March 2023 arXiv papers — page 50

Yuta Yoshitake, Mai Nishimura, Shohei Nobuhara, Ko Nishino

We propose a novel method for joint estimation of shape and pose of rigid objects from their sequentially observed RGB-D images. In sharp contrast to past approaches that rely on complex non-linear optimization, we propose to formulate it as a neural optimization that learns to efficiently estimate the shape and pose. We introduce Deep Directional Distance F

Alessandro Summer, Cecilia Chiaracane, Mark T. Mitchison, John Goold

Quantum statistical mechanics allows us to extract thermodynamic information from a microscopic description of a many-body system. A key step is the calculation of the density of states, from which the partition function and all finite-temperature equilibrium thermodynamic quantities can be calculated. In this work, we devise and implement a quantum algorith

Maximilian F. Steffen

The multi-index model with sparse dimension reduction matrix is a popular approach to circumvent the curse of dimensionality in a high-dimensional regression setting. Building on the single-index analysis by Alquier, P. & Biau, G. (Journal of Machine Learning Research 14 (2013) 243-280), we develop a PAC-Bayesian estimation method for a possibly misspecified

Willi Menapace, Aliaksandr Siarohin, Stéphane Lathuilière, Panos Achlioptas

Neural video game simulators emerged as powerful tools to generate and edit videos. Their idea is to represent games as the evolution of an environment's state driven by the actions of its agents. While such a paradigm enables users to play a game action-by-action, its rigidity precludes more semantic forms of control. To overcome this limitation, we augment

Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu

Humans naturally perceive surrounding scenes by unifying sound and sight in a first-person view. Likewise, machines are advanced to approach human intelligence by learning with multisensory inputs from an egocentric perspective. In this paper, we explore the challenging egocentric audio-visual object localization task and observe that 1) egomotion commonly e

Atticus Stonestrom

`Definable amenability' of a definable group is the model-theoretic analogue of amenability of a discrete group; precisely, a definable group is said to be definably amenable if it admits a translation-invariant finitely additive probability measure on its definable subsets. We prove a combinatorial characterization of definable amenability for groups de

Ioannis Argyriou, Alistair Glasse, David R. Law, Alvaro Labiano

The Medium-Resolution Spectrometer (MRS) provides one of the four operating modes of the Mid-Infrared Instrument (MIRI) on board the James Webb Space Telescope (JWST). The MRS is an integral field spectrometer, measuring the spatial and spectral distributions of light across the 5-28 $\mu m$ wavelength range with a spectral resolving power between 3700-1300.

Jim Skulte, Jayson G. Cosme, Ludwig Mathey

Using an atom-cavity platform, we propose to combine the effective gauge phase of rotated neutral atoms and the superradiant phase transition to build a highly sensitive and fast quantum rotation sensor. The atoms in a well-controlled array of Bose-Einstein condensates are coupled to a single light mode of an optical cavity. The photon emission from the cavi

Benjamin W. Toulson, Diptarka Hait, Davide Faccialà, Daniel M. Neumark

The UV photochemistry of small heteroaromatic molecules serves as a testbed for understanding fundamental photoinduced transformations in moderately complex compounds, including isomerization, ring-opening, and molecular dissociation. Here, a combined experimental-theoretical study of 268 nm UV light-induced dynamics in 2-iodothiophene (C$_4$H$_3$IS) is perf

M. V. Dolgopolik

A hypodifferential is a compact family of affine mappings that defines a local max-type approximation of a nonsmooth convex function. We present a general theory of hypodifferentials of nonsmooth convex functions defined on a Banach space. In particular, we provide complete characterizations of hypodifferentiability and hypodifferentials of nonsmooth convex

Tobias Haug, M. S. Kim

Generalization is the ability of machine learning models to make accurate predictions on new data by learning from training data. However, understanding generalization of quantum machine learning models has been a major challenge. Here, we introduce the data quantum Fisher information metric (DQFIM). It describes the capacity of variational quantum algorithm

