July 2023 arXiv papers — page 21

Monica Verducci, Natascia Vignaroli, Chiara Roda, Vincenzo Cavasinni

The $\tau$ lepton anomalous magnetic moment: $a_\tau = \frac{g_{\tau}-2}{2}$ was measured, so far, with a precision of only several percents despite its highly sensitivity to physics beyond the Standard Model such as compositeness or Supersymmetry. A new study is presented to improve the sensitivity of the $a_\tau $ measurement with photon-photon interaction

A. Bahlaoui, Y. Zahidi

The paper discusses the chiral tunnelling of charge carriers through double barrier structure in twisted graphene bilayer. The theoretical analysis investigates the transmission probability for various system parameters under both symmetric and asymmetric barrier conditions. The results reveal that the transmission probability of quasiparticles in the $K$ co

Marios Constantinides, Edyta Bogucka, Daniele Quercia, Susanna Kallio

Many guidelines for responsible AI have been suggested to help AI practitioners in the development of ethical and responsible AI systems. However, these guidelines are often neither grounded in regulation nor usable by different roles, from developers to decision makers. To bridge this gap, we developed a four-step method to generate a list of responsible AI

Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami

Similarity metrics have played a significant role in computer vision to capture the underlying semantics of images. In recent years, advanced similarity metrics, such as the Learned Perceptual Image Patch Similarity (LPIPS), have emerged. These metrics leverage deep features extracted from trained neural networks and have demonstrated a remarkable ability to

A. Belley, T. Miyagi, S. R. Stroberg, J. D. Holt

Neutrinos are perhaps the most elusive known particles in the universe. We know they have some nonzero mass, but unlike all other particles, the absolute scale remains unknown. In addition, their fundamental nature is uncertain; they can either be their own antiparticles or exist as distinct neutrinos and antineutrinos. The observation of the hypothetical pr

Keoni Castellano, Rachidi B. Salako

We study a diffusive SIS epidemic model with the mass-action transmission mechanism and show, under appropriate assumptions on the parameters, the existence of multiple endemic equilibria (EE). Our results answer some open questions on previous studies related to disease extinction or persistence when $\mathcal{R}_0<1$ and the multiplicity of EE solutions wh

Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain

We investigate the fixed-budget best-arm identification (BAI) problem for linear bandits in a potentially non-stationary environment. Given a finite arm set $\mathcal{X}\subset\mathbb{R}^d$, a fixed budget $T$, and an unpredictable sequence of parameters $\left\lbrace\theta_t\right\rbrace_{t=1}^{T}$, an algorithm will aim to correctly identify the best arm $

Aekta Aggarwal, Ganesh Vaidya

We study a class of nonlinear nonlocal conservation laws with discontinuous flux, modeling crowd dynamics and traffic flow, without any additional conditions on finiteness/discreteness of the set of discontinuities or on the monotonicity of the kernel/the discontinuous coefficient. Strong compactness of the Godunov and Lax-Friedrichs type approximations is p

David P. Nicholls, Liet Vo

The scattering of electromagnetic waves by three--dimensional periodic structures is important for many problems of crucial scientific and engineering interest. Due to the complexity and three-dimensional nature of these waves, the fast, accurate, and reliable numerical simulations of these are indispensable for engineers and scientists alike. For this, High

Christis Katsouris

We consider Wald type statistics designed for joint predictability and structural break testing based on the instrumentation method of Phillips and Magdalinos (2009). We show that under the assumption of nonstationary predictors: (i) the tests based on the OLS estimators converge to a nonstandard limiting distribution which depends on the nuisance coefficien

Liqi Wang, Qiya Hu

Dropout as a regularization technique is widely used in fully connected layers while is less effective in convolutional layers. Therefore more structured forms of dropout have been proposed to regularize convolutional networks. The disadvantage of these methods is that the randomness introduced causes inconsistency between training and inference. In this pap

Thomas M Henderson, Fei Gao, Gustavo E. Scuseria

The Jordan-Wigner transformation is a powerful tool for converting systems of spins into systems of fermions, or vice versa. While this mapping is exact, the transformation itself depends on the labeling of the spins. One consequence of this dependence is that approximate solutions of a Jordan-Wigner--transformed Hamiltonian may depend on the (physically inc

Alexander Vishik

In this paper we prove the conjecture claiming that, over a flexible field, isotropic Chow groups coincide with numerical Chow groups (with ${\Bbb{F}}_p$-coefficients). This shows that Isotropic Chow motives coincide with Numerical Chow motives. In particular, homs between such objects are finite groups and $\otimes$ has no zero-divisors. It provides a large

