December 2024 arXiv papers — page 65

Aneesha Guna, Parth Ganeriwala, Siddhartha Bhattacharyya

With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system for object detection and tracking. The c

Sophie Kriz

This is the first in a series of papers on type I Howe duality for finite fields, concerning the restriction of an oscillator representation of the symplectic group to a product of a symplectic and an orthogonal group. The goal of the series is describing this restriction completely explicitly. Applications (described in the third paper of the series) includ

Gabriel Cardoso, Jean-Marie Stéphan, Alexander G. Abanov

We present Monte Carlo simulations of the two-dimensional one-component plasma (2D OCP) confined to a cylindrical geometry, focusing on density profiles, fluctuations, and their connection to bulk correlation functions. The cylindrical geometry eliminates geometric frustration, allowing for a precise study of boundary density oscillations, the dependence on

Marcos A. Reyes-Martinez, Alain Kadar, Steven Dunne, Sharon C. Glotzer

Interconnected networks of rigid struts are critical for application in lightweight, load-bearing structures. However, accurately modeling stress distribution in these strut lattices poses significant computational challenges due to its strong dependence on organizational patterns, boundary conditions, and collective effects. Leveraging two-dimensional strut

Nathan Berkovits, Ulisses M. Portugal

Using the hybrid formalism with manifest $N=1$ $d=4$ spacetime supersymmetry, we construct the quadratic term in the heterotic superstring field theory action. As in open superstring field theory using the hybrid formalism, the heterotic string field theory action is constructed with three string fields and the massless sector describes $N=1$ $d=10$ supergra

Denis Prokopenko, David F. A. Lloyd, Amedeo Chiribiri, Daniel Rueckert

Dynamic fetal heart magnetic resonance imaging (MRI) presents unique challenges due to the fast heart rate of the fetus compared to adult subjects and uncontrolled fetal motion. This requires high temporal and spatial resolutions over a large field of view, in order to encompass surrounding maternal anatomy. In this work, we introduce Dynamic Cardiac Reconst

Reza Shirkavand, Peiran Yu, Shangqian Gao, Gowthami Somepalli

Recent advances in diffusion generative models have yielded remarkable progress. While the quality of generated content continues to improve, these models have grown considerably in size and complexity. This increasing computational burden poses significant challenges, particularly in resource-constrained deployment scenarios such as mobile devices. The comb

Shivani Aggarwal, Saumya Singh, Dinkar Mishra, Bhupesh Kumar

This study presents an investigation of second harmonic generation (SHG) resulting from interaction of radially polarized laser beam propagating in homogeneous, unmagnetized plasma. Lorentz force and continuity equations have been used to derive the radial and axial current density components. Further, using these densities in the wave equation leads to anal

Davide Rinaldi, Radim Filip, Dario Gerace, Giacomo Guarnieri

Quantum batteries represent one of the most promising applications of quantum thermodynamics, whose goal is not only to store energy inside small quantum systems but also to potentially leverage genuine quantum effects to outperform classical counterparts. In this context, however, energy fluctuations become extremely relevant and have a significant impact o

Alex Q. Costa, Pedro D. S. Silva, Manoel M. Ferreira

We investigate the optical properties of bi-isotropic materials under the anomalous Hall effect (AHE) of axion electrodynamics. Four refractive indices associated with circularly polarized waves are achieved, implying circular birefringence with rotatory power endowed with double sign reversal, an exotic optical signature for chiral dielectrics. The Kerr rot

Mohammadreza Khodaparast Klidbari, Naser Souri, Zahra Sadat Habibolahi, Hamid Montazeri Hedeshi

Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid multiport isolated DC-DC converter for u

Zhian Jia

We introduce weak Hopf symmetry as a tool to explore (1+1)-dimensional topological phases with non-invertible symmetries. Drawing inspiration from Symmetry Topological Field Theory (SymTFT), we construct a lattice model featuring two boundary conditions: one that encodes topological symmetry and another that governs non-topological dynamics. This cluster lad

Ryan Rizaldy, Tian Zhou, Sougato Bose, Anupam Mazumdar

The rotation of a nanoparticle in a quantum system has many applications, from theory to experiments. This paper will treat nanoparticle rotational dynamics for spin-embedded nanorotors. We will model it as a rigid body that properly treats the rotation in the co-frame of the nanorotor in the presence of external fields. Besides rotation, we will further inv

