October 2023 arXiv papers — page 163

Mijail Szczerban, Mikael Mazur, Lauren Dallachiesa, Haïk Mardoyan

We propose and implement a novel fibre sensing control device and associated sensing control plane that effectively controls backscatter and polarization-based fibre sensing. We experimentally demonstrate in a fibre network that this device and associated control plane can achieve sub-us response time.

Károly Bezdek, Zsolt Lángi

We study translative arrangements of centrally symmetric convex domains in the plane (resp., of congruent balls in the Euclidean $3$-space) that neither pack nor cover. We define their soft density depending on a soft parameter and prove that the largest soft density for soft translative packings of a centrally symmetric convex domain with $3$-fold rotationa

Sia Gholami, Marwan Omar

Transformer models have revolutionized natural language processing with their unparalleled ability to grasp complex contextual relationships. However, the vast number of parameters in these models has raised concerns regarding computational efficiency, environmental impact, and deployability on resource-limited platforms. To address these challenges, this pa

Ryan Gupta, Minkyu Kim, Juliana T Rodriguez, Kyle Morgenstein

This paper introduces LIVE: Lidar Informed Visual Search focused on the problem of multi-robot (MR) planning and execution for robust visual detection of multiple objects. We perform extensive real-world experiments with a two-robot team in an indoor apartment setting. LIVE acts as a perception module that detects unmapped obstacles, or Short Term Features (

Andrei Grekov, Nikita Nekrasov

Affine analogues of the Q-functions are constructed using folded instantons partition functions. They are shown to be the solutions of the quantum spectral curve of the N-body elliptic Calogero-Moser (eCM) system, the quantum Krichever curve. They also solve the elliptic analogue of the quantum Wronskian equation. In the companion paper we present the quantu

Thomas M. Hehn, Tribhuvanesh Orekondy, Ori Shental, Arash Behboodi

Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a transformer-based neural network architecture that enables predicting link-level properties from maps of vari

Steven Chen, Oleg Kargaltsev, Hui Yang, Jeremy Hare

We present a collection of classified X-ray sources in Globular Clusters (GCs) observed by the Chandra X-ray Observatory (CXO), including active binaries, cataclysmic variables, millisecond pulsars, and low-mass X-ray binaries. We cross-match the most accurate published positions from multiwavelength observations of these sources to the Chandra Source Catalo

Michael Butler, Masoud Ghezelbash

In this article, we consider a class of four-dimensional Einstein-Maxwell theory which is coupled non-minimally to a scalar field and the Gauss-Bonnet invariant. We mainly use the numerical methods to find the solutions to the theory, with the NUT twist. We find explicitly the numerical solutions to all of the field equations. To find the appropriate consist

Jiarui Hai, Helin Wang, Dongchao Yang, Karan Thakkar

Common target sound extraction (TSE) approaches primarily relied on discriminative approaches in order to separate the target sound while minimizing interference from the unwanted sources, with varying success in separating the target from the background. This study introduces DPM-TSE, a first generative method based on diffusion probabilistic modeling (DPM)

Yuhang Hu, Judah Goldfeder, Zhizhuo Zhang, Xinyue Zhu

For robots to truly collaborate and assist humans, they must understand not only logic and instructions, but also the subtle emotions, aesthetics, and feelings that define our humanity. Human art and aesthetics are among the most elusive concepts-often difficult even for people to articulate-and without grasping these fundamentals, robots will be unable to h

Pablo González, Alejandro Moreo, Fabrizio Sebastiani

Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the labelled data on which the predictor has been trained and the unlabelled data are not IID, i.e., suffer from dataset shift. To date, quantification methods have mostly been tested o

Iman Mirzadeh, Keivan Alizadeh, Sachin Mehta, Carlo C Del Mundo

Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant challenges for deployment on resource-constrained devices. Despite recent trends favoring alternative activation functions such as GELU or SiLU, known for increased computation, th

Brian Liu, Yujia Zhang, Shane G. Henderson, David B. Shmoys

During the COVID-19 pandemic, safely implementing in-person indoor instruction was a high priority for universities nationwide. To support this effort at the University, we developed a mathematical model for estimating the risk of SARS-CoV-2 transmission in university classrooms. This model was used to evaluate combinations of feasible interventions for clas

Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang

Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of tokens in language. Knowledge graphs (KGs) have different entity and relation vocabularies that generally do not overlap. The key challenge of designing foundation models on KGs is to l

