May 2022 arXiv papers — page 141

Driss Bennis, Rachid El Maaouy, Juan Ramón García Rozas, Luis Oyonarte

A model structure on a category is a formal way of introducing a homotopy theory on that category, and if the model structure is abelian and hereditary, its homotopy category is known to be triangulated. So a good way to both build and model a triangulated category is to build a hereditary abelian model structure. Given a ring $R$ and a (non necessarily semi

Alessandro Lonardi, Diego Baptista, Caterina De Bacco

In classification tasks, it is crucial to meaningfully exploit the information contained in data. While much of the work in addressing these tasks is devoted to building complex algorithmic infrastructures to process inputs in a black-box fashion, less is known about how to exploit the various facets of the data, before inputting this into an algorithm. Here

David Yallup, Timo Janßen, Steffen Schumann, Will Handley

We present the first application of a Nested Sampling algorithm to explore the high-dimensional phase space of particle collision events. We describe the adaptation of the algorithm, designed to perform Bayesian inference computations, to the integration of partonic scattering cross sections and the generation of individual events distributed according to th

Iker de las Heras, Benjamin Klopsch, Andoni Zozaya

The degree of commutativity of a finite group is the probability that two uniformly and randomly chosen elements commute. This notion extends naturally to finitely generated groups $G$: the degree of commutativity $\text{dc}_S(G)$, with respect to a given finite generating set $S$, results from considering the fractions of commuting pairs of elements in incr

Karolina Stańczak, Edoardo Ponti, Lucas Torroba Hennigen, Ryan Cotterell

The success of multilingual pre-trained models is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision. However, it remains unclear how these models learn to generalise across languages. In this work, we conjecture that multilingual pre-trained models can derive language-universal abst

David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, Julia Kreutzer

Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages are not well represented on the web and therefore excluded from the large-scale crawls used to create datasets. Furthe

Temenuzhka Avramova, Riccardo Maderna, Alessandro Benetton, Christian Cardenio

Space missions are characterized by long distances, difficult or unavailable communication and high operating costs. Moreover, complexity has been constantly increasing in recent years. For this reason, improving the autonomy of space operators is an attractive goal to increase the mission reward with lower costs. This paper proposes an onboard scheduler, th

Belle Collaboration, L. K. Li, A. J. Schwartz, K. Kinoshita

We present measurements of the branching fractions for the singly Cabibbo-suppressed decays $D^+\to K^{+}K^{-}π^{+}π^{0}$ and $D_s^{+}\to K^{+}π^{-}π^{+}π^{0}$, and the doubly Cabibbo-suppressed decay $D^{+}\to K^{+}π^{-}π^{+}π^{0}$, based on 980 ${\rm fb}^{-1}$ of data recorded by the Belle experiment at the KEKB $e^{+}e^{-}$ collider. We measure these mode

Bijan Bagchi, Rahul Ghosh, Christiane Quesne

We investigate the (1+1)-dimensional position-dependent mass Dirac equation within the confines of so(2,1) potential algebra by utilizing the character of a spatial varying Fermi velocity. We examine the combined effects of the two when the Dirac equation is equipped with an external pseuodoscalar potential. Solutions of the three cases induced by so(2, 1) a

J. E. Rodríguez Camargo

In this work we develop geometric Sen theory for rigid analytic spaces, generalizing the previous work of Pan for curves. We also extend the axiomatic Sen-Tate formalism of Berger-Colmez to a certain class of locally analytic representations.

G. Bougas, S. I. Mistakidis, P. Giannakeas, P. Schmelcher

A scheme is proposed to dynamically excite distinct eigenstate superpositions in three-body Bose-Fermi mixtures confined in a two-dimensional harmonic trap. The system is initialized in a non-interacting state with a variable spatial extent, and the scattering lengths are subsequently quenched. For spatial widths smaller than the three-body harmonic oscillat

Detlef Lehmann

As a typical quantum many body problem, we consider the time evolution of density matrix elements in the Bose-Hubbard model. For an arbitrary initial state, these quantities can be obtained from an SDE or stochastic differential equation system. To this SDE system, a Girsanov transformation can be applied. This has the effect that all the information from th

Tommy McElvanney, Miriam Backens

In the one-way model of measurement-based quantum computation (MBQC), computation proceeds via measurements on some standard resource state. So-called flow conditions ensure that the overall computation is deterministic in a suitable sense, with Pauli flow being the most general of these. Existing work on rewriting MBQC patterns while preserving the existenc

Tom Hope, Doug Downey, Oren Etzioni, Daniel S. Weld

We stand at the foot of a significant inflection in the trajectory of scientific discovery. As society continues on its fast-paced digital transformation, so does humankind's collective scientific knowledge and discourse. We now read and write papers in digitized form, and a great deal of the formal and informal processes of science are captured digitall

