February 2024 arXiv papers

Alessandra F. Lütz, Lucas Wardil

Pluralistic ignorance is a social-psychological phenomenon that occurs when individuals privately hold beliefs that differ from perceived group norms. Traditional models, based on opinion dynamics with private and public states, fail to account for a key aspect: when nonexpression aligns with normative behavior, initial social pressure can induce pluralistic

Benjamin Cohen-Wang, Joshua Vendrow, Aleksander Madry

Pre-training is a widely used approach to develop models that are robust to distribution shifts. However, in practice, its effectiveness varies: fine-tuning a pre-trained model improves robustness significantly in some cases but not at all in others (compared to training from scratch). In this work, we seek to characterize the failure modes that pre-training

Hamza Jnane, Simon C Benjamin

Electron spins in semiconductor devices are highly promising building blocks for quantum processors (QPs). Commercial semiconductor foundries can create QPs using the same processes employed for conventional chips, once the QP design is suitably specified. There is a vast accessible design space; to identify the most promising options for fabrication, one re

Yuanwei Liu, Chongjun Ouyang, Zhiguo Ding, Robert Schober

The evolution of wireless communications has been significantly influenced by remarkable advancements in multiple access (MA) technologies over the past five decades, shaping the landscape of modern connectivity. Within this context, a comprehensive tutorial review is presented, focusing on representative MA techniques developed over the past 50 years. The f

Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier

When deployed in the world, a learning agent such as a recommender system or a chatbot often repeatedly interacts with another learning agent (such as a user) over time. In many such two-agent systems, each agent learns separately and the rewards of the two agents are not perfectly aligned. To better understand such cases, we examine the learning dynamics of

D. R. Ernst, A. Bortolon, C. S. Chang, S. Ku

Multi-machine empirical scaling predicts an extremely narrow heat exhaust layer in future high magnetic field tokamaks, producing high power densities that require mitigation. In the experiments presented, the width of this exhaust layer is nearly doubled using actuators to increase turbulent transport in the plasma edge. This is achieved in low collisionali

Maryam Soltani, Ghasem Akbari, Nader Montazerin

Particle Image Velocimetry (PIV) data is a valuable asset in fluid mechanics. It is capable of visualizing flow structures even in complex physics scenarios, such as the flow at the exit of the rotor of a centrifugal fan. Machine learning is also a successful companion to PIV in order to increase data resolution or impute experimental gaps. While classical a

Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon

Data similarity assumptions have traditionally been relied upon to understand the convergence behaviors of federated learning methods. Unfortunately, this approach often demands fine-tuning step sizes based on the level of data similarity. When data similarity is low, these small step sizes result in an unacceptably slow convergence speed for federated metho

Carlos Ansótegui, Jordi Levy

Quantum Annealers are basically quantum computers that with high probability can optimize certain quadratic functions on Boolean variables in constant time. These functions are basically the Hamiltonian of Ising models that reach the ground energy state, with a high probability, after an annealing process. They have been proposed as a way to solve SAT. These

Karina Halevy, Anna Sotnikova, Badr AlKhamissi, Syrielle Montariol

Model editing has emerged as a cost-effective strategy to update knowledge stored in language models. However, model editing can have unintended consequences after edits are applied: information unrelated to the edits can also be changed, and other general behaviors of the model can be wrongly altered. In this work, we investigate how model editing methods u

Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang

A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical modeling to characterize and simulate its defining features. By constructing digital twins for disease processes, we can perform in-silico simulations that mimic patients' health conditions and counterfactual outcomes under hypothetical interventions in a virtua

Chenhao Fang, Xiaohan Li, Zezhong Fan, Jianpeng Xu

Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry. The provision of precise product attribute values is fundamental in ensuring high-quality recommendations and enhancing customer satisfaction. The recently emerging Large Language Models (LLMs) have demonstrated state-of-th

Safouane El Ghazouali, Youssef Mhirit, Ali Oukhrid, Umberto Michelucci

In the realm of computer vision, the integration of advanced techniques into the processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmenta

