October 2022 arXiv papers — page 64

Ahmed Shalabi, Laura J. Henderson, Robert B. Mann

The potential breakdown of the notion of a metric at high energy scales could imply the existence of a fundamental minimal length scale below which distances cannot be resolved. One approach to realizing this minimum length scale is construct a quantum field theory with a bandlimit on the field. We report on an investigation of the effects of imposing a band

Nitesh Kumar, Kumar Dheenadayalan, Suprabath Reddy, Sumant Kulkarni

Demand forecasting applications have immensely benefited from the state-of-the-art Deep Learning methods used for time series forecasting. Traditional uni-modal models are predominantly seasonality driven which attempt to model the demand as a function of historic sales along with information on holidays and promotional events. However, accurate and robust s

José María Jorquera Valero, Pedro Miguel Sánchez Sánchez, Manuel Gil Pérez, Alberto Huertas Celdrán

Trust, security, and privacy are three of the major pillars to assemble the fifth generation network and beyond. Despite such pillars are principally interconnected, they arise a multitude of challenges to be addressed separately. 5G ought to offer flexible and pervasive computing capabilities across multiple domains according to user demands and assuring tr

Gaoming Wang

In this paper, we consider a Generalized Bernstein Theorem for a type of generalized minimal surfaces, namely minimal Plateau surfaces. We show that if an orientable minimal Plateau surface is stable and has quadratic area growth in $\mathbb{R}^3 $, then it is flat.

Alice Chen, Niayesh Afshordi

Halo Models of large scale structure provide powerful and indispensable tools for phenomenological understanding of the clustering of matter in the Universe. While the halo model builds structures out of the superposition of haloes, defining halo profiles in their outskirts - beyond their virial radii - becomes increasingly ambiguous, as one cannot assign ma

Dalton Lunga, Yingjie Hu, Shawn Newsam, Song Gao

Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption. However, the efficient design and implementation of GeoAI systems face many open challenges. This is mainly due to the lack of non-standardized approaches to artificial intelligence tool development, inadequate platforms, and a lack of multidisciplinary eng

Hannah Chen, Yangfeng Ji, David Evans

Traditional (fickle) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different prediction. Conversely, obstinate adversarial examples occur when an adversary finds a small perturbation that preserves the classifier's prediction but changes the true label of an

Alexios P. Polychronakos

Braneworlds winding and spinning around an extra compact dimension manifest superluminal propagation for fields penetrating the bulk. This propagation is either irreducibly anisotropic or one that becomes isotropic in a special frame, depending on the brane's motion and orientation in the bulk. For a class of boosted observers on the brane the wavefront of s

Dao Thanh Hai

Conventional wisdom in designing the optical switching nodes is rooted in the intuition that when an optical channel crossing an intermediate node, it should be maximally isolated from other optical channels to avoid interference. Such long-established paradigm perceiving the interference of optical channels transiting at the same node as an adversarial fact

Matthew J. Hayes

We develop a new method to infer the temporal, geometric, and energetic properties of galaxy outflows, by combining stellar spectral modeling to infer starburst ages, and absorption lines to measure velocities. If winds are accelerated with time during a starburst event, then these two measurements enable us to solve for the wind radius, similarly to length

Stanley Snelson, Eduardo V. Teixeira

We investigate Bernoulli free boundary problems prescribing infinite jump conditions. The mathematical set-up leads to the analysis of non-differentiable minimization problems of the form $\int \left(\nabla u\cdot (A(x)\nabla u) + \varphi(x) 1_{\{u>0\}}\right) \,\mathrm{d}x \to \text{min}$, where $A(x)$ is an elliptic matrix with bounded, measurable coeffici

Amr Ahmadain, Alexander Frenkel, Krishnendu Ray, Ronak M Soni

We identify the microstates of the non supersymmetric, asymptotically flat 2d black hole in the dual c=1 matrix quantum mechanics (MQM). We calculate the partition function of the theory using Hamiltonian methods and reproduce one of two conflicting results found by Kazakov and Tseytlin. We find the entropy by counting states and the energy by solving the Sc

