May 2023 arXiv papers — page 3

J. van de Sande, A. Fraser-McKelvie, D. B. Fisher, M. Martig

We present GECKOS (Generalising Edge-on galaxies and their Chemical bimodalities, Kinematics, and Outflows out to Solar environments), a new ESO VLT/MUSE large program. The main aim of GECKOS is to reveal the variation in key physical processes of disk formation by connecting Galactic Archaeology with integral field spectroscopic observations of nearby galax

Maria Tikhanovskaya, Ali Lavasani, Matthew P. A. Fisher, Sagar Vijay

The linear cross-entropy (LXE) has been recently proposed as a scalable probe of the measurement-driven phase transition between volume- and area-law-entangled phases of pure-state trajectories in certain monitored quantum circuits. Here, we demonstrate that the LXE can distinguish distinct area-law-entangled phases of monitored circuits with symmetries, and

Manoj K. Joshi, Christian Kokail, Rick van Bijnen, Florian Kranzl

Entanglement is a distinguishing feature of quantum many-body systems, and uncovering the entanglement structure for large particle numbers in quantum simulation experiments is a fundamental challenge in quantum information science. Here we perform experimental investigations of entanglement based on the entanglement Hamiltonian, as an effective description

Andrea Caputo, Michael Geller, Giuseppe Rossi

Light scalar and pseudoscalar particles are compelling dark matter candidates, with a vast running experimental program to discover them. Previous studies have shown that these light fields can generate sizable isocurvature perturbations in high scale inflationary models. Thereby, dark matter existence and cosmic microwave background measurements impose an u

Souvik Banerjee, Moritz Dorband, Johanna Erdmenger, Anna-Lena Weigel

We show how geometric phases may be used to fully describe quantum systems, with or without gravity, by providing knowledge about the geometry and topology of its Hilbert space. We find a direct relation between geometric phases and von Neumann algebras. In particular, we show that a vanishing geometric phase implies the existence of a well-defined trace fun

Shayan Majidy, William F. Braasch, Aleksander Lasek, Twesh Upadhyaya

Thermodynamic systems typically conserve quantities ("charges") such as energy and particle number. The charges are often assumed implicitly to commute with each other. Yet quantum phenomena such as uncertainty relations rely on observables' failure to commute. How do noncommuting charges affect thermodynamic phenomena? This question, upon arising at the int

T. Corbett, A. Martin

We present the inclusive calculations of a Higgs boson produced in associated with massive vector bosons in the Standard Model Effective Field Theory (SMEFT) to order 1/$\Lambda^4$ for the 13 TeV LHC. The calculations include the decay of the vector boson into massless constituents and are done using the geometric formulation of the SMEFT supplemented by the

Syuhei Iguro, Yuji Omura

We discuss a model with a SU$(2)_L$ doublet vector leptoquark (LQ), motivated by the recent experimental results relating to the lepton universality of $\overline{B} \to D^{(*)} \tau \overline{\nu}_\tau$. We find that scalar operators predicted by the LQ are favored to explain the deviations, taking into account the recent LHCb result. We investigate the ext

Ariane Dekker, Ian Holst, Dan Hooper, Giovani Leone

The LHAASO Collaboration has recently reported a measurement of the diffuse gamma-ray emission from the Galactic Plane at energies between 10 TeV and 1 PeV. While this emission is brighter than that expected from cosmic-ray interactions in the interstellar medium alone, we show that the intensity, spectrum, and morphology of this excess are in good agreement

Horng Sheng Chia, Thomas D. P. Edwards, Digvijay Wadekar, Aaron Zimmerman

We report results on the first matched-filtering search for binaries with compact objects having large tidal deformabilities in the LIGO-Virgo gravitational wave (GW) data. The tidal deformability of a body is quantified by the ``Love number" $\Lambda \propto \hskip 1pt (r/m)^5$, where $r/m$ is the body's (inverse) compactness. Due to its strong dependence o

