April 2024 arXiv papers — page 76

Aleksandra Olejak, Jakub Klencki, Xiao-Tian Xu, Chen Wang

The growing database of gravitational-wave (GW) detections with the binary black holes (BHs) merging in the distant Universe contains subtle insights into their formation scenarios. One of the puzzling properties of detected GW sources is the possible (anti)correlation between mass ratio q of BH-BH binaries and their effective spin. We use rapid binary evolu

Osama Karkout, Andreas Papaefstathiou, Marieke Postma, Gilberto Tetlalmatzi-Xolocotzi

The production of three Higgs bosons at hadron colliders can be enhanced by a double-resonant effect in the $\mathbb{Z}_2$-symmetric two-real-singlet extension of the Standard Model, making it potentially observable in future LHC runs. The production rate is maximized for large scalar couplings, which prompts us to carefully reconsider the perturbativity con

Hengrui Zhu, Frans Pretorius, Sizheng Ma, Robert Owen

We numerically investigate the imprints of gravitational radiation-reaction driven changes to a black hole's mass and spin on the corresponding ringdown waveform. We do so by comparing the dynamics of a perturbed black hole evolved with the full (nonlinear) versus linearized Einstein equations. As expected, we find that the quasinormal mode amplitudes extrac

Diyar Agin, Benjamin Fuks, Mark D. Goodsell, Taylor Murphy

The most recent searches by the ATLAS and CMS Collaborations in final states with soft leptons and missing transverse energy show mild excesses predominantly associated with dilepton invariant masses of about 10-20 GeV, which can result from decays of electroweakinos that are heavier than the lightest neutralino by O(10) GeV. On the other hand, these analyse

Thomas W. Grimm, Damian van de Heisteeg

Identifying flux vacua in string theory with stabilized complex structure moduli presents a significant challenge, necessitating the minimization of a scalar potential complicated by infinitely many exponential corrections. In order to obtain exact results we connect three central topics: transcendentality or algebraicity of coupling functions, emergent symm

Itai Linial, Brian D. Metzger

A modest fraction of the stars in galactic nuclei fed towards the central supermassive black hole (SMBH) approach on low-eccentricity orbits driven by gravitational-wave radiation (extreme mass ratio inspiral, EMRI). In the likely event that a gaseous accretion disk is created in the nucleus during this slow inspiral (e.g., via an independent tidal-disruptio

Bernhard Putzer, Lucas V. Pupim, Mathias S. Scheurer

Motivated by recent experiments demonstrating the creation of atomically sharp interfaces between hexagonal sapphire and cubic SrTiO$_3$ with finite twist, we here develop and study a general electronic band theory for this novel class of moir\'e heterostructures. We take into account the three-dimensional nature of the two crystals, allow for arbitrary comb

Songwei Ge, Aniruddha Mahapatra, Gaurav Parmar, Jun-Yan Zhu

Fr\'echet Video Distance (FVD), a prominent metric for evaluating video generation models, is known to conflict with human perception occasionally. In this paper, we aim to explore the extent of FVD's bias toward per-frame quality over temporal realism and identify its sources. We first quantify the FVD's sensitivity to the temporal axis by decoupling the fr

Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng

We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations. Most of the Blink tasks can be solved by humans "within a blink" (e.g., relative depth estimation, visual correspondence, forensics detection, and multi-view reasoning). However, we find these perception-d

Junyu Xie, Charig Yang, Weidi Xie, Andrew Zisserman

The objective of this paper is motion segmentation -- discovering and segmenting the moving objects in a video. This is a much studied area with numerous careful, and sometimes complex, approaches and training schemes including: self-supervised learning, learning from synthetic datasets, object-centric representations, amodal representations, and many more.

Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou

Video super-resolution (VSR) approaches have shown impressive temporal consistency in upsampled videos. However, these approaches tend to generate blurrier results than their image counterparts as they are limited in their generative capability. This raises a fundamental question: can we extend the success of a generative image upsampler to the VSR task whil

Reka Team, Aitor Ormazabal, Che Zheng, Cyprien de Masson d'Autume

We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images, video, and audio inputs. This technical report discusses details of training some of these models and provides comprehensive evaluation results. We show that Reka Edge and Reka Fl

