December 2023 arXiv papers — page 52

S. J. Brennan, S. Schulze, R. Lunnan, J. Sollerman

SN 2021adxl is a slowly evolving, luminous, Type IIn supernova with asymmetric emission line profiles, similar to the well-studied SN 2010jl. We present extensive optical, near-ultraviolet, and near-infrared photometry and spectroscopy covering ~1.5 years post discovery. SN 2021adxl occurred in an unusual environment, atop a vigorously star-forming region th

Matthew Lamsey, You Liang Tan, Meredith D. Wells, Madeline Beatty

Physical therapy (PT) is a key component of many rehabilitation regimens, such as treatments for Parkinson's disease (PD). However, there are shortages of physical therapists and adherence to self-guided PT is low. Robots have the potential to support physical therapists and increase adherence to self-guided PT, but prior robotic systems have been large and

Gia Dvali, Manuel Ettengruber, Anja Stuhlfauth

Neutrons and neutrinos are natural probes for new physics. Since they carry no conserved gauge quantum numbers, both can easily mix with the fermions from hidden sectors. A particularly interesting effect is the oscillation of a neutron or a neutrino into a fermion propagating in large extra dimensions. In fact, such a mixing has been identified as the possi

Pierluigi Zama Ramirez, Luca De Luigi, Daniele Sirocchi, Adriano Cardace

In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes. When applied to 3D data, NFs offer a solution to the fragmentation and limitations associated with prevalent discrete representations. However, given that NFs are essentially neural networks, it remains

Zhenisbek Tagay, Ralph Romero, N. P. Armitage

We present a new method for high precision measurements of polarization rotation in the frequency range from 0.2 to 2.2 THz using a fiber coupled time-domain THz spectrometer. A free standing wire-grid polarizer splits THz light into orthogonal components which are then measured by two separate detectors simultaneously. We theoretically model the uncertainti

Melissa van Beekveld, Mrinal Dasgupta, Basem Kamal El-Menoufi, Silvia Ferrario Ravasio

In this article, we document version 0.1 of the PanScales code for parton shower simulations. With the help of a few examples, we discuss basic usage of the code, including tests of logarithmic accuracy of parton showers. We expose some of the numerical techniques underlying the logarithmic tests and include a description of how users can implement their own

Michael D. Brown, Adam Meily, Brian Fairservice, Akshay Sood

Software debloating tools seek to improve program security and performance by removing unnecessary code, called bloat. While many techniques have been proposed, several barriers to their adoption have emerged. Namely, debloating tools are highly specialized, making it difficult for adopters to find the right type of tool for their needs. This is further hind

Michael Unger, Glennys R. Farrar

The Telescope Array Collaboration recently reported the detection of a cosmic-ray particle, "Amaterasu", with an extremely high energy of $2.4\times10^{20}$ eV. Here we investigate its probable charge and the locus of its production. Interpreted as a primary iron nucleus or slightly stripped fragment, the event fits well within the existing paradigm for UHEC

Paul Bourgade, Patrick Lopatto, Ofer Zeitouni

We determine to leading order the maximum of the characteristic polynomial for Wigner matrices and $\beta$-ensembles. In the special case of Gaussian-divisible Wigner matrices, our method provides universality of the maximum up to tightness. These are the first universal results on the Fyodorov--Hiary--Keating conjectures for these models, and in particular

Ping-Hsuan Tsai, Paul Fischer, Traian Iliescu

Reg-ROMs are stabilization strategies that leverage spatial filtering to alleviate the spurious numerical oscillations generally displayed by the classical G-ROM in under-resolved numerical simulations of turbulent flows. In this paper, we propose a new Reg-ROM, the time-relaxation ROM (TR-ROM), which filters the marginally resolved scales. We compare the ne

Junwu Zhang, Zhenyu Tang, Yatian Pang, Xinhua Cheng

Recent one image to 3D generation methods commonly adopt Score Distillation Sampling (SDS). Despite the impressive results, there are multiple deficiencies including multi-view inconsistency, over-saturated and over-smoothed textures, as well as the slow generation speed. To address these deficiencies, we present Repaint123 to alleviate multi-view bias as we

Delia Kesner, Shane Ó Conchúir

We study Milner's lambda-calculus with partial substitutions. Particularly, we show confluence on terms and metaterms, preservation of \b{eta}-strong normalisation and characterisation of strongly normalisable terms via an intersection typing discipline. The results on terms transfer to Milner's bigraphical model of the calculus. We relate Milner's calculus

