April 2023 arXiv papers — page 88

Jiahao Liu, Jingjie Yeo

Amorphous silica ($a-SiO_2$) is a widely used inorganic material. Interestingly, the relationship between the local atomic structures of $a-SiO_2$ and their effects on ductility and fracture is seldom explored. Here, we combine large-scale molecular dynamics simulations and machine learning methods to examine the molecular deformations and fracture mechanism

R. Garg, S. Dellmann, C. Lederer-Woods, C. G. Bruno

Tantalum-180m is nature's rarest (quasi) stable isotope and its astrophysical origin is an open question. A possible production site of this isotope is the slow neutron capture process in Asymptotic Giant Branch stars, where it can be produced via neutron capture reactions on unstable $^{179}$Ta. We report a new measurement of the $^{179}$Ta($n,\gamma$)$^{18

Gengchen Mai, Weiming Huang, Jin Sun, Suhang Song

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to develop foundation models for geospatial ar

Christian G. Parigger

The interpretation of optical spectra requires thorough comprehension of quantum mechanics, especially understanding the concept of angular momentum operators. Suppose now that a transformation from laboratory-fixed to molecule-attached coordinates, by invoking the correspondence principle, induces reversed angular momentum operator identities. However, the

Ashraf Haddad, Najwa Aaraj, Preslav Nakov, Septimiu Fabian Mare

In recent years, a proliferation of cyber-security threats and diversity has been on the rise culminating in an increase in their reporting and analysis. To counter that, many non-profit organizations have emerged in this domain, such as MITRE and OSWAP, which have been actively tracking vulnerabilities, and publishing defense recommendations in standardized

Brendan J. Meade

Computational earthquake sequence models provide generative estimates of the time, location, and size of synthetic seismic events that can be compared with observed earthquake histories and assessed as rupture forecasts. Here we describe a three-dimensional probabilistic earthquake sequence model that produces slip event time series constrained across geomet

Luigi Ranno, Jia Xu Brian Sia, Khoi Phuong Dao, Juejun Hu

Photonics has been one of the primary beneficiaries of advanced silicon manufacturing. By leveraging on mature complementary metal-oxide-semiconductor (CMOS) process nodes, unprecedented device uniformities and scalability have been achieved at low costs. However, some functionalities, such as optical memory, Pockels modulation, and magnetooptical activity,

Hainan Xu, Fei Jia, Somshubra Majumdar, He Huang

This paper introduces a novel Token-and-Duration Transducer (TDT) architecture for sequence-to-sequence tasks. TDT extends conventional RNN-Transducer architectures by jointly predicting both a token and its duration, i.e. the number of input frames covered by the emitted token. This is achieved by using a joint network with two outputs which are independent

Eduard Petiska

ChatGPT (GPT) has become one of the most talked-about innovations in recent years, with over 100 million users worldwide. However, there is still limited knowledge about the sources of information GPT utilizes. As a result, we carried out a study focusing on the sources of information within the field of environmental science. In our study, we asked GPT to i

Ole Richter, Yannan Xing, Michele De Marchi, Carsten Nielsen

Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System on Chip (SoC), featuring an event-based

Jian Qiu

The `brane quantisation' is a quantisation procedure developed by Gukov and Witten \cite{Gukov:2008ve}. We implement this idea by combining it with the tilting theory and the minimal resolutions. This way, we can realistically compute the deformation quantisation on the space of observables acting on the Hilbert space. We apply this procedure to certain quan

Wojciech Jablonski, Ludwig Reich

We study the algebraic structure of the groups of solutions of the third Acz{\'e}l-Jabotinsky differential equation $(H\circ \Phi)(x)=\frac{d\Phi}{d x}\cdot H(x)$ in the rings of formal power series and truncated formal power series $k[[x]]_s$, where $s$ is either a positive integer or $s=\infty$ and $H\in k[[x]]_s$ is given. We give also the detailed descri

Tao Yu, Runseng Feng, Ruoyu Feng, Jinming Liu

Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first attempt to the mask-free image inpainting and propose a new paradigm of ``clicking and filling'', which is named as Inpaint Anything (IA). The core idea behind IA is to combine the s