Piotr Dacko

In this paper we provide a local construction of a Sasakian manifold given a K\"ahler manifold. Obatined in this way manifold we call Sasakian lift of K\"ahler base. Almost contact metric structure is determined by the operation of the lift of vector fields - idea similar to lifts in Ehresmann connections. We show that Sasakian lift inherits geometry very cl

Tobias Damm, Martin Redmann

In this paper, we consider a model reduction technique for stabilizable and detectable stochastic systems. It is based on a pair of Gramians that we analyze in terms of well-posedness. Subsequently, dominant subspaces of the stochastic systems are identified exploiting these Gramians. An associated balancing related scheme is proposed that removes unimportan

Nick Rekuski

We show the kernel sheaf associated to a sufficiently positive torsion-free sheaf of rank 1 is slope stable. Furthermore, we are able to give an explicit bound for "sufficiently positive." This settles a conjecture of Ein-Lazarsfeld-Mustopa. The main technical lemma is a bound on the number of global sections of a torsion-free, globally generated sheaf in te

Oskar Nordenfors, Fredrik Ohlsson, Axel Flinth

We investigate the optimization of neural networks on symmetric data, and compare the strategy of constraining the architecture to be equivariant to that of using data augmentation. Our analysis reveals that that the relative geometry of the admissible and the equivariant layers, respectively, plays a key role. Under natural assumptions on the data, network,

D. I. Palade, L. M. Pomârjanschi, M. Ghită

The diffusive transport in two-dimensional incompressible turbulent fields is investigated with the aid of high-quality direct numerical simulations. Three classes of turbulence spectra that are able to capture both short and long-range time-space correlations and oscillating features are employed. We report novel scaling laws that depart from the $\gamma=7/

Huan-Xiang Zhou

Power law distributions are widely observed in chemical physics, geophysics, biology, and beyond. The independent variable x of these distributions has an obligatory lower bound and in many cases also an upper bound. Estimating these bounds from sample data is notoriously difficult, with a recent method involving O(N^3) operations, where N denotes sample siz

Haoxuan You, Mandy Guo, Zhecan Wang, Kai-Wei Chang

The field of vision and language has witnessed a proliferation of pre-trained foundation models. Most existing methods are independently pre-trained with contrastive objective like CLIP, image-to-text generative objective like PaLI, or text-to-image generative objective like Parti. However, the three objectives can be pre-trained on the same data, image-text

Tsao-Hsien Chen

Let $G$ be a split connected reductive group over a non-archimedan local field $F$. The depth zero stable Bernstein conjecture asserts that there is an algebra isomorphism between the depth zero stable Bernstein center of $G(F)$ and the ring of functions on the moduli of tame Langlands parameters. An approach to the depth zero stable Bernstein conjecture was

Matteo Torcoli, Emanuël A. P. Habets

In TV services, dialogue level personalization is key to meeting user preferences and needs. When dialogue and background sounds are not separately available from the production stage, Dialogue Separation (DS) can estimate them to enable personalization. DS was shown to provide clear benefits for the end user. Still, the estimated signals are not perfect, an

Hanjia Lyu, Arsal Imtiaz, Yufei Zhao, Jiebo Luo

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the diseas

Xavier Tannier, Perceval Wajsbürt, Alice Calliger, Basile Dura

The objective of this study is to address the critical issue of de-identification of clinical reports in order to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP) in

Dana Cohen-Bar, Elad Richardson, Gal Metzer, Raja Giryes

Recent breakthroughs in text-guided image generation have led to remarkable progress in the field of 3D synthesis from text. By optimizing neural radiance fields (NeRF) directly from text, recent methods are able to produce remarkable results. Yet, these methods are limited in their control of each object's placement or appearance, as they represent the scen

Tung Nguyen, Alex Scott, Paul Seymour

Two subgraphs $A,B$ of a graph $G$ are anticomplete if they are vertex-disjoint and there are no edges joining them. Is it true that if $G$ is a graph with bounded clique number, and sufficiently large chromatic number, then it has two anticomplete subgraphs, both with large chromatic number? This is a question raised by El-Zahar and Erd\H{o}s in 1986, and r