Alexander Sidorenko

Let $H_k^r$ denote an $r$-uniform hypergraph with $k$ edges and $r+1$ vertices, where $k \leq r+1$ (it is easy to see that such a hypergraph is unique up to isomorphism). The known general bounds on its Tur\'{a}n density are $\pi(H_k^r) \leq \frac{k-2}{r}$ for all $k \geq 3$, and $\pi(H_3^r) \geq 2^{1-r}$ for $k=3$. We prove that $\pi(H_k^r) \geq (C_k - o(1)

Leo Torres

We completely characterize the conditions under which a complex unitary number is an eigenvalue of the non-backtracking matrix of an undirected graph. Further, we provide a closed formula to compute its geometric multiplicity and describe an algorithm to compute this multiplicity without making a single matrix computation. The algorithm has time complexity t

Xinyi Yu, Jianan Hu, Yuehai Fan, Wancai Zheng

Robot navigation in dynamic environments shared with humans is an important but challenging task, which suffers from performance deterioration as the crowd grows. In this paper, multi-subgoal robot navigation approach based on deep reinforcement learning is proposed, which can reason about more comprehensive relationships among all agents (robot and humans).

Willem Boon, Tim Veenstra, Marjolein Dijkstra, René van Roij

Using both analytic and numerical analyses of the Poisson-Nernst-Planck equations we theoretically investigate the electric conductivity of a conical channel, which in accordance with recent experiments exhibits a strong non-linear pressure dependence. This mechanosensitive diodic behavior stems from the pressure-sensitive build-up or depletion of salt in th

Wenchao Shi, Rui Peng, Tai-Xing Liu, Songlin Lyu

We calculate the deuteron charge, electric quadrupole, and magnetic form factors up to next-to-next-to-leading order in chiral effective field theory, treating subleading corrections, especially that of chiral nuclear forces, in perturbation theory. We examine the power counting based on naive dimensional analysis by investigating the ultraviolet cutoff vari

Zeki Can Seskir, Ramis Korkmaz, Arsev Umur Aydinoglu

The second quantum revolution has been producing groundbreaking scientific and technological outputs since the early 2000s; however, the scientific literature on the impact of this revolution on the industry, specifically on start-ups, is limited. In this paper, we present a landscaping study with a gathered dataset of 441 companies from 42 countries that we

Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós, Juan C. SanMiguel

Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge. In t

Antoni Kijowski, Qing Liu, Xiaodan Zhou

This paper is concerned with a PDE-based approach to the horizontally quasiconvex (h-quasiconvex for short) envelope of a given continuous function in the Heisenberg group. We provide a characterization for upper semicontinuous, h-quasiconvex functions in terms of the viscosity subsolution to a first-order nonlocal Hamilton-Jacobi equation. We also construct

Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Sebastiano Vascon

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack t

Yuichi Motoyama, Kazuyoshi Yoshimi, Takeo Kato, Synge Todo

In our current era, numerical simulations have become indispensable theoretical and experimental tools for use in daily research activities, particularly in the materials science fields. However, the installation processes for such simulations frequently become problematic because they depend strongly on the device environment, and troubleshooting those proc

Michael Thomas Smith, Magnus Ross, Joel Ssematimba, Pablo A. Alvarado

Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively the calibration can be transferred using low-cost, mobile sensors. However inferring the calibration (with uncertainty) becomes difficult. We prop

Amin Aghababaie Dastgerdi, Behrouz Mirza, Naresh Dadhich

We wish to carry forward to higher dimensions the insightful and novel method of obtaining the Kerr metric proposed by one of us [Gen. Relativ. Gravit. 45, 2383 (2013)] for deriving the Myers-Perry rotating black hole metric. We begin with a flat spacetime metric written in oblate spheroidal coordinates (ellipsoidal geometry) appropriate for the inclusion of

Devika Tharakkal, Andrew P. Snodin, Graeme R. Sarson, Anvar Shukurov

The spatial distribution of cosmic ray (CR) particles in the interstellar medium (ISM) is of major importance in radio astronomy, where its knowledge is essential for the interpretation of observations, and in theoretical astrophysics, where CR contribute to the structure and dynamics of the ISM. Local inhomogeneities in interstellar magnetic field strength

Weiming Feng, Heng Guo, Jiaheng Wang

We study the sampling problem for the ferromagnetic Ising model with consistent external fields, and in particular, Swendsen-Wang dynamics on this model. We introduce a new grand model unifying two closely related models: the subgraph world and the random cluster model. Through this new viewpoint, we show: (1) polynomial mixing time bounds for Swendsen-Wang

Takashi Tsuchimochi, Yoohee Ryo, Seiichiro L. Ten-no

Quantum imaginary time evolution (QITE) is a recently proposed quantum-classical hybrid algorithm that is guaranteed to reach the lowest state of system. In this study, we present several improvements on QITE, mainly focusing on molecular applications. We analyze the derivation of the underlying QITE equation order-by-order, and suggest a modification that i

Hamidreza Kasaei, Songsong Xiong

Service robots are integrating more and more into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them in an open-ended fashion. Furthermore, such robots must be able to recognize a wide range of object categories. In this paper, we present a lifelong e