Tianhao Xie, Eugene Belilovsky, Sudhir Mudur, Tiberiu Popa

Direct mesh editing and deformation are key components in the geometric modeling and animation pipeline. Mesh editing methods are typically framed as optimization problems combining user-specified vertex constraints with a regularizer that determines the position of the rest of the vertices. The choice of the regularizer is key to the realism and authenticit

Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi

Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance. Reasoning on graphs is essential for drawing inferences about the relationships between entities in a complex system, and to identify hidden patterns and trends. Despite the remark

Maximilian H. Kriebel, Paweł Tecmer, Marta Gałyńska, Aleksandra Leszczyk

We scrutinize how to accelerate the bottleneck operations of Pythonic coupled cluster implementations performed on a \texttt{NVIDIA} Tesla V100S PCIe 32GB (rev 1a) Graphics Processing Unit (GPU). The \texttt{NVIDIA} Compute Unified Device Architecture (CUDA) API is interacted with via \texttt{CuPy}, an open-source library for Python, designed as a \texttt{Nu

Santosh Adhikari, Bishnu Bhusal, Prashant Ghimire, Anil Shrestha

Virtual Try-On (trying clothes virtually) is a promising application of the Generative Adversarial Network (GAN). However, it is an arduous task to transfer the desired clothing item onto the corresponding regions of a human body because of varying body size, pose, and occlusions like hair and overlapped clothes. In this paper, we try to produce photo-realis

Zining Zhu, Frank Rudzicz

Text-based explanation is a particularly promising approach in explainable AI, but the evaluation of text explanations is method-dependent. We argue that placing the explanations on an information-theoretic framework could unify the evaluations of two popular text explanation methods: rationale and natural language explanations (NLE). This framework consider

L. Tosi, I. Lobato, M. F. Goffman, C. Metzger

We explore the effects of driving a cavity at a large photon number in a circuit-QED experiment where the ``matter-like'' part corresponds to an unique Andreev level in a superconducting weak link. The three many-body states of the weak link, corresponding to the occupation of the Andreev level by 0, 1 or 2 quasiparticles, lead to different cavity frequency

T. A. Moura, M. L. Lana Júnior, C. H. V. da Silva, L. R. Américo

We present a novel approach to determine the optical properties of materials in the nanoscale range using optical tweezers (OT). Fluorescent polymer-based nanostructures (pdots) are optically trapped in a Gaussian beam OT and the trap stiffness is studied as a function of various parameters of interest. We explicitly show that properties such as the refracti

L. Perotto, R. Adam, P. Ade, H. Ajeddig

The NIKA2 camera operating at the IRAM 30 m telescope excels in high-angular resolution mapping of the thermal Sunyaev-Zeldovich effect towards galaxy clusters at intermediate and high-redshift. As part of the NIKA2 guaranteed time, the SZ Large Program (LPSZ) aims at tSZ-mapping a representative sample of SZ-selected galaxy clusters in the catalogues of the

Xiaolin Liu, Zhoujian Cao, Zong-Hong Zhu

Waveform models are important to gravitational wave data analysis. People recently pay much attention to the waveform model construction for eccentric binary black hole coalescence. Several Effective-One-Body Numerical-Relativity waveform models of eccentric binary black hole coalescence have been constructed. But none of them can treat orbit eccentricity an

Muhammad Osama Khan, Junbang Liang, Chun-Kai Wang, Shan Yang

Pre-training has been an important ingredient in developing strong monocular depth estimation models in recent years. For instance, self-supervised learning (SSL) is particularly effective by alleviating the need for large datasets with dense ground-truth depth maps. However, despite these improvements, our study reveals that the later layers of the SOTA SSL

Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown

Pre-trained multimodal foundation models have demonstrated remarkable generalizability but pose challenges for deployment due to their large sizes. One effective approach to reducing their sizes is layerwise distillation, wherein small student models are trained to match the hidden representations of large teacher models at each layer. Motivated by our obser

Simone Creo, Michael Hinz, Maria Rosaria Lancia

We consider non-local energy forms of fractional Laplace type on quasicircles and prove that they can be approximated by similar energy forms on polygonal curves. The approximation is in terms of generalized Mosco convergence along a sequence of varying Hilbert spaces. The domains of the energy forms are the natural trace spaces, and we focus on the case of

