May 2022 arXiv papers — page 8

Krzysztof A. Maliszewski, Piotr Kolenderski, Varvara Vetrova, Sylwia M. Kolenderska

Artefacts in quantum-mimic Optical Coherence Tomography are considered detrimental because they scramble the images even for the simplest objects. They are a side effect of autocorrelation which is used in the quantum entanglement mimicking algorithm behind this method. Interestingly, the autocorrelation imprints certain characteristics onto an artefact - it

Devamanyu Hazarika, Yingting Li, Bo Cheng, Shuai Zhao

Building robust multimodal models are crucial for achieving reliable deployment in the wild. Despite its importance, less attention has been paid to identifying and improving the robustness of Multimodal Sentiment Analysis (MSA) models. In this work, we hope to address that by (i) Proposing simple diagnostic checks for modality robustness in a trained multim

Seher Karakuzu, Andy Tanjaroon Ly, Peizhi Mai, James Neuhaus

Several state-of-the-art numerical methods have observed static or fluctuating spin and charge stripes in doped two-dimensional Hubbard models, suggesting that these orders play a significant role in shaping the cuprate phase diagram. Many experiments, however, also indicate that the cuprates have strong electron-phonon ($e$-ph) coupling, and it is unclear h

Giorgio Giannone, Didrik Nielsen, Ole Winther

Denoising diffusion probabilistic models (DDPM) are powerful hierarchical latent variable models with remarkable sample generation quality and training stability. These properties can be attributed to parameter sharing in the generative hierarchy, as well as a parameter-free diffusion-based inference procedure. In this paper, we present Few-Shot Diffusion Mo

Samer B. Nashed, Saaduddin Mahmud, Claudia V. Goldman, Shlomo Zilberstein

We introduce a novel framework for causal explanations of stochastic, sequential decision-making systems built on the well-studied structural causal model paradigm for causal reasoning. This single framework can identify multiple, semantically distinct explanations for agent actions -- something not previously possible. In this paper, we establish exact meth

Zhimei Ren, Rina Foygel Barber

Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomness inherent to the method, different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is undesirable in practice. In this pa

Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba

We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target distribution, which is particularly helpfu

Carlos Muli, Sangyoung Park, Mingming Liu

Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature. However, a general consensus on the energy consumption model is yet to be reached at present. As a result, there are many variations that attempt to create models that range in complexity with a focus on different aspects.

Zhou Zhang, Yulin Pan

We study the energy transfer by exact resonances for surface gravity waves in a finite periodic spatial domain. Based on a kinematic model simulating the generation of active wave modes in a finite discrete wavenumber space $\mathcal{S}_R$, we examine the possibility of direct and inverse energy cascades. More specifically, we set an initially excited region

Maxim Ziatdinov, Yongtao Liu, Kyle Kelley, Rama Vasudevan

Recent progress in machine learning methods, and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs), have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods,

Stefaan Vaes, Bram Verjans

We prove the first orbit equivalence superrigidity results for actions of type III$_\lambda$ when $\lambda \neq 1$. These actions arise as skew products of actions of dense subgroups of $SL(n,\mathbb{R})$ on the sphere $S^{n-1}$ and they can have any prescribed associated flow.

Laurent Chauvin, William Wells, Matthew Toews

This paper proposes to extend local image features in 3D to include invariance to discrete symmetry including inversion of spatial axes and image contrast. A binary feature sign $s \in \{-1,+1\}$ is defined as the sign of the Laplacian operator $\nabla^2$, and used to obtain a descriptor that is invariant to image sign inversion $s \rightarrow -s$ and 3D par

Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke

In retail (e.g., grocery stores, apparel shops, online retailers), inventory managers have to balance short-term risk (no items to sell) with long-term-risk (over ordering leading to product waste). This balancing task is made especially hard due to the lack of information about future customer purchases. In this paper, we study the problem of restocking a g

