May 2023 arXiv papers — page 99

Aravinda Ramakrishnan Srinivasan, Julian Schumann, Yueyang Wang, Yi-Shin Lin

Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is a need to complement naturalistic datasets of road user interaction with targeted, controlled study data. This paper describes a dataset collected in a simulator study conducted in

Chanwoo Chun, Daniel D. Lee

We investigate how sparse neural activity affects the generalization performance of a deep Bayesian neural network at the large width limit. To this end, we derive a neural network Gaussian Process (NNGP) kernel with rectified linear unit (ReLU) activation and a predetermined fraction of active neurons. Using the NNGP kernel, we observe that the sparser netw

Photios A. Stavrou, Yanina Shkel, Marios Kountouris

We consider a remote source coding problem subject to a {distortion function}. Contrary to the use of the classical separable distortion criterion, herein we consider the more general, $f$-separable distortion measure and study its implications on the characterization of the minimum achievable rates (also called $f$-separable indirect rate distortion functio

Daniel Waelchli, Pascal Weber, Petros Koumoutsakos

The discovery of individual objectives in collective behavior of complex dynamical systems such as fish schools and bacteria colonies is a long-standing challenge. Inverse reinforcement learning is a potent approach for addressing this challenge but its applicability to dynamical systems, involving continuous state-action spaces and multiple interacting agen

Jialin Yuan, Ye Yu, Gaurav Mittal, Matthew Hall

There is a rapidly growing need for multimodal content moderation (CM) as more and more content on social media is multimodal in nature. Existing unimodal CM systems may fail to catch harmful content that crosses modalities (e.g., memes or videos), which may lead to severe consequences. In this paper, we present a novel CM model, Asymmetric Mixed-Modal Moder

Francesco Giannini, Stefano Fioravanti, Oguzhan Keskin, Alisia Maria Lupidi

The rise of Artificial Intelligence (AI) recently empowered researchers to investigate hard mathematical problems which eluded traditional approaches for decades. Yet, the use of AI in Universal Algebra (UA) -- one of the fields laying the foundations of modern mathematics -- is still completely unexplored. This work proposes the first use of AI to investiga

Nathanaël Fijalkow, C. Aiswarya, Guy Avni, Nathalie Bertrand

The objective of this book is to give a comprehensive presentation of the research field concerned with infinite duration games on graphs. Historically, these game models appeared in the study of automata and logic, and they later became important for program verification and synthesis. They have many more applications, in particular some of the models inves

Bhagath Sreenarayanan, Marta Vicencio, Shuang Bai, Bingyu Lu

The growth of the semiconductor and solar industry has been exponential in the last two decades due to the computing and energy demands of the world. Silicon (Si) is one of the main constituents for both sectors and, thus, is used in large quantities. As a result, a lot of Si waste is generated mainly by these two industries. For a sustainable world, the cir

Federico Errica, Mathias Niepert

We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a paren

Andres Fernandez Herrero, Emmett Lennen, Svetlana Makarova

We construct moduli spaces of objects in an abelian category satisfying some finiteness hypotheses. Our approach is based on the work of Artin-Zhang and the intrinsic construction of moduli spaces for stacks developed by Alper-Halpern-Leistner-Heinloth.

Len van Deurzen, Jashan Singhal, Jimy Encomendero, Naomi Pieczulewski

Using low-temperature cathodoluminescence spectroscopy, we study the properties of N- and Al-polar AlN layers grown by molecular beam epitaxy on bulk AlN{0001}. Compared to the bulk AlN substrate, layers of both polarities feature a suppression of deep-level luminescence, a total absence of the prevalent donor with an exciton binding energy of 28 meV, and a

Anton Orlichenko, Gang Qu, Ziyu Zhou, Zhengming Ding

Functional connectivity (FC) is one of the most common inputs to fMRI-based predictive models, due to a combination of its simplicity and robustness. However, there may be a lack of theoretical models for the generation of FC. In this work, we present a straightforward decomposition of FC into a set of basis states of sine waves with an additional jitter com

Sudarshan Adiga, Xin Xiao, Ravi Tandon, Bane Vasic

Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagation (NBP), which unfolds the belief propagation (BP) iterations into a deep neural network and the parameters are trained in a data-driven manner. NBP decoders have been shown to im

