September 2022 arXiv papers — page 2

Jeremy A. Collins, Patrick Grady, Charles C. Kemp

Robotic manipulation can benefit from wrist-mounted force/torque (F/T) sensors, but conventional F/T sensors can be expensive, difficult to install, and damaged by high loads. We present Visual Force/Torque Sensing (VFTS), a method that visually estimates the 6-axis F/T measurement that would be reported by a conventional F/T sensor. In contrast to approache

Venkatraman Renganathan, Joshua Pilipovsky, Panagiotis Tsiotras

This article extends the optimal covariance steering (CS) problem for discrete time linear stochastic systems modeled using moment-based ambiguity sets. To hedge against the uncertainty in the state distributions while performing covariance steering, distributionally robust risk constraints are employed during the optimal allocation of the risk. Specifically

Valen E. Johnson, Sandipan Pramanik, Rachael Shudde

Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. Drawbacks of Bayes factors are their dependence on prior specifications that define null and alternative hypotheses and difficulties encountered in their computation. To address these problems, we define Bayes factor functions (BFF) directly from common te

Jan Christoph Schlegel, Mateusz Kwaśnicki, Akaki Mamageishvili

We study axiomatic foundations for different classes of constant-function automated market makers (CFMMs). We focus particularly on separability and on different invariance properties under scaling. Our main results are an axiomatic characterization of a natural generalization of constant product market makers (CPMMs), popular in decentralized finance, on th

Kim Klinger-Logan, Stephen D. Miller, Danylo Radchenko

We complete the program, initiated in a 2015 paper of Green, Miller, and Vanhove, of directly constructing the automorphic solution to the string theory $D^6 R^4$ differential equation $(\Delta-12)f=-E_{3/2}^2$ for $SL(2,\Z)$. The construction is via a type of Poincar\'e series, and requires explicitly evaluating a particular double integral. We also show ho

Daniel Peck, Gaspare Da Fies

The radial (penny-shaped) model of hydraulic fracture is considered. The tangential traction on the fracture walls is incorporated, including an updated evaluation of the energy release rate (fracture criterion), system asymptotics and the need to account for stagnant zone formation near the injection point. The impact of incorporating the shear stress on th

Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan

Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible sequences given the context, the model probabilities often do not accurately rank-order generated sequences by quality. This has been empir

Mavina Nikandrou, Lu Yu, Alessandro Suglia, Ioannis Konstas

Continual learning aims to train a model incrementally on a sequence of tasks without forgetting previous knowledge. Although continual learning has been widely studied in computer vision, its application to Vision+Language tasks is not that straightforward, as settings can be parameterized in multiple ways according to their input modalities. In this paper,

Ranwa Al Mallah, David Lopez

Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets by enabling the training of a model across nodes holding their data samples locally. To achieve decentralized federated learning, blockchain-based FL was proposed as a distributed FL architecture. In decentralized FL, the

CMS Collaboration

A search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state is presented. The analysis is based on proton-proton collision data recorded by the CMS detector in 2016-2018 at a centre-of-mass energy of 13 TeV at the CERN LHC, corresponding to an integrated luminosity of 138 fb$^{-1}$. The search is sensitive to r

Divakar Vashisth, Tapan Mukerji

Petrophysical inversion is an important aspect of reservoir modeling. However due to the lack of a unique and straightforward relationship between seismic traces and rock properties, predicting petrophysical properties directly from seismic data is a complex task. Many studies have attempted to identify the direct end-to-end link using supervised machine lea

Babatunji Omoniwa, Boris Galkin, Ivana Dusparic

Unmanned aerial vehicles (UAVs) are increasingly deployed to provide wireless connectivity to static and mobile ground users in situations of increased network demand or points of failure in existing terrestrial cellular infrastructure. However, UAVs are energy-constrained and experience the challenge of interference from nearby UAV cells sharing the same fr