Kalen Patton, Matteo Russo, Sahil Singla

Optimization problems often involve vector norms, which has led to extensive research on developing algorithms that can handle objectives beyond the $\ell_p$ norms. Our work introduces the concept of submodular norms, which are a versatile type of norms that possess marginal properties similar to submodular set functions. We show that submodular norms can ac

Enes Krijestorac, Danijela Cabric

Prediction of wireless channel gain (CG) across space is a necessary tool for many important wireless network design problems. In this paper, we develop prediction methods that use environment-specific features, namely building maps and CG measurements, to achieve high prediction accuracy. We assume that measurements are collected using a swarm of coordinate

Sunpreet Arora, Andrew Beams, Panagiotis Chatzigiannis, Sebastian Meiser

One of the main goals of financial institutions (FIs) today is combating fraud and financial crime. To this end, FIs use sophisticated machine-learning models trained using data collected from their customers. The output of machine learning models may be manually reviewed for critical use cases, e.g., determining the likelihood of a transaction being anomalo

Sayan Banerjee, Amarjit Budhiraja, Peter Rudzis

The infinite Atlas model describes the evolution of a countable collection of Brownian particles on the real line, where the lowest particle is given a drift of $\gamma \in [0,\infty)$. We study equilibrium fluctuations for the Atlas model when the system of particles starts from an inhomogeneous stationary profile with exponentially growing density. We show

Patrick Tinsley, Sandip Purnapatra, Mahsa Mitcheff, Aidan Boyd

This paper describes the results of the 2023 edition of the ''LivDet'' series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark. Clarkson

Saumya Sinha, John Fasullo, R. Steven Nerem, Claire Monteleoni

Sea surface height observations provided by satellite altimetry since 1993 show a rising rate (3.4 mm/year) for global mean sea level. While on average, sea level has risen 10 cm over the last 30 years, there is considerable regional variation in the sea level change. Through this work, we predict sea level trends 30 years into the future at a 2-degree spati

Minxing Zhang, Michael Backes, Xiao Zhang

This paper revisits the robust overfitting phenomenon of adversarial training. Observing that models with better robust generalization performance are less certain in predicting adversarially generated training inputs, we argue that overconfidence in predicting adversarial examples is a potential cause. Therefore, we hypothesize that generating less certain

Heiko Kabutz, Alexander Hedrick, Parker McDonnell, Kaushik Jayaram

Soft compliant microrobots have the potential to deliver significant societal impact when deployed in applications such as search and rescue. In this research we present mCLARI, a body compliant quadrupedal microrobot of 20mm neutral body length and 0.97g, improving on its larger predecessor, CLARI. This robot has four independently actuated leg modules with

Muxin Han, Qiaoyin Pan

Infrared divergence is a common feature of spinfoam models with a vanishing cosmological constant but is expected to disappear in presence of a non-vanishing cosmological constant. In this paper, we investigate the spinfoam amplitude with cosmological constant introduced in arXiv:2109.00034 on the melon graph, which is known as the melonic radiative correcti

Zixi Zhang, Balint Szekely, Pedro Gimenes, Greg Chadwick

Hardware design verification (DV) is a process that checks the functional equivalence of a hardware design against its specifications, improving hardware reliability and robustness. A key task in the DV process is the test stimuli generation, which creates a set of conditions or inputs for testing. These test conditions are often complex and specific to the

Daniel Severo, Lucas Theis, Johannes Ballé

We show how perceptual embeddings of the visual system can be constructed at inference-time with no training data or deep neural network features. Our perceptual embeddings are solutions to a weighted least squares (WLS) problem, defined at the pixel-level, and solved at inference-time, that can capture global and local image characteristics. The distance in

Jinxia Cen, J. Vanterler da C. Sousa, Wei Wu

In this present paper, we introduce and study a dynamical systems involving fractional derivative operator and nonlocal condition, which is constituted of a fractional evolution equation and a time-dependent variational inequality, and is named as fractional partial differential variational inequality (FPDVI, for short). By employing the estimates involving

David Hansen

We formulate some refinements and complements to the categorical local Langlands conjecture of Fargues-Scholze. In particular, we state the expected compatibilities with Eisenstein series and duality, and explain some of their consequences. We also begin the process of matching t-structures on both sides. Notably, we introduce the so-called hadal t-structure

