July 2023 arXiv papers — page 61

Xuepeng Wang, Roderich Moessner, Debanjan Chowdhury

Bosonic collective modes are ubiquitous in metals, but over a wide range of energy and momenta suffer from Landau damping, decaying into the continuum of particle-hole excitations. Here we point out that interactions can suppress this decay, protecting a finite fraction of the total spectral weight associated with the collective mode, e.g. a plasmon. The und

Jakub Meixner, Justyna Rychły-Gruszecka, Mirosław Werwiński

The aim of this work is to investigate the structural and magnetic characteristics of Fe thin films with a triangular (hexagonal) lattice surfaces (fcc (111) and bcc (111)). The properties of these structures have been calculated using density functional theory (DFT) implemented in the full-potential local-orbital(FPLO) code. The results indicate a structura

Vaddadi Sai Rahul, Debajyoti Chakraborty

We leverage the fast physics simulator, MuJoCo to run tasks in a continuous control environment and reveal details like the observation space, action space, rewards, etc. for each task. We benchmark value-based methods for continuous control by comparing Q-learning and SARSA through a discretization approach, and using them as baselines, progressively moving

Adam Freese, Gerald A. Miller

We obtain two-dimensional relativistic densities and currents of energy and momentum in a proton at rest. These densities are obtained at surfaces of fixed light front time, which physically corresponds to using an alternative synchronization convention. Mathematically, this is done using tilted light front coordinates, which consist of light front time and

Federica Maria Surace, Olexei Motrunich

Integrable models are characterized by the existence of stable excitations that can propagate indefinitely without decaying. This includes multi-magnon bound states in the celebrated XXZ spin chain model and its integrable Floquet counterpart. A recent Google Quantum AI experiment [A. Morvan et al., Nature 612, 240 (2022)] realizing the Floquet model demonst

S. Demidov, B. Farkhtdinov, D. Levkov

We study multiparticle production in the unbroken $(3+1)$-dimensional $\lambda\phi^4$ theory using the semiclassical method of singular solutions. We show that the probabilities of these processes are exponentially suppressed in terms of a small coupling constant $\lambda \ll 1$ if the multiplicity of the final state is large: $n \gg 1$. At $ n \ll \lambda^{

Elena Hoemann, Stefan Heigl, Andreas Burkert

The onset of star formation is set by the collapse of filaments in the interstellar medium. From a theoretical point of view, an isolated cylindrical filament forms cores via the edge effect. Due to the self-gravity of a filament, the strong increase in acceleration at both ends leads to a pile-up of matter which collapses into cores. However, this effect is

Adithi Udupa, Samudra Sur, Sourav Nandy, Arnab Sen

We study a one-dimensional spin-$1/2$ model with three-spin interactions and a transverse magnetic field $h$. The model has a $Z_2 \times Z_2$ symmetry, and a duality between $h$ and $1/h$. The self-dual point at $h=1$ is a quantum critical point with a continuous phase transition. We compute the critical exponents $z$, $\beta$, $\gamma$ and $\nu$, and the c

Pascal Baßler, Markus Heinrich, Martin Kliesch

Multi-qubit entangling interactions arise naturally in several quantum computing platforms and promise advantages over traditional two-qubit gates. In particular, a fixed multi-qubit Ising-type interaction together with single-qubit X-gates can be used to synthesize global ZZ-gates (GZZ gates). In this work, we first show that the synthesis of such quantum g

Bridget Ratcliffe, Ivan Minchev, Gabriele Cescutti, Emanuele Spitoni

The relative enrichment of s-process to $\alpha$-elements ([s/$\alpha$]) has been linked with age, providing a potentially useful avenue in exploring the Milky Way's chemical evolution. However, the age--[s/$\alpha$] relationship is non-universal, with dependencies on metallicity and current location in the Galaxy. In this work, we examine these chemical clo

Christopher Munna

We derive new terms in the post-Newtonian (PN) expansion of the generalized redshift invariant $\langle u^t \rangle_\tau$ for a small body in eccentric, equatorial orbit about a massive Kerr black hole. The series is computed analytically using the Teukolsky formalism for first-order black hole perturbation theory (BHPT), along with the CCK method for metric

Tobias Golling, Gregor Kasieczka, Claudius Krause, Radha Mastandrea

Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM is assumed to be localized in at least one known variable. While there have been many method

