October 2022 arXiv papers — page 134

Huan Liu, Ahmed Eldib, Lili Chen, Bin Wang

Purpose: A novel rotating gamma stereotactic radiosurgery (SRS) system (Galaxy RTi) with real-time image guidance technology has been developed for high-precision SRS and frameless fractionated stereotactic radiotherapy (SRT). This work investigated the dosimetric quality of Galaxy by comparing both the machine treatment parameters and plan dosimetry paramet

Tamás Erdélyi

Let either $R_k(t) := |P_k(e^{it})|^2$ or $R_k(t) := |Q_k(e^{it})|^2$, where $P_k$ and $Q_k$ are the usual Rudin-Shapiro polynomials of degree $n-1$ with $n=2^k$. The graphs of the trigonometric polynomials $R_k$ on the period suggest many zeros of $R_k(t)-n$ in a dense fashion on the period. Let ${\Cal N}(I,R_k-n)$ denote the number of zeros, counted with m

Kai Jiang, ShiFeng Li, Pingwen Zhang

Quasiperiodic systems are important space-filling ordered structures, without decay and translational invariance. How to solve quasiperiodic systems accurately and efficiently is of great challenge. A useful approach, the projection method (PM) [J. Comput. Phys., 256: 428, 2014], has been proposed to compute quasiperiodic systems. Various studies have demons

Zhicheng Tong, Jiayin Du, Yong Li

In this paper, we study the Hamiltonian systems $ H\left( {y,x,\xi ,\varepsilon } \right) = \left\langle {\omega \left( \xi \right),y} \right\rangle + \varepsilon P\left( {y,x,\xi ,\varepsilon } \right) $, where $ \omega $ and $ P $ are continuous about $ \xi $. We prove that persistent invariant tori possess the same frequency as the unperturbed tori, under

Xiaocong Yang, James Y. Huang, Wenxuan Zhou, Muhao Chen

Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretrained model to downstream tasks. In this work, we introduce PASTA, in which we only modify the special token representations (e.g., [SEP] and [CLS] in BERT) before the self-attention module at each layer in Transformer-based models. PASTA achieves comparable pe

Christian M. Heil, Anvay Patil, Bram Vanthournout, Saranshu Singla

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce non-iridescent, saturated colors using nanoparticle assemblies. Mixtures of nanoparticles varying in particle chemistry (or complex refractive indices) and particle size have additional emergent properties that impact the color produced. For such compl

Milo M Lin

Living systems efficiently use chemical fuel to do work, process information, and assemble patterns despite thermal noise. Whether high efficiency arises from general principles or specific fine-tuning is unknown. Here, applying a recent mapping from nonequilibrium systems to battery-resistor circuits, I derive an analytic expression for the efficiency of an

Qianbi Yu, Dongnan Liu, Chaoyi Zhang, Xinwen Zhang

Deep learning-based segmentation methods have been widely employed for automatic glaucoma diagnosis and prognosis. In practice, fundus images obtained by different fundus cameras vary significantly in terms of illumination and intensity. Although recent unsupervised domain adaptation (UDA) methods enhance the models' generalization ability on the unlabeled t

Teague Tomesh, Nicholas Allen, Daniel Dilley, Zain Saleem

The computational capabilities of near-term quantum computers are limited by the noisy execution of gate operations and a limited number of physical qubits. Hybrid variational algorithms are well-suited to near-term quantum devices because they allow for a wide range of tradeoffs between the amount of quantum and classical resources used to solve a problem.

Zutong Li, Lei Yang

The explosion of user-generated videos stimulates a great demand for no-reference video quality assessment (NR-VQA). Inspired by our observation on the actions of human annotation, we put forward a Divide and Conquer Video Quality Estimator (DCVQE) for NR-VQA. Starting from extracting the frame-level quality embeddings (QE), our proposal splits the whole seq

João Henrique Andrade, Juncheng Wei

We classify entire positive singular solutions to a family of critical sixth order equations in the punctured space with a non-removable singularity at the origin. More precisely, we show that when the origin is a non-removable singularity, solutions are given by a singular radial factor times a periodic solution to a sixth order IVP with constant coefficien

Immanuel Ben Porat

The Liouville equation with non-constant magnetic field is obtained as a limit in the Planck constant \hbar of the Heisenberg equation with the same magnetic field. The convergence is with respect to an appropriate semi-classical pseudo distance, and consequently with respect to the Monge-Kantorovich distance. Uniform estimates both in \epsilon and \hbar are