Pavel Perezhogin, Andrey Glazunov

Ocean models at intermediate resolution (1/4 degree), which partially resolve mesoscale eddies, can be seen as Large eddy simulations (LES) of the primitive equations, in which the effect of unresolved eddies must be parameterized. In this work, we propose new subgrid models that are consistent with the physics of two-dimensional (2D) flows. We analyze subgr

M. A. Ganaie, M. Tanveer, I. Beheshti, N. Ahmad

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data an

Vikrant Singhal

We present the first $\varepsilon$-differentially private, computationally efficient algorithm that estimates the means of product distributions over $\{0,1\}^d$ accurately in total-variation distance, whilst attaining the optimal sample complexity to within polylogarithmic factors. The prior work had either solved this problem efficiently and optimally unde

Volker Hohmann

Background. Hearing aid technology has proven successful in the rehabilitation of hearing loss, but its performance is still limited in difficult everyday conditions characterized by noise and reverberation. Objectives. Introduction to the current state of hearing aid technology and presentation of the current state of research and future development. Method

Sajidah Ahmed, Odd Erik Garcia, Adam Q Kuang, Brian LaBombard

Intermittent plasma fluctuations in the boundary region of the Alcator C-Mod device were comprehensively investigated using data time-series from gas puff imaging and mirror Langmuir probe diagnostics. Fluctuations were sampled during stationary plasma conditions in ohmically heated, lower single null diverted configurations with scans in both line-averaged

Bruno Anghinoni, Mikko Partanen, Nelson G. C. Astrath

We present a detailed derivation of the electromagnetic force density and pressure in linear dielectric media according to the so-called Microscopic Amp\`ere formulation, which considers the classical dipolar sources in matter along with the hidden momentum contribution. It is seen that, among the other formulations existing in the literature, our proposal i

Feras Al Taha, Shuhao Yan, Eilyan Bitar

This paper proposes a distributionally robust approach to regret optimal control of discrete-time linear dynamical systems with quadratic costs subject to a stochastic additive disturbance on the state process. The underlying probability distribution of the disturbance process is unknown, but assumed to lie in a given ball of distributions defined in terms o

Bayu Setiaji, Purwoko Haryadi Santoso, Khafidh Nur Aziz, Heri Retnawati

Science literacy is being fostered by science education community including the Indonesian education system. Science literacy and technology course has been designed and implemented to strengthen the national initiative empowering scientifically literate Indonesian society. This paper is intended to evaluate to what degree this course can be performed by non

Ahmed Zahari, Bouzid Mosbahi, Imed Basdouri

The basic objective of this research work is to investigate the stucture of BiHom-associative trialgebras.\,In this respect we build up one important class of BiHom-trialgebras and determine properties of right, left and middle operations in BiHom-associative trialgebras.\,We provide a classification of $n$-dimensional BiHom-trialgebras for $n\leq3$.\,Relyin

Minati De, Ratnadip Mandal, Satyam Singh

We investigate the geometric hitting set problem in the online setup for the range space $\Sigma=({\cal P},{\cal S})$, where the set $\P\subset\mathbb{R}^2$ is a collection of $n$ points and the set $\cal S$ is a family of geometric objects in $\mathbb{R}^2$. In the online setting, the geometric objects arrive one by one. Upon the arrival of an object, an on

Eyal Rozenberg, Daniel Freedman

We study the problem of learning conditional distributions of the form $p(G | \hat G)$, where $G$ and $\hat G$ are two 3D graphs, using continuous normalizing flows. We derive a semi-equivariance condition on the flow which ensures that conditional invariance to rigid motions holds. We demonstrate the effectiveness of the technique in the molecular setting o

Wolfgang Bock, Vyacheslav Futorny, Mikhail Neklyudov

In this paper we show variant of the spectral theorem using an algebraic Jordan-Schwinger map. The advantage of this approach is that we don't have restriction of normality on the class of operators we consider. On the other side, we have the restriction that the class of operators we consider should be of weighted Hilbert-Schmidt class.

