April 2023 arXiv papers — page 122

Marwan Shaban, Adam J. Rocke

Simulators for the RISC-V instruction set architecture (ISA) are useful for teaching assembly language and modern CPU architecture concepts. The Assembly/Simulation Platform for Illustration of RISC-V in Education (ASPIRE) is an integrated RISC-V assembler and simulator used to illustrate these concepts and evaluate algorithms to generate machine language co

Daniel Mejia-Rodriguez, Alexander A. Kunitsa, Edoardo Aprà, Niranjan Govind

We report computations of the vertical ionization potentials within the $GW$ approximation of the near-complete series of first-row transition metal (V-Cu) aqua ions in their most common oxidation states, i.e. V$^{3+}$, Cr$^{3+}$, Cr$^{2+}$, Mn$^{2+}$, Fe$^{3+}$, Fe$^{2+}$, Co$^{2+}$, Ni$^{2+}$, and Cu$^{2+}$. The $d$-orbital occupancy of these systems spans

Tamas Rudas, Wicher Bergsma

Marginal models involve restrictions on the conditional and marginal association structure of a set of categorical variables. They generalize log-linear models for contingency tables, which are the fundamental tools for modelling the conditional association structure. This chapter gives an overview of the development of marginal models during the past 20 yea

J. E. Alba-Arroyo, S. F. Caballero-Benitez, R. Jauregui

A theoretical analysis of the rotational dynamics induced by off axis binary collisions of quantum droplets constituted by ultracold atoms is reported. We focus on quantum droplets formed by degenerate dilute Bose gases made up from binary mixtures of alkaline atoms under feasible experimental conditions. The stability of the ground state is known to be long

Giorgos Kordopatis-Zilos, Giorgos Tolias, Christos Tzelepis, Ioannis Kompatsiaris

We introduce S$^2$VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks after fine-tuning. Here, in contrast to prior work, SSL is used to perform video similarity learning and address multiple retrieval and det

Jackie Baek, Shixin Wang

We study online weighted bipartite matching of reusable resources where an adversarial sequence of requests for resources arrive over time. A resource that is matched is 'used' for a random duration, drawn independently from a resource-dependent distribution, after which it returns and is able to be matched again. We study the performance of the greedy polic

Adam Gosztolai, Robert L. Peach, Alexis Arnaudon, Mauricio Barahona

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation learning method, MARBLE, that decomposes on-manifold dynamics into local flow fields and maps them into a co

Sahar Aljalbout, Gilles Falquet, Didier Buchs

Wikidata is a knowledge graph increasingly adopted by many communities for diverse applications. Wikidata statements are annotated with qualifier-value pairs that are used to depict information, such as the validity context of the statement, its causality, provenances, etc. Handling the qualifiers in reasoning is a challenging problem. When defining inferenc

Garrett Bingham

Automated machine learning (AutoML) methods improve upon existing models by optimizing various aspects of their design. While present methods focus on hyperparameters and neural network topologies, other aspects of neural network design can be optimized as well. To further the state of the art in AutoML, this dissertation introduces techniques for discoverin

Minghao Chen, Iro Laina, Andrea Vedaldi

Recent diffusion-based generators can produce high-quality images from textual prompts. However, they often disregard textual instructions that specify the spatial layout of the composition. We propose a simple approach that achieves robust layout control without the need for training or fine-tuning of the image generator. Our technique manipulates the cross

Sijie Zhu, Zhe Lin, Scott Cohen, Jason Kuen

We investigate the problem of automatically placing an object into a background image for image compositing. Given a background image and a segmented object, the goal is to train a model to predict plausible placements (location and scale) of the object for compositing. The quality of the composite image highly depends on the predicted location/scale. Existi

Valentina Harizanov, Keshav Srinivasan

A cohesive power of a structure is an effective analog of the classical ultrapower of a structure. We start with a computable structure, and consider its countable ultrapower over a cohesive set of natural numbers. A cohesive set is an infinite set of natural numbers that is indecomposable with respect to computably enumerable sets. It plays the role of an u