Lev Klebanov

There is given a characterization of the geometric distribution by the independence of linear forms with random coefficients. The result is a discrete analog of the corresponding theorem on exponential distribution. The property of linear statistics independence is also a characterization of Poisson law. Keywords: geometric distribution; exponential distribu

Thinh Phan, Duc Le, Patel Brijesh, Donald Adjeroh

Electrocardiogram (ECG) signal is one of the most effective sources of information mainly employed for the diagnosis and prediction of cardiovascular diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly, single modality ECG (i.e. time series) cannot convey its complete characteristics, thus, exploiting both time and time-frequency modali

Bart Kieboom, Maria Bartzioka, Matin Jafarian

This paper studies the problem of output regulation for a class of nonlinear systems experiencing matched input disturbances. It is assumed that the disturbance signal is generated by an external autonomous dynamical system. First, we show that for a class of nonlinear systems admitting a finite-dimensional Koopman representation, the problem is equivalent t

James M Shook, Bing Wei

A graph is $k$-chordal if it does not have an induced cycle with length greater than $k$. We call a graph chordal if it is $3$-chordal. Let $G$ be a graph. The distance between the vertices $x$ and $y$, denoted by $d_{G}(x,y)$, is the length of a shortest path from $x$ to $y$ in $G$. The eccentricity of a vertex $x$ is defined as $\epsilon_{G}(x)= \max\{d_{G

Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

Differentially private (DP) optimization is the standard paradigm to learn large neural networks that are accurate and privacy-preserving. The computational cost for DP deep learning, however, is notoriously heavy due to the per-sample gradient clipping. Existing DP implementations are 2-1000X more costly in time and space complexity than the standard (non-p

Koresh Khateri, Karthik Soma, Mahdi Pourgholi, Mohsen Montazeri

The topology of a robotic swarm affects the convergence speed of consensus and the mobility of the robots. In this paper, we prove the existence of a complete set of local topology manipulation operations that allow the transformation of a swarm topology. The set is complete in the sense that any other possible set of manipulation operations can be performed

Leandro von Werra, Lewis Tunstall, Abhishek Thakur, Alexandra Sasha Luccioni

Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and datasets in ML. Evaluate is a library to support best practices for measurements, metrics, and comparisons of

Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis

We study the problem of differentially private (DP) fine-tuning of large pre-trained models -- a recent privacy-preserving approach suitable for solving downstream tasks with sensitive data. Existing work has demonstrated that high accuracy is possible under strong privacy constraint, yet requires significant computational overhead or modifications to the ne

Kevin Xia, Yushu Pan, Elias Bareinboim

Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the determination of blame and responsibility, credit assignment, and regret. In this paper, we study the evaluation of counterf

Goetz Graefe, Thanh Do

Recent work shows how offset-value coding speeds up database query execution, not only sorting but also duplicate removal and grouping (aggregation) in sorted streams, order-preserving exchange (shuffle), merge join, and more. It already saves thousands of CPUs in Google's Napa and F1 Query systems, e.g., in grouping algorithms and in log-structured merge-fo

Pieter Belmans, Chiara Damiolini, Hans Franzen, Victoria Hoskins

We give a moduli-theoretic treatment of the existence and properties of moduli spaces of semistable quiver representations, avoiding methods from geometric invariant theory. Using the existence criteria of Alper--Halpern-Leistner--Heinloth, we show that for many stability functions, the stack of semistable representations admits an adequate moduli space, and

Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu

Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit at least one of two limitations. First, time is assumed to be discretized, so if the time data is continuous, the user must determine the discretization and discard precise time i

Lilia Anguelova, Calin Iuliu Lazaroiu

We derive consistency conditions for sustained slow roll and rapid turn inflation in two-field cosmological models with oriented scalar field space, which imply that inflationary models with field-space trajectories of this type are non-generic. In particular, we show that third order adiabatic slow roll, together with large and slowly varying turn rate, req

Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani

Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual re

Harlan Campbell, Paul Gustafson

In many common situations, a Bayesian credible interval will be, given the same data, very similar to a frequentist confidence interval, and researchers will interpret these intervals in a similar fashion. However, no predictable similarity exists when credible intervals are based on model-averaged posteriors whenever one of the two nested models under consi