Alexey Uvarov, Daniil Rabinovich, Olga Lakhmanskaya, Kirill Lakhmanskiy

Variational quantum algorithms have emerged as a cornerstone of contemporary quantum algorithms research. Practical implementations of these algorithms, despite offering certain levels of robustness against systematic errors, show a decline in performance due to the presence of stochastic errors and limited coherence time. In this work, we develop a recipe f

Aubin Courty, Kyriakos Destounis, Paolo Pani

Recent studies have shown that quasinormal modes suffer from spectral instabilities, a frailty of black holes that leads to disproportional migration of their spectra in the complex plane when black-hole effective potentials are modified by minuscule perturbations. Similar results have been found with the mathematical notion of the pseudospectrum which was r

Henning Bahl, Seth Koren, Lian-Tao Wang

The discovery of the top quark, the most massive elementary particle yet known, has given us a distinct window into investigating the physics of the Standard Model and Beyond. With a plethora of top quarks to be produced in the High Luminosity era of the LHC, the exploration of its rare decays holds great promise in revealing potential new physics phenomena.

Alim Ablat, Marco Guzzi, Keping Xie, Sayipjamal Dulat

The impact of recent LHC top-quark pair production single differential cross section measurements at 13 TeV collision energy on the structure of the proton is explored. In particular, the impact of these high-precision data on the gluon and other parton distribution functions (PDFs) of the proton at intermediate and large partonic momentum fraction $x$ is an

Matthew Kirk, Shohei Okawa, Keyun Wu

Upcoming neutrino telescopes promise a new window onto the interactions of neutrinos with matter at ultrahigh energies ($E_\nu = 10^7$-$10^{10}$ GeV), and the possibility to detect deviations from the Standard Model predictions. In this paper, we update previous predictions for the enhancement of the neutrino-nucleon cross-section for motivated leptoquark mo

Georgios K. Karananas, Mikhail Shaposhnikov, Sebastian Zell

We find the conditions under which scale-invariant Einstein-Cartan gravity with scalar matter fields leads to an approximate conformal invariance of the flat space particle theory up to energies of the order of the Planck mass. In the minimal setup, these models, in addition to the fields of the Standard Model and the graviton, contain only one extra particl

E. Pizzati, G. P. Rosotti, B. Tabone

Constraining the strength of gas turbulence in protoplanetary discs is an open problem that has relevant implications for the physics of gas accretion and planet formation. In this work, we gauge the amount of turbulence in 6 of the discs observed in the DSHARP programme by indirectly measuring the vertical distribution of their dust component. We employ the

Smadar Naoz, Zoltan Haiman

Extreme mass ratio inspirals (EMRIs) take place when a stellar-mass black hole (BH) merges with a supermassive black hole (SMBH). The gravitational wave emission from such an event is expected to be detectable by the future Laser Interferometer Space Antenna (LISA) and other mHz detectors. It was recently suggested that the EMRI rate in SMBH binary systems i

Florian Goertz, Álvaro Pastor-Gutiérrez, Jan M. Pawlowski

Composite Higgs extensions of the Standard Model provide an explanation for the large hierarchies between the Yukawa couplings. We study their realisation in the context of fundamental partial compositeness where the Standard Model fermions mix linearly with bound states of the new sector, consisting of a fermion and a scalar. The properties of this composit

Richard J. Parker, Christina Schoettler

Short-lived radioisotopes, in particular 26-Al and 60-Fe, are thought to contribute to the internal heating of the Earth, but are significantly more abundant in the Solar System compared to the Interstellar Medium. The presence of their decay products in the oldest Solar System objects argues for their inclusion in the Sun's protoplanetary disc almost immedi

D. M. Rowan, T. Jayasinghe, M. A. Tucker, C. Y. Lam

The identification and characterization of massive ($\gtrsim 0.8~M_\odot$) white dwarfs is challenging in part due to their low luminosity. Here we present two candidate single-lined spectroscopic binaries, Gaia DR3 4014708864481651840 and 5811237403155163520, with K-dwarf primaries and optically dark companions. Both have orbital periods of $P\sim 0.45$ day

Collin Lewin, Erin Kara, Edward M. Cackett, Dan Wilkins

UV and optical continuum reverberation mapping is powerful for probing the accretion disk and inner broad-line region. However, recent reverberation mapping campaigns in the X-ray, UV, and optical have found lags consistently longer than those expected from the standard disk reprocessing picture. The largest discrepancy to-date was recently reported in Mrk 3