Daniel Engelsman, Itzik Klein

Modern sensors play a pivotal role in many operating platforms, as they manage to track the platform dynamics at a relatively low manufacturing costs. Their widespread use can be found starting from autonomous vehicles, through tactical platforms, and ending with household appliances in daily use. Upon leaving the factory, the calibrated sensor starts accumu

D. A. Ilyukhin

In the paper the Fermat-Torricelli problem is considered. The problem asks a point minimizing the sum of distances to arbitrarily given points in d-dimensional real normed spaces. Various generalizations of this problem are outlined, current methods of solving and some recent results in this area are presented. The aim of the article is to find an answer to

Jinmeng Rao, Song Gao, Michelle Miller, Alfonso Morales

Quantifying the resilience in the food system is important for food security issues. In this work, we present a geospatial knowledge graph (GeoKG)-based method for measuring the resilience of a multi-commodity flow network. Specifically, we develop a CFS-GeoKG ontology to describe geospatial semantics of a multi-commodity flow network comprehensively, and de

Endri Kacupaj, Kuldeep Singh, Maria Maleshkova, Jens Lehmann

This paper addresses the task of conversational question answering (ConvQA) over knowledge graphs (KGs). The majority of existing ConvQA methods rely on full supervision signals with a strict assumption of the availability of gold logical forms of queries to extract answers from the KG. However, creating such a gold logical form is not viable for each potent

Hannah Valenty, Jennifer Rittenhouse West, Fatiha Benmokhtar, Douglas W. Higinbotham

A nuclear physics example of statistical bootstrap is used on the MARATHON data nucleon structure function ratio, $\frac{F_2^n}{F_2^p}$, in the quark momentum fraction $x_B\rightarrow0$ and $x_B\rightarrow1$ regions. The extrapolated $F_2$ ratio value as quark momentum fraction $x_B\rightarrow 1$ approaches 0.4 and this value is compared to theoretical predi

Ayush Khot, Mark S. Neubauer, Avik Roy

Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input-output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from to

Sandip Roy, Subir Sarker, Mengran Xue

A notion of disturbance propagation stability is defined for dynamical network processes, in terms of decrescence of an input-output energy metric along cutsets away from the disturbance source. A characterization of the disturbance propagation notion is developed for a canonical model for synchronization of linearly-coupled homogeneous subsystems. Specifica

Nicholas Rosa, Tom Drummond, Mehrtash Harandi

Naively trained AI models can be heavily biased. This can be particularly problematic when the biases involve legally or morally protected attributes such as ethnic background, age or gender. Existing solutions to this problem come at the cost of extra computation, unstable adversarial optimisation or have losses on the feature space structure that are disco

Roberto Casadio, Alexander Kamenshchik, Iberê Kuntz

It is generally believed that a full-fledged theory of quantum gravity should exhibit background independence and diffeomorphism invariance. In its most general form, the latter comprises field redefinitions, which are diffeomorphisms in configuration space. We show that any path-integral approach to quantum gravity leads to a tension between these propertie

Lu Gan, Connor Lee, Soon-Jo Chung

This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network. Our method does not require any thermal annotations or co-registered RGB-thermal pairs, enabling robots to perform visual tasks at night and in adverse weather conditions

Patrick Perrine, Trevor Kirkby

Digitally synthesizing human motion is an inherently complex process, which can create obstacles in application areas such as virtual reality. We offer a new approach for predicting human motion, KP-RNN, a neural network which can integrate easily with existing image processing and generation pipelines. We utilize a new human motion dataset of performance ar

Zixian Ma, Rose Wang, Li Fei-Fei, Michael Bernstein

Modern multi-agent reinforcement learning frameworks rely on centralized training and reward shaping to perform well. However, centralized training and dense rewards are not readily available in the real world. Current multi-agent algorithms struggle to learn in the alternative setup of decentralized training or sparse rewards. To address these issues, we pr

Yifei Pan

For a Lipschitz function $f$ on an open set in $\mathbb{R}^n$, we consider the $L^{n}$ integrability of the quotient $\frac{|\nabla f|}{|f|}$ over its natural domain of definition.