Matthew Danish, SM Labib, Britta Ricker, Marco Helbich

Street View Imagery (SVI) is a valuable data source for studies (e.g., environmental assessments, green space identification or land cover classification). While commercial SVI is available, such providers commonly restrict copying or reuse in ways necessary for research. Open SVI datasets are readily available from less restrictive sources, such as Mapillar

Guanxuan Wu, Allison Sullivan

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built to automated testing and debugging of their implementations after they are built. Unfortunately, the model itself needs to be correct to gain these benefits. Alloy is a commonly used modeling language that has

Yuta Kimoto, Hidetoshi Masuda, Takeshi Seki, Yoichi Nii

We found signatures of current-induced sliding motion in helimagnetic $\mathrm{Mn}\mathrm{Au}_2$ thin films. An abrupt change in differential resistivity occurred at a threshold bias current in the helimagnetic state, whereas it was absent in the induced ferromagnetic state. Broadband voltage noise also emerged above the threshold current in the helimagnetic

Yunyi Zhang, Ruozhen Yang, Xueqiang Xu, Rui Li

Hierarchical text classification aims to categorize each document into a set of classes in a label taxonomy, which is a fundamental web text mining task with broad applications such as web content analysis and semantic indexing. Most earlier works focus on fully or semi-supervised methods that require a large amount of human annotated data which is costly an

Giovanni P. Galdi, Tatsuki Yamamoto

We study the nonhomogeneous boundary value problem for the steady-state Navier-Stokes equations under the slip boundary conditions in two-dimensional multiply-connected bounded domains. Employing the approach of Korobkov-Pileckas-Russo (Ann. Math. 181(2), 769-807, 2015), we prove that this problem has a solution if the friction coefficient is sufficiently la

Nishchay Suri, Jason Saied, Davide Venturelli

We present a general condition to obtain subspaces that decay uniformly in a system governed by the Lindblad master equation and use them to perform error mitigated quantum computation. The expectation values of dynamics encoded in such subspaces are unbiased estimators of noise-free expectation values. In analogy to the decoherence free subspaces which are

Raj Agrawal, Sam Witty, Andy Zane, Eli Bingham

Many practical problems involve estimating low dimensional statistical quantities with high-dimensional models and datasets. Several approaches address these estimation tasks based on the theory of influence functions, such as debiased/double ML or targeted minimum loss estimation. This paper introduces \textit{Monte Carlo Efficient Influence Functions} (MC-

Nicole Sabina Ticea, Srinivas Raghu, Yi-Ming Wu

An indispensable ingredient for pair density wave (PDW) superconductivity is the presence of an attractive pairing interaction at finite momentum. Here, we show how this condition can be met with straightforward electron-density interactions in multiband systems. The electron-density interaction, when projected to the band basis, acquires form factors with n

Mahsa Mozafari-Nia, Salimeh Yasaei Sekeh

Despite the impressive performance of deep neural networks (DNNs), their computational complexity and storage space consumption have led to the concept of network compression. While DNN compression techniques such as pruning and low-rank decomposition have been extensively studied, there has been insufficient attention paid to their theoretical explanation.

Yu Wang

Interest is increasing among political scientists in leveraging the extensive information available in images. However, the challenge of interpreting these images lies in the need for specialized knowledge in computer vision and access to specialized hardware. As a result, image analysis has been limited to a relatively small group within the political scien

Yuqiao Wen, Behzad Shayegh, Chenyang Huang, Yanshuai Cao

The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions. Alternatively, zero-shot translation can be accomplished by pivoting through a third language (e.g., English). In our work, we observe that both direct and pivot translations are nois

Behzad Shayegh, Yuqiao Wen, Lili Mou

We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model in the literature. We propose to build an ensemble of different runs of the existing discontinuous parser by averaging the predicted trees, to stabilize and boost performance. To begin with, we provide comprehensive compu

Polina Arsenteva, Mohamed Amine Benadjaoud, Hervé Cardot

In modern experimental science, there is a common problem of estimating the coefficients of a linear regression in a context where the variables of interest cannot be observed simultaneously. When there is a categorical variable that is observed on all statistical units, we consider two estimators of linear regression that take this additional information in

