November 2022 arXiv papers — page 42

Jianbo De, Xuekai Ma, Fan Yin, Jiahuan Ren

Integrated electro-optical switches are essential as one of the fundamental elements in the development of modern optoelectronics. As an architecture for photonic systems, exciton polaritons, that are hybrid bosonic quasiparticles that possess unique properties derived from both excitons and photons, have shown much promise. For this system, we demonstrate a

S. Tang, B. King

In many-cycle plane waves at intermediate intensities, the nonlinear trident process can be well-approximated by the two sequential steps of nonlinear Compton scattering of a polarised real photon followed by its transformation into an electron-positron pair via nonlinear Breit-Wheeler pair creation. We investigate this two-step process in the intermediate i

Dror Orgad, Vadim Oganesyan, Sarang Gopalakrishnan

We explore the effects of spatial locality on the dynamics of random quantum systems subject to a Markovian noise. To this end, we study a model in which the system Hamiltonian and its couplings to the noise are random matrices whose entries decay as power laws of distance, with distinct exponents $\alpha_H, \alpha_L$. The steady state is always featureless,

Zongyu Dai, Zhiqi Bu, Qi Long

Missing data are ubiquitous in real world applications and, if not adequately handled, may lead to the loss of information and biased findings in downstream analysis. Particularly, high-dimensional incomplete data with a moderate sample size, such as analysis of multi-omics data, present daunting challenges. Imputation is arguably the most popular method for

Simon Sekavčnik, Janis Nötzel

Quantum information processing technology offers several communication strategies, which offer capacity advantages over classical technologies. However, advantages typically arise only in very particular communication scenarios which are of limited use in public networks. Most importantly, striking capacity advantages have so far been found only for cases wh

Sethupathy Subramanian, Dinshaw S. Balsara, Deepak Bhoriya, Harish Kumar

GPU computing is expected to play an integral part in all modern Exascale supercomputers. It is also expected that higher order Godunov schemes will make up about a significant fraction of the application mix on such supercomputers. It is, therefore, very important to prepare the community of users of higher order schemes for hyperbolic PDEs for this emergin

Jozsef Solymosi, Joshua Zahl

We prove a new Elekes-Szab\'o type estimate on the size of the intersection of a Cartesian product $A\times B\times C$ with an algebraic surface $\{f=0\}$ over the reals. In particular, if $A,B,C$ are sets of $N$ real numbers and $f$ is a trivariate polynomial, then either $f$ has a special form that encodes additive group structure (for example $f(x,y,x) =

Chang-Goo Kim, Jeong-Gyu Kim, Munan Gong, Eve C. Ostriker

Massive, young stars are the main source of energy that maintains multiphase structure and turbulence in the interstellar medium (ISM), and without this "feedback" the star formation rate (SFR) would be much higher than is observed. Rapid energy loss in the ISM and efficient energy recovery by stellar feedback lead to co-regulation of SFRs and the ISM state.

Valentina Shumovskaia, Mert Kayaalp, Mert Cemri, Ali H. Sayed

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly

Davin Choo, Yuval Dagan, Constantinos Daskalakis, Anthimos Vardis Kandiros

We provide time- and sample-efficient algorithms for learning and testing latent-tree Ising models, i.e. Ising models that may only be observed at their leaf nodes. On the learning side, we obtain efficient algorithms for learning a tree-structured Ising model whose leaf node distribution is close in Total Variation Distance, improving on the results of prio

Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai

Currently, attention mechanism becomes a standard fixture in most state-of-the-art natural language processing (NLP) models, not only due to outstanding performance it could gain, but also due to plausible innate explanation for the behaviors of neural architectures it provides, which is notoriously difficult to analyze. However, recent studies show that att

Ratmir Miftachov, Georg Keilbar, Wolfgang Karl Härdle

This paper fills the limited statistical understanding of Shapley values as a variable importance measure from a nonparametric (or smoothing) perspective. We introduce population-level \textit{Shapley curves} to measure the true variable importance, determined by the conditional expectation function and the distribution of covariates. Having defined the esti

Yi Lin, Yiannis Loizides, Reyer Sjamaar, Yanli Song

We extend the Marsden-Weinstein reduction theorem and the Darboux-Moser-Weinstein theorem to symplectic Lie algebroids. We also obtain a coisotropic embedding theorem for symplectic Lie algebroids.