V. Kulinskii, A. Katts

In this work we study the global isomorphism between the liquid-vapor equilibrium of the hard-core attractive Yukawa fluid (HCAYF) and that of the Lattice Gas (LG) model of the Ising-like type. The applicability of the global isomorphism transformation and dependence of its parameters on the screening length of the Yukawa potential are discussed. These param

Jie Xu

We give sufficient and "almost" necessary conditions for the prescribed scalar curvature problems within the conformal class of a Riemannian metric $ g $ for both closed manifolds and compact manifolds with boundary, including the interesting cases $ \mathbb{S}^{n} $ or some quotient of $ \mathbb{S}^{n} $, in dimensions $ n \geqslant 3 $, provided that the f

Aitor Alvarez-Gila, Joost van de Weijer, Yaxing Wang, Estibaliz Garrote

We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view

Takuya Hara

Studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain poorly understood. Based on a simple but essential model, this study found that the bottleneck formed by the largest mismatch between demand and power generation profiles determines the optimal cap

Rui Portocarrero Sarmento, Douglas O. Cardoso, João Gama, Pavel Brazdil

There has been a significant effort by the research community to address the problem of providing methods to organize documentation with the help of information Retrieval methods. In this report paper, we present several experiments with some stream analysis methods to explore streams of text documents. We use only dynamic algorithms to explore, analyze, and

Junjie Dong, Rebecca A. Fischer, Lars P. Stixrude, Carolina R. Lithgow-Bertelloni

Water has been stored in the Martian mantle since its formation, primarily in nominally anhydrous minerals. The short-lived early hydrosphere and intermittently flowing water on the Martian surface may have been supplied and replenished by magmatic degassing of water from the mantle. Estimating the water storage capacity of the solid Martian mantle places im

Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig

Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. This additional source of uncertainty, due to limited computation, is entirely ignored when using the approximate posterior. Therefore in practice, GP models are often as much about

Ce Zheng, Matias Mendieta, Taojiannan Yang, Guo-Jun Qi

Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose estimation (3D HPE), and human mesh reconstruction (HMR) tasks. In these tasks, feature map representations of the human structural information are often extracted first from the image by a CNN (such as HRNet)

Victor Y. Pan

The DLG root-squaring iterations, due to Dandelin 1826 and rediscovered by Lobachevsky 1834 and Graeffe 1837, have been the main approach to root-finding for a univariate polynomial p(x) in the 19th century and beyond, but not so nowadays because these iterations are prone to severe numerical stability problems. Trying to avoid these problems we have found s

Benjamin Schwendinger, Florian Schwendinger, Laura Vana

Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The $\textsf{R}$ package $\texttt{holiglm}$ provides functionality to model and fit holistic generali

Vladimir Yu. Protasov, Rinat Kamalov

We address the stability problem for linear switching systems with mode-dependent restrictions on the switching intervals. Their lengths can be bounded as from below (the guaranteed dwell-time) as from above. The upper bounds make this problem quite different from the classical case: a stable system can consist of unstable matrices, it may not possess Lyapun

Angelo G. Menezes, Gustavo de Moura, Cézanne Alves, André C. P. L. F. de Carvalho

The field of Continual Learning investigates the ability to learn consecutive tasks without losing performance on those previously learned. Its focus has been mainly on incremental classification tasks. We believe that research in continual object detection deserves even more attention due to its vast range of applications in robotics and autonomous vehicles

Weijie Zhao, Yingjie Lao, Ping Li

Tree models are very widely used in practice of machine learning and data mining. In this paper, we study the problem of model integrity authentication in tree models. In general, the task of model integrity authentication is the design \& implementation of mechanisms for checking/detecting whether the model deployed for the end-users has been tampered with

Xia Chen, Tong Guo, Martin Kriegel, Philipp Geyer

The performance gap between predicted and actual energy consumption in the building domain remains an unsolved problem in practice. The gap exists differently in both current mainstream methods: the first-principles model and the machine learning (ML) model. Inspired by the concept of time-series decomposition to identify different uncertainties, we proposed