Konstantinos Kritos, Ricarda S. Beckmann, Joseph Silk, Emanuele Berti

Supermassive black holes are prevalent at the centers of massive galaxies, and their masses scale with galaxy properties, increasing evidence suggesting that these trends continue to low stellar masses. Seeds are needed for supermassive black holes, especially at the highest redshifts explored by the James Webb Space Telescope. We study the hierarchical merg

R. Johanna Zijderveld, Isidora Araya Day, Anton R. Akhmerov

The surface states of intrinsic higher order topological phases are protected by the spatial symmetries of a finite sample. This property makes the existing scattering theory of topological invariants inapplicable because the scattering geometry is either incompatible with the symmetry or does not probe the bulk topology. We resolve this obstacle by using a

Shahar Hod

Black-hole spacetimes that possess stationary equatorial matter rings are known to exist in general relativity. We here reveal the existence of black-hole spacetimes that support {\it non}-equatorial matter rings. In particular, it is proved that rapidly-rotating Kerr black holes in the dimensionless large-spin regime ${\bar a}>{\bar a}_{\text{crit}}= \sqrt{

Rafał Świętek, Miroslav Hopjan, Carlo Vanoni, Antonello Scardicchio

In most noninteracting quantum systems, the scaling theory of localization predicts one-parameter scaling flow in both ergodic and localized regimes. On the other hand, it is expected that the one-parameter scaling hypothesis breaks down for interacting systems that exhibit the many-body ergodicity breaking transition. Here we introduce a scaling theory of f

Peter Lunts, Aavishkar A. Patel, Subir Sachdev

We describe the evolution of low-temperature thermopower across Fermi-volume-changing quantum phase transitions in Kondo lattice models without translational symmetry breaking. This transition moves from a heavy Fermi liquid with a conventional Luttinger-volume large Fermi surface to a 'FL*' state, characterized by a small Fermi surface and a spin liquid wit

Thomas Dyer, Will Barker, Damianos Iosifidis

The cosmology of metric-affine gravity is studied for the general, parity preserving action quadratic in curvature, torsion and non-metricity. The model contains 27 a priori independent couplings in addition to the Einstein constant. Linear and higher order relations between the quadratic operators in a Friedmann--Lemaitre--Robertson--Walker spacetime are ob

Yolanda Jiménez-Teja, Javier Román, Kim HyeongHan, Jose M. Vílchez

Detection and study of the intracluster light in rich clusters of galaxies has been a problem of long standing challenge and interest. Using the lowest surface brightness images of the Coma cluster of galaxies in the g and r bands, from the Halos and Environment of Nearby Galaxies (HERON) Coma Cluster Project, we obtained the most extensive image of intraclu

J. R. Peláez, P. Rabán, J. Ruiz de Elvira

We provide new global parametrizations of $\pi\pi \to \pi\pi$ scattering for the S2, P, D, F, and G partial waves up to at least 1.8 GeV, easy to implement for phenomenological use. With earlier S0-wave parametrizations, slightly updated here, they reproduce previous partial wave dispersion analyses up to the $\pi\omega$ threshold. In addition, these new par

Jason T. Hinkle, Katie Auchettl, Willem B. Hoogendam, Anna V. Payne

We present observations of ASASSN-22ci (AT2022dbl), a nearby tidal disruption event (TDE) discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) at a distance of d$_L \simeq 125$ Mpc. Roughly two years after the initial ASAS-SN discovery, a second flare was detected coincident with ASASSN-22ci. UV/optical photometry and optical spectroscopy indi

Gabriele Calliari, Marco Di Liberto, Hannes Pichler, Torsten V. Zache

Simulating the real-time dynamics of quantum field theories (QFTs) is one of the most promising applications of quantum simulators. Regularizing a bosonic QFT for quantum simulation purposes typically involves a truncation in Hilbert space in addition to a discretization of space. Here, we discuss how to perform such a regularization of scalar QFTs by explic