Maria-Florina Balcan, Steve Hanneke, Rattana Pukdee, Dravyansh Sharma

The problem of designing learners that provide guarantees that their predictions are provably correct is of increasing importance in machine learning. However, learning theoretic guarantees have only been considered in very specific settings. In this work, we consider the design and analysis of reliable learners in challenging test-time environments as encou

Yunxiao Shi, Hong Cai, Amin Ansari, Fatih Porikli

The ubiquitous multi-camera setup on modern autonomous vehicles provides an opportunity to construct surround-view depth. Existing methods, however, either perform independent monocular depth estimations on each camera or rely on computationally heavy self attention mechanisms. In this paper, we propose a novel guided attention architecture, EGA-Depth, which

Xueying Ding, Nikita Seleznev, Senthil Kumar, C. Bayan Bruss

Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few. While the literature is abundant in effective detection algorithms due to this practical relevance, autonomous anomaly detection is rarely used in real-world scenarios. Especially in high-stakes application

Dimitris Papadimitriou, Jingqi Li

Inferring unknown constraints is a challenging and crucial problem in many robotics applications. When only expert demonstrations are available, it becomes essential to infer the unknown domain constraints to deploy additional agents effectively. In this work, we propose an approach to infer affine constraints in control tasks after observing expert demonstr

Flemming Holtorf, Frank Schäfer, Julian Arnold, Christopher Rackauckas

The limits of quantum feedback control have immediate consequences for quantum information science at large, yet remain largely unexplored. Here, we combine quantum filtering theory and moment-sum-of-squares techniques to construct a hierarchy of convex optimization problems that furnish monotonically improving, computable bounds on the best attainable perfo

Abhishek Sharma, Sonali Parbhoo, Omer Gottesman, Finale Doshi-Velez

Model-based reinforcement learning (MBRL) provides a way to learn a transition model of the environment, which can then be used to plan personalized policies for different patient cohorts and to understand the dynamics involved in the decision-making process. However, standard MBRL algorithms are either sensitive to changes in the reward function or achieve

Maurizio Grasselli, Andrea Poiatti

We consider a phase-field model which describes the interactions between the blood flow and the thrombus. The latter is supposed to be a viscoelastic material. The potential describing the cohesive energy of the mixture is assumed to be of Flory-Huggins type (i.e. logarithmic). This ensures the boundedness from below of the dissipation energy. In the two dim

Maurizio Grasselli, Andrea Poiatti

We consider a multi-component version of the conserved Allen-Cahn equation proposed by J. Rubinstein and P. Sternberg in 1992 as an alternative model for phase separation. In our case, the free energy is characterized by a mixing entropy density which belongs to a large class of physically relevant entropies like, e.g., the Boltzmann-Gibbs entropy. We establ

Andre N. Souza

We formulate a data-driven method for constructing finite volume discretizations of a dynamical system's underlying Continuity / Fokker-Planck equation. A method is employed that allows for flexibility in partitioning state space, generalizes to function spaces, applies to arbitrarily long sequences of time-series data, is robust to noise, and quantifies unc

Herman Rull, Markus Fischer, Stefan Kuhn

Prediction of chemical shift in NMR using machine learning methods is typically done with the maximum amount of data available to achieve the best results. In some cases, such large amounts of data are not available, e.g. for heteronuclei. We demonstrate a novel machine learning model which is able to achieve good results with comparatively low amounts of da

David Khatami, Daniel Kasen

The interaction of supernova ejecta with a surrounding circumstellar medium (CSM) generates a strong shock which can convert the ejecta kinetic energy into observable radiation. Given the diversity of potential CSM structures (arising from diverse mass loss processes such as late-stage stellar outbursts, binary interaction, and winds), the resulting transien

Xiang Ma, Haijian Sun, Rose Qingyang Hu, Yi Qian

This paper presents an approximate wireless communication scheme for federated learning (FL) model aggregation in the uplink transmission. We consider a realistic channel that reveals bit errors during FL model exchange in wireless networks. Our study demonstrates that random bit errors during model transmission can significantly affect FL performance. To ov

Hans-Christian Herbig

I present a universal method, called pivotal condensation, for calculating stoichiometric factors of chemical reactions. It can be done by hand, even for rather complicated reactions. The main trick, which I call kernel pivotal condensation (ker pc), to calculate the kernel of a matrix might be of independent interest. The discussion is elaborated for matric