ALICE Collaboration

The interactions of kaons (K) and antikaons ($\mathrm{\overline{K}}$) with few nucleons (N) were studied so far using kaonic atom data and measurements of kaon production and interaction yields in nuclei. Some details of the three-body KNN and $\mathrm{\overline{K}}$NN dynamics are still not well understood, mainly due to the overlap with multi-nucleon inter

Frederick Choi, Sajjadur Rahman, Hannah Kim, Dan Zhang

Data science workflows are human-centered processes involving on-demand programming and analysis. While programmable and interactive interfaces such as widgets embedded within computational notebooks are suitable for these workflows, they lack robust state management capabilities and do not support user-defined customization of the interactive components. Th

Yunhai Han, Mandy Xie, Ye Zhao, Harish Ravichandar

Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity to initialization, and the considerable technical expertise

G. M. Bernstein, B. J. Holler, R. Navarro-Escamilla, P. H. Bernardinelli

We combine photometry of Eris from a 6-month campaign on the Palomar 60-inch telescope in 2015, a 1-month Hubble Space Telescope WFC3 campaign in 2018, and Dark Energy Survey data spanning 2013--2018 to determine a light curve of definitive period $15.771\pm 0.008$~days (1-$\sigma$ formal uncertainties), with nearly sinusoidal shape and peak-to-peak flux var

Lars Hesselholt, Piotr Pstragowski

Whatever it is that animates anima and breathes life into higher algebra, this something leaves its trace in the structure of a Dirac ring on the homotopy groups of a commutative algebra in spectra. In the prequel to this paper, we developed the commutative algebra of Dirac rings and defined the category of Dirac schemes. Here, we first embed this category i

David Gamarnik, Mihyun Kang, Pawel Pralat

The semi-random graph process is a single player game in which the player is initially presented an empty graph on $n$ vertices. In each round, a vertex $u$ is presented to the player independently and uniformly at random. The player then adaptively selects a vertex $v$, and adds the edge $uv$ to the graph. For a fixed monotone graph property, the objective

Prashanth Ramesh, Yibo Chen, Petra Räder, Svenja Morsbach

Self-propelling active matter relies on the conversion of energy from the undirected, nanoscopic scale to directed, macroscopic motion. One of the challenges in the design of synthetic active matter lies in the control of dynamic states, or motility gaits. Here, we present an experimental system of self-propelling droplets with thermally controllable and rev

Y. Sarkis, Aguilar-Arevalo, Juan Carlos D'Olivo

We present a model of the ionization efficiency, or quenching factor, for low-energy nuclear recoils, based on a solution to Lindhard integral equation with binding energy and apply it to the calculation of the relative scintillation efficiency and charge yield for nuclear recoils in noble liquid detectors. The quenching model incorporates a constant average

Aneeshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Subhadeep Koley

In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best to achieve this synergy, for both the c

Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusio

T. Cisneros-Pérez, M. A. Hernández-Ruíz, A. Gutiérrez-Rodríguez, E. Cruz-Albaro

This paper investigates the effects of parameters in the Bestest Little Higgs Model (BLHM) on rare flavor-changing decays of the top quark. As a result, flavor-changing phenomena are introduced in the BLHM for the first time. In this study, we incorporate new flavor mixing terms between the light quarks of the Standard Model (SM) and the fermions and bosons

Kimet Jusufi, Ahmad Sheykhi, Salvatore Capozziello

We disclose a close correspondence between Verlinde's Emergent Gravity (VEG) theory and the non-local gravity theories. Such non-local effects can play crucial role at small distances as well as in large scale structures. In particular, we argue that the emergent gravity effectively is a manifestation of the entanglement entropy and can modify Newton's law o

Á. P. Horváth

We define and examine nonlinear potential by Bessel convolution with Bessel kernel. We investigate removable sets with respect to Laplace-Bessel inequality. By studying the maximal and fractional maximal measure, a Wolff type inequality is proved. Finally the relation of B-$p$ capacity and B-Lipschitz mapping, and the B-$p$ capacity and weighted Hausdorff me