Shengcao Cao, Jiuxiang Gu, Jason Kuen, Hao Tan

Open-world entity segmentation, as an emerging computer vision task, aims at segmenting entities in images without being restricted by pre-defined classes, offering impressive generalization capabilities on unseen images and concepts. Despite its promise, existing entity segmentation methods like Segment Anything Model (SAM) rely heavily on costly expert ann

Xinyue Wei, Kai Zhang, Sai Bi, Hao Tan

We propose MeshLRM, a novel LRM-based approach that can reconstruct a high-quality mesh from merely four input images in less than one second. Different from previous large reconstruction models (LRMs) that focus on NeRF-based reconstruction, MeshLRM incorporates differentiable mesh extraction and rendering within the LRM framework. This allows for end-to-en

Jens Hertkorn, Philipp Stürmer, Koushik Mukherjee, Kevin S. H. Ng

We theoretically investigate elementary excitations of dipolar quantum gases across the superfluid to supersolid phase transition in a toroidal trap. We show how decoupled first sound, second sound, and Higgs modes emerge by following their origin from superfluid modes across the transition. The structure of these excitations reveals the interplay between cr

Yufei Ye, Abhinav Gupta, Kris Kitani, Shubham Tulsiani

We propose G-HOP, a denoising diffusion based generative prior for hand-object interactions that allows modeling both the 3D object and a human hand, conditioned on the object category. To learn a 3D spatial diffusion model that can capture this joint distribution, we represent the human hand via a skeletal distance field to obtain a representation aligned w

Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman

We introduce a novel diffusion transformer, LazyDiffusion, that generates partial image updates efficiently. Our approach targets interactive image editing applications in which, starting from a blank canvas or an image, a user specifies a sequence of localized image modifications using binary masks and text prompts. Our generator operates in two phases. Fir

C. J. Xin, Shengyuan Lu, Jiayu Yang, Amirhassan Shams-Ansari

Recent advancements in thin-film lithium niobate (TFLN) photonics have led to a new generation of high-performance electro-optic devices, including modulators, frequency combs, and microwave-to-optical transducers. However, the broader adoption of TFLN-based devices that rely on all-optical nonlinearities have been limited by the sensitivity of quasi-phase m

David Gao, Srivatsav Kunnawalkam Elayavalli, Gregory Patchell, Hui Tan

We extract a precise internal description of the sequential commutation equivalence relation introduced in [KEP23] for tracial von Neumann algebras. As an application we prove that if a tracial von Neumann algebra $N$ is generated by unitaries $\{u_i\}_{i\in \mathbb{N}}$ such that $u_i\sim u_j$ (i.e, there exists a finite set of Haar unitaries $\{w_i\}_{i=1}

Isabella Liu, Hao Su, Xiaolong Wang

Modern 3D engines and graphics pipelines require mesh as a memory-efficient representation, which allows efficient rendering, geometry processing, texture editing, and many other downstream operations. However, it is still highly difficult to obtain high-quality mesh in terms of detailed structure and time consistency from dynamic observations. To this end,

Théo Gieruc, Marius Kästingschäfer, Sebastian Bernhard, Mathieu Salzmann

Current 3D reconstruction techniques struggle to infer unbounded scenes from a few images faithfully. Specifically, existing methods have high computational demands, require detailed pose information, and cannot reconstruct occluded regions reliably. We introduce 6Img-to-3D, an efficient, scalable transformer-based encoder-renderer method for single-shot ima

Siyuan Zhou, Yilun Du, Jiaben Chen, Yandong Li

Text-to-video models have demonstrated substantial potential in robotic decision-making, enabling the imagination of realistic plans of future actions as well as accurate environment simulation. However, one major issue in such models is generalization -- models are limited to synthesizing videos subject to language instructions similar to those seen at trai

Yiwen Kou, Zixiang Chen, Quanquan Gu, Sham M. Kakade

The $k$-sparse parity problem is a classical problem in computational complexity and algorithmic theory, serving as a key benchmark for understanding computational classes. In this paper, we solve the $k$-sparse parity problem with sign stochastic gradient descent, a variant of stochastic gradient descent (SGD) on two-layer fully-connected neural networks. W

Rafael L. Greenblatt, Eveliina Peltola

We point out that the construction of a martingale observable describing the spin interface of the two-dimensional Ising model extends to a class of non-integrable variants of the two-dimensional Ising model, and express it in terms of Grassmann integrals. Under a conjecture about the scaling limit of this object, which is similar to some results recently ob