Yoshimasa Hidaka, Dan Kondo

We address emergent higher-form symmetry in Higgs phases with superfluidity. The emergent symmetry appears if a matter field is invariant under a transformation of a common subgroup of gauge and global $\mathrm{U}(1)$ symmetries. We explicitly construct the symmetry generator that is topological and gauge invariant in a low-energy effective theory. Such emer

Francesco Zappa, Sebastiano Bernuzzi, David Radice, Albino Perego

We present a systematic numerical relativity study of the impact of different treatment of microphysics and grid resolution in binary neutron star mergers. We consider series of simulations at multiple resolutions comparing hydrodynamics, neutrino leakage scheme, leakage augmented with the M0 scheme and the more consistent M1 transport scheme. Additionally,

Dominik S. Wild, Álvaro M. Alhambra

Recent progress in the development of quantum technologies has enabled the direct investigation of dynamics of increasingly complex quantum many-body systems. This motivates the study of the complexity of classical algorithms for this problem in order to benchmark quantum simulators and to delineate the regime of quantum advantage. Here we present classical

Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman

In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis. Typically, algorithms select individual collision events for preservation and store the complete experimental response. A relatively new alternative strategy is to additionally save a partial record for a

Jiaqing Bi, Min-Kai Lin, Ruobing Dong

As one of the most commonly observed disk substructures, dust rings from high-resolution disk surveys appear to have different radial widths. Recent observations on PDS 70 and AB Aur reveal not only planets in the disk, but also the accompanying wide dust rings. We use three-dimensional dust-and-gas disk simulations to study whether gap-opening planets are r

Farida Farsian, Federico Marulli, Lauro Moscardini, Carlo Giocoli

Upcoming cosmological surveys will provide unprecedented amount of data, which will require innovative statistical methods to maximize the scientific exploitation. Standard cosmological analyses based on abundances, two-point and higher-order statistics of cosmic tracers have been widely used to investigate the properties of the cosmic web and Large Scale St

Peizhi Mai, Jinchao Zhao, Benjamin E. Feldman, Philip W. Phillips

In non-interacting systems, bands from non-trivial topology emerge strictly at half-filling and exhibit either the quantum anomalous Hall or spin Hall effects. Here we show using determinantal quantum Monte Carlo and an exactly solvable strongly interacting model that these topological states now shift to quarter filling. A topological Mott insulator is the

Ruotian Gong, Guanghui He, Xingyu Gao, Peng Ju

Optically active spin defects in van der Waals materials are promising platforms for modern quantum technologies. Here we investigate the coherent dynamics of strongly interacting ensembles of negatively charged boron-vacancy ($\mathrm{V}_{\mathrm{B}}^-$) centers in hexagonal boron nitride (hBN) with varying defect density. By employing advanced dynamical de

Panos Betzios, Olga Papadoulaki

We analyse models of Matrix Quantum Mechanics in the double scaling limit that contain non-singlet states. The finite temperature partition function of such systems contains non-trivial winding modes (vortices) and is expressed in terms of a group theoretic sum over representations. We then focus in the case when the first winding mode is dominant (model of

Dan Stefan Eniceicu, Raghu Mahajan, Pronobesh Maity, Chitraang Murdia

We compute the ZZ annulus one-point function of the cosmological constant operator in non-critical string theory, regulating divergences from the boundaries of moduli space using string field theory. We identify a subtle issue in a previous analysis of these divergences, which was done in the context of the $c=1$ string theory, and where it had led to a mism

Beiwen Tian, Liyi Luo, Hao Zhao, Guyue Zhou

Recently, 3D scenes parsing with deep learning approaches has been a heating topic. However, current methods with fully-supervised models require manually annotated point-wise supervision which is extremely user-unfriendly and time-consuming to obtain. As such, training 3D scene parsing models with sparse supervision is an intriguing alternative. We term thi