E. S. Moreira

This paper reports calculations and analysis of the effects of a perfect conducting wall of a very large spherical shell on the stable thermodynamic equilibrium of a black hole sitting at the centre of the shell which is filled with Electromagnetic blackbody radiation. A parallel is drawn with the case where electromagnetic is replaced by scalar blackbody ra

Alexey A. Dmitriev, Mikhail V. Rybin

Exceptional points in an optical dimer of spheres, which have the same size and operate in the spectral region of the dipolar resonance, are considered. By choosing different materials of these spheres, we can offset the radiative loss and create a gain-loss contrast to achieve a parity-time (PT)-symmetric dimer. In this case, an exceptional point correspond

Andrei Tudor Patrascu

In order to show that the graviton is a quantum entity an experiment is proposed that can show that quantum entanglement is produced by means of an exchange of gravitons. For this to be possible, one has to be able to witness the entanglement between the two objects considered in the experiment and to be able to eliminate other sources of entanglement like t

Matthew Yeung, Lu-Ting Chou, Marco Turchetti, Felix Ritzkowsky

Electronic frequency mixers are fundamental building blocks of electronic systems. Harmonic frequency mixing in particular enables broadband electromagnetic signal analysis across octaves of spectrum using a single local oscillator. However, conventional harmonic frequency mixers do not operate beyond hundreds of GHz to a few THz. If extended to the petahert

Monika Sanoria, Raghunath Chelakkot, Amitabha Nandi

Homogeneous active Brownian particle (ABP) systems with purely repulsive interactions are considered to have simple phase behavior, but various physical attributes of active entities can lead to variation in the collective dynamics. Recent studies have shown that even homogeneous ABPs exhibit complex behavior due to an interplay between particle softness and

Florin Catrina, Mikhail I. Ostrovskii

The main goal of this paper is to improve the result of Ostrovskii (2012) on the finite determination of bilipschitz and coarse embeddability of locally finite metric spaces into Banach spaces. There are two directions of the improvement: (1) Substantial decrease of distortion (from about $3000$ to $3+\ep$) is achieved by replacing the barycentric gluing by

Kenny Peng, Manish Raghavan, Emma Pierson, Jon Kleinberg

In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories). As such, real-world recommender systems often explicitly incorporate diversity separately from accuracy. This approach, however, leaves a basic quest

F. Bao, L. Bauer, A. E. Rubio Lopez, Z. Jacob

Photon statistics of an optical field can be used for quantum optical sensing in low light level scenarios free of bulky optical components. However, photon-number-resolving detection to unravel the photon statistics is challenging. Here, we propose a novel detection approach, that we call `photon discerning', which uses adaptive photon thresholding for phot

Prashant Kocherlakota, Ramesh Narayan, Koushik Chatterjee, Alejandro Cruz-Osorio

Accretion of magnetized gas on compact astrophysical objects such as black holes has been successfully modeled using general relativistic magnetohydrodynamic (GRMHD) simulations. These simulations have largely been performed in the Kerr metric, which describes the spacetime of a vacuum and stationary spinning black hole (BH) in general relativity (GR). The s

Chuanxia Zheng, Andrea Vedaldi

Vector Quantisation (VQ) is experiencing a comeback in machine learning, where it is increasingly used in representation learning. However, optimizing the codevectors in existing VQ-VAE is not entirely trivial. A problem is codebook collapse, where only a small subset of codevectors receive gradients useful for their optimisation, whereas a majority of them

Akim Ndlovu, Hilson Shrestha, Lane T. Harrison

Choropleth maps have been studied and extended in many ways to counteract the many biases that can occur when using them. Two recent techniques, Surprise metrics and Value Suppressing Uncertainty Palettes (VSUPs), offer promising solutions but have yet to be tested empirically with users of visualizations. In this paper, we explore how well people can make u

Sophia R. Vaughan, Timothy D. Gebhard, Kimberly Bott, Sarah L. Casewell

NASA is engaged in planning for a Habitable Worlds Observatory (HabWorlds), a coronagraphic space mission to detect rocky planets in habitable zones and establish their habitability. Surface liquid water is central to the definition of planetary habitability. Photometric and polarimetric phase curves of starlight reflected by an exoplanet can reveal ocean gl