Spencer A Hill, Destiny Zamir Meyers, Adam H Sobel, Michela Biasutti

Extreme rainfall in the Indian summer monsoon can be destructive and deadly. Although El Ni\~no/ events in the equatorial Pacific make dry days and whole summers more likely throughout India, their influence on daily extremes is not well established. Despite this summer-mean drying effect, we show using observational data spanning 1901-2020 that El Ni\~no in

Boqin Song, Yuyang Xie, Wei-Jian Li, Hui Liu

The Kondo lattice, describing a grid of the local magnetic moments coupling to itinerant electrons, is a fertile ground of strongly correlated states in condensed matter physics. While the Kagome lattice has long been predicted to host Kondo physics with exotic magnetism and nontrivial topology, no experimental realization has been achieved. Here, we report

P. P. Avelino

In the context of $f(R,T)$ gravity and other modified theories of gravity, the knowledge of the first order variation of the trace $T$ of the energy-momentum tensor with respect to the metric is essential for an accurate characterization of the gravitational field. In this paper, by considering a paradigmatic example of a perfect fluid whose dynamics is desc

Xiaotang Gai, Chenyi Zhou, Jiaxiang Liu, Yang Feng

Medical Visual Question Answering (MedVQA), which offers language responses to image-based medical inquiries, represents a challenging task and significant advancement in healthcare. It assists medical experts to swiftly interpret medical images, thereby enabling faster and more accurate diagnoses. However, the model interpretability and transparency of exis

Siva Darbha, Milan Kornjača, Fangli Liu, Jan Balewski

Metastable states arise in a range of quantum systems and can be observed in various dynamical scenarios, including decay, bubble nucleation, and long-lived oscillations. The phenomenology of metastable states has been examined in quantum many-body systems, notably in 1D ferromagnetic Ising spin systems and superfluids. In this paper, we study long-lived osc

Marcus Babin, Simone Fabian, Michael Reibe, Lukas Spantzel

The usage of a GaAsP (gallium arsenide phosphide) photomultiplier for microscopical imaging allows the evaluation of low-light luminescent objects. We designed a setup for collecting a confocal microscopic image signal, which is divided into 14 equal-sized input channels. The division is achieved with a beamsplitter and two fiber bundles consisting of seven

Marco Arazzi, Serena Nicolazzo, Antonino Nocera

Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data. Typically, in a VFL scenario, the labels of the samples are kept private from all the parties except for the aggregating server, that is the label owner. Nevertheless, recent works discovered that b

Sina Sharifi, Taha Entesari, Bardia Safaei, Vishal M. Patel

One of the challenges for neural networks in real-life applications is the overconfident errors these models make when the data is not from the original training distribution. Addressing this issue is known as Out-of-Distribution (OOD) detection. Many state-of-the-art OOD methods employ an auxiliary dataset as a surrogate for OOD data during training to achi

Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh, Fei Zhou

An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from si

Sarah Dean, Evan Dong, Meena Jagadeesan, Liu Leqi

As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of AI systems rarely accounts for how AI and users shape one another. In this position paper, we argue for the development of formal interaction models which mathematically specify ho

Asaf Yehudai, Elron Bendel

We present FastFit, a method, and a Python package design to provide fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. FastFit utilizes a novel approach integrating batch contrastive learning and token-level similarity score. Compared to existing few-shot learning packages, such as SetFit, Transformer

Zihua Guo, Luc Molinet

We revisit the local well-posedness for the KP-I equation. We obtain unconditional local well-posedness in $H^{s,0}({\mathbb R}^2)$ for $s>3/4$ and unconditional global well-posedness in the energy space. We also prove the global existence of perturbations with finite energy of non decaying smooth global solutions.

L. Nortmann, F. Lesjak, F. Yan, D. Cont

General circulation models of gas giant exoplanets predict equatorial jets that drive inhomogeneities in the atmospheric physical parameters across the planetary surface. We studied the transmission spectrum of the hot Jupiter WASP-127\,b during one transit in the K band with CRIRES$^+$. Telluric and stellar signals were removed from the data using SYSREM. T

Nils Graef

He and Hofmann (arXiv:2311.01906) detailed a skipless transformer without the V and P (post-attention projection) linear layers, which reduces the total number of weights. However, this scheme is only applicable to MHA (multi-head attention), but not for MQA (multi-query attention) and GQA (grouped-query attention). The latter schemes are used by many popula