Justyn Snarski-Adamski, Mirosław Werwiński

New hard magnetic materials with zero or low rare earth content are in demand due to the high prices of the rare earth metals. Among the candidates for such materials, we consider MnB, FeB and their alloys, because previous experiments suggest that FeB has a relatively high magnetic hardness of about 0.83 at room temperature. Using first-principles calculati

Tao Jiang, Wei Yu

This paper addresses the design of transmit precoder and receive combiner matrices to support $N_{\rm s}$ independent data streams over a time-division duplex (TDD) point-to-point massive multiple-input multiple-output (MIMO) channel with either a fully digital or a hybrid structure. The optimal precoder and combiner design amounts to finding the top-$N_{\rm

Georges Dupret, Konstantin Sozinov, Carmen Barcena Gonzalez, Ziggy Zacks

Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and services to remain relevant, desirable, and profitable. Data and experimentation to test product hypotheses are k

Shanghua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, with most models narrowly focused on specific areas, such as time series forecasting. Unifying predictive and generative time series tasks within a single model remains challenging.

Yungang Lu

This paper primarily focuses on the investigation of the distribution of certain crucial operators with respect to significant states on the (q,2)-Fock space, for instance, the vacuum distribution of the field operator.

Amartya Shankha Biswas, Ruidi Cao, Cassandra Marcussen, Edward Pyne

We initiate the study of Local Computation Algorithms on average case inputs. In the Local Computation Algorithm (LCA) model, we are given probe access to a huge graph, and asked to answer membership queries about some combinatorial structure on the graph, answering each query with sublinear work. For instance, an LCA for the $k$-spanner problem gives access

Sui He

Prompt engineering has shown potential for improving translation quality in LLMs. However, the possibility of using translation concepts in prompt design remains largely underexplored. Against this backdrop, the current paper discusses the effectiveness of incorporating the conceptual tool of translation brief and the personas of translator and author into p

Xiaoqiang Wang, Lingfei Wu, Tengfei Ma, Bang Liu

Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. However, such a paradigm fails to comprehensively differentiate the fine-grained language and cognitive skills, rendering the lack of sufficient interpretation to LLMs' capabilities. In this paper, we present FAC$^2$E, a framewo

Miao Li, Ming-Bin Chen, Bo Tang, Shengbin Hou

We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focused on four facets of writing proficiency and six facets of safety adherence, and it comprises manually and carefully designed 1,267 test sample

Rouven Essig, Ryan Plestid, Aman Singal

Solid-state detectors with a low energy threshold have several applications, including searches of non-relativistic halo dark-matter particles with sub-GeV masses. When searching for relativistic, beyond-the-Standard-Model particles with enhanced cross sections for small energy transfers, a small detector with a low energy threshold may have better sensitivi

Maria Emelianenko, Guy B. Oldaker

While there exists a rich array of matrix column subset selection problem (CSSP) algorithms for use with interpolative and CUR-type decompositions, their use can often become prohibitive as the size of the input matrix increases. In an effort to address these issues, the authors in \cite{emelianenko2024adaptive} developed a general framework that pairs a col

Christophe Lacave, Matthieu Ménard, Catherine Sulem

A central object in the analysis of the water wave problem is the Dirichlet-Neumann operator. This paper is devoted to the study of its spectrum in the context of the water wave system linearized near equilibrium in a domain with a variable bottom, assumed to be a $C^2$ periodic function. We use the analyticity of the Dirichlet-Neumann operator with respect

Michael Unterkalmsteiner, Waleed Abdeen

Introduction: Taxonomies capture knowledge about a particular domain in a succinct manner and establish a common understanding among peers. Researchers use taxonomies to convey information about a particular knowledge area or to support automation tasks, and practitioners use them to enable communication beyond organizational boundaries. Aims: Despite this i

Michele L. Silverstein, Thomas Barclay, Joshua E. Schlieder, Karen A. Collins

The nearby LHS 1678 (TOI-696) system contains two confirmed planets and a wide-orbit, likely-brown-dwarf companion, which orbit an M2 dwarf with a unique evolutionary history. The host star occupies a narrow "gap" in the HR diagram lower main sequence, associated with the M dwarf fully convective boundary and long-term luminosity fluctuations. This s