Sahil Hegde, Steven R. Furlanetto

The formation of the first stars marks a watershed moment in the history of our universe. As the first luminous structures, these stars (also known as Population III, or Pop III stars) seed the first galaxies and begin the process of reionization. We construct an analytic model to self-consistently trace the formation of Pop III stars inside minihalos in the

Stefano Bolognesi, Kenichi Konishi, Andrea Luzio

We study the dynamics of $SU(N)$ chiral gauge theories with massless fermions belonging to various combinations of the symmetric, antisymmetric or fundamental representations. We limit ourselves to the gauge-anomaly-free and asymptotically free systems. 't Hooft anomaly-matching conditions severely limit the possible RG flows. In vectorlike theories such as

Arpan De, Hashem Mohammad, Yiren Wang, Rajkumar Kubendran

Deoxyribonucleic acid (DNA) has emerged as a promising building block for next-generation ultra-high density storage devices. Although DNA has high durability and extremely high density in nature, its potential as the basis of storage devices is currently hindered by limitations such as expensive and complex fabrication processes and time-consuming read-writ

M. J. Taranchuk, L. J. Cummings, T. A. Driscoll, R. J. Braun

Motivated by problems arising in tear film dynamics, we present a model for the extensional flow of thin sheets of nematic liquid crystal. The rod-like molecules of these substances impart an elastic contribution to its response. We rescale a weakly elastic model due to Cummings et al. [European Journal of Applied Mathematics 25 (2014): 397-423] to describe

Boris Volkov, Alexander Pechen

Quantum control is necessary for a variety of modern quantum technologies as it allows to optimally manipulate quantum systems. An important problem in quantum control is to establish whether the control objective functional has trapping behaviour or no, namely if it has or no traps -- controls from which it is difficult to escape by local search optimizatio

Lauri Hella, Kerkko Luosto, Jouko Väänänen

We introduce three measures of complexity for families of sets. Each of the three measures, that we call dimensions, is defined in terms of the minimal number of convex subfamilies that are needed for covering the given family: for upper dimension, the subfamilies are required to contain a unique maximal set, for dual upper dimension a unique minimal set, an

Joseph Compton, Shrawan Kumar

We prove sign-alternation of the product structure constants in the basis dual to the basis consisting of the structure sheaves of Schubert varieties in the torus-equivariant Grothendieck group of coherent sheaves on the partial flag varieties $G/P$ associated to an arbitrary symmetrizable Kac-Moody group $G$, where $P$ is any parabolic subgroup of finite ty

Nisarg Ujjainkar, Jingwen Leng, Yuhao Zhu

Image processing algorithms are prime targets for hardware acceleration as they are commonly used in resource- and power-limited applications. Today's image processing accelerator designs make rigid assumptions about the algorithm structures and/or on-chip memory resources. As a result, they either have narrow applicability or result in inefficient designs.

Nicholas Botzer, Tim Weninger

Vast amounts of human communication occurs online. These digital traces of natural human communication along with recent advances in natural language processing technology provide for computational analysis of these discussions. In the study of social networks the typical perspective is to view users as nodes and concepts as flowing through and among the use

Iztok Banič, Goran Erceg, Judy Kennedy, Chris Mouron

Many continua that admit a transitive homeomorphism may be found in the literature. The circle is probably the simplest non-degenerate continuum that admits such a homeomorphism. On the other hand, most of the known examples of such continua have a complicated topological structure. For example, they are {indecomposable} (such as the pseudo-arc or the Knaste

Jean-Christophe Pain

In this work, we propose a double-series representation of the difference between two dilogarithms with specific arguments. The summation is derived combining a formula we recently found for the so-called Grotendieck-Krivine constant, and an identity obtained by Lima for the difference of two dilogarithms with arguments $\sqrt{2}-1$ and $1-\sqrt{2}$ respecti

Kristian van Kuijk, Mark Dirksen, Christof Seiler

UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put physical fitness and endurance of riders to the test. The coaches of Team Jumbo-Visma have long been responsible for predicting the energy needs of each rider of the Dutch team for every race on the calendar. Those must be estimated to ensure riders have the energy a

Fateme Abedi, Dave Witte Morris, Javanshir Rezaee, M. Reza Salarian

We give a computer-assisted proof that if $G$ is a finite group of order $8pq$, where $p$ and $q$ are distinct primes, then every connected Cayley graph on $G$ has a hamiltonian cycle.