Riki Dutta, Sagardeep Talukdar, Gautam Kumar Saharia, Sudipta Nandy

We propose the Hirota bilinearization of the Fokas-Lenells derivative nonlinear Schrodinger equation with a non-vanishing background. The bilinear method is applied using an auxilary function to obtain the dark one soliton solution, dark two soliton solution and eventually the scheme for obtaining dark N soliton solutions. The use of auxilary function in bil

Sanjana Agarwal

The Morava $E$-theories, $E_{n}$, are complex-oriented $2$-periodic ring spectra, with homotopy groups $\mathbb{W}_{\mathbb{F}_{p^{n}}}[[u_{1}, u_{2}, ... , u_{n-1}]][u,u^{-1}]$. Here $\mathbb{W}$ denotes the Witt vector ring. $E_{n}$ is a Landweber exact spectrum and hence uniquely determined by this ring as $BP_{\ast}$-algebra. Algebraic $K$-theory of $E_{

Davide Parise, Alessandro Pigati, Daniel Stern

We develop the asymptotic analysis as $\epsilon\to 0$ for the natural gradient flow of the self-dual $U(1)$-Higgs energies $$E_{\epsilon}(u,\nabla)=\int_M\left(|\nabla u|^2+\epsilon^2|F_{\nabla}|^2+\frac{(1-|u|^2)^2}{4\epsilon^2}\right)$$ on Hermitian line bundles over closed manifolds $(M^n,g)$ of dimension $n\ge 3$, showing that solutions converge in a mea

Mohammed Mechacha

In this paper, we establish congruences (mod $p^2$) involving the quadrinomial coefficients $\dbinom{np-1}{p-1}_{3}$ and $\dbinom{np-1}{\frac{p-1}{2}}_{3}$. This is an analogue of congruences involving the trinomial coefficients $\dbinom{np-1}{p-1}_{2}$ and $\dbinom{np-1}{\frac{p-1}{2}}_{2}$ due to Elkhiri and Mihoubi.

Stephen Hannon, Bradley C. Whitmore, Janice C. Lee, David A. Thilker

Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used

Ehsan Paki, Sabrina Baudart, Atila Poro

We performed the first photometric study of the CSS J003106.8+313347 W Ursae Majoris (W UMa)-type system based on ground-based observations. We extracted times of minima from our observations and proposed a linear ephemeris based on the increasing incline of the orbital period using a Markov chain Monte Carlo (MCMC) approach. The PHOEBE Python code and the M

Xiangyu Wang, Jingsen Zhu, Qi Ye, Yuchi Huo

With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation. While previous works have explored NeRF editing from various perspectives, they are restricted in editing fl

Thomas Caleb, Gianmario Merisio, Pierluigi Di Lizia, Francesco Topputo

Ballistic capture orbits offer safer Mars injection at longer transfer time. However, the search for such an extremely rare event is a computationally intensive process. Indeed, it requires the propagation of a grid sampling the whole search space. This work proposes a novel ballistic capture search algorithm based on Taylor differential algebra propagation.

Guiqin Zhao, Lianlei Shan, Weiqiang Wang

Change detection based on remote sensing images has been a prominent area of interest in the field of remote sensing. Deep networks have demonstrated significant success in detecting changes in bi-temporal remote sensing images and have found applications in various fields. Given the degradation of natural environments and the frequent occurrence of natural

Dong Fan, Aydin Ozcan, Pengbo Lyu, Guillaume Maurin

Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. Herein, we designed in silico a highly stable NiF$_2$(pyrazine)$_2$ 2D MOF with a two-periodic wine-rack architecture. Extensive first-principles calculations and Molecular Dynamics

Marianthi Bataki, Constantinos Skordis, Tom Zlosnik

The Aether Scalar Tensor (AeST) theory is an extension of General Relativity (GR), proposed for addressing galactic and cosmological observations without dark matter. By casting the AeST theory into a $3+1$ form, we determine its full non-perturbative Hamiltonian formulation and analyse the resulting constraints. We find the presence of four first class and

Rossella Gamba, Matteo Breschi, Sebastiano Bernuzzi, Alessandro Nagar

Gravitational wave astronomy pipelines rely on template waveform models for searches and parameter estimation purposes. For coalescing binary neutron stars (BNS), such models need to accurately reproduce numerical relativity (NR) up to merger, in order to provide robust estimate of the stars' equation of state - dependent parameters. In this work we present