Marta Lewicka

In this paper, we study flexibility of weak solutions to the Monge-Amp\`ere system (MA) via convex integration. This new system of Pdes is an extension of the Monge-Amp\`ere equation in $d=2$ dimensions, naturally arising from the prescribed curvature problem and closely related to the classical problem of isometric immersions (II). Our main result achieves

Jacopo Niedda, Giacomo Gradenigo, Luca Leuzzi, Giorgio Parisi

By means of enhanced Monte Carlo numerical simulations parallelized on GPU's we study the critical properties of the spin-glass-like model for the mode-locked glassy random laser, a $4$-spin model with complex spins with a global spherical constraint and quenched random interactions. Using two different boundary conditions for the mode frequencies we identif

Shuo Liu, Jun Zeng, Koushil Sreenath, Calin A. Belta

Safety is one of the fundamental challenges in control theory. Recently, multi-step optimal control problems for discrete-time dynamical systems were formulated to enforce stability, while subject to input constraints as well as safety-critical requirements using discrete-time control barrier functions within a model predictive control (MPC) framework. Exist

Katarzyna Reluga, Ting Ye, Qingyuan Zhao

Regression adjustment is broadly applied in randomized trials under the premise that it usually improves the precision of a treatment effect estimator. However, previous work has shown that this is not always true. To further understand this phenomenon, we develop a unified comparison of the asymptotic variance of a class of linear regression-adjusted estima

Aida Kostikova, Benjamin Paassen, Dominik Beese, Ole Pütz

Solidarity is a crucial concept to understand social relations in societies. In this paper, we explore fine-grained solidarity frames to study solidarity towards women and migrants in German parliamentary debates between 1867 and 2022. Using 2,864 manually annotated text snippets (with a cost exceeding 18k Euro), we evaluate large language models (LLMs) like

Zhijie Fan, Michael Lacey, Ji Li, Manasa N. Vempati

This article provides a deeper study of the Riesz transform commutators associated with the Neumann Laplacian operator $\Delta_N$ on $\mathbb R^n$. Along the line of singular value estimates for Riesz transform commutators established by Janson--Wolff and Rochberg--Semmes, we establish a full range of Schatten-$p$ class characterization for these commutators

Erman Cineli, Viktor L. Ginzburg, Basak Z. Gurel

We study lower semi-continuity properties of the volume, i.e., the surface area, of a closed Lagrangian manifold with respect to the Hofer- and $\gamma$-distance on a class of monotone Lagrangian submanifolds Hamiltonian isotopic to each other. We prove that volume is $\gamma$-lower semi-continuous in two cases. In the first one the volume form comes from a

Biswarup Paul

Quarkonium production has long been regarded as a potential signature of deconfinement in nucleus-nucleus collisions. Recently, the production of J/$\psi$ via regeneration within the quark-gluon plasma (QGP) or at the phase boundary has been identified as an important ingredient for the interpretation of quarkonium production results from lead-lead collision

Antonin Chambolle, Vito Crismale

We give a new, simpler proof of a compactness result in $GSBD^p$, $p>1$, by the same authors, which is also valid in $GBD$ (the case $p=1$), and shows that bounded sequences converge a.e., after removal of a suitable sequence of piecewise infinitesimal rigid motions, subject to a fixed partition.

Zhixin Lu, Guo Meng, Roman Hatzky, Matthias Hoelzl

In this work, we focus on the development of the particle-in-cell scheme and the application to the studies of Alfv\'en waves and energetic particle physics in tokamak plasmas. The $\delta f$ and full $f$ schemes are formulated on the same footing adopting mixed variables and the pullback scheme for electromagnetic problems. The TRIMEG-GKX code [Lu et al. J.

Biswarup Paul

Charmonium production is a probe sensitive to deconfinement in nucleus--nucleus collisions. The production of J/$\psi$ via regeneration within the QGP or at the phase boundary has been identified as an important ingredient for the description of the observed centrality and $p_{\rm T}$ dependence at the LHC. $\psi$(2S) production relative to J/$\psi$ is one p

Juncheng Wei, Qidi Zhang, Yifu Zhou

We consider the following Cauchy problem for the four-dimensional energy critical heat equation \begin{equation*} \begin{cases} u_t=\Delta u+u^{3} ~&\mbox{ in }~ {\mathbb R}^4 \times (0,\infty),\\ u(x,0)=u_0(x) ~&\mbox{ in }~ {\mathbb R}^4. \end{cases} \end{equation*} We construct a positive infinite time blow-up solution $u(x,t)$ with the blow-up rate $ \|