Ben Eckardt, Yixuan Li

We study, from the perspective of supersymmetry and space-time Killing spinors, the local brane densities involved in 1/4-BPS intersecting brane systems. In particular, we classify the possible local brane structures that have maximal (16) supersymmetries in 1/4-BPS intersecting brane backgrounds. Applied to BPS black holes, this classification reveals the a

Alexander Impertro, Simon Karch, Julian F. Wienand, SeungJung Huh

Quantum gas microscopes have revolutionized quantum simulations with ultracold atoms, allowing to measure local observables and snapshots of quantum states. However, measurements so far were mostly carried out in the occupation basis. Here, we demonstrate how all kinetic operators, such as kinetic energy or current operators, can be measured and manipulated

George Doran, Ricardo Monteiro, Sam Wikeley

We investigate the integrability anomalies arising in the self-dual sectors of gravity and Yang-Mills theory, focusing on their connection to both the chiral anomaly and the trace anomaly. The anomalies in the self-dual sectors generate the one-loop all-plus amplitudes of gravitons and gluons, and have recently been studied via twistor constructions. On the

Alexander Dobrick, Julian Hölz, Markus Kunze

It is a widely acknowledged fact that the product of two positive strong Feller operators on a Polish space $E$ enjoys the ultra Feller property. We present a functional analytic proof of this fact that allows us to drop the assumption that the operators are positive and also extends the applicability of this result to more general state spaces. As it turns

Zixiang Wei, Yiting Wang, Lichao Sun, Athanasios V. Vasilakos

Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a non-trivial task. Some endeavors have been recently made to enhance low-light images using convolutional neural networks (CNNs). However, they have low efficiency in learning the structural information and diverse illuminatio

Pablo M. Rodriguez Bertorello, Jean Rodmond Junior Laguerre

Data is stored in both structured and unstructured form. Querying both, to power natural language conversations, is a challenge. This paper introduces dIR, Discrete Information Retrieval, providing a unified interface to query both free text and structured knowledge. Specifically, a Large Language Model (LLM) transforms text into expressive representation. A

Bharath Hebbe Madhusudhana, Karatzyna Krzyzanowska, Malcolm Boshier

Recent progress in quantum technologies with ultracold atoms has been propelled by spatially fine-tuned control of lasers and diffraction-limited imaging. The state-of-the-art precision of optical alignment to achieve this fine-tuning is reaching the limits of manual control. Here, we show how to automate this process. One of the elementary techniques of man

Jeff Achter, Sebastian Casalaina-Martin

After Jacobians of curves, Prym varieties are perhaps the next most studied abelian varieties. They turn out to be quite useful in a number of contexts. For technical reasons, there does not appear to be any systematic treatment of Prym varieties in characteristic 2, and due to our recent interest in this topic, the purpose of this paper is to fill in that g

Jeff Achter, Sebastian Casalaina-Martin, Jonathan Wise

We provide some conditions for the image of a morphism of abelian schemes to again be an abelian scheme. For context, in characteristic 0, the image is always an abelian scheme; in mixed and positive characteristic the image can fail to be an abelian scheme, and so it is in this setting that the conditions we provide are pertinent.

Danilo Amigo, Felipe Lepe, Gonzalo Rivera

We introduce non conforming virtual elements to approximate the eigenvalues and eigenfunctions of the two dimensional acoustic vibration problem. We focus our attention on the pressure formulation of the acoustic vibration problem in order to discretize it with a suitable non conforming virtual space for $\mathrm{H}^1$. With the aid of the theory of non-comp

Hao-Chen Wang, Meilin Yu, Heng Xiao

In this study, we present a universal nonlinear numerical scheme design method enabled by multi-agent reinforcement learning (MARL). Different from contemporary supervised-learning-based and reinforcement-learning-based approaches, no reference data and special numerical treatments are used in the MARL-based method developed here; instead, a first-principle-

Gunther Gust, Alexander Schlüter, Stefan Feuerriegel, Ignacio Úbeda

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence imp

Eugenio Clerico, Benjamin Guedj

We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value. This keeps the network in a lazy training regime, where the dynamics can be linearised around the initialisation. The standard neural tangent kernel (NTK) governs the evolution during the t