Dalila Pîrvu, Junwu Huang, Matthew C. Johnson

We study anisotropic (patchy) screening induced by the resonant conversion of cosmic microwave background (CMB) photons into dark-sector massive vector bosons (dark photons) as they cross non-linear large scale structure (LSS). Resonant conversion takes place through the kinetic mixing of the photon with the dark photon, one of the simplest low energy extens

Nicholas Flinner, Michael A. Tucker, John F. Beacom, Benjamin J. Shappee

We analyze pre-explosion ultraviolet (UV) imaging of the nearby Type II supernova SN 2023ixf in search of precursor variability. No outbursts are seen in observations obtained 15-20 yr prior to explosion to a limit of $L_{NUV} \approx 1000~L_{sun}$ and $L_{NUV} \approx 2000~L_{sun}$. The time period of these non-detections roughly corresponds to changes in t

Ido Ben-Dayan, Utkarsh Kumar, Udaykrishna Thattarampilly, Amresh Verma

A stochastic gravitational wave background is a prediction of a number of astrophysical and cosmological phenomena including early Universe Cosmology. Recently, the NANOGrav Collaboration reported conclusive evidence for a stochastic gravitational-wave background. We analyze the NANOGrav signal assuming it is of primordial origin including the reheating phas

Damiano F. G. Fiorillo, Georg Raffelt, Edoardo Vitagliano

Neutrino-neutrino scattering could have a large secret component that would turn neutrinos within a supernova (SN) core into a self-coupled fluid. Neutrino transport within the SN core, emission from its surface, expansion into space, and the flux spectrum and time structure at Earth might all be affected. We examine these questions from first principles. Fi

Nikhil Sarin, Brian D. Metzger

The cooling envelope model for tidal disruption events (TDE) postulates that while the stellar debris streams rapidly dissipate their bulk kinetic energy (``circularize"), this does not necessarily imply rapid feeding of the supermassive black hole (SMBH). The bound material instead forms a large pressure-supported envelope which powers optical/UV emission a

Avery E. Broderick, Kiana Salehi, Boris Georgiev

With the imaging and characterization of the horizon-scale images of M87* and Sgr A* by the Event Horizon Telescope (EHT), it has become possible to resolve the near-horizon region of astrophysical black holes. As a result, there has been considerable interest in the implications of the measurement of the shadow size, i.e., the asymptotic photon ring. We exp

Agnes Valenti, Vladimir Calvera, Steven A. Kivelson, Erez Berg

The two-dimensional electron gas is of fundamental importance in quantum many-body physics. We study a minimal extension of this model with $C_4$ (as opposed to full rotational) symmetry and an electronic dispersion with two valleys with anisotropic effective masses. Using variational Monte Carlo simulations, we find a broad intermediate range of densities w

Jennifer Schober, Igor Rogachevskii, Axel Brandenburg

In the standard model of particle physics, the chiral anomaly can occur in relativistic plasmas and plays a role in the early Universe, protoneutron stars, heavy-ion collisions, and quantum materials. It gives rise to a magnetic instability if the number densities of left- and right-handed electrically charged fermions are unequal. Using direct numerical sim

Marco Fedele, Felix Wuest, Ulrich Nierste

Leptoquarks with masses between 2 TeV and 50 TeV are commonly invoked to explain deviations between data and Standard-Model (SM) predictions of several observables in the decays $b\to c\tau \bar\nu$ and $b\to s \ell^+\ell^-$ with $\ell=e,\mu$. While Leptoquarks appear in theories unifying quarks and leptons, the corresponding unification scale $M_{QLU}$ is t

Renato Renner, Ramona Wolf

A recent publication by the NSA assessing the usability of quantum cryptography has generated significant attention, concluding that this technology is not recommended for use. Here, we reply to this criticism and argue that some of the points raised are unjustified, whereas others are problematic now but can be expected to be resolved in the foreseeable fut

Damiano F. G. Fiorillo, Georg Raffelt, Edoardo Vitagliano

When hypothetical neutrino secret interactions ($\nu$SI) are large, they form a fluid in a supernova (SN) core, flow out with sonic speed, and stream away as a fireball. For the first time, we tackle the complete dynamical problem and solve all steps, systematically using relativistic hydrodynamics. The impact on SN physics and the neutrino signal is remarka