David Robinson, Camille Avestruz, Nickolay Y. Gnedin, Vadim A. Semenov

Gas cooling and heating rates are vital components of hydrodynamic simulations. However, they are computationally expensive to evaluate exactly with chemical networks or photoionization codes. We compare two different approximation schemes for gas cooling and heating in an idealized simulation of an isolated galaxy. One approximation is based on a polynomial

Marcus Stålhammar, Lukas Rødland

The advent of non-Hermitian physics has enriched the plethora of topological phases to include phenomena without Hermitian counterparts. Despite being among the most well-studied uniquely non-Hermitian features, the topological properties of multifold exceptional points, $n$-fold spectral degeneracies (EP$n$s) at which also the corresponding eigenvectors coa

Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann

We propose an unsupervised instruction-based image editing approach that removes the need for ground-truth edited images during training. Existing methods rely on supervised learning with triplets of input images, ground-truth edited images, and edit instructions. These triplets are typically generated either by existing editing methods, introducing biases,

Tao Xie, Xi Chen, Zhen Xu, Yiman Xie

Reconstructing complex reflections in real-world scenes from 2D images is essential for achieving photorealistic novel view synthesis. Existing methods that utilize environment maps to model reflections from distant lighting often struggle with high-frequency reflection details and fail to account for near-field reflections. In this work, we introduce EnvGS,

Hanlin Wang, Hao Ouyang, Qiuyu Wang, Wen Wang

The intuitive nature of drag-based interaction has led to its growing adoption for controlling object trajectories in image-to-video synthesis. Still, existing methods that perform dragging in the 2D space usually face ambiguity when handling out-of-plane movements. In this work, we augment the interaction with a new dimension, i.e., the depth dimension, suc

Qihao Liu, Xi Yin, Alan Yuille, Andrew Brown

Diffusion models, and their generalization, flow matching, have had a remarkable impact on the field of media generation. Here, the conventional approach is to learn the complex mapping from a simple source distribution of Gaussian noise to the target media distribution. For cross-modal tasks such as text-to-image generation, this same mapping from noise to

Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya

We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically a

João Carreira, Dilara Gokay, Michael King, Chuhan Zhang

Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations on semantic-related tasks $\unicode{x2013}$ action classification, ImageNet classification, etc. In this paper we focus on evaluating self-supervised learning on non-semantic vision tasks that are more spatial (3D) and

Hadi Alzayer, Philipp Henzler, Jonathan T. Barron, Jia-Bin Huang

Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for more specular objects whose appearance strongly depends on the viewing direction. Some prior approaches model appearance v

Philip Whittington, Gregor Bachmann, Tiago Pimentel

In this work, we prove the NP-completeness of two variants of tokenisation, defined as the problem of compressing a dataset to at most $\delta$ symbols by either finding a vocabulary directly (direct tokenisation), or selecting a sequence of merge operations (bottom-up tokenisation).

Muntasir Wahed, Kiet A. Nguyen, Adheesh Sunil Juvekar, Xinzhuo Li

Despite significant advancements in Large Vision-Language Models (LVLMs)' capabilities, existing pixel-grounding models operate in single-image settings, limiting their ability to perform detailed, fine-grained comparisons across multiple images. Conversely, current multi-image understanding models lack pixel-level grounding. Our work addresses this gap by i

Shuo Xing, Chengyuan Qian, Yuping Wang, Hongyuan Hua

Since the advent of Multimodal Large Language Models (MLLMs), they have made a significant impact across a wide range of real-world applications, particularly in Autonomous Driving (AD). Their ability to process complex visual data and reason about intricate driving scenarios has paved the way for a new paradigm in end-to-end AD systems. However, the progres

Yatian Pang, Peng Jin, Shuo Yang, Bin Lin

Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision tasks. Pioneering works introduce NTP to autoregressive visual generation tasks. In this work, we rethink the NTP for autoregressive image generation and extend it to a novel Next Patc

Sofiia Dubova, Kevin Yang

We study a class of Gaussian random band matrices of dimension $N \times N$ and band-width $W$. We show that delocalization holds for bulk eigenvectors and that quantum diffusion holds for the resolvent, all under the assumption that $W \gg N^{8/11}$. Our analysis is based on a flow method, and a refinement of it may lead to an improvement on the condition $