Mohammad Amin Shabani, Sepidehsadat Hosseini, Yasutaka Furukawa

The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a single-step noise as the continuous quantity to precisely invert the continuous forward process; and 2) the final 2D coordinate as the discrete quantity to establish geometric incide

Zeyu Cao, Yuchi Ma, Zhou Zhang

In the U.S., corn is the most produced crop and has been an essential part of the American diet. To meet the demand for supply chain management and regional food security, accurate and timely large-scale corn yield prediction is attracting more attention in precision agriculture. Recently, remote sensing technology and machine learning methods have been wide

Jorge Calvo-Zaragoza, Alexander Pacha, Elona Shatri

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop

Gaetano Fiore

We simplify and complete the construction of fully $O(D)$-equivariant fuzzy spheres $S^d_L$, for all dimensions $d\equiv D-1$, initiated in [G. Fiore, F. Pisacane, J. Geom. Phys. 132 (2018), 423]. This is based on imposing a suitable energy cutoff on a quantum particle in $\mathbb{R}^D$ in a confining potential well $V(r)$ with a very sharp minimum on the sp

Adam Dziedzic, Christopher A Choquette-Choo, Natalie Dullerud, Vinith Menon Suriyakumar

Private multi-winner voting is the task of revealing $k$-hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine learning literature despite its prevalence in many domains such as healthcare. We propose three new DP multi-winner mechanisms: Binary, $\tau$, and Powerset voting. Binary voting oper

L. P. Chitta, D. B. Seaton, C. Downs, C. E. DeForest

The solar wind consists of continuous streams of charged particles that escape into the heliosphere from the Sun, and is split into fast and slow components, with the fast wind emerging from the interiors of coronal holes. Near the ecliptic plane, the fast wind from low-latitude coronal holes is interspersed with a highly structured slow solar wind, the sour

Mumin Jin, Prashant Serai, Jilong Wu, Andros Tjandra

Most people who have tried to learn a foreign language would have experienced difficulties understanding or speaking with a native speaker's accent. For native speakers, understanding or speaking a new accent is likewise a difficult task. An accent conversion system that changes a speaker's accent but preserves that speaker's voice identity, such as timbre a

F. E. Onah, E. García Herrera, J. A. Ruelas-Galván, G. Juárez Rangel

We present a Lie algebraic approach to a Hamiltonian class covering driven, parametric quantum harmonic oscillators where the parameter set -- mass, frequency, driving strength, and parametric pumping -- is time-dependent. Our unitary-transformation-based approach provides a solution to our general quadratic time-dependent quantum harmonic model. As an examp

Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati, Srikanth Ronanki

In this work, we define barge-in verification as a supervised learning task where audio-only information is used to classify user spoken dialogue into true and false barge-ins. Following the success of pre-trained models, we use low-level speech representations from a self-supervised representation learning model for our downstream classification task. Furth

Scott Armstrong, Benjamin Fehrman, Jessica Lin

We prove quantitative estimates on the the parabolic Green function and the stationary invariant measure in the context of stochasic homogenization of elliptic equations in nondivergence form. We consequently obtain a quenched, local CLT for the corresponding diffusion process and a quantitative ergodicity estimate for the environmental process. Each of thes

Shteryo Nozharov, Petya Koralova-Nozharova

The research is done in the context of the upcoming introduction of new European legislation for the first time for regulation of minimum wage at European level. Its purpose is to identify the direction and strength of the correlation amongst changes of minimum wage and unemployment rate in the context of conflicting findings of the scientific literature. Th

A. Stern, A. Pinzul

Using the exact solutions to the field equation for a massive scalar field on noncommutative $AdS_2$, we apply the $AdS/CFT$ correspondence principle to obtain an exact result for the associated two-point function on the conformal boundary. The answer satisfies conformal invariance and has the correct commutative limit and massless limit.