Christopher V. Cappiello, Michael Jafs, Aaron C. Vincent

We study the morphology of the 511 keV signal that could be produced by exciting dark matter (XDM) in the Milky Way. In this model, collisions between dark matter particles excite the dark matter to a state that can then decay back to the ground state, releasing an electron-positron pair. These electrons and positrons would then annihilate, producing 511 keV

Ben Forrest, Brian C. Lemaux, Ekta Shah, Priti Staab

Motivated by spectroscopic confirmation of three overdense regions in the COSMOS field at $z\sim3.35$, we analyze the uniquely deep multi-wavelength photometry and extensive spectroscopy available in the field to identify any further related structure. We construct a three dimensional density map using the Voronoi tesselation Monte Carlo method and find addi

L. Sironi, L. Comisso, R. Golant

The mechanisms that generate "seed" magnetic fields in our Universe and that amplify them throughout cosmic time remain poorly understood. By means of fully-kinetic particle-in-cell simulations of turbulent, initially unmagnetized plasmas, we study the genesis of magnetic fields via the Weibel instability and follow their dynamo growth up to near-equipartiti

Hector Afonso G. Cruz, Andy D. Goulding, Jenny E. Greene

We study the broad-band spectral energy distribution (SED) of the prototypical low-mass active galactic nucleus (AGN) in NGC 4395. We jointly model the optical through mid-infrared SED with a combination of galaxy and AGN light, and find that on arcsecond scales, the AGN dominates at most wavelengths. However, there is still some ambiguity about emission fro

Gokhan Alkac, Gokcen Deniz Ozen, Hikmet Ozsahin, Gun Suer

Recently, it was discovered that lower-dimensional versions of Lovelock gravity exist as scalar-tensor theories that are examples of Horndeski gravity. We study the thermodynamics of the static black hole solutions in these theories up to cubic order through Euclidean methods. Considering solutions with spherical, planar and hyperbolic event horizons ($k=+1,

Carlota Andres, Fabio Dominguez, Jack Holguin, Cyrille Marquet

Heavy quarks created in heavy-ion collisions serve as an excellent probe of the produced quark-gluon plasma (QGP). The radiation pattern of jets formed from heavy quarks as they traverse the QGP exhibits a particularly interesting structure due to the interplay of two competing effects: the suppression of small-angle radiation, also known as the ``dead-cone'

R. Vanon, P. V. F. Edelmann, R. P. Ratnasingam, A. Varghese

We present 3D full star simulations, reaching up to 90% of the total stellar radius, for three $7M_\odot$ stars of different ages (ZAMS, midMS and TAMS). A comparison with several theoretical prescriptions shows the generation spectra for all three ages are dominated by convective plumes. Two distinct overshooting layers are observed, with most plumes stoppe

Kazuyuki Sugimura, Tomoaki Matsumoto, Takashi Hosokawa, Shingo Hirano

We study the formation of Pop III stars by performing radiation hydrodynamics simulations for three different initial clouds extracted from cosmological hydrodynamics simulations. Starting from the cloud collapse stage, we follow the growth of protostars by accretion for $\sim 10^5$ yr until the radiative feedback from the protostars suppresses the accretion

Arjun Somayazulu, Changan Chen, Kristen Grauman

Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. Existing methods assume access to paired training data, where the audio is observed in both source and target environments, but this limits the diversity of training data or requires the use of simulated data or heuristics to create paired

Marc Botet Colomer, Pier Luigi Dovesi, Theodoros Panagiotakopoulos, Joao Frederico Carvalho

The goal of Online Domain Adaptation for semantic segmentation is to handle unforeseeable domain changes that occur during deployment, like sudden weather events. However, the high computational costs associated with brute-force adaptation make this paradigm unfeasible for real-world applications. In this paper we propose HAMLET, a Hardware-Aware Modular Lea

Shankar Balasubramanian, Tongyang Li, Aram Harrow

There are few known exponential speedups for quantum algorithms and these tend to fall into even fewer families. One speedup that has mostly resisted generalization is the use of quantum walks to traverse the welded-tree graph, due to Childs, Cleve, Deotto, Farhi, Gutmann, and Spielman. We show how to generalize this to a large class of hierarchical graphs i

Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevitably from real-world corruptions and perturbations and are struggled to provide reliable depth predictions und

Nils Schöneberg, Léo Vacher, J. D. F. Dias, Martim M. C. D. Carvalho

Our current best guess for a unified theory of gravitation and quantum field theory (string theory) generically predicts a set of requirements for a consistently quantized theory, the Swampland criteria. Refined versions of these criteria have recently been shown to be in mild tension with cosmological observations. We summarize the status of the current imp

