November 2022 arXiv papers — page 57

Shihong Liao, Peter H. Johansson, Matias Mannerkoski, Dimitrios Irodotou

We introduce a new model for the accretion and feedback of supermassive black hole (SMBH) binaries to the KETJU code, which enables us to resolve the evolution of SMBH binaries down to separations of tens of Schwarzschild radii in gas-rich galaxy mergers. Our subgrid binary accretion model extends the widely used Bondi--Hoyle--Lyttleton accretion into the bi

Zixia Wei, Yasushi Yoneta

Disentangled black hole microstates are atypical states in holographic CFTs whose gravity duals do not have smooth horizons. If there exist sufficiently many disentangled microstates to account for the entire black hole entropy, then any black hole microstate can be written as a superposition of states without smooth horizons. We show that there exist suffic

Yu-Jie Liu, Adam Smith, Michael Knap, Frank Pollmann

Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. Here, we propose a model-independent protocol for training QCNNs to discover order parameters that are unchanged under phase-preserving perturbations. We initiate the training sequence with the fixed-point wavefunctions of the quantum phase

Henri Jukkala

We derive field-theoretic local quantum transport equations which can describe quantum coherence. Our methods are based on Kadanoff--Baym equations derived in the Schwinger--Keldysh closed time path formalism of non-equilibrium quantum field theory. We focus on spatially homogeneous and isotropic systems and mixing fermions with a time-dependent mass and a w

Zechang Sun, Yuan-Sen Ting, Zheng Cai

Since their first discovery, quasars have been essential probes of the distant Universe. However, due to our limited knowledge of its nature, predicting the intrinsic quasar continua has bottlenecked their usage. Existing methods of quasar continuum recovery often rely on a limited number of high-quality quasar spectra, which might not capture the full diver

Felix Pat, Stéphanie Juneau, Vanessa Böhm, Ragadeepika Pucha

Optical spectra of galaxies and quasars from large cosmological surveys are used to measure redshifts and infer distances. They are also rich with information on the intrinsic properties of these astronomical objects. However, their physical interpretation can be challenging due to the substantial number of degrees of freedom, various sources of noise, and d

Minwoo Suh

By extending the method developed by Arav, Gauntlett, Roberts and Rosen, we construct supersymmetric $AdS_2\times{\Sigma}$ solutions of gauged $\mathcal{N}=8$ supergravity which are asymptotic to the $SU(3)\times{U}(1)$-invariant Warner fixed point, where ${\Sigma}$ is a spindle. The Warner fixed point is dual to the mass-deformed ABJM theory. The solutions

Goni Halevi, Belinda Wu, Philipp Moesta, Ore Gottlieb

Long-duration gamma-ray bursts (lGRBs) originate in relativistic collimated outflows -- jets -- that drill their way out of collapsing massive stars. Accurately modeling this process requires realistic stellar profiles for the jets to propagate through and break out of. Most previous studies have used simple power laws or pre-collapse models for massive star

Francesco Giovanni Celiberto

We bring evidence that the recently discovered property of natural stability of the high-energy resummation is directly connected to the fragmentation mechanism of heavy hadrons. As a phenomenological support, we provide predictions for differential distributions sensitive to heavy-hadron tags, calculated at the next-to-leading logarithmic level of the hybri

Laura Scholz-Diaz, Ignacio Martin-Navarro, Jesus Falcon-Barroso

Nearby galaxies are the end result of their cosmological evolution, which is predicted to be influenced by the growth of their host dark matter halos. This co-evolution potentially leaves signatures in present-day observed galaxy properties, which might be essential to further understand how the growth and properties of galaxies are connected to those of the

Adeline Crépieux, Emile Pangburn, Louis Haurie, Oladunjoye A. Awoga

We study the emergence of electronic edge states in superconducting (SC) monolayer, bilayer, and trilayer graphene for both spin-singlet and spin-triplet SC order parameters. We focus mostly on the gapped chiral $p+ip'$- and $d+id'$-wave SC states that show a non-zero Chern number and a corresponding number of edge states. For the $p+ip'$-wave state, we obse