Kausik Ghosh, Zechuan Zheng

This paper explores the numerical conformal bootstrap in general spacetime dimensions through the lens of a distinct category of analytic functionals, previously employed in two-dimensional studies. We extend the application of these functionals to a more comprehensive backdrop, demonstrating their adaptability and efficacy in general spacetime dimensions ab

Rahul Ramesh, Dylan Nelson

We introduce Project GIBLE (Gas Is Better resoLved around galaxiEs), a suite of cosmological zoom-in simulations where gas in the circumgalactic medium (CGM) is preferentially simulated at ultra-high numerical resolution. Our initial sample consists of eight galaxies, all selected as Milky Way-like galaxies at $z=0$ from the TNG50 simulation. Using the same

Silvia Ferrario Ravasio, Keith Hamilton, Alexander Karlberg, Gavin P. Salam

The accuracy of parton-shower simulations is often a limiting factor in the interpretation of data from high-energy colliders. We present the first formulation of parton showers with accuracy one order beyond state-of-the-art next-to-leading logarithms, for classes of observable that are dominantly sensitive to low-energy (soft) emissions, specifically non-g

Mateusz Krawczyk, Jarosław Pawłowski, Maciej M. Maśka, Katarzyna Roszak

We build a machine learning model to detect correlations in a three-qubit system using a neural network trained in an unsupervised manner on randomly generated states. The network is forced to recognize separable states, and correlated states are detected as anomalies. Quite surprisingly, we find that the proposed detector performs much better at distinguish

Mandar Karandikar, Cristiano Porciani, Oliver Hahn

The Effective Field Theory of Large-Scale Structure (EFTofLSS) attempts to amend some of the shortcomings of the traditional perturbative methods used in cosmology. It models the evolution of long-wavelength perturbations above a cutoff scale without the need for a detailed description of the short-wavelength ones. Short-scale physics is encoded in the coeff

Philipp Böer, Matthias Neubert, Michel Stillger

It has been known for many years that jet cross sections at hadron colliders exhibit double-logarithmic corrections starting at four-loop order, arising from two soft Glauber-gluon interactions between the two colliding partons. The resummation of these "super-leading logarithms" has been achieved only recently by means of a renormalization-group treatment i

Chenxin An, Shansan Gong, Ming Zhong, Xingjian Zhao

Recently, there has been growing interest in extending the context length of large language models (LLMs), aiming to effectively process long inputs of one turn or conversations with more extensive histories. While proprietary models such as GPT-4 and Claude can largely preserve the reasoning ability in an extended context, open-source models are still progr

Michele Del Zotto, Marco Fazzi, Suvendu Giri

We begin a study of the Higgs branch of six-dimensional $(1,0)$ little string theories governing the worldvolumes of heterotic ALE instantons. We give a description of this space by constructing the corresponding magnetic quiver. The latter is a three-dimensional $\mathcal{N}=4$ quiver gauge theory that flows in the infrared to a fixed point whose quantum co

Yanshu Zhang, Shichong Peng, Alireza Moazeni, Ke Li

Learning accurate and parsimonious point cloud representations of scene surfaces from scratch remains a challenge in 3D representation learning. Existing point-based methods often suffer from the vanishing gradient problem or require a large number of points to accurately model scene geometry and texture. To address these limitations, we propose Proximity At

Yedid Hoshen

In this perspective paper, we argue that the dominant paradigm in anomaly detection cannot scale indefinitely and will eventually hit fundamental limits. This is due to the a no free lunch principle for anomaly detection. These limitations can be overcome when there are strong tasks priors, as is the case for many industrial tasks. When such priors do not ex

Uma Girish, Ran Raz, Wei Zhan

In this note, we observe that quantum logspace computations are verifiable by classical logspace algorithms, with unconditional security. More precisely, every language in BQL has an (information-theoretically secure) streaming proof with a quantum logspace prover and a classical logspace verifier. The prover provides a polynomial-length proof that is stream

K. McGee, S. Kim, K. Elliott, A. Ganshyn

Nitrogen-doping and furnace-baking are advanced high-Q0 recipes developed for 1.3 GHz TESLA-type cavities. These treatments will significantly benefit the high-Q0 linear accelerator community if they can be successfully adapted to different cavity styles and frequencies. Strong frequency- and geometry- dependence of these recipes makes the technology transfe