Ryuichiro Kitano, Motoo Suzuki, Wen Yin

Ignorance of the initial condition for the axion dynamics in the early Universe has led us to consider an $O(1)$ valued initial amplitude, and that prefers the decay constant, $F_a$, of the QCD axion to be an intermediate scale such as $10^{12}$ GeV in order to explain the dark matter abundance. We explore a cosmological scenario of $F_a$ being much larger t

Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong

Nowadays, autonomous cars can drive smoothly in ordinary cases, and it is widely recognized that realistic sensor simulation will play a critical role in solving remaining corner cases by simulating them. To this end, we propose an autonomous driving simulator based upon neural radiance fields (NeRFs). Compared with existing works, ours has three notable fea

William Shen, Ge Yang, Alan Yu, Jansen Wong

Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed understanding of 3D geometry, which is often lacking in 2D image features. This work bridges this 2D-to-3D gap for robotic manipulation by leveraging distilled feature fields to combine

Erik Rye, Dave Levin

Today's network measurements rely heavily on Internet-wide scanning, employing tools like ZMap that are capable of quickly iterating over the entire IPv4 address space. Unfortunately, IPv6's vast address space poses an existential threat for Internet-wide scans and traditional network measurement techniques. To address this reality, efforts are underway to d

Rob Klabbers, Michelangelo Preti, István M. Szécsényi

We study a family of higher-twist Regge trajectories in $\mathcal{N}=4$ supersymmetric Yang-Mills theory using the Quantum Spectral Curve. We explore the many-sheeted Riemann surface connecting the different trajectories and show the interplay between the degenerate non-local operators known as horizontal trajectories. We resolve their degeneracy analyticall

Michael Bleher

The Haydys-Witten equations are partial differential equations on five-dimensional Riemannian manifolds that are equipped with a non-vanishing vector field $v$. Conjecturally, their solutions determine the Floer differential in a gauge-theoretic approach to Khovanov homology. This article introduces a certain decoupled version of the Haydys-Witten equations,

Yang Zheng, Adam W. Harley, Bokui Shen, Gordon Wetzstein

We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world moti

Clément Guerner, Tianyu Liu, Anej Svete, Alexander Warstadt

The linear subspace hypothesis (Bolukbasi et al., 2016) states that, in a language model's representation space, all information about a concept such as verbal number is encoded in a linear subspace. Prior work has relied on auxiliary classification tasks to identify and evaluate candidate subspaces that might give support for this hypothesis. We instead giv

Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko

Approaches to recommendation are typically evaluated in one of two ways: (1) via a (simulated) online experiment, often seen as the gold standard, or (2) via some offline evaluation procedure, where the goal is to approximate the outcome of an online experiment. Several offline evaluation metrics have been adopted in the literature, inspired by ranking metri

Alex Costanzino, Pierluigi Zama Ramirez, Matteo Poggi, Fabio Tosi

Inferring the depth of transparent or mirror (ToM) surfaces represents a hard challenge for either sensors, algorithms, or deep networks. We propose a simple pipeline for learning to estimate depth properly for such surfaces with neural networks, without requiring any ground-truth annotation. We unveil how to obtain reliable pseudo labels by in-painting ToM

Qiao Jin, Zifeng Wang, Charalampos S. Floudas, Fangyuan Chen

Patient recruitment is challenging for clinical trials. We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models. TrialGPT comprises three modules: it first performs large-scale filtering to retrieve candidate trials (TrialGPT-Retrieval); then predicts criterion-level patient eligibility (TrialGPT-Matc

Sirina Prasad, Qizhou Zhang, James Moran, Yue Cao

MWC 349A is one of the rare stars known to have hydrogen radio recombination line (RRL) masers. The bright maser emission makes it possible to study the dynamics of the system at milli-arcsecond (mas) precision. We present Atacama Large Millimeter/submillimeter Array (ALMA) observations of the 1.4 mm and 0.8 mm continuum emission of MWC 349A, as well as the

Kecheng Zheng, Wei Wu, Ruili Feng, Kai Zhu

Prompt tuning and adapter tuning have shown great potential in transferring pre-trained vision-language models (VLMs) to various downstream tasks. In this work, we design a new type of tuning method, termed as regularized mask tuning, which masks the network parameters through a learnable selection. Inspired by neural pathways, we argue that the knowledge re

Zdenek Dvorak, Liana Yepremyan

We show that Erd\H{o}s-R\'enyi random graphs $G(n,p)$ with constant density $p<1$ have correspondence chromatic number $O(n/\sqrt{\log n})$; this matches a prediction from linear Hadwiger's conjecture for correspondence coloring. The proof follows from a simple sufficient condition for correspondence colorability in terms of the numbers of independent sets.