Campbell McLauchlan, Benjamin Béri

Majorana zero modes (MZMs) are promising candidates for topologically-protected quantum computing hardware, however their large-scale use will likely require quantum error correction. Majorana surface codes (MSCs) have been proposed to achieve this. However, many MSC properties remain unexplored. We present a unified framework for MSC "twist defects" $\unico

Yuanfeng Xu, M. G. Vergniory, Da-Shuai Ma, Juan L. Mañes

Phonons play a crucial role in many properties of solid state systems, such as thermal and electrical conductivity, neutron scattering and associated effects or superconductivity. Hence, it is expected that topological phonons will also lead to rich and unconventional physics and the search of materials hosting topological phonons becomes a priority in the f

Ofer Aharony, Gabriel Cuomo, Zohar Komargodski, Márk Mezei

We study the low-energy limit of Wilson lines (charged impurities) in conformal gauge theories in 2+1 and 3+1 dimensions. As a function of the representation of the Wilson line, certain defect operators can become marginal, leading to interesting renormalization group flows and for sufficiently large representations to complete or partial screening by charge

Giuliano Iorio, Michela Mapelli, Guglielmo Costa, Mario Spera

Population-synthesis codes are an unique tool to explore the parameter space of massive binary star evolution and binary compact object (BCO) formation. Most population-synthesis codes are based on the same stellar evolution model, limiting our ability to explore the main uncertainties. Here, we present the new version of the code SEVN, which overcomes this

Oleg Lebedev, Timofey Solomko, Jong-Hyun Yoon

We study postinflationary scalar dark matter production via its non-minimal coupling to gravity. During the inflaton oscillation epoch, dark matter is produced resonantly for a sufficiently large non-minimal coupling $\xi\gtrsim 5$. We find that backreaction on the curvature and rescattering effects typically become important for the values of $\xi$ above $3

Dorottya Demszky, Heather Hill

Classroom discourse is a core medium of instruction - analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can help improve instruction. The dataset co

Rainer Weinberger, Kung-Yi Su, Kristian Ehlert, Christoph Pfrommer

Feedback driven by jets from active galactic nuclei is believed to be responsible for reducing cooling flows in cool-core galaxy clusters. We use simulations to model feedback from hydrodynamic jets in isolated halos. While the jet propagation converges only after the diameter of the jet is well resolved, reliable predictions about the effects these jets hav

Daniel P. Thorngren, Eve J. Lee, Eric D. Lopez

The hot Saturn population exhibits a boundary in mass-radius space, such that no planets are observed at a density less than $\sim$0.1 g cm$^{-3}$. Yet, planet interior structure models can readily construct such objects as the natural result of radius inflation. Here, we investigate the role XUV-driven mass-loss plays in sculpting the density boundary by co

Thomas W. Grimm, Stefano Lanza, Thomas van Vuren

A longstanding conjecture states that global symmetries should be absent in quantum gravity. By investigating large classes of Type IIB four-dimensional $\mathcal{N}=2$ effective field theories, we enlist the potential generalized global symmetries that could be present and explore how they are avoided. Crucial ingredients that arise in such effective field

Shing Yan Li, Washington Taylor

We study the massless charged spectrum of U(1) gauge fields in F-theory that arise from flux breaking of a nonabelian group. The U(1) charges that arise in this way can be very large. In particular, using vertical flux breaking, we construct an explicit 4D F-theory model with a U(1) decoupled from other gauge sectors, in which the massless/light fields have

N. Engler, J. Milli, R. Gratton, S. Ulmer-Moll

We present new optical and near-IR images of debris disk around the F-type star HD 114082. We obtained direct imaging observations and analysed the TESS photometric time series data of this target with a goal to search for planetary companions and to characterise the morphology of the debris disk and the scattering properties of dust particles. HD 114082 was

Ben Allanach, Joe Davighi

We juxtapose global fits of two bottom-up models (an $S_3$ scalar leptoquark model and a ${B_3-L_2}$ $Z^\prime$ model) of \bsll\ anomalies to flavour data in order to quantify statistical preference or lack thereof. The leptoquark model couples directly to left-handed di-muon pairs, whereas the $Z^\prime$ model couples to di-muon pairs with a vector-like cou