Nisarg A. Shah, Shameema Sikder, S. Swaroop Vedula, Vishal M. Patel

Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for surgical step recognition either rely on separate, multi-stage modeling of spatial and temporal information or operate on short-range temporal resolution when learned jointly. Howev

Shreya Anand, Peter T. H. Pang, Mattia Bulla, Michael W. Coughlin

GW170817 and its associated electromagnetic counterpart AT2017gfo continue to be a treasure trove as observations and modeling continue. Recent precision astrometry of AT2017gfo with the Hubble Space Telescope combined with previous constraints from Very Long Baseline Interferometry (VLBI) constraints narrowed down the inclination angle to 19-25 deg (90\% co

Timo I. Denk, Yu Takagi, Takuya Matsuyama, Andrea Agostinelli

The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. In this paper, we introduce a method for reconstructing music from brain activity, captured using functional magnetic resonance imaging (fMRI). Our approach uses either music retrieval or the MusicLM music generatio

Ming Li, Jie Wu, Xionghui Wang, Chen Chen

The paradigm of large-scale pre-training followed by downstream fine-tuning has been widely employed in various object detection algorithms. In this paper, we reveal discrepancies in data, model, and task between the pre-training and fine-tuning procedure in existing practices, which implicitly limit the detector's performance, generalization ability, and co

Elnaz Kabir, Vivek Srikrishnan, M. Vivienne Liu, Scott Steinschneider

The variability and intermittency of renewable energy sources pose several challenges for power systems operations, including energy curtailment and price volatility. In power systems with considerable renewable sources, co-variability in renewable energy supply and electricity load can intensify these outcomes. In this study, we examine the impacts of renew

Yanjun Wang, Qingping Sun, Wenjia Wang, Jun Ling

Human mesh reconstruction from a single image is challenging in the presence of occlusion, which can be caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that l

Oscar Michel, Anand Bhattad, Eli VanderBilt, Ranjay Krishna

Existing image editing tools, while powerful, typically disregard the underlying 3D geometry from which the image is projected. As a result, edits made using these tools may become detached from the geometry and lighting conditions that are at the foundation of the image formation process. In this work, we formulate the newt ask of language-guided 3D-aware e

Andrew M. Graff, Todd E. Humphreys

This paper proposes a method of passively estimating the parameters of frequency-modulated-continuous-wave (FMCW) radar signals with a wide range of structural parameter values and analyzes how a malicious actor could employ such estimates to track and spoof a target radar. When radars are implemented to support automated driver assistance systems, an intell

Artur Avila

We show that a one-frequency analytic SL(2,R) cocycle with Diophantine rotation vector is analytically linearizable if and only if the Lyapunov exponent is zero through a complex neighborhood of the circle. More generally, we show (without any arithmetic assumptions) that regularity implies almost reducibility, i.e., the range of validity of the perturbative

Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis

On-screen game footage contains rich contextual information that players process when playing and experiencing a game. Learning pixel representations of games can benefit artificial intelligence across several downstream tasks including game-playing agents, procedural content generation, and player modelling. The generalizability of these methods, however, r

Muriel Figueredo Franco, Fabian Künzler, Jan von der Assen, Chao Feng

Digitization increases business opportunities and the risk of companies being victims of devastating cyberattacks. Therefore, managing risk exposure and cybersecurity strategies is essential for digitized companies that want to survive in competitive markets. However, understanding company-specific risks and quantifying their associated costs is not trivial.

A. Flórez, J. Jones-Pérez, A. Gurrola, C. Rodriguez

Leptoquarks (LQs) are hypothetical particles that appear in various extensions of the Standard Model (SM) that can explain observed differences between SM theory predictions and experimental results. The production of these particles has been widely studied at various experiments, most recently at the Large Hadron Collider (LHC), and stringent bounds have be

Caitlin Sim, Kesheng Wu, Alex Sim, Inder Monga

Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area

Jacob R. Taylor, Jay D. Sau, Sankar Das Sarma

We develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance data in order to obtain the disorder details in the system. The inversion carried out through machine learning using different disorder parametrizations turns out to be unique in

The COSINUS Collaboration, G. Angloher, M. R. Bharadwaj, I. Dafinei

Sodium iodide (NaI) based cryogenic scintillating calorimeters using quantum sensors for signal read out have shown promising first results towards a model-independent test of the annually modulating signal detected by the DAMA/LIBRA dark matter experiment. The COSINUS collaboration has previously reported on the first above-ground measurements using a dual

Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Vincent Lepetit

We propose a simple three-stage approach to segment unseen objects in RGB images using their CAD models. Leveraging recent powerful foundation models, DINOv2 and Segment Anything, we create descriptors and generate proposals, including binary masks for a given input RGB image. By matching proposals with reference descriptors created from CAD models, we achie

COSINUS Collaboration, G. Angloher, M. R. Bharadwaj, I. Dafinei

The COSINUS direct dark matter experiment situated at Laboratori Nazionali del Gran Sasso in Italy is set to investigate the nature of the annually modulating signal detected by the DAMA/LIBRA experiment. COSINUS has already demonstrated that sodium iodide crystals can be operated at mK temperature as cryogenic scintillating calorimeters using transition edg

Anjeza Bekolli, Luis A. Guardiola, Ana Meca

In this paper, we focus on decentralized agricultural supply chains consisting of multiple non-competing distributors satisfying the demand of their respective markets. These distributors source a single product from a farmer through an agricultural cooperative, operating in a single period. The agents have the ability to coordinate their actions to maximize

Izhar Oppenheim

The main result of this paper is that all affine isometric actions of higher rank Steinberg groups over commutative rings on uniformly convex Banach spaces have a fixed point. We consider Steinberg groups over classical root systems and our analysis covers almost all such Steinberg groups excluding a single rank 2 case. The proof of our main result stems fro

Sridhar Chellappa, Lihong Feng, Peter Benner

Accurate error estimation is crucial in model order reduction, both to obtain small reduced-order models and to certify their accuracy when deployed in downstream applications such as digital twins. In existing a posteriori error estimation approaches, knowledge about the time integration scheme is mandatory, e.g., the residual-based error estimators propose

Graham R. Brown, Joshua Gowdy, Bill Spence

We explore the double copy for self-dual gauge and gravitational fields on self-dual background spacetimes. We consider backgrounds associated to solutions of the second Plebanski equation and describe results with different gauge-fixing conditions. Finally we discuss the kinematic and $w$-algebras and the double copy, identifying modified Poisson structures

Vassilis Digalakis

Differential privacy is the state-of-the-art definition for privacy, guaranteeing that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this thesis, we develop differentially private algorithms to analyze distributed and streaming data. In the distributed model, we consider the part

David Mitrouskas, Peter Pickl

We consider $N$ trapped bosons in the mean-field limit with coupling constant $\lambda_N=1 / (N-1)$. The ground state of such systems exhibits Bose--Einstein condensation. We prove that the probability of finding $\ell$ particles outside the condensate wave function decays exponentially in $\ell $.

Ahmad Reza Haj Saeedi Sadegh, Shiqi Liu, Yiannis Loizides, Jesus Sanchez

We study equivariant families of Dirac operators on the source fibers of a Lie groupoid with a closed space of units and equipped with an action of an auxiliary compact Lie group. We use the Getzler rescaling method to derive a fixed-point formula for the pairing of a trace with the K-theory class of such a family. For the pair groupoid of a closed manifold,

Daniel S. Katz, Ben Clifford, Yadu Babuji, Kevin Hunter Kesling

This position paper describes the Parsl open source research software project and its various phases over seven years. It defines four types of research software engineers (RSEs) who have been important to the project in those phases; we believe this is also applicable to other research software projects.

Erich Peter Klement, Damjana Kokol Bukovšek, Blaž Mojškerc, Matjaž Omladič

We discuss avoidance of sure loss and coherence results for semicopulas and standardized functions, i.e., for grounded, 1-increasing functions with value $1$ at $(1,1,\ldots, 1)$. We characterize the existence of a $k$-increasing $n$-variate function $C$ fulfilling $A\leq C\leq B$ for standardized $n$-variate functions $A,B$ and discuss the method for constr

Alexander Liu

In this endeavor, we developed a comprehensive system that processes integrated visual features derived from video frames captured by a regular camera, along with depth details obtained from a point cloud scanner. This system is designed to anticipate driving actions, encompassing both vehicle speed and steering angle. To ensure its reliability, we conducted

Lê Thành Dũng Nguyên, Camille Noûs, Cécilia Pradic

We consider a notion of planarity for two-way finite automata and transducers, inspired by Temperley-Lieb monoids of planar diagrams. We show that this restriction captures star-free languages and first-order transductions.