Jobst Landgrebe, Barry Smith

The view of nature we adopt in the natural attitude is determined by common sense, without which we could not survive. Classical physics is modelled on this common-sense view of nature, and uses mathematics to formalise our natural understanding of the causes and effects we observe in time and space when we select subsystems of nature for modelling. But in m

Amina Abdurrahman, Akshay Venkatesh

We propose (and prove under some restrictions) that the square class of the central value of the $L$-function of an everywhere unramified symplectic Galois representation is given by a universal cohomological formula. This phenomenon is parallel to the appearance of metaplectic groups in quantization. In the course of the proof we also establish a topologica

Brian R. Pauw, Sofya Laskina, Aakash Naik, Glen J. Smales

We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is on the same scale as the s

Jinjing Zhu, Haotian Bai, Lin Wang

Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention in ViT for direct domain alignment. However, as the performance of cross-attention highly relies on the quality of pseudo labels for targeted samples, it becomes less effective when

Yue Li, Timoteo Colnaghi, Yilun Gong, Huaide Zhang

In solids, chemical short-range order (CSRO) refers to the self-organisation of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurati

Henrique Santos Lima, Constantino Tsallis

The thermal conductivity of a $d=1$ lattice of ferromagnetically coupled planar rotators is studied through molecular dynamics. Two different types of anisotropies (local and in the coupling) are assumed in the inertial XY model. In the limit of extreme anisotropy, both models approach the Ising model and its thermal conductivity $\kappa$, which, at high tem

Anirudh Pradhan, Gopikant Goswami, Aroonkumar Beesham

In this work, we have developed an FLRW type model of a universe which displays transition from deceleration in the past to the acceleration at the present. For this, we have considered field equations of $f(R,T)$ gravity and have taken $f(R,T) = R + 2 \lambda T$, $\lambda$ being an arbitrary constant. We have estimated the $\lambda$ parameter in such a way

ALICE Collaboration

The production cross section of inclusive J/$\psi$ pairs in pp collisions at a centre-of-mass energy $\sqrt{s} = 13$ TeV is measured with ALICE. The measurement is performed for J/$\psi$ in the rapidity interval $2.5 < y < 4.0$ and for transverse momentum $p_{\rm T} > 0$. The production cross section of inclusive J/$\psi$ pairs is reported to be $10.3 \pm 2.

Bram de Wilde, Anindo Saha, Maarten de Rooij, Henkjan Huisman

Diffusion models for text-to-image generation, known for their efficiency, accessibility, and quality, have gained popularity. While inference with these systems on consumer-grade GPUs is increasingly feasible, training from scratch requires large captioned datasets and significant computational resources. In medical image generation, the limited availabilit

Ö. Deniz Akyildiz, Francesca Romana Crucinio, Mark Girolami, Tim Johnston

We develop a class of interacting particle systems for implementing a maximum marginal likelihood estimation (MMLE) procedure to estimate the parameters of a latent variable model. We achieve this by formulating a continuous-time interacting particle system which can be seen as a Langevin diffusion over an extended state space of parameters and latent variab

Dominic Gerber, Lawrence A. Wilen, Eric R. Dufresne, Robert W. Style

Damage caused by freezing wet, porous materials is a widespread problem, but is hard to predict or control. Here, we show that polycrystallinity makes a great difference to the stress build-up process that underpins this damage. Unfrozen water in grain-boundary grooves feeds ice growth at temperatures below the freezing temperature, leading to the fast build

Dan Romik

Viazovska proved that the $E_8$ lattice sphere packing is the densest sphere packing in 8 dimensions. Her proof relies on two inequalities between functions defined in terms of modular and quasimodular forms. We give a direct proof of these inequalities that does not rely on computer calculations.