Kailai Yang, Shaoxiong Ji, Tianlin Zhang, Qianqian Xie

The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis. However, existing relevant studies bear several limitations, including inadequate evaluations, lack of prompting strategies, and ignorance of exploring LLMs for explainability. To bridge these gaps, we comprehensively evaluate the mental

Pavan K Mangipudi, Janise McNair

This paper presents a survey of the state of the art in research related to handovers employing software defined networking (SDN) enabled architectures, serving multiple coexisting radio access technologies. As the industrial roll-out of cellular services continues to evolve, it brings with it the coexistence of various IP based networks such as 5G NR, LTE,

Caleb Ji

Grothendieck proposed a theory of regular polyhedra over finite fields in Section 4 of \textit{Esquisse d'un Programme}. He isolates certain key parameters from the automorphism groups of regular polyhedra, which can be extended to any genus and specialized to various rings. In this note we give an interpretation of his sketched theory which explains some of

Ziwei Fan, Ke Xu, Zhang Dong, Hao Peng

Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce inter

Walid Al Misba, Harindra S. Mavikumbure, Md Mahadi Rajib, Daniel L. Marino

In this study, we have shown autonomous long-term prediction with a spintronic physical reservoir. Due to the short-term memory property of the magnetization dynamics, non-linearity arises in the reservoir states which could be used for long-term prediction tasks using simple linear regression for online training. During the prediction stage, the output is d

Frits Veerman, Isabelle Schneider

The aim of this paper is to investigate the use of Pyragas control on the stability of stationary, localised coherent structures in a general class of two-component, singularly perturbed, reaction-diffusion systems. We use noninvasive Pyragas-like proportional feedback control to stabilise a singular pulse solution to a two-component, singularly perturbed re

Ishak Hajjej, Caroline Hillairet, Mohamed Mnif

This article studies the problem of evaluating the information that a Principal lacks when establishing an incentive contract with an Agent whose effort is not observable. The Principal ("she") pays a continuous rent to the Agent ("he"), while the latter gives a best response characterized by his effort, until a terminal date decided by the Principal when sh

David Fernández-Duque, Konstantinos Papafilippou

The topological $\mu$-calculus has gathered attention in recent years as a powerful framework for representation of spatial knowledge. In particular, spatial relations can be represented over finite structures in the guise of weakly transitive wK4 frames. In this paper we show that the topological $\mu$-calculus is equivalent to a simple fragment based on a

Dominik Dürrschnabel, Gerd Stumme

Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factor

Vinod Raman, Unique Subedi, Ambuj Tewari

Multilabel ranking is a central task in machine learning. However, the most fundamental question of learnability in a multilabel ranking setting with relevance-score feedback remains unanswered. In this work, we characterize the learnability of multilabel ranking problems in both batch and online settings for a large family of ranking losses. Along the way,

Guang Ping He

We give a general proof showing that once irreversible processes are involved, a class of projective measurements is impossible. Applying this no-go result to the Schroedinger's cat paradox implies that if something is claimed to be a real Schroedinger's cat, there will be no measurable difference between it and a trivial classical mixture of ordinary cats i

Pu, Yi, Sara Achour

Binary spatter code (BSC)-based hyperdimensional computing (HDC) is a highly error-resilient approximate computational paradigm suited for error-prone, emerging hardware platforms. In BSC HDC, the basic datatype is a hypervector, a typically large binary vector, where the size of the hypervector has a significant impact on the fidelity and resource usage of

M. R. Combi, T. Mäkinen, J. -L. Bertaux, E. Quémerais

In 2021 and 2022 the hydrogen comae of three long period comets, C/2020 S3 (Erasmus), C/2021 A1 (Leonard) and C/2021 O3 (PanSTARRS) were observed with the Solar Wind ANisotropies (SWAN) all-sky hydrogen Lyman-alpha camera on the SOlar and Heliosphere Observer (SOHO) satellite. SWAN obtains nearly daily full-sky images of the hydrogen Lyman-alpha distribution