Sofia Taylor, Aditya Rangarajan, Noah Rhodes, Jonathan Snodgrass

This paper presents the California Test System (CATS), a synthetic transmission grid in California that can be used by the public for power systems policy research without revealing any critical energy information. The proposed synthetic grid combines publicly available geographic data of California's electric infrastructure, such as the actual locations of

Donatella Romano

After hydrogen and helium, oxygen, carbon, and nitrogen - hereinafter, the CNO elements - are the most abundant species in the universe. They are observed in all kinds of astrophysical environments, from the smallest to the largest scales, and are at the basis of all known forms of life, hence, the constituents of any biomarker. As such, their study proves c

Siqi Liang, Yan Sun, Faming Liang

Sufficient dimension reduction is a powerful tool to extract core information hidden in the high-dimensional data and has potentially many important applications in machine learning tasks. However, the existing nonlinear sufficient dimension reduction methods often lack the scalability necessary for dealing with large-scale data. We propose a new type of sto

Bálint Farkas, Béla NAgy, Szilárd Gy. Révész

Following P. Fenton, we investigate sum of translates functions $F(\mathbf{x},t):=J(t)+\sum_{j=1}^n \nu_j K(t-x_j)$, where $J:[0,1]\to {\underline{\mathbb{R}}}:=\mathbb{R}\cup\{-\infty\}$ is a "sufficiently non-degenerate" and upper-bounded "field function", and $K:[-1,1]\to {\underline{\mathbb{R}}}$ is a fixed "kernel function", concave both on $(-1,0)$ and

Jonathan Chapman, Sam Chow

We study the Ramsey properties of equations $a_1P(x_1) + \cdots + a_sP(x_s) = b$, where $a_1,\ldots,a_s,b$ are integers, and $P$ is an integer polynomial of degree $d$. Provided there are at least $(1+o(1))d^2$ variables, we show that Rado's criterion and an intersectivity condition completely characterise which equations of this form admit monochromatic sol

Michael Goldstein

We prove that GOE random band matrix localization length is $\le C\left(\log W\right)^3 W^2$, where $W$ is the width of the band and $C$ is an absolute constant. Our method consists of Green function edge-to-edge vector action approach to the Schenker method. That allows to split and decouple the action, so that it becomes transparent that \emph{the magnitud

Jean-Robert Argand, Michael Bertrand

This is a translation from French into English of Argand's "Reflexions sur la nouvelle th\'eorie des imaginaires, suivies d'une application \`a la d\'emonstration d'un th\'eor\`eme d'analise", published in 1815. Argand reprises the method of representing complex numbers as points in the plane, which he first introduced in 1806. He takes up complex addition,

Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik, Arnold Smeulders

Symmetries built into a neural network have appeared to be very beneficial for a wide range of tasks as it saves the data to learn them. We depart from the position that when symmetries are not built into a model a priori, it is advantageous for robust networks to learn symmetries directly from the data to fit a task function. In this paper, we present a met

Enrique Mestre, Diego F. Torres, Emma de Oña Wilhelmi, Josep Martí

The Cherenkov Telescope Array will provide the deepest survey of the Galactic Plane performed at very-high-energy gamma-rays. Consequently, this survey will unavoidably face the challenge of source confusion, i.e., the non-unique attribution of signal to a source due to multiple overlapping sources. Among the known populations of Galactic gamma-ray sources a

Ofek Lauber Bonomo, Shlomi Reuveni

Random motion in disordered media is sensitive to the presence of obstacles that prevent atoms, molecules, and other particles from moving freely in space. When obstacles are static, a sharp transition between confined motion and free diffusion occurs at a critical obstacle density: the percolation threshold. To test if this conventional wisdom continues to

Gabriela Jaramillo, Lidia Mrad, Tracy L. Stepien

The May--Leonard model was introduced to examine the behavior of three competing populations where rich dynamics, such as limit cycles and nonperiodic cyclic solutions, arise. In this work, we perturb the system by adding the capability of global mutations, allowing one species to evolve to the other two in a linear manner. We find that for small mutation ra