Hadas Orgad, Yonatan Belinkov

Considerable efforts to measure and mitigate gender bias in recent years have led to the introduction of an abundance of tasks, datasets, and metrics used in this vein. In this position paper, we assess the current paradigm of gender bias evaluation and identify several flaws in it. First, we highlight the importance of extrinsic bias metrics that measure ho

Kevin Zhang, Zhiqiang Shen

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (

Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda, Xiaofeng Wang

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in practice to annotate all the labels in every training image. Existing works on partial-label learning focus on the case where each training image is labeled with only a subset of its po

Alex Gu, Tamara Mitrovska, Daniela Velez, Jacob Andreas

We introduce ObSynth, an interactive system leveraging the domain knowledge embedded in large language models (LLMs) to help users design object models from high level natural language prompts. This is an example of specification reification, the process of taking a high-level, potentially vague specification and reifying it into a more concrete form. We eva

Matteo Poggi, Andrea Conti, Stefano Mattoccia

This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acquisition. Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network by modulating the plane-sweep cost volume built during the forward

Yoonho Lee, Annie S. Chen, Fahim Tajwar, Ananya Kumar

A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task. This paper shows that in such settings, selectively fine-tuning a subset of layers (which we term surgical fine-tuning) matches or outperforms commonly used fine-tuning ap

Ashlesha Patil, Yosef P. Jacobson, Don Towsley, Saikat Guha

Measurement-Based Quantum Computing (MBQC), proposed in 2001 is a model of quantum computing that achieves quantum computation by performing a series of adaptive single-qubit measurements on an entangled cluster state. Our project is aimed at introducing MBQC to a wide audience ranging from high school students to quantum computing researchers through a Tang

Xin Liu, Zhongdao Wang, Yali Li, Shengjin Wang

A mainstream type of current self-supervised learning methods pursues a general-purpose representation that can be well transferred to downstream tasks, typically by optimizing on a given pretext task such as instance discrimination. In this work, we argue that existing pretext tasks inevitably introduce biases into the learned representation, which in turn

Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg

We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fracture simulation is powered by a recent physically based algorithm that efficiently generates a variety of fracture modes of an object. Existing shape assembly datasets decompose obj

M. E. Shirokov

We consider methods for obtaining local lower bounds on characteristics of quantum (correspondingly, classical) systems, i.e. lower bounds valid in the trace norm $\epsilon$-neighborhood of a given state (correspondingly, probability distribution). The main attention is paid to infinite-dimensional systems.

R. Li, N. Sparveris, H. Atac, M. K. Jones

The visible world is founded on the proton, the only composite building block of matter that is stable in nature. Consequently, understanding the formation of matter relies on explaining the dynamics and the properties of the proton's bound state.A fundamental property of the proton involves the response of the system to an external electromagnetic field. It

Max Sokolich, David Rivas, Zameer Hussain Shah, Sambeeta Das

We report a closed-loop control system for paramagnetic catalytically self-propelled Janus microrobots. We achieve this control by employing electromagnetic coils that direct the magnetic field in a desired orientation to steer the microrobots. The microrobots move due to the catalytic decomposition of hydrogen peroxide, during which they align themselves to

Tan Dao, Sergey S. Pershoguba, Jiadong Zang

Electrons moving through a noncoplanar magnetic texture acquire a Berry phase, which can be described as an effective magnetic field. This effect is known as the topological Hall effect and has been observed in topological spin textures. Motivated by recent experimental realizations, here we study the Hall effect in a nontopological magnetic texture known as

Gal Kronenberg, Shoham Letzter, Alexey Pokrovskiy, Liana Yepremyan

A common problem in graph colouring seeks to decompose the edge set of a given graph into few similar and simple subgraphs, under certain divisibility conditions. In 1987 Wormald conjectured that the edges of every cubic graph on $4n$ vertices can be partitioned into two isomorphic linear forests. We prove this conjecture for large connected cubic graphs. Ou

George Cooper

We prove that any compactified universal Jacobian over any stack of stable maps, defined using torsion-free sheaves which are Gieseker semistable with respect to a relatively ample invertible sheaf over the universal curve, admits a projective good moduli space which can be constructed using GIT, and that the same is true for analogues parametrising semistab