Paul Bressler, Juan Diego Rojas

DQ-algebroids locally defined on a symplectic manifold form a 2-gerbe. By adapting the method of P. Deligne to the setting of DQ-algebroids we show that this 2-gerbe admits a canonical global section, namely that every symplectic manifold admits a canonical DQ-algebroid quantizing the structure sheaf. The construction relies on methods of non-abelian cohomol

Saikat Nandy, Scott H. Holan, Michael Schweinberger

Many models for spatial and spatio-temporal data assume that "near things are more related than distant things," which is known as the first law of geography. While geography may be important, it may not be all-important, for at least two reasons. First, technology helps bridge distance, so that regions separated by large distances may be more similar than w

Xin Xing, Lin Lin

Coupled cluster theory is one of the most popular post-Hartree-Fock methods for ab initio molecular quantum chemistry. The finite-size error of the correlation energy in periodic coupled cluster calculations for three-dimensional insulating systems has been observed to satisfy the inverse volume scaling, even in the absence of any correction schemes. This is

Shilpa Sarkar, Kuldeep Singh, Indranil Chattopadhyay, Philippe Laurent

We investigate two-temperature accretion flows onto strongly magnetized compact stars. Matter is accreted in the form of an accretion disc upto the disc radius ($r_{\rm d}$), where, the magnetic pressure exceeds both the gas and ram pressure and thereafter the matter is channelled along the field lines onto the poles. We solve the equations of motion self-co

G. Scandariato, F. Borsa, A. S. Bonomo, B. S. Gaudi

Hot giant planets like MASCARA-1 b are expected to have thermally inverted atmospheres, that makes them perfect laboratory for the atmospheric characterization through high-resolution spectroscopy. Nonetheless, previous attempts of detecting the atmosphere of MASCARA-1 b in transmission have led to negative results. In this paper we aim at the detection of t

Bijaya Acharya, Laura Elisa Marcucci, Lucas Platter

We calculate the $S$-factor for proton-proton fusion using chiral effective field theory interactions and currents. By performing order-by-order calculations with a variety of chiral interactions that are regularized and calibrated in different ways, we assess the uncertainty in the $S$-factor from the truncation of the effective field theory expansion and f

Kartik Krishna, Steven L. Brunton, Zhuoyuan Song

Finite-time Lyapunov exponents (FTLEs) provide a powerful approach to compute time-varying analogs of invariant manifolds in unsteady fluid flow fields. These manifolds are useful to visualize the transport mechanisms of passive tracers advecting with the flow. However, many vehicles and mobile sensors are not passive, but are instead actuated according to s

Aman Rangapur, Haoran Wang

Large language models have gained considerable interest for their impressive performance on various tasks. Among these models, ChatGPT developed by OpenAI has become extremely popular among early adopters who even regard it as a disruptive technology in many fields like customer service, education, healthcare, and finance. It is essential to comprehend the o

Paolo Celli, Antonio Palermo

In this work, we discuss the realization of mechanical devices with non-reciprocal attributes enabled by inertia-amplifying, time-modulated mechanisms. Our fundamental building-block features a mass, connected to a fixed ground through a spring and to a moving base through a mechanism-based inerter. Through analytical derivations and numerical simulations, w

Amit Kumar Singh Yadav, Kratika Bhagtani, Ziyue Xiang, Paolo Bestagini

Tools to generate high quality synthetic speech signal that is perceptually indistinguishable from speech recorded from human speakers are easily available. Several approaches have been proposed for detecting synthetic speech. Many of these approaches use deep learning methods as a black box without providing reasoning for the decisions they make. This limit

Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu

Image inpainting refers to the task of generating a complete, natural image based on a partially revealed reference image. Recently, many research interests have been focused on addressing this problem using fixed diffusion models. These approaches typically directly replace the revealed region of the intermediate or final generated images with that of the r

Michael Muehlebach

We consider adaptive decision-making problems where an agent optimizes a cumulative performance objective by repeatedly choosing among a finite set of options. Compared to the classical prediction-with-expert-advice set-up, we consider situations where losses are constrained and derive algorithms that exploit the additional structure in optimal and computati