Adriano Fragomeni, Michael Wray, Dima Damen

In this paper, we re-examine the task of cross-modal clip-sentence retrieval, where the clip is part of a longer untrimmed video. When the clip is short or visually ambiguous, knowledge of its local temporal context (i.e. surrounding video segments) can be used to improve the retrieval performance. We propose Context Transformer (ConTra); an encoder architec

Brian Allen, Edward Bryden, Demetre Kazaras

Work of D. Stern and Bray-Kazaras-Khuri-Stern provide differential-geometric identities which relate the scalar curvature of Riemannian 3-manifolds to global invariants in terms of harmonic functions. These quantitative formulas are useful for stability results and show promise for more applications of this type. In this paper, we analyze harmonic maps to fl

Giovanni Colavizza

Non-Fungible Tokens (NFTs) have recently surged to mainstream attention by allowing the exchange of digital assets via blockchains. NFTs have also been adopted by artists to sell digital art. One of the promises of NFTs is broadening participation to the arts market, a traditionally closed and opaque system, to sustain a wider and more diverse set of artists

Suchuan Dong, Yiran Wang

We present a method for computing the inverse parameters and the solution field to inverse parametric PDEs based on randomized neural networks. This extends the local extreme learning machine technique originally developed for forward PDEs to inverse problems. We develop three algorithms for training the neural network to solve the inverse PDE problem. The f

Preethi Seshadri, Pouya Pezeshkpour, Sameer Singh

Recently, there has been an increase in efforts to understand how large language models (LLMs) propagate and amplify social biases. Several works have utilized templates for fairness evaluation, which allow researchers to quantify social biases in the absence of test sets with protected attribute labels. While template evaluation can be a convenient and help

Mostafa Hassanlou, Ebrahim Abbasi, Mehdi Kanani Arpatapeh, Sepideh Nasresfahani

In this paper, we consider product-type operators $T^m_{u,v,\varphi}$ from minimal M\"{o}bius invariant spaces into Zygmund type spaces. So some characterizations for boundedness and essential norm of these operators are obtained. As a result some conditions for the compactness will be given.

J. John Soundar Jerome, Yohann Bachelier, Delphine Doppler, Christophe Lehmann

The dynamics of a thin low-density polyethylene sheet subject to periodic forcing due to B\'enard-K\`arm\`an vortices in a long narrow water channel is investigated here. In particular, the time-averaged sheet deflection and its oscillation amplitude are considered. The former is first illustrated to be well-approximated by the static equilibrium between the

Aaditya Ramdas, Jianbo Chen, Martin J. Wainwright, Michael I. Jordan

This paper designs methods for decentralized multiple hypothesis testing on graphs that are equipped with provable guarantees on the false discovery rate (FDR). We consider the setting where distinct agents reside on the nodes of an undirected graph, and each agent possesses p-values corresponding to one or more hypotheses local to its node. Each agent must

James Motes, Tan Chen, Timothy Bretl, Marco Morales

We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitude faster than existing methods and successfully plans for problems with up to twenty objects, more than three times as many objects as comparable methods. We achieve this improvem

Arian Nadjimzadah

A. Iosevich and K. Taylor showed that compact subsets of $\mathbb R^d$ with Hausdorff dimension greater than $(d+1)/2$ contain trees with gaps in an open interval. Under the same dimensional threshold, we prove the analogous result where distance is replaced by the dot product. We additionally show that the gaps of embedded trees of dot products are prevalen

Gorjan Radevski, Dusan Grujicic, Matthew Blaschko, Marie-Francine Moens

The focal point of egocentric video understanding is modelling hand-object interactions. Standard models -- CNNs, Vision Transformers, etc. -- which receive RGB frames as input perform well, however, their performance improves further by employing additional modalities such as object detections, optical flow, audio, etc. as input. The added complexity of the

Bharath Hebbe Madhusudhana

Accurate and precise control of large quantum systems is paramount to achieve practical advantages on quantum devices. Therefore, benchmarking the hardware errors in quantum computers has drawn significant attention lately. Existing benchmarks for digital quantum computers involve averaging the global fidelity over a large set of quantum circuits and are the

Maik Punke, Steven M. Wise, Axel Voigt, Marco Salvalaglio

We optimize a numerical time-stabilization routine for the phase-field crystal (PFC) models of solidification. By numerical experiments, we showcase that our approach can improve the accuracy of underlying time integration schemes by a few orders of magnitude. We investigate different time integration schemes. Moreover, as a prototypical example for applicat

