October 2024 arXiv papers — page 12

Mohammad Shahmoradi, Ahmet Yelboğa, Eduard Alarcón, Korkut Kaan Tokgöz

Wireless Network-on-Chip (WNoC) systems enable low-latency communication in many-core platforms through short-range wireless links. However, the power consumption of integrated transceivers (TRXs), dominated by that of the RF front-end circuitry, remains a major challenge. Moreover, the optimal operating frequency is still unclear, as bandwidth, energy effic

Daniel Barros, Paula Fraga-Lamas, Tiago M. Fernandez-Carames, Sergio Ivan Lopes

The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-ce

Michal Sedlák, Robert Stárek, Nikola Horová, Michal Mičuda

We address the fundamental task of converting $n$ uses of an unknown unitary transformation into a quantum state (i.e., storage) and later retrieval of the transformation. Specifically, we consider the case where the unknown unitary is selected with equal prior probability from two options. First, we prove that the optimal storage strategy involves the seque

Yuting Bai, Gabriel Cardoso, Rajae Malek, Qing-Dong Jiang

We investigate the geometric response of chiral superfluids when coupled to a dynamic background geometry. We find that geometry fluctuations, represented by the flexural mode, interact with the superfluid phase fluctuations (the Goldstone mode). Starting from a minimally coupled theory, we derive the equilibrium conditions for a static background defined by

Vinicius Lima, Umit Karabiyik

Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This domain has been extensively studied by researchers in sociology, psychology, and statistics. More recently, it has drawn interest from computer scientists, especially with advances in artif

V. Shah, K. Shin, C. Leung, W. Fong

We report the discovery of the repeating fast radio burst source FRB 20240209A using the CHIME/FRB telescope. We have detected 22 bursts from this repeater between February and July 2024, six of which were also recorded at the Outrigger station KKO. The 66-km long CHIME-KKO baseline can provide single-pulse FRB localizations along one dimension with $2^{\pri

Jhordan Silveira de Borba, Jonas Maziero

The first artificial quantum neuron models followed a similar path to classic models, as they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test, and implement a neuron model that works with continuous values in a quantum computer. Through simulations, w

Alice M. Eltvedt, T. Shanks, N. Metcalfe, B. Ansarinejad

We exploit the VST ATLAS quasar/QSO catalogue to perform three measurements of the quasar halo mass profile. First, we make a new estimate of the angular auto-correlation function of $\approx230,000$ ATLAS quasars with $z_{photo}\lesssim 2.5$ and $17<g<22$. By comparing with the $\Lambda$CDM mass clustering correlation function, we measure the quasar bias to

Keiichi Namikoshi, David A. Shamma, Rumen Iliev, Jingchao Fang

Behavior change interventions are important to coordinate societal action across a wide array of important applications, including the adoption of electrified vehicles to reduce emissions. Prior work has demonstrated that interventions for behavior must be personalized, and that the intervention that is most effective on average across a large group can resu

João Daniel Silva, Joao Magalhaes, Devis Tuia, Bruno Martins

Methods based on Contrastive Language-Image Pre-training (CLIP) are nowadays extensively used in support of vision-and-language tasks involving remote sensing data, such as cross-modal retrieval. The adaptation of CLIP to this specific domain has relied on model fine-tuning with the standard contrastive objective, using existing human-labeled image-caption d

Stefan Rothe, Fabio Barbosa, Jürgen W. Czarske, Filipe M. Ferreira

Programmable optical devices provide performance enhancement and flexibility to spatial multiplexing systems enabling transmission of tributaries in high-order eigenmodes of spatially-diverse transmission media, like multimode fiber (MMF). Wavefront shaping with spatial light modulators (SLMs) facilitates scalability of the transmission media by allowing for

Amin Ranem, John Kalkhof, Anirban Mukhopadhyay

Continual learning (CL) in medical imaging presents a unique challenge, requiring models to adapt to new domains while retaining previously acquired knowledge. We introduce NCAdapt, a Neural Cellular Automata (NCA) based method designed to address this challenge. NCAdapt features a domain-specific multi-head structure, integrating adaptable convolutional lay

Mohsen Hafez-Torbati

The antiferromagnetic quantum Hall insulator (AFQHI), where one of the spin components is in the quantum Hall state and the other in the trivial state, is an established phase emerging as a result of the Hubbard repulsion in spinful quantum Hall systems. The stabilization of the AFQHI requires a charge order preventing the effect of the spin-flip transformat