Shuo Xing, Hongyuan Hua, Xiangbo Gao, Shenzhe Zhu

Recent advancements in large vision language models (VLMs) tailored for autonomous driving (AD) have shown strong scene understanding and reasoning capabilities, making them undeniable candidates for end-to-end driving systems. However, limited work exists on studying the trustworthiness of DriveVLMs -- a critical factor that directly impacts public transpor

Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen

Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through next token prediction. Recently, in image generation, VAR proposes scale-wise autoregressive modeling, which extends the next token prediction to the next scale prediction, preserv

Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng

This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks. LongBench v2 consists of 503 challenging multiple-choice questions, with contexts ranging from 8k to 2M words, across six major task categories: single-document QA, mul

Benjamin A. Levitan, Yuval Oreg, Erez Berg

We propose a mechanism which can generate supercurrents in spin-orbit coupled superconductors with charged magnetic inclusions. The basic idea is that through spin-orbit interaction, the in-plane electric field near the edge of each inclusion appears to the electrons as an effective spin-dependent gauge field; if Cooper pairs can be partially spin polarized,

Amber Yijia Zheng, Raymond A. Yeh

Model immunization is an emerging direction that aims to mitigate the potential risk of misuse associated with open-sourced models and advancing adaptation methods. The idea is to make the released models' weights difficult to fine-tune on certain harmful applications, hence the name ``immunized''. Recent work on model immunization focuses on the single-conc

Craig Lawrie, Lorenzo Mansi, Marcus Sperling, Zhenghao Zhong

We present an algorithm to extract the Coulomb branch Hasse diagram of orthosymplectic 3d $\mathcal{N}=4$ quiver gauge theories. The algorithm systematically predicts all descendant theories arising from Coulomb branch Higgsing, thereby detailing the stratification of the symplectic singularity defined by the initial Coulomb branch. Leveraging the Lie algebr

Abir Ghosh, Chethan Krishnan, Adinath Mahalingam

We synthesize and sharpen various observations about sub-AdS holography in the literature to associate a central charge to sub-AdS scales of AdS x $X$ geometries. A key ingredient in our proposal is the idea that the AdS length is the length of the long string in a stack of $N$ $backreacted$ D-branes. This allows us to make statements about sub-AdS scales by

Wang Zhao, Yan-Pei Cao, Jiale Xu, Yuejiang Dong

Procedural Content Generation (PCG) is powerful in creating high-quality 3D contents, yet controlling it to produce desired shapes is difficult and often requires extensive parameter tuning. Inverse Procedural Content Generation aims to automatically find the best parameters under the input condition. However, existing sampling-based and neural network-based

Chenxu Zhou, Lvchang Fu, Sida Peng, Yunzhi Yan

This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving scenarios. Recent approaches utilize neural radiance fields combined with the physical modeling of LiDAR sensors to achieve high-fidelity re-simulation results. Unfortunately, these methods face limitations due to high computational demands in large-scale scenes and cannot p

Yong Zhang

In this work, we prove the new factorization pattern for tree-level Yang-Mills (YM) amplitudes proposed in a companion paper. This pattern reveals a decomposition of amplitudes into a sum of gluings of lower-point amplitudes under specific kinematic constraints, making the hidden zeros of YM amplitudes manifest. Utilizing the Cachazo-He-Yuan (CHY) formalism,

Reuven Balkin, Noam Burger, Jonathan L. Feng, Yael Shadmi

We study a simple class of flavored scalar models, in which the couplings of a new light scalar to standard-model fermions are controlled by the flavor symmetry responsible for fermion masses and mixings. The scalar couplings are then aligned with the Yukawa matrices, with small but nonzero flavor-violating entries. $D$-meson decays are an important source o

Theodore McKeever, Owen Diba, Ahsan Nazir

With the development of any quantum technology comes a need for precise control of quantum systems. Here, we evaluate the impact of control noise on a quantum Otto cycle. Whilst it is postulated that noiseless quantum engines can approach maximal Otto efficiency in finite times, the existence of white noise on the controls is shown to negatively affect avera

Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu

Vector-quantized networks (VQNs) have exhibited remarkable performance across various tasks, yet they are prone to training instability, which complicates the training process due to the necessity for techniques such as subtle initialization and model distillation. In this study, we identify the local minima issue as the primary cause of this instability. To

Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui

Multiple-choice question (MCQ) datasets like Massive Multitask Language Understanding (MMLU) are widely used to evaluate the commonsense, understanding, and problem-solving abilities of large language models (LLMs). However, the open-source nature of these benchmarks and the broad sources of training data for LLMs have inevitably led to benchmark contaminati

Soeren Wengerowsky, Stefano Duranti, Lukas Heller, Hugues de Riedmatten

Quantum Frequency Conversion (QFC) is a widely used technique to interface atomic systems with the telecom band in order to facilitate propagation over longer distances in fiber. Here we demonstrate the difference-frequency conversion from 606 nm to 1552 nm of microsecond-long weak coherent pulses at the single photon level compatible with Pr$^{3+}$:Y$_2$SiO

Denis G. Rancourt, Joseph Hickey

A parameter $\psi$ was recently defined and introduced into the epidemiological modelling scientific literature, and is being accepted. The said parameter was used to argue that there was a disproportionate risk of infection incurred by vaccinated persons due to contacts with unvaccinated persons during the declared COVID-19 pandemic. Opposing published resu

Moayed Haji-Ali, Willi Menapace, Aliaksandr Siarohin, Ivan Skorokhodov

We propose AV-Link, a unified framework for Video-to-Audio (A2V) and Audio-to-Video (A2V) generation that leverages the activations of frozen video and audio diffusion models for temporally-aligned cross-modal conditioning. The key to our framework is a Fusion Block that facilitates bidirectional information exchange between video and audio diffusion models

Sagar Soni, Akshay Dudhane, Hiyam Debary, Mustansar Fiaz

Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and {resource management}. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor moda

Daniel Russo, Stefano Menini, Jacopo Staiano, Marco Guerini

Natural Language Processing and Generation systems have recently shown the potential to complement and streamline the costly and time-consuming job of professional fact-checkers. In this work, we lift several constraints of current state-of-the-art pipelines for automated fact-checking based on the Retrieval-Augmented Generation (RAG) paradigm. Our goal is t

Weijia Shi, Xiaochuang Han, Chunting Zhou, Weixin Liang

We present LMFusion, a framework for empowering pretrained text-only large language models (LLMs) with multimodal generative capabilities, enabling them to understand and generate both text and images in arbitrary sequences. LMFusion leverages existing Llama-3's weights for processing texts autoregressively while introducing additional and parallel transform

Stefano Veroni, Alexandru Paler, Giacomo Giudice

We show that universal quantum computation can be concretely made fault-tolerant without mid-circuit measurements. To this end, we introduce a measurement-free deformation protocol of the Bacon-Shor code to realize a logical $\mathit{CCZ}$ gate. Combined with a fold-transversal logical Hadamard gate, this enables a universal set of fault-tolerant operations

Runze Liu, Prasun Datta, Anirudh Nakra, Chau-Wai Wong

The rapid development of the semiconductor industry and the ubiquity of electronic devices have led to a significant increase in the counterfeiting of integrated circuits (ICs). This poses a major threat to public health, the banking industry, and military defense sectors that are heavily reliant on electronic systems. The electronic physically unclonable fu

Or Madar, Ohad Fried

Image tiling -- the seamless connection of disparate images to create a coherent visual field -- is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to

Simon Frieder, Jonas Bayer, Sam Looi, Jacob Loader

The datasets and benchmarks commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several shortcomings and misdirections. These range from a restricted scope of mathematical complexity to limited fidelity in capturing aspects beyond the final, written proof (e.g. motivati

Marco Ambrosini, Eliezer Rabinovici, Adrián Sánchez-Garrido, Ruth Shir

In this paper we study the notion of complexity under time evolution in chaotic quantum systems with holographic duals. Continuing on from our previous work, we turn our attention to the issue of Krylov complexity upon the insertion of a class of single-particle operators in the double-scaled SYK model. Such an operator is described by a matter-chord inserti

Alexey Milekhin

It has been conjectured that the size of the black hole interior captures the quantum gate complexity of the underlying boundary evolution. In this short note we aim to provide a further microscopic evidence for this by directly relating the area of a certain codimension-two surface traversing the interior to the depth of the quantum circuit. Our arguments a

