack uniquely determines the isomorphism class of the curve $X$ and the rank of the vector bundles. The case of trivial determinant, rank $2$ and ge

Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach

Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights. Whereas the classical interventional interpretation of counterfactuals has been studied extensively, backtracking constitutes a less studied alternative where all causal laws are kept intact. In the present work, we introdu

Deli Yu, Teng Xi, Jianwei Li, Baopu Li

Recently, Vision Transformers (ViTs) have attracted a lot of attention in the field of computer vision. Generally, the powerful representative capacity of ViTs mainly benefits from the self-attention mechanism, which has a high computation complexity. To accelerate ViTs, we propose an integrated compression pipeline based on observed heterogeneous attention

Kyuyeon Kim, Junsik Jung, Woo Jae Kim, Sung-Eui Yoon

Humans can easily imagine a scene from auditory information based on their prior knowledge of audio-visual events. In this paper, we mimic this innate human ability in deep learning models to improve the quality of video inpainting. To implement the prior knowledge, we first train the audio-visual network, which learns the correspondence between auditory and

Maksymilian Bujok, Miroslav Rozložník, Agata Smoktunowicz, Alicja Smoktunowicz

In this paper we give the detailed error analysis of two algorithms $W_1$ and $W_2$ for computing the symplectic factorization of a symmetric positive definite and symplectic matrix $A \in \mathbb R^{2n \times 2n}$ in the form $A=LL^T$, where $L \in \mathbb R^{2n \times 2n}$ is a symplectic block lower triangular matrix. We prove that Algorithm $W_2$ is nume

Anshul Verma, Sanjeet K. Patel, Pavan K. Aluri, Sukanta Panda

We study the Bianchi-I cosmological model motivated by signals of statistical isotropy violation seen in cosmic microwave background (CMB) observations and others. To that end, we consider various kinds of anisotropic matter that source anisotropy in our model, specifically Cosmic strings, Magnetic fields, Domain walls and Lorentz violation generated magneti

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

We present the first search for heavy neutral leptons (HNL) decaying into $\nu e^+e^-$ or $\nu\pi^0$ final states in a liquid-argon time projection chamber using data collected with the MicroBooNE detector. The data were recorded synchronously with the NuMI neutrino beam from Fermilab's Main Injector corresponding to a total exposure of $7.01 \times 10^{20}$

Lang Qin, Yao Zhang, Hongru Liang, Jun Wang

Accurate knowledge selection is critical in knowledge-grounded dialogue systems. Towards a closer look at it, we offer a novel perspective to organize existing literature, i.e., knowledge selection coupled with, after, and before generation. We focus on the third under-explored category of study, which can not only select knowledge accurately in advance, but

Çağkan Yapar, Fabian Jaensch, Ron Levie, Gitta Kutyniok

To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this short overview paper, we briefly describe the pathloss prediction problem, the provided datasets, the challenge task and the challenge evaluation methodology.

M. Rouble, G. Smecher, A. Anderson, P. S. Barry

We present RF-ICE, a novel readout platform for microwave kinetic inductance detectors (MKIDs), optimized for use on millimeter-wavelength telescopes. The RF-ICE system extends ICE, a versatile, mature signal processing platform currently in use on telescopes around the world, into a new operational domain with MKIDs biased with gigahertz carriers. The syste

Svenja M. Griesbach, Max Klimm, Philipp Warode, Theresa Ziemke

We study the optimal provision of information for two natural performance measures of queuing systems: throughput and makespan. A set of parallel links is equipped with deterministic capacities and stochastic travel times where the latter depend on a realized scenario. A continuum of flow particles arrives at the system at a constant rate. A system operator

James East, Victoria Gould, Craig Miller, Thomas Quinn-Gregson

For a semigroup $S$ whose universal right congruence is finitely generated (or, equivalently, a semigroup satisfying the homological finiteness property of being type right-$FP_1$), the right diameter of $S$ is a parameter that expresses how `far apart' elements of $S$ can be from each other, in a certain sense. To be more precise, for each finite generating