Tianrui Ma, Yu Feng, Xuan Zhang, Yuhao Zhu

CMOS Image Sensors (CIS) are fundamental to emerging visual computing applications. While conventional CIS are purely imaging devices for capturing images, increasingly CIS integrate processing capabilities such as Deep Neural Network (DNN). Computational CIS expand the architecture design space, but to date no comprehensive energy model exists. This paper p

Shuyang Bai, He Tang

We consider a class of stationary processes exhibiting both long-range dependence and heavy tails. Separate limit theorems for sums and for extremes have been established recently in literature with novel objects appearing in the limits. In this article, we establish the joint sum-max limit theorems for this class of processes. In the finite-variance case, t

Robert Kaufman, David Kirsh

Why do explainable AI (XAI) explanations in radiology, despite their promise of transparency, still fail to gain human trust? Current XAI approaches provide justification for predictions, however, these do not meet practitioners' needs. These XAI explanations lack intuitive coverage of the evidentiary basis for a given classification, posing a significant ba

Biswajit Pabi, Jakub Šebesta, Richard Korytár, Oren Tal

In contrast to silicon-based transistors, single molecule junctions can be gated by simple mechanical means. Specifically, charge can be transferred between the junction's electrodes and its molecular bridge when the interelectrode distance is modified, leading to variations in the electronic transport properties of the junction. While this effect has been s

Chithra H. Sharma, Marta Prada, Jan-Hendrik Schmidt, Isabel Gonz'alez D'iaz-Palacio

The physics of moir'e superlattices and the resulting formation of mini-bands in van der Waals materials have opened up an exciting new field in condensed matter physics. These systems exhibit a rich phase diagram of novel physical phenomena and exotic correlated phases that emerge in the low-dispersing bands. Transition metal dichalcogenides, in particular,

Chuanqi Xu, Ferhat Erata, Jakub Szefer

With the rapidly growing interest in quantum computing also grows the importance of securing these quantum computers from various physical attacks. Constantly increasing qubit counts and improvements to the fidelity of the quantum computers hold great promise for the ability of these computers to run novel algorithms with highly sensitive intellectual proper

Rodrigo A. González, Angel L. Cedeño, María Coronel, Juan C. Agüero

This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurements of a continuous-time signal whenever it crosses fixed and regularly partitioned thresholds. The knowledge of the intersample behavior of the

Mattia Bruno, Daniel Hoying, Taku Izubuchi, Christoph Lehner

The two pion channel in Lattice QCD has long been a primary testing ground for studying multiparticle scattering in finite volume QCD. With the development of sophisticated techniques such as distillation, it is possible to carefully study two-pion scattering in order to constrain associated low-energy constants. In this work, correlation functions with mult

Rodrigo A. González, Koen Tiels, Tom Oomen

Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control. Some of these applications use measurement devices that sample equidistantly in the amplitude domain. The aim of this paper is to develop a non-parametric estimator of the impulse respon

Andrea Bulgarelli, Marco Panero

We study the entanglement entropy in lattice field theory using a simulation algorithm based on Jarzynski's theorem. We focus on the entropic c-function for the Ising model in two and in three dimensions: after validating our algorithm against known analytical results from conformal field theory in two dimensions, we present novel results for the three-dimen

Niel de Beaudrap, Richard D. P. East

The ZX calculus and ZH calculus use diagrams to denote and compute properties of quantum operations, using `rewrite rules' to transform between diagrams which denote the same operator through a functorial semantic map. Different semantic maps give rise to different rewrite systems, which may prove more convenient for different purposes. Using discrete measur

Leo Radzihovsky, Anatoly Kuklov, Nikolay Prokof'ev, Boris Svistunov

Recently, it has been argued by Kuklov et al., that unusual features associated with the superflow-through-solid effect observed in solid He4 can be explained by unique properties of dilute distribution of superfluid edge dislocations. We demonstrate that stability of supercurrents controlled by quantum phase slips (instantons), and other exotic infrared pro