Samuel J. Garratt, Ehud Altman

We study the problem of observing quantum collective phenomena emerging from large numbers of measurements. These phenomena are difficult to observe in conventional experiments because, in order to distinguish the effects of measurement from dephasing, it is necessary to post-select on sets of measurement outcomes whose Born probabilities are exponentially s

Shubham Goel, Georgios Pavlakos, Jathushan Rajasegaran, Angjoo Kanazawa

We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficult to reconstruct from single images. To

Ke Huang, DinhDuy Vu, Sankar Das Sarma, Xiao Li

We study the many-body localization (MBL) transition in a generalized Aubry-Andre model (also known as the GPD model) introduced in Phys. Rev. Lett. 114, 146601 (2015). In contrast to MBL in other disordered or quasiperiodic models, the interaction seems to unexpectedly enhance MBL in the GPD model in some parameter ranges. To understand this counter-intuiti

Junxing Hu, Hongwen Zhang, Zerui Chen, Mengcheng Li

Reconstructing hand-held objects from monocular RGB images is an appealing yet challenging task. In this task, contacts between hands and objects provide important cues for recovering the 3D geometry of the hand-held objects. Though recent works have employed implicit functions to achieve impressive progress, they ignore formulating contacts in their framewo

Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi

Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective and scalable methods for training transferable vision models using paired image and text data. CLIP models are trained using contrastive loss, which typically relies on data augmentations to prevent overfitting and shortcuts. However, in the CLIP training paradigm, data augmen

Alex Jinpeng Wang, Kevin Qinghong Lin, David Junhao Zhang, Stan Weixian Lei

This paper examines the problems of severe image-text misalignment and high redundancy in the widely-used large-scale Vision-Language Pre-Training (VLP) datasets. To address these issues, we propose an efficient and straightforward Vision-Language learning algorithm called TL;DR, which aims to compress the existing large VLP data into a small, high-quality s

Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping

Images generated by diffusion models like Stable Diffusion are increasingly widespread. Recent works and even lawsuits have shown that these models are prone to replicating their training data, unbeknownst to the user. In this paper, we first analyze this memorization problem in text-to-image diffusion models. While it is widely believed that duplicated imag

Trinnhallen Brisley, Gordon Ross, Daniel Paulin, Jake Easto

Hawkes processes are a class of self-exciting point processes that are used to model complex phenomena. While most applications of Hawkes processes assume that event data occurs in continuous-time, the less-studied discrete-time version of the process is more appropriate in some situations. In this work, we develop methodology for the efficient implementatio

Tim O. Höhn, Etienne Staub, Guillaume Brochier, Nelson Darkwah Oppong

We present measurements of three distinctive state-(in)dependent wavelengths for the $^1\text{S}_{0}-^3\text{P}_{0}$ clock transition in $^{174}\text{Yb}$ atoms. Specifically, we determine two magic wavelengths at $652.281(21)\,$THz and $542.50205(19)\,$THz, where the differential light shift on the $^1\text{S}_{0}-^3\text{P}_{0}$ clock transition vanishes,

David Aristoff, Mats Johnson, Danny Perez

Stochastic dynamics, such as molecular dynamics, are important in many scientific applications. However, summarizing and analyzing the results of such simulations is often challenging, due to the high dimension in which simulations are carried out, and consequently to the very large amount of data that is typically generated. Coarse graining is a popular tec

Ruizhi Shao, Jingxiang Sun, Cheng Peng, Zerong Zheng

We introduce Control4D, an innovative framework for editing dynamic 4D portraits using text instructions. Our method addresses the prevalent challenges in 4D editing, notably the inefficiencies of existing 4D representations and the inconsistent editing effect caused by diffusion-based editors. We first propose GaussianPlanes, a novel 4D representation that

Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang

Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametrized Markov Chain with hundreds of steps

Noëmi Aepli, Çağrı Çöltekin, Rob Van Der Goot, Tommi Jauhiainen

This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2023. Three separate shared tasks were included this year: Slot and intent detection for l