Ziyu Wang, Yuting Wu, Yongmo Park, Sangmin Yoo

Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. In this paper, we identify a potential security vulnerability wherein an adversary can reconstruct t

M. A. Simão, O. Gibaru, P. Neto

Online recognition of gestures is critical for intuitive human-robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This pap

Guangpu Zhu, Lailai Zhu

Self-propulsion of chemically active droplet and phoretic disk has been widely studied; however, most research overlooks the influence of disk shape on swimming dynamics. Inspired by the experimentally observed prolate composite droplets and elliptic camphor disks, we employ simulations to investigate the phoretic dynamics of an elliptic disk that uniformly

Shivanand Kundargi, Tejas Anvekar, Ramesh Ashok Tabib, Uma Mudenagudi

Point clouds offer comprehensive and precise data regarding the contour and configuration of objects. Employing such geometric and topological 3D information of objects in class incremental learning can aid endless application in 3D-computer vision. Well known 3D-point cloud class incremental learning methods for addressing catastrophic forgetting generally

Kalil Bernardino, Mauro C. C. Ribeiro

Ionic liquids have generated interest in applications as lubricants and as additives to conventional lubricants due to their unique physical properties. In these applications, the liquid thin film can be subjected simultaneously to extremely high shear and loads in addition to nanoconfinement effects. Here, we use molecular dynamics simulations with a coarse

Duncan Laurie

We first construct an action of the extended double affine braid group $\mathcal{\ddot{B}}$ on the quantum toroidal algebra $U_{q}(\mathfrak{g}_{\mathrm{tor}})$ in untwisted and twisted types. As a crucial step in the proof, we obtain a finite Drinfeld new style presentation for a broad class of quantum affinizations. In the simply laced cases, using our act

Enrique Fernández-Martínez, Manuel González-López, Josu Hernández-García, Matheus Hostert

The existence of right-handed neutrinos, or heavy neutral leptons (HNLs), is strongly motivated by the observation of neutrino masses and mixing. The mass of these new particles could lie below the electroweak scale, making them accessible to low-energy laboratory experiments. Additional new physics at high energies can mediate new interactions between the S

David J. Abramovitch, Jin-Jian Zhou, Jernej Mravlje, Antoine Georges

Electron-electron ($e$-$e$) and electron-phonon ($e$-ph) interactions are challenging to describe in correlated materials, where their joint effects govern unconventional transport, phase transitions, and superconductivity. Here we combine first-principles $e$-ph calculations with dynamical mean field theory (DMFT) as a step toward a unified description of $

Christopher J. Lindsay, J. M. Joel Ong, Sarbani Basu

Asteroseismology has been used extensively in recent years to study the interior structure and physical processes of main sequence stars. We consider prospects for using pressure modes (p-modes) near the frequency of maximum oscillation power to probe the structure of the near-core layers of main sequence stars with convective cores by constructing stellar m

Feras Al Taha, Tyrone Vincent, Eilyan Bitar

The increasing prevalence of electric vehicles (EVs) in the transportation sector will introduce a large number of highly flexible electric loads that EV aggregators can pool and control to provide energy and ancillary services to the wholesale electricity market. To integrate large populations of EVs into electricity market operations, aggregators must expr

Pablo Bermejo, Borja Aizpurua, Roman Orus

Machine learning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent and alike. The overall performance is dependent on the appearance of local minima and barren plateaus, which slow-down calculations and lead to non-optimal solutions. In practice, this results in dram

Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang

Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcomes, and unfairness, with potentially serious consequences. Consequently, aligning these models with human ethics and preferences is an essential step toward ensuring their responsib

Brian Mintz, Feng Fu

Cultures around the world show varying levels of conservatism. While maintaining traditional ideas prevents wrong ones from being embraced, it also slows or prevents adaptation to new times. Without exploration there can be no improvement, but often this effort is wasted as it fails to produce better results, making it better to exploit the best known option