Angel Niebla-Montero, Ivan Froiz-Miguez, Paula Fraga-Lamas, Tiago M. Fernandez-Carames

Wireless communications are critical in the constantly changing environment of IoT and RFID technologies, where thousands of devices can be deployed across a wide range of scenarios. Whether connecting to cloud servers or local fog/edge devices, maintaining seamless communications is difficult, especially in demanding contexts like industrial warehouses or r

Ahmed Akib Jawad Karim, Shahria Hoque, Md. Golam Rabiul Alam, Md. Zia Uddin

Organizational success in todays competitive employment market depends on choosing the right staff. This work evaluates software engineer profiles using an automated staff selection method based on advanced natural language processing (NLP) techniques. A fresh dataset was generated by collecting LinkedIn profiles with important attributes like education, exp

H. W. T. Morgan, R. Elwell, J. E. S. Terhune, H. B. Tran Tan

The recent laser excitation of the 229Th isomeric transition in a solid-state host opens the door for a portable solid-state nuclear optical clock. However, at present the vacuum-ultraviolet laser systems required for clock operation are not conducive to a fieldable form factor. Here, we propose a possible solution to this problem by using 229Th-doped nonlin

Connor T. Hann, Kyungjoo Noh, Harald Putterman, Matthew H. Matheny

Dissipative cat qubits are a promising physical platform for quantum computing, since their large noise bias can enable more hardware-efficient quantum error correction. In this work we theoretically study the long-term prospects of a hybrid cat-transmon quantum computing architecture where dissipative cat qubits play the role of data qubits, and error syndr

Pablo Carrasco, Gonzalo Muñoz

The non-convex nature of trained neural networks has created significant obstacles in their incorporation into optimization models. In this context, Anderson et al. (2020) provided a framework to obtain the convex hull of the graph of a piecewise linear convex activation function composed with an affine function; this effectively convexifies activations such

Stefan Rothe, Kabish Wisal, Chun-Wei Chen, Mert Ercan

Multimode fibers provide a promising platform for realizing high-power laser amplifiers with suppressed nonlinearities and instabilities. The potential degradation of optical beam quality has been a major concern for highly multimode fiber amplifiers. We show numerically that the beam propagation factor M2 of a single-frequency multimode fiber amplifier can

Samuel W. Flint, Ali M. Keshk, Robert Dyer, Hamid Bagheri

Statically typed languages offer numerous benefits to developers, such as improved code quality and reduced runtime errors, but they also require the overhead of manual type annotations. To mitigate this burden, language designers have started incorporating support for type inference, where the compiler infers the type of a variable based on its declaration/

Axel Klawonn, Martin Lanser, Janine Weber

In many modern computer application problems, the classification of image data plays an important role. Among many different supervised machine learning models, convolutional neural networks (CNNs) and linear discriminant analysis (LDA) as well as sophisticated variants thereof are popular techniques. In this work, two different domain decomposed CNN models

Philip Boalch

Any algebraic connection on a vector bundle on a smooth complex algebraic curve determines an irregular class and in turn a fission tree at each puncture. The fission trees are the discrete data classifying the admissible deformation classes. Here we explain how to count the fission trees with given slope and number of leaves, in the untwisted case. This als

Ivan Froiz-Miguez, Paula Fraga-Lamas, Tiago M. Fernandez-Carames

One of the main limitations for the development and deployment of many Green Radio Frequency Identification (RFID) and Internet of Things (IoT) systems is the access to energy sources. In this aspect batteries are the main option to be used in energy constrained scenarios, but their use is limited to certain cases, either because of the constraints imposed b

Seunghan Lee, Juri Hong, Kibok Lee, Taeyoung Park

Mamba has recently emerged as a promising alternative to Transformers, offering near-linear complexity in processing sequential data. However, while channels in time series (TS) data have no specific order in general, recent studies have adopted Mamba to capture channel dependencies (CD) in TS, introducing a sequential order bias. To address this issue, we p

Shi Chen, Aleksey Cherman, Gongjun Choi, Maria Neuzil

We discuss unusual $\theta$ terms that can appear in field theories that allow global vortices. These `Cheshire $\theta$ terms' induce Aharonov-Bohm effects for some particles that move around vortices. For example, a Cheshire $\theta$ term can appear in QCD coupled to an axion and induces Aharonov-Bohm effects for baryons and leptons moving around axion str