Jörg Frauendiener, Chris Stevens

Recently, Friedrich's Generalized Conformal Field Equations (GCFE) have been implemented numerically and global quantities such as the Bondi energy and the Bondi-Sachs mass loss have been successfully calculated directly on null-infinity. Although being an attractive option for studying global quantities by way of local differential geometrical methods, how

Karl P. Olson, Laurence D. Marks

The underlying mechanisms responsible for triboelectricity have yet to be completely understood. We have previously proposed a model which explains charge transfer in non-metals via band bending due to electromechanical, especially flexoelectric, effects at deformed asperities coupled with work function differences. Here, we investigate whether the shape of

Amin Izadyar, Shiva Zamani

This paper investigates how changes in investor base is related to idiosyncratic volatility in cryptocurrency markets. For each cryptocurrency, we set change in its subreddit followers as a proxy for the change in its investor base, and find out that the latter can significantly increase cryptocurrencies idiosyncratic volatility. This finding is not subsumed

Jose Avila, Guillermo Ortiz, Sergio Troncoso

For each finite primitive subgroup $G$ of $\operatorname{PGL}_4(\mathbb{C})$, we find all the smooth $G$-invariant quartic surfaces. We also find all the faithful representations in $\operatorname{PGL}_4(\mathbb{C})$ of the smooth quartic $G$-invariant surfaces by the groups: $\mathfrak{A}_5,\mathfrak{S}_5, \operatorname{PSL_2(\mathbb{F}_7)},\mathfrak{A}_6,\

Salvador Moreno-Rodríguez, Antonio Alex-Amor, Pablo Padilla, Juan F. Valenzuela-Valdés

In this paper, we study the scattering and diffraction phenomena in time-modulated metamaterials of metallic nature by means of Floquet equivalent circuits. Concretely, we focus on a time-periodic screen that alternates between "metal" and "air" states. We generalize our previous approaches by introducing the concepts of "macroperiod" and "duty cycle" to the

A. Pinzul, A. Stern

We study the exact solutions of both, massless and massive, scalar field theory on the noncommutative $AdS_2$. We also discuss some important limits in order to compare with known results.

Luca Cassia, Nicolo Piazzalunga, Maxim Zabzine

We consider generalizations of equivariant volumes of abelian GIT quotients obtained as partition functions of 1d, 2d, and 3d supersymmetric GLSM on $S^1$, $D^2$ and $D^2 \times S^1$, respectively. We define these objects and study their dependence on equivariant parameters for non-compact toric K\"ahler quotients. We generalize the finite-difference equatio

MAGIC Collaboration, V. A. Acciari, T. Aniello, S. Ansoldi

The BL Lac object 1ES 0647+250 is one of the few distant $\gamma$-ray emitting blazars detected at very high energies (VHE, $\gtrsim$100 GeV) during a non-flaring state. It was detected with the MAGIC telescopes during its low activity in the years 2009-2011, as well as during three flaring activities in the years 2014, 2019 and 2020, with the highest VHE fl

Sangchul Oh, Sabre Kais

Random circuit sampling, the task to sample bit strings from a random unitary operator, has been performed to demonstrate quantum advantage on the Sycamore quantum processor with 53 qubits and on the Zuchongzhi quantum processor with 56 and 61 qubits. Recently, it has been claimed that classical computers using tensor network simulation could catch on curren

Mikołaj Krupski

A Tychonoff space $X$ is called $\kappa$-pseudocompact if for every continuous mapping $f$ of $X$ into $\mathbb{R}^\kappa$ the image $f(X)$ is compact. This notion generalizes pseudocompactness and gives a stratification of spaces lying between pseudocompact and compact spaces. It is well known that pseudocompactness of $X$ is determined by the uniform struc

Márton Pósfai, Balázs Szegedy, Iva Bačić, Luka Blagojević

The emergence of detailed maps of physical networks, like the brain connectome, vascular networks, or composite networks in metamaterials, whose nodes and links are physical entities, have demonstrated the limits of the current network science toolset. Link physicality imposes a non-crossing condition that affects both the evolution and the structure of a ne