Trevor J. Chan, Chamith S. Rajapakse

Deep learning methods for accelerated MRI achieve state-of-the-art results but largely ignore additional speedups possible with noncartesian sampling trajectories. To address this gap, we created a generative diffusion model-based reconstruction algorithm for multi-coil highly undersampled spiral MRI. This model uses conditioning during training as well as f

Siva Darbha, Milan Kornjača, Fangli Liu, Jan Balewski

Metastable states of quantum many-body systems with confinement offer a means to simulate false vacuum phenomenology, including non-equilibrium dynamical processes like decay by nucleation, in truncated limits. Recent work has examined the decay process in 1D ferromagnetic Ising spins and superfluids. In this paper, we study nucleation dynamics in 1D antifer

Julian Ost, Tanushree Banerjee, Mario Bijelic, Felix Heide

Today, most methods for image understanding tasks rely on feed-forward neural networks. While this approach has allowed for empirical accuracy, efficiency, and task adaptation via fine-tuning, it also comes with fundamental disadvantages. Existing networks often struggle to generalize across different datasets, even on the same task. By design, these network

Rafael Rafailov, Joey Hejna, Ryan Park, Chelsea Finn

Reinforcement Learning From Human Feedback (RLHF) has been critical to the success of the latest generation of generative AI models. In response to the complex nature of the classical RLHF pipeline, direct alignment algorithms such as Direct Preference Optimization (DPO) have emerged as an alternative approach. Although DPO solves the same objective as the s

Renata Ferrero, Roberto Percacci

We consider a quantum scalar field in a classical (Euclidean) De Sitter background, whose radius is fixed dynamically by Einstein's equations. In the case of a free scalar, it has been shown by Becker and Reuter that if one regulates the quantum effective action by putting a cutoff $N$ on the modes of the quantum field, the radius is driven dynamically to in

Pablo Sanchez-Martin, Kinaan Aamir Khan, Isabel Valera

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks. However, most GNN architectures aggregate information from all nodes and edges in a graph, regardless of their relevance to the task at hand, thus hindering the interpretability of their predictions. In contrast to prior work, in this paper we propo

Jingmin Sun, Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

Foundation models, such as large language models, have demonstrated success in addressing various language and image processing tasks. In this work, we introduce a multi-modal foundation model for scientific problems, named PROSE-PDE. Our model, designed for bi-modality to bi-modality learning, is a multi-operator learning approach which can predict future s

Julen Untzaga, Silvia Bonoli, David Izquierdo-Villalba, Mar Mezcua

A population of non-stellar black holes ($\gtrsim$100 M$_{\odot}$) has been long predicted to wander the Milky Way. We aim to characterize this population by using the L-Galaxies semi-analytical model applied on top of the high resolution Millennium-II merger trees. Our results predict $\sim$10 wandering black holes with masses $\sim$2 $\times$ 10$^{3}$ M$_{

Hang Hua, Yolo Yunlong Tang, Chenliang Xu, Jiebo Luo

Video summarization aims to create short, accurate, and cohesive summaries of longer videos. Despite the existence of various video summarization datasets, a notable limitation is their limited amount of source videos, which hampers the effective training of advanced large vision-language models (VLMs). Additionally, most existing datasets are created for vi

Mengyuan Liu, Zhongbin Fang, Xia Li, Joachim M. Buhmann

The rise of large-scale models has catalyzed in-context learning as a powerful approach for multitasking, particularly in natural language and image processing. However, its application to 3D point cloud tasks has been largely unexplored. In this paper, we introduce Point-In-Context (PIC), a pioneering framework for 3D point cloud understanding that leverage

A. Begnoni, L. Valbusa Dall'Armi, D. Bertacca, A. Raccanelli

Measurements of the luminosity distance of propagating gravitational waves can provide invaluable information on the geometry and content of our Universe. Due to the clustering of cosmic structures, in realistic situations we need to average the luminosity distance of events coming from patches inside a volume. In this work we evaluate, in a gauge-invariant

Rirong Yuan

In this paper, we study a broad class of fully nonlinear elliptic equations on Hermitian manifolds. On one hand, under the optimal structural assumptions we derive $C^{2,\alpha}$-estimate for solutions of the equations on closed Hermitian manifolds. On the other hand, we treat the Dirichlet problem. In both cases, we prove the existence theorems with unbound