Yanran Ding, Charles Khazoom, Matthew Chignoli, Sangbae Kim

This paper proposes a novel orientation-aware model predictive control (MPC) for dynamic humanoid walking that can plan footstep locations online. Instead of a point-mass model, this work uses the augmented single rigid body model (aSRBM) to enable the MPC to leverage orientation dynamics and stepping strategy within a unified optimization framework. With th

Leandro M. de Lima, Renato A. Krohling

Skin cancer is one of the most common types of cancer in the world. Different computer-aided diagnosis systems have been proposed to tackle skin lesion diagnosis, most of them based in deep convolutional neural networks. However, recent advances in computer vision achieved state-of-art results in many tasks, notably Transformer-based networks. We explore and

Patricio Gaete, Kimet Jusufi, Piero Nicolini

In this paper, we present a family of regular black hole solutions in the presence of charge and angular momentum. We also discuss the related thermodynamics and we comment about the black hole life cycle during the balding and spin down phases. Interestingly the static solution resembles the Ay\'on-Beato--Garc\'ia spacetime, provided the T-duality scale is

Jacob Azoulay, Nico Carballal

This work optimizes a lithium-ion battery charging schedule while considering a joint revenue and battery degradation model. The study extends the work of Meheswari et. al. to encourage battery usage/charging at optimal intervals depending on energy cost forecasts. This paper utilizes central difference Nesterov momentum gradient descent to come to optimal c

Thomas Heller, Hartmut Kaiser, Patrick Diehl, Dietmar Fey

On the way to Exascale, programmers face the increasing challenge of having to support multiple hardware architectures from the same code base. At the same time, portability of code and performance are increasingly difficult to achieve as hardware architectures are becoming more and more diverse. Today's heterogeneous systems often include two or more comple

Yinghao Aaron Li, Cong Han, Nima Mesgarani

Text-to-Speech (TTS) has recently seen great progress in synthesizing high-quality speech owing to the rapid development of parallel TTS systems, but producing speech with naturalistic prosodic variations, speaking styles and emotional tones remains challenging. Moreover, since duration and speech are generated separately, parallel TTS models still have prob

Zhu-Fang Cui, Daniele Binosi, Craig D. Roberts, Sebastian M. Schmidt

Theory suggests that in high-energy elastic hadron+hadron scattering, $t$-channel exchange of a family of colourless crossing-odd states -- the odderon -- may generate differences between $p\bar p$ and $pp$ cross-sections in the neighbourhood of the diffractive minimum. Using a mathematical approach based on interpolation via continued fractions enhanced by

Moshe Kimhi, Tal Rozen, Avi Mendelson, Chaim Baskin

Quantized neural networks are well known for reducing the latency, power consumption, and model size without significant harm to the performance. This makes them highly appropriate for systems with limited resources and low power capacity. Mixed-precision quantization offers better utilization of customized hardware that supports arithmetic operations at dif

Lequn Wang, Thorsten Joachims

Many large-scale recommender systems consist of two stages. The first stage efficiently screens the complete pool of items for a small subset of promising candidates, from which the second-stage model curates the final recommendations. In this paper, we investigate how to ensure group fairness to the items in this two-stage architecture. In particular, we fi

Francisco Bauzá Mingueza, Mario Floría, Jesús Gómez-Gardeñes, Alex Arenas

Many complex networked systems exhibit volatile dynamic interactions among their vertices, whose order and persistence reverberate on the outcome of dynamical processes taking place on them. To quantify and characterize the similarity of the snapshots of a time-varying network -- a proxy for the persistence,-- we present a study on the persistence of the int

Oliver Slumbers, David Henry Mguni, Stephen Marcus McAleer, Stefano B. Blumberg

In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game theory (GT) assumes that agents are not affected by risk from other agents and only strive to maximise their expected utility. For example, in hybrid human-AI driving systems, it is