Davis M. Welakuh, Spyros Tserkis, Scott E. Smart, Prineha Narang

The generation and control of entanglement in a quantum mechanical system is a critical element of nearly all quantum applications. Molecular systems are a promising candidate, with numerous degrees of freedom able to be targeted. However, knowledge of inter-system entanglement mechanisms in such systems is limited. In this work, we demonstrate the generatio

Luigi Alfonsi, Charles A. S. Young

We propose a global geometric framework which allows one to encode a natural non-perturbative generalisation of usual Batalin-Vilkovisky (BV-)theory. Namely, we construct a concrete model of derived differential geometry, whose geometric objects are formal derived smooth stacks, i.e. stacks on formal derived smooth manifolds, together with a notion of differ

Giorgio Orlando, Thomas Flöss, P. Daniel Meerburg, Joseph Silk

The 21-cm brightness temperature fluctuation from the Dark Ages ($z \simeq 30-100$) will allow us to probe the inflationary epoch on very small scales ($>0.1 \, \mbox{Mpc}^{-1}$), inaccessible to cosmic microwave background experiments. Combined with the possibility to collect information from different redshift slices, the 21-cm bispectrum has the potential

Saleh Momeni, Bagher BabaAli

Handwriting recognition is a challenging and critical problem in the fields of pattern recognition and machine learning, with applications spanning a wide range of domains. In this paper, we focus on the specific issue of recognizing offline Arabic handwritten text. Existing approaches typically utilize a combination of convolutional neural networks for imag

Andreas Filipp, Yiping Shu, Ruediger Pakmor, Sherry H. Suyu

Understanding the evolution of galaxies provides crucial insights into a broad range of aspects in astrophysics, including structure formation and growth, the nature of dark energy and dark matter, baryonic physics, and more. It is, however, infeasible to track the evolutionary processes of individual galaxies in real time given their long timescales. As a r

Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr

Because "out-of-the-box" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures -- so-called "jailbreaks" against LLMs -- these attacks have required significant hu

Zihan Zhang, Richard Liu, Kfir Aberman, Rana Hanocka

The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently explored in the motion domain. In this work, we propose to adapt the gradual diffusion concept (operating along

Lorenzo Pellegrini, Guido Borghi, Annalisa Franco, Davide Maltoni

Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based training procedure, make the development of robust models particularly challenging. We hypothesize that the recent Continual Learning (CL) paradigm may represent an effective solu

Alexander K. Hartmann, Alexandre Krajenbrink, Pierre Le Doussal

We consider a discrete-time random walk on a one-dimensional lattice with space and time-dependent random jump probabilities, known as the Beta random walk. We are interested in the probability that, for a given realization of the jump probabilities (a sample), a walker starting at the origin at time $t=0$ is at position beyond $\xi \sqrt{T/2}$ at time $T$.

Nick Alonso, Jeff Krichmar

An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed (non-i.i.d.) way. Further, synaptic plasticity in the brain depends only on information local to synapses. Deep networks, on the other hand, typical

Akanksha Saran, Jacob Alber, Cyril Zhang, Ann Paradiso

Gaze tracking devices have the potential to greatly expand interactivity, yet miscalibration remains a significant barrier to use. As devices miscalibrate, people tend to compensate by intentionally offsetting their gaze, which makes detecting miscalibration from eye signals difficult. To help address this problem, we propose a novel approach to seamless cal

Anne Tanguy

Amorphous materials are also distinguished from crystals by their thermal properties. The structural disorder seems to be responsible both for a significant increase in heat capacity compared to crystals of the same composition, but also for a significant decrease in thermal conductivity. The temperature dependence of thermal conductivity, unusual for common

Arash Arabi Ardehali, Hare Krishna

We consider the $\mathcal{N}=(2,2)$ AdS$_3$/CFT$_2$ dualities proposed by Eberhardt, where the bulk geometry is AdS$_3\times(S^3\times T^4)/\mathbb{Z}_k$, and the CFT is a deformation of the symmetric orbifold of the supersymmetric sigma model $T^4/\mathbb{Z}_k$ (with $k=2,\ 3,\ 4,\ 6$). The elliptic genera of the two sides vanish due to fermionic zero modes