Rohan Bhambhoria, Samuel Dahan, Jonathan Li, Xiaodan Zhu

This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. It suggests leveraging foundational models enhanced by domain-specific knowledge to overcome these issues. The paper advocates for creating open-source legal AI systems to improve accu

T. Joseph W. Lazio

Detection of radio emission from Jupiter was identified quickly as being due to its planetary-scale magnetic field. Subsequent spacecraft investigations have revealed that many of the planets, and even some moons, either have or have had large-scale magnetic fields. In the case of the Earth, Jupiter, Saturn, Uranus, and Neptune, the their magnetic fields are

Ronghuan Wu, Wanchao Su, Kede Ma, Jing Liao

Clipart, a pre-made art form, offers a convenient and efficient way of creating visual content. However, traditional workflows for animating static clipart are laborious and time-consuming, involving steps like rigging, keyframing, and inbetweening. Recent advancements in text-to-video generation hold great potential in resolving this challenge. Nevertheless

Maurício Matos, Thiago Werlang, Daniel Valente

Thermophoresis is the migration of a particle due to a thermal gradient. Here, we theoretically uncover the quantum version of thermophoresis. As a proof of principle, we analytically find a thermophoretic force on a trapped quantum particle having three energy levels in $\Lambda$ configuration. We then consider a model of N sites, each coupled to its first

Shuai Li, Ming Gong, Yu-Hang Li, Hua Jiang

Axion insulators possess a quantized axion field $\theta=\pi$ protected by combined lattice and time-reversal symmetry, holding great potential for device applications in layertronics and quantum computing. Here, we propose a high-spin axion insulator (HSAI) defined in large spin-$s$ representation, which maintains the same inherent symmetry but possesses a

Jonathan Gordon, Bernardo F. de Aguiar, João Rebouças, Guilherme Brando

Year 1 results of the Legacy Survey of Space and Time (LSST) will provide tighter constraints on small-scale cosmology, beyond the validity of linear perturbation theory. This heightens the demand for a computationally affordable prescription that can accurately capture nonlinearities in beyond-$\Lambda$CDM models. The COmoving Lagrangian Acceleration (COLA)

Harum Ahmed, Ohad Shemmer, Brandon Matthews, Cooper Dix

We present the rest-frame ultraviolet-optical spectral properties of 65 broad absorption line (BAL) quasars from the Gemini Near Infrared Spectrograph-Distant Quasar Survey (GNIRS-DQS). These properties are compared with those of 195 non-BAL quasars from GNIRS-DQS in order to identify the drivers for the appearance of BALs in quasar spectra. In particular, w

Nicolay Rusnachenko, Anton Golubev, Natalia Loukachevitch

In this paper we investigate the use of decoder-based generative transformers for extracting sentiment towards the named entities in Russian news articles. We study sentiment analysis capabilities of instruction-tuned large language models (LLMs). We consider the dataset of RuSentNE-2023 in our study. The first group of experiments was aimed at the evaluatio

Yinzhu Jin, Matthew B. Dwyer, P. Thomas Fletcher

This paper introduces a new technique to measure the feature dependency of neural network models. The motivation is to better understand a model by querying whether it is using information from human-understandable features, e.g., anatomical shape, volume, or image texture. Our method is based on the principle that if a model is dependent on a feature, then

Mohsen Mohammadi Jajin, J. Enrique Vázquez-Lozano, Iñigo Liberal

Polarization-dependent dynamical properties of light as the spin angular momentum (SAM), helicity, and chirality are conserved quantities in free-space. Despite their similarities on account of their relationship with a circular state of polarization, SAM, helicity, and chirality emerge from distinct symmetries, which endows them with different physical mean

Spencer Carmichael, Rahul Agrawal, Ram Vasudevan, Katherine A. Skinner

Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers. However, the analogous robotics capability, visual place recognition (VPR) with limited field of view cameras under 180 degree rotations, has proven to be challenging to achieve. To address this problem, this paper presents Same Place Opposing Traject

Priya Viji, Constantin Tormann, Clemens Göhler, Martijn Kemerink

Hot-carrier solar cells use the photon excess energy, that is, the energy exceeding the absorber bandgap, to do additional work. These devices have the potential to beat the upper limit for the photovoltaic power conversion efficiency set by near-equilibrium thermodynamics. However, since their conceptual inception in 1982, no experimental realization that w