L. Balogh, C. Beaufort, A. Brossard, J. F. Caron

The New Experiments With Spheres-Gas (NEWS-G) collaboration intends to achieve $\mathrm{sub-GeV/c^{2}}$ Weakly Interacting Massive Particles (WIMPs) detection using Spherical Proportional Counters (SPCs). SPCs are gaseous detectors relying on ionization with a single ionization electron energy threshold. The latest generation of SPC for direct dark matter se

Andrey A. Shoom, Sumit Kumar, N. V. Krishnendu

We consider the massive graviton phenomenological model based on the graviton's dispersion terms included into phase of gravitational wave's waveform. Such model was already considered in many works but it was based on a single leading-order dispersion term only. Here we derive a relation between relativistic gravitons emission and absorption time intervals

M. Ghasemi, A. A. Talebi, N. Mehdipoor

A graph is half-arc-transitive if its automorphism group acts transitively on its vertex set, edge set, but not its arc set. In this paper, we study all tetravalent half-arc-transitive graphs of order $12p$.

Susanne Bradley, Chen Greif

We derive bounds on the eigenvalues of saddle-point matrices with singular leading blocks. The technique of proof is based on augmentation. Our bounds depend on the principal angles between the ranges or kernels of the matrix blocks. Numerical experiments validate our analytical findings.

Giovanni Longobardi, Corrado Zanella

In this paper we present results concerning the stabilizer $G_f$ in $\mathrm{GL}(2,q^n)$ of the subspace $U_f=\{(x,f(x))\colon x\in\mathbb F_{q^n}[x]\}$, $f(x)$ a scattered linearized polynomial in $\mathbb F_{q^n}[x]$. Each $G_f$ contains the $q-1$ maps $(x,y)\mapsto(ax,ay)$, $a\in\mathbb F_{q}^*$. By virtue of the results of Beard (1972) and Willett (1973)

Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen

Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device. While prior FL optimizations tackled the data and system heterogeneity challenges faced by FL, they assume the two are independent of each other. This fundamental assumption is not reflective of real-world, large-sca

Birk Torpmann-Hagen, Vajira Thambawita, Kyrre Glette, Pål Halvorsen

Generalizability is seen as one of the major challenges in deep learning, in particular in the domain of medical imaging, where a change of hospital or in imaging routines can lead to a complete failure of a model. To tackle this, we introduce Consistency Training, a training procedure and alternative to data augmentation based on maximizing models' predicti

Hui Chen, Fan Jiang, Yu Ge, Hyowon Kim

Radio localization is a key enabler for joint communication and sensing in the fifth/sixth generation (5G/6G) communication systems. With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array. However,

Andrea Raffo, Chiara Romanengo, Bianca Falcidieno, Silvia Biasotti

The automatic creation of geometric models from point clouds has numerous applications in CAD (e.g., reverse engineering, manufacturing, assembling) and, more in general, in shape modelling and processing. Given a segmented point cloud representing a man-made object, we propose a method for recognizing simple geometric primitives and their interrelationships

Robert Janczewski, Krzysztof Turowski, Bartłomiej Wróblewski

Recently, Behr introduced a notion of the chromatic index of signed graphs and proved that for every signed graph $(G$, $\sigma)$ it holds that \[ \Delta(G)\leq\chi'(G\text{, }\sigma)\leq\Delta(G)+1\text{,} \] where $\Delta(G)$ is the maximum degree of $G$ and $\chi'$ denotes its chromatic index. In general, the chromatic index of $(G$, $\sigma)$ depends on

Arip Asadulaev, Vitaly Shutov, Alexander Korotin, Alexander Panfilov

We present a novel algorithm for domain adaptation using optimal transport. In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the target domain. In our method, we use optimal transport to map target samples to the domain named source fiction. This domain differs from the source but is accurately classified by the