Sung Hak Lim, Kailash A. Raman, Matthew R. Buckley, David Shih

Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires "upsampling" the star particles into individual stars following the same phase-space density. In this paper, we introduce two new upsampling method

Maissam Barkeshli, Yu-An Chen, Po-Shen Hsin, Ryohei Kobayashi

A large class of gapped phases of matter can be described by topological finite group gauge theories. In this paper we show how such gauge theories possess a higher-group global symmetry, which we study in detail. We derive the $d$-group global symmetry and its 't Hooft anomaly for topological finite group gauge theories in $(d+1)$ space-time dimensions, inc

Justin Wasserman, Karmesh Yadav, Girish Chowdhary, Abhinav Gupta

Realistic long-horizon tasks like image-goal navigation involve exploratory and exploitative phases. Assigned with an image of the goal, an embodied agent must explore to discover the goal, i.e., search efficiently using learned priors. Once the goal is discovered, the agent must accurately calibrate the last-mile of navigation to the goal. As with any robus

Sergio Contreras, Raul E. Angulo, Jonás Chaves-Montero, Simon D. M. White

The spatial distribution of galaxies and their gravitational lensing signal offer complementary tests of galaxy formation physics and cosmology. However, their synergy can only be fully exploited if both probes are modelled accurately and consistently. In this paper, we demonstrate that this can be achieved using an extension of Subhalo Abundance Matching, d

Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar

In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in less structured environments that remain beyond the reach of current robots. Prior works built reorientation systems assuming one or many of the following: reorienting only specific objects with simple shapes, limited range of reorientation, slow o

Yaniv Nikankin, Niv Haim, Michal Irani

Diffusion models exhibited tremendous progress in image and video generation, exceeding GANs in quality and diversity. However, they are usually trained on very large datasets and are not naturally adapted to manipulate a given input image or video. In this paper we show how this can be resolved by training a diffusion model on a single input image or video.

Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu

We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More specifically, the input layout consists of one or more semantic regions with free-form text descriptions and adjustable precision levels, which can be set based on the desired contr

Dhananjay Raju, Georgios Bakirtzis, Ufuk Topcu

Fault diagnosis is the problem of determining a set of faulty system components that explain discrepancies between observed and expected behavior. Due to the intrinsic relation between observations and sensors placed on a system, sensors' fault diagnosis and placement are mutually dependent. Consequently, it is imperative to solve the fault diagnosis and sen

Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey

End-to-end automatic speech recognition systems represent the state of the art, but they rely on thousands of hours of manually annotated speech for training, as well as heavyweight computation for inference. Of course, this impedes commercialization since most companies lack vast human and computational resources. In this paper, we explore training and depl

Jeremy Strockoz, Daniil S. Antonenko, Dmitri LaBelle, Jorn W. F. Venderbos

Magnetic frustration can lead to peculiar magnetic orderings that break a discrete symmetry of the lattice in addition to the fundamental magnetic symmetries (i.e., spin rotation invariance and time-reversal symmetry). In this work, we focus on frustrated quantum magnets and study the nature of the quantum phase transition between a paramagnet and a magnetic

Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt, Federico Tombari

Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views. While showing impressive performance, it relies on the availability of dense input views with highly accurate camera poses, thus limiting its application in real-world scenarios. In this work, we introduce Sparse Pose Adjusting Radiance Fi

Or Zamir

We present a general method to convert algorithms into faster algorithms for almost-regular input instances. Informally, an almost-regular input is an input in which the maximum degree is larger than the average degree by at most a constant factor. This family of inputs vastly generalizes several families of inputs for which we commonly have improved algorit

Ted Xiao, Harris Chan, Pierre Sermanet, Ayzaan Wahid

In recent years, much progress has been made in learning robotic manipulation policies that follow natural language instructions. Such methods typically learn from corpora of robot-language data that was either collected with specific tasks in mind or expensively re-labelled by humans with rich language descriptions in hindsight. Recently, large-scale pretra