Clara Giménez-Arteaga, Gabriel B. Brammer, Danilo Marchesini, Luis Colina

We present a sample of 24 local star-forming galaxies observed with broad- and narrow-band photometry from the Hubble Space Telescope, that are part of the GOALS survey of local luminous and ultra-luminous infrared galaxies. With narrow-band filters around the emission lines H$\alpha$ (and [NII]) and Pa$\beta$, we obtain robust estimates of the dust attenuat

Rodrigo Bañuelos, Mateusz Kwaśnicki

The long-standing conjecture that for $p \in (1, \infty)$ the $\ell^p(\mathbb Z)$ norm of the Riesz--Titchmarsh discrete Hilbert transform is the same as the $L^p(\mathbb R)$ norm of the classical Hilbert transform, is verified when $p = 2 n$ or $\frac{p}{p - 1} = 2 n$, for $n \in \mathbb N$. The proof, which is algebraic in nature, depends in a crucial way

Jacob King, William Ryan, Richard D. Wesel

We extend earlier work on the design of convolutional code-specific CRC codes to $Q$-ary alphabets, with an eye toward $Q$-ary orthogonal signaling. Starting with distance-spectrum optimal, zero-terminated, $Q$-ary convolutional codes, we design $Q$-ary CRC codes so that the CRC/convolutional concatenation is distance-spectrum optimal. The $Q$-ary code symbo

Siddhartha Banerjee, Sean R. Sinclair, Milind Tambe, Lily Xu

Most real-world deployments of bandit algorithms exist somewhere in between the offline and online set-up, where some historical data is available upfront and additional data is collected dynamically online. How best to incorporate historical data to "warm start" bandit algorithms is an open question: naively initializing reward estimates using all historica

Henning Bahl, Marcela Carena, Nina M. Coyle, Aurora Ireland

Two Higgs doublet models (2HDM) provide the low energy effective field theory (EFT) description in many well motivated extensions of the Standard Model. It is therefore relevant to study their properties, as well as the theoretical constraints on these models. In this article we concentrate on three relevant requirements for the validity of the 2HDM framewor

Yue Yu

On the Sb-layer of the Kagome superconductor CsV$_3$Sb$_5$, pair density wave states have been observed. When the high-temperature charge orderings are treated as static backgrounds, these PDW states exhibit the same wavevector in the effective 2D Brillouin zone. Interestingly, these PDW states break the same symmetry on the surface. Considering the presence

Daniel Fortunato

We introduce a fast direct solver for variable-coefficient elliptic partial differential equations on surfaces based on the hierarchical Poincar\'e-Steklov method. The method takes as input an unstructured, high-order quadrilateral mesh of a surface and discretizes surface differential operators on each element using a high-order spectral collocation scheme.

Arjun Dey, David F. Mross

We study entanglement spectra of gapped states on the surfaces of symmetry-protected topological phases. These surface states carry anomalies that do not allow them to be terminated by a trivial state. Their entanglement spectra are dominated by non-universal features, which reflect the underlying bulk. We introduce a modified type of entanglement spectra th

Sin Kyu Kang, Jinheung Kim, Soojin Lee, Jeonghyeon Song

The type-I two-Higgs-doublet model in the inverted Higgs scenario can retain the theoretical stability all the way up to the Planck scale. The Planck-cutoff scale, $\Lambda_{\rm cut}^{\rm Planck}$, directly impacts the mass spectra such that all the extra Higgs boson masses should be light below about 160 GeV. However, the observation of the light masses of

Anja Butter, Theo Heimel, Till Martini, Sascha Peitzsch

The matrix element method is widely considered the ultimate LHC inference tool for small event numbers. We show how a combination of two conditional generative neural networks encodes the QCD radiation and detector effects without any simplifying assumptions, while keeping the computation of likelihoods for individual events numerically efficient. We illustr

Alex May, Jonathan Sorce, Beni Yoshida

In the AdS/CFT correspondence, bulk causal structure has consequences for boundary entanglement. In quantum information science, causal structures can be replaced by distributed entanglement for the purposes of information processing. In this work, we deepen the understanding of both of these statements, and their relationship, with a number of new results.