Everton de Matos, Markku Ahvenjärvi

Virtualization plays an essential role in providing security to computational systems by isolating execution environments. Many software solutions, called hypervisors, have been proposed to provide virtualization capabilities. However, only a few were designed for being deployed at the edge of the network, in devices with fewer computation resources when com

K. L. Seyler, A. Ron, D. Van Beveren, C. R. Rotundu

We report magnetic optical second-harmonic generation (SHG) polarimetry and imaging on Sr$_2$Cu$_3$O$_4$Cl$_2$, which allows direct visualization of the mesoscopic antiferromagnetic (AFM) structure of a parent cuprate. Temperature- and magnetic-field-dependent SHG reveals large domains with 90$^{\circ}$ relative orientations that are stabilized by a combinat

Joseph Johnson, Ricky Ini Liu

We study piecewise-linear and birational lifts of Sch\"utzenberger promotion, evacuation, and the RSK correspondence defined in terms of toggles. Using this perspective, we prove that certain chain statistics in rectangles shift predictably under the action of these maps. We then use this to construct piecewise-linear and birational versions of Rubey's bijec

Wenlong Deng, Lang Lang, Zhen Liu, Bin Liu

In light of the smoothness property brought by skip connections in ResNet, this paper proposed the Skip Logit to introduce the skip connection mechanism that fits arbitrary DNN dimensions and embraces similar properties to ResNet. Meta Tanh Normalization (MTN) is designed to learn variance information and stabilize the training process. With these delicate d

Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric P. Xing

Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text problems often suffer from various data

O. S. Deiev, I. S. Timchenko, S. M. Olejnik, S. M. Potin

In experiments on the electron linac LUE-40 of RDC "Accelerator" NSC KIPT, the flux-averaged cross-sections $\langle{\sigma(E_{\rm{\gamma max}})}\rangle_{\rm{exp}}$ of photonuclear reactions ${^{100}\rm{Mo}}(\gamma,n)^{99}\rm{Mo}$, ${^{27}\rm{Al}}(\gamma,x)^{24}\rm{Na}$, ${^{93}\rm{Nb}}(\gamma,n)^{92m}\rm{Nb}$, ${^{93}\rm{Nb}}(\gamma,3n)^{90}\rm{Nb}$, and ${

Pierrick Pochelu

Deep learning (DL) has been widely adopted those last years but they are computing-intensive method. Therefore, scientists proposed diverse optimization to accelerate their predictions for end-user applications. However, no single inference framework currently dominates in terms of performance. This paper takes a holistic approach to conduct an empirical com

Greg Dresden, Yike Li

Using elementary methods, we establish old and new relations between binomial coefficients, Fibonacci numbers, Lucas numbers, and more.

Dionysis Theodosis, Iasson Karafyllis, George Titakis, Ioannis Papamichail

In this paper, a new nonlinear heat equation is studied that arises as a model of the collective behavior of automated vehicles. The properties of the solutions of this equation are studied by introducing the appropriate notion of a weak solution that requires certain entropy-like conditions. To obtain an approximation of the solution of the nonlinear heat e

Tianbo Ji, Chenyang Lyu, Gareth Jones, Liting Zhou

Question Generation (QG) aims to automate the task of composing questions for a passage with a set of chosen answers found within the passage. In recent years, the introduction of neural generation models has resulted in substantial improvements of automatically generated questions in terms of quality, especially compared to traditional approaches that emplo

Samy Jelassi, David Dobre, Arthur Mensch, Yuanzhi Li

Adaptive methods are a crucial component widely used for training generative adversarial networks (GANs). While there has been some work to pinpoint the "marginal value of adaptive methods" in standard tasks, it remains unclear why they are still critical for GAN training. In this paper, we formally study how adaptive methods help train GANs; inspired by the

Milo Grillo, Yunpeng Han, Agnieszka Werpachowska

We propose a simple and efficient approach to generate a prediction intervals (PI) for approximated and forecasted trends. Our method leverages a weighted asymmetric loss function to estimate the lower and upper bounds of the PI, with the weights determined by its coverage probability. We provide a concise mathematical proof of the method, show how it can be

Duc Nguyen, Anderson Zhang

The Rasch model is one of the most fundamental models in \emph{item response theory} and has wide-ranging applications from education testing to recommendation systems. In a universe with $n$ users and $m$ items, the Rasch model assumes that the binary response $X_{li} \in \{0,1\}$ of a user $l$ with parameter $\theta^*_l$ to an item $i$ with parameter $\bet