Ilija Rakic, Mukund Rangamani, Gustavo J. Turiaci

We examine the thermodynamics of a near-extremal Kerr black hole, and demonstrate that the geometry behaves as an ordinary quantum system with a vanishingly small degeneracy at low temperatures. This is in contrast with the classical analysis, which instead predicts a macroscopic entropy for the extremal Kerr black hole. Our results follow from a careful ana

Daniel Flores

We derive, via the Hardy-Littlewood method, an asymptotic formula for the number of integral zeros of a particular class of weighted quartic forms under the assumption of non-singular local solubility. Our polynomials $F({\mathbf x},{\mathbf y}) \in \mathbb{Z}[x_1,\ldots,x_{s_1},y_1,\ldots,y_{s_2}]$ satisfy the condition that $F(\lambda^2 {\mathbf x}, \lambd

Yorick L. A. Schmerwitz, Vilhjálmur Ásgeirsson, Hannes Jónsson

In reaction path optimization, such as the calculation of a minimum energy path (MEP) between given reactant and product configurations of atoms, it is advantageous to start with an initial guess where close proximity of atoms is avoided and bonds are not unnecessarily broken only to be reformed later. When the configurations of the atoms are described with

Arianna Vasini, Emanuele Spitoni, Francesca Matteucci

The diagram depicting the abundance ratios [Mg/Mn] vs. [Al/Fe] has gained attention in recent literature as a valuable tool for exploring fundamental aspects of the evolution of the Milky Way and the Local Group. In particular, this combination of elements is supposed to be highly sensitive to the star formation history (SFH), unveiled by the imprints left o

Brett A. Halperin, Stephanie M. Lukin

In this paper, we collect an anthology of 100 visual stories from authors who participated in our systematic creative process of improvised story-building based on image sequences. Following close reading and thematic analysis of our anthology, we present five themes that characterize the variations found in this creative visual storytelling process: (1) Nar

Dongjie Chen, Sen-ching S. Cheung, Chen-Nee Chuah

High-dimensional data are widely used in the era of deep learning with numerous applications. However, certain data which has sensitive information are not allowed to be shared without privacy protection. In this paper, we propose a novel differentially private data releasing method called Differentially Private Data Publishing with Gaussian Optimized Model

N. A. Tashkeev, A. I. Zenchuk

We study the long distance (0,1)-excitation state restoring in the linear open chain governed by the XX-Hamiltonian. We show that restoring the 1-order coherence matrix results in restoring the 1-excitation block of the 0-order coherence matrix, so that only one 0-excitation element of the density matrix remain unrestored. Such restoring also scales the conc

Emmanuel Balogun, Sonia Martin, Anthony Degleris, Ram Rajagopal

Coordination of distributed energy resources is critical for electricity grid management. Although nodal pricing schemes can mitigate congestion and voltage deviations, the resulting prices are not necessarily equitable. In this work, we leverage market mechanisms for DER coordination and propose a daily dynamic nodal pricing scheme that incorporates equity.

Guang Shi, Kenneth S. Schweizer

Biomolecular condensates formed through the phase separation of proteins and nucleic acids are widely observed, offering a fundamental means of organizing intracellular materials in a membrane-less fashion. Traditionally, these condensates have been regarded as homogeneous isotropic liquids. However, in analogy with some synthetic copolymer systems, our rece

Ross Wakefield, Anthony Laing, Yogesh N. Joglekar

Over the past decade classical optical systems with gain or loss, modelled by non-Hermitian parity-time symmetric Hamiltonians, have been deeply investigated. Yet, their applicability to the quantum domain with number-resolved photonic states is fundamentally voided by quantum-limited amplifier noise. Here, we show that second-quantised Hermitian Hamiltonian

Aleksandr A. Movsisian, Sergey P. Vyatchanin

Broadband multidimensional variational measurement allows to overcome Standard Quantum Limit (SQL) of a classical mechanical force detection, resulting from quantum back action, which perturbs evolution of a mechanical oscillator. In this optomechanic scheme detection of a resonant signal force acting on a linear mechanical oscillator coupled to a system wit

Tzu-Yuan Lin, Minghan Zhu, Maani Ghaffari

This paper proposes an equivariant neural network that takes data in any semi-simple Lie algebra as input. The corresponding group acts on the Lie algebra as adjoint operations, making our proposed network adjoint-equivariant. Our framework generalizes the Vector Neurons, a simple $\mathrm{SO}(3)$-equivariant network, from 3-D Euclidean space to Lie algebra