Yuchen Ma, Yanbei Chen, Zeynep Akata

Recent advances have indicated the strengths of self-supervised pre-training for improving representation learning on downstream tasks. Existing works often utilize self-supervised pre-trained models by fine-tuning on downstream tasks. However, fine-tuning does not generalize to the case when one needs to build a customized model architecture different from

Solenne Collomb Xiaolei Chen, Jean-Philippe Tinnes, Thomas Schenk, Olivier Ferry

The analysis and understanding of creep damage of Grade 91 steel welded joints is an important topic in the energy industry. Creep tests on welded joints were carried out at 600$^{\circ}$C, 100MPa and then interrupted at 0%, 10%, 30%, 50%, 80% of the expected life and after failure. Creep damage is characterised by cavity bands located exclusively in the cor

Jan de Boer, Viktor Jahnke, Keun-Young Kim, Juan F. Pedraza

We construct worldsheet traversable wormholes by considering the effects of a double-trace deformation, $\delta\mathcal{L}\sim h\partial\phi_L\partial\phi_R$, coupling the endpoints of an open string in AdS space. The operator deforming the theory is irrelevant and makes the boundaries bend inward toward the IR. This effect, reminiscent of two-dimensional di

Leron Borsten, Branislav Jurco, Hyungrok Kim, Tommaso Macrelli

We analyze theories with color-kinematics duality from an algebraic perspective and find that any such theory has an underlying BV${}^{\color{gray} \blacksquare}$-algebra structure, extending the ideas of arXiv:1912.03110. Conversely, we show that any theory with a BV${}^{\color{gray} \blacksquare}$-algebra features a kinematic Lie algebra that controls inte

André Eberhard, Houssam Metni, Georg Fahland, Alexander Stroh

Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed systems, training time could be reduced from several days to

Richard Mayr, Eric Munday

We study Markov decision processes (MDPs) with a countably infinite number of states. The $\limsup$ (resp. $\liminf$) threshold objective is to maximize the probability that the $\limsup$ (resp. $\liminf$) of the infinite sequence of directly seen rewards is non-negative. We establish the complete picture of the strategy complexity of these objectives, i.e.,

Sohag Kabir, Koorosh Aslansefat, Prosanta Gope, Felician Campean

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source ove

Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen

Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional reinforcement learning (RL) with the help of the perception capability of deep learning and has been widely applied in real-world problems.While model-free RL, as a class of efficient DRL methods, performs the learning of state representations simultaneously with policy learning

Travis Gagie

Consider a text $T [1..n]$ prefixed by a reference sequence $R = T [1..\ell]$. We show how, given $R$ and the $z'$-phrase relative Lempel-Ziv parse of $T [\ell + 1..n]$ with respect to $R$, we can build the LZ77 parse of $T$ in $n\,\mathrm{polylog} (n)$ time and $O (\ell + z')$ total space.

Marco Costa, Rashmish K. Mishra, Sonali Verma

Present and upcoming neutrino experiments can have considerable sensitivity to dark sectors that interact feebly with the Standard Model. We consider dark sectors interacting with the SM through irrelevant portals that are motivated on general principles. We derive bounds on such scenarios by considering decays of dark sector excitations inside the neutrino

Kai Shen, Yichong Leng, Xu Tan, Siliang Tang

Text error correction aims to correct the errors in text sequences such as those typed by humans or generated by speech recognition models. Previous error correction methods usually take the source (incorrect) sentence as encoder input and generate the target (correct) sentence through the decoder. Since the error rate of the incorrect sentence is usually lo

Keqiang Sun, Shangzhe Wu, Ning Zhang, Zhaoyang Huang

Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, previous methods focus on controllable 2D image generative model

Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz, Edward Raff

Sequence processing has long been a central area of machine learning research. Recurrent neural nets have been successful in processing sequences for a number of tasks; however, they are known to be both ineffective and computationally expensive when applied to very long sequences. Compression-based methods have demonstrated more robustness when processing s