Hayeong Song, John Stasko

Journalists and visualization designers include visualizations in their articles and storytelling tools to deliver their message effectively. But design decisions they make to represent information, such as the graphical dimensions they choose and the viewer's familiarity with the content can impact the viewer's perceived credibility of charts. Especially in

Gautam Yadav

This paper presents a comprehensive exploration of leveraging Large Language Models (LLMs), specifically GPT-4, in the field of instructional design. With a focus on scaling evidence-based instructional design expertise, our research aims to bridge the gap between theoretical educational studies and practical implementation. We discuss the benefits and limit

Adam Graham-Squire, David McCune

Ranked-choice voting anomalies such as monotonicity paradoxes have been extensively studied through creating hypothetical examples and generating elections under various models of voter behavior. However, very few real-world examples of such voting paradoxes have been found and analyzed. We investigate two single-transferable vote elections from Scotland tha

Anya Li, Bhala Ranganathan, Feng Pan, Mickey Zhang

Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various b

Jessy Lin, Nicholas Tomlin, Jacob Andreas, Jason Eisner

We describe a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans via natural language to help them make complex decisions. We formalize three domains in which users face everyday decisions: (1) choosing an assignment of reviewers to conference papers, (2) plan

Hamed Manouchehri Kousha, Sina Hooshangi, Aliakbar Abolhasani

Ultra-light scalar fields and their non-interacting class, the so-called fuzzy dark matter (FDM), are candidates for dark matter, introduced to solve the small-scale problems of the standard cold dark matter. In this paper, we address whether the small-scale effects, specifically the quantum pressure, could leave sizable imprints on the large-scale statistic

Bo Hu, Yuheng Bu, José C. Príncipe

This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in multiview systems. By framing view similarities as dependencies and ensuring contrastivity by imposing orthonormality, HFMCA achieves faster convergence and increased stability in

Yuxiang Lu, Yuhang Yao, Syed A. Jafar

Inspired by recent work by Christensen and Popovski on secure $2$-user product computation for finite-fields of prime-order over a quantum multiple access channel, the generalization to $K$ users and arbitrary finite fields is explored. Asymptotically optimal (capacity-achieving for large alphabet) schemes are proposed. Additionally, the capacity of modulo-$

Alex Dunbar, Lars Ruthotto

We propose an alternating minimization heuristic for regression over the space of tropical rational functions with fixed exponents. The method alternates between fitting the numerator and denominator terms via tropical polynomial regression, which is known to admit a closed form solution. We demonstrate the behavior of the alternating minimization method exp

Xiaoling Wu, Zhuqing Wang, Fan Yang, Ruochen Gao

The notion of spontaneous symmetry breaking has been well established to characterize classical and quantum phase transitions of matter, such as in condensation, crystallization or quantum magnetism. Generalizations of this paradigm to the time dimension can lead to a time crystal phase, which spontaneously breaks the time translation symmetry of the system.

Anurag Anshu, Srinivasan Arunachalam

We survey various recent results that rigorously study the complexity of learning quantum states. These include progress on quantum tomography, learning physical quantum states, alternate learning models to tomography and learning classical functions encoded as quantum states. We highlight how these results are paving the way for a highly successful theory w

Zihao Wen, Yifan Zhang, Xinhong Chen, Jianping Wang

In autonomous driving, an accurate understanding of environment, e.g., the vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks such as trajectory prediction and motion planning. Environment information comes from high-definition (HD) map and historical trajectories of vehicles. Due to the heterogeneity of the map

Fabrizio Cipollini, Giampiero M. Gallo, Alessandro Palandri

We focus on the time-varying modeling of VaR at a given coverage $\tau$, assessing whether the quantiles of the distribution of the returns standardized by their conditional means and standard deviations exhibit predictable dynamics. Models are evaluated via simulation, determining the merits of the asymmetric Mean Absolute Deviation as a loss function to ra