Peter Kunz, Jens Lassen, Corina Andreoiu, Fatima H. Garcia

The high-intensity proton beam of the TRIUMF 500 MeV cyclotron offers unique opportunities to produce rare isotopes by irradiating a variety of targets. In particular, the ISAC (Isotope Separation and ACceleration) facility provides the infrastructure to deliver customized rare ion beams for fundamental research in nuclear physics, astrophysics, material sci

Christian D. Blakely

Bayesian networks (BN) are directed acyclic graphical (DAG) models that have been adopted into many fields for their strengths in transparency, interpretability, probabilistic reasoning, and causal modeling. Given a set of data, one hurdle towards using BNs is in building the network graph from the data that properly handles dependencies, whether correlated

Robert Carlson

The spectral theory of the Laplace differential operator for biregular quantum graphs is developed. Trees are studied in detail. Generating functions for closed non backtracking walks appear when resolvents for trees are related to resolvents for biregular graphs they cover. The relationship between resolvent traces for finite graphs and walk generating func

Samuel McCauley, Benjamin Moseley, Aidin Niaparast, Shikha Singh

A growing line of work shows how learned predictions can be used to break through worst-case barriers to improve the running time of an algorithm. However, incorporating predictions into data structures with strong theoretical guarantees remains underdeveloped. This paper takes a step in this direction by showing that predictions can be leveraged in the fund

Vasileios Vasilopoulos, Suveer Garg, Pedro Piacenza, Jinwook Huh

We introduce Reactive Action and Motion Planner (RAMP), which combines the strengths of sampling-based and reactive approaches for motion planning. In essence, RAMP is a hierarchical approach where a novel variant of a Model Predictive Path Integral (MPPI) controller is used to generate trajectories which are then followed asynchronously by a local vector fi

Josiane Mothe, Md Zia Ullah

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches and these optimized parameters are then used as the system configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selective query expansion is one such an appr

Philip Lynch, Maarten van de Meent, Niels Warburton

We develop the first model for extreme mass-ratio inspirals (EMRIs) with misaligned angular momentum and primary spin, and zero eccentricity -- also known as quasi-spherical inspirals -- evolving under the influence of the first-order in mass ratio gravitational self-force. The forcing terms are provided by an efficient spectral interpolation of the first-or

Kishore Babu Nampalle, Pradeep Singh, Vivek Narayan Uppala, Sumit Gangwar

In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on tweaking the architectures to attain better performance without giving due consideration to the representation of data.

Atzin David Ruiz Pérez, Salvador Escobar Guerrero, Ma. Del Rocío Nava Lara, Jorge-Alejandro Reyes-Esqueda

Room temperature strong coupling from CdSeS/Zn quantum-dots embedded into a tunable porous-silicon Fibonacci-conjugated array could be observed when exciton's energy was tuned either to the photonic-edge or the defect in the middle of the pseudo-bandgap region of the 1D cavity. Both, the photonic-edge and the defect could be identified as topological edge mo

Zihan Wang, Kai Zhao, Yongquan He, Zhumin Chen

Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs. These embedding methods require that all test entities are observed at training time, resulting in a time-consuming retraining process for out-of-knowledge-graph (OOKG) entities. To address this issue, current inductive knowledge embe

Saksham Gupta, Gust Verbruggen, Mukul Singh, Sumit Gulwani

Automation platforms aim to automate repetitive tasks using workflows, which start with a trigger and then perform a series of actions. However, with many possible actions, the user has to search for the desired action at each step, which hinders the speed of flow development. We propose a personalized transformer model that recommends the next item at each

Orlando Luongo, Hernando Quevedo

We evaluate the effects of repulsive gravity using first order geometric invariants, \textit{i.e.}, the Ricci scalar and the eigenvalues of the Riemann curvature tensor, for three regular black holes, namely the Bardeen, Hayward, and Dymnikova spacetimes. To examine the repulsive effects, we calculate their respective onsets and regions of repulsive gravity.