A. A. Milner, P. C. E. Stamp, V. Milner

Superfluid 4He, the first superfluid ever discovered, is in some ways the least well understood. Unlike 3He superfluid, or the variety of Bose-Einstein condensates of ultracold gases, superfluid 4He is a very dense liquid of strongly interacting quasiparticles. The theory is then necessarily phenomenological: the quasiparticle properties are found from exper

Antoine Pommerol, Nicolas Thomas, Miguel Almeida, Mattew Read

The Colour and Stereo Surface Science Imaging System (CaSSIS) of the ExoMars Trace Gas Orbiter returns on average twenty images per day of the Martian surface, most of them in 3 or 4 colours and some of them in stereo. CaSSIS uses a push-frame approach to acquire colour images, with four bandpass filters deposited directly above the sensor and an imaging cad

Fahrettin Sarcan, Nicola J. Fairbairn, Panaiot Zotev, Toby Severs-Millard

Focused ion beam (FIB) has been used as an effective tool for precise nanoscale fabrication. It has recently been employed to tailor defect engineering in functional nanomaterials such as two-dimensional transition metal dichalcogenides (TMDCs), providing desirable properties in TMDC-based optoelectronic devices. However, the damage caused by the FIB irradia

R. A. Konoplya, A. Zhidenko

Hod's proposal claims that the least damped quasinormal mode of a black hole must have the imaginary part smaller than half of the surface gravity at the event horizon. The Strong Cosmic Censorship in General Relativity implies that this bound must be even weaker: half of the surface gravity at the Cauchy horizon. The appealing question is whether these boun

Holger Boche, Ullrich J. Mönich, Yannik N. Böck, Frank H. P. Fitzek

We consider the task of converting different digital descriptions of analog bandlimited signals and systems into each other, with a rigorous application of mathematical computability theory. Albeit very fundamental, the problem appears in the scope of digital twinning, an emerging concept in the field of digital processing of analog information that is regul

Saeid K. Dehkordi, Giuseppe Caire

In this paper, we develop an active sensing strategy for a millimeter wave (mmWave) band Integrated Sensing and Communication (ISAC) system adopting a realistic hybrid digital-analog (HDA) architecture. To maintain a desired SNR level, initial beam acquisition (BA) must be established prior to data transmission. In the considered setup, a Base Station (BS) T

Tong Jian, Zifeng Wang, Yanzhi Wang, Jennifer Dy

Adversarial pruning compresses models while preserving robustness. Current methods require access to adversarial examples during pruning. This significantly hampers training efficiency. Moreover, as new adversarial attacks and training methods develop at a rapid rate, adversarial pruning methods need to be modified accordingly to keep up. In this work, we pr

Germán E. Melo-Acosta, Freddy Duitama-Muñoz, Julián D. Arias-Londoño

Training of Machine Learning (ML) models in real contexts often deals with big data sets and high-class imbalance samples where the class of interest is unrepresented (minority class). Practical solutions using classical ML models address the problem of large data sets using parallel/distributed implementations of training algorithms, approximate model-based

S. Metayer, D. Mouhanna, S. Teber

In this conference report, we present a recent field theoretic renormalization group analysis of flat polymerized membranes at three-loop order by the present authors [Phys. Rev. E 105, L012603 (2022)].

Minrui Xu, Dusit Niyato, Zhaohui Yang, Zehui Xiong

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose

Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur

Domain-specific language understanding requires integrating multiple pieces of relevant contextual information. For example, we see both suicide and depression-related behavior (multiple contexts) in the text ``I have a gun and feel pretty bad about my life, and it wouldn't be the worst thing if I didn't wake up tomorrow''. Domain specificity in self-attenti

Lucas Hutter, Rafael Chaves, Ranieri Nery, George Moreno

Causal influences are at the core of any empirical science, the reason why its quantification is of paramount relevance for the mathematical theory of causality and applications. Quantum correlations, however, challenge our notion of cause and effect, implying that tools and concepts developed over the years having in mind a classical world, have to be reeva

Reetam Majumder, Nagaraj K. Neerchal, Amita Mehta

Stochastic precipitation generators (SPGs) are a class of statistical models which generate synthetic data that can simulate dry and wet rainfall stretches for long durations. Generated precipitation time series data are used in climate projections, impact assessment of extreme weather events, and water resource and agricultural management. We construct an S