Gregory Moille, Pradyoth Shandilya, Alioune Niang, Curtis Menyuk

Kerr-induced synchronization (KIS) provides a new key tool for the control and stabilization of the repetition rate of a cavity soliton frequency comb. It enables direct external control of a given comb tooth of a dissipative Kerr soliton (DKS) thanks to its capture by an injected reference laser. Efficient KIS requires its coupling energy to be sufficiently

Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian

Finetuning LLMs with LoRA has gained significant popularity due to its simplicity and effectiveness. Often, users may even find pluggable, community-shared LoRAs to enhance their base models for a specific downstream task of interest; enjoying a powerful, efficient, yet customized LLM experience with negligible investment. However, this convenient share-and-

Jonathan Zong, Isabella Pedraza Pineros, Mengzhu Katie Chen, Daniel Hajas

We present Umwelt, an authoring environment for interactive multimodal data representations. In contrast to prior approaches, which center the visual modality, Umwelt treats visualization, sonification, and textual description as coequal representations: they are all derived from a shared abstract data model, such that no modality is prioritized over the oth

Per Bäck, Johan Richter

We introduce hom-associative versions of the higher order Weyl algebras, generalizing the construction of the first hom-associative Weyl algebras. We then show that the higher order hom-associative Weyl algebras are simple, and that all their one-sided ideals are principal.

CMS Collaboration

A search for heavy neutral leptons (HNLs) of Majorana or Dirac type using proton-proton collision data at $\sqrt{s}$ = 13 TeV is presented. The data were collected by the CMS experiment at the CERN LHC and correspond to an integrated luminosity of 138 fb$^{-1}$. Events with three charged leptons (electrons, muons, and hadronically decaying tau leptons) are s

Gorav Jindal, Joël Ouaknine

The Skolem Problem asks, given an integer linear recurrence sequence (LRS), to determine whether the sequence contains a zero term or not. Its decidability is a longstanding open problem in theoretical computer science and automata theory. Currently, decidability is only known for LRS of order at most 4. On the other hand, the sole known complexity result is

Lvzhou Chen, Alexander J. Rasmussen

We study a skew product transformation associated to an irrational rotation of the circle [0,1]/~. This skew product keeps track of the number of times an orbit of the rotation lands in the two complementary intervals of {0,1/2} in the circle. We show that under certain conditions on the continued fraction expansion of the irrational number defining the rota

Raghavv Goel, Mukul Gagrani, Wonseok Jeon, Junyoung Park

Text generation with Large Language Models (LLMs) is known to be memory bound due to the combination of their auto-regressive nature, huge parameter counts, and limited memory bandwidths, often resulting in low token rates. Speculative decoding has been proposed as a solution for LLM inference acceleration. However, since draft models are often unavailable i

Tianyi Zhang, Li Zhang, Zhaoyi Hou, Ziyu Wang

Planning in a text-based environment continues to be a major challenge for AI systems. Recent approaches have used language models to predict a planning domain definition (e.g., PDDL) but have only been evaluated in closed-domain simulated environments. To address this, we present Proc2PDDL , the first dataset containing open-domain procedural texts paired w

M. Eltohfa, Xinghan Wang, Colton M. Griffin, F. Robicheaux

One-dimensional systems, such as nanowires or electrons moving along strong magnetic field lines, have peculiar thermalization physics. The binary collision of point-like particles, typically the dominant process for reaching thermal equilibrium in higher dimensional systems, cannot thermalize a 1D system. We study how dilute classical 1D gases thermalize th

Mateo Galdeano, Leander Stecker

We construct solutions to the heterotic G$_2$ system on almost contact metric manifolds with reduced characteristic holonomy. We focus on $3$-$(α,δ)$-Sasaki manifolds and $(α,δ)$-Sasaki manifolds, the latter being a convenient reformulation of spin $η$-Einstein $α$-Sasaki manifolds. Investigating a $1$-parameter family of G$_2$-connections on the tangent bun

Md Shahin Alam, Alexandr Kazakov, Mujeeb Ahmad, Rajibul Islam

We report measurements of the electrical resistivity ($ρ$) and thermoelectric power (S) in a thin film of strained single-crystalline $α$-Sn grown by molecular beam epitaxy on an insulating substrate. The temperature (T) dependence of the resistivity of $α$-Sn can be divided into two regions:below T* $\approx$ 135 K $ρ$(T) shows a metallic-like behaviour, wh