Álvaro Nodar, Ruben Esteban, Unai Muniain, Michael J. Steel

We use a recently derived gauge-invariant formulation of the problem of a two-level system coupled to an optical cavity, to explore the transition between the weak, and the ultra-strong coupling regimes of light-matter interaction. We explore this transition using the intensity correlations $g^{(2)}(\tau)$ of the emitted light, and find strong, unbounded bun

Hunter T. Nelson, Evangelos Piliouras, Kyle Connelly, Edwin Barnes

Noise-induced gate errors remain one of the main obstacles to realizing a broad range of quantum information technologies. Dynamical error suppression using carefully designed control schemes is critical for overcoming this challenge. Such schemes must be able to correct against multiple noise sources simultaneously afflicting a qubit in order to reach error

Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez

Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most computationally expensive simulations involve calorimeter showers. Advances in deep generative modelling - particularly in the realm of high-dimensional data - have opened the possi

Gianluca Cavoto, Angelo Esposito, Guglielmo Papiri, Antonio Davide Polosa

Forthcoming experiments like Project8 and Ptolemy aim at investigating with high precision the end-point of the tritium $\beta$-decay spectrum sensitive to the neutrino mass. In light of this, using the standard parametrization in terms of nuclear polar form factors, we analyze the complete relativistic expression for the spectrum of the $\beta$-electron emi

Mir Afrasiar, Jaydeep Kumar Basak, Ashish Chandra, Gautam Sengupta

We obtain the reflected entropy for bipartite mixed state configurations of two adjacent and disjoint intervals at a finite temperature in $BCFT_2$s with two distinct boundaries through a replica technique in the large central charge limit. Subsequently these field theory results are reproduced from bulk computations involving the entanglement wedge cross se

Joan Bernabeu, Alberto Cortijo

We analyze from a microscopic point of view the feasibility of a hydrodynamic regime in a type-I Weyl semimetal driven by electron-electron interactions mediated by virtual phonons. Considering also the effects of of impurities and the absorption/emission of real phonons, the electric and thermal conductivities are derived. At temperatures $T$ above the Bloc

Zhiqiang Yan, Tereza Jerabkova, Pavel Kroupa

The stellar initial mass function (IMF) is commonly interpreted to be a scale-invariant probability density distribution function (PDF) such that many small clusters yield the same IMF as one massive cluster of the same combined number of stars. Observations of the galaxy-wide IMF challenge this as dwarf galaxies do not form as many massive stars as expected

Fabio Apruzzi, Oren Bergman, Hee-Cheol Kim, Christoph F. Uhlemann

$\mathbb{Z}_n$ S-folds of 5d SCFTs, including $T_N$, which lead to brane webs with $E_{6,7,8}$ 7-branes were discussed recently. We generalize the construction to `fractional quotients', which are based on $\mathbb{Z}_n$ actions linking multiple copies of the seed theory and lead to $H_{0,1,2}$ 7-branes. We provide the holographic duals for both classes. Thi

Zahra Raissi, Edwin Barnes, Sophia E. Economou

We propose and analyze deterministic protocols to generate qudit photonic graph states from quantum emitters. We show that our approach can be applied to generate any qudit graph state, and we exemplify it by constructing protocols to generate one- and two-dimensional qudit cluster states, absolutely maximally entangled states, and logical states of quantum

Daniel Butter, Falk Hassler, Christopher N. Pope, Haoyu Zhang

Recent progress in generalised geometry and extended field theories suggests a deep connection between consistent truncations and dualities, which is not immediately obvious. A prime example is generalised Scherk-Schwarz reductions in double field theory, which have been shown to be in one-to-one correspondence with Poisson-Lie T-duality. Here we demonstrate

Mohammad Akhond, Andrea Legramandi, Carlos Nunez, Leonardo Santilli

We enlarge the dictionary between matrix models for long linear quivers preserving eight supercharges in $d=5$ and $d=3$ and type IIB supergravity backgrounds with AdS$_{d+1}$ factors. We introduce mass deformations of the field theory that break the quiver into a collection of interacting linear quivers, which are decoupled at the end of the RG flow. We fin

Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem

Given a pair of models with similar training set performance, it is natural to assume that the model that possesses simpler internal representations would exhibit better generalization. In this work, we provide empirical evidence for this intuition through an analysis of the intrinsic dimension (ID) of model activations, which can be thought of as the minima

Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu

This paper presents a new perspective of self-supervised learning based on extending heat equation into high dimensional feature space. In particular, we remove time dependence by steady-state condition, and extend the remaining 2D Laplacian from x--y isotropic to linear correlated. Furthermore, we simplify it by splitting x and y axes as two first-order lin

Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang

Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive approach will cause obvious fusing artifac

Yuan Li, Zhi-Hao Lin, David Forsyth, Jia-Bin Huang

Physical simulations produce excellent predictions of weather effects. Neural radiance fields produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse physical simulations with NeRF models of scenes, producing realistic movies of physical phenomena in those scenes. Our application -- Climate NeRF -- allows people to visualize what

Austin Patel, Andrew Wang, Ilija Radosavovic, Jitendra Malik

We study the problem of imitating object interactions from Internet videos. This requires understanding the hand-object interactions in 4D, spatially in 3D and over time, which is challenging due to mutual hand-object occlusions. In this paper we make two main contributions: (1) a novel reconstruction technique RHOV (Reconstructing Hands and Objects from Vid

Ryan Burgert, Kanchana Ranasinghe, Xiang Li, Michael S. Ryoo

Recently, text-to-image diffusion models have shown remarkable capabilities in creating realistic images from natural language prompts. However, few works have explored using these models for semantic localization or grounding. In this work, we explore how an off-the-shelf text-to-image diffusion model, trained without exposure to localization information, c

Chiheon Kim, Doyup Lee, Saehoon Kim, Minsu Cho

Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based

Zhen Xing, Qi Dai, Han Hu, Jingjing Chen

Semi-supervised action recognition is a challenging but critical task due to the high cost of video annotations. Existing approaches mainly use convolutional neural networks, yet current revolutionary vision transformer models have been less explored. In this paper, we investigate the use of transformer models under the SSL setting for action recognition. To

Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan

AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length of generated videos are far from satisfactory. Diffusion models have shown remarkable results recently but require signi

Nikolai Kalischek, Torben Peters, Jan D. Wegner, Konrad Schindler

Probabilistic denoising diffusion models (DDMs) have set a new standard for 2D image generation. Extending DDMs for 3D content creation is an active field of research. Here, we propose TetraDiffusion, a diffusion model that operates on a tetrahedral partitioning of 3D space to enable efficient, high-resolution 3D shape generation. Our model introduces operat

James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla

Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data. Typical solutions for this continual learning problem require extensive rehearsal of previously seen data, which increases memory costs and may violate data privacy. Recently, the emergence of large-scale pre

Kunal Relia

Fairness in multiwinner elections, a growing line of research in computational social choice, primarily concerns the use of constraints to ensure fairness. Recent work proposed a model to find a diverse \emph{and} representative committee and studied the model's computational aspects. However, the work gave complexity results under major assumptions on how t

Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li

Recently, large-scale text-to-image (T2I) models have shown impressive performance in generating high-fidelity images, but with limited controllability, e.g., precisely specifying the content in a specific region with a free-form text description. In this paper, we propose an effective technique for such regional control in T2I generation. We augment T2I mod

A. Yanes-Díaz, S. Rueda-Teruel, R. Bello, D. Lozano-Pérez

The Observatorio Astrof\'isico de Javalambre (OAJ{\dag}1) in Spain is a young astronomical facility, conceived and developed from the beginning as a fully automated observatory with the main goal of optimizing the processes in the scientific and general operation of the Observatory. The OAJ has been particularly conceived for carrying out large sky surveys w

Aurora Ireland, Seth Koren

Asymmetric reheating is a generic requirement for models of dark sectors with light species, but its implementation is usually in tension with unique phenomenologies otherwise possible in compelling theories containing dark copies of the Standard Model. We present a simple module to implement asymmetric reheating during a $\mathbb{Z}_2$-breaking phase above