Verónica Becher, Stephen Jackson, Dominik Kwietniak, Bill Mance

Let $b\ge 2$ be an integer. We show that the set of real numbers that are Poisson generic in base $b$ is $\boldsymbol{\Pi}^0_3$-complete in the Borel hierarchy of subsets of the real line. Furthermore, the set of real numbers that are Borel normal in base $b$ and not Poisson generic in base $b$ is complete for the class given by the differences between $\bol

Sarthak Ahuja, Mohammad Kachuee, Fateme Sheikholeslami, Weiqing Liu

Off-Policy reinforcement learning has been a driving force for the state-of-the-art conversational AIs leading to more natural humanagent interactions and improving the user satisfaction for goal-oriented agents. However, in large-scale commercial settings, it is often challenging to balance between policy improvements and experience continuity on the broad

Yiguang Zhang, Kristen Altenburger, Poppy Zhang, Tsutomu Okano

With the rapid development of digital platforms, users can now interact in endless ways from writing business reviews and comments to sharing information with their friends and followers. As a result, organizations have numerous digital social networks available for graph learning problems with little guidance on how to select the right graph or how to combi

Martina Cardillo, Andrea Giuliani

The recent detection of 12 gamma-ray Galactic sources well above E > 100 TeV by the LHAASO observatory has been a breakthrough in the context of Cosmic Ray (CR) origin search. Although most of these sources are unidentified, they are often spatially correlated with leptonic accelerators, like pulsar and pulsar wind nebulae (PWNe). This dramatically affects t

Jozef Fecenko, Enno Diekema

The main goal of the paper is to prove that the sequence of functions $f(x), Df(x), \dots, D^{2n+1}f(x)$, where $f(x)$ is $x^n\sin x$ or $x^n\cos x$ are linearly independent. Or more generally: that the sequence of functions $D^kf(x), D^{k+1}f(x), \dots, D^{2n+k+1}f(x)$, $k\in \mathbb{N}$ is linearly independent. The problem is solved by a suitable transform

Mohamad Hossein Idjadi, Andrea Blanco-Redondo

Here, an enhanced frequency noise discriminator is proposed using a cavity-coupled Mach-Zehnder interferometer and demonstrated on a general-purpose programmable integrated photonics platform. The frequency noise measurement sensitivity is similar to the well-known Pound-Drever-Hall architecture but owns a passive, simpler, and more compact design. The propo

Ziyuan Chen, Ying Yang, Fang Yao

Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the matrix completion and compressed sensing models as special cases. In this work we propose a general framework for dynamic matrix recovery of low-rank matrices that evolve smoothly o

Peter L. Kaulfuss, Paul M. Alsing, Richard J. Birrittella, Dashiell L. P. Vitullo

We investigate the effect of backscattering on the Hong-Ou-Mandel manifold (HOMM) that manifests in double-bus mircoring resonators (MRRs). The HOMM represents higher-dimensional parameter solutions for the complete destructive interference of coincident detection in the HOM effect. To model the backscattering, we introduce a set of internal `beam splitters'

A. Zlotnik, T. Lomonosov

We deal with the reduced four-equation model for the dynamics of heterogeneous compressible binary mixtures with the stiffened gas equations of state. We study its further reduced form, with the excluded volume concentrations, and with a quadratic equation for the common pressure of the components; this form can be called a quasi-homogeneous form. We prove n

Yan Levin, Amin Bakhshandeh

We present a simulation method that allows us to calculate the titration curves for systems undergoing protonation/deprotonation reactions -- such as charged colloidal suspensions with acidic/basic surface groups, polyelectrolytes, polyampholytes, proteins, etc. The new approach allows us to simultaneously obtain titration curves both for systems in contact

Bryn Davies, Gregory J. Chaplain, Timothy A. Starkey, Richard V. Craster

The rainbow trapping phenomenon of graded metamaterials can be combined with the fractal spectra of quasiperiodic waveguides to give a metamaterial that performs fractal rainbow trapping. This is achieved through a graded cut-and-project algorithm that yields a projected geometry for which the effective projection angle is graded along its length. As a resul