Yiming Wang, Chandan Setty, Shouvik Sur, Liyang Chen

Shot noise measures out-of-equilibrium current fluctuations and is a powerful tool to probe the nature of current-carrying excitations in quantum systems. Recent shot noise measurements in the heavy fermion strange metal YbRh$_2$Si$_2$ exhibit a strong suppression of the Fano factor ($F$) -- the ratio of the current noise to the average current in the DC lim

Karthik Shetty, Annette Birkhold, Srikrishna Jaganathan, Norbert Strobel

We introduce PLIKS (Pseudo-Linear Inverse Kinematic Solver) for reconstruction of a 3D mesh of the human body from a single 2D image. Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences. We approach the task as a model-i

Sivan Doveh, Assaf Arbelle, Sivan Harary, Rameswar Panda

Vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a challenge. We introduce the collective notion of Structured Vision&Language Concepts (SVLC) which includes object attributes, relations, and states which are present in the text and v

Jason Crann, David W. Kribs, Rajesh Pereira

We show that any finite dimensional von Neumann algebra admits an orthonormal unitary basis with respect to its standard trace. We also show that a finite dimensional von Neumann subalgebra of $M_n(\mathbb{C})$ admits an orthonormal unitary basis under normalized matrix trace if and only if the normalized matrix trace and standard trace of the von Neumann su

Ryan Alweiss, Brice Huang, Mark Sellke

We verify an explicit inequality conjectured recently by Gilmer, thus proving that for any nonempty union-closed family $F \subseteq 2^{[n]}$, some $i\in [n]$ is contained in at least a $\frac{3-\sqrt{5}}{2} \approx 0.38$ fraction of the sets in $F$. One case, an explicit one-variable inequality, is checked by computer calculation.

Pei-Hong Gu

It is firmly believed that the axion for solving the strong CP problem must come from the spontaneous breaking of an anomalous Peccei-Quinn global symmetry. Here we show a new possibility that the axion can be induced by a pseudo Goldstone seesaw mechanism although the Peccei-Quinn symmetry is exactly forbidden. Specifically, after the spontaneous breaking o

Harry Buhrman, Noah Linden, Laura Mančinska, Ashley Montanaro

Majority vote is a basic method for amplifying correct outcomes that is widely used in computer science and beyond. While it can amplify the correctness of a quantum device with classical output, the analogous procedure for quantum output is not known. We introduce quantum majority vote as the following task: given a product state $|\psi_1\rangle \otimes \do

Sajjad Karimnejad, Amin Amiri Delouei, Hakan Basagaoglu, Mohsen Nazari

Modeling and direct numerical simulation of particle-laden flows have a tremendous variety of applications in science and engineering across a vast spectrum of scales from pollution dispersion in the atmosphere, to fluidization in the combustion process, to aerosol deposition in spray medication, along with many others. Due to their strongly nonlinear and mu

Xin Wen, Bingchen Zhao, Xiaojuan Qi

Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. Previous studies argued that parametric classifiers are prone to overfitting to seen categories, and endorsed using a non-parametric classifier formed with semi-supervised k-means. However, in this study, we investigate

Bernhard Haeupler, Jonas Huebotter, Mohsen Ghaffari

This paper extends and generalizes the well-known cut-matching game framework and provides a novel cut-strategy that produces constant-hop expanders. Constant-hop expanders are a significant strengthening of regular expanders with the additional guarantee that any demand can be (obliviously) routed along constant-hop flow-paths - in contrast to the $\Omega(\

Steven Sivek, Mehdi Yazdi

Bill Thurston proved that taut foliations of hyperbolic 3-manifolds have Euler classes of norm at most one, and conjectured that any integral second cohomology class of norm equal to one is realised as the Euler class of some taut foliation. Recent work of the second author, joint with David Gabai, has produced counterexamples to this conjecture. Since tight

Noah Bergam, Emily Allaway, Kathleen McKeown

We analyze publicly available US Supreme Court documents using automated stance detection. In the first phase of our work, we investigate the extent to which the Court's public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument transcripts. We then compare these language-based metrics t

Anirban Kundu, Alain Le Yaouanc, Poulami Mondal, François Richard

In view of the future Higgs factories, this work presents the status of scalar searches at the LHC with an emphasis on the H(650) resonance which has been observed in WW, ZZ and h(95)h(125) channels, with a cumulative evidence of about 7 s.d. global significance. Its interpretation in models, restricted to extension of the scalar sector by SU(2) singlets and