Marius Memmel, Jacob Berg, Bingqing Chen, Abhishek Gupta

Robot learning is witnessing a significant increase in the size, diversity, and complexity of pre-collected datasets, mirroring trends in domains such as natural language processing and computer vision. Many robot learning methods treat such datasets as multi-task expert data and learn a multi-task, generalist policy by training broadly across them. Notably,

Jorge Noreña, Thiago S. Pereira, Sean K. Reynolds

We investigate the dominant physical effects of superhorizon fluctuations in a flat FLRW universe, focusing on whether the combined evolution of scalar and tensor adiabatic modes in the near-horizon regime could lead to geometries beyond those predicted by the conventional separate-universe approach. Assuming a matter-dominated universe and working to first

Talal Ahmed Chowdhury, Kwangmin Yu, Muhammad Asaduzzaman, Raza Sabbir Sufian

Quantum computers are emerging technologies expected to become important tools for exploring various aspects of fundamental physics in the future. Therefore, we pose the question of whether quantum computers can help us to study the Page curve and the black hole information dynamics, which has been a key focus in fundamental physics. In this regard, we rigor

Pierre C Bellec, Tobias Fritz

A casino offers the following game. There are three cups each containing a die. You are being told that the dice in the cups are all the same, but possibly nonstandard. For a bet of \$1, the game master shakes all three cups and lets you choose one of them. You win \$2 if the die in your cup displays at least the average of the other two, and you lose otherw

Sylvain Carrozza, Aidan Chatwin-Davies, Philipp A. Hoehn, Fabio M. Mele

In a gauge theory, a collection of kinematical degrees of freedom is used to redundantly describe a smaller amount of gauge-invariant information. In a quantum error correcting code (QECC), a collection of computational degrees of freedom that make up a device's physical layer is used to redundantly encode a smaller amount of logical information. We elaborat

Aman Chaturvedi, Daniel Nichols, Siddharth Singh, Abhinav Bhatele

Large Language Model (LLM) based coding tools have been tremendously successful as software development assistants, yet they are often designed for general purpose programming tasks and perform poorly for more specialized domains such as high performance computing. Creating specialized models and tools for these domains is crucial towards gaining the benefit

Federico Castagna, Isabel Sassoon, Simon Parsons

Studies have underscored how, regardless of the recent breakthrough and swift advances in AI research, even state-of-the-art Large Language models (LLMs) continue to struggle when performing logical and mathematical reasoning. The results seem to suggest that LLMs still work as (highly advanced) data pattern identifiers, scoring poorly when attempting to gen

Lukas Aichberger, Kajetan Schweighofer, Sepp Hochreiter

Large Language Models (LLMs) are increasingly employed in real-world applications, driving the need to evaluate the trustworthiness of their generated text. To this end, reliable uncertainty estimation is essential. Leading uncertainty estimation methods generate and analyze multiple output sequences, which is computationally expensive and impractical at sca

Antonin Brossollet, Etienne Lempereur, Stéphane Mallat, Giulio Biroli

Estimating the effective energy, $E_\text{eff}$ of a stationary probability distribution is a challenge for non-equilibrium steady states. Its solution could offer a novel framework for describing and analyzing non-equilibrium systems. In this work, we address this issue within the context of scalar active matter, focusing on the continuum field theory of Ac

Francisco Lobo, Elsa Prada, Pablo San-Jose

Predictions of topological p-wave superconductivity and Majorana zero modes (MZMs) in hybrid superconductor-semiconductor nanowires have been difficult to realize experimentally. Consequently, researchers are actively exploring alternative platforms for MZMs. In this work, we theoretically study depleted nanowires with intrinsic superconductivity (as opposed

Cristina Cicali, Martino Calzavara, Eloisa Cuestas, Tommaso Calarco Robert Zeier

We focus on the optimization of neutral atom transport and transfer between optical tweezers, both critical steps towards the implementation of quantum processors and simulators. We consider four different types of experimentally relevant pulses: piece-wise linear, piece-wise quadratic, minimum jerk, and a family of hybrid linear and minimum jerk ramps. We a

Lorenzo Mercuri, Andrea Perchiazzo, Edit Rroji

A self-exciting point process with a continuous-time autoregressive moving average intensity process, named CARMA(p,q)-Hawkes model, has recently been introduced. The model generalizes the Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p,q) model where the associated state process is driven by the counting process itself. The pr