Alexander J. Millar, Steven M. Anlage, Rustam Balafendiev, Pavel Belov

We summarise the recent progress of the Axion Longitudinal Plasma HAloscope (ALPHA) Consortium, a new experimental collaboration to build a plasma haloscope to search for axions and dark photons. The plasma haloscope is a novel method for the detection of the resonant conversion of light dark matter to photons. ALPHA will be sensitive to QCD axions over almo

Patrycja Łydżba, Marcin Mierzejewski, Marcos Rigol, Lev Vidmar

Thermalization (generalized thermalization) in nonintegrable (integrable) quantum systems requires two ingredients: equilibration and agreement with the predictions of the Gibbs (generalized Gibbs) ensemble. We prove that observables that exhibit eigenstate thermalization in single-particle sector equilibrate in many-body sectors of quantum-chaotic quadratic

Anson Hook, Junwu Huang

We explore a novel IR phase of electromagnetism and place constraints on it. The usual IR modification of electromagnetism, the Higgs phase, involves adding a photon mass for the gauge field $A_\mu$, which screens electric fields and confines magnetic fields. We explore the confined phase resulting from adding a mass term for the dual photon, which screens m

Nicole M. Khusid, Chiara M. F. Mingarelli, Priyamvada Natarajan, J. Andrew Casey-Clyde

Supermassive black hole binary systems (SMBHBs) should be the most powerful sources of gravitational waves (GWs) in the Universe. Once Pulsar Timing Arrays (PTAs) detect the stochastic GW background from their cosmic merger history, searching for individually resolvable binaries will take on new importance. Since these individual SMBHBs are expected to be ra

Valentin Benedetti, Horacio Casini, Pedro J. Martinez

We study generalized free fields (GFF) from the point of view of information measures. We first review conformal GFF, their holographic representation, and the ambiguities in the assignation of algebras to regions that arise in these theories. Then we study the mutual information (MI) in several geometric configurations. The MI displays unusual features at t

Sambuddha Sanyal, Alexander Wietek, John Sous

We study a model of a hole-doped collinear Ising antiferromagnet on the honeycomb lattice as a route toward the realization of subsystem symmetry. We find nearly exact conservation of dipole symmetry verified both numerically with exact diagonalization (ED) on finite clusters and analytically with perturbation theory. The emergent symmetry forbids the motion

Jun Tsujimura, Yasusada Nambu

Entanglement entropy (EE) is a quantitative measure of the effective degrees of freedom and the correlation between the sub-systems of a physical system. Using the replica trick, we can obtain the EE by evaluating the entanglement Renyi entropy (ERE). The ERE is a $q$-analogue of the EE and expressed by the $q$ replicated partition function. In the semi-clas

Ricardo Yarza, Rosa Wallace Everson, Enrico Ramirez-Ruiz

Common-envelope evolution is a stage in binary system evolution in which a giant star engulfs a companion. The standard energy formalism is an analytical framework to estimate the amount of energy transferred from the companion's shrinking orbit into the envelope of the star that engulfed it. We show analytically that this energy transfer is larger than pred

Ananthan Karunakaran, Dennis Zaritsky

We extend the Ultra-Diffuse Galaxy (UDG) abundance relation, $N_{UDG}-M_{200}$, to lower halo mass hosts $(M_{200}\sim10^{11.6-12.2}M_{\odot})$. We select UDG satellites from published catalogs of dwarf satellite galaxies around Milky Way analogs, namely the Exploration of Local Volume Satellites (ELVES) survey, Satellite Around Galactic Analogs (SAGA) surve