Sahar Soltanieh, Ali Etemad, Javad Hashemi

This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning of electrocardiogram (ECG) signals and identifies the best parameters. The baseline of our proposed self-supervised framework consists of two main parts: the contrastive learning and the downstream task. In the first stage, we train an e

Althea V. Moorhead, Tiffany Clements, Denis Vida

Meteor showers occur when streams of meteoroids originating from a common source intersect the Earth. There will be small dissimilarities between the direction of motion of different meteoroids within a stream, and these small differences will act to broaden the radiant, or apparent point of origin, of the shower. This dispersion in meteor radiant can be par

Takayuki Iguchi, Andrés F. Barrientos, Eric Chicken, Debajyoti Sinha

In Statistical Process Control, control charts are often used to detect undesirable behavior of sequentially observed quality characteristics. Designing a control chart with desirably low False Alarm Rate (FAR) and detection delay ($ARL_1$) is an important challenge especially when the sampling rate is high and the control chart has an In-Control Average Run

Adrián Hernández, José M. Amigó

The connectome is a wiring diagram mapping all the neural connections in the brain. At the cellular level, it provides a map of the neurons and synapses within a part or all of the brain of an organism. In recent years, significant advances have been made in the study of the connectome via network science and graph theory. This analysis is fundamental to und

Deborah Gefang, Stephen G. Hall, George S. Tavlas

This paper proposes a fast two-stage variational Bayesian (VB) algorithm to estimate unrestricted panel spatial autoregressive models. Using Dirichlet-Laplace priors, we are able to uncover the spatial relationships between cross-sectional units without imposing any a priori restrictions. Monte Carlo experiments show that our approach works well for both lon

Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand.

SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations. However, existing attacks focus solely on altering the black-box model it

Geoffrey Pritchard, Mark C. Wilson

We make a detailed analysis of three key algorithms (Serial Dictatorship and the naive and adaptive variants of the Boston algorithm) for the housing allocation problem, under the assumption that agent preferences are chosen iid uniformly from linear orders on the items. We compute limiting distributions (with respect to some common utility functions) as $n\

Hui Chen, Ahmed Elzanaty, Reza Ghazalian, Musa Furkan Keskin

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. {For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation.} However, with increased array size in extr

Md. Ariful Islam, Md. Antonin Islam, Md. Amzad Hossain Jacky, Md. Al-Amin

Healthcare data is sensitive and requires great protection. Encrypted electronic health records (EHRs) contain personal and sensitive data such as names and addresses. Having access to patient data benefits all of them. This paper proposes a blockchain-based distributed healthcare application platform for Bangladeshi public and private healthcare providers.

Giuliano Benenti, Giulio Casati, Fabio Marchesoni, Jiao Wang

A dynamical model of a highly efficient heat engine is proposed, where an applied temperature difference maintains the motion of particles around the circuit consisting of two asymmetric narrow channels, in one of which the current flows against the applied thermodynamic forces. Numerical simulations and linear-response analysis suggest that, in the absence

Nguyen Dang

Competitions such as the MiniZinc Challenges or the SAT competitions have been very useful sources for comparing performance of different solving approaches and for advancing the state-of-the-arts of the fields. Traditional competition setting often focuses on producing a ranking between solvers based on their average performance across a wide range of bench

Jan Andre Fagereng, Vajira Thambawita, Andrea M. Storås, Sravanthi Parasa

Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) systems to detect polyps can improve detection accuracy and efficiency and save the time of the domain experts called endoscopists. Lack of annotated data is a common challenge when b

Torkan Gholamalizadeh, Sune Darkner, Peter Lempel Søndergaard, Kenny Erleben

Studying different types of tooth movements can help us to better understand the force systems used for tooth position correction in orthodontic treatments. This study considers a more realistic force system in tooth movement modeling across different patients and investigates the effect of the couple force direction on the position of the center of rotation