Qingxiu Dong, Jingjing Xu, Lingpeng Kong, Zhifang Sui

Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? Existing LLMs may generate distinct responses for different prompts. In this paper, we study the problem of quantifying knowledge contained in an LLM regarding a given set of facts. We propose KaRR, a statistical approach to asses

Md Riajul Haque, Essodjolo Kpatcha, Debaprasad Maity, Yann Mambrini

Post-inflationary reheating phase is usually said to be solely governed by the decay of coherently oscillating inflaton into radiation. In this submission, we explore a new avenue toward reheating through the evaporation of primordial black holes (PBHs). After the inflation, if PBHs form, depending on its initial mass, abundance, and inflaton coupling with t

Junyi Peng, Oldřich Plchot, Themos Stafylakis, Ladislav Mošner

Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle the benefits of large-scale datasets and Transformer structures from the limitations of the pre-training. In this paper, we introduce a hierarchical training approach, named self-pr

Tyler McCabe, Caleb Redshaw, Lillian Otteson, Rogier A. Windhorst

We present the results of deep, ground based U-band imaging with the Large Binocular Telescope of the Cosmic Evolution Survey (COSMOS) field as part of the near-UV imaging program, UVCANDELS. We utilize a seeing sorted stacking method along with night-to-night relative transparency corrections to create optimal depth and optimal resolution mosaics in the U-b

Xin Zhou, Botao Hao, Jian Kang, Tor Lattimore

A brain-computer interface (BCI) is a technology that enables direct communication between the brain and an external device or computer system. It allows individuals to interact with the device using only their thoughts, and holds immense potential for a wide range of applications in medicine, rehabilitation, and human augmentation. An electroencephalogram (

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximu

Vincenzo Maria Schimmenti, Giuseppe Petrillo, Alberto Rosso, Francois P. Landes

We present a machine learning approach for the aftershock forecasting of Japanese earthquake catalogue from 2015 to 2019. Our method takes as sole input the ground surface deformation as measured by Global Positioning System (GPS) stations at the day of the mainshock, and processes it with a Convolutional Neural Network (CNN), thus capturing the input's spat

Jorge R. Bolaños-Servín

We prove the assymptotic equivalence of a sequence of block diagonal matrices with Toeplitz blocks. The blocks are the principal submatrices of an originating Toeplitz sequence with generating symbol of the Wiener class. As an application, using the invariance of certain \textit{diagonal} and \textit{cyclic-diagonal} operator subspaces of the GKSL generators

Shenyuan Liang, Pavan Turaga, Anuj Srivastava

This paper investigates the challenge of learning image manifolds, specifically pose manifolds, of 3D objects using limited training data. It proposes a DNN approach to manifold learning and for predicting images of objects for novel, continuous 3D rotations. The approach uses two distinct concepts: (1) Geometric Style-GAN (Geom-SGAN), which maps images to l

Viktor Moskvoretskii, Anton Frolov, Denis Kuznetsov

Currently, dialogue systems have achieved high performance in processing text-based communication. However, they have not yet effectively incorporated visual information, which poses a significant challenge. Furthermore, existing models that incorporate images in dialogue generation focus on discussing the image itself. Our proposed approach presents a novel

Luca Orusa, Damiano Caprioli

Understanding the conditions conducive to particle acceleration at collisionless, non-relativistic shocks is important for the origin of cosmic rays. We use hybrid (kinetic ions -- fluid electrons) kinetic simulations to investigate particle acceleration and magnetic field amplification at non-relativistic, weakly magnetized, quasi-perpendicular shocks. So f

Sourojit Ghosh, Aylin Caliskan

In this multicultural age, language translation is one of the most performed tasks, and it is becoming increasingly AI-moderated and automated. As a novel AI system, ChatGPT claims to be proficient in such translation tasks and in this paper, we put that claim to the test. Specifically, we examine ChatGPT's accuracy in translating between English and languag

Lailani Kenoly, A. Ken Luu, Celia Toral, Roseanne M. Cheng

We compare the evolution of binary systems evolved in the MESA stellar evolution code to those in the COSMIC population synthesis code. Our aim is to convey the robustness of the equations that model binary evolution in the COSMIC code, particularly for the cases of high mass stars with closely orbiting compact object companions. Our larger goal is to accura