Anastasia A. Golubtsova, Nikita S. Tsegelnik

We study Wilson loops in holographic duals of the $\mathcal{N}=4$ SYM quark-gluon plasma. For this we consider the Schwarzschild-$AdS_5$ and Kerr-$AdS_5$ black holes, which are dual to the non-rotating and rotating QGPs, correspondingly. From temporal Wilson loops we find the heavy quark potentials in both backgrounds. For the temperature above the critical

Nadia Ben Atti, Gema M. Diaz--Toca, Henri Lombardi

We propose a slight modification of the Berlekamp-Massey Algorithm for obtaining the minimal polynomial of a given linearly recurrent sequence. Such a modification enables to explain it in a simpler way and to adapt it to lazy evaluation.

Sheng Shen, Shijia Yang, Tianjun Zhang, Bohan Zhai

Prompt Tuning, conditioning on task-specific learned prompt vectors, has emerged as a data-efficient and parameter-efficient method for adapting large pretrained vision-language models to multiple downstream tasks. However, existing approaches usually consider learning prompt vectors for each task independently from scratch, thereby failing to exploit the ri

Kefan Dong, Tengyu Ma

Real-world machine learning applications often involve deploying neural networks to domains that are not seen in the training time. Hence, we need to understand the extrapolation of nonlinear models -- under what conditions on the distributions and function class, models can be guaranteed to extrapolate to new test distributions. The question is very challen

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson

In this work, we study the task of estimating the numbers of distinct and $k$-occurring items in a time window under the constraint of differential privacy (DP). We consider several variants depending on whether the queries are on general time windows (between times $t_1$ and $t_2$), or are restricted to being cumulative (between times $1$ and $t_2$), and de

Dario Beraldo, Massimo Pippi

We apply methods of derived and non-commutative algebraic geometry to understand intersection theoretic phenomena on arithmetic schemes. Specifically, we categorify Bloch's intersection number (in the formulation provided by Kato--Saito). Combining this with To\"en--Vezzosi's non-commutative Chern character, we obtain a generalization of Bloch conductor conj

M. H. Saadat, B. Gjorgiev, L. Das, G. Sansavini

Physics-informed neural networks (PINNs) numerically approximate the solution of a partial differential equation (PDE) by incorporating the residual of the PDE along with its initial/boundary conditions into the loss function. In spite of their partial success, PINNs are known to struggle even in simple cases where the closed-form analytical solution is avai

Kai Xu

We study closed orientable surfaces satisfying the spectral condition $\lambda_1(-\Delta+\beta K)\geq\lambda\geq0$, where $\beta$ is a positive constant and $K$ is the Gauss curvature. This condition naturally arises for stable minimal surfaces in 3-manifolds with positive scalar curvature. We show isoperimetric inequalities, area growth theorems and diamete

Songling Shan

In 1956, Tutte proved the celebrated theorem that every 4-connected planar graph is hamiltonian. This result implies that every more than $\frac{3}{2}$-tough planar graph on at least three vertices is hamiltonian and so has a 2-factor. Owens in 1999 constructed non-hamiltonian maximal planar graphs of toughness arbitrarily close to $\frac{3}{2}$ and asked wh

Alexander Müller-Hermes

We show that the quantum generalization of the $2$-Wasserstein distance proposed by Chakrabarti et al. is not monotone under partial traces. This disproves a recent conjecture by Friedland et al. Finally, we propose a stabilized version of the original definition, which we show to be monotone under the application of general quantum channels.