Pilar Coloma, Jacobo López-Pavón, Laura Molina-Bueno, Salvador Urrea

The exquisite capabilities of liquid Argon Time Projection Chambers make them ideal to search for weakly interacting particles in Beyond the Standard Model scenarios. Given their location at CERN the ProtoDUNE detectors may be exposed to a flux of such particles, produced in the collisions of 400 GeV protons (extracted from the Super Proton Synchrotron accel

Lizvette Villafaña, Peter R. Williams, Tommaso Treu, Brendon J. Brewer

We combine our dynamical modeling black hole mass measurements from the Lick AGN Monitoring Project 2016 sample with measured cross-correlation time lags and line widths to recover individual scale factors, f, used in traditional reverberation mapping analyses. We extend our sample by including prior results from Code for AGN Reverberation and Modeling of Em

Tomas Codina, Olaf Hohm, Barton Zwiebach

We report two surprising results on $\alpha'$ corrections in string theory restricted to massless fields. First, for critical dimension Bianchi type I cosmologies with $q$ scale factors only $q-1$ of them have non-trivial $\alpha'$ corrections. In particular, for FRW backgrounds all $\alpha'$ corrections are trivial. Second, in non-critical dimensions, all t

Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee

Large decoder-only language models (LMs) can be largely improved in terms of perplexity by retrieval (e.g., RETRO), but its impact on text generation quality and downstream task accuracy is unclear. Thus, it is still an open question: shall we pretrain large autoregressive LMs with retrieval? To answer it, we perform a comprehensive study on a scalable pre-t

Wout Merbis, Turmoli Neogi, Arash Ranjbar

We reconsider the Hamiltonian reduction of the action for three dimensional AdS supergravity and $W_3$ higher spin AdS gravity in the Chern-Simons formulation under asymptotically anti-de Sitter boundary conditions. We show that the reduction gives two copies of chiral bosons on the boundary. In particular, we take into account the holonomy of the Chern-Simo

Meysam Motaharfar, Parampreet Singh

It was recently shown that tunneling wavefunction proposal is consistent with loop quantum geometry corrections including both holonomy and inverse scale factor corrections in the gravitational part of a spatially closed isotropic model with a positive cosmological constant. However, in presence of an inflationary potential the initial singularity is kinetic

Astrid Eichhorn, Shouryya Ray

The bound on the proton lifetime is one of a small handful of observations that constrains physics not far from the Planck scale. This calls for a calculation of the proton lifetime in quantum gravity. Here, we calculate how quantum fluctuations of the metric impact four-fermion interactions which mediate proton decay. We find that quantum gravity lowers the

Vidya Sagar, Ritumoni Sarma

There are exactly two non-commutative rings of size $4$, namely, $E = \langle a, b ~\vert ~ 2a = 2b = 0, a^2 = a, b^2 = b, ab= a, ba = b\rangle$ and its opposite ring $F$. These rings are non-unital. A subset $D$ of $E^m$ is defined with the help of simplicial complexes, and utilized to construct linear left-$E$-codes $C^L_D=\{(v\cdot d)_{d\in D} : v\in E^m\

Jorge Miguel-Ramiro, Ferran Riera-Sàbat, Wolfgang Dür

W states are a valuable resource for various quantum information tasks, and several protocols to generate them have been proposed and implemented. We introduce a quantum repeater protocol to efficiently distribute three-qubit W states over arbitrary distances in a 2D triangular quantum network with polylogarithmic overhead, thereby enabling these application

M. Perna, S. Arribas, M. Marshall, F. D'Eugenio

LBQS 0302-0019 is a blue quasar (QSO) at z ~ 3.3, hosting powerful outflows, and residing in a complex environment consisting of an obscured AGN candidate, and multiple companions, all within 30 kpc in projection. We use JWST NIRSpec IFS observations to characterise the ionized gas in this complex system. We develop a procedure to correct for the spurious os

Alexander Bednyakov, Johan Henriksson, Stefanos R. Kousvos

We perform a comprehensive perturbative study of the operator spectrum in multi-scalar theories with hypercubic global symmetry. This includes working out symmetry representations and their corresponding tensor structures. These structures are then used to compute the anomalous dimensions of scalar operators with up to four fields and arbitrary representatio