Daniel Bissinger, Rolf Farnsteiner

Let $\mathbb{k}$ be an algebraically closed field. Connections between representations of the generalized Kronecker quivers $K_r$ and vector bundles on $\mathbb{P}^{r-1}$ have been known for quite some time. This article is concerned with a particular aspect of this correspondence, involving more generally Steiner bundles on Grassmannians $\mathrm{Gr}_d(\mat

Ankit Aggarwal, Florian Ecker, Daniel Grumiller, Dmitri Vassilevich

Carroll black holes with an associated Carroll temperature were introduced recently. So far, it is unclear if they exhibit a Hawking-like effect. To solve this, we study scalar fields on Carroll black hole backgrounds. Inspired by anomaly methods, we derive a Hawking-like energy-momentum tensor compatible with the Carroll temperature and the Stefan-Boltzmann

Seigo Kikura, Rui Asaoka, Masato Koashi, Yuuki Tokunaga

We propose a scheme for generating a high-purity single photon on the basis of cavity quantum electrodynamics (QED). This scheme employs a four-level system including two excited states, two ground states, and two driving lasers; this structure allows the suppression of the re-excitation process due to the atomic decay, which is known to significantly degrad

Suyuchen Wang, Ivan Kobyzev, Peng Lu, Mehdi Rezagholizadeh

This paper addresses the challenge of train-short-test-long (TSTL) scenarios in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE), where models pre-trained on shorter sequences face difficulty with out-of-distribution (OOD) token positions in longer sequences. We introduce Resonance RoPE, a novel approach designed to narrow the gene

Mariam Campbell, Sante Carloni, Peter K. S. Dunsby, Nolene F. Naidu

We present a covariant description of non-vacuum static spherically symmetric spacetimes in $f(R)$ gravity applying the (1+1+2) covariant formalism. The propagation equations are then used to derive a covariant and dimensionless form of the Tolman-Oppenheimer-Volkoff (TOV) equations. We then give a solution strategy to these equations and obtain some new exa

Arjun Mirani, Patrick Hayden

Efficiently learning an unknown Hamiltonian given access to its dynamics is a problem of interest for quantum metrology, many-body physics and machine learning. A fundamental question is whether learning can be performed at the Heisenberg limit, where the Hamiltonian evolution time scales inversely with the error, $\varepsilon$, in the reconstructed paramete

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clarified to what extent LVLMs possess the ability to understand the knowledge necessary for explaining images, the complex relationships between various pieces of knowledge, and how th

Md Tahmid Rahman Laskar, Elena Khasanova, Xue-Yong Fu, Cheng Chen

This work focuses on the task of query-based meeting summarization in which the summary of a context (meeting transcript) is generated in response to a specific query. When using Large Language Models (LLMs) for this task, usually a new call to the LLM inference endpoint/API is triggered for each new query, even if the context stays the same. However, repeat

Nathan Steinle, Davide Gerosa, Martin G. H. Krause

The precession of astrophysical jets produced by active-galactic nuclei is likely related to the dynamics of the accretion disks surrounding the central supermassive black holes (BHs) from which jets are launched. The two main mechanisms that can drive jet precession arise from Lense-Thirring precession and tidal torquing. These can explain direct and indire

Ana Ines Ennis, Juan Pablo Caso, Lilia Patricia Bassino

We present a wide-field study of the globular cluster systems (GCS) of the elliptical galaxy NGC 3640 and its companion NGC 3641, based on observations from Gemini Multi-Object Spectrograph/Gemini using the g'r'i' filters. NGC 3640 is a shell galaxy which presents a complex morphology, which previous studies have indicated as the sign of a recent

Marco Golla, Marco Marengon

For every $n \ge 3$, we construct 2-component links in $S^{n+1}$ that are a split by an integer homology $n$-sphere, but not by $S^n$. In the special case $n=3$, i.e. that of 2-links in $S^4$, we produce an infinite family of links $L_\ell$ and of integer homology spheres $Y_\ell$ such that the link $L_\ell$ is (topologically or smoothly) split by $Y_\ell$ a