Minwoo Suh

We construct supersymmetric $AdS_3\times{\Sigma}$ solutions with baryonic charge in the Betti-vector truncation of five-dimensional gauged $\mathcal{N}=4$ supergravity where ${\Sigma}$ is a spindle. The truncation is obtained from type IIB supergravity on $AdS_5\times{T}^{1,1}$. The solutions realize supersymmetry by the anti-twist. The dual field theories a

Syed Talal Wasim, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan

Adopting contrastive image-text pretrained models like CLIP towards video classification has gained attention due to its cost-effectiveness and competitive performance. However, recent works in this area face a trade-off. Finetuning the pretrained model to achieve strong supervised performance results in low zero-shot generalization. Similarly, freezing the

Sora Shiratani, Kazumasa A. Takeuchi, Daiki Nishiguchi

We report a novel route to active turbulence, observed in numerical simulations of a polar active fluid model under confinement. To deal with large-scale computations with arbitrary geometries, we developed a GPU-based scheme that can be used for any boundary shape in a unified manner. For the circular confinement, as the radius was increased, we found a ser

Tyler Corbett, Jay Desai, O. J. P. Eboli, M. C. Gonzalez-Garcia

We perform a complete study of the electroweak precision observables and electroweak gauge boson pair production in terms of the SMEFT up to ${\cal O}(1/\Lambda^4)$ under the assumption of universal, C and P conserving new physics. We show that the analysis of data from those two sectors allows us to obtain closed constraints in the relevant parameter space

Paul Caucal, Farid Salazar, Björn Schenke, Tomasz Stebel

In JHEP 11 (2022) 169, we performed the first complete computation of the back-to-back inclusive di-jet cross-section in Deeply Inelastic Scattering (DIS) at small $x_{\rm Bj}$ to next-to-leading order (NLO) in the Color Glass Condensate effective field theory (CGC EFT). We demonstrate here that for di-jets with relative transverse momentum $P_\perp$ and tra

Sayan Banerjee, Mathias S. Scheurer

The superconducting diode effect refers to an asymmetry in the critical supercurrent $J_c(\hat{n})$ along opposite directions, $J_c(\hat{n})\neq J_c(-\hat{n})$. While the basic symmetry requirements for this effect are known, it is, for junction-free systems, difficult to capture within current theoretical models the large current asymmetries $J_c(\hat{n})/J

Renaud Boussarie, Matthias Burkardt, Martha Constantinou, William Detmold

This handbook provides a comprehensive review of transverse-momentum-dependent parton distribution functions and fragmentation functions, commonly referred to as transverse momentum distributions (TMDs). TMDs describe the distribution of partons inside the proton and other hadrons with respect to both their longitudinal and transverse momenta. They provide u

Yoonseok Hwang, Penghao Zhu, Taylor L. Hughes

Spin-momentum locking plays a fundamental role in spintronics and, more broadly, is an important concept in condensed matter physics. In 2D and 3D, spin-momentum locking typically does not allow spin-conservation because the spin-1/2 operators of electrons anticommute. Instead, here we study spin-momentum locking terms with conserved, commuting pseudospins b

Bobby Samir Acharya, Michele Del Zotto, Jonathan J. Heckman, Max Hubner

One of the general strategies for realizing a wide class of interacting QFTs is via junctions and intersections of higher-dimensional bulk theories. In the context of string/M-theory, this includes many $D > 4$ superconformal field theories (SCFTs) coupled to an IR free bulk. Gauging the flavor symmetries of these theories and allowing position dependent gau

Jordan Flitter, Cyril Creque-Sarbinowski, Marc Kamionkowski, Liang Dai

Compensated isocurvature perturbations (CIPs) are perturbations to the primordial baryon density that are accompanied by dark-matter-density perturbations so that the total matter density is unperturbed. Such CIPs, which may arise in some multi-field inflationary models, can be long-lived and only weakly constrained by current cosmological measurements. Here

Govert Nijs, Bruno Scheihing-Hitschfeld, Xiaojun Yao

Previous studies have shown that a gauge-invariant correlation function of two chromoelectric fields connected by a straight timelike adjoint Wilson line encodes crucial information about quark-gluon plasma (QGP) that determines the dynamics of small-sized quarkonium in the medium. Motivated by the successes of holographic calculations to describe strongly c