Torres Cruz, Fred Garcia Jimenez, Angel Raul Quispe Bravo, Eder Ander

This article presents DataXploreFines, an innovative Shiny application that revolutionizes data exploration, analysis, and visualization. The application offers functionalities for data loading, management, summarization, basic graphs, advanced analysis, and contact. Users can upload their datasets in popular formats like CSV or Excel, explore the data struc

Victor B. Valera, Mauricio Bustamante, Christian Glaser

The discovery of ultra-high-energy (UHE) neutrinos has the potential to offer unique insight into fundamental questions. To capitalize on the upcoming opportunity provided by new UHE neutrino telescopes, we provide state-of-the-art forecasts of the discovery of a diffuse flux of UHE neutrinos over the next 10-20 years, focusing on neutrino radio-detection in

Justin Schirmann, Selma Franca, Felix Flicker, Adolfo G. Grushin

The discovery of the Hat, an aperiodic monotile, has revealed novel mathematical aspects of aperiodic tilings. However, the physics of particles propagating in such a setting remains unexplored. In this work we study spectral and transport properties of a tight-binding model defined on the Hat. We find that (i) the spectral function displays striking similar

Sofia Gonzalez-Garcia, Shengqi Sang, Timothy H. Hsieh, Sergio Boixo

Projected entangled pair states (PEPS) offer memory-efficient representations of some quantum many-body states that obey an entanglement area law, and are the basis for classical simulations of ground states in two-dimensional (2d) condensed matter systems. However, rigorous results show that exactly computing observables from a 2d PEPS state is generically

Fahim Faisal Niloy, Kishor Kumar Bhaumik, Simon S. Woo

Existing high-resolution satellite image forgery localization methods rely on patch-based or downsampling-based training. Both of these training methods have major drawbacks, such as inaccurate boundaries between pristine and forged regions, the generation of unwanted artifacts, etc. To tackle the aforementioned challenges, inspired by the high-resolution im

Nicholas Carlini

Large language models (LLMs) are now highly capable at a diverse range of tasks. This paper studies whether or not GPT-4, one such LLM, is capable of assisting researchers in the field of adversarial machine learning. As a case study, we evaluate the robustness of AI-Guardian, a recent defense to adversarial examples published at IEEE S&P 2023, a top compute

Álvaro Duenas-Vidal, Jorge Segovia

In Classical Dynamics, Eisenhart lift connects the dynamics of null geodesics in a Brinkmann spacetime with a continuous family of Hamiltonian systems by means of a suitable projection. In this work we explore the possibility of building a model for quantum dynamics of massless particles propagating inside a Brinkmann spacetime from the Einsenhart lift. As a

Victor B. Valera, Mauricio Bustamante, Christian Glaser

The investigation of neutrino interactions with matter serves as a valuable tool for understanding the fundamental structure of nucleons and potentially uncovering novel physics phenomena. To date, the neutrino-nucleon cross section has been examined across a range of energies spanning from a few hundred MeV to PeV. However, the pursuit of ultra-high-energy

Marcel Torne, Max Balsells, Zihan Wang, Samedh Desai

Exploration and reward specification are fundamental and intertwined challenges for reinforcement learning. Solving sequential decision-making tasks requiring expansive exploration requires either careful design of reward functions or the use of novelty-seeking exploration bonuses. Human supervisors can provide effective guidance in the loop to direct the ex

Jamie A. P. Law-Smith, Georges Obied, Anirudh Prabhu, Cumrun Vafa

In the dark dimension scenario, which predicts an extra dimension of micron scale, dark gravitons (KK modes) are a natural dark matter candidate. In this paper, we study observable features of this model. In particular, their decay to standard matter fields can distort the CMB and impact other astrophysical signals. Using this we place bounds on the paramete

Zhao-Sai Jia, Zhen-Hua Zhang, Gang Li, Feng-Kun Guo

With the assumptions that the $T_{cc}^+$ discovered at LHCb is a $D^{*}D$ hadronic molecule, using a nonrelativistic effective field theory we calculate the radiative partial widths of $T_{cc}^* \to D^*D\gamma$ with $T_{cc}^*$ being a $D^{*}D^{*}$ shallow bound state and the heavy-quark-spin partner of $T_{cc}^+$. The $I=0$ $D^*D$ rescattering effect with th