Taiki Kawamori, Peter G. Schunemann, Vitaly Gruzdev, Konstantin L. Vodopyanov

We report a study of high-order multi-photon absorption, nonlinear refraction, and their anisotropy in four notable mid-infrared \c{hi}(2) crystals: GaP, ZnSe, GaSe and ZGP using the Z- scan method and 2.35-{\mu}m femtosecond pulses with peak intensity in excess of 200 GW/cm2. We found that the nonlinear absorption obeys a perturbation model with multi-photo

Yiming Ren, Chengfeng Zhao, Yannan He, Peishan Cong

We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up conveniently and worn lightly. Specifically, to fully utilize the global geometry information captured by LiDAR and local dynamic motion

Vladimir Monakhov, Vajira Thambawita, Pål Halvorsen, Michael A. Riegler

The interest for video anomaly detection systems has gained traction for the past few years. The current approaches use deep learning to perform anomaly detection in videos, but this approach has multiple problems. For starters, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly det

Emily Sullivan, Philippe Verreault-Julien

People are increasingly subject to algorithmic decisions, and it is generally agreed that end-users should be provided an explanation or rationale for these decisions. There are different purposes that explanations can have, such as increasing user trust in the system or allowing users to contest the decision. One specific purpose that is gaining more tracti

V. K. Oikonomou

In this work we shall consider the effects of a geometrically generated post-inflationary era on the energy spectrum of the primordial gravitational waves. Specifically, we shall consider a post-inflationary constant equation of state era, generated by the synergistic effect of $f(R)$ gravity and of radiation and matter perfect fluids. Two cases of interest

Eli Passov, Eli David, Nathan S. Netanyahu

The rise of neural network (NN) applications has prompted an increased interest in compression, with a particular focus on channel pruning, which does not require any additional hardware. Most pruning methods employ either single-layer operations or global schemes to determine which channels to remove followed by fine-tuning of the network. In this paper we

Arip Asadulaev, Alexander Korotin, Vage Egiazarian, Petr Mokrov

We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals. In contrast to common Euclidean costs, i.e., $\ell^1$ or $\ell^2$, such functionals provide more flexibility and allow using auxiliary information, such as class labels, to construct the required transport map. Existing methods for genera

A. Castillo-Ramirez, M. Sanchez-Alvarez, A. Vazquez-Aceves, A. Zaldivar-Corichi

Let $G$ be a group and let $A$ be a finite set with at least two elements. A cellular automaton (CA) over $A^G$ is a function $\tau : A^G \to A^G$ defined via a finite memory set $S \subseteq G$ and a local function $\mu :A^S \to A$. The goal of this paper is to introduce the definition of a generalized cellular automaton (GCA) $\tau : A^G \to A^H$, where $H

Angtian Wang, Peng Wang, Jian Sun, Adam Kortylewski

The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation of object 3D geometry from meshes and point clouds. On the other hand, current state-of-the-art (SoTA) differentiable renderers, Liu et al. (2019), use rasterization to collect tri

Henry Sowerby, Zhiyuan Zhou, Michael L. Littman

To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward function for the environment, arguably the most important knob designers have in interacting with RL agents. Although many reward functions induce the same optimal behavior (Ng et al., 1999), in practice, some of them result in faster learning than others. In t

Hengjie Yang, Recep Can Yavas, Victoria Kostina, Richard D. Wesel

Incremental redundancy with ACK/NACK feedback produces a variable-length stop-feedback (VLSF) code constrained to have $m$ decoding times, with an ACK/NACK feedback to the transmitter at each decoding time. This paper focuses on the numerical evaluation of the maximal achievable rate of random VLSF codes as a function of $m$ for the binary-input additive whi

İbrahim Halil Efendioğlu

Metaverse is a virtual universe that combines the physical world and the digital world. People can socialize, play games and even shop with their avatars created in this virtual environment. Metaverse, which is growing very fast in terms of investment, is both a profitable and risky area for consumers. In order to enter the Metaverse for investment purposes,

Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury

Online imitation learning is the problem of how best to mimic expert demonstrations, given access to the environment or an accurate simulator. Prior work has shown that in the infinite sample regime, exact moment matching achieves value equivalence to the expert policy. However, in the finite sample regime, even if one has no optimization error, empirical va

Djuna Croon, Seyda Ipek, David McKeen

We generalise existing constraints on primordial black holes to dark objects with extended sizes using the aLIGO design sensitivity. We show that LIGO is sensitive to dark objects with radius $O(10-10^3~{\rm km})$ if they make up more than $\sim O(10^{-2}-10^{-3})$ of dark matter.