Weicheng Kuo, Yin Cui, Xiuye Gu, AJ Piergiovanni

We present F-VLM, a simple open-vocabulary object detection method built upon Frozen Vision and Language Models. F-VLM simplifies the current multi-stage training pipeline by eliminating the need for knowledge distillation or detection-tailored pretraining. Surprisingly, we observe that a frozen VLM: 1) retains the locality-sensitive features necessary for d

Emilio H. S. Sousa, A. Vidiella-Barranco, J. A. Roversi

We investigate the generation and transfer of entangled states between two coupled microtoroidal cavities considering two different types of couplings, namely i) via a bridge qubit and ii) via evanescent fields. The cavities support two counter-propagating whispering-gallery modes (WGMs) that may also interact with each other. We firstly show that it is poss

Zifan Shi, Yinghao Xu, Yujun Shen, Deli Zhao

3D-aware image synthesis aims at learning a generative model that can render photo-realistic 2D images while capturing decent underlying 3D shapes. A popular solution is to adopt the generative adversarial network (GAN) and replace the generator with a 3D renderer, where volume rendering with neural radiance field (NeRF) is commonly used. Despite the advance

Hang Zheng, Y-H. Xia

In this paper, persistence of solitary wave solutions of the regularized long wave equation with small perturbations are investigated by the geometric singular perturbation theory. Two different kinds of the perturbations are considered in this paper: one is the weak backward diffusion and dissipation, the other is the Marangoni effects. Indeed, the solitary

Wenjie Li, Shu-Tao Xia, Jiangke Fan, Teng Zhang

Traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its application to advertising systems. To this end, we advocate a new practical learning setting, Semi-VFL (Vertical Semi-Federated Learning), for real-wor

Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu

With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we propose a general framework that unifies model-based and model-free RL, and an Admissible Bellman Characterization (ABC) class that subsumes nearly all Markov Decision Process (MDP) m

José Luis González, Antonio Laface

In this expository note we discuss a class of graded algebras named Cox rings, which are naturally associated to algebraic varieties generalizing the homogeneous coordinate rings of projective spaces. Whenever the Cox ring is finitely generated, the variety admits a quotient presentation by a quasitorus, which resembles the quotient construction of the proje

Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li

Sketch-and-extrude is a common and intuitive modeling process in computer aided design. This paper studies the problem of learning the shape given in the form of point clouds by inverse sketch-and-extrude. We present ExtrudeNet, an unsupervised end-to-end network for discovering sketch and extrude from point clouds. Behind ExtrudeNet are two new technical co

Yannick Gullentops, Sonja Hohloch

On a 4-dimensional compact symplectic manifold, we study how suitable perturbations of a toric system to a family of completely integrable systems with $\mathbb{S}^1$-symmetry lead to various hyperbolic-regular singularities. We compute and visualise associated phenomena like flaps, swallowtails, and $k$-stacked tori for $k \in \{2, 3, 4\}$ and give an upper

Tsutomu Nakamura

We give a result of Auslander-Ringel-Tachikawa type for Gorenstein-projective modules over a complete Gorenstein order. In particular, we prove that a complete Gorenstein order is of finite Cohen-Macaulay representation type if and only if every indecomposable pure-injective object in the stable category of Gorenstein-projective modules is compact.

Enrique Guerrero, David A. Strubbe

Sliding of two-dimensional materials is critical for their application as solid lubricants for space, and also relevant for strain engineering and device fabrication. Dopants such as Ni surprisingly improve lubrication in MoS$_2$, despite formation of interlayer bonds by intercalated Ni, and the mechanism has remained unclear. While sliding on the atomistic

Alexandre Belsley

Absorption spectroscopy is a widely used technique that permits the detection and characterization of gas species at low concentrations. We propose a sensing strategy combining the advantages of frequency modulation spectroscopy with the reduced noise properties accessible by squeezing the probe state. A homodyne detection scheme allows the simultaneous meas