Serhii Kryhin, Subir Sachdev, Pavel A. Volkov

We show that nonlinear transport responses in strange metals are strong, larger by a factor of $E_F/T$ than in Fermi liquids. Within the two-dimensional Yukawa-Sachdev-Ye-Kitaev model of a Fermi surface with a spatially random coupling to a critical scalar, the third order conductivity is found to diverge as $1/T$ at low $T$, indicating the existence of a vo

Shiyu Zhang, Yang Su, Xuepeng Chen, Min Fang

We study the physical properties and 3D distribution of molecular clouds (MCs) toward the Cygnus region using the MWISP CO survey and Gaia DR3 data. Based on Gaussian decomposition and clustering for $\rm ^{13}CO$ lines, over 70% of the fluxes are recovered. With the identification result of $\rm ^{13}CO$ structures, two models are designed to measure the di

Daria Gangardt, Alessandro Alberto Trani, Clément Bonnerot, Davide Gerosa

Models of accretion discs surrounding active galactic nuclei (AGNs) find vast applications in high-energy astrophysics. The broad strategy is to parametrize some of the key disc properties such as gas density and temperature as a function of the radial coordinate from a given set of assumptions on the underlying physics. Two of the most popular approaches in

Aidan P. Reddy, Nisarga Paul, Ahmed Abouelkomsan, Liang Fu

Motivated by the recent discovery of fractional quantum anomalous Hall states in moiré systems, we consider the possibility of realizing non-Abelian phases in topological minibands. We study a family of moiré systems, skyrmion Chern band models, which can be realized in two-dimensional semiconductor-magnet heterostructures and also capture the essence of twi

Jonah S. Peter, Raphael Holzinger, Stefan Ostermann, Susanne F. Yelin

Light-harvesting via the transport and trapping of optically-induced electronic excitations is of fundamental interest to the design of new energy efficient quantum technologies. Using a paradigmatic quantum optical model, we study the influence of coherence, entanglement, and cooperative dissipation on the transport and capture of excitation energy. In part

Marco Marengon, Clayton McDonald

We show that there exists a link with 2 components which is not smoothly slice in $\mathbb{CP}^2 \# \overline{\mathbb{CP}^2}$. By contrast, it is well-known that every knot (i.e., link with 1 component) is smoothly slice therein. Our proof uses classical topological and smooth obstructions, as well as constructive arguments to exploit the symmetries of the p

Manato Fujimoto, Daniel E. Parker, Junkai Dong, Eslam Khalaf

The rise of moiré materials has led to experimental realizations of integer and fractional Chern insulators in small or vanishing magnetic fields. At the same time, a set of minimal conditions sufficient to guarantee a Abelian fractional state in a flat band were identified, namely "ideal" or "vortexable" quantum geometry. Such vortexable ban

David Benisty, Supriya Pan, Denitsa Staicova, Eleonora Di Valentino

We investigated a possible interaction between cold dark matter and dark energy, corresponding to a well-known interacting dark energy model discussed in the literature within the context of resolving the Hubble tension. We put constraints on it in a novel way, by creating new likelihoods with an analytical marginalization over the Hubble parameter $H_0$, th

Julian May-Mann, Mark R. Hirsbrunner, Lei Gioia, Taylor L. Hughes

Three-dimensional Dirac semimetals can be driven into an insulating state by coupling to a charge density wave (CDW) order. Here, we consider the quantized crystalline responses of such charge-ordered Dirac semimetals, which we dub Dirac-CDW insulators, in which charge is bound to disclination defects of the lattice. Using analytic and numeric methods we sho

Xingrui Song, Flavio Salvati, Chandrashekhar Gaikwad, Nicole Yunger Halpern

The goal of quantum metrology is to improve measurements' sensitivities by harnessing quantum resources. Metrologists often aim to maximize the quantum Fisher information, which bounds the measurement setup's sensitivity. In studies of fundamental limits on metrology, a paradigmatic setup features a qubit (spin-half system) subject to an unknown rota