Joseph T. Lizier, Frank Bauer, Fatihcan M. Atay, Jürgen Jost

Synchronization phenomena on networks have attracted much attention in studies of neural, social, economic, and biological systems, yet we still lack a systematic understanding of how relative synchronizability relates to underlying network structure. Indeed, this question is of central importance to the key theme of how dynamics on networks relate to their

Ted Thorbeck, Zhihao Xiao, Archana Kamal, Luke C. G. Govia

It has long been known that the lifetimes of superconducting qubits suffer during readout, increasing readout errors. We show that this degradation is due to the anti-Zeno effect, as readout-induced dephasing broadens the qubit so that it overlaps 'hot spots' of strong dissipation, likely due to two-level systems in the qubit's bath. Using a flux-tunable qub

Hao Shao, Letian Wang, Ruobing Chen, Steven L. Waslander

The large-scale deployment of autonomous vehicles is yet to come, and one of the major remaining challenges lies in urban dense traffic scenarios. In such cases, it remains challenging to predict the future evolution of the scene and future behaviors of objects, and to deal with rare adverse events such as the sudden appearance of occluded objects. In this p

Baturalp Yalcin, Haixiang Zhang, Javad Lavaei, Murat Arcak

This paper investigates the system identification problem for linear discrete-time systems under adversaries and analyzes two lasso-type estimators. We examine both asymptotic and non-asymptotic properties of these estimators in two separate scenarios, corresponding to deterministic and stochastic models for the attack times. Since the samples collected from

Prasanna Rout, Nikos Papadopoulos, Fernando Peñaranda, Kenji Watanabe

Bilayer graphene encapsulated in tungsten diselenide can host a weak topological phase with pairs of helical edge states. The electrical tunability of this phase makes it an ideal platform to investigate unique topological effects at zero magnetic field, such as topological superconductivity. Here we couple the helical edges of such a heterostructure to a su

Yue Wang, Alvaro Velasquez, George Atia, Ashley Prater-Bennette

Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In this paper, we focus on the robust average-reward MDPs under the model-free setting. We first theoretically characterize the structure of solutions to the robust average-reward Bellman equation, which

Youtan Yin, Zhoujie Fu, Fan Yang, Guosheng Lin

The emergence of Neural Radiance Fields (NeRF) for novel view synthesis has increased interest in 3D scene editing. An essential task in editing is removing objects from a scene while ensuring visual reasonability and multiview consistency. However, current methods face challenges such as time-consuming object labeling, limited capability to remove specific

Chenyu Liu, Xinliang Zhou, Yang Liu

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in EEG signal processing. Transformer models can capture the global dependencies in EEG signals through a self-attention mechanism, while CNN models can capture local features such as sawtooth waves. In this work, we propose an end-to-end neural epilepsy detec

Steven Hoehner, Júlia Novaes

It is shown that for a given log-concave function, its symmetric decreasing rearrangement is always harder to approximate in the symmetric difference metric by inner log-linearizations with a fixed number of break points. This extends a classical result of Macbeath (1951) from convex bodies to a functional setting.

Shahzar Rizvi, Mariel Pettee, Benjamin Nachman

The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of data- or simulation-driven models for which computing the likelihood ratio can be very difficult or even imposs

Daniel Gonçalves, Bruno Brogni Uggioni

In this paper, we develop the basic theory of the shadowing property for local homeomorphisms of metric locally compact spaces, with a focus on applications to edge shift spaces connected with C*-algebra theory. For the local homeomorphism (the Deaconu-Renault system) associated with a direct graph, we completely characterize the shadowing property in terms

Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca

Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data. However, while many real-world graphs are directed, the majority of today's GNN models discard this information altogether by simply making the graph undirected. The reasons for this are historical: 1) many early variants of spectral GNNs explicitly required und

Keping Xie, Bei Zhou, T. J. Hobbs

In this work, we complete our CT18qed study with the neutron's photon parton distribution function (PDF), which is essential for the nucleus scattering phenomenology. Two methods, CT18lux and CT18qed, based on the LUXqed formalism and the DGLAP evolution, respectively, to determine the neutron's photon PDF have been presented. Various low-$Q^2$ non-perturbat