Max Geier, Svend Krøjer, Felix von Oppen, Charles M. Marcus

If a quantum dot is coupled to a topological superconductor via tunneling contacts, each contact hosts a Majorana zero mode in the limit of zero transmission. Close to a resonance and at a finite contact transparency, the resonant level in the quantum dot couples the Majorana modes, but a ground state degeneracy per fermion parity subspace remains if the num

Keith Doore, Erik B. Monson, Rafael T. Eufrasio, Bret D. Lehmer

We present an updated version of Lightning, a galaxy spectral energy distribution (SED) fitting code that can model X-ray to submillimeter observations. The models in Lightning include the options to contain contributions from stellar populations, dust attenuation and emission, and active galactic nuclei (AGN). X-ray emission, when utilized, can be modeled a

Johanna Erdmenger, Bastian Heß, Ioannis Matthaiakakis, René Meyer

General relativity (GR) as described in terms of curvature by the Einstein-Hilbert action is dynamically equivalent to theories of gravity formulated in terms of spacetime torsion or non-metricity. This forms what is called the geometrical trinity of gravity. The theories corresponding to this trinity are, apart from GR, the teleparallel (TEGR) and symmetric

Andreas P. Braun, Bernardo Fraiman, Mariana Graña, Severin Lüst

The tadpole conjecture proposes that complex structure moduli stabilisation by fluxes that have low tadpole charge can be realised only at special points in moduli space, leading generically to (large) gauge symmetries. Here we provide an exhaustive survey of the gauge symmetries arising in F-theory flux compactifications on products of attractive $\mbox{K3}

Guillaume Rostagni, Jack D. Shergold

We present a comprehensive discussion of the Stodolsky effect for dark matter (DM), and discuss two techniques to measure the effect and constrain the DM parameter space. The Stodolsky effect is the spin-dependent shift in the energy of a Standard Model (SM) fermion sitting in a bath of neutrinos. This effect, which scales linearly in the effective coupling,

Keith Inight, Boris Gänsicke, Elmé Breedt, Henry Israel

We present a catalogue of 507 cataclysmic variables (CVs) observed in SDSS I to IV including 70 new classifications collated from multiple archival data sets. This represents the largest sample of CVs with high-quality and homogeneous optical spectroscopy. We have used this sample to derive unbiased space densities and period distributions for the major sub-

Anirban Roy, Dariannette Valentín-Martínez, Kailai Wang, Nicholas Battaglia

Mapping of multiple lines such as the fine-structure emission from [CII] (157.7 $\mu \text{m}$), [OIII] (52 \& 88.4 $\mu \text{m}$), and rotational emission lines from CO are of particular interest for upcoming line intensity mapping (LIM) experiments at millimetre wavelengths, due to their brightness features. Several upcoming experiments aim to cover a bro

Francisco Albergaria, G. C. Branco, José Filipe Bastos, J. I. Silva-Marcos

We suggest a simple relation between the quark and the lepton mixing within the framework of type-I seesaw mechanism. We show that within our ansatz the empirical King-Mohapatra-Smirnov relation, which suggests a connection between the CKM and PMNS mixing where $|V^\text{PMNS}_{13}|\approx \frac{1}{\sqrt{2}}\sin(\theta_C) $, can be derived. This is possible

Yossef Zenati, C. Albertus, M. Ángeles Pérez-García, Joseph Silk

We calculate the neutrino luminosity in an astrophysical scenario where dark matter is captured by a neutron star which eventually implodes to form a low mass black hole. The Trojan horse scenario involves the collapse of a neutron star (NS) due to the accumulation of a critical amount of dark matter (DM) during its lifetime. As a result, a central disk form

Javier Campos, Zhen Dong, Javier Duarte, Amir Gholami

We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware. Our approach leverages Hessian-aware quantization (HAWQ) of NNs, the Quantized Open Neural Network Exchange (QONNX) intermediate representati

Patrick Emonts, Erez Zohar

Fermionic Gaussian Projected Entangled Pair States are fermionic tensor network state constructions which describe the physics of ground states of non-interacting fermionic Hamiltonians. As non-interacting states, one may study and analyze them very efficiently, in both analytical and numerical means. Recently it was shown that they may be used as the starti