Jonathon P. Misiewicz, Francesco A. Evangelista

We study the performance of our previously proposed Projective Quantum Eigensolver (PQE) on IBM's quantum hardware in conjunction with error mitigation techniques. For a single qubit model of H$_2$, we find that we are able to obtain energies within 4 millihartree (2.5 kcal/mol) of the exact energy along the entire potential energy curve, with the accuracy l

Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar, Dan Alistarh

It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity constraints, or impose sparsity after the fact, altering the network's general behavior. In this paper, we demonstrate, for the first time, that sparsity can instead be incorporated

Antoine Ayache, Yimin Xiao

Non-Gaussian Harmonizable Fractional Stable Motion (HFSM) is a natural and important extension of the well-known Fractional Brownian Motion to the framework of heavy-tailed stable distributions. It was introduced several decades ago; however its properties are far from being completely understood. In our present paper we determine the optimal power of the lo

Johann Huber, François Hélénon, Hippolyte Watrelot, Faiz Ben Amar

Robotic grasping refers to making a robotic system pick an object by applying forces and torques on its surface. Many recent studies use data-driven approaches to address grasping, but the sparse reward nature of this task made the learning process challenging to bootstrap. To avoid constraining the operational space, an increasing number of works propose gr

Amir Khazraei, Miroslav Pajic

In this work, we focus on analyzing vulnerability of nonlinear dynamical control systems to stealthy false data injection attacks on sensors. We start by defining the stealthiness notion in the most general form where an attack is considered stealthy if it would be undetected by any intrusion detector, i.e., any intrusion detector could not do better than a

Aditya Narayan Ravi, Ilan Shomorony

Federated Learning (FL) is a distributed machine learning approach to learn models on decentralized heterogeneous data, without the need for clients to share their data. Many existing FL approaches assume that all clients have equal importance and construct a global objective based on all clients. We consider a version of FL we call Prioritized FL, where the

Misbah Shahzadi, Martin Kolos, Rabia Saleem, Yousaf Habib

In this paper, we explore the chaotic signatures of the geodesic dynamics for particles moving in the slowly rotating Hartle-Thorne spacetime; an approximate solution of vacuum Einstein field equations describing the exterior of a massive, deformed, and slowly rotating compact object. We employ the numerical study to examine the geodesics of prolate and obla

Padmaksha Roy, Jaganmohan Chandrasekaran, Erin Lanus, Laura Freeman

Machine learning (ML) is increasingly being deployed in critical systems. The data dependence of ML makes securing data used to train and test ML-enabled systems of utmost importance. While the field of cybersecurity has well-established practices for securing information, ML-enabled systems create new attack vectors. Furthermore, data science and cybersecur

I. Pope, K. Mori, M. Abdelmaguid, J. D. Gelfand

G106.3$+$2.7, commonly considered a composite supernova remnant (SNR), is characterized by a boomerang-shaped pulsar wind nebula (PWN) and two distinct ("head" & "tail") regions in the radio band. A discovery of very-high-energy (VHE) gamma-ray emission ($E_\gamma > 100$ GeV) followed by the recent detection of ultra-high-energy (UHE) gamma-ray emission ($E_

Natalie Packham

Stress testing refers to the application of adverse financial or macroeconomic scenarios to a portfolio. For this purpose, financial or macroeconomic risk factors are linked with asset returns, typically via a factor model. We expand the range of risk factors by adapting dimension-reduction techniques from unsupervised learning, namely PCA and autoencoders.

Pablo Andújar Guerrero, Margaret E. M. Thomas

We study the properties of topological spaces $(X,\tau)$, where $X$ is a definable set in an o-minimal structure and the topology $\tau$ on $X$ has a basis that is (uniformly) definable. Examples of such spaces include the canonical euclidean topology on definable sets, definable order topologies, definable quotient spaces and definable metric spaces. We use

D. Espinosa-Gomez, F. Ramirez-Zavaleta, E. S. Tututi

The flavor-violating decay of a new neutral massive gauge boson $Z^\prime\to\mu e$ is analyzed in the context of extended models, in which this particle emerges. By means of the analysis of the $\mu\to e\gamma$ decay, $\mu-e$ conversion process in nuclei and the $\mu \to e e^{+}e^{-}$ decay, the strength of the $Z^\prime\mu e$ coupling is estimated and used