Matthias Klein, Daniel Hernández-Lang, Joseph J Mohr, Sebastian Bocquet

We present the RASS-MCMF catalog of 8,465 X-ray selected galaxy clusters over 25,000 deg$^2$ of extragalactic sky. The accumulation of deep, multiband optical imaging data, the development of the Multi-Component Matched Filter cluster confirmation algorithm (MCMF), and the release of the DESI Legacy Survey DR10 catalog makes it possible -- for the first time

Alberto Silvio Chiappa, Alessandro Marin Vargas, Ann Zixiang Huang, Alexander Mathis

In Reinforcement Learning, agents learn policies by exploring and interacting with the environment. Due to the curse of dimensionality, learning policies that map high-dimensional sensory input to motor output is particularly challenging. During training, state of the art methods (SAC, PPO, etc.) explore the environment by perturbing the actuation with indep

Thomas Read

For a profinite group $G$ we describe an abelian group $W_G(R; M)$ of $G$-typical Witt vectors with coefficients in an $R$-module $M$ (where $R$ is a commutative ring). This simultaneously generalises the ring $W_G(R)$ of Dress and Siebeneicher and the Witt vectors with coefficients $W(R; M)$ of Dotto, Krause, Nikolaus and Patchkoria, both of which extend th

Gabriel M. Azevedo, Ana L. Chies-Santos, Rogério Riffel, Jean M. Gomes

We present a spatially resolved stellar population analysis of 61 jellyfish galaxies and 47 control galaxies observed with ESO/MUSE attempting to understand the general trends of the stellar populations as a function of the stripping intensity and mass. This is the public sample from the GASP programme, with $0.01 < z < 0.15$ and $8.9 <\log(M_{\star}/M_{\odo

Matt Wilson, Nick Ormrod

We reconstruct the transformations of quantum theory using a physically motivated postulate. This postulate states that transformations should be locally applicable, and recovers the linear isometries from pure quantum theory, as well as the completely positive, trace-preserving maps from mixed quantum theory. Notably, in the pure case, linearity with respec

Matan Levy, Rami Ben-Ari, Nir Darshan, Dani Lischinski

Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. However, existing image retrieval approaches typically address the case of a single query-to-image round, and the use of chats for image retrieval has been mostly overlooked. In this

Mark Pupilli

We present an experimental neural path tracer designed to exploit the large on-chip memory of Graphcore intelligence-processing-units (IPUs). This open source renderer demonstrates how to map path tracing to the novel software and hardware architecture and is a useful tool for analysing in-cache neural-rendering scenarios. Such scenarios will be increasingly

B. Stray, O. Ennis, S. Hedges, S. Dey

This paper outlines the centralised design and production of the Ultra-High-Vacuum sidearm and Laser-Stabilisation systems for the AION Ultra-Cold Strontium Laboratories. Commissioning data on the residual gas and steady-state pressures in the sidearm chambers, on magnetic field quality, on laser stabilisation, and on the loading rate for the 3D Magneto-Opti

Md Ashikuzzaman, Ali K. Z. Tehrani, Hassan Rivaz

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate

Imane Nedjar, Mohammed Brahimi, Said Mahmoudi, Khadidja Abi Ayad

Computer aided detection and diagnosis systems based on deep learning have shown promising performance in breast cancer detection. However, there are cases where the obtained results lack justification. In this study, our objective is to highlight the regions of interest used by a convolutional neural network (CNN) for classifying histological images as beni

Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen

Multi-objective learning (MOL) problems often arise in emerging machine learning problems when there are multiple learning criteria, data modalities, or learning tasks. Different from single-objective learning, one of the critical challenges in MOL is the potential conflict among different objectives during the iterative optimization process. Recent works ha

Arvind Pillai, Subigya Nepal, Andrew Campbell

Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions. We envision that mobile sensing data can be used to detect these anomalies. However, the human-centered nature of the problem, combined with the infrequency and uniqueness of these events makes it challenging fo