Tobias Barker

We show that if $v$ is a smooth suitable weak solution to the Navier-Stokes equations on $B(0,4)\times (0,T_*)$, that possesses a singular point $(x_0,T_*)\in B(0,4)\times \{T_*\}$, then for all $\delta>0$ sufficiently small one necessarily has $$\lim\sup_{t\uparrow T_*} \frac{\|v(\cdot,t)\|_{L^{3}(B(x_0,\delta))}}{\Big(\log\log\log\Big(\frac{1}{(T_*-t)^{\fr

Hsin-Yu Liu, Xiaohan Fu, Bharathan Balaji, Rajesh Gupta

Batch reinforcement learning (BRL) is an emerging research area in the RL community. It learns exclusively from static datasets (i.e. replay buffers) without interaction with the environment. In the offline settings, existing replay experiences are used as prior knowledge for BRL models to find the optimal policy. Thus, generating replay buffers is crucial f

Ouail Kitouni, Niklas Nolte, Mike Williams

A novel neural architecture was recently developed that enforces an exact upper bound on the Lipschitz constant of the model by constraining the norm of its weights in a minimal way, resulting in higher expressiveness compared to other techniques. We present a new and interesting direction for this architecture: estimation of the Wasserstein metric (Earth Mo

Thiago Nunes, Daniel Schwabe

Information exploration tasks are inherently complex, ill-structured, and involve sequences of actions usually spread over many sessions. When exploring a dataset, users tend to experiment higher degrees of uncertainty, mostly raised by knowledge gaps concerning the information sources, the task, and the efficiency of the chosen exploration actions, strategi

Frederike Lübeck, Charlotte Bunne, Gabriele Gut, Jacobo Sarabia del Castillo

Comparing unpaired samples of a distribution or population taken at different points in time is a fundamental task in many application domains where measuring populations is destructive and cannot be done repeatedly on the same sample, such as in single-cell biology. Optimal transport (OT) can solve this challenge by learning an optimal coupling of samples a

Dong Xiao, Zuoqiang Shi, Siyu Li, Bailin Deng

Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in the implicit space and propose a novel approach to orient point cloud normals by incorporating isovalue

Tim Murray-Browne

Against Interaction Design is a short manifesto that distils a position that's emerged through a decade of creating interactive art. I intend it here as a provocation and a speculation on an alternative future relationship between people and machines.

Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Tim Genewein

Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rather than an explicit modeling assumption. Bayes-optimal agents are risk-neutral, since they solely attune to the expected return, and ambiguity-neutral, since they act in new situat

G. E. Volovik

Diakonov theory of quantum gravity, in which tetrads emerge as the bilinear combinations of the fermionis fields,\cite{Diakonov2011} suggests that in general relativity the metric may have dimension 2, i.e. $[g_{\mu\nu}]=1/[L]^2$. Several other approaches to quantum gravity, including the model of superplastic vacuum and $BF$-theories of gravity support this

Timothy Greco, Daniel E. Koditschek

This technical report provides a more thorough treatment of the proofs and derivations contained in a recent conference paper submission. The description of the anchoring controller is reproduced here without abridgement, and additional appendices provide a clearer account of the implementation details.

Jayesh K. Gupta, Johannes Brandstetter

Partial differential equations (PDEs) are central to describing complex physical system simulations. Their expensive solution techniques have led to an increased interest in deep neural network based surrogates. However, the practical utility of training such surrogates is contingent on their ability to model complex multi-scale spatio-temporal phenomena. Va

Yanxi Li, Derek S. Young, Julien Gori, Olivier Rioul

Fitts' law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered "in the wild") typically display noticeable p

S Ashwin Hebbar, Rajesh K Mishra, Sravan Kumar Ankireddy, Ashok V Makkuva

In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of inte

Omid Amini, Lucas Gierczak

We develop a purely combinatorial theory of limit linear series on metric graphs. This will be based on the formalisms of hypercube rank functions and slope structures. We provide a full classification of combinatorial limit linear series of rank one, and discuss connections to other concepts in tropical algebra and combinatorial algebraic geometry.