Vega-Hernandez, Mayrim, Galan-Garcia, Lidice

Objective: We seek stable Electrophysiological Source Imaging (ESI) biomarkers associated with Gait Speed (GS) as a measure of functional decline. Towards this end we determine the predictive value of ESI activation and connectivity patterns of resting-state EEG Theta rhythm on physical performance decline measured by a slowing GS in aging individuals. Metho

David Abel, André Barreto, Benjamin Van Roy, Doina Precup

In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward. However, this perspective is based on a restricted view of learning as finding a solution, rather than treating learning as endless adaptation. In contrast, continual reinforcement learning refers to the setting in wh

Aritra Bhowmick, Sachchidanand Prasad

In this article, we investigate the cut locus of closed (not necessarily compact) submanifolds in a forward complete Finsler manifold. We explore the deformation and characterization of the cut locus, extending the results of Basu and the second author (\emph{Algebraic and Geometric Topology}, 2023). Given a submanifold $N$, we consider an $N$-geodesic loop

David Abel, André Barreto, Hado van Hasselt, Benjamin Van Roy

When has an agent converged? Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence: An agent converges when its behavior or performance in each environment state stops changing. However, as we shift the focus of our learning problem from the environment's state to the agent's state, the concept of an a

Lorenzo Valbusa Dall'Armi, Alina Mierna, Sabino Matarrese, Angelo Ricciardone

The non-thermal nature of the cosmological gravitational wave background (CGWB) makes it difficult to define the initial condition for the graviton distribution, which determines the initial contribution to the CGWB anisotropies. Specifically, unlike Cosmic Microwave Background (CMB) photons, primordial gravitational waves (GWs) are not necessarily subject t

Konstantinos Ameranis, Antares Chen, Adela DePavia, Lorenzo Orecchia

The development of simple and fast hypergraph spectral methods has been hindered by the lack of numerical algorithms for simulating heat diffusions and computing fundamental objects, such as Personalized PageRank vectors, over hypergraphs. In this paper, we overcome this challenge by designing two novel algorithmic primitives. The first is a simple, easy-to-

Sébastien Philippe, Susan L. Alzner, Gilbert P. Compo, Mason Grimshaw

Between 1945 and 1962, the United States conducted 101 atmospheric nuclear weapon tests in New Mexico and Nevada, dispersing radioactive fallout across the contiguous United States. We reconstruct the first 10 days of fission-product deposition from the 94 non-zero-yield tests using U.S. government test data, benchmarked particle-size distributions, and the

Fabio Bacchini, Lorenzo Di Biagio, Giampiero M. Gallo, Vincenzo Spinelli

The main component of the NextGeneration EU (NGEU) program is the Recovery and Resilience Facility (RRF), spanning an implementation period between 2021 and 2026. The RRF also includes a monitoring system: every six months, each country is required to send an update on the progress of the plan against 14 common indicators, measured on specific quantitative s

Jason M. Pittman, Shaho Alaee

Cyber threats, such as advanced persistent threats (APTs), ransomware, and zero-day exploits, are rapidly evolving and demand improved security measures. Honeypots and honeynets, as deceptive systems, offer valuable insights into attacker behavior, helping researchers and practitioners develop innovative defense strategies and enhance detection mechanisms. H

Steve Phelps, Rebecca Ranson

AI Alignment is often presented as an interaction between a single designer and an artificial agent in which the designer attempts to ensure the agent's behavior is consistent with its purpose, and risks arise solely because of conflicts caused by inadvertent misalignment between the utility function intended by the designer and the resulting internal utilit

Anand Pillay, Atticus Stonestrom

We give an example of an NIP theory $T$ in which there is a formula that does not fork over $\varnothing$ but has measure $0$ under any global $\varnothing$-invariant Keisler measure, and we show that this cannot occur if $T$ is also first-order amenable.