David T. Stephen, Rahul Nandkishore, Jian-Hao Zhang

A catalyst is a substance that enables otherwise impossible transformations between states of a system, without being consumed in the process. In this work, we apply the notion of catalysts to many-body quantum physics. In particular, we construct catalysts that enable transformations between different symmetry-protected topological (SPT) phases of matter us

Yoshifumi Nakata, Yuki Takeuchi, Martin Kliesch, Andrew Darmawan

We investigate unitary and state $t$-designs from a computational complexity perspective. First, we address the problems of computing frame potentials that characterize (approximate) $t$-designs. We present a quantum algorithm for computing frame potentials and establish the following: (1) exact computation can be achieved by a single query to a $\# \textsf{

Pavan Hosur

Recent advancements in the realizations of superconducting diodes have pushed the diode coefficient $\eta$ towards its theoretical maximum of $\eta=1$. In this work, we describe the construction of logic gates NOT, AND, OR, NAND and NOR using superconducting diodes with $\eta\approx1$ by exploiting their dynamically tunable polarity. We then argue that funda

Remya Ajai A S, Nithin Nagaraj

Inspired by the human brain's structure and function, Artificial Neural Networks (ANN) were developed for data classification. However, existing Neural Networks, including Deep Neural Networks, do not mimic the brain's rich structure. They lack key features such as randomness and neuron heterogeneity, which are inherently chaotic in their firing behavior. Ne

Clare Burrage, Benjamin Elder, Yeray Garcia del Castillo, Joerg Jaeckel

Wave-like dark matter may feature quadratic couplings to ordinary matter. This carries profound consequences for the phenomenologies of such models. It changes the dark matter density around dense objects made from ordinary matter such as planets and stars, thereby changing the sensitivity of direct detection experiments on Earth as well as implying forces o

Chenyang An, Shima Imani, Feng Yao, Chengyu Dong

In the field of large language model (LLM)-based proof generation, despite extensive training on large datasets such as ArXiv, LLMs still exhibit only modest performance on proving tasks of moderate difficulty. We believe that this is partly due to the widespread presence of suboptimal ordering within the data for each proof used in training. For example, pu

Lu Shen, Casey Papovich, Jasleen Matharu, Nor Pirzkal

We use JWST/NIRISS slitless spectroscopy from the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey to investigate the physical condition of star-forming galaxies at $1.7 < z < 3.4$. At these redshifts, the deep NGDEEP NIRISS slitless spectroscopy covers the [O II]$\lambda\lambda$3726,3729, [O III]$\lambda\lambda$4959,5007, H$\beta$ and H

Kylar Greene, Aurora Ireland, Gordan Krnjaic, Yuhsin Tsai

A B-mode polarization signal in the cosmic microwave background (CMB) is widely regarded as smoking gun evidence for gravitational waves produced during inflation. Here, we demonstrate that tensor perturbations sourced during non-inflationary epochs can yield non-negligible B-mode signals, which can in principle complicate the interpretation of future observ

Diego Blas, Vitor Cardoso, Jose María Ezquiaga

Black holes are extreme outcomes of General Relativity, and can form through a variety of ways, including gravitational collapse of massive stars, or quantum fluctuations in the early universe. Here, we ask the question of whether they can form via focusing of radiation by compact binaries or intense lasers, or via trapping at the light ring of black holes.

Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai

We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent. Massive stars with a Zero Age Main Sequence mass of more than about 10 $\mathrm{M_\odot}$ explode as core-collapse supernovae (CCSNe), which play a critical role in galaxy formation. The energy released by CCSNe

Michael C. H. Yeung, Gabriele Ponti, Michael J. Freyberg, Konrad Dennerl

The SRG/eROSITA All-Sky Surveys (eRASSs) combine the advantages of complete sky coverage and the energy resolution provided by the charge couple device and offer the most holistic and detailed view of the diffuse soft X-ray background (SXRB) to date. The first eRASS (eRASS1) was completed at solar minimum, when solar wind charge exchange emission was minimal

Christopher K Jones, Michaela Leung, Chenyi Tu, Saleheh Ebadirad

The Hadean, once thought to be uninhabitable and tumultuous, has more recently been recontextualized as a clement time in which oceans, land, and life likely appeared on Earth. This non-exhaustive chapter follows multiple threads from planet formation to the origin of life. We place significant emphasis on the solar system context for the Earth, the timing a