Dmitry Shatilov

The efficiency of colliders for physics is largely determined by their luminosity, while most of the energy consumed by high-energy $e^+e^-$ colliders is proportional to the total beam current. Thus, the energy efficiency is mainly determined by the specific luminosity that needs to be maximized. One of the most effective ways to achieve this is by using the

Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou

The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a

Annegret Burtscher, Leonardo García-Heveling

No Hopf-Rinow Theorem is possible in Lorentzian Geometry. Nonetheless, we prove that a spacetime is globally hyperbolic if and only if it is metrically complete with respect to the null distance of a time function. Our approach is based on the observation that null distances behave particularly well for weak temporal functions in terms of regularity and caus

Alex Glyn-Davies, Connor Duffin, Ö. Deniz Akyildiz, Mark Girolami

Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from

Efe Onaran, Soledad Villar

The shuffled linear regression problem aims to recover linear relationships in datasets where the correspondence between input and output is unknown. This problem arises in a wide range of applications including survey data, in which one needs to decide whether the anonymity of the responses can be preserved while uncovering significant statistical connectio

Philippe Moreillon

We consider the free additive convolution semigroup $\lbrace \mu^{\boxplus t}:\,t\ge 1\rbrace$ and determine the local behavior of the density of $\mu^{\boxplus t}$ at the endpoints and at any singular point of its support. We then study the free additive convolution of two multi-cut probability measures and show that its density decays either as a square ro

Harshit Yadav

Let $U:\mathcal{C}\rightarrow\mathcal{D}$ be a strong monoidal functor between abelian monoidal categories admitting a right adjoint $R$, such that $R$ is exact, faithful and the adjunction $U\dashv R$ is coHopf. Building on the work of Balan, we show that $R$ is separable (resp., special) Frobenius monoidal if and only if $R(\mathbb{1}_{\mathcal{D}})$ is a

Maan Qraitem, Kate Saenko, Bryan A. Plummer

Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups $B$ (\eg Female) within class labels $Y$ (\eg Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant archi

Šárka Todorova-Nová

A brief overview of properties of the model of quantized fragmentation of a helical QCD string is followed by the discussion of colour reconnection models and of the ways to reproduce the evolution of the average transverse momentum in hadronic events, as function of charged particle multiplicity. Reference to recent experimental data is included.

Cael Warner, Loïc Markley, Kenneth J. Chau

The polarization density of a broadband electrodynamic lattice-Boltzmann method (ELBM) is generalized to represent frequency-dispersion of materials interacting with electromagnetic waves. The frequency-dependent refractive index and extinction coefficient are modeled using complex-conjugate pole-residue pairs in an auxiliary-differential-equation (ADE). Ele

Matyáš Boháček, Zhuo Cao, Marek Hrúz

Sign language recognition could significantly improve the user experience for d/Deaf people with the general consumer technology, such as IoT devices or videoconferencing. However, current sign language recognition architectures are usually computationally heavy and require robust GPU-equipped hardware to run in real-time. Some models aim for lower-end devic

Xin Xiong, Ivor Cribben

Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently s

Nafis Irtija, Jim Plusquellic, Eirini Eleni Tsiropoulou, Joshua Goldberg

Electronic control systems used for quantum computing have become increasingly complex as multiple qubit technologies employ larger numbers of qubits with higher fidelity targets. Whereas the control systems for different technologies share some similarities, parameters like pulse duration, throughput, real-time feedback, and latency requirements vary widely

Olga Trapeznikova

We calculate the Euler characteristic of associated vector bundles over the moduli spaces of stable parabolic bundles on smooth curves. Our method is based on a wall-crossing technique from Geometric Invariant Theory, certain iterated residue calculus and the tautological Hecke correspondence. Our work was motivated by the results of Teleman and Woodward on

Melvyn B. Nathanson

This is an exposition of Poincar\'e's 1883 paper, ``Sur les \'equations alg\'ebriques,'' which gives an important refinement of Descartes's rule of signs and was a precursor of P\' olya's Positivstellensatz.