Forrest Iandola, Stanislav Pidhorskyi, Igor Santesteban, Divam Gupta

Gaussian-based human avatars have achieved an unprecedented level of visual fidelity. However, existing approaches based on high-capacity neural networks typically require a desktop GPU to achieve real-time performance for a single avatar, and it remains non-trivial to animate and render such avatars on mobile devices including a standalone VR headset due to

Wei Tseu

We identify two distinct approaches to the derived equivalence for the stratified Mukai flop of cotangent bundles of Grassmannians -- one induced by the geometric categorical sl(2) action, and the other through the magic window category of graded matrix factorizations on the gauged Landau-Ginzburg model -- via the Kn\"orrer periodicity.

Sibin Yang, Gabe Schumm, Anders W. Sandvik

We study the dynamic structure factor $S(k,\omega)$ of the spin-1/2 chain with long-range, power-law decaying unfrustrated (sign alternating) Heisenberg interactions $J_r \sim (-1)^{r-1} r^{-\alpha}$ by means of stochastic analytic continuation (SAC) of imaginary-time correlations computed by quantum Monte Carlo calculations. We do so in both the long-range

P. M. Chichura, A. Rahlin, A. J. Anderson, B. Ansarinejad

We present improvements to the pointing accuracy of the South Pole Telescope (SPT) using machine learning. The ability of the SPT to point accurately at the sky is limited by its structural imperfections, which are impacted by the extreme weather at the South Pole. Pointing accuracy is particularly important during SPT participation in observing campaigns wi

Junjia Liu, Zhuo Li, Minghao Yu, Zhipeng Dong

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid i

Pedro Sales Rodriguez, John M. Robinson, Paul Niklas Jepsen, Zhiyang He

Realizing universal fault-tolerant quantum computation is a key goal in quantum information science. By encoding quantum information into logical qubits utilizing quantum error correcting codes, physical errors can be detected and corrected, enabling substantial reduction in logical error rates. However, the set of logical operations that can be easily imple

Miguel A. Benitez, Andre H. Hoang, Vicent Mateu, Iain W. Stewart

We update a previous N$^3$LL$^\prime$+${\cal O}(\alpha_s^3)$ determination of the strong coupling from a global fit to thrust data by including newly available perturbative ingredients, upgrading the renormalization scales to include a fully canonical scaling region, and implementing the log resummation in a way which ensures the integrated cross section is

Jessica Woodgate, Paul Marshall, Nirav Ajmeri

Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL-E, a method to create ethical norm-learning agents. RAWL-E agents operationalise maximin, a fairness principle from Rawlsian ethics, in t

Serafina Kamp, Benjamin Fish

Machine learning has grown in popularity to help assign resources and make decisions about users, which can result in discrimination. This includes hiring markets, where employers have increasingly been interested in using automated tools to help hire candidates. In response, there has been significant effort to understand and mitigate the sources of discrim

Tin-Yau Tam, Xiang Xiang Wang

In this paper we obtain inequalities for the geometric mean of elements in the Grassmannians. These inequalities reflect the elliptic geometry of the Grassmannians as Riemannian manifolds. These include Semi-Parallelogram Law, Law of Cosines and geodesic triangle inequalities.

Alain Couvreur, Rakhi Pratihar, Nihan Tanısalı, Ilaria Zappatore

Twisted generalized Reed-Solomon (TGRS) codes constitute an interesting family of evaluation codes, containing a large class of maximum distance separable codes non-equivalent to generalized Reed-Solomon (GRS) ones. Moreover, the Schur squares of TGRS codes may be much larger than those of GRS codes with same dimension. Exploiting these structural difference

Jiacheng Zhang, Jie Wu, Weifeng Chen, Yatai Ji

Video diffusion models (VDMs) have demonstrated remarkable capabilities in text-to-video (T2V) generation. Despite their success, VDMs still suffer from degraded image quality and flickering artifacts. To address these issues, some approaches have introduced preference learning to exploit human feedback to enhance the video generation. However, these methods