Kirill Vishniakov, Eric Xing, Zhiqiang Shen

The recent progress in self-supervised learning has successfully combined Masked Image Modeling (MIM) with Siamese Networks, harnessing the strengths of both methodologies. Nonetheless, certain challenges persist when integrating conventional erase-based masking within Siamese ConvNets. Two primary concerns are: (1) The continuous data processing nature of C

Ilmar Gahramanov, Batuhan Keskin, Dilara Kosva, Mustafa Mullahasanoglu

We study Bailey pairs construction for hyperbolic hypergeometric integral identities acquired via the duality of lens partitions functions for the three-dimensional $\mathcal N=2$ supersymmetric gauge theories on $S_b^3/\mathbb{Z}_r$. The novel Bailey pairs are constructed for the star-triangle relation, the star-star relation and the pentagon identity. The

Abhimanyu Susobhanan

Pulsar Timing Array (PTA) experiments are expected to be sensitive to gravitational waves (GWs) emitted by individual supermassive black hole binaries (SMBHBs) inspiralling along eccentric orbits. We compare the computational cost of different methods of computing the PTA signals induced by relativistic eccentric SMBHBs, namely approximate analytic expressio

Timo Eichhorn, Christian Hoelbling, Philip Rouenhoff, Lukas Varnhorst

At fine lattice spacings, lattice simulations are plagued by slow (topological) modes that give rise to large autocorrelation times. These, in turn, lead to statistical and systematic errors that are difficult to estimate. We study the problem and possible algorithmic solutions in 4-dimensional SU(3) gauge theory, with special focus on instanton updates and

Pulkit Gopalani, Anirbit Mukherjee

In this note, we consider appropriately regularized $\ell_2-$empirical risk of depth $2$ nets with any number of gates and show bounds on how the empirical loss evolves for SGD iterates on it -- for arbitrary data and if the activation is adequately smooth and bounded like sigmoid and tanh. This in turn leads to a proof of global convergence of SGD for a spe

Joshua Villarreal, Daniel Winklehner, Daniel Koser, Janet Marie Conrad

Radio-Frequency Quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to r

Zhiheng Zhang

In this note, we first bound the intersection number of the regular simplicial partitions.

Robert Siddall, Raphael Zufferey, Sophie Armanini, Ketao Zhang

Biomimetic and Bioinspired design is not only a potent resource for roboticists looking to develop robust engineering systems or understand the natural world. It is also a uniquely accessible entry point into science and technology. Every person on Earth constantly interacts with nature, and most people have an intuitive sense of animal and plant behavior, e

Sebastian Mizera

Singularities, such as poles and branch points, play a crucial role in investigating the analytic properties of scattering amplitudes that inform new computational techniques. In this note, we point out that scattering amplitudes can also have another class of singularities called natural boundaries of analyticity. They create a barrier beyond which analytic

P. Drmota, D. Main, D. P. Nadlinger, B. C. Nichol

We integrate a long-lived memory qubit into a mixed-species trapped-ion quantum network node. Ion-photon entanglement first generated with a network qubit in Sr-88 is transferred to Ca-43 with 0.977(7) fidelity, and mapped to a robust memory qubit. We then entangle the network qubit with a second photon, without affecting the memory qubit. We perform quantum

Giacomo De Palma, Dario Trevisan

We propose a generalization of the Wasserstein distance of order 1 to quantum spin systems on the lattice $\mathbb{Z}^d$, which we call specific quantum $W_1$ distance. The proposal is based on the $W_1$ distance for qudits of [De Palma et al., IEEE Trans. Inf. Theory 67, 6627 (2021)] and recovers Ornstein's $\bar{d}$-distance for the quantum states whose ma

Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla

Bagging is a commonly used ensemble technique in statistics and machine learning to improve the performance of prediction procedures. In this paper, we study the prediction risk of variants of bagged predictors under the proportional asymptotics regime, in which the ratio of the number of features to the number of observations converges to a constant. Specif

Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry

A cognitive radar is a constrained utility maximizer that adapts its sensing mode in response to a changing environment. If an adversary can estimate the utility function of a cognitive radar, it can determine the radar's sensing strategy and mitigate the radar performance via electronic countermeasures (ECM). This paper discusses how a cognitive radar can {

Joe Kileel, Kathlén Kohn

In this survey article, we present interactions between algebraic geometry and computer vision, which have recently come under the header of algebraic vision. The subject has given new insights in multiple view geometry and its application to 3D scene reconstruction and carried a host of novel problems and ideas back into algebraic geometry.

Muhammad Umair Nasir, Michael Beukman, Steven James, Christopher Wesley Cleghorn

In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly challenging environments. Unlike previous open-ended approaches that optimize agents using a fixed neural network topology, we hypothesize that generalization can be improved by allowing agents' controllers to become more com

Karina Ruzaeva, Jan-Christopher Cohrs, Keitaro Kasahara, Dietrich Kohlheyer

Cell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates. Unlike in common multiple object tracking, in microbial live-cell experiments cells are growing, moving, and dividing over time, to form cell colonies that are densely packed in mono-layer structures. With increasing cell

Walter W. Golay, Robert L. Mutel, Dani Lipman, Manuel Güdel

We searched for thermal gyro-synchrotron radio emission from a sample of five radio-loud stars whose X-ray coronae contain a hot ($T_e>10^7$ K) thermal component. We used the JVLA to measure Stokes I and V/I spectral energy distributions (SEDs) over the frequency range 15--45 GHz, determining the best-fitting model parameters using power-law and thermal gyro

Souheib Allout, Abderrahmane Belkacem, Abdelghani Zeghib

We revisit the classification of Lorentz homogeneous spaces of dimension $3$, and relax usual completeness assumptions. In particular, non-unimodular elliptic plane waves, and only them, are neither locally symmetric nor locally isometric to a left-invariant Lorentz metric on a $3$-dimensional Lie group. We characterize homogeneous plane waves in dimension $

McKenzie Black, Changhui Tan

We consider the compressible Euler system with a family of nonlinear velocity alignments. The system is a nonlinear extension of the Euler-alignment system in collective dynamics. We show the asymptotic emergent phenomena of the system: alignment and flocking. Different types of nonlinearity and nonlocal communication protocols are investigated, resulting in

Min Jun Jo, Junha Kim

We analyze the asymptotic stability of the quasi-linearly stratified densities in the 2D inviscid incompressible porous medium equation on $\bbR^2$ with respect to the buoyancy frequency $N$. Our target density of stratification is the sum of the large background linear profile with its slope $N$ and the small perturbation that could be both non-linear and n

Shamindra Shrotriya, Matey Neykov

We study the classical problem of deriving minimax rates for density estimation over convex density classes. Building on the pioneering work of Le Cam (1973), Birge (1983, 1986), Wong and Shen (1995), Yang and Barron (1999), we determine the exact (up to constants) minimax rate over any convex density class. This work thus extends these known results by demo

Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu

Imitation learning offers a promising path for robots to learn general-purpose behaviors, but traditionally has exhibited limited scalability due to high data supervision requirements and brittle generalization. Inspired by recent advances in multi-task imitation learning, we investigate the use of prior data from previous tasks to facilitate learning novel

Georgios Daskalopoulos, Chikako Mese

We present some results about harmonic maps with possibly infinite energy from punctured Riemann surfaces to CAT(0) spaces. In particular, we give precise estimates of their energy growth near the punctures and prove their uniqueness.