David Clausen, Xiang Tang, Li-Sheng Tseng

On symplectic manifolds, we introduce a Morse-type complex with elements generated by pairs of critical points of a Morse function. The differential of the complex consists of gradient flows and an integration of the symplectic structure over spaces of gradient flow lines. Using the Witten deformation method, we prove that the cohomology of this complex is i

Jinghan Jia, Shashank Srikant, Tamara Mitrovska, Chuang Gan

We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models. Different from existing works, we show that code obfuscation, a standard code transformation operation, provides novel means to generate complementary `views' of a code that enable us to achieve both robust and accurate code models. To

Weijia Shao, Fikret Sivrikaya, Sahin Albayrak

In this paper, we propose and analyse a family of generalised stochastic composite mirror descent algorithms. With adaptive step sizes, the proposed algorithms converge without requiring prior knowledge of the problem. Combined with an entropy-like update-generating function, these algorithms perform gradient descent in the space equipped with the maximum no

E. Pérez-Montero, M. Lanzara, A. Ortiz-Gil, M. Villaverde

Astroaccesible is an outreach project hosted by the Instituto de Astrof\'{\i}sica de Andaluc\'{\i}a - CSIC aimed at the teaching and popularisation of the astronomy among all publics independently of their capabilities and abilities, paying special attention to the collective of blind and visually impaired (BVI). Among the different strategies and resources

Amin Esfahani, Gulcin M. Muslu

In this paper, we study the generalized Boussinesq equation as a model for the water wave problem with surface tension. Initially, we investigate the initial value problem within Sobolev spaces, deriving conditions under which solutions are either global or experience blow-up in time. Subsequently, we extend our analysis to Bessel potential and modulation sp

Yichen Fu, I. Y. Dodin, Hong Qin

In inhomogeneous media, electromagnetic-wave rays deviate from the trajectories predicted by the leading-order geometrical optics. This effect, called the spin Hall effect of light, is typically neglected in ray-tracing codes used for modeling waves in plasmas. Here, we demonstrate that the spin Hall effect can be significant for radiofrequency waves in toro

Mohammad Mahdi Johari, Camilla Carta, François Fleuret

We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorporate the latest advances in Neural Radianc

Ngoc-Quan Pham, Jan Niehues, Alexander Waibel

Multilingual speech recognition with neural networks is often implemented with batch-learning, when all of the languages are available before training. An ability to add new languages after the prior training sessions can be economically beneficial, but the main challenge is catastrophic forgetting. In this work, we combine the qualities of weight factorizat

R. K. Cochrane, C. C. Hayward, D. Angles-Alcazar, R. S. Somerville

Recent years have seen growing interest in post-processing cosmological simulations with radiative transfer codes to predict observable fluxes for simulated galaxies. However, this can be slow, and requires a number of assumptions in cases where simulations do not resolve the ISM. Zoom-in simulations better resolve the detailed structure of the ISM and the g

Zineng Tang, Jaemin Cho, Jie Lei, Mohit Bansal

We present Perceiver-VL, a vision-and-language framework that efficiently handles high-dimensional multimodal inputs such as long videos and text. Powered by the iterative latent cross-attention of Perceiver, our framework scales with linear complexity, in contrast to the quadratic complexity of self-attention used in many state-of-the-art transformer-based

Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee

Graphical models are an important tool in exploring relationships between variables in complex, multivariate data. Methods for learning such graphical models are well developed in the case where all variables are either continuous or discrete, including in high-dimensions. However, in many applications data span variables of different types (e.g. continuous,

Nico Potyka, Xiang Yin, Francesca Toni

Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large, their decision making process is often incomprehensible. In order to reason about the decision process, we propose representing it as an argumentation problem. We generalize suffi

Romain Branchereau

We consider the Kudla-Millson theta series associated to a quadratic space of signature $(N,N)$. By combining a `see-saw' argument with the Siegel-Weil formula, we show that its (regularized) integral along a torus attached to a totally real field of degree $N$ is the diagonal restriction of an Eisenstein series. It allows us to express the Fourier coefficie

Lorenzo Bartolini, Sven Bjarke Gudnason, Josef Leutgeb, Anton Rebhan

Description of nuclear matter in the core of neutron stars eludes the main tools of investigation of QCD, such as perturbation theory and the lattice formulation of the theory. Recently, the application of the holographic paradigm (both via top-down and bottom-up models) to this task has led to many encouraging results, both qualitatively and quantitatively.