Diego H. Correa, Maximiliano G. Ferro, Victor I. Giraldo-Rivera

The open string dual to a 1/6 BPS Wilson line in the ${\cal N} = 6$ super Chern-Simons-matter theory is coupled to a flat Kalb-Ramond field. We show that the resulting boundary term imposes mixed boundary conditions on the fields that describe the fluctuations on the world-sheet. These boundary conditions fix a combination of the derivatives of the fluctuati

Pierre C. Bellec, Takuya Koriyama

We consider unregularized robust M-estimators for linear models under Gaussian design and heavy-tailed noise, in the proportional asymptotics regime where the sample size n and the number of features p are both increasing such that $p/n \to \gamma\in (0,1)$. An estimator of the out-of-sample error of a robust M-estimator is analyzed and proved to be consiste

Edward Frenkel, David Hernandez

Generalized Baxter's TQ-relations and the QQ-system are systems of algebraic relations in the category O of representations of the Borel subalgebra of the quantum affine algebra U_q(g^), which we established in our earlier works arXiv:1308.3444 and arXiv:1606.05301. In the present paper, we conjecture a family of analogous relations labeled by elements of th

Antoni Torrens

We present a family of local involutive integral bounded residuated lattice-ordered commutative monoids (involutive residuated lattices, for short) having Boolean term, radical term (see \cite{CT12} and \cite{T23}), and satisfying GAP (Generalized Appel property) (see \cite{T23}).The construction of this family is based in the examples given in \cite[Subsect

Pierre C. Bellec, Takuya Koriyama

Major progress has been made in the previous decade to characterize the asymptotic behavior of regularized M-estimators in high-dimensional regression problems in the proportional asymptotic regime where the sample size $n$ and the number of features $p$ are increasing simultaneously such that $n/p\to \delta \in(0,\infty)$, using powerful tools such as Appro

Marco Cirelli, Caterina Doglioni, Federica Petricca

We introduce the initiative for Dark Matter in Europe and beyond (iDMEu), a collective effort by a group of particle and astroparticle physicists to set up an online resource meta-repository, a common discussion platform and a series of meetings on everything concerning Dark Matter. This document serves as a status report as well as a citable item concerning

Rajesh Shrestha, Bowen Xie

In recent years, diffusion models have gained popularity for their ability to generate higher-quality images in comparison to GAN models. However, like any other large generative models, these models require a huge amount of data, computational resources, and meticulous tuning for successful training. This poses a significant challenge, rendering it infeasib

Saurabh Saxena, Junhwa Hur, Charles Herrmann, Deqing Sun

While methods for monocular depth estimation have made significant strides on standard benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the joint modeling of indoor and outdoor scenes, which often exhibit significantly different distributions of RGB and depth, and the depth-scale ambiguity due to unknown camera intrinsics. R

Max Leopold Frisch Sbarra, Mattia Mecchia

We consider 3-manifolds admitting the action of an involution such that its space of orbits is homeomorphic to $S^3.$ Such involutions are called \textit{hyperelliptic} as the manifolds admitting such an action. We prove that the sectional 2-rank of a finite group acting on a 3-manifold and containing a hyperelliptic involution with fixed-point set with two

Tomisin Awosika, Raj Mani Shukla, Bernardi Pranggono

Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions, but some factors make the development of effective and efficient fraud detection systems a challenge. On

Sreetama Das, Stefano Martina, Filippo Caruso

Geometric deep learning refers to the scenario in which the symmetries of a dataset are used to constrain the parameter space of a neural network and thus, improve their trainability and generalization. Recently this idea has been incorporated into the field of quantum machine learning, which has given rise to equivariant quantum neural networks (EQNNs). In

James Fardeen, Peter McGill, Scott E. Perkins, William A. Dawson

Primordial Black Holes (PBHs) could explain some fraction of dark matter and shed light on many areas of early-universe physics. Despite over half a century of research interest, a PBH population has so far eluded detection. The most competitive constraints on the fraction of dark matter comprised of PBHs ($f_{\rm DM}$) in the $(10^{-9}-10)M_{\odot}$ mass-ra

Jonathan Brokman, Roy Betser, Rotem Turjeman, Tom Berkov

As neural networks grow in scale, their training becomes both computationally demanding and rich in dynamics. Amidst the flourishing interest in these training dynamics, we present a novel observation: Parameters during training exhibit intrinsic correlations over time. Capitalizing on this, we introduce Correlation Mode Decomposition (CMD). This algorithm c

Kohtaro Tadaki

The notion of probability plays a crucial role in quantum mechanics. It appears in quantum mechanics as the Born rule. In modern mathematics which describes quantum mechanics, however, probability theory means nothing other than measure theory, and therefore any operational characterization of the notion of probability is still missing in quantum mechanics.