Amelie Schreiber

The following article is an application of commutative algebra to the study of multiparameter persistent homology in topological data analysis. In particular, the theory of finite free resolutions of modules over polynomial rings is applied to multiparameter persistent modules. The generic structure of such resolutions and the classifying spaces involved are

Débora A. F. Albanez, Maicon J. Benvenutti

In this paper, we study analytically the long-time behavior of three-dimensional Brinkman-Forchheimer-extended Darcy model, in the context that the parameters related to the damping nonlinear term are unknown. This work is inspired by the approach firstly introduced for two-dimensional Navier-Stokes equations by Carlson, Hudson and Larios. We show estimates

Xiao Liu, Yansong Feng, Jizhi Tang, Chengang Hu

People can acquire knowledge in an unsupervised manner by reading, and compose the knowledge to make novel combinations. In this paper, we investigate whether pretrained language models can perform compositional generalization in a realistic setting: recipe generation. We design the counterfactual recipe generation task, which asks models to modify a base re

Nestor Mirabal

The origin of ultra-high-energy cosmic rays (UHECRs, E $> 10^{18}$ eV) is one of the great mysteries of modern astrophysics. It has been suggested that UHECRs could be accelerated in gamma-ray bursts (GRBs) and engine-driven supernovae (SNe). Here we report the discovery of a 1.4 teraelectronvolt (TeV) photon offset 0.97$^{\circ}$ from the site of the nearby

Chunyu Qiang, Jianhua Tao, Ruibo Fu, Zhengqi Wen

Current end-to-end code-switching Text-to-Speech (TTS) can already generate high quality two languages speech in the same utterance with single speaker bilingual corpora. When the speakers of the bilingual corpora are different, the naturalness and consistency of the code-switching TTS will be poor. The cross-lingual embedding layers structure we proposed ma

Guillaume Couairon, Jakob Verbeek, Holger Schwenk, Matthieu Cord

Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Sema

Yashar Akrami, Stefano Anselmi, Craig J. Copi, Johannes R. Eskilt

The shortest distance around the Universe through us is unlikely to be much larger than the horizon diameter if microwave background anomalies are due to cosmic topology. We show that observational constraints from the lack of matched temperature circles in the microwave background leave many possibilities for such topologies. We evaluate the detectability o

Simon Hofsäss, J. Eduardo Padilla-Castillo, Sid C. Wright, Sebastian Kray

We present absolute frequency measurements of the ${}^{1}\text{P}_1 \leftarrow {}^{1}\text{S}_0$ ($229$nm) and ${}^{3}\text{P}_1 \leftarrow {}^{1}\text{S}_0$ ($326$nm) transitions for all naturally occurring isotopes of cadmium. The isotope shifts and hyperfine intervals of the fermionic isotopes are determined with an accuracy of 3.3MHz. We find that quantu

Luis Garma-Oehmichen, Héctor Hernández-Toledo, Erik Aquino-Ortíz, Luis Martinez-Medina

Most secular effects produced by stellar bars strongly depend on the pattern speed. Unfortunately, it is also the most difficult observational parameter to estimate. In this work, we measured the bar pattern speed of 97 Milky-Way Analogue galaxies from the MaNGA survey using the Tremaine-Weinberg method. The sample was selected by constraining the stellar ma

Jheng-Wei Su, Chi-Han Peng, Peter Wonka, Hung-Kuo Chu

Reconstructing 3D layouts from multiple $360^{\circ}$ panoramas has received increasing attention recently as estimating a complete layout of a large-scale and complex room from a single panorama is very difficult. The state-of-the-art method, called PSMNet, introduces the first learning-based framework that jointly estimates the room layout and registration

Elia Fioravanti, Ivan Levcovitz, Michah Sageev

We show that for many right-angled Artin and Coxeter groups, all cocompact cubulations coarsely look the same: they induce the same coarse median structure on the group. These are the first examples of non-hyperbolic groups with this property. For all graph products of finite groups and for Coxeter groups with no irreducible affine parabolic subgroups of ran

Nicholas Blum, Andre Li, Kevin O'Keeffe, Oleg Kogan

We investigate the effects of delayed interactions in a population of ``swarmalators", generalizations of phase oscillators that both synchronize in time and swarm through space. We discover two steady collective states: a state in which swarmalators are essentially motionless in a disk arranged in a pseudo-crystalline order, and a boiling state in which the