Mengke Zhan, Muhammad Alif bin Razali, Ayush Moitra, Cheng-Gang Xie

A multiphase flowmeter (MPFM) is used in the upstream oil and gas industry for continuous, in-line, real-time, oil-gas-water flow measurement without fluid separation. An MPFM typically consists of phase-fraction (holdup) and velocity (or flow rate) measurements. It is desirable to have homogeneous flow at the measurement location so that the phase-fraction

Juan Garcia-Bellido, Alexandros Papageorgiou, Marco Peloso, Lorenzo Sorbo

The coupling between a pseudo-scalar inflaton and a gauge field leads to an amount of additional density perturbations and gravitational waves (GWs) that is strongly sensitive to the inflaton speed. This naturally results in enhanced GWs at (relatively) small scales that exited the horizon well after the CMB ones, and that can be probed by a variety of GW ob

Emily L. Hunt, Sabine Reffert

Data from the Gaia satellite are revolutionising our understanding of the Milky Way. With every new data release, there is a need to update the census of open clusters. We aim to conduct a blind, all-sky search for open clusters using 729 million sources from Gaia DR3 down to magnitude $G\sim20$, creating a homogeneous catalogue of clusters including many ne

Chenguang Su, Qian Liu, Tianjiao Liang

The detection and cross-section measurement of Coherent Elastic Neutrino-Nucleus Scattering (CE{\nu}NS) are vital for particle physics, astrophysics, and nuclear physics. Therefore, a new CE{\nu}NS detection experiment is proposed in China. Undoped CsI crystals, each coupled with two Photon Multiplier Tubes (PMTs), will be cooled down to 77K and placed at th

Simon C. Marshall, Jordi Tura, Vedran Dunjko

Small numbers of qubits are one of the primary constraints on the near-term deployment of advantageous quantum computing. To mitigate this constraint, techniques have been developed to break up a large quantum computation into smaller computations. While this work is sometimes called circuit knitting or divide and quantum we generically refer to it as circui

Pierre Suret, Martin Dufour, Giacomo Roberti, Gennady El

We report an optical fiber experiment in which we investigate the interaction between an individual soliton and a dense soliton gas. We evidence a refraction phenomenon where the tracer soliton experiences an effective velocity change due to its interaction with the optical soliton gas. This interaction results in a significant spatial shift that is measured

Marvin Edelmann, Mikhail Pergament, Franz X. Kärtner

In this work, we identify and characterize intra-pulse intensity noise shaping by saturable absorbers applied in mode-locked lasers and ultra-low noise nonlinear fiber amplifiers. Reshaped intra-pulse intensity noise distributions are shown to be inevitably interconnected with self-amplitude modulation, the fundamental physical mechanism for initiation and s

Juhao Liang, Chen Zhang, Zhengyang Tang, Jie Fu

New retrieval tasks have always been emerging, thus urging the development of new retrieval models. However, instantiating a retrieval model for each new retrieval task is resource-intensive and time-consuming, especially for a retrieval model that employs a large-scale pre-trained language model. To address this issue, we shift to a novel retrieval paradigm

Joseph Vargovich, Fabio Santos, Jacob Penney, Marco A. Gerosa

Developers often struggle to navigate an Open Source Software (OSS) project's issue-tracking system and find a suitable task. Proper issue labeling can aid task selection, but current tools are limited to classifying the issues according to their type (e.g., bug, question, good first issue, feature, etc.). In contrast, this paper presents a tool (GiveMeLabel

Satoshi Nakajima, Yasuhiro Utsumi

We investigate a symmetric logarithmic derivative (SLD) Fisher information for kinetic uncertainty relations (KURs) of open quantum systems described by the GKSL quantum master equation with and without the detailed balance condition. In a quantum kinetic uncertainty relation derived by Vu and Saito [Phys. Rev. Lett. 128, 140602 (2022)], the Fisher informati

Thong Nguyen, Sean MacAvaney, Andrew Yates

Learned sparse retrieval (LSR) is a family of first-stage retrieval methods that are trained to generate sparse lexical representations of queries and documents for use with an inverted index. Many LSR methods have been recently introduced, with Splade models achieving state-of-the-art performance on MSMarco. Despite similarities in their model architectures

Stefano Nardean, Massimiliano Ferronato, Ahmad Abushaikha

This work proposes an original preconditioner that couples the Constrained Pressure Residual (CPR) method with block preconditioning for the efficient solution of the linearized systems of equations arising from fully implicit multiphase flow models. This preconditioner, denoted as Block CPR (BCPR), is specifically designed for Lagrange multipliers-based flo