Haoxuan Xu, Songning Lai, Xianyang Li, Yang Yang

Car detection, particularly through camera vision, has become a major focus in the field of computer vision and has gained widespread adoption. While current car detection systems are capable of good detection, reliable detection can still be challenging due to factors such as proximity between the car, light intensity, and environmental visibility. To addre

Zhong-Qiu Wang, Shinji Watanabe

In reverberant conditions with multiple concurrent speakers, each microphone acquires a mixture signal of multiple speakers at a different location. In over-determined conditions where the microphones out-number speakers, we can narrow down the solutions to speaker images and realize unsupervised speech separation by leveraging each mixture signal as a const

Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas

We propose a novel machine learning framework for solving optimization problems governed by large-scale partial differential equations (PDEs) with high-dimensional random parameters. Such optimization under uncertainty (OUU) problems may be computational prohibitive using classical methods, particularly when a large number of samples is needed to evaluate ri

Chase Walker, Sumit Jha, Kenny Chen, Rickard Ewetz

Attribution algorithms are frequently employed to explain the decisions of neural network models. Integrated Gradients (IG) is an influential attribution method due to its strong axiomatic foundation. The algorithm is based on integrating the gradients along a path from a reference image to the input image. Unfortunately, it can be observed that gradients co

Federico Glaudo

We prove that a subset of the hypercube $(0,1)^d$ with volume sufficiently close to $\frac12$ has (relative) perimeter greater than or equal to $1$. This settles a conjecture by Brezis and Bruckstein. We also prove that, in contrast with what happens for the high-dimensional sphere $\mathbb S^d$, the isoperimetric profile of the hypercube $(0,1)^d$ does not

Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards

In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even state-of-the-art models still regularly produce logical mistakes. To train more reliable models, we can turn either to outcome supervision, which provides feedback for a final result, or process supervision, which provides feed

Yi Zhang, Xiaoyu Shi, Dasong Li, Xiaogang Wang

Diffusion Probabilistic Models (DPMs) have recently shown remarkable performance in image generation tasks, which are capable of generating highly realistic images. When adopting DPMs for image restoration tasks, the crucial aspect lies in how to integrate the conditional information to guide the DPMs to generate accurate and natural output, which has been l

Krish Kabra, Guha Balakrishnan

Perceptual metrics, like the Fr\'echet Inception Distance (FID), are widely used to assess the similarity between synthetically generated and ground truth (real) images. The key idea behind these metrics is to compute errors in a deep feature space that captures perceptually and semantically rich image features. Despite their popularity, the effect that diff

Xiaoyuan Guo, Kezhen Chen, Jinmeng Rao, Yawen Zhang

We present LOWA, a novel method for localizing objects with attributes effectively in the wild. It aims to address the insufficiency of current open-vocabulary object detectors, which are limited by the lack of instance-level attribute classification and rare class names. To train LOWA, we propose a hybrid vision-language training strategy to learn object de

Charalambos Themistocleous, Kyrana Tsapkini, Dimitrios Kokkinakis

Speech, language, and communication deficits are present in most neurodegenerative syndromes. They enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression as part of traditional neurological assessment. Nevertheless, standard speech and language evaluation is time-consuming and resource-intensive for cl

Leon Weber, Barbara Plank

Manually annotated datasets are crucial for training and evaluating Natural Language Processing models. However, recent work has discovered that even widely-used benchmark datasets contain a substantial number of erroneous annotations. This problem has been addressed with Annotation Error Detection (AED) models, which can flag such errors for human re-annota

Junan Chen, Josephine Monica, Wei-Lun Chao, Mark Campbell

The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making accurate predictions, especially for safety-critical applications such as self-driving cars. This paper proposes our approach to uncertainty quantification in the context of visual lo

Deniz Koyuncu, Alex Gittens, Bülent Yener, Moti Yung

Inference of causal structures from observational data is a key component of causal machine learning; in practice, this data may be incompletely observed. Prior work has demonstrated that adversarial perturbations of completely observed training data may be used to force the learning of inaccurate causal structural models (SCMs). However, when the data can b