Jesse Thaler, Sokratis Trifinopoulos

The Cabibbo-Kobayashi-Maskawa (CKM) matrix, which controls flavor mixing between the three generations of quark fermions, is a key input to the Standard Model of particle physics. In this paper, we identify a surprising connection between quantum entanglement and the degree of quark mixing. Focusing on a specific limit of $2 \to 2$ quark scattering mediated

Cameron Beetar, Mariana Carrillo González, Sumer Jaitly, Théo Keseman

The double copy relates gravitational theories to the square of gauge theories. While it is well understood in flat backgrounds, its precise realisation around curved spacetimes remains an open question. In this paper, we construct a classical double copy for cohomology class representatives in the minitwistor space of hyperbolic spacetimes. We find that the

Alice M. Eltvedt, T. Shanks, 1 N. Metcalfe, B. Ansarinejad

We cross-correlate a low-contamination subset of the VST ATLAS g < 22.5 quasar catalogue with g < 21.5 galaxy clusters, r < 21 galaxies and r < 19.5 Luminous Red Galaxies (LRGs) to probe their halo mass profiles via quasar magnification bias caused by weak lensing. In the case of galaxy clusters we find that at small scales their mass profiles are well fitte

Benjamin J. Gibson, Gail Zasowski, Anil Seth, Dimitri A. Gadotti

We describe a novel framework to model galaxy spectra with two cospatial stellar populations, such as may represent a bulge & bar or thick & thin disc, and apply it to APOGEE spectra in the inner $\sim$2 kpc of M31, as well as to stacked spectra representative of the northern and southern parts of M31's disc ($R\sim4-7$ kpc). We use a custom M31 photometric

Vadim A. Semenov

Modeling unresolved turbulence in astrophysical gasdynamic simulations can improve the modeling of other subgrid processes dependent on the turbulent structure of gas: from flame propagation in the interiors of combusting white dwarfs to star formation and chemical reaction rates in the interstellar medium, and nonthermal pressure support of circum- and inte

Thomas W. Grimm, Mick van Vliet

We initiate a study of the complexity of quantum field theories (QFTs) by proposing a measure of information contained in a QFT and its observables. We show that from minimal assertions, one is naturally led to measure complexity by two integers, called format and degree, which characterize the information content of the functions and domains required to spe

Matthew E. Orr, Douglas Rennehan

We present a simple model for the number distribution of maximally star-forming clumps in rotating disk galaxies, at high-$z$ with high gas surface densities. By combining assumptions surrounding marginal stability of disks against gravitational fragmentation and collapse (i.e., Toomre's $Q\approx 1$), star cluster formation efficiency scaling with local gas

T. Eftekhari, Y. Dong, W. Fong, V. Shah

The discovery and localization of FRB20240209A by the Canadian Hydrogen Intensity Mapping Fast Radio Burst (CHIME/FRB) experiment marks the first repeating FRB localized with the CHIME/FRB Outriggers and adds to the small sample of repeating FRBs with associated host galaxies. Here we present Keck and Gemini observations of the host that reveal a redshift $z

Teresa Panurach, Kristen C. Dage, Ryan Urquhart, Richard M. Plotkin

Ultraluminous X-ray sources (ULXs) were once largely believed to be powered by super-Eddington accretion onto stellar-mass black holes, although in some rare cases, ULXs also serve as potential candidates for (sub-Eddington) intermediate mass black holes. However, a total of eight ULXs have now been confirmed to be powered by neutron stars, thanks to observe

Pawel Caputa, Bowen Chen, Ross W. McDonald, Joan Simón

We demonstrate a precise relation between the rate of complexity of quantum states excited by local operators in two-dimensional conformal field theories and the radial momentum of particles in 3-dimensional Anti-de Sitter spacetimes. Similar relations have been anticipated based on qualitative models for operator growth. Here, we make this correspondence sh

Andrea Guerrieri, Kelian Häring, Ning Su

We propose a novel strategy to fit experimental data using a UV complete amplitude ansatz satisfying the constraints of Analyticity, Crossing, and Unitarity. We focus on $\pi\pi$ scattering combining both experimental and lattice data. The fit strategy requires using S-matrix Bootstrap methods and non-convex Particle Swarm Optimization techniques. Using this

Jie Zhu, Yixiong Chen, Mingyu Ding, Ping Luo

Text-to-image diffusion has attracted vast attention due to its impressive image-generation capabilities. However, when it comes to human-centric text-to-image generation, particularly in the context of faces and hands, the results often fall short of naturalness due to insufficient training priors. We alleviate the issue in this work from two perspectives.