ALICE Collaboration

The correlations between different moments of two flow amplitudes, extracted with the recently developed asymmetric cumulants, are measured in Pb$-$Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV recorded by the ALICE detector at the CERN Large Hadron Collider. The magnitudes of the measured observables show a dependence on the different moments as well as o

Kevin L. Haglin

Educators must make decisions about learner expectations and skills on which to focus when it comes to laboratory activities. There are various approaches but the general pattern is to encourage students to measure ordered pairs, plot a graph to establish linear dependence, and then compute the slope of the best-fit line for an eventual scientific conclusion

Yi Huang, Xiaoguang Tu, Gui Fu, Tingting Liu

Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknow low-light conditions. In this paper, we propose to incorporate

Vincenzo Fiorentino, Stefan Weigert

A collapse-free version of quantum theory is examined to systematically study the role of the projection postulate. This foil theory assumes "passive" measurements that do not update quantum states although measurement outcomes still occur probabilistically, and in accordance with Born's rule. The Hilbert space setting of quantum theory is retained. "Passive

Pratik Suchde, Christian Leithäuser, Jörg Kuhnert, Stéphane P. A. Bordas

Meshfree Lagrangian frameworks for free surface flow simulations do not conserve fluid volume. Meshfree particle methods like SPH are not mimetic, in the sense that discrete mass conservation does not imply discrete volume conservation. On the other hand, meshfree collocation methods typically do not use any notion of mass. As a result, they are neither mass

Teddy Mekonnen, Zeky Murra-Anton, Bobak Pakzad-Hurson

We consider sequential search by an agent who cannot observe the quality of goods but can acquire information by buying signals from a profit-maximizing principal with limited commitment power. The principal can charge higher prices for more informative signals in any period, but high prices in the future discourage continued search by the agent, thereby red

Muhammad Aaqib, Aftab Ali, Liming Chen, Omar Nibouche

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schem

Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting

The rise in malicious usage of large language models, such as fake content creation and academic plagiarism, has motivated the development of approaches that identify AI-generated text, including those based on watermarking or outlier detection. However, the robustness of these detection algorithms to paraphrases of AI-generated text remains unclear. To stre

Do June Min, Andreas Stolcke, Anirudh Raju, Colin Vaz

Current endpointing (EP) solutions learn in a supervised framework, which does not allow the model to incorporate feedback and improve in an online setting. Also, it is a common practice to utilize costly grid-search to find the best configuration for an endpointing model. In this paper, we aim to provide a solution for adaptive endpointing by proposing an e

Nabil Bouamara, Sébastien Laurent, Shuping Shi

We introduce a simple tool to control for false discoveries and identify individual signals in scenarios involving many tests, dependent test statistics, and potentially sparse signals. The tool applies the Cauchy combination test recursively on a sequence of expanding subsets of $p$-values and is referred to as the sequential Cauchy combination test. While

Dinkar Juyal, Siddhant Shingi, Syed Ashar Javed, Harshith Padigela

Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. In pathology images, there is another level of imb

Haijin Zeng, Kai Feng, Shaoguang Huang, Jiezhang Cao

Hyperspectral imaging systems that use multispectral filter arrays (MSFA) capture only one spectral component in each pixel. Hyperspectral demosaicing is used to recover the non-measured components. While deep learning methods have shown promise in this area, they still suffer from several challenges, including limited modeling of non-local dependencies, lac

Wei-Xuan Chang, Zi-Xiang Li

Here we develop a new scheme of projective quantum Monte-Carlo (QMC) simulation combining unbiased zero-temperature (projective) determinant QMC and variational Monte-Carlo based on Gutzwiller projection wave function, dubbed as ``Gutzwiller projection QMC''. The numerical results demonstrate that employment of Gutzwiller projection trial wave function with

Michael Hausenblas

Observability, in cloud native systems, is the capability to continuously generate and discover actionable insights, based on signals from the system under observation. How do you know what insights are the most useful ones? What signals should you be using to generate insights? This article discusses challenges arising when rolling out observability in orga