Ming Xie, Mohammad Hafezi, Sankar Das Sarma

Moiré superlattices based on two-dimensional transition metal dichalcogenides (TMDs) have emerged as a highly versatile and fruitful platform for exploring correlated topological electronic phases. One of the most remarkable examples is the recently discovered fractional quantum anomalous Hall effect (FQAHE) under zero magnetic field. Here we propose a minim

Gary T. Horowitz, Maciej Kolanowski, Grant N. Remmen, Jorge E. Santos

It was recently shown that (near-)extremal Kerr black holes are sensitive probes of small higher-derivative corrections to general relativity. In particular, these corrections produce diverging tidal forces on the horizon in the extremal limit. We show that adding a black hole charge makes this effect qualitatively stronger. Higher-derivative corrections to

Christian Kragh Jespersen, Charles L. Steinhardt, Rachel S. Somerville, Christopher C. Lovell

The discovery of extremely luminous galaxies at ultra-high redshifts ($z\gtrsim 8$) has challenged galaxy formation models. Most analyses of this tension have not accounted for the variance due to field-to-field clustering, which causes the number counts of galaxies to vary greatly in excess of Poisson noise. This super-Poissonian variance is often referred

Muyang Li, Tianle Cai, Jiaxin Cao, Qinsheng Zhang

Diffusion models have achieved great success in synthesizing high-quality images. However, generating high-resolution images with diffusion models is still challenging due to the enormous computational costs, resulting in a prohibitive latency for interactive applications. In this paper, we propose DistriFusion to tackle this problem by leveraging parallelis

Jarosław Pawłowski, Pankaj Kumar, Kenji Watanabe, Takashi Taniguchi

Spin-valley properties in two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDC) has attracted significant interest due to the possible applications in quantum computing. Spin-valley properties can be exploited in TMDC quantum dot (QD) with well-resolved energy levels. This requires smaller QDs, especially in material systems with heavy

Sangwoon Kim, Antonia Bronars, Parag Patre, Alberto Rodriguez

We introduce a novel approach that combines tactile estimation and control for in-hand object manipulation. By integrating measurements from robot kinematics and an image-based tactile sensor, our framework estimates and tracks object pose while simultaneously generating motion plans in a receding horizon fashion to control the pose of a grasped object. This

Yang-Zhi Chou, Yuting Tan, Fengcheng Wu, Sankar Das Sarma

We study theoretically the magic-angle twisted bilayer graphene with proximity-induced Ising and Rashba spin-orbit couplings on the top layer. Topological flat bands (with three distinct phases) are generically realized by the spin-orbit couplings. Using a mean field analysis, we find that (partial) valley polarization prevails for a wide range of doping, su

Lingchen Yang, Gaspard Zoss, Prashanth Chandran, Markus Gross

Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits. Today's methods are data-driven, where the actuations for finite elements are inferred from captured skin

Charlie T. Mpetha, Giuseppe Congedo, Andy Taylor, Martin A. Hendry

Gravitational waves from binary mergers at cosmological distances will experience weak lensing by large scale structure. This causes a (de-)magnification, $μ$, of the wave amplitude, and a degenerate modification to the inferred luminosity distance $d_L$. To address this the uncertainty on $d_L$ is increased according to the dispersion of the magnification d

Weiyun Wang, Yiming Ren, Haowen Luo, Tiantong Li

We present the All-Seeing Project V2: a new model and dataset designed for understanding object relations in images. Specifically, we propose the All-Seeing Model V2 (ASMv2) that integrates the formulation of text generation, object localization, and relation comprehension into a relation conversation (ReC) task. Leveraging this unified task, our model excel

Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang

Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC). Despite its notable successes, AIGC still faces hurdles such as updating knowledge, handling long-tail data, mitigating data leakage, and managing high training and inferenc

Cai Dieball, Aljaž Godec

We prove that the transport of any differentiable scalar observable in $d$-dimensional non-equilibrium systems is bounded from above by the total entropy production scaled by the amount the observation "stretches" microscopic coordinates. The result--a time-integrated generalized speed limit--reflects the thermodynamic cost of transport of observable

Ameya Prabhu, Vishaal Udandarao, Philip Torr, Matthias Bethge

Standardized benchmarks drive progress in machine learning. However, with repeated testing, the risk of overfitting grows as algorithms over-exploit benchmark idiosyncrasies. In our work, we seek to mitigate this challenge by compiling ever-expanding large-scale benchmarks called Lifelong Benchmarks. These benchmarks introduce a major challenge: the high cos