Zhixue Zhao, Nikolaos Aletras

Feature attribution methods (FAs) are popular approaches for providing insights into the model reasoning process of making predictions. The more faithful a FA is, the more accurately it reflects which parts of the input are more important for the prediction. Widely used faithfulness metrics, such as sufficiency and comprehensiveness use a hard erasure criter

Giuseppe Del Vecchio Del Vecchio, Márton Kormos, Benjamin Doyon, Alvise Bastianello

We present the first exact theory and analytical formulas for the large-scale phase fluctuations in the sine-Gordon model, valid in all regimes of the field theory, for arbitrary temperatures and interaction strengths. Our result is based on the Ballistic Fluctuation Theory combined with Generalized Hydrodynamics, and can be seen as an exact ``dressing" of t

Cynthia Yan

We study further the duality between semiclassical AdS3 and formal CFT2 ensembles. First, we study torus wormholes (Maldacena-Maoz wormholes with two torus boundaries) with one insertion or two insertions on each boundary and find that they give non-decaying contribution to the product of two torus one-point or two-point functions at late-time. Second, we st

Paolo Ciatti, Bruno Franchi, Yannick Sire

We derive several properties of the heat equation with the Hodge operator associated with the Rumin complex on Heisenberg groups and prove several properties of the fundamental solution. As an application, we use the heat kernel for Rumin's differential forms to construct a Calder\'on reproducing formula on Rumin forms.

Dina Traykova, Rodrigo Vicente, Katy Clough, Thomas Helfer

We use numerical simulations of scalar field dark matter evolving on a moving black hole background to confirm the regime of validity of (semi-)analytic expressions derived from first principles for both dynamical friction and momentum accretion in the relativistic regime. We cover both small and large clouds (relative to the de Broglie wavelength of the sca

David S. Berman, Marc S. Klinger, Alexander G. Stapleton

In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian Renormalization. The main insight of Bayesian Renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent RG scale quantifying the distinguishability between

Ralph Blumenhagen, Aleksandar Gligovic, Antonia Paraskevopoulou

We explore the Emergence Proposal for the moduli metric and the gauge couplings in a concrete model with 7 saxionic and 7 axionic moduli fields, namely the compactification of the type IIA superstring on a 6-dimensional toroidal orbifold. We show that consistency requires integrating out precisely the 12 towers of light particle species arising from KK and s

Niccolò Cribiori, Dieter Lust, Carmine Montella

We analyse particle species and the species scale in quantum gravity from a thermodynamic perspective. In close analogy to black hole thermodynamics, we propose that particle species own an entropy and a temperature, which is determined by the species scale. This is identical to the Bekenstein-Hawking entropy of a corresponding minimal black hole and agrees

Simone Mastrogiovanni, Danny Laghi, Rachel Gray, Giada Caneva Santoro

Gravitational wave (GW) sources at cosmological distances can be used to probe the expansion rate of the Universe. GWs directly provide a distance estimation of the source but no direct information on its redshift. The optimal scenario to obtain a redshift is through the direct identification of an electromagnetic (EM) counterpart and its host galaxy. With a

Hui Peng, Yu Yu

Interloper contamination due to line misidentification is an important issue in the future low-resolution spectroscopic surveys. We realize that the algorithm previously used for photometric redshift self-calibration, with minor modifications, can be particularly applicable to calibrate the interloper bias. In order to explore the robustness of the modified

Maxime Trépanier

I study the two-dimensional defects of the $d$ dimensional critical $O(N)$ model and the defect RG flows between them. By combining the $\epsilon$-expansion around $d = 4$ and $d = 6$ as well as large $N$ techniques, I find new conformal defects and examine their behavior across dimensions and at various $N$. I discuss how some of these fixed points relate t

Miguel Murça, Duarte Magano, Yasser Omar

Despite the promise that fault-tolerant quantum computers can efficiently solve classically intractable problems, it remains a major challenge to find quantum algorithms that may reach computational advantage in the present era of noisy, small-scale quantum hardware. Thus, there is substantial ongoing effort to create new quantum algorithms (or adapt existin