Santiago Arenas-Velilla, Emilien Joly

In this paper, we explore some links between transforms derived by Stein's method and concentration inequalities. In particular, we show that the stochastic domination of the zero bias transform of a random variable is equivalent to sub-Gaussian concentration. For this purpose a new stochastic order is considered. In a second time, we study the case of funct

Ketevi A. Assamagan, Breese Quinn, Kenneth Bloom, Veronique Boisvert

This is the summary report of the Community Engagement Frontier for the Snowmass 2021 study of the future of particle physics. The report discusses a number of general issues of importance to the particle physics community, including (1) the relation of universities, national laboratories, and industry, (2) career paths for scientists engaged in particle phy

Leonard Gamberg, Zhong-Bo Kang, Ding Yu Shao, John Terry

We study transverse momentum dependent factorization and resummation at sub-leading power in Drell-Yan and semi-inclusive deep inelastic scattering. In these processes the sub-leading power contributions to the cross section enter as a kinematic power correction to the leptonic tensor, and the kinematic, intrinsic, and dynamic sub-leading contributions to th

Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh

Sample-efficient offline reinforcement learning (RL) with linear function approximation has recently been studied extensively. Much of prior work has yielded the minimax-optimal bound of $\tilde{\mathcal{O}}(\frac{1}{\sqrt{K}})$, with $K$ being the number of episodes in the offline data. In this work, we seek to understand instance-dependent bounds for offli

Yan-Qi Wang, Chunxiao Liu, Yuan-Ming Lu

We consider two-dimensional (2d) quantum many-body systems with long-range orders, where the only gapless excitations in the spectrum are Goldstone modes of spontaneously broken continuous symmetries. To understand the interplay between classical long-range order of local order parameters and quantum order of long-range entanglement in the ground states, we

Yuelang Xu, Lizhen Wang, Xiaochen Zhao, Hongwen Zhang

With NeRF widely used for facial reenactment, recent methods can recover photo-realistic 3D head avatar from just a monocular video. Unfortunately, the training process of the NeRF-based methods is quite time-consuming, as MLP used in the NeRF-based methods is inefficient and requires too many iterations to converge. To overcome this problem, we propose Avat

Steven Dale Cutkosky, Smita Praharaj

We extend the asymptotic Samuel function of an ideal to a filtration and show that many of the good properties of this function for an ideal are true for filtrations. There are, however, interesting differences, which we explore. We study the notion of projective equivalence of filtrations and the relation between the asymptotic Samuel function and the multi

Hy Ginsberg

We present the number of totally symmetric quasigroups (equivalently, totally symmetric Latin squares) of order 16, as well as the number of isomorphism classes, and extend previously published results to include information on the number and classes of medial totally symmetric quasigroups for all orders up to and including 16.

Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang

The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes including semantic elements, object shapes, etc. Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. The pre-traine

Ning Zhang, Francesco Nex, George Vosselman, Norman Kerle

Self-supervised monocular depth estimation that does not require ground truth for training has attracted attention in recent years. It is of high interest to design lightweight but effective models so that they can be deployed on edge devices. Many existing architectures benefit from using heavier backbones at the expense of model sizes. This paper achieves

Laurie Berrie, Kellyn F. Arnold, Georgia D. Tomova, Mark S. Gilthorpe

Deterministic variables are variables that are fully explained by one or more parent variables. They commonly arise when a variable has been algebraically constructed from one or more parent variables, as with composite variables, and in compositional data, where the 'whole' variable is determined from its 'parts'. This article introduces how deterministic v

O. Savchuk, R. V. Poberezhnyuk, A. Motornenko, J. Steinheimer

The time evolution of particle number fluctuations in nuclear collisions at intermediate energies ($E_{\rm lab} = 1.23-10A$ GeV) is studied by means of the UrQMD-3.5 transport model. The transport description incorporates baryonic interactions through a density-dependent potential. This allows for an implementation of a first order phase transition including