Michele Colombo, Riccardo Diamanti, Luca Bastianelli, Gabriele Gradoni

The performance and functionalities of a commercial fifth generation base station are evaluated inside the reverberation chamber at the mmWave frequency range. The base station capability to operates in different propagation environment conditions reproduced by the reverberation chamber is investigated. Throughput, modulation code scheme and beamforming are

Alexander Lenz, Maria Laura Piscopo, Aleksey V. Rusov

Motivated by the recent measurements of CP violating effects in singly Cabibbo suppressed $D^0$ decays, we revisit the theoretical predictions of these channels. Using up-to-date values for the decay constants and form factors, we find already within naive QCD factorisation surprisingly good agreement between the central values of the branching ratios and th

Lívia S. Rocha, Jorge E. Horvath, Lucas M. de Sá, Gustavo Y. Chinen

Matter at ultra-high densities finds a physical realization inside neutron stars. One key property is their maximum mass, which has far-reaching implications for astrophysics and the equation of state of ultra dense matter. In this work, we employ Bayesian analysis to scrutinize the mass distribution and maximum mass threshold of galactic neutron stars. We c

Daniel Ávila, Alberto Guijosa, Rafael Olmedo

In recent years, interesting curved-space extensions of nonrelativistic (NR) string theory have been very actively pursued, where the background has a structure that is a stringy generalization of Newton-Cartan geometry. Here we show that the natural black branes of the NR theory, sourced by the familiar repertoire of stringy objects, generally have a differ

Pratik Shah, Jenna Lester, Jana G Deflino, Vinay Pai

Tools, models and statistical methods for signal processing and medical image analysis and training deep learning models to create research prototypes for eventual clinical applications are of special interest to the biomedical imaging community. But material and optical properties of biological tissues are complex and not easily captured by imaging devices.

M. Polkas, S. Bonoli, E. Bortolas, D. Izquierdo-Villalba

Stars can be ripped apart by tidal forces in the vicinity of a massive black hole (MBH), causing luminous flares known as tidal disruption events (TDEs). These events could be contributing to the mass growth of intermediate-mass MBHs, and new samples from transient surveys can provide useful information on this growth channel. This work aims to study the dem

Anna Schroeder, Matthias Heller, Mariami Gachechiladze

Measurement-based quantum computing (MBQC) is a promising approach to reducing circuit depth in noisy intermediate-scale quantum algorithms such as the Variational Quantum Eigensolver (VQE). Unlike gate-based computing, MBQC employs local measurements on a preprepared resource state, offering a trade-off between circuit depth and qubit count. Ensuring determ

Amit Rozner, Barak Battash, Ofir Lindenbaum, Lior Wolf

We study the problem of performing face verification with an efficient neural model $f$. The efficiency of $f$ stems from simplifying the face verification problem from an embedding nearest neighbor search into a binary problem; each user has its own neural network $f$. To allow information sharing between different individuals in the training set, we do not

Ting-Kuo Chen, Cheng-Wei Chiang, Sven Heinemeyer, Georg Weiglein

CMS and ATLAS have reported small excesses in the search for low-mass Higgs bosons in the di-photon decay channel at exactly the same mass, $95.4~$GeV. These searches rely on improved analysis techniques, enhancing in particular the discrimination against the $Z \to e^+e^-$ background. In models beyond the Standard Model (SM) that extend the Higgs sector wit

Junru Lin, Asen Nachkov, Songyou Peng, Luc Van Gool

In this work, we address the challenge of deploying Neural Radiance Field (NeRFs) in Simultaneous Localization and Mapping (SLAM) under the condition of lacking depth information, relying solely on RGB inputs. The key to unlocking the full potential of NeRF in such a challenging context lies in the integration of real-world priors. A crucial prior we explore

John Ellis, Vincent Vennin, David Wands

This is a review article for The Review of Particle Physics 2026 (aka the Particle Data Book), appearing as Chapter 23. It forms a compact review of our understanding of cosmological inflation near the end of 2025. Topics included are Scalar Field Cosmology; Primordial Perturbations from Inflation; Models; Model Comparison; Constraints on Reheating; Beyond S