Hengyue Liang, Buyun Liang, Le Peng, Ying Cui

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations entails solving nontrivial constrained optimization problems. Existing numerical algorithms that are commonly used to solve them in practice predominantly rely on projected gradient, and mostly handle perturbations modeled by the $\ell_1$, $\ell_2$ and $\ell_\infty

D. I. Palade, L. M. Pomârjanschi

We propose an analytical approximation for the modified Bessel function of the second kind $K_\nu$. The approximation is derived from an exponential ansatz imposing global constrains. It yields local and global errors of less than one percent and a speed-up in the computing time of $3$ orders in magnitude in comparison with traditional approaches. We demonst

Kehan Li, Yian Zhao, Zhennan Wang, Zesen Cheng

Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis. Typically, massive and expansive pixel-level annotations are spent to train deep models by object-oriented interactions with manually labeled object masks. In

Honghuai Fang

We investigate the Berezin-Toeplitz operators that operate on the geometric quantized space corresponding to the $SO(3)$-Witten-Chern-Simons theory. We conjecture that the $SO(3)$-Berezin-Toeplitz operators quantized from the A-polynomial annihilate the corresponding $SO(3)$-knot states.

Haijin Zeng, Kai Feng, Jiezhang Cao, Shaoguang Huang

Pixel binning based Quad sensors have emerged as a promising solution to overcome the hardware limitations of compact cameras in low-light imaging. However, binning results in lower spatial resolution and non-Bayer CFA artifacts. To address these challenges, we propose a dual-head joint remosaicing and denoising network (DJRD), which enables the conversion o

Ce Zheng, Xianpeng Liu, Qucheng Peng, Tianfu Wu

Human mesh recovery (HMR) provides rich human body information for various real-world applications. While image-based HMR methods have achieved impressive results, they often struggle to recover humans in dynamic scenarios, leading to temporal inconsistencies and non-smooth 3D motion predictions due to the absence of human motion. In contrast, video-based ap

Pitchaporn Rewatbowornwong, Nattanat Chatthee, Ekapol Chuangsuwanich, Supasorn Suwajanakorn

CLIP has enabled new and exciting joint vision-language applications, one of which is open-vocabulary segmentation, which can locate any segment given an arbitrary text query. In our research, we ask whether it is possible to discover semantic segments without any user guidance in the form of text queries or predefined classes, and label them using natural l

Ivan Stelmakh, John Wieting, Sarina Xi, Graham Neubig

Many peer-review venues are using algorithms to assign submissions to reviewers. The crux of such automated approaches is the notion of the "similarity score" -- a numerical estimate of the expertise of a reviewer in reviewing a paper -- and many algorithms have been proposed to compute these scores. However, these algorithms have not been subjected to a pri

Benjamin Kenwright

Transformations in the field of computer graphics and geometry are one of the most important concepts for efficient manipulation and control of objects in 2-dimensional and 3-dimensional space. Transformations take many forms each with their advantages and disadvantages. A particularly powerful tool for representing transforms in a unified form are dual-quat

Andrea Barresi, Antoine Boulet, Gabriel Wlazłowski, Piotr Magierski

We investigate the life cycle of the large amplitude Higgs mode in strongly interacting superfluid Fermi gas. Through numerical simulations with time-dependent density-functional theory and the technique of the interaction quench, we verify the previous theoretical predictions on the mode's frequency. Next, we demonstrate that the mode is dynamically unstabl

Arnold D. Kim, Chrysoula Tsogka

Motivated by applications in unmanned aerial based ground penetrating radar for detecting buried landmines, we consider the problem of imaging small point like scatterers situated in a lossy medium below a random rough surface. Both the random rough surface and the absorption in the lossy medium significantly impede the target detection and imaging process.