Gabriel Grand, Valerio Pepe, Jacob Andreas, Joshua B. Tenenbaum

Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions given limited cognitive resources? We study these tradeoffs in a classic grounded question-asking task based on the board game Battleship. Our language-informed program sampling (L

Qi Chen, Xiaoxi Chen, Haorui Song, Zhiwei Xiong

Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. However, success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and, furthermore, the resulting AI models being capable of detecting real tumor

Andrew L. Miller, Nancy Aggarwal, Sébastien Clesse, Federico De Lillo

Gravitational waves from sub-solar mass inspiraling compact objects would provide almost smoking-gun evidence for primordial black holes (PBHs). We perform the first search for inspiraling planetary-mass compact objects in equal-mass and highly asymmetric mass-ratio binaries using data from the first half of the LIGO-Virgo-KAGRA third observing run. Though w

Kate Sanders, Nathaniel Weir, Benjamin Van Durme

It is challenging for models to understand complex, multimodal content such as television clips, and this is in part because video-language models often rely on single-modality reasoning and lack interpretability. To combat these issues we propose TV-TREES, the first multimodal entailment tree generator. TV-TREES serves as an approach to video understanding

Anna Julia Poser, Ronald Redmer

We are interested in the influence of cloudy atmospheres on the thermal radius evolution of warm exoplanets from an interior modelling perspective. By applying a physically motivated but simple parameterized cloud model, we obtain the atmospheric $P$-$T$ structure that is connected to the adiabatic interior at the self-consistently calculated radiative-conve

Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu

Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies concentrate on fully pre-trained LLMs to better understand and improve LLMs' trustworthiness. In this paper, to reveal the untapped potential of pre-training, we pioneer the exploration of LLMs' trustworthiness during this period, focusing on five key dimensions: re

Jenny Seidenschwarz, Aljoša Ošep, Francesco Ferroni, Simon Lucey

We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory signals to train 3D object detectors in Lidar data without manual supervision. We re-think this approach and su

Pranav Shetty, Aishat Adeboye, Sonakshi Gupta, Chao Zhang

We present a simulation of various active learning strategies for the discovery of polymer solar cell donor/acceptor pairs using data extracted from the literature spanning $\sim$20 years by a natural language processing pipeline. While data-driven methods have been well established to discover novel materials faster than Edisonian trial-and-error approaches

Ionut Chifan, Adrian Ioana, Denis Osin, Bin Sun

For a class of wreath-like product groups with property (T), we describe explicitly all the embeddings between their von Neumann algebras. This allows us to provide a continuum of ICC groups with property (T) whose von Neumann algebras are pairwise non (stably) embeddable. We also give a construction of groups in this class only having inner injective homomo

Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh

Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification. The latest goal is disentanglement: the construction of multiple estimators that are each tailored to one and only one source of uncertainty. This paper presents the f

Jonathan Oppenheim, Andrea Russo

We consider a proposed alternative to quantum gravity, in which the spacetime metric is treated as classical, even while matter fields remain quantum. Consistency of the theory necessarily requires that the metric evolve stochastically. Here, we show that this stochastic behaviour leads to a modification of general relativity at low accelerations. In the low

Jay Armas, Giorgos Batzios, Akash Jain

Higher-group symmetries are combinations of higher-form symmetries which appear in various field theories. In this paper, we explain how higher-group symmetries arise in 10d and 11d supergravities when the latter are coupled to brane sources. Motivated by this observation, we study field theories at zero and finite temperature invariant under a class of cont

Maxime Darrin, Philippe Formont, Jackie Chi Kit Cheung, Pablo Piantanida

Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that assesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probabili

David Heurtel-Depeiges, Charles C. Margossian, Ruben Ohana, Bruno Régaldo-Saint Blancard

In recent years, denoising problems have become intertwined with the development of deep generative models. In particular, diffusion models are trained like denoisers, and the distribution they model coincide with denoising priors in the Bayesian picture. However, denoising through diffusion-based posterior sampling requires the noise level and covariance to