Claudio Saccon

We study the problem $-\Delta u=\lambda u-u^{-1}$ with a Neumann boundary condition; the peculiarity being the presence of the singular term $-u^{-1}$. We point out that the minus sign in front of the negative power of $u$ is particularly challenging, since no convexity argument can be invoked. Using bifurcation techniques we are able to prove the existence

Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov

Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images. Central to these algorithms is the diffusion process, a set of equations which maps data to noise in a way that can significantly affect performance. In this paper, we explore whether the diffusion process can be learned from data. Our work is grounded in Bayes

Alexander C. Sobotka, Adrienne L. Erickcek, Tristan L. Smith

We derive constraints on the injection of free-streaming dark radiation after big bang nucleosynthesis (BBN) by considering the decay of a massive hidden sector particle into dark radiation. Such a scenario has the potential to alleviate the Hubble tension by introducing a new energy component to the evolution of the early universe. We employ observations of

Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta

We argue that interpretations of machine learning (ML) models or the model-building process can be seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics. We address both researchers and practitioners, calling attention to the benefits of a un

Felix Ivander, Lachlan P. Lindoy, Joonho Lee

Studies of the dynamics of a quantum system coupled to baths are typically performed by utilizing the Nakajima-Zwanzig memory kernel (${\mathcal{K}}$) or the influence functions ($\mathbf{{I}}$), especially when the dynamics exhibit memory effects (i.e., non-Markovian). Despite their significance, the formal connection between the memory kernel and the influ

Vinzenz Thoma, Michael Curry, Niao He, Sven Seuken

Many real-world auctions are dynamic processes, in which bidders interact and report information over multiple rounds with the auctioneer. The sequential decision making aspect paired with imperfect information renders analyzing the incentive properties of such auctions much more challenging than in the static case. It is clear that bidders often have incent

Nils Gluth, Thomas Guhr, Alfred Hucht

We consider an Ising model with quenched surface disorder, the disorder average of the free energy is the main object of interest. Explicit expressions for the free energy distribution are difficult to obtain if the quenched surface spins take values of $\pm 1$. Thus, we choose a different approach and model the surface disorder by Gaussian random matrices.

A. R. Taylor, S. Sekhar, L. Heino, A. M. M. Scaife

The MeerKAT International GigaHertz Tiered Extragalactic Exploration (MIGHTEE) is one of the MeerKAT large survey projects, designed to pathfind SKA key science. MIGHTEE is undertaking deep radio imaging of four well observed fields (COSMOS, XMM-LSS, ELAIS S1 and CDFS) totaling 20 square degrees to $\mu$Jy sensitivities. Broadband imaging observations betwee

Samuel Forbes

We fit the exponent of the Pareto distribution, that is equivalent or can approximate the continuous power law distribution given a cutoff point, using linear regression (LR). We use LR on the logged variables of the empirical tail (one minus the empirical cumulative distribution function). We find the distribution of the consistent LR estimator and an appro

John M. Scanlon, Kristofer D. Kusano, Laura A. Fraade-Blanar, Timothy L. McMurry

With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the US, we are now approaching an inflection point, where the process of retrospectively evaluating ADS safety impact can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a "benchmark" crash rate. This study a

Peter Bubenik

We give necessary and sufficient conditions for certain pushouts of topological spaces in the category of Cech's closure spaces to agree with their pushout in the category of topological spaces. We prove that in these two categories, the constructions of cell complexes by a finite sequence of closed cell attachments, which attach arbitrarily many cells at a

Niklas Wagner, Daniele Guerci, Andrew J. Millis, Giorgio Sangiovanni

We introduce a real-space slave rotor theory of the physics of topological Mott insulators, using the Kane-Mele-Hubbard model as an example, and use it to show that a topological gap in the Green function zeros corresponds to a gap in the bulk spinon spectrum and that a zero edge mode corresponds to a spinon edge mode. We then consider an interface between a

Deep Mehta, Kartik Rawool, Subodh Gujar, Bowen Xu

Automating software development processes through the orchestration of GitHub Action workflows has revolutionized the efficiency and agility of software delivery pipelines. This paper presents a detailed investigation into the use of Large Language Models (LLMs) specifically, GPT 3.5 and GPT 4 to generate and evaluate GitHub Action workflows for DevOps tasks