Jun Yan Lau

I present an unbiased method of mapping particles to distribution functions and vice versa. This method alone defines the canonical formulation of statistical mechanics, since it can be used to derive the principle of maximum entropy in both Boltzmann's paradigm and Gibbs' paradigm. A rigorous definition of the macrostate enables application of this statisti

Philip F. Hopkins, Jonathan Squire, Eliot Quataert, Norman Murray

Recent numerical cosmological radiation-magnetohydrodynamic-thermochemical-star formation simulations have resolved the formation of quasar accretion disks with Eddington or super-Eddington accretion rates onto supermassive black holes (SMBHs) down to a few hundred gravitational radii. These 'flux-frozen' and hyper-magnetized disks appear to be qualitatively

Philip F. Hopkins, Jonathan Squire, Kung-Yi Su, Ulrich P. Steinwandel

In a companion paper, we reported the self-consistent formation of quasar accretion disks with inflow rates $\sim 10\,{\rm M_{\odot}\,yr^{-1}}$ down to <300 Schwarzschild radii from cosmological radiation-magneto-thermochemical-hydrodynamical galaxy and star formation simulations. We see the formation of a well-defined, steady-state accretion disk which is s

Jayatee Kanwar, Inga Kamp, Peter Woitke, Christian Rab

The analysis of the mid-infrared spectra helps understanding the composition of the gas in the inner, dense and warm terrestrial planet forming region of disks around young stars. ALMA has detected hydrocarbons in the outer regions of the planet forming disk and Spitzer detected \ce{C2H2} in the inner regions. JWST- MIRI provides high spectral resolution obs

David C. Smolinski, Denis Wittor, Franco Vazza, Marcus Brüggen

Radio relics are giant sources of diffuse synchrotron radio emission in the outskirts of galaxy clusters that are associated with shocks in the intracluster medium. Still, the origin of relativistic particles that make up relics is not fully understood. For most relics, diffusive shock acceleration (DSA) of thermal electrons is not efficient enough to explai

ChangHoon Hahn, Connor Bottrell, Khee-Gan Lee

We present ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$, a new machine learning approach for inferring the mass of host dark matter halos, $M_h$, from the photometry and morphology of galaxies. ${\rm H{\scriptsize ALO}F{\scriptsize LOW}}$ uses simulation-based inference with normalizing flows to conduct rigorous Bayesian inference. It is trained on state-of-

Adam R. Solomon

We explore the duality invariance of the Maxwell and linearized Einstein-Hilbert actions on a non-rotating black hole background. On shell these symmetries are electric-magnetic duality and Chandrasekhar duality, respectively. Off shell they lead to conserved quantities; we demonstrate that one of the consequences of these conservation laws is that even- and

Sihan Yuan, Tom Abel, Risa H. Wechsler

We present the methodology for deriving accurate and reliable cosmological constraints from non-linear scales (<50Mpc/h) with k-th nearest neighbor (kNN) statistics. We detail our methods for choosing robust minimum scale cuts and validating galaxy-halo connection models. Using cross-validation, we identify the galaxy-halo model that ensures both good fits a

Stefan Birnkammer, Johannes Knolle, Michael Knap

Mesonic bound states of domain walls can be stabilized in quasi one-dimensional magnetic compounds. Here, we theoretically study the Raman light scattering response of a twisted Kitaev chain with tilted magnetic fields as a minimal model for confinement in $\mathrm{CoNb}_{2}\mathrm{O}_{6}$. By both numerical matrix product states and few-domain wall variatio

François Mernier, Yuanyuan Su, Maxim Markevitch, Congyao Zhang

Synthesized in the cores of stars and supernovae, most metals disperse over cosmic scales and are ultimately deposited well outside the gravitational potential of their host galaxies. Since their presence is well visible through their X-ray emission lines in the hot gas pervading galaxy clusters, measuring metal abundances in the intracluster medium (ICM) of

A. Bolamperti, A. Zanella, U. Meštrić, E. Vanzella

We study the ultraviolet (UV) continuum $\beta$ slope of a sample of 166 clumps, individual star-forming regions observed in high redshift galaxies. They are hosted by 67 galaxies with redshift between 2 and 6.2, strongly lensed by the Hubble Frontier Fields cluster of galaxies MACS J0416.1-2403. The $\beta$ slope is sensitive to a variety of physical proper