Neil Epstein, Jay Shapiro

A ring is *unit-additive* if a sum of units is always either a unit or nilpotent. For example, $k[X]$ and $k[X]/(X^2)$ are unit-additive, but $\mathbb Z$ is not. We prove a wide-ranging theorem about unit-additivity in semigroup rings, showing among other things that an affine semigroup ring $A[M]$ is unit-additive if and only if $A$ is unit-additive and $M$

Mingqiao Ye, Lei Ke, Siyuan Li, Yu-Wing Tai

Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not competitive in diverse domains. Moreover, these methods still struggle to very accurately estimate the object bounding boxes in complex environments. We introduce Cascade-DETR for high

Melanie Graf, Marco van den Beld-Serrano

Given an extendible spacetime one may ask how much, if any, uniqueness can in general be expected of the extension. Locally, this question was considered and comprehensively answered in a recent paper of Sbierski, where he obtains local uniqueness results for anchored spacetime extensions of similar character to earlier work for conformal boundaries by Chru\

Roberto Corral López, Samir Suweis, Sandro Azaele, Miguel A. Muñoz

Laboratory experiments with bacterial colonies, under well-controlled conditions often lead to evolutionary diversification, where at least two ecotypes emerge from an initially monomorphic population. Empirical evidence suggests that such ''evolutionary branching'' occurs stochastically, even under fixed and stable conditions. This stochastic nature is char

Neel Guha, Mayee F. Chen, Kush Bhatia, Azalia Mirhoseini

Recent work has shown that language models' (LMs) prompt-based learning capabilities make them well suited for automating data labeling in domains where manual annotation is expensive. The challenge is that while writing an initial prompt is cheap, improving a prompt is costly -- practitioners often require significant labeled data in order to evaluate the i

Yijun Dong, Kevin Miller, Qi Lei, Rachel Ward

Despite the empirical success and practical significance of (relational) knowledge distillation that matches (the relations of) features between teacher and student models, the corresponding theoretical interpretations remain limited for various knowledge distillation paradigms. In this work, we take an initial step toward a theoretical understanding of rela

Jana Reker

We compute the deterministic approximation for mixed fluctuation moments of products of deterministic matrices and general Sobolev functions of Wigner matrices. Restricting to polynomials, our formulas reproduce recent results of [Male, Mingo, Pech\'e, Speicher 2022], showing that the underlying combinatorics of non-crossing partitions and annular non-crossi

Alex Townsend-Teague, Julio Magdalena de la Fuente, Markus Kesselring

Floquet codes are a recently discovered type of quantum error correction code. They can be thought of as generalising stabilizer codes and subsystem codes, by allowing the logical Pauli operators of the code to vary dynamically over time. In this work, we use the ZX-calculus to create new Floquet codes that are in a definable sense equivalent to known stabil

Jana Reker

Consider the random variable $\mathrm{Tr}( f_1(W)A_1\dots f_k(W)A_k)$ where $W$ is an $N\times N$ Hermitian Wigner matrix, $k\in\mathbb{N}$, and choose (possibly $N$-dependent) regular functions $f_1,\dots, f_k$ as well as bounded deterministic matrices $A_1,\dots,A_k$. We give a functional central limit theorem showing that the fluctuations around the expec

Qian Wan, Siying Hu, Yu Zhang, Piaohong Wang

Prewriting is the process of discovering and developing ideas before a first draft, which requires divergent thinking and often implies unstructured strategies such as diagramming, outlining, free-writing, etc. Although large language models (LLMs) have been demonstrated to be useful for a variety of tasks including creative writing, little is known about ho

Vivek Wadhia, Nicholas Chancellor, Viv Kendon

The rapid development of quantum computing has led to increasing interest in quantum algorithms for a variety of different applications. Quantum walks have also experienced a surge in interest due to their potential use in quantum algorithms. Using the qiskit software package, we test how accurately the current generation of quantum computers provided by IBM

Farshid Soltani

I derive a smooth and global Kruskal-Szekeres coordinate chart for the maximal extension of the non-extremal Reissner-Nordstr\"om geometry that provides a generalization to the standard inner and outer Kruskal-Szekeres coordinates. The Kruskal-Szekeres diagram associated to this coordinate chart, whose existence is an interesting fact in and on itself, provi

Qian Wan, Zhicong Lu

VTubers, or Virtual YouTubers, are live streamers who create streaming content using animated 2D or 3D virtual avatars. In recent years, there has been a significant increase in the number of VTuber creators and viewers across the globe. This practise has drawn research attention into topics such as viewers' engagement behaviors and perceptions, however, as

Jera Hensel, Jürgen Giesl

There are many techniques and tools for termination of C programs, but up to now they were not very powerful for termination proofs of programs whose termination depends on recursive data structures like lists. We present the first approach that extends powerful techniques for termination analysis of C programs (with memory allocation and explicit pointer ar