Emilie Dufault, Faranak Bahrami, Alenna Streeter, Xiaohan Yao

Recent theoretical studies have suggested that the low-energy Hamiltonian of honeycomb cobaltate systems could be dominated by anisotropic Kitaev interactions. Motivated by the theory, a honeycomb layered material Na$_{2}$Co$_{2}$TeO$_{6}$ with a hexagonal unit cell has been studied and found to exhibit antiferromagnetic (AFM) ordering at 27 K with two spin

Miguel A. Prado Reynoso, D. J. Nader, Jorge Chávez-Carlos, B. E. Ordaz-Mendoza

The quasi-energy spectrum recently measured in experiments with a squeeze-driven superconducting Kerr oscillator showed good agreement with the energy spectrum of its corresponding static effective Hamiltonian. The experiments also demonstrated that the dynamics of low-energy states can be explained with the same emergent static effective model. The spectrum

Juan Román-Roche, Víctor Herráiz-López, David Zueco

We present an exact analytical solution for quantum strong long-range models in the canonical ensemble by extending the classical solution proposed in [Campa et al., J. Phys. A 36, 6897 (2003)]. Specifically, we utilize the equivalence between generalized Dicke models and interacting quantum models as a generalization of the Hubbard-Stratonovich transformati

Hao-Lin Li, Zhe Ren, Ming-Lei Xiao, Jiang-Hao Yu

We construct the on-shell amplitude basis and the corresponding effective operators for generic modified gravity theory, such as pure gravity with higher derivatives, scalar-tensor gravity, Einstein-Yang-Mills, etc. Taking the Weyl tensor as the building block, we utilize the Young tensor technique to obtain independent operators, without equation of motion

Grant M. Kennedy, Joshua B. Lovell, Paul Kalas, Michael P. Fitzgerald

At 7.7 pc, the A-type star Fomalhaut hosts a bright debris disk with multiple radial components. The disk is eccentric and misaligned, strongly suggesting that it is sculpted by interaction with one or more planets. Compact sources are now being detected with JWST, suggesting that new planet detections may be imminent. However, to confirm such sources as com

Di Zhu, Zheng-Yang Zhuang, Zhigang Wu, Zhongbo Yan

Bringing magnetic metals into superconducting states represents an important approach for realizing unconventional superconductors and potentially even topological superconductors. Altermagnetism, classified as a third basic collinear magnetic phase, gives rise to intriguing momentum-dependent spin-splitting of the band structure, and results in an even numb

Leah Jenks, Lyla Choi, Macarena Lagos, Nicolás Yunes

Gravitational parity violation arises in a variety of theories beyond general relativity. Gravitational waves in such theories have their propagation altered, leading to birefringence effects in both the amplitude and speed of the wave. In this work, we introduce a generalized, theory-motivated parametrization scheme to study parity violation in gravitationa

Valentin Crépel, Nicolas Regnault, Raquel Queiroz

The observation of zero field fractional quantum Hall analogs in twisted transition metal dichalcogenides (TMDs) asks for a deeper understanding of what mechanisms lead to topological flat bands in two-dimensional heterostructures, and what makes TMDs an excellent platform for topologically ordered phases, surpassing twisted bilayer graphene. To this aim, we

João Barata, Jean-Paul Blaizot, Yacine Mehtar-Tani

We study the time evolution of the density matrix of a high energy quark propagating in a dense QCD medium where it undergoes elastic collisions (radiation is ignored in the present study). The medium is modeled as a stochastic color field with a Gaussian correlation function. This allows us to eliminate the medium degrees of freedom and obtain a simple mast

Anja Butter, Nathan Huetsch, Sofia Palacios Schweitzer, Tilman Plehn

We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks exce

Guangxuan Xiao, Tianwei Yin, William T. Freeman, Frédo Durand

Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend featu

Songwei Ge, Seungjun Nah, Guilin Liu, Tyler Poon

Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy. While off-the-shelf billion-scale datasets for image generation are available, collecting similar video data of the same scale is still challenging. Also

Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao

Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning. The planning task involves predicting the trajectory of the ego vehicle based on inputs from both internal intention and the external environment, and manipulating the vehicle accordingly. Most existing works evaluate their performance on the

Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du

The mixture proportions of pretraining data domains (e.g., Wikipedia, books, web text) greatly affect language model (LM) performance. In this paper, we propose Domain Reweighting with Minimax Optimization (DoReMi), which first trains a small proxy model using group distributionally robust optimization (Group DRO) over domains to produce domain weights (mixt

Francesco Sinigaglia, Francisco-Shu Kitaura, Kentaro Nagamine, Yuri Oku

We present an improved analytical model to predict the Lyman-alpha forest at the field level in redshift space from the dark matter field, expanding upon the widely-used Fluctuating Gunn-Peterson approximation (FGPA). In particular, we introduce the dependence on the cosmic web environment (knots, filaments, sheets, voids) in the model, thereby effectively a

Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca

Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model, trading inference speed at the cost of the translation quality. In this paper, we propose to address the prob

Allyson Brodzeller, Kyle Dawson, Stephen Bailey, Jiaxi Yu

Millions of quasar spectra will be collected by the Dark Energy Spectroscopic Instrument (DESI), leading to a four-fold increase in the number of known quasars. High accuracy quasar classification is essential to tighten constraints on cosmological parameters measured at the highest redshifts DESI observes ($z>2.0$). We present the spectral templates for ide

Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman

Learning from human feedback has been shown to be effective at aligning language models with human preferences. Past work has often relied on Reinforcement Learning from Human Feedback (RLHF), which optimizes the language model using reward scores assigned from a reward model trained on human preference data. In this work we show how the recently introduced

Kyle Vedder, Neehar Peri, Nathaniel Chodosh, Ishan Khatri

Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process full-size point clouds, making them unusable as computer vision primitives for real-time applications such as o

Sen Lin, Gianmarco Mengaldo, Romit Maulik

The detection of anomalies or transitions in complex dynamical systems is of critical importance to various applications. In this study, we propose the use of machine learning to detect changepoints for high-dimensional dynamical systems. Here, changepoints indicate instances in time when the underlying dynamical system has a fundamentally different characte

Vladimir Bashmakov, Michele Del Zotto, Azeem Hasan

In this paper we study the geometric origin of non-invertible symmetries of 2d theories arising from the reduction of 6d $(2,0)$ theories on four-manifolds. This generalizes and extends our previous results in the context of class $\mathcal S$ theories to a wider realm of models. In particular, we find that relative 2d field theories, such as the chiral boso

Marziyeh Rezaei, Sevda Molani, Negar Firoozeh, Hossein Abbasi

Du e to rapid population growth and the need to use artificial intelligence to make quick decisions, developing a machine learning-based disease detection model and abnormality identification system has greatly improved the level of medical diagnosis Since COVID-19 has become one of the most severe diseases in the world, developing an automatic COVID-19 dete

Rabah Ouldnoughi, Chia-Wen Kuo, Zsolt Kira

Generalized Category Discovery (GCD) requires a model to both classify known categories and cluster unknown categories in unlabeled data. Prior methods leveraged self-supervised pre-training combined with supervised fine-tuning on the labeled data, followed by simple clustering methods. In this paper, we posit that such methods are still prone to poor perfor

Zezhou Huang, Pranav Subramaniam, Raul Castro Fernandez, Eugene Wu

AutoML services provide a way for non-expert users to benefit from high-quality ML models without worrying about model design and deployment, in exchange for a charge per hour ($21.252 for VertexAI). However, existing AutoML services are model-centric, in that they are limited to extracting features and searching for models from initial training data-they ar

Yidi Shao, Chen Change Loy, Bo Dai

Mimicking realistic dynamics in 3D garment animations is a challenging task due to the complex nature of multi-layered garments and the variety of outer forces involved. Existing approaches mostly focus on single-layered garments driven by only human bodies and struggle to handle general scenarios. In this paper, we propose a novel data-driven method, called

Stefania Druga, Nancy Otero

How can AI enhance creative coding experiences for families? This study explores the potential of large language models (LLMs) in helping families with creative coding using Scratch. Based on our previous user study involving a prototype AI assistant, we devised three evaluation scenarios to determine if LLMs could help families comprehend game code, debug p