Pavel Orlov, Georgy V. Shlyapnikov, Denis V. Kurlov

The quantum geometric tensor has established itself as a general framework for the analysis and detection of equilibrium phase transitions in isolated quantum systems. We propose a novel generalization of the quantum geometric tensor, which offers a universal approach to studying phase transitions in non-Hermitian quantum systems. Our generalization is based

Samuel Coward, Theo Drane, Emiliano Morini, George Constantinides

Industrial datapath designers consider dynamic power consumption to be a key metric. Arithmetic circuits contribute a major component of total chip power consumption and are therefore a common target for power optimization. While arithmetic circuit area and dynamic power consumption are often correlated, there is also a tradeoff to consider, as additional ga

Germano D'Abramo

One of the most widespread interpretations of the mass-energy equivalence establishes that not only can mass be transformed into energy (e.g., through nuclear fission, fusion, or annihilation) but that every type of energy also has mass (via the mass-energy equivalence formula). Here, we show that this is not always the case. With the help a few thought expe

Nick Feng, Lina Marsso, S. Getir Yaman, Isobel Standen

Normative non-functional requirements specify constraints that a system must observe in order to avoid violations of social, legal, ethical, empathetic, and cultural norms. As these requirements are typically defined by non-technical system stakeholders with different expertise and priorities (ethicists, lawyers, social scientists, etc.), ensuring their well

Claudio Llosa Isenrich

Given a finitely presented group $G$ and a surjective homomorphism $G\to \mathbb{Z}^n$ with finitely presented kernel $K$, we give an upper bound on the Dehn function of $K$ in terms of an area-radius pair for $G$. As a consequence we obtain that finitely presented coabelian subgroups of hyperbolic groups have polynomially bounded Dehn function. This general

Nupur Kumari, Grace Su, Richard Zhang, Taesung Park

Model customization introduces new concepts to existing text-to-image models, enabling the generation of these new concepts/objects in novel contexts. However, such methods lack accurate camera view control with respect to the new object, and users must resort to prompt engineering (e.g., adding ``top-view'') to achieve coarse view control. In this work, we

E. Emanuel Rapsch

A general theory of stochastic decision forests is developed to bridge two concepts of information flow: decision trees and refined partitions on the one side, filtrations from probability theory on the other. Instead of the traditional "nature" agent, this framework uses a single lottery draw to select a tree of a given decision forest. Each "personal" agen

Yutian Bu, Chenyu He, Li Wang, Jiamao Lin

Research has shown that many young and intermediate-age clusters (younger than $\sim$2 Gyr) have extended main sequences and main-sequence turnoffs (eMSTOs), which cannot be adequately described by a single isochrone. The reason for the extended main sequences is now known, with the most probable cause being the fast rotation of stars. However, a significant

Christoph Reich, Oliver Hahn, Daniel Cremers, Stefan Roth

Resource-constrained hardware, such as edge devices or cell phones, often rely on cloud servers to provide the required computational resources for inference in deep vision models. However, transferring image and video data from an edge or mobile device to a cloud server requires coding to deal with network constraints. The use of standardized codecs, such a

Lukas Brunke, Siqi Zhou, Mingxuan Che, Angela P. Schoellig

Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement learning. Here, safety is defined as staying in a pre-defined set, the safe set, that adheres to the system's state constraints, e.g., as given by lane boundaries for a self-driving

Mads M. Lund, Fan Yang, Victor Rueskov Christiansen, Danil Kornovan

Coherent manipulation of quantum states of light is key to photonic quantum information processing. In this Letter, we show that a passive two-level nonlinearity suffices to implement non-Gaussian quantum operations on propagating field modes. In particular, the collective light-matter interaction can efficiently extract a single photon from a multi-photon i

Lorenzo Gavassino

We provide rigorous criteria to determine whether the non-hydrodynamic sector of a relativistic kinetic theory is gapless. These general criteria apply to both Boltzmann's equation and approximate models thereof, provided that the latter are consistent with thermodynamic principles. As an application of these criteria, we prove that the non-hydrodynamic sect

Imen Rjaiba

We give an explicit description of three operad structures on the species composition $p \circ q$, where $q$ is any given positive operad, and where $p$ is the NAP operad, or a shuffle version of the magmatic operad Mag. No distributive law between $p$ and $q$ is assumed.