Yanzhao Zou, Dorien Herremans

Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes a

Nick Luiken, Matteo Ravasi, Claire E. Birnie

To limit the time, cost, and environmental impact associated with the acquisition of seismic data, in recent decades considerable effort has been put into so-called simultaneous shooting acquisitions, where seismic sources are fired at short time intervals between each other. As a consequence, waves originating from consecutive shots are entangled within the

Florian Evéquoz, Johan Rochel, Vijay Keswani, L. Elisa Celis

Elections are the central institution of democratic processes, and often the elected body -- in either public or private governance -- is a committee of individuals. To ensure the legitimacy of elected bodies, the electoral processes should guarantee that diverse groups are represented, in particular members of groups that are marginalized due to gender, eth

L. V. Elnikova, A. N. Ozerin, V. G. Shevchenko, A. T. Ponomarenko

Conducting polymers have wide technological applications in sensors, actuators, electric and optical devices, solar cells etc. To improve their operational performance, mechanical, thermal, electrical and optical properties, such polymers are doped with carbon allotrope nanofillers. Functionality of the novel nanocomposite polymers may be stipulated by size

Spencer Leslie, Aaron Pollack

We define a notion of modular forms of half-integral weight on the quaternionic exceptional groups. We prove that they have a well-behaved notion of Fourier coefficients, which are complex numbers defined up to multiplication by $\pm 1$. We analyze the minimal modular form $\Theta_{F_4}$ on the double cover of $F_4$, following Loke--Savin and Ginzburg. Using

D. Arous, J. L. Lie, B. V. Håland, M. Børsting

Spatially fractionated radiation therapy (SFRT or GRID) is an approach to deliver high local radiation doses in an 'on-off' pattern. To better appraise the radiobiological effects from GRID, a framework to link local radiation dose to clonogenic survival needs to be developed. A549 (lung) cancer cells were irradiated in T25 cm$^2$ flasks using 220 kV X-rays

Niklas Metzger, Christopher Hahn, Julian Siber, Frederik Schmitt

In this paper, we study the computation of how much an input token in a Transformer model influences its prediction. We formalize a method to construct a flow network out of the attention values of encoder-only Transformer models and extend it to general Transformer architectures including an auto-regressive decoder. We show that running a maxflow algorithm

Nicha Leethochawalit, Guido Roberts-Borsani, Takahiro Morishita, Michele Trenti

We determine the bright end of the rest-frame UV luminosity function (UVLF) at $z=8-10$ by selecting bright $z\gtrsim 8$ photometric candidates from the largest systematic compilation of HST (pure-)parallel observations to date, the Super-Brightest-of-Reionizing-Galaxies (SuperBoRG) data set. The data set includes $\sim300$ independent sightlines from WFC3 o

Steffen Gielen, Lucía Menéndez-Pidal

We explore the consequences of requiring that quantum theories of gravity be unitary, mostly focusing on simple cosmological models to illustrate the main points. We show that unitarity for a clock that encounters a classical singularity at finite time implies quantum singularity resolution, but for a clock that encounters future infinity at finite time lead

Gavin Parpart, Carlos Gonzalez, Terrence C. Stewart, Edward Kim

The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using

J. I. Katz

Recent CHIME/FRB observations of the periodic repeating FRB 180916B have produced a homogeneous sample of 44 bursts. These permit a redetermination of the modulation period and phase window, in agreement with earlier results. If the periodicity results from the precession of an accretion disc, in analogy with those of Her X-1, SS 433, and many other superorb

I. A. Tlyustangelov

In this work we prove a criterion for an algebraic continued fraction to have a proper palindromic symmetry in dimension $4$. As a multidimensional generalization of continued fractions, we consider Klein polyhedra.