Irmak Guzey, Yinlong Dai, Georgy Savva, Raunaq Bhirangi

Training robots directly from human videos is an emerging area in robotics and computer vision. While there has been notable progress with two-fingered grippers, learning autonomous tasks for multi-fingered robot hands in this way remains challenging. A key reason for this difficulty is that a policy trained on human hands may not directly transfer to a robo

Jonathan McManus, Vitaliy Kurlin

The fundamental model of any periodic crystal is a periodic set of points at all atomic centres. Since crystal structures are determined in a rigid form, their strongest equivalence is rigid motion (composition of translations and rotations) or isometry (also including reflections). The recent classification of periodic point sets under rigid motion used a c

Anurag Bagchi, Zhipeng Bao, Yu-Xiong Wang, Pavel Tokmakov

We present REM, a framework for segmenting a wide range of concepts in video that can be described through natural language. Our method leverages the universal visual-language mapping learned by video diffusion models on Internet-scale data by fine-tuning them on small-scale Referring Object Segmentation datasets. Our key insight is to preserve the entirety

HaRu K. Park, SungBin Lee

Spin-$1$ chain models have been extensively studied in condensed matter physics, significantly advancing our understanding of quantum magnetism and low-dimensional systems, which exhibit unique properties compared to their spin-$1/2$ counterparts. Despite substantial progress in this area, providing a rigorous proof of nonintegrability for the bilinear-biqua

Gen Li, Changxiao Cai

Score-based diffusion models, while achieving minimax optimality for sampling, are often hampered by slow sampling speeds due to the high computational burden of score function evaluations. Despite the recent remarkable empirical advances in speeding up the score-based samplers, theoretical understanding of acceleration techniques remains largely limited. To

Michał Pietruszka, Łukasz Borchmann, Aleksander Jędrosz, Paweł Morawiecki

We present a benchmark for large language models designed to tackle one of the most knowledge-intensive tasks in data science: writing feature engineering code, which requires domain knowledge in addition to a deep understanding of the underlying problem and data structure. The model is provided with a dataset description in a prompt and asked to generate co

Adam Artymowicz, Hamza Fawzi, Omar Fawzi, Samuel O. Scalet

We consider the problem of learning the Hamiltonian of a quantum system from estimates of Gibbs-state expectation values. Various methods for achieving this task were proposed recently, both from a practical and theoretical point of view. On the one hand, some practical algorithms have been implemented and used to analyze experimental data but these algorith

Ola Shorinwa, Matthew Devlin, Elliot W. Hawkes, Mac Schwager

We present DisCo, a distributed algorithm for contact-rich, multi-robot tasks. DisCo is a distributed contact-implicit trajectory optimization algorithm, which allows a group of robots to optimize a time sequence of forces to objects and to their environment to accomplish tasks such as collaborative manipulation, robot team sports, and modular robot locomoti

Tamas Gombor

The overlaps between integrable matrix product states (MPS) and Bethe states are important in both the non-equilibrium statistical physics and the AdS/CFT duality. We present the general MPS overlap formula. The result is a product of a ratio of Gaudin determinants and a prefactor. The Gaudin determinants depend on the spin chain but not on the MPS. The MPS

Richard D. Barney, Yunxiang Liao, Victor Galitski

Quantum circuits have become a powerful tool in the study of many-body quantum physics, providing insights into both fast-thermalizing chaotic and non-thermalizing integrable many-body dynamics. In this work, we explore a distinct intermediate class - glassy quantum systems - where thermalization occurs, but over very long timescales. We introduce and analyz

Ahmed Akib Jawad Karim, Kazi Hafiz Md. Asad, Md. Golam Rabiul Alam

This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT based model for natural language processing applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related conce

Qingyu Shi, Lu Qi, Jianzong Wu, Jinbin Bai

Customized image generation is essential for creating personalized content based on user prompts, allowing large-scale text-to-image diffusion models to more effectively meet individual needs. However, existing models often neglect the relationships between customized objects in generated images. In contrast, this work addresses this gap by focusing on relat

Bin Wu, Shinnosuke Takamichi, Sakriani Sakti, Satoshi Nakamura

The marmoset, a highly vocal primate, is a key model for studying social-communicative behavior. Unlike human speech, marmoset vocalizations are less structured, highly variable, and recorded in noisy, low-resource conditions. Learning marmoset communication requires joint call segmentation, classification, and caller identification -- challenging domain tas