Simon Badger, Matteo Becchetti, Nicolò Giraudo, Simone Zoia

We compute the differential equations for the two remaining integral topologies contributing to the leading colour two-loop amplitudes for $pp \rightarrow t\bar{t}j$. We derive differential equations for the master integrals by solving the integration-by-parts identities over finite fields. Of the two systems of differential equations, one is presented in ca

Daniela Cadamuro, Markus B. Fröb, Carolina Moreira Ferrera

We consider the massless Sine-Gordon model in de Sitter spacetime, in the regime $\beta^2 < 4 \pi$ and using the framework of perturbative algebraic quantum field theory. We show that a Fock space representation exists for the free massless field, but that the natural one-parameter family of vacuum-like states breaks the de Sitter boost symmetries. We prove

Yannick Deller, Martin Gärttner, Tobias Haas, Markus K. Oberthaler

We investigate the information extractable from measurement distributions of two non-commuting spin observables in a multi-well spin-1 Bose-Einstein condensate. We provide a variety of analytic and numerical evidence that suitably chosen classical entropies and classical mutual informations thereof contain the typical feature of quantum entropies known in qu

Jiayi Liang, Haotian Liu, Hongteng Xu, Dixin Luo

As a significant step for human face modeling, editing, and generation, face landmarking aims at extracting facial keypoints from images. A generalizable face landmarker is required in practice because real-world facial images, e.g., the avatars in animations and games, are often stylized in various ways. However, achieving generalizable face landmarking is

Yannick Deller, Martin Gärttner, Tobias Haas, Markus K. Oberthaler

The scaling of local quantum entropies is of utmost interest for characterizing quantum fields, many-body systems, and gravity. Despite their importance, theoretically and experimentally accessing quantum entropies is challenging as they are nonlinear functionals of the underlying quantum state. Here, we show that suitably chosen classical entropies capture

Tobias Haas

The area law-like scaling of local quantum entropies is the central characteristic of the entanglement inherent in quantum fields, many-body systems, and spacetime. Whilst the area law is primarily associated with the entanglement structure of the underlying quantum state, we here show that it equally manifests in classical entropies over measurement distrib

Simon Nik

Time series in real-world applications often have missing observations, making typical analytical methods unsuitable. One method for dealing with missing data is the concept of amplitude modulation. While this principle works with any data, here, missing data for unbounded and bounded count time series are investigated, where tailor-made dispersion and skewn

Zhaofeng Wu, Ananth Balashankar, Yoon Kim, Jacob Eisenstein

Aligning language models (LMs) based on human-annotated preference data is a crucial step in obtaining practical and performant LM-based systems. However, multilingual human preference data are difficult to obtain at scale, making it challenging to extend this framework to diverse languages. In this work, we evaluate a simple approach for zero-shot cross-lin

Jiangbo Yu, Graeme McKinley

Unleashing the synergies among rapidly evolving mobility technologies in a multi-stakeholder setting presents unique challenges and opportunities for addressing urban transportation problems. This paper introduces a novel synthetic participatory method that critically leverages large language models (LLMs) to create digital avatars representing diverse stake

Dong-Liang Fang, Yu-Feng Li, Yi-Yu Zhang, Jing-Yu Zhu

In this work we discuss the neutrino mass dependent nuclear matrix element (NME) of the neutrinoless double beta decay process and derive the limit on the parameter space of the minimal Type-I seesaw model from the current available experimental data as well as the future sensitivities from the next-generation experiments. Both the explicit many-body calcula

Defne E. Ozan, Luca Magri

In one calculation, adjoint sensitivity analysis provides the gradient of a quantity of interest with respect to all system's parameters. Conventionally, adjoint solvers need to be implemented by differentiating computational models, which can be a cumbersome task and is code-specific. To propose an adjoint solver that is not code-specific, we develop a data

Jun Han, Zixiang Chen, Yongqian Li, Yiwen Kou

Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e.g., disease progression prediction, clinical trial design, and health economics and outcomes research. Despite wide usability, their sensitive nature raises privacy and confidentially concerns, which limit potential use cases. To tackle

Ana Luiza Tenório, Hugo Luiz Mariano

In this paper, we present a generalization of Grothendieck pretopologies -- suited for semicartesian categories with equalizers $C$ -- leading to a closed monoidal category of sheaves, instead of closed cartesian category. This is proved through a different sheafification process, which is the left adjoint functor of the suitable inclusion functor but does n