Kyler Siegel, Yuan Yao

Let $B^{2n}(R)$ denote the closed $2n$-dimensional symplectic ball of area $R$, and let $\Sigma_g(L)$ be a closed symplectic surface of genus $g$ and area $L$. We prove that there is a symplectic embedding $\bigsqcup_{i=1}^k B^4(R_i) \times \Sigma_g (L) \overset{s}\hookrightarrow \operatorname{int}(B^4(R))\times \Sigma_g (L)$ if and only if there exists a sy

Shiu-hong Kao, Jierun Chen, S. H. Gary Chan

Knowledge distillation (KD) has been recognized as an effective tool to compress and accelerate models. However, current KD approaches generally suffer from an accuracy drop and/or an excruciatingly long distillation process. In this paper, we tackle the issue by first providing a new insight into a phenomenon that we call the Inter-Block Optimization Entang

Emily N Peterson, Rachel C. Nethery, Jarvis T. Chen, Loni P. Tabb

Monitoring small-area geographical population trends in opioid mortality has large scale implications to informing preventative resource allocation. A common approach to obtain small area estimates of opioid mortality is to use a standard disease mapping approach in which population-at-risk estimates are treated as fixed and known. Assuming fixed populations

Chang Teng, Yunchuan Ma, Guorong Li, Yuankai Qi

Describing video content according to users' needs is a long-held goal. Although existing video captioning methods have made significant progress, the generated captions may not focus on the entity that users are particularly interested in. To address this problem, we propose a new video captioning task, Subject-Oriented Video Captioning (SOVC), which aims t

Luigi Ranno, Jia Xu Brian Sia, Cosmin Popescu, Drew Weninger

As silicon photonics transitions from research to commercial deployment, packaging solutions that efficiently couple light into highly-compact and functional sub-micron silicon waveguides are imperative but remain challenging. The 220 nm silicon-on-insulator (SOI) platform, poised to enable large-scale integration, is the most widely adopted by foundries, re

Rémy Larue, Jérémie Quevillon, Roman Zwicky

We derive constraints on the four dimensional energy-momentum tensor from gravitational and gauge anomalies. Our work can be considered an extension of Duff's analysis [1] to include parity-odd terms and explicit symmetry breaking. The constraints imply the absence of the parity-odd $R\tilde R$ and $F\tilde F$ terms, for theories whose symmetries are compati

Omar Nagib, P. Huft, A. Safari, M. Saffman

We propose a scheme for two-qubit gates between a flying photon and an atom in a cavity. The atom-photon gate setup consists of a cavity and a Mach-Zehnder interferometer with doubly degenerate ground and excited state energy levels mediating the atom-light interaction. We provide an error analysis of the gate and model important errors, including spatial mo

Oscar Dabrowski, Jean-Luc Falcone, Antoine Klauser, Julien Songeon

MRI, a widespread non-invasive medical imaging modality, is highly sensitive to patient motion. Despite many attempts over the years, motion correction remains a difficult problem and there is no general method applicable to all situations. We propose a retrospective method for motion estimation and correction to tackle the problem of in-plane rigid-body mot

Weiwei Gu, Anant Sah, Nakul Gopalan

We present a framework for robots to learn novel visual concepts and tasks via in-situ linguistic interactions with human users. Previous approaches have either used large pre-trained visual models to infer novel objects zero-shot, or added novel concepts along with their attributes and representations to a concept hierarchy. We extend the approaches that fo

Jean V. Alves, Diogo Leitão, Sérgio Jesus, Marco O. P. Sampaio

Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilities in hybrid decision-making systems. In such systems, human availability and domain-specific concerns introduce difficulties, while obtaining human predictions for training and e

Lukas Broers, Ludwig Mathey

We present the non-linear DC photoconductivity of graphene under strong infra-red (IR) radiation. The photoconductivity is obtained as the response to a strong DC electric field, with field strengths outside of the linear-response regime, while the IR radiation is described by a strong AC electric field. The conductivity displays two distinct regimes in whic

Octave Mariotti, Oisin Mac Aodha, Hakan Bilen

Recent progress in self-supervised representation learning has resulted in models that are capable of extracting image features that are not only effective at encoding image level, but also pixel-level, semantics. These features have been shown to be effective for dense visual semantic correspondence estimation, even outperforming fully-supervised methods. N