Roger Bellido-Peralta, Fabio Leoni, Carles Calero, Giancarlo Franzese

The separation of liquid mixture components is relevant to many applications -- ranging from water purification to biofuel production -- and is a growing concern related to the UN Sustainable Development Goals (SDGs), such as ``Clean water and Sanitation'' and ``Affordable and clean energy''. One promising technique is using graphene slit-pores as filters, o

Chantal Pellegrini, Matthias Keicher, Ege Özsoy, Petra Jiraskova

Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making. However, such systems usually need to be trained on large amounts of annotated data, which often is scarce in the medical domain. Zero-shot methods address this challenge by allowing a flexible adaption to new settings with different clinical findin

Maks Sorokin, Chuyuan Fu, Jie Tan, C. Karen Liu

As robots become more prevalent, optimizing their design for better performance and efficiency is becoming increasingly important. However, current robot design practices overlook the impact of perception and design choices on a robot's learning capabilities. To address this gap, we propose a comprehensive methodology that accounts for the interplay between

M. Arruabarrena, A. Leonardo, A. Ayuela

Time-Dependent Density Functional Theory (TDDFT) has been currently established as a computationally cheaper, yet effective, alternative to the Many-Body Perturbation Theory (MBPT) for calculating the optical properties of solids. Within the Linear Response formalism, the optical absorption spectra are in good agreement with experiments, as well as the direc

Xin Wang, Yi Zhuo, Shunlong Li

This paper proposes a damage detection method based on the train-induced responses of high-speed railway box girder. Under the coupling effects of bending and torsion, the traditional damage detection method based on the Euler beam theory cannot be applied. In this research, the box girder section is divided into different components based on the plate eleme

E. Campedel, A. Caranti, I. Del Corso

Let $p, q$ be distinct primes, with $p > 2$. In a previous paper we classified the Hopf-Galois structures on Galois extensions of degree $p^{2} q$, when the Sylow $p$-subgroups of the Galois group are cyclic. This is equivalent to classifying the skew braces of order $p^2q$, for which the Sylow $p$-subgroups of the multiplicative group is cyclic. In this pap

Jeremy Wilkerson

This study presents a novel model for invertible sentence embeddings using a residual recurrent network trained on an unsupervised encoding task. Rather than the probabilistic outputs common to neural machine translation models, our approach employs a regression-based output layer to reconstruct the input sequence's word vectors. The model achieves high accu

Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García

Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from MLM of unlabelled in-domain free text) and task knowledge (f

Sebastian Dill, Susi Zhihan, Maurice Rohr, Maziar Sharbafi

With recent advancements in computer vision as well as machine learning (ML), video-based at-home exercise evaluation systems have become a popular topic of current research. However, performance depends heavily on the amount of available training data. Since labeled datasets specific to exercising are rare, we propose a method that makes use of the abundanc

Dhruv Saxena, Maxim Likhachev

We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding MAPF abstraction of this problem.

Viachaslau I. Murashka

The Hawkes graph $\Gamma_H(G)$ of $G$ is the directed graph whose vertex set coincides with $\pi(G)$ and it has the edge $(p, q)$ whenever $q\in\pi(G/O_{p',p}(G))$. The Sylow graph $\Gamma_s(G)$ of $G$ is the directed graph with vertex set $\pi(G)$ and $(p, q)$ is an edge of $\Gamma_s(G)$ whenever $q \in\pi(N_G(P)/PC_G(P))$ for some Sylow $p$-subgroup $P$ of

Julian Heeck, Anil Thapa

The Zee model provides a simple model for one-loop Majorana neutrino masses. The new scalars can furthermore explain the long-standing deviation in the muon's magnetic moment and the recent CDF measurement of the $W$-boson mass. Together, these observations yield predictions for lepton flavor violating processes that are almost entirely testable in the near

Yuki Shirai, Devesh K. Jha, Arvind U. Raghunathan

Planning and control for uncertain contact systems is challenging as it is not clear how to propagate uncertainty for planning. Contact-rich tasks can be modeled efficiently using complementarity constraints among other techniques. In this paper, we present a stochastic optimization technique with chance constraints for systems with stochastic complementarit

Wouter Jansen, Erik Verreycken, Anthony Schenck, Jean-Edouard Blanquart

Within academia and industry, there has been a need for expansive simulation frameworks that include model-based simulation of sensors, mobile vehicles, and the environment around them. To this end, the modular, real-time, and open-source AirSim framework has been a popular community-built system that fulfills some of those needs. However, the framework requ