Hongbo Zhao, Lue Fan, Yuntao Chen, Haochen Wang

In this paper, we propose OpenSatMap, a fine-grained, high-resolution satellite dataset for large-scale map construction. Map construction is one of the foundations of the transportation industry, such as navigation and autonomous driving. Extracting road structures from satellite images is an efficient way to construct large-scale maps. However, existing sa

Yining Hong, Beide Liu, Maxine Wu, Yuanhao Zhai

Human beings are endowed with a complementary learning system, which bridges the slow learning of general world dynamics with fast storage of episodic memory from a new experience. Previous video generation models, however, primarily focus on slow learning by pre-training on vast amounts of data, overlooking the fast learning phase crucial for episodic memor

Christine Gaßner

This paper focuses on the set HAC of 1-1 Ackermann axioms of choice in second-order predicate logic with Henkin interpretation (HPL). To answer a question posed by Michael Rathjen, we restrict the proof that the basic Fraenkel model of second order is a model of all n-m Ackermann axioms to the case where the Ackermann axioms are in HAC. In the second part, w

Matteo Citterio, Marco D'Errico, Gabriele Visentin

We introduce a novel Dynamic Graph Neural Network (DGNN) architecture for solving conditional $m$-steps ahead forecasting problems in temporal financial networks. The proposed DGNN is validated on simulated data from a temporal financial network model capturing stylized features of Interest Rate Swaps (IRSs) transaction networks, where financial entities tra

Yanke Song, Jonathan Lorraine, Weili Nie, Karsten Kreis

Diffusion models achieve high-quality sample generation at the cost of a lengthy multistep inference procedure. To overcome this, diffusion distillation techniques produce student generators capable of matching or surpassing the teacher in a single step. However, the student model's inference speed is limited by the size of the teacher architecture, preventi

Ioannis Caragiannis, Evi Micha, Nisarg Shah

We revisit the recently developed framework of proportionally fair clustering, where the goal is to provide group fairness guarantees that become stronger for groups of data points (agents) that are large and cohesive. Prior work applies this framework to centroid clustering, where the loss of an agent is its distance to the centroid assigned to its cluster.

Qidong Yang, Weicheng Zhu, Joseph Keslin, Laure Zanna

Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often desirable to quantify the uncertainty associated with the prediction (instead of just determining the most likely sequence, as in language modeling). In this paper, we propose a Mont

Lara Bohnenblust, Lucile Cangemi, Henrik Johansson, Paolo Pichini

Quantum higher-spin theory applied to Compton amplitudes has proven to be surprisingly useful for elucidating Kerr black hole dynamics. Here we apply the framework to compute scattering amplitudes and observables for a binary system of two rotating black holes, at second post-Minkowskian order, and to all orders in the spin-multipole expansion for certain qu

Shouvanik Chakrabarti, Dylan Herman, Guneykan Ozgul, Shuchen Zhu

We analyze generalizations of quantum algorithms based on the short path framework first proposed by Hastings~[\textit{Quantum} 2, 78 (2018)], which has been extended and shown by Dalzell~et~al.~[STOC~'23] to achieve super-Grover speedups for certain binary optimization problems. We demonstrate that, under some commonly satisfied technical conditions, an app

Benedikt Wilde, Manuel Kaiser, Malte Reinschmidt, Andreas Günther

Hybrid quantum systems are highly promising platforms for addressing important challenges of quantum information science and quantum sensing. Their implementation, however, is technologically non-trivial, since each component typically has unique experimental requirements. Here, we work towards a hybrid system consisting of a superconducting on-chip microwav

R. Leal Martir, E. A. Jagla, D. Rubi, M. J. Sánchez

The control and manipulation of filamentary resistive switching (FRS) is essential for practical applications in fields like non-volatile memories and neuromorphic computing. However, key aspects of the dynamics of conductive filament formation and their influence on device resistance remain incompletely understood. In this work we study FRS in binary oxides

Tianyu Yang, Lisen Dai, Xiangqi Wang, Minhao Cheng

Machine unlearning (MU) has gained significant attention as a means to remove specific data from trained models without requiring a full retraining process. While progress has been made in unimodal domains like text and image classification, unlearning in multimodal models remains relatively underexplored. In this work, we address the unique challenges of un