Hasti Narimanzadeh, Arash Badie-Modiri, Iuliia Smirnova, Ted Hsuan Yun Chen

How to better reduce measurement variability and bias introduced by subjectivity in crowdsourced labelling remains an open question. We introduce a theoretical framework for understanding how random error and measurement bias enter into crowdsourced annotations of subjective constructs. We then propose a pipeline that combines pairwise comparison labelling w

Yunbo Zhang, Deepak Gopinath, Yuting Ye, Jessica Hodgins

We present a method for reproducing complex multi-character interactions for physically simulated humanoid characters using deep reinforcement learning. Our method learns control policies for characters that imitate not only individual motions, but also the interactions between characters, while maintaining balance and matching the complexity of reference da

Rabindranath Bag, Sijie Xu, Nicholas E. Sherman, Lalit Yadav

The emergence of a quantum spin liquid (QSL), a state of matter that can result when electron spins are highly correlated but do not become ordered, has been the subject of a considerable body of research in condensed matter physics. Spin liquid states have been proposed as hosts for high-temperature superconductivity and can host topological properties with

Tabassum S. Tanvir, Mark R. Krumholz

Dust is important for star formation because it is the crucial component that couples gas to stellar radiation fields, allowing radiation feedback to influence gas fragmentation and thus the stellar initial mass function (IMF). Variations in dust abundance therefore provide a potential avenue by which variation in galaxy metallicity might affect the IMF. In

Lu Yin

The 21-cm signal is the most important measurement for us to understand physics during cosmic dawn. It is the key for us to understand the expansion history of the Universe and the nature of dark energy. In this paper, we focused on the characteristic 21-cm power spectrum of a special dynamic dark energy - the Interacting Chevallier-Polarski-Linder (ICPL) mo

Sarthak Duary

We explore the flat limit of massless scalar scattering in $\mathrm{AdS}_2$. We derive the $1 \to 1$ $\mathcal{S}$-matrix from the CFT $2$-point function. We show a key property of the $2 \to 2$ $\mathcal{S}$-matrix in $2d$, where the contact interaction in the flat limit gives momentum conserving delta function. We show the factorization of the $n \to n$ $\

Hoang Dinh Van, Lander Hermans, Wendy Lowen

We introduce a symmetric operad $\square p$ ("box-op") which describes a certain calculus of rectangular labeled ``boxes''. Algebras over $\square p$, which we call box operads, have appeared under the name of fc multicategories in work by Leinster \cite{LeinsterFcmulticategories1999}. In our main result, we endow a suitable (graded, zero differential) total

Antonio Capone, Maurizio Decina, Aldo Milan, Marco Petracca

This paper deals with the challenge of modeling the performance of planned ultrabroadband access networks while maintaining technological neutrality and accuracy in measurable quality. We highlight the importance of such modeling also for addressing public funding policies compared to models mainly based on the maximum nominal speed of the access networks, t

Peirui Ji, Chenjiang Qian, Jonathan J. Finley, Shuming Yang

Two-dimensional (2D) heterostructures integrated into nanophotonic cavities have emerged as a promising approach towards novel photonic and opto-electronic devices. However, the thickness of the 2D heterostructure has a strong influence on the resonance frequency of the nanocavity. For a single cavity, the resonance frequency shifts approximately linearly wi

Justin Diamond, Markus Lill

We propose a novel approach for predicting protein-peptide interactions using a bi-modal transformer architecture that learns an inter-facial joint distribution of residual contacts. The current data sets for crystallized protein-peptide complexes are limited, making it difficult to accurately predict interactions between proteins and peptides. To address th

Muhammad Zaki Almuzakki, Bayu Jayawardhana

This letter studies the problem of cooperative nearest-neighbor control of multi-agent systems where each agent can only realize a finite set of control points. Under the assumption that the underlying graph representing the communication network between agents is connected and the interior of the convex hull of all finite actions of each agent contains the