Seth Grable

In this paper, I calculate the large $N$ limit of marginal $O(N)$ models with non-polynomial potentials in arbitrary odd dimensions $d$. This results in a new class of interacting pure conformal field theories (CFTs) in $d=3+4n$ for any $n \in \mathbb{Z}_+$. Similarly, in $d=3+4n$ I calculate the thermal entropy for all couplings on $R^{2+4n} \times S^1$ for

Chao Fan, Yu-Heng Chien, Ali Mostafavi

Ambient exposure to fine particulate matters of diameters smaller than 2.5{\mu}m (PM2.5) has been identified as one critical cause for respiratory disease. Disparities in exposure to PM2.5 among income groups at individual residences are known to exist and are easy to calculate. Existing approaches for exposure assessment, however, do not capture the exposur

Andreas Reiserer

A future quantum network will consist of quantum processors that are connected by quantum channels, just like conventional computers are wired up to form the Internet. In contrast to classical devices, however, the entanglement and non-local correlations available in a quantum-controlled system facilitate novel fundamental tests of quantum theory. In additio

Ehsan Saleh, Saba Ghaffari, Timothy Bretl, Matthew West

In this paper, we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape. By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic dynamics (up to the initial state distribution). Since deterministic policy regularization is imp

Miklós Bóna, Ryan R. Martin

We prove the endomorphism conjecture for graded posets whose largest Whitney number is at most 4. In particular, this implies the endomorphism conjecture is true for graded posets of width at most 4.

Chitrarth Prasad, Datta V. Gaitonde

Turbulence modeling has the potential to revolutionize high-speed vehicle design by serving as a co-equal partner to costly and challenging ground and flight testing. However, the fundamental assumptions that make turbulence modeling such an appealing alternative to its scale-resolved counterparts also degrade its accuracy for practical high-speed configurat

Guy Tennenholtz, Nadav Merlis, Lior Shani, Shie Mannor

We present the problem of reinforcement learning with exogenous termination. We define the Termination Markov Decision Process (TerMDP), an extension of the MDP framework, in which episodes may be interrupted by an external non-Markovian observer. This formulation accounts for numerous real-world situations, such as a human interrupting an autonomous driving

Randall S. Dumont, Tom Rivlin

A curious feature of quantum tunneling known as the MacColl-Hartman effect results in the numerical observation that particles can traverse a barrier with effective superluminal speed. However, because tunneling is never certain, any attempt to use this effect to send a signal faster than light would require sending many particles. In this work, we consider

Lizhen Nie, Veronika Rockova

For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function. We survey existing work on both Bayesian parametric inference with Gibbs posteriors as well as Bayesian n

Peter A. Boyle, Bipasha Chakraborty, Christine T. H. Davies, Thomas DeGrand

Lattice quantum chromodynamics has proven to be an indispensable method to determine nonperturbative strong contributions to weak decay processes. In this white paper for the Snowmass community planning process we highlight achievements and future avenues of research for lattice calculations of weak $b$ and $c$ quark decays, and point out how these calculati

Kai Wang, Lily Xu, Aparna Taneja, Milind Tambe

Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled. Solving RMABs requires information on transition dynamics, which are often unknown upfront. To plan in RMAB settings with unknown transitions, we propose

Salvador Bosch, Josep Ferré-Borrull, Jordi Sancho-Parramon

Thin film technology is a most relevant field in terms of number of different applications and, even more, in how widespread the use of the technology is. Virtually all optical devices involving light beams or with imaging capabilities available on the market contain thin film assemblies that are crucial for enhancing the practical performances of the device