Yang Bai, Sida Lu, Nicholas Orlofsky

This paper calculates the stochastic gravitational wave background from dark binaries with finite-range attractive dark forces, complementing previous works which consider long-range dark forces. The finiteness of the dark force range can dramatically modify both the initial distributions and evolution histories of the binaries. The generated gravitational w

Margot Niels, Tom Vanackere, Tom Vandekerckhove, Stijn Poelman

The integrated photonics CMOS-compatible silicon nitride (SiN) platform is praised for its low propagation loss, but is limited by its lack of active functionalities such as a strong Pockels coefficient and intrinsic \c{hi}(2) nonlinearity. In this paper, we demonstrate the integration of centimetre-long thin-film lithium niobate (TFLN) devices on a SiN plat

Yatai Ji, Jiacheng Zhang, Jie Wu, Shilong Zhang

Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos. However, achieving the desired output often entails multiple revisions and iterative inference to refine user-provided prompts. Current automatic methods for refin

Gerasim Kokarev

We study relationships between asymptotic geometry of submanifolds in the hyperbolic space and their regularity properties near the ideal boundary, revisiting some of the related results in the literature. In particular, we discuss hypotheses when minimal submanifolds meet the ideal boundary orthogonally, and compute the essential spectrum of the Laplace ope

J. Soto-Oton

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino oscillation experiment aiming to measure the oscillation parameters with an unprecedented precision that will allow determining the CP violation phase in the leptonic sector and the neutrino mass ordering. The Far Detector of DUNE will consist of four 17 kton liquid argon Time Projec

J. van Roestel, A. C. Rodriguez, P. Szkody, A. J. Brown

We present the discovery of 14 new (and recovery of 4 known) low accretion rate magnetic white dwarfs in post-common envelope binaries that emit strong cyclotron emission using the Zwicky Transient Facility (ZTF) light curves, doubling the known sample size. In addition, we discovered a candidate magnetic period bouncer and recovered three known ones. We con

Taryn Imamura, Teresa A. Kent, Rebecca E. Taylor, Sarah Bergbreiter

Microswimmers are sub-millimeter swimming microrobots that show potential as a platform for controllable locomotion in applications including targeted cargo delivery and minimally invasive surgery. To be viable for these target applications, microswimmers will eventually need to be able to navigate in environments with dynamic fluid flows and forces. Experim

Peidong Wang, Ming Wang, Zhiming Ma, Xiaocui Yang

Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addition, traditional training approaches rely too much on expert-labeled data, setting a ceiling on the performance of LLMs. To address this issue, we propose a novel paradigm named LA

Bastian Jäckl, Yannick Metz, Udo Schlegel, Daniel A. Keim

Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the object level. Similarly to autoencoder-based image models, object-centric approaches have been trained on the unsupervised reconstruction loss of images encoded by RGB color spaces. In our work, we challenge the common

Xin-Yi Liu, Yue-Liang Wu, Zhen Fang

We investigate the QCD phase transition and its phase structure within Einstein-Maxwell-Dilaton-scalar system and compare the results with those obtained from the Einstein-Maxwell-Dilaton system. It is shown that both models reproduce behavior consistent with lattice QCD. In particular, the Einstein-Maxwell-Dilaton-scalar system exhibits a first-order phase

Kelsie Taylor, Derek Davis, Rico K. L. Lo

For gravitationally lensed type II signals, the phase of the dominant (2, 2) mode and the higher order (3, 3) mode is offset by $-\pi/12$, or roughly -0.26 radians. Using this, we develop a test for type II imagery by allowing the phases of the (2,2) and (3,3) modes to vary separately and introducing a new waveform parameter to represent the phase offset bet

Kunal Marwaha, Adrian She, James Sud

We design two variational algorithms to optimize specific 2-local Hamiltonians defined on graphs. Our algorithms are inspired by the Quantum Approximate Optimization Algorithm. We develop formulae to analyze the energy achieved by these algorithms with high probability over random regular graphs in the infinite-size limit, using techniques from [arXiv:2110.1

Johann Hugon, Paul Schmitt, Anthony Busson, Francesco Bronzino

Modern networks increasingly rely on machine learning models for real-time insights, including traffic classification, application quality of experience inference, and intrusion detection. However, existing approaches prioritize prediction accuracy without considering deployment constraints or the dynamism of network traffic, leading to potentially suboptima