P. Crozet, J. Morin, A. J. Ross, S. Bellotti

Molecular bands of metal oxides and hydrides dominate the optical and near-infrared spectra of M dwarfs. High-resolution spectra of these bands have immense potential for determining many properties of these stars, such as effective temperature, surface gravity, elemental abundances, radial velocity, or surface magnetic fields. Techniques are being developed

Zeyu Yun, Juexiao Zhang, Yann LeCun, Yubei Chen

Unsupervised representation learning has seen tremendous progress. However, it is constrained by its reliance on domain specific stationarity and topology, a limitation not found in biological intelligence systems. For instance, unlike computer vision, human vision can process visual signals sampled from highly irregular and non-stationary sensors. We introd

Seth Musser, Hart Goldman, T. Senthil

Hall viscosity is a nondissipative viscosity occurring in systems with broken time-reversal symmetry, such as quantum Hall phases and $p+ip$ superfluids. Despite Hall viscosity's expected ubiquity and past observations in: classical soft matter, optical, and graphene systems, it has yet to be measured experimentally in any macroscopic quantum state of matter

Frédéric Dux, Cameron Lemon, Frédéric Courbin, Dominique Sluse

We report the discovery of PSJ2107-1611, a fold-configuration 4.3"-separation quadruply lensed quasar with a bright lensed arc. It was discovered using a convolutional neural network on Pan-STARRS gri images of pre-selected quasar candidates with multiple nearby Pan-STARRS detections. Spectroscopic follow-up with EFOSC2 on the ESO 3.58m New Technology Telesc

Alvise Bastianello

The sine-Gordon model captures the low-energy effective dynamics of a wealth of one-dimensional quantum systems, stimulating the experimental efforts in building a versatile quantum simulator of this field theory and fueling the parallel development of new theoretical toolkits able to capture far-from-equilibrium settings. In this work, we analyze the realiz

Ali Rostami Shirazi, Pouria Khalaj, Hosein Haghi

This is the second paper in a series, which studies the likelihood that some globular clusters (GCs) of the Milky Way (MW) could have originated from a dwarf satellite galaxy (DSG). Using a large suite of three-body simulations we determine the present-day orbital properties of 154 GCs that could have escaped from 41 MW DSGs over the past $8\,\mathrm{Gyrs}$.

Cheryne Jonay, Tianci Zhou

One indication of thermalization time is subsystem entanglement reaching thermal values. Recent studies on local quantum circuits reveal two exponential stages with decay rates $r_1$ and $r_2$ of the purity before and after thermalization. We provide an entanglement membrane theory interpretation, with $r_1$ corresponding to the domain wall free energy. Circ

Akhil Premkumar

Generative diffusion models synthesize new samples by reversing a diffusive process that converts a given data set to generic noise. This is accomplished by training a neural network to match the gradient of the log of the probability distribution of a given data set, also called the score. By casting reverse diffusion as an optimal control problem, we show

Mark Ho-Yeuk Cheung, Emanuele Berti, Vishal Baibhav, Roberto Cotesta

In general relativity, when two black holes merge they produce a rotating (Kerr) black hole remnant. According to perturbation theory, the remnant emits "ringdown" radiation: a superposition of exponentials with characteristic complex frequencies that depend only on the remnant's mass and spin. While the goal of the black hole spectroscopy program is to meas

José Calderón-Infante, Matilda Delgado, Angel M. Uranga

The scale at which quantum gravity becomes manifest, the species scale $\Lambda_s$, has recently been argued to take values parametrically lower than the Planck scale. We use black holes of vanishing horizon area (small black holes) in effective field theories coupled to quantum gravity to shed light on how the three different physical manifestations of the

Tamiko Masuda, J. P. E. Hadden, David P. Lake, Matthew Mitchell

Fiber-coupled microdisks are a promising platform for enhancing the spontaneous emission from color centers in diamond. The measured cavity-enhanced emission from the microdisk is governed by the effective volume ($V$) of each cavity mode, the cavity quality factor ($Q$), and the coupling between the microdisk and the fiber. Here we observe photoluminescence

Andrew F. Luo, Margaret M. Henderson, Michael J. Tarr, Leila Wehbe

Understanding the functional organization of higher visual cortex is a central focus in neuroscience. Past studies have primarily mapped the visual and semantic selectivity of neural populations using hand-selected stimuli, which may potentially bias results towards pre-existing hypotheses of visual cortex functionality. Moving beyond conventional approaches