Lindsay Popowski, Yutong Zhang, Michael S. Bernstein

In spite of efforts to increase participation, many online groups struggle to survive past the initial days, as members leave and activity atrophies. We argue that a main assumption of online group design -- that groups ask nothing of their members beyond lurking -- may be preventing many of these groups from sustaining a critical mass of participation. In t

Ziyao Shangguan, Chuhan Li, Yuxuan Ding, Yanan Zheng

Existing benchmarks often highlight the remarkable performance achieved by state-of-the-art Multimodal Foundation Models (MFMs) in leveraging temporal context for video understanding. However, how well do the models truly perform visual temporal reasoning? Our study of existing benchmarks shows that this capability of MFMs is likely overestimated as many que

Ryota Inagaki, Tanya Khovanova, Austin Luo

Chip-firing is a combinatorial game played on a graph in which we place and disperse chips on vertices until a stable state is reached. We study a chip-firing variant played on an infinite rooted directed $k$-ary tree, where we place $k^\ell$ chips on the root for some positive integer $\ell$, and we say a vertex $v$ can fire if it has at least $k$ chips. A

Elias R. Most, Hai-Yang Wang

Merging supermassive black hole (SMBH) binaries will likely be surrounded by a circumbinary accretion disk. Close to merger, gravitational radiation-driven inspiral will happen on timescales faster than the effective viscous time at the disk cavity wall, leading to a decoupling of the inner binary dynamics from the surrounding gaseous environment. Here we pe

Anqi Gong, Joseph M. Renes

We give a fault tolerant construction for error correction and computation using two punctured quantum Reed-Muller (PQRM) codes. In particular, we consider the $[[127,1,15]]$ self-dual doubly-even code that has transversal Clifford gates (CNOT, H, S) and the triply-even $[[127,1,7]]$ code that has transversal T and CNOT gates. We show that code switching bet

Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, Wei-Chih Hung

We introduce EMMA, an End-to-end Multimodal Model for Autonomous driving. Built upon a multi-modal large language model foundation like Gemini, EMMA directly maps raw camera sensor data into various driving-specific outputs, including planner trajectories, perception objects, and road graph elements. EMMA maximizes the utility of world knowledge from the pre

Apoorv Khandelwal, Tian Yun, Nihal V. Nayak, Jack Merullo

Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In this paper, we seek to clarify this assumption. We first survey academic researchers to learn about their available compute and then empirically measure the time to replicate mode

Alexander W. Criswell, Steven Rieck, Vuk Mandic

The Laser Interferometer Space Antenna (LISA) will feature a prominent anisotropic astrophysical stochastic gravitational wave signal, arising from the tens of millions of unresolved mHz white dwarf binaries in the Milky Way: the Galactic foreground. While proper characterization of the Galactic foreground as a noise source will be crucial for every LISA sci

Gerrit Bauch, Manuel Foerster

We model the communication of narratives as a cheap-talk game under model uncertainty. The sender has private information about the true data generating process of publicly observable data. The receiver is uncertain about how to interpret the data, but aware of the sender's incentives to strategically provide interpretations ("narratives"). We introduce a ge

Annika Rudolph, Irene Tamborra, Ore Gottlieb

Building on a general relativistic magnetohydrodynamic simulation of a short gamma-ray burst (sGRB) jet with initial magnetization $\sigma_0=150$, propagating through the dynamical ejecta from a binary neutron star merger, we identify regions of energy dissipation driven by magnetic reconnection and collisionless sub-shocks within different scenarios. We sol

Damir Bečirević, Svjetlana Fajfer, Nejc Košnik, Lovre Pavičić

We explain the difference between the measured decay widths of $b \to c \tau \nu$ processes and of $B\to K\nu\bar{\nu}$ and their values predicted in the Standard Model by introducing the right-handed interactions both to quarks and to leptons. At low energy scales, in addition to the Standard Model particles, we assume the presence of an additional neutral

Annalisa Baldi, Giovanna Citti, Giovanni Cupini

The aim of this paper is to study a Laplace-type operator and its fundamental solution on the characteristic plane in the Heisenberg group $\mathbb{H}^2$. We introduce a conformal version of the Laplacian and we prove that the distance induced by the immersion in the ambient space is a good approximation of its fundamental solution. We provide in particular

Vishwa Dewage, Mishko Mitkovski

We use quantum harmonic analysis for densely defined operators to provide a simplified proof of the Berger-Coburn theorem for boundedness of Toeplitz operators. In addition, we revisit compactness and Schatten-class membership of densely defined Toeplitz operators.