Stefano Antonini, Brianna Grado-White, Shao-Kai Jian, Brian Swingle

We extend the results of arXiv:2209.12903 by studying local projective measurements performed on subregions of two copies of a CFT${}_2$ in the thermofield double state and investigating their consequences on the bulk double-sided black hole holographic dual. We focus on CFTs defined on an infinite line and consider measurements of both finite and semi-infin

Xuejian Shen, Josh Borrow, Mark Vogelsberger, Enrico Garaldi

Using high-resolution cosmological radiation-hydrodynamic (RHD) simulations (THESAN-HR), we explore the impact of alternative dark matter (altDM) models on galaxies during the Epoch of Reionization. The simulations adopt the IllustrisTNG galaxy formation model. We focus on altDM models that exhibit small-scale suppression of the matter power spectrum, namely

Daniel R. Rybarczyk, Snezana Stanimirovic, Antoine Gusdorf

Interstellar shocks, a key element of stellar feedback processes, shape the structure of the interstellar medium (ISM) and are essential for the chemistry, thermodynamics, and kinematics of interstellar gas. Powerful, high-velocity shocks are driven by stellar winds, young supernova explosions, more evolved supernova remnants, cloud-cloud collisions, and pro

José P. S. Lemos, O. B. Zaslavskii

We study a spherical black hole surrounded by a hot self-gravitating thin shell in the canonical ensemble, i.e., a black hole and a hot shell inside a heat reservoir acting as a boundary with its area and temperature fixed. To work out the quantum partition function, from which the thermodynamics of the system follows, we use the Euclidean path integral appr

Farzan Vafa, David R. Nelson, Amin Doostmohammadi

Using the Born-Oppenheimer approximation, we present a general description of topological defects dynamics in $p$-atic materials on curved surfaces, and simplify it in the case of active nematics. We find that activity induces a geometric contribution to the motility of the $+1/2$ defect. Moreover, in the case of a cone, the simplest example of a geometry wi

Songwei Ge, Taesung Park, Jun-Yan Zhu, Jia-Bin Huang

Plain text has become a prevalent interface for text-to-image synthesis. However, its limited customization options hinder users from accurately describing desired outputs. For example, plain text makes it hard to specify continuous quantities, such as the precise RGB color value or importance of each word. Furthermore, creating detailed text prompts for com

Shaoyuan Xie, Lingdong Kong, Wenwei Zhang, Jiawei Ren

The recent advances in camera-based bird's eye view (BEV) representation exhibit great potential for in-vehicle 3D perception. Despite the substantial progress achieved on standard benchmarks, the robustness of BEV algorithms has not been thoroughly examined, which is critical for safe operations. To bridge this gap, we introduce RoboBEV, a comprehensive ben

Xueyan Zou, Jianwei Yang, Hao Zhang, Feng Li

In this work, we present SEEM, a promptable and interactive model for segmenting everything everywhere all at once in an image, as shown in Fig.1. In SEEM, we propose a novel decoding mechanism that enables diverse prompting for all types of segmentation tasks, aiming at a universal segmentation interface that behaves like large language models (LLMs). More

Sida Peng, Yunzhi Yan, Qing Shuai, Hujun Bao

This paper introduces a novel representation of volumetric videos for real-time view synthesis of dynamic scenes. Recent advances in neural scene representations demonstrate their remarkable capability to model and render complex static scenes, but extending them to represent dynamic scenes is not straightforward due to their slow rendering speed or high sto

Ziyan Huang, Haoyu Wang, Zhongying Deng, Jin Ye

Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions. Further scaling them up to higher orders of magnitude is rarely explored. An overarching goal of explor

Jonathan Crabbé, Mihaela van der Schaar

Interpretability methods are valuable only if their explanations faithfully describe the explained model. In this work, we consider neural networks whose predictions are invariant under a specific symmetry group. This includes popular architectures, ranging from convolutional to graph neural networks. Any explanation that faithfully explains this type of mod

Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian

3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance

Francisco Escudero Gutiérrez

We give a new presentation of the main result of Arunachalam, Bri\"et and Palazuelos (SICOMP'19) and show that quantum query algorithms are characterized by a new class of polynomials which we call Fourier completely bounded polynomials. We conjecture that all such polynomials have an influential variable. This conjecture is weaker than the famous Aaronson-A

Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi

Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation. Despite that, their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models, such as GPT-3. Here we explore the idea

Zheng Ding, Xuaner Zhang, Zhihao Xia, Lars Jebe

We address the problem of learning person-specific facial priors from a small number (e.g., 20) of portrait photos of the same person. This enables us to edit this specific person's facial appearance, such as expression and lighting, while preserving their identity and high-frequency facial details. Key to our approach, which we dub DiffusionRig, is a diffus

Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan

Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-training on another CD dataset and then fine-tuning on the target benchmark. This current strategy is driven by the fact that transformers typically require a large amount of training d

Kaloian D. Lozanov, Misao Sasaki, Volodymyr Takhistov

Cosmological solitonic objects such as monopoles, cosmic strings, domain walls, oscillons and Q-balls often appear in theories of the early Universe. We demonstrate that such scenarios are generically accompanied by a novel production source of gravitational waves stemming from soliton isocurvature perturbations. The resulting induced universal gravitational

Kalpdrum Passi, Shervin Assari, Amir Hossein Zolfaghari

The year 2020 was a challenging year known mainly as the pandemic year. However, the notable event of George Floyd's killing broke many humans' hearts and made them protest on social media and the streets as well. In this research, we studied the hashtag "BlackLivesMatter," and some of its adversary contentions regarding George Floyd's demise in 2020 on Twit

Liliane Momeni, Mathilde Caron, Arsha Nagrani, Andrew Zisserman

Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language models based on CLIP have been shown to have limited verb understanding and to rely extensively on nouns, restricting their performance in real-world video applications that require

Saeed Saadatnejad, Mehrshad Mirmohammadi, Matin Daghyani, Parham Saremi

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited uncertainty analysis have hindered progress in the field. To address this, we first develop an open-sour

Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan

Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-

Carlos Rodriguez-Pardo, Melania Prieto-Martin, Dan Casas, Elena Garces

We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera. Our approach enables to create mechanically-correct digital representations of real-world textile materials, which is a fundamental step for many interactive design and engineering applications. As opposed to existing capture methods, which

Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir

Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual inform

Cai Dieball, Gerrit Wellecke, Aljaž Godec

It was predicted and recently experimentally confirmed that systems with microscopically reversible dynamics in locally quadratic potentials warm up faster than they cool down. This thermal relaxation asymmetry challenged the local-equilibrium paradigm valid near equilibrium. Because the intuition and proof hinged on the dynamics obeying detailed balance, it

Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban

Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when each form of support will yield better outcomes? In this work, we posit that personalizing access to decision support tools can be an effective mechanism for instantiating the appropriate use of AI assistance. Specifically, we propose the gener

Jiatao Gu, Qingzhe Gao, Shuangfei Zhai, Baoquan Chen

Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulties in acquiring 3D ground truth data for training. On the other hand, 3D GANs that integrate implicit 3D representations into GANs have shown remarkable 3D-aware generation when tr

Marko Ristic, Richard O'Shaughnessy, V. Ashley Villar, Ryan T. Wollaeger

In this work, we present a simple interpolation methodology for spectroscopic time series, based on conventional interpolation techniques (random forests) implemented in widely-available libraries. We demonstrate that our existing library of simulations is sufficient for training, producing interpolated spectra that respond sensitively to varied ejecta param

Gevorg Martirosyan, Christopher J. Ho, Jiří Etrych, Yansheng Zhang

We explore the dynamics of a tuneable box-trapped Bose gas under strong periodic forcing in the presence of weak disorder. In absence of interparticle interactions, the interplay of the drive and disorder results in an isotropic nonthermal momentum distribution that shows subdiffusive dynamic scaling, with sublinear energy growth and the universal scaling fu