Yuchen Zhu, Yufeng Zhang, Zhaoran Wang, Zhuoran Yang

This paper studies minimax optimization problems defined over infinite-dimensional function classes of overparameterized two-layer neural networks. In particular, we consider the minimax optimization problem stemming from estimating linear functional equations defined by conditional expectations, where the objective functions are quadratic in the functional

R. Elwell, Christian Schneider, Justin Jeet, J. E. S. Terhune

LiSrAlF$_6$ crystals doped with $^{229}$Th are used in a laser-based search for the nuclear isomeric transition. Two spectroscopic features near the nuclear transition energy are observed. The first is a broad excitation feature that produces red-shifted fluorescence that decays with a timescale of a few seconds. The second is a narrow, laser-linewidth-limit

Rishi R. Paudel, Thomas Barclay, Allison Youngblood, Elisa V. Quintana

We present a comprehensive multiwavelength investigation into flares and activity in nearby M~dwarf stars. We leverage the most extensive contemporaneous dataset obtained through the Transiting Exoplanet Sky Survey (TESS), Kepler/K2, the Neil Gehrels Swift Observatory (\textit{Swift}), and the Hubble Space Telescope (HST), spanning the optical and near-ultra

Md Adnan Arefeen, Biplob Debnath, Md Yusuf Sarwar Uddin, Srimat Chakradhar

Retrieval-augmented generation (RAG) systems combine the strengths of language generation and information retrieval to power many real-world applications like chatbots. Use of RAG for understanding of videos is appealing but there are two critical limitations. One-time, upfront conversion of all content in large corpus of videos into text descriptions entail

Marius Memmel, Andrew Wagenmaker, Chuning Zhu, Patrick Yin

Model-free control strategies such as reinforcement learning have shown the ability to learn control strategies without requiring an accurate model or simulator of the world. While this is appealing due to the lack of modeling requirements, such methods can be sample inefficient, making them impractical in many real-world domains. On the other hand, model-ba

Shilpa Samdani, Yaqi Rong, Birte Coester, Amit Kumar Shukla

FM1/NM/FM2 trilayers have garnered considerable attention because of their potential in spintronic applications. A thorough investigation of the spin transport properties of these trilayers is therefore important. Asymmetric trilayers, particularly those including Platinum (Pt) as a spacer are less explored. Pt mediates exchange coupling between the two FM l

Suyash Vardhan Singh, Rakeshkumar Mahto

The demand for low power processing is increasing due to mobile and portable devices. In a processor unit, an adder is an important building block since it is used in Floating Point Units (FPU) and Arithmetic Logic Units (ALU). Also, pipeline techniques are used extensively to improve the throughput of the processing unit. To implement a pipeline requires ad

Shiwen Kou, Chungang Yang, Mingji Wu

Intent-driven Networks (IDNs) are crucial in enhancing network management efficiency by enabling the translation of high-level intents into executable configurations via a top-down approach. The escalating complexity of network architectures, however, has led to a semantic gap between these intents and their actual configurations, posing significant challeng

Manuel Ruivo de Oliveira

We rigorously establish the existence of many free boundary minimal annuli with boundary in a geodesic sphere of $\mathbb{S}^3$. These arise as compact subdomains of a one-parameter family of complete minimal immersions of $\mathbb{R} \times \mathbb{S}^1$ into $\mathbb{S}^3$ described by do Carmo and Dajczer. While the immersed free boundary minimal annuli w

Nathan Priddis, Mark Shoemaker, Yaoxiong Wen

We prove the crepant transformation conjecture for relative Grassmann flops over a smooth base $B$. We show that the $I$-functions of the respective GIT quotients are related by analytic continuation and a symplectic transformation. We verify that the symplectic transformation is compatible with Iritani's integral structure, that is, that it is induced by a

Wendelin Lutz, Qaasim Shafi, Rachel Webb

We prove an explicit form of the Crepant Transformation Conjecture for Grassmannian flops. Our approach uses abelianization to first relate the restrictions of the Lagrangian cones to degree-2 classes, and then deduces the general result using ``explicit reconstruction'' (also known as the method of big I-functions).

Federico Ambrosino, Ran Luo, Yi-Nan Wang, Yi Zhang

We explore new aspects of internal fermionic shifting symmetries, present in physical systems such as free Dirac spinors and p-form tensor-spinor fields. We propose a novel procedure to gauge these global symmetries, which also introduces a new St\"uckelberg mechanism to give a mass to free fermionic fields. Furthermore, we find new magnetic fermionic symmet