Zhaoyang Cao, Han Yu, Huiyuan Yang, Akane Sano

Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications. However, in real-world applications, generic models are usually more favorable due to new-user-adaptation issues and system complexities, etc. To improve the performance of the generic model,

Andrei Rabusov, Daniel Greenwald, Stephan Paul

We present simulation studies in preparation for analyzing $\tau^-\to\pi^-\pi^+\pi^-\nu_\tau$ in data from the Belle experiment at the KEK $\mathrm{e}^+\mathrm{e}^-$ collider. Analyzing this decay can shed light on the $\mathrm{a}_1(1260)$ and $\mathrm{a}_1(1420)$ resonances and yield results that improve measurement of the $\tau$ electric and magnetic dipol

Xin Wang, Hong Chen, Si'ao Tang, Zihao Wu

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form. The process of separating underlying factors of variation into variables with semantic meaning benefits in learning explainable representations of data, which imitates the meani

Zixin Zhu, Yixuan Wei, Jianfeng Wang, Zhe Gan

The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one. We present a diffusion-based captioning model, dubbed the name DDCap, to allow more decoding flexibility. Unlike image generation, where the output is continuous and redundant with a fixed length, texts in image captions are categorical and s

Divesh Aggarwal, Huck Bennett, Zvika Brakerski, Alexander Golovnev

We study the complexity of lattice problems in a world where algorithms, reductions, and protocols can run in superpolynomial time, revisiting four foundational results: two worst-case to average-case reductions and two protocols. We also show a novel protocol. 1. We prove that secret-key cryptography exists if $\widetilde{O}(\sqrt{n})$-approximate SVP is ha

Tien Thanh Le, Yusheng Ji, John C. S Lui

Distributed access control is a crucial component for massive machine type communication (mMTC). In this communication scenario, centralized resource allocation is not scalable because resource configurations have to be sent frequently from the base station to a massive number of devices. We investigate distributed reinforcement learning for resource selecti

Erhan Bayraktar, Qi Feng, Zhaoyu Zhang

In this work, we study the deep signature algorithms for path-dependent options. We extend the backward scheme in [Hur\'e-Pham-Warin. Mathematics of Computation 89, no. 324 (2020)] for state-dependent FBSDEs with reflections to path-dependent FBSDEs with reflections, by adding the signature layer to the backward scheme. Our algorithm applies to both European

Joshua Lockhart, Daniele Magazzeni, Manuela Veloso

The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means to ensure that a neural network based classifier bases its predictions solely on human understandable concepts. The concept labels, or rationales as we refer to them, are learned by the concept labeling component of the CBM. Another component learns to predict the target classification

Zachary Chase, Shachar Lovett

A set system is called union closed if for any two sets in the set system their union is also in the set system. Gilmer recently proved that in any union closed set system some element belongs to at least a $0.01$ fraction of sets, and conjectured that his technique can be pushed to the constant $\frac{3-\sqrt{5}}{2}$. We verify his conjecture; show that it

Zhandos A. Moldabekov, Mani Lokamani, Jan Vorberger, Attila Cangi

We assess the accuracy of common hybrid exchange-correlation (XC) functionals (PBE0, PBE0-1/3, HSE06, HSE03, and B3LYP) within Kohn-Sham density functional theory (KS-DFT) for the harmonically perturbed electron gas at parameters relevant for the challenging conditions of warm dense matter. Generated by laser-induced compression and heating in the laboratory

Zihao Wang, Yingyu Yang, Maxime Sermesant, Herve Delingette

Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared to other imaging modalities. Traditional (non-learning) registration approaches rely on the iterative optimization of a similarity metric which is usually costly in time complexity.

Vivek Kumar Tiwari

The vacuum effective potential and phase diagram for the three (2+1) flavor quark-meson model have been computed and compared in an extended mean-field approximation (e-MFA) where the model parameters are fixed by using different renormalization prescriptions after including quark one-loop vacuum fluctuations. When the vacuum one-loop divergence is regulariz

Szabolcs Mikulás

We have a quick look at various finite model properties for residuated semigroups. In particular, we solve Problem 19.17 from Relation Algebras by Games by Hirsch and Hodkinson.