Damian R Sowinski, Jonathan Carroll-Nellenback, Robert N Markwick, Jordi Piñero

We explore the application of a new theory of Semantic Information to the well-motivated problem of a resource foraging agent. Semantic information is defined as the subset of correlations, measured via the transfer entropy, between agent $A$ and environment $E$ that is necessary for the agent to maintain its viability $V$. Viability, in turn, is endogenousl

Hadi Alzayer, Abdullah Abuolaim, Leung Chun Chan, Yang Yang

Smartphone cameras today are increasingly approaching the versatility and quality of professional cameras through a combination of hardware and software advancements. However, fixed aperture remains a key limitation, preventing users from controlling the depth of field (DoF) of captured images. At the same time, many smartphones now have multiple cameras wit

Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang

We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color map

Chen Wei, Karttikeya Mangalam, Po-Yao Huang, Yanghao Li

There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representations in light of recent interest in denoising diffusion models. While directly pre-training with diffusion models does not produce strong representations, we condition diffusion mod

Mingyu Ding, Yikang Shen, Lijie Fan, Zhenfang Chen

Humans possess a versatile mechanism for extracting structured representations of our visual world. When looking at an image, we can decompose the scene into entities and their parts as well as obtain the dependencies between them. To mimic such capability, we propose Visual Dependency Transformers (DependencyViT) that can induce visual dependencies without

Songbo Xie, Daniel Younis, Yuhan Mei, Joseph H. Eberly

Genuine multipartite entanglement is crucial for quantum information and related technologies but quantifying it has been a long-standing challenge. Most proposed measures do not meet the ``genuine'' requirement, making them unsuitable for many applications. In this work, we propose a journey toward addressing this issue by introducing an unexpected relation

Akshay Krishnan, Amit Raj, Xianling Zhang, Alexandra Carlson

Neural fields have recently enjoyed great success in representing and rendering 3D scenes. However, most state-of-the-art implicit representations model static or dynamic scenes as a whole, with minor variations. Existing work on learning disentangled world and object neural fields do not consider the problem of composing objects into different world neural

Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li

Artificial agents have traditionally been trained to maximize reward, which may incentivize power-seeking and deception, analogous to how next-token prediction in language models (LMs) may incentivize toxicity. So do agents naturally learn to be Machiavellian? And how do we measure these behaviors in general-purpose models such as GPT-4? Towards answering th

Laurenz Aisenpreis, Gustav Gyrst, Vedran Sekara

Ensuring transparency and integrity in political communication on climate change has arguably never been more important than today. Yet we know little about how politicians focus on, talk about, and portray climate change on social media. Here we study it from the perspective of political advertisement. We use Meta's Ad Library to collect 602,546 ads that ha

Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley

Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed. In this paper, we present the first attempt to use GPT-4 to generate instruction-following data for LLM finetuning. Ou

Jung Hoon Han, Ethan Lake, Sunghan Ro

We study diffusion in systems of classical particles whose dynamics conserves the total center of mass. This conservation law leads to several interesting consequences. In finite systems, it allows for equilibrium distributions that are exponentially localized near system boundaries. It also yields an unusual approach to equilibrium, which in $d$ dimensions

Youngjoon Jang, Kyeongha Rho, Jong-Bin Woo, Hyeongkeun Lee

The goal of this paper is to synthesise talking faces with controllable facial motions. To achieve this goal, we propose two key ideas. The first is to establish a canonical space where every face has the same motion patterns but different identities. The second is to navigate a multimodal motion space that only represents motion-related features while elimi

Jiawei Ren, Cunjun Yu, Siwei Chen, Xiao Ma

Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer from heavy reward engineering, high variance, and slow convergence with hard explorations. Specifically, they usually take tens of hours or even days of training to mimic a simple

Anffany Chen, Yifei Guan, Patrick M. Lenggenhager, Joseph Maciejko

Particles hopping on a two-dimensional hyperbolic lattice feature unconventional energy spectra and wave functions that provide a largely uncharted platform for topological phases of matter beyond the Euclidean paradigm. Using real-space topological markers as well as Chern numbers defined in the higher-dimensional momentum space of hyperbolic band theory, w