Angkana Rüland, Theresa M. Simon

We classify all exactly stress-free solutions to the cubic-to-trigonal phase transformation within the geometrically linearized theory of elasticity, showing that only simple laminates and crossing-twin structures can occur. In particular, we prove that although this transformation is closely related to the cubic-to-orthorhombic phase transformation, all its

Mathias Lechner, Ramin Hasani, Alexander Amini, Tsun-Hsuan Wang

There is an ever-growing zoo of modern neural network models that can efficiently learn end-to-end control from visual observations. These advanced deep models, ranging from convolutional to patch-based networks, have been extensively tested on offline image classification and regression tasks. In this paper, we study these vision architectures with respect

Arash Bahari Kordabad, Mario Zanon, Sebastien Gros

This paper shows that the optimal policy and value functions of a Markov Decision Process (MDP), either discounted or not, can be captured by a finite-horizon undiscounted Optimal Control Problem (OCP), even if based on an inexact model. This can be achieved by selecting a proper stage cost and terminal cost for the OCP. A very useful particular case of OCP

Gennadi Saiko, Alexandre Douplik

Purpose: Visualization and monitoring of capillary loops in dermis and mucosa are interesting for various clinical applications, including rheumatology, early cancer, and shock detection. However, the limitations of existing imaging technologies are not well understood. Therefore, this study aimed to elucidate peculiarities of the subsurface defect visualiza

Olivier Bokanowski, Xavier Warin, Averil Prost

We consider a deterministic optimal control problem with a maximum running cost functional, in a finite horizon context, and propose deep neural network approximations for Bellman's dynamic programming principle, corresponding also to some first-order Hamilton-Jacobi-Bellman equations. This work follows the lines of Hur\'e et al. (SIAM J. Numer. Anal., vol.

Hakima Bessaih, Annie Millet

We prove that an implicit time Euler scheme for the 2D-Boussinesq model on the torus $D$ converges. Various moment of the $W^{1,2}$-norms of the velocity and temperature, as well as their discretizations, are computed. We obtain the optimal speed of convergence in probability, and a logarithmic speed of convergence in $L^2(\Omega)$. These results are deduced

Antoine Grandjean, A. -M. Lagrange, N. Meunier, G. Chauvin

The Scorpius-Centaurus (Sco-Cen) young and nearby massive star-forming region is particularly well suited for extrasolar planet searches with both direct imaging and radial velocity (RV) techniques. The RV search, however, is challenging, as the stars are faster rotators on average than their older stellar counterparts of similar spectral types. Moreover, th

Thiago S. Gomides, Evangelos Kranakis, Ioannis Lambadaris, Yannis Viniotis

As the automotive industry is developing autonomous driving systems and vehicular networks, attention to truck platooning has increased as a way to reduce costs (fuel consumption) and improve efficiency in the highway. Recent research in this area has focused mainly on the aerodynamics, network stability, and longitudinal control of platoons. However, the sy

Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka

Score-based generative models (SGMs) learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise. These perturbed data densities are linked together by the Fokker-Planck equation (FPE), a partial differential equation (PDE) governing the spatial-temporal evolution of a density under

Sergiy Borodachov

We use methods of approximation theory to find the absolute minima on the sphere of the potential of spherical $(2m-3)$-designs with a non-trivial index $2m$ that are contained in a union of $m$ parallel hyperplanes, $m\geq 2$, whose locations satisfy certain additional assumptions. The interaction between points is described by a function of the dot product

Yunhao Li, Zhenbo Yu, Yucheng Zhu, Bingbing Ni

Human motion synthesis is a long-standing problem with various applications in digital twins and the Metaverse. However, modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions and consequently they usually produce unrealistic human motions. In order to solve this problem, we propose a system ``

Aayushman Ghosh, Sayan Sarkar, Sukhdev Roy

All-optical devices are essential for next generation ultrafast, ultralow-power and ultrahigh bandwidth information processing systems. Silicon microring resonators (SiMRR) provide a versatile platform for all-optical switching and CMOS-compatible computing, with added advantages of high Q-factor, tunability, compactness, cascadability and scalability. A det

Byron Tasseff, Tameem Albash, Zachary Morrell, Marc Vuffray

Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to ev