Michele Arzano, Giulia Gubitosi, José Javier Relancio

We review the main features of models where relativistic symmetries are deformed at the Planck scale. We cover the motivations and links to other quantum gravity approaches. We describe in some detail the most studied theoretical frameworks, including Hopf algebras, relative locality, and other scenarios with deformed momentum space geometry. We discuss poss

Stephanie Blanke, Christina Brandt

Magnetic particle imaging is a relatively new tracer-based medical imaging technique exploiting the non-linear magnetization response of magnetic nanoparticles to changing magnetic fields. If the data are generated by using a field-free line, the sampling geometry resembles the one in computerized tomography. Indeed, for an ideal field-free line rotating onl

Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo

Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still limited and only constrained to the classification task. In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unlea

Dimitris Achlioptas, Amrit Daswaney, Periklis A. Papakonstantinou

Applying deep learning to solve real-life instances of hard combinatorial problems has tremendous potential. Research in this direction has focused on the Boolean satisfiability (SAT) problem, both because of its theoretical centrality and practical importance. A major roadblock faced, though, is that training sets are restricted to random formulas of size s

H. Harbrecht, M. Multerer, O. Schenk, Ch. Schwab

We propose a sparse algebra for samplet compressed kernel matrices, to enable efficient scattered data analysis. We show the compression of kernel matrices by means of samplets produces optimally sparse matrices in a certain S-format. It can be performed in cost and memory that scale essentially linearly with the matrix size $N$, for kernels of finite differ

Alice E. A. Allen, Alexandre Tkatchenko

Learning from data has led to substantial advances in a multitude of disciplines, including text and multimedia search, speech recognition, and autonomous-vehicle navigation. Can machine learning enable similar leaps in the natural and social sciences? This is certainly the expectation in many scientific fields and recent years have seen a plethora of applic

Christian Peterson

Exact solutions are found for Euler's equations of rigid body motion for general asymmetrical bodies under the influence of torque by using Jacobi elliptic functions. Differential equations are determined for the amplitudes and the parameters of the elliptic functions. The solution is then applied to the detumbling of a satellite with arbitrary initial rotat

Yangxiao Lu, Yuqiao Chen, Nicholas Ruozzi, Yu Xiang

Segmenting unseen objects from images is a critical perception skill that a robot needs to acquire. In robot manipulation, it can facilitate a robot to grasp and manipulate unseen objects. Mean shift clustering is a widely used method for image segmentation tasks. However, the traditional mean shift clustering algorithm is not differentiable, making it diffi

Samia Touileb, Debora Nozza

Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exist in selected Scandinavian language models. We examine nine models, covering Danish, Swedish, and Norwegian, by manually creat

James O. Cochran, Grace L. Callaghan, Miles J. G. Caven, Suzanne M. Fielding

We study theoretically the dynamical process of yielding in cyclically sheared amorphous materials, within a thermal elastoplastic model and the soft glassy rheology model. Within both models we find an initially slow accumulation, over many cycles after the inception of shear, of low levels of damage in the form strain heterogeneity across the sample. This

Zs. Bognár, Á. Sódor, Gy. Mező

Context. Thanks to photometric space missions, we have access to more and more information on the properties of white dwarf stars, especially pulsating ones. In the case of pulsators, we have the opportunity to get an insight into their otherwise hidden interiors by the means of asteroseismology. In addition to space-based observations, we also take advantag

John Ormerod, Weichang Yu, Mohammad Javad Davoudabadi, Yuhao Li

Mean-field variational Bayes is a fast and scalable approach to approximate Bayesian inference, but its independence assumptions often lead to underestimated posterior uncertainty. We introduce moment propagation (MP), a framework for improving marginal posterior approximations by propagating conditional posterior moment information between parameter blocks

Dario Pavllo, David Joseph Tan, Marie-Julie Rakotosaona, Federico Tombari

Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arbitrary topologies. Recent work in this area, however, has mostly focused on synthetic datasets where exact ground-truth poses are known, and has overlooked pose estimation, which i

Iraj Daizadeh

Is the complexity of medical product (medicines and medical devices) regulation impacting innovation in the US? If so, how? Here, this question is investigated as follows: Various novel proxy metrics of regulation (FDA-issued guidelines) and innovation (corresponding FDA-registrations) from 1976-2020 are used to determine interdependence, a concept relying o