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruc

Nizar El Idrissi, Samir Kabbaj

A relaxation of the notion of invariant set, known as $k$-quasi-invariant set, has appeared several times in the literature in relation to group dynamics. The results obtained in this context depend on the fact that the dynamic is generated by a group. In our work, we consider the notions of invariant and 1-internally-quasi-invariant sets as applied to an ac

Jaehui Hwang, Dongyoon Han, Byeongho Heo, Song Park

In recent years, many deep neural architectures have been developed for image classification. Whether they are similar or dissimilar and what factors contribute to their (dis)similarities remains curious. To address this question, we aim to design a quantitative and scalable similarity measure between neural architectures. We propose Similarity by Attack Tra

Tameem Albash, Conor Smith, Quinn Campbell, Andrew D. Baczewski

A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamiltonians. However, the standard variational algorithms used to update or train the ANN parameters can get trapped in local minima, especially for frustrated systems and even if the rep

Amani Almalki, Longin Jan Latecki

The computer-assisted radiologic informative report is currently emerging in dental practice to facilitate dental care and reduce time consumption in manual panoramic radiographic interpretation. However, the amount of dental radiographs for training is very limited, particularly from the point of view of deep learning. This study aims to utilize recent self

Abhinav Shanbhag

Americans across demographic groups tend to have low financial literacy, with low-income people and minorities at highest risk. This opens the door to the exploitation of unbanked low-income families through high-interest alternative financial services. This paper studies the causes and effects of financial illiteracy and exclusion in the most at-risk demogr

Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin

A natural goal in multiagent learning besides finding equilibria is to learn rationalizable behavior, where players learn to avoid iteratively dominated actions. However, even in the basic setting of multiplayer general-sum games, existing algorithms require a number of samples exponential in the number of players to learn rationalizable equilibria under ban

Mohit Pandey, Tristan Zaborniak, Hans Melo, Alexey Galda

Molecular docking, which aims to find the most stable interacting configuration of a set of molecules, is of critical importance to drug discovery. Although a considerable number of classical algorithms have been developed to carry out molecular docking, most focus on the limiting case of docking two molecules. Since the number of possible configurations of

Luca Rizzi, Francesco Zucconi

We define a new notion of supported global deformation class for a semistable family of complex varieties over a curve $f\colon X\to B$. We use this notion to study when $X$, possibly up to a finite covering, has a generically finite morphism onto a product $B\times Y$ with $Y$ of general type.

Yi Tay, Jason Wei, Hyung Won Chung, Vinh Q. Tran

Scaling language models improves performance but comes with significant computational costs. This paper proposes UL2R, a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. The key idea is to continue training a state-of-the-art large language model (e.g., PaLM) on a few more st

Philipp Ketz

In this paper, we use the results in Andrews and Cheng (2012), extended to allow for parameters to be near or at the boundary of the parameter space, to derive the asymptotic distributions of the two test statistics that are used in the two-step (testing) procedure proposed by Pedersen and Rahbek (2019). The latter aims at testing the null hypothesis that a

A. Nourou Issa

A representation theory for Bol algebras is proposed. For a suitable (2,3)-cohomology theory for Bol algebras, we define a (2,3)-coboundary with companion and next we define a (2,3)-cohomology group. Deformations of Bol algebras are investigated. In particular, one-parameter infinitesimal deformations of Bol algebras are characterized in terms of Bol algebra

Eleftherios Theodosiadis

In this text we make study of the geometry of the solutions to the radial and chordal Loewner PDEs, for a particular choice of time-dependent driving measures with multiple point masses.