Jose A. R. Cembranos, David García-López, Zoe G. del Toro

The Aharonov-Bohm effect is a genuine quantum effect typically characterized by a measurable phase shift in the wave function for a charged particle that encircles an electromagnetic field located in a region inaccessible to the mentioned particle. However, this definition is not possible in the majority of the phase space descriptions since they are based o

Dimitrios Boucharas, Christos Androutsos, George Gkois, Vassilis Tsakanikas

During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessme

Franklin Liou, Hsin-Zon Tsai, Zachary A. H. Goodwin, Andrew S. Aikawa

Solid-liquid phase transitions are fundamental physical processes, but atomically-resolved microscopy has yet to capture both the solid and liquid dynamics for such a transition. We have developed a new technique for controlling the melting and freezing of 2D molecular layers on a graphene field-effect transistor (FET) that allows us to image phase transitio

Enrico Pasqualetto

We prove that the category of Banach $L^0$-modules over a given $\sigma$-finite measure space is both complete and cocomplete, which means that it admits all small limits and colimits.

Aneesha Sampath, Victoria Lin, Louis-Philippe Morency

Many machine learning tasks -- particularly those in affective computing -- are inherently subjective. When asked to classify facial expressions or to rate an individual's attractiveness, humans may disagree with one another, and no single answer may be objectively correct. However, machine learning datasets commonly have just one "ground truth" label for ea

Mihai Christodorescu, Maliheh Shirvanian, Shams Zawoad

Application authentication is typically performed using some form of secret credentials such as cryptographic keys, passwords, or API keys. Since clients are responsible for securely storing and managing the keys, this approach is vulnerable to attacks on clients. Similarly a centrally managed key store is also susceptible to various attacks and if compromis

Siddharth Agrawal, Keyur D. Joshi

Detection and recognition of a licence plate is important when automating weighbridge services. While many large databases are available for Latin and Chinese alphanumeric license plates, data for Indian License Plates is inadequate. In particular, databases of Indian commercial truck license plates are inadequate, despite the fact that commercial vehicle li

A. Pujol, G. J. M. Luna, K. Mukai, J. L. Sokoloski

Symbiotic binaries sometimes hide their symbiotic nature for significant periods of time. There is mounting observational evidence that in those symbiotics that are powered solely by accretion of red-giant's wind material onto a white dwarf, without any quasi-steady shell burning on the surface of the white dwarf, the characteristic emission lines in the opt

V. P. Kruglov, I. R. Sataev

We present a modified complex-valued Shimizu -- Morioka system with uniformly hyperbolic attractor. The numerically observed attractor in Poincar\'{e} cross-section is topologically close to Smale -- Williams solenoid. The arguments of the complex variables undergo Bernoulli-type map, essential for Smale -- Williams attractor, due to the geometrical arrangem

Elena Peña Tapia, Giannicola Scarpa, Alejandro Pozas-Kerstjens

This paper presents, via an explicit example with a real-world dataset, a hands-on introduction to the field of quantum machine learning (QML). We focus on the case of learning with a single qubit, using data re-uploading techniques. After a discussion of the relevant background in quantum computing and machine learning we provide a thorough explanation of t

Nikolai Kalischek, Rodrigo C. Daudt, Torben Peters, Reinhard Furrer

The well-documented presence of texture bias in modern convolutional neural networks has led to a plethora of algorithms that promote an emphasis on shape cues, often to support generalization to new domains. Yet, common datasets, benchmarks and general model selection strategies are missing, and there is no agreed, rigorous evaluation protocol. In this pape

Philip G. Judge

Physicists have long known that the Sun's magnetic fields make its corona much hotter than the surface of the star itself. But how -- and why -- those fields transport and deposit their energy is still a mystery, as Philip G Judge explains

Sara Atito, Muhammad Awais, Wenwu Wang, Mark D Plumbley

Transformers, which were originally developed for natural language processing, have recently generated significant interest in the computer vision and audio communities due to their flexibility in learning long-range relationships. Constrained by the data hungry nature of transformers and the limited amount of labelled data, most transformer-based models for