Xiaolin Fang, Bo-Ruei Huang, Jiayuan Mao, Jasmine Shone

Generalization to novel object configurations and instances across diverse tasks and environments is a critical challenge in robotics. Keypoint-based representations have been proven effective as a succinct representation for capturing essential object features, and for establishing a reference frame in action prediction, enabling data-efficient learning of

Sarah Blackwell, Puttipong Pongtanapaisan, Hanh Vo

We extend the Wirtinger number of links, an invariant originally defined by Blair, Kjuchukova, Velazquez, and Villanueva in terms of extending initial colorings of some strands of a diagram to the entire diagram, to spatial graphs. We prove that the Wirtinger number equals the bridge index of spatial graphs, and we implement an algorithm in Python which give

Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal, Kung-Hsiang Huang

As large language models (LLMs) expand into performing as agents for real-world applications beyond traditional NLP tasks, evaluating their robustness becomes increasingly important. However, existing benchmarks often overlook critical dimensions like cultural and social awareness. To address these, we introduce CASA, a benchmark designed to assess LLM agent

Songfu Cai, Fei Han, Xuanyu Cao

We introduce the framework of performative control, where the policy chosen by the controller affects the underlying dynamics of the control system. This results in a sequence of policy-dependent system state data with policy-dependent temporal correlations. Following the recent literature on performative prediction [21], we introduce the concept of a perfor

Ritvik Ramanan Radhakrishnan, Vincent Tassion

We discuss a general method to prove quantitative improvements on correlation inequalities and apply it to arm estimates for Bernoulli bond percolation on the square lattice. Our first result is that the two-arm exponent is strictly larger than twice the one-arm exponent and can be seen as a quantitative improvement on the Harris-FKG inequality. This answers

R. Navarro-Carrera, P. Rinaldi, K. I. Caputi, E. Iani

We analyze a sample of 4500 photometrically-selected Ha emitter galaxies at redshifts z~2 and z~4-6 selected from James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam) medium-band images in the Great Observatories Origins Deep Survey South (GOODS-S). The bulk (80%) of the galaxies in our sample have stellar masses lower than 10^8 Msun, with a media

Max McGinley, Wen Wei Ho, Daniel Malz

We analyse the entanglement structure of states generated by random constant-depth two-dimensional quantum circuits, followed by projective measurements of a subset of sites. By deriving a rigorous lower bound on the average entanglement entropy of such post-measurement states, we prove that macroscopic long-ranged entanglement is generated above some consta

Yehe Liu, Alexander Krull, Hector Basevi, Ales Leonardis

Quanta image sensors, such as SPAD arrays, are an emerging sensor technology, producing 1-bit arrays representing photon detection events over exposures as short as a few nanoseconds. In practice, raw data are post-processed using heavy spatiotemporal binning to create more useful and interpretable images at the cost of degrading spatiotemporal resolution. I

Gloria Buriticá, Sebastian Engelke

The problem of regression extrapolation, or out-of-distribution generalization, arises when predictions are required at test points outside the range of the training data. In such cases, the non-parametric guarantees for regression methods from both statistics and machine learning typically fail. Based on the theory of tail dependence, we propose a novel sta

Chen Ziwen, Zexiang Xu, Li Fuxin

We propose a novel online, point-based 3D reconstruction method from posed monocular RGB videos. Our model maintains a global point cloud representation of the scene, continuously updating the features and 3D locations of points as new images are observed. It expands the point cloud with newly detected points while carefully removing redundancies. The point

Giacomo Petrillo

Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique based on an ensemble of decision trees. It is part of the toolbox of many statisticians. The overall statistical quality of the regression is typically higher than other generic alternatives, and it requires less manual tuning, making it a good default choice. However,

Yiling Chen, Shi Feng, Fang-Yi Yu

Comparison data elicited from people are fundamental to many machine learning tasks, including reinforcement learning from human feedback for large language models and estimating ranking models. They are typically subjective and not directly verifiable. How to truthfully elicit such comparison data from rational individuals? We design peer prediction mechani

Matteo G. Mecattaf, Ben Slater, Marko Tešić, Jonathan Prunty

As general-purpose tools, Large Language Models (LLMs) must often reason about everyday physical environments. In a question-and-answer capacity, understanding the interactions of physical objects may be necessary to give appropriate responses. Moreover, LLMs are increasingly used as reasoning engines in agentic systems, designing and controlling their actio