Alberto Verjovsky, Ricardo F. Vila-Freyer

We prove, using the celebrated result by Spitzer about winding of planar Brownian motion, and the existence of harmonic morphisms $f:M\to{\mathbb S}^1$ representing cohomology classes in $\text{H}^1(M,\mathbb Z)$, that there is a stochastic process $H_t:{\mathcal C}(M)\to{\text{Hom}(\text{H}^1(M;\mathbb R), \mathbb R)}\simeq{\text{H}_1(M;\mathbb R)}$ ($t\in[

Francesco Albarelli, Marco G. Genoni

In this manuscript we present a pedagogical introduction to continuously monitored quantum systems. We start by giving a simplified derivation of the Markovian master equation in Lindblad form, in the spirit of collision models and input-output theory, which describes the unconditional dynamics of a continuously monitored system. The same formalism is then e

Gerd Niestegge

The interplay between the algebraic structure (operator algebras) for the quantum observables and the convex structure of the state space has been explored for a long time and most advanced results are due to Alfsen and Shultz. Here we present a more elementary approach with a more generic structure for the observables, which focuses on the transition probab

Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen

To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving high-resolution wind fields left the simulations impractical to test different time lengths and model configur

Rahul Chand, Yashoteja Prabhu, Pratyush Kumar

With the tremendous success of large transformer models in natural language understanding, down-sizing them for cost-effective deployments has become critical. Recent studies have explored the low-rank weight factorization techniques which are efficient to train, and apply out-of-the-box to any transformer architecture. Unfortunately, the low-rank assumption

Dieter Lust, Joaquin Masias, Benjamin Muntz, Marco Scalisi

We argue that the Starobinsky model of inflation, realised via an $R^2$ term in the Lagrangian, can originate from quantum effects due to a tower of light species. By means of two separate arguments, we show how this implies that the scale of the $R^2$ term must be of order of the species scale $\Lambda_s$, namely the energy at which gravity becomes strongly

Martin Frankland, Sebastian H. Martensen, Marius Thaule

We introduce Toda brackets for n-angulated categories and show that the various definitions of Toda brackets coincide. We prove juggling formulas for these Toda brackets generalizing the triangulated case. Following that, we generalize a theorem due to Heller in the triangulated setting to the setting of n-angulated categories. We also provide several exampl

Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, André Freitas

Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces. To combine the controllability of VAE latent spaces with the state-of-the-art performance of recent large language models (LLMs), we present in this work LlaMaVAE

Karl Christ

As in algebraic geometry, an effective divisor class on a vertex-weighted graph is called special if also its residual class is effective. We study the question, when this is true already on the level of divisors; that is, when there exists an effective divisor in the class whose residual is effective as well, so called uniform divisors. We show that uniform

Baptiste Claudon, Julien Zylberman, César Feniou, Fabrice Debbasch

Controlled operations are fundamental building blocks of quantum algorithms. Decomposing $n$-control-NOT gates ($C^n(X)$) into arbitrary single-qubit and CNOT gates, is a crucial but non-trivial task. This study introduces $C^n(X)$ circuits outperforming previous methods in the asymptotic and non-asymptotic regimes. Three distinct decompositions are presente

Julian Külshammer

In this survey article we propose the notion of a bound quiver for an exact category generalising the classical concept of the Gabriel quiver and its relation for a module category as certain ring extension. The notion is motivated by joint work of the author with Vanessa Miemietz, Steffen Koenig and Sergiy Ovsienko in the case where the exact category is th

Alexander Menovschikov

We provide the estimates for the constant in the weighted Poincar\'e inequality for a special class of planar domains and weights. Based on this, we prove the lower bounds for the first non-zero eigenvalue $\mu_\rho$ of the Neumann Laplacian with density $\rho$. These estimates depend on the density function and the geometry of the domain. In particular, it

G. Encinas-Lago, M. Rossanese, V. Sciancalepore, Marco Di Renzo

Reconfigurable Intelligent Surfaces (RIS) have emerged as a disruptive technology with the potential to revolutionize wireless communication systems. In this paper, we present RIShield, a novel application of RIS technology specifically designed for radiation-sensitive environments. The aim of RIShield is to enable electromagnetic blackouts, preventing radia

Nicolas Boullé, Astrid Herremans, Daan Huybrechs

Functions with singularities are notoriously difficult to approximate with conventional approximation schemes. In computational applications, they are often resolved with low-order piecewise polynomials, multilevel schemes, or other types of grading strategies. Rational functions are an exception to this rule: for univariate functions with point singularitie