Manuel Odelli, Vladimir M. Stojanovic, Andreas Ruschhaupt

We investigate a time-efficient and robust preparation of spin-squeezed states -- a class of states of interest for quantum-enhanced metrology -- in internal bosonic Josephson junctions with a time-dependent nonlinear coupling strength between atoms in two different hyperfine states. We treat this state-preparation problem, which had previously been addresse

Dominic T. Meiers, Georg von Freymann

Considering light transport in disordered media, the medium is often treated as an effective medium requiring accurate evaluation of an effective refractive index. Because of its simplicity, the Maxwell-Garnett (MG) mixing rule is widely used, although its restriction to particles much smaller than the wavelength is rarely satisfied. Using 3D finite-differen

Andrew Nemec, Theerapat Tansuwannont

The quantum Hamming bound was originally put forward as an upper bound on the parameters of nondegenerate quantum codes, but over the past few decades much work has been done to show that many degenerate quantum codes must also obey this bound. In this paper, we show that there is a Hamming-like bound stricter than the quantum Hamming bound that applies to d

Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein

Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling

John E. McCarthy

We define a random commuting $d$-tuple of $n$-by-$n$ matrices to be a random variable that takes values in the set of commuting $d$-tuples and has a distribution that is a rapidly decaying continuous weight on this algebraic set. In the Hermitian case, we characterize the eigenvalue distribution as $n$ tends to infinity. In the non-Hermitian case, we get a f

Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson

Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process (GP) surrogate models which are easy to optimize and support exact inference. While standard GP surrogates have been well-established in Bayesian optimization, Bayesian neural netw

Miroslav Popovic, Marko Popovic, Ivan Kastelan, Miodrag Djukic

Nowadays many researchers are developing various distributed and decentralized frameworks for federated learning algorithms. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. In this paper, we present our solution to that challenge called Python Testbed for Federated Learning Algorithms. T

Steve Macenski, Shrijit Singh, Francisco Martin, Jonatan Gines

The accelerated deployment of service robots have spawned a number of algorithm variations to better handle real-world conditions. Many local trajectory planning techniques have been deployed on practical robot systems successfully. While most formulations of Dynamic Window Approach and Model Predictive Control can progress along paths and optimize for addit

Ruining Deng, Yanwei Li, Peize Li, Jiacheng Wang

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell type

Nunzio A. Letizia, Nicola Novello, Andrea M. Tonello

Estimating mutual information accurately is pivotal across diverse applications, from machine learning to communications and biology, enabling us to gain insights into the inner mechanisms of complex systems. Yet, dealing with high-dimensional data presents a formidable challenge, due to its size and the presence of intricate relationships. Recently proposed

Bohong Wang, Qinglai Guo, Tian Xia, Qiang Li

With the development of Internet of Things (IoT) and big data technology, the data value is increasingly explored in multiple practical scenarios, including electricity transactions. However, the isolation of IoT data among several entities makes it difficult to achieve optimal allocation of data resources and convert data resources into real economic value,

Dongseong Hwang, Changwan Ryu, Khe Chai Sim

RNN-T is currently considered the industry standard in ASR due to its exceptional WERs in various benchmark tests and its ability to support seamless streaming and longform transcription. However, its biggest drawback lies in the significant discrepancy between its training and inference objectives. During training, RNN-T maximizes all alignment probabilitie

Alexei Ilyin, Ari Laptev

In this paper we prove Lieb--Thirring inequalities for magnetic Schr\"odinger operators on the torus, where the constants in the inequalities depend on the magnetic flux.