Tereza Klimošová, Vibha Sahlot

The question of whether 3-Coloring can be solved in polynomial-time for the diameter two graphs is a well-known open problem in the area of algorithmic graph theory. We study the problem restricted to graph classes that avoid cycles of given lengths as induced subgraphs. Martin et. al. [CIAC 2021] showed that the problem is polynomial-time solvable for $C_5$

G. Dhananjay, M. Hemlata, Saravanan Matheshwaran, Sivasurender Chandran

Dense bacterial suspensions display collective motion exhibiting coherent flow structures reminiscent of turbulent flows. In contrast to inertial turbulence, understanding the microscopic dynamics of bacterial fluid elements undergoing collective motion is in its incipient stages. Here, we report experiments revealing correlations between the microscopic dyn

Renbo Tu, Colin White, Jean Kossaifi, Boris Bonev

Neural operators, such as Fourier Neural Operators (FNO), form a principled approach for learning solution operators for PDEs and other mappings between function spaces. However, many real-world problems require high-resolution training data, and the training time and limited GPU memory pose big barriers. One solution is to train neural operators in mixed pr

Ahmet Burak Yildirim, Hamza Pehlivan, Bahri Batuhan Bilecen, Aysegul Dundar

Recent inversion methods have shown that real images can be inverted into StyleGAN's latent space and numerous edits can be achieved on those images thanks to the semantically rich feature representations of well-trained GAN models. However, extensive research has also shown that image inversion is challenging due to the trade-off between high-fidelity recon

Tung Nguyen, Alex Scott, Paul Seymour

The Erd\H{o}s-Hajnal conjecture says that, for every graph $H$, there exists $c>0$ such that every $H$-free graph on $n$ vertices has a clique or stable set of size at least $n^c$. In this paper we are concerned with the case when $H$ is a path. The conjecture has been proved for paths with at most five vertices, but not for longer paths. We prove that the c

Henrique Rubira, Fabian Schmidt

The effective field theory of large-scale structure allows for a consistent perturbative bias expansion of the rest-frame galaxy density field. In this work, we present a systematic approach to renormalize galaxy bias and stochastic parameters using a finite cutoff scale $\Lambda$. We derive the differential equations of the Wilson-Polchinski renormalization

Peter Keevash, Noam Lifshitz

A recently fertile strand of research in Group Theory is developing non-abelian analogues of classical combinatorial results for arithmetic Cayley graphs, describing properties such as growth, expansion, mixing, diameter, etc. We consider these problems for the symmetric and alternating groups. The case of normal Cayley graphs (those generated by unions of c

Guiqin Zhao

Inspired by deep convolution segmentation algorithms, scene text detectors break the performance ceiling of datasets steadily. However, these methods often encounter threshold selection bottlenecks and have poor performance on text instances with extreme aspect ratios. In this paper, we propose to automatically learn the discriminate segmentation threshold,

Vincent A. Thomas, Robert J. Kares

2D and 3D numerical simulations with the adaptive mesh refinement Eulerian radiation-hydrocode RAGE are used to investigate hydrodynamic disruption of asymmetrically driven ICF implosions. A central aspect of this phenomenon is the connection between drive asymmetry and the generation of turbulence in the DT fuel. Long wavelength deviations from spherical sy

Yu Zhang, Zhimin Wang, Min Li, Caimei Liu

The primary objective of the JUNO experiment is to determine the ordering of neutrino masses using a 20-kton liquid-scintillator detector. The 20-inch photomultiplier tube (PMT) plays a crucial role in achieving excellent energy resolution of at least 3% at 1 MeV. Understanding the characteristics and features of the PMT is vital for comprehending the detect

Milo Z. Trujillo, Laurent Hébert-Dufresne, James Bagrow

Decentralized architecture offers a robust and flexible structure for online platforms, since centralized moderation and computation can be easy to disrupt with targeted attacks. However, a platform offering a decentralized architecture does not guarantee that users will use it in a decentralized way, and measuring the centralization of socio-technical netwo

Pascal Junior Tikeng Notsawo

Despite the recent growth of theoretical studies and empirical successes of neural networks, gradient backpropagation is still the most widely used algorithm for training such networks. On the one hand, we have deterministic or full gradient (FG) approaches that have a cost proportional to the amount of training data used but have a linear convergence rate,