Rohit Chikkaraddy, Junyang Huang, Dean Kos, Eoin Elliott

Plasmonic nano-antennas can focus light to nanometre length-scales providing intense field enhancements. For the tightest optical confinements (0.5-5 nm) achieved in plasmonic gaps, the gap spacing, refractive index, and facet width play a dominant role in determining the optical properties making tuning through antenna shape challenging. We show here that c

Dennis Scheidt, Pedro A. Quinto Su

In this work we compare the Canonical and Hadamard bases for in-situ wavefront correction of a focused Gaussian beam using a spatial light modulator (SLM). The beam is perturbed with a transparent optical element (sparse) or a random scatterer (both prevent focusing at a single spot). The phase corrections are implemented with different basis sizes ($N=64, 2

Ryan Faulkner, Daniel Zoran

The ability to carve the world into useful abstractions in order to reason about time and space is a crucial component of intelligence. In order to successfully perceive and act effectively using senses we must parse and compress large amounts of information for further downstream reasoning to take place, allowing increasingly complex concepts to emerge. If

Haroldo V. Ribeiro, Matthew R. Acre, Jacob D. Faulkner, Leonardo R. da Cunha

Despite significant efforts devoted to understanding the underlying complexity and emergence of collective movement in animal groups, the role of different external settings on this type of movement remains largely unexplored. Here, by combining time series analysis and complex network tools, we present an extensive investigation of the effects of shady envi

Sisi Zhou, Spyridon Michalakis, Tuvia Gefen

Measurement noise is a major source of noise in quantum metrology. Here, we explore preprocessing protocols that apply quantum controls to the quantum sensor state prior to the final noisy measurement (but after the unknown parameter has been imparted), aiming to maximize the estimation precision. We define the quantum preprocessing-optimized Fisher informat

Diego Martinez, Luis Riazuelo, Luis Montano

Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots. Recent deep reinforcement learning approaches show promising results to solve the problem, but it is not solved yet, as they typically assume no robot kinodynamic restrictions, holonomic movement or perfect environment knowledge. Moreover, most algorithms fai

Alan Inglis, Andrew Parnell, Catherine Hurley

We present vivid, an R package for visualizing variable importance and variable interactions in machine learning models. The package provides a range of displays including heatmap and graph-based displays for viewing variable importance and interaction jointly and partial dependence plots in both a matrix layout and an alternative layout emphasizing importan

Axel Flinth, Ingo Roth, Gerhard Wunder

The hierarchical sparsity framework, and in particular the HiHTP algorithm, has been successfully applied to many relevant communication engineering problems recently, particularly when the signal space is hierarchically structured. In this paper, the applicability of the HiHTP algorithm for solving the bi-sparse blind deconvolution problem is studied. The b

Clemens von Korff Schmising, Somnath Jana, Kelvin Yao, Martin Hennecke

The ultrafast and element-specific response of magnetic systems containing ferromagnetic 3d transition metals and 4d/5d heavy metals is of interest both from a fundamental as well as an applied research perspective. However, to date no consensus about the main microscopic processes describing the interplay between intrinsic 3d and induced 4d/5d magnetic mome

David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani

A major problem of deep neural networks for image classification is their vulnerability to domain changes at test-time. Recent methods have proposed to address this problem with test-time training (TTT), where a two-branch model is trained to learn a main classification task and also a self-supervised task used to perform test-time adaptation. However, these

Chen Qian, Haoyu Zhang, Yuncheng Gao, Mingyang Han

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion weighted imaging (DWI) MRI acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than sin

Hoseong Cho, Seungryul Baek

This report describes the 2nd place solution to the ECCV 2022 Human Body, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras Challenge: Action Recognition. This challenge aims to recognize hand-object interaction in an egocentric view. We propose a framework that estimates keypoints of two hands and an object with a Transformer-based keypoin

David Kern, Etienne Mann, Cristina Manolache, Renata Picciotto

We introduce a derived enhancement of the moduli space of sections defined by Chang-Li, and we compute its tangent complex. Special cases of this moduli space include stable maps and stable quasi-maps. As an application, we prove that G-theoretic stable map and quasi-map invariants of projective spaces are equal.

Soumyadip Ghosh, Yingdong Lu, Tomasz Nowicki, Edith Zhang

The mean field variational inference (MFVI) formulation restricts the general Bayesian inference problem to the subspace of product measures. We present a framework to analyze MFVI algorithms, which is inspired by a similar development for general variational Bayesian formulations. Our approach enables the MFVI problem to be represented in three different ma