James Davies

We prove that every finite colouring of the plane contains a monochromatic pair of points at an odd distance from each other.

Hung Nghiep Tran, Atsuhiro Takasu

Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs. Tensor-decomposition-based models, such as ComplEx, provide a good trade-off between efficiency and expressiveness, that is crucial because of the large size of real world knowledge graphs. The recent multi-partition embedding interaction (MEI) model subsume

Antti Koskela, Marlon Tobaben, Antti Honkela

Individual privacy accounting enables bounding differential privacy (DP) loss individually for each participant involved in the analysis. This can be informative as often the individual privacy losses are considerably smaller than those indicated by the DP bounds that are based on considering worst-case bounds at each data access. In order to account for the

Mahdi Morafah, Saeed Vahidian, Chen Chen, Mubarak Shah

Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises. To cope with the statistical heterogeneity, previous works incorporated a proximal term in local optimization or modified the model aggregation scheme at the server side or advocated clustere

Alex Damian, Eshaan Nichani, Jason D. Lee

Traditional analyses of gradient descent show that when the largest eigenvalue of the Hessian, also known as the sharpness $S(\theta)$, is bounded by $2/\eta$, training is "stable" and the training loss decreases monotonically. Recent works, however, have observed that this assumption does not hold when training modern neural networks with full batch or larg

Nour-Eddine Abouelkhir, Hanane EL Hadfi, Abdallah Slaoui, Rachid Ahl Laamara

In statistical estimation theory, it has been shown previously that the Wigner-Yanase skew information is bounded by the quantum Fisher information associated with the phase parameter. Besides, the quantum Cram\'er-Rao inequality is expressed in terms of skew information. Since these two fundamental quantities are based on the concept of quantum uncertainty,

Som Kanjilal, Vivek Pandey, Arun Kumar Pati

Efficient certification and quantification of high dimensional entanglement of composite systems are challenging both theoretically as well as experimentally. Here, we demonstrate that several entanglement detection methods can be implemented efficiently in a Mach-Zehnder Interferometric set-up. In particular, we demonstrate how to measure the linear entropy

M. S. Hernandez, M. R. Schreiber, S. G. Parsons, B. T. Gänsicke

The white dwarf binary pathways survey is dedicated to studying the origin and evolution of binaries containing a white dwarf and an intermediate-mass secondary star of the spectral type A, F, G, or K (WD+AFGK). Here we present CPD-65\,264, a new post common envelope binary with an orbital period of 1.37\,days that contains a massive white dwarf ($ 0.86\pm 0

Wen-Qing Guo, Yichao Li, Xiaoyuan Huang, Yin-Zhe Ma

The Galactic center $\gamma$-ray excess, detected by the Fermi-LAT, is a very attractive tentative signal from dark matter annihilation. Searching for associated synchrotron emissions can test the dark matter interpretation for this excess. We geared the Five-hundred-meter Aperture Spherical radio Telescope (FAST) towards Coma Berenices (a dwarf Spheroidal g

Skanda Koppula, Yazhe Li, Evan Shelhamer, Andrew Jaegle

Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training. Most prior work has done so by increasing pre-training computation by adding complex data augmentation, multiple views, or lengthy training schedules. In this work, we investigate a related, but orthogonal q

Umberto Michelucci, Francesca Venturini

The application of machine learning to physics problems is widely found in the scientific literature. Both regression and classification problems are addressed by a large array of techniques that involve learning algorithms. Unfortunately, the measurement errors of the data used to train machine learning models are almost always neglected. This leads to esti

Ekta A. Shah, Jeyhan S. Kartaltepe, Christina T. Magagnoli, Isabella G. Cox

Observations and simulations of interacting galaxies and mergers in the local universe have shown that interactions can significantly enhance the star formation rates (SFR) and fueling of Active Galactic Nuclei (AGN). However, at higher redshift, some simulations suggest that the level of star formation enhancement induced by interactions is lower due to the