Xiaodong Wei, Riccardo Puppi, Pablo Antolin, Annalisa Buffa

We present a novel stabilized isogeometric formulation for the Stokes problem, where the geometry of interest is obtained via overlapping NURBS (non-uniform rational B-spline) patches, i.e., one patch on top of another in an arbitrary but predefined hierarchical order. All the visible regions constitute the computational domain, whereas independent patches a

Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu

Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics. In this paper, we present a structured overview of the resea

Jishnu Ray Chowdhury, Cornelia Caragea

We explore different ways to utilize position-based cross-attention in seq2seq networks to enable length generalization in algorithmic tasks. We show that a simple approach of interpolating the original and reversed encoded representations combined with relative attention allows near-perfect length generalization for both forward and reverse lookup tasks or

Maxwell Crouse, Ramon Astudillo, Tahira Naseem, Subhajit Chaudhury

We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser. Conceptually, LOCCO can be viewed as a form of self-learning where the semantic parser being trained is used to generate annotations for unlabeled text that are then used as new supervision. To increase the quality of

Yusuf Karli, Daniel A. Vajner, Florian Kappe, Paul C. A. Hagen

Quantum communication networks rely on quantum cryptographic protocols including quantum key distribution (QKD) using single photons. A critical element regarding the security of QKD protocols is the photon number coherence (PNC), i.e. the phase relation between the zero and one-photon Fock state, which critically depends on the excitation scheme. Thus, to o

Lucas Napolitano, Agnesh Pandey, Adam D. Myers, Ting-Wen Lan

We present findings of the detection of Magnesium II (Mg II, {\lambda} = 2796, 2803 {\AA}) absorbers from the early data release of the Dark Energy Spectroscopic Instrument (DESI). DESI is projected to obtain spectroscopy of approximately 3 million quasars (QSOs), of which over 99% are anticipated to be at redshifts greater than z > 0.3, such that DESI would

Raja Al-Bahou, Julia Bruner, Helen Moore, Ali Zarrinpar

Liver transplantation continues to be the gold standard for treating patients with end-stage liver diseases. However, despite the huge success of liver transplantation in improving patient outcomes, long term graft survival continues to be a major problem. The current clinical practice in the management of liver transplant patients is centered around immunos

Nikitha Rao, Jason Tsay, Kiran Kate, Vincent J. Hellendoorn

Low-code programming allows citizen developers to create programs with minimal coding effort, typically via visual (e.g. drag-and-drop) interfaces. In parallel, recent AI-powered tools such as Copilot and ChatGPT generate programs from natural language instructions. We argue that these modalities are complementary: tools like ChatGPT greatly reduce the need

Dinesh Verma, Eden Figueroa, Gabriella Carini, Mark Ritter

Quantum Communications Networks using the properties of qubits, namely state superposition, no-cloning and entanglement, can enable the exchange of information in a very secure manner across optical links or free space. New innovations enable the use of optical repeaters as well as multi-cast communication in the networks. Some types of quantum communication

Riccardo W. Maffucci

A $3$-polytope is a $3$-connected, planar graph. It is called unigraphic if it does not share its vertex degree sequence with any other $3$-polytope, up to graph isomorphism. The classification of unigraphic $3$-polytopes appears to be a difficult problem. In this paper we prove that, apart from pyramids, all unigraphic $3$-polytopes have no $n$-gonal faces

Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa

We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restricted optimization problem by modelling ta

Daniel Jannai, Amos Meron, Barak Lenz, Yoav Levine

We present "Human or Not?", an online game inspired by the Turing test, that measures the capability of AI chatbots to mimic humans in dialog, and of humans to tell bots from other humans. Over the course of a month, the game was played by over 1.5 million users who engaged in anonymous two-minute chat sessions with either another human or an AI language mod

Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner

A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling, classifier-guided diffusion modeling is a promising founda

Xiang-dong Hou

Two rational functions $f,g\in\Bbb F_q(X)$ are said to be {\em equivalent} if there exist $\phi,\psi\in\Bbb F_q(X)$ of degree one such that $g=\phi\circ f\circ\psi$. We give an explicit formula for the number of equivalence classes of rational functions of a given degree in $\Bbb F_q(X)$. This result should provide guidance for the current and future work on

Jacob N. Gamsky, Philip T. Metzger

The physical state of the lunar soil in the permanently shadowed craters of the moon is inferred from experimental investigation. The permanently shadowed craters do not undergo the same thermal cycling experienced by other parts of the moon and therefore could be slightly less compacted. This study is significant because excavating, roving, and landing inte