Cheng-I Jeff Lai, Freda Shi, Puyuan Peng, Yoon Kim

We study phrase structure induction from visually-grounded speech. The core idea is to first segment the speech waveform into sequences of word segments, and subsequently induce phrase structure using the inferred segment-level continuous representations. We present the Audio-Visual Neural Syntax Learner (AV-NSL) that learns phrase structure by listening to

Zeqiang Lai, Xizhou Zhu, Jifeng Dai, Yu Qiao

The revolution of artificial intelligence content generation has been rapidly accelerated with the booming text-to-image (T2I) diffusion models. Within just two years of development, it was unprecedentedly of high-quality, diversity, and creativity that the state-of-the-art models could generate. However, a prevalent limitation persists in the effective comm

Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim

Data visualization is a powerful tool for exploring and communicating insights in various domains. To automate visualization choice for datasets, a task known as visualization recommendation has been proposed. Various machine-learning-based approaches have been developed for this purpose, but they often require a large corpus of dataset-visualization pairs f

Ivan Fumagalli, Mattia Corti, Nicola Parolini, Paola F. Antonietti

A comprehensive mathematical model of the multiphysics flow of blood and Cerebrospinal Fluid (CSF) in the brain can be expressed as the coupling of a poromechanics system and Stokes' equations: the first describes fluids filtration through the cerebral tissue and the tissue's elastic response, while the latter models the flow of the CSF in the brain ventricl

Kyungmin Kim, Sumin Lim, Kyujin Shin, Gwonhak Lee

The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge that motivates research on compact system representations to reduce algorithm complexity. Toward this end, qua

Carlo Bosio, Mark W. Mueller

The joint optimization of physical parameters and controllers in robotic systems is challenging. This is due to the difficulties of predicting the effect that changes in physical parameters have on final performances. At the same time, physical and morphological modifications can improve robot capabilities, perhaps completely unlocking new skills and tasks.

Eleonora Lopez, Eleonora Chiarantano, Eleonora Grassucci, Danilo Comminiello

Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reactions, thus providing more reliable information. Existing deep learning-based methods still rely on extracted handcrafted features, not taking full advantage of the learning ability of

Xiaoting Zhou, Yi-Chun Hung, Baokai Wang, Arun Bansil

Unlike the spin-1/2 fermions, the Lieb and Dice lattices both host triply-degenerate low-energy excitations. Here, we discuss Moir\'e structures involving twisted bilayers of these lattices, which are shown to exhibit a tunable number of isolated flat bands near the Fermi level. These flat bands remain isolated from the high-energy bands even in the presence

Cedric Westphal, Lin Han, Richard Li

This document surveys recent and current developments in LEO satellite networking. It presents a brief overview of satellite networking in order to contextualize the issue. It then focuses on current research work in emerging domains, such as Machine Learning, SDN, low latency networking, green networking, Information-Centric Networks, etc. For each, it pres

E. P. Inyang, E. P. Inyang, I. O. Akpan, E. S. William

In this study, a quarkonium potential is adopted as the quark-antiquark interaction potential for predicting the mass spectra of heavy mesons. We solved the radial Schr\"odinger equation analytically using the series expansion method and obtained the energy eigenvalues. The present results are applied for predicting the mass spectra of heavy mesons such as c

Chaoqi Liang, Lifeng Qiao, Peng Ye, Nanqing Dong

With the success of large-scale pre-training in language tasks, there is an increasing trend of applying it to the domain of life sciences. In particular, pre-training methods based on DNA sequences have received increasing attention because of their potential to capture general information about genes. However, existing pre-training methods for DNA sequence

Keke Long, Xiaowei Shi, Zhiwei Chen, Yuan Wang

Automated vehicle (AV) shuttles are emerging mobility technologies that have been widely piloted and deployed. Public attitude is critical to the deployment progress and the overall social benefits of automated vehicle (AV) technologies. The AV shuttle demonstration was regarded as a good way for possible attitude improvements. However, not all existing AV s