Miguel Simão, Pedro Neto, Olivier Gibaru

We propose a novel way of solving the issue of classification of out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in the Generative Adversarial Network (GAN) framework. A generative model augments the data set in an online fashion with new samples and stochastic target vectors, while a discriminative model determines the class of th

Siyang Li, Stefano Casertano, Adam G. Riess

The tip of the red giant branch (TRGB) is a standard candle that can be used to help refine the determination of the Hubble constant. $Gaia$ Data Release 3 (DR3) provides synthetic photometry constructed from low-resolution BP/RP spectra for Milky Way field stars that can be used to directly calibrate the luminosity of the TRGB in the Johnson-Cousins I band,

D. Scolnic, A. G. Riess, J. Wu, S. Li

The Tip of the Red Giant Branch (TRGB) provides a luminous standard candle for constructing distance ladders to measure the Hubble constant. In practice its measurements via edge-detection response (EDR) are complicated by the apparent fuzziness of the tip and the multi-peak landscape of the EDR. As a result, it can be difficult to replicate due to a case-by

M. L. Davis, S. Parolo, C. Reichl, W. Dietsche

Bilayers consisting of two-dimensional (2D) electron and hole gases separated by a 10 nm thick AlGaAs barrier are formed by charge accumulation in epitaxially grown GaAs. Both vertical and lateral electric transport are measured in the millikelvin temperature range. The conductivity between the layers shows a sharp tunnel resonance at a density of $1.1 \cdot

Mathis Grelier, David A. Sivak, Jannik Ehrich

Molecular motors fulfill critical functions within all living beings. Understanding their underlying working principles is therefore of great interest. Here we develop a simple model inspired by the two-component biomolecular motor Fo-F1 ATP synthase. We analyze its energetics and characterize information flows between the machine's components. At maximum ou

Yusuke O. Nakai, Nobuyuki Okuma, Daichi Nakamura, Kenji Shimomura

The non-Hermitian skin effects are representative phenomena intrinsic to non-Hermitian systems: the energy spectra and eigenstates under the open boundary condition (OBC) drastically differ from those under the periodic boundary condition (PBC). Whereas a non-trivial topology under the PBC characterizes the non-Hermitian skin effects, their proper measure un

J. R. Paudel, M. Terilli, T. -C. Wu, J. D. Grassi

Interfacial charge transfer in oxide heterostructures gives rise to a rich variety of electronic and magnetic phenomena. Designing heterostructures where one of the thin-film components exhibits a metal-insulator transition opens a promising avenue for controlling such phenomena both statically and dynamically. In this letter, we utilize a combination of dep

Jordi Pérez-Guijarro, Alba Pagès-Zamora, Javier R. Fonollosa

The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantage are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems

Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin

We consider an important problem in scientific discovery, namely identifying sparse governing equations for nonlinear dynamical systems. This involves solving sparse ridge regression problems to provable optimality in order to determine which terms drive the underlying dynamics. We propose a fast algorithm, OKRidge, for sparse ridge regression, using a novel

Roman Lucrezi, Pedro P. Ferreira, Markus Aichhorn, Christoph Heil

In this work, we resolve conflicting experimental and theoretical findings related to the dynamical stability and superconducting properties of $Fm\overline{3}m$-LuH$_3$, which was recently suggested as the parent phase harboring room-temperature superconductivity at near-ambient pressures. Including temperature and quantum anharmonic lattice effects in our

Zhipeng Huang, Manuel Bridger, Oscar A Naranjo-Montoya, Alexander Tarasevitch

Understanding heterogeneous charge transfer is crucial if we are to build the best electrolyzers, fuel cells and photoelectrochemical water splitting devices that chemistry allows. Because the elementary processes involved have timescales ranging from femto- to milliseconds, direct simulation is not generally possible. Model Hamiltonian approaches thus have

Yushi Hamaguchi

We introduce a new framework of Markovian lifts of stochastic Volterra integral equations (SVIEs for short) with completely monotone kernels. We define the state space of the Markovian lift as a separable Hilbert space which incorporates the singularity or regularity of the kernel into the definition. We show that the solution of an SVIE is represented by th