Colin Rylands, Katja Klobas, Filiberto Ares, Pasquale Calabrese

The highly complicated nature of far from equilibrium systems can lead to a complete breakdown of the physical intuition developed in equilibrium. A famous example of this is the Mpemba effect, which states that non-equilibrium states may relax faster when they are further from equilibrium or, put another way, hot water can freeze faster than warm water. Des

Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie

Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models. Though the Transformer architecture has fundamentally no limits on the input sequence lengths it can process, the choice of position encoding used during training can limit the perform

Esha Saha, Giang Tran

Diffusion probabilistic models have been successfully used to generate data from noise. However, most diffusion models are computationally expensive and difficult to interpret with a lack of theoretical justification. Random feature models on the other hand have gained popularity due to their interpretability but their application to complex machine learning

Xiaoxiao Sun, Yue Yao, Shengjin Wang, Hongdong Li

For object re-identification (re-ID), learning from synthetic data has become a promising strategy to cheaply acquire large-scale annotated datasets and effective models, with few privacy concerns. Many interesting research problems arise from this strategy, e.g., how to reduce the domain gap between synthetic source and real-world target. To facilitate deve

Francesco D'Angelo, Maksym Andriushchenko, Aditya Varre, Nicolas Flammarion

Weight decay is a broadly used technique for training state-of-the-art deep networks from image classification to large language models. Despite its widespread usage and being extensively studied in the classical literature, its role remains poorly understood for deep learning. In this work, we highlight that the role of weight decay in modern deep learning

Xiaoxiao Sun, Xingjian Leng, Zijian Wang, Yang Yang

Analyzing model performance in various unseen environments is a critical research problem in the machine learning community. To study this problem, it is important to construct a testbed with out-of-distribution test sets that have broad coverage of environmental discrepancies. However, existing testbeds typically either have a small number of domains or are

Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar

Offline policy learning is aimed at learning decision-making policies using existing datasets of trajectories without collecting additional data. The primary motivation for using reinforcement learning (RL) instead of supervised learning techniques such as behavior cloning is to find a policy that achieves a higher average return than the trajectories consti

Peiran Xu, Zeyu Wang, Jieru Mei, Liangqiong Qu

Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data leakage. While recent studies posit that Vision Transformer (ViT) outperforms Convolutional Neural Networks (CNNs) in addressing data heterogeneity in FL, the specific architectural

Yang Yue, Rui Lu, Bingyi Kang, Shiji Song

The divergence of the Q-value estimation has been a prominent issue in offline RL, where the agent has no access to real dynamics. Traditional beliefs attribute this instability to querying out-of-distribution actions when bootstrapping value targets. Though this issue can be alleviated with policy constraints or conservative Q estimation, a theoretical unde

Corwin Shiu, Steven J. Benton, Jeffrey P. Filippini, Aurélien A. Fraisse

We present an implementation of a Bayesian mixture model using Hamiltonian Monte Carlo (HMC) techniques to search for spatial separation of Galactic dust populations. Utilizing intensity measurements from Planck High Frequency Instrument (HFI), we apply this model to high-latitude Galactic dust emission. Our analysis reveals a strong preference for a spatial

Mohamed ElKabbash, Sivan Trajtenberg-Mills, Isaac Harris, Saumil Bandyopadhyay

Integrating nanophotonics with electronics promises revolutionary applications, from LiDAR to holographic displays. Although silicon photonics is maturing, realizing active nanophotonics in the ubiquitous bulk CMOS processes remains challenging. We introduce a fabless approach to embed active nanophotonics in bulk CMOS by co-designing the back-end-of-line me

Fangyuan Xu, Weijia Shi, Eunsol Choi

Retrieving documents and prepending them in-context at inference time improves performance of language model (LMs) on a wide range of tasks. However, these documents, often spanning hundreds of words, make inference substantially more expensive. We propose compressing the retrieved documents into textual summaries prior to in-context integration. This not on

Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley

Text retrieval plays a crucial role in incorporating factual knowledge for decision making into language processing pipelines, ranging from chat-based web search to question answering systems. Current state-of-the-art text retrieval models leverage pre-trained large language models (LLMs) to achieve competitive performance, but training LLM-based retrievers

Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang

While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of LMs in reasoning, acting, and planning. By leve