Zinuo You, Andreas Geiger, Anpei Chen

We present NeLF-Pro, a novel representation to model and reconstruct light fields in diverse natural scenes that vary in extent and spatial granularity. In contrast to previous fast reconstruction methods that represent the 3D scene globally, we model the light field of a scene as a set of local light field feature probes, parameterized with position and mul

Dario Andrea Bini, Fabio Durastante, Sooyeong Kim, Beatrice Meini

Given a stochastic matrix $P$ partitioned in four blocks $P_{ij}$, $i,j=1,2$, Kemeny's constant $\kappa(P)$ is expressed in terms of Kemeny's constants of the stochastic complements $P_1=P_{11}+P_{12}(I-P_{22})^{-1}P_{21}$, and $P_2=P_{22}+P_{21}(I-P_{11})^{-1}P_{12}$. Specific cases concerning periodic Markov chains and Kronecker products of stochastic matr

Mathis Guéneau, Léo Touzo

The connection between absorbing boundary conditions and hard walls is well established in the mathematical literature for a variety of stochastic models, including for instance the Brownian motion. In this paper we explore this duality for a different type of process which is of particular interest in physics and biology, namely the run-tumble-particle, a t

Kazi Toufique Elahi, Tasnuva Binte Rahman, Shakil Shahriar, Samir Sarker

Memes have become a distinctive and effective form of communication in the digital era, attracting online communities and cutting across cultural barriers. Even though memes are frequently linked with humor, they have an amazing capacity to convey a wide range of emotions, including happiness, sarcasm, frustration, and more. Understanding and interpreting th

Guillermo Reyes-Peraza, Santiago Avila, Violeta Gonzalez-Perez, Daniel Lopez-Cano

Emission line galaxies (ELGs) are targeted by the new generation of spectroscopic surveys to make unprecedented measurements in cosmology from their distribution. Accurately interpreting this data requires understanding the imprints imposed by the physics of galaxy formation and evolution on galaxy clustering. In this work we utilize a semi-analytical model

Branislav Rabatin, David C. Collins

Turbulence is a key process in many astrophysical systems. In this work we explore the statistics of thermal and kinetic energy of isothermal, supersonic, turbulent gas. We develop analytic formulas for the PDF of thermal and kinetic energies and their joint PDF. We compare these analytical models with a suite of simulations with a fixed resolution of $1024^

Cecilia Lunardini, Joshua Loeffler, Mainak Mukhopadhyay, Matthew J. Hurley

When a star undergoes core collapse, a vast amount of energy is released in a ~10 s long burst of neutrinos of all species. Inverse beta decay in the star's hydrogen envelope causes an electromagnetic cascade which ultimately results in a flare of gamma rays - an "echo" of the neutrino burst - at the characteristic energy of 0.511 MeV. We study the phenomeno

Filipe S. Ribeiro, Pedro D. S. Silva, Manoel M. Ferreira

In this work, we study the propagation and absorption of plasma waves in the context of the Maxwell-Carroll-Field-Jackiw (MCFJ) electrodynamics with a purely spacelike background playing the role of the anomalous Hall conductivity, concerning the anomalous Hall current. Such a current is also found in an axion field which increases linearly with a space coor

K. B. Gubbels, J. Y. Ypma, C. W. Oosterlee

We introduce a class of copulas that we call Principal Component Copulas (PCCs). This class combines the strong points of copula-based techniques with principal component analysis (PCA), which results in flexibility when modelling tail dependence along the most important directions in high-dimensional data. We obtain theoretical results for PCCs that are imp

Giorgio Calderone, Francesco Guarneri, Matteo Porru, Stefano Cristiani

Context. The identification of bright QSOs is of great importance to probe the intergalactic medium and address open questions in cosmology. Several approaches have been adopted to find such sources in currently available photometric surveys, including machine learning methods. However, the rarity of bright QSOs at high redshifts compared to contaminating so

Neeraj Kumar Singh, Koyel Ghosh, Joy Mahapatra, Utpal Garain

Warning: This paper contains examples of the language that some people may find offensive. Detecting and reducing hateful, abusive, offensive comments is a critical and challenging task on social media. Moreover, few studies aim to mitigate the intensity of hate speech. While studies have shown that context-level semantics are crucial for detecting hateful c