Pieter van Goor, Robert Mahony

Visual-Inertial Odometry (VIO) is the problem of estimating a robot's trajectory by combining information from an inertial measurement unit (IMU) and a camera, and is of great interest to the robotics community. This paper develops a novel Lie group symmetry for the VIO problem and applies the recently proposed equivariant filter. The proposed symmetry is co

Francesco Chiariello, Fabrizio Maria Maggi, Fabio Patrizi

We propose Answer Set Programming (ASP) as an approach for modeling and solving problems from the area of Declarative Process Mining (DPM). We consider here three classical problems, namely, Log Generation, Conformance Checking, and Query Checking. These problems are addressed from both a control-flow and a data-aware perspective. The approach is based on th

Kay Jin Lim, Jialin Wang

This paper focuses on the rank varieties for modules over a group algebra $\mathbb{F}E$ where $E$ is an elementary abelian $p$-group and $p$ is the characteristic of an algebraically closed field $\mathbb{F}$. In the first part, we give a sufficient condition for a Green vertex of an indecomposable module containing an elementary abelian $p$-group $E$ in ter

Prithviraj Ammanabrolu, Liwei Jiang, Maarten Sap, Hannaneh Hajishirzi

We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives and interacts with a world through natural language. Such interactive agents are often trained via reinforcement learning to optimize task performance, even when such rewards may

Ruoyu Yin, Eli Barkai

Classical first-passage times under restart are used in a wide variety of models, yet the quantum version of the problem still misses key concepts. We study the quantum hitting time with restart using a monitored quantum walk. The restart strategy eliminates the problem of dark states, i.e. cases where the particle evades detection, while maintaining the bal

Yuki Tatsunami, Masato Taki

In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contra

Peter C. Nwilo., Chukwuma J. Okolie, Johanson C. Onyegbula, Ikenna D. Arungwa

Google Earth is the most popular virtual globe in use today. Given its popularity and usefulness, most users do not pay close attention to the positional accuracy of the imagery, and there is limited information on the subject. This study evaluates the horizontal accuracy of historical GE imagery at four epochs between year 2000 and 2018, and the vertical ac

Youhuan Yang, Lei Sun, Leyu Dai, Song Guo

Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e., not approaching the lower bound of robustness). To solve this problem, this paper uses the proposed decouple space method to divide the classifier into

Youhuan Yang, Lei Sun, Leyu Dai, Song Guo

Deep neural networks are widely used in various fields because of their powerful performance. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks, i.e., adding a slight perturbation to the input will make the model obtain wrong results. This is especially dangerous for some systems with high-security requirement

Bo Zou, Yu Zhou, Yan Zhou, Yanyan Wu

The emerging Au-assisted exfoliation technique provides a wealth of large-area and high-quality ultrathin two-dimensional (2D) materials compared with traditional tape-based exfoliation. Fast, damage-free, and reliable determination of the layer number of such 2D films is essential to study layer-dependent physics and promote device applications. Here, an op

Constantinos Psomas, Ghassan M. Kraidy, Kai-Kit Wong, Ioannis Krikidis

Reconfigurability is a desired characteristic of future communication networks. From a transceiver's standpoint, this can be materialized through the implementation of fluid antennas (FAs). An FA consists of a dielectric holder, in which a radiating liquid moves between pre-defined locations (called ports) that serve as the transceiver's antennas. Du

Weichao Liang, Nina H. Amini

This paper generalizes the results in [30] concerning feedback stabilization of target states for N-level quantum angular momentum systems undergoing quantum non-demolition measurements (QND) in absence of the knowledge about initial states and parameters. Here we consider multiple measurement operators and study the stabilization toward a chosen target subs

Léo Exibard, Emmanuel Filiot, Ayrat Khalimov

We study synthesis of reactive systems interacting with environments using an infinite data domain. A popular formalism for specifying and modelling such systems is register automata and transducers. They extend finite-state automata by adding registers to store data values and to compare the incoming data values against stored ones. Synthesis from nondeterm

Aldo Glielmo, Iuri Macocco, Diego Doimo, Matteo Carli

DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in toy cas

Yu Yang, Qing-Shan Jia, Zhanbo Xu, Xiaohong Guan

By enabling the nodes or agents to solve small-sized subproblems to achieve coordination, distributed algorithms are favored by many networked systems for efficient and scalable computation. While for convex problems, substantial distributed algorithms are available, the results for the more broad nonconvex counterparts are extremely lacking. This paper deve

Rong Qiang Wei

We suggest a closed solution for the roots of polynomial trinomial algebraic equation $$z^n+xz^{n-1}-1=0$$ with an appropriate $x$. This solution is a minor modification to the work of Mikhalkin (Mikhalkin E N, 2006. On solving general algebraic equations by integrals of elementary functions, Siberian Mathematical Jounral, 47(2), 301-306). This modification,

Christopher Sun, Jai Sharma, Milind Maiti

Computational methods to accelerate natural disaster response include change detection, map alignment, and vision-aided navigation. Current software functions optimally only on near-nadir images, though off-nadir images are often the first sources of information following a natural disaster. The use of off-nadir images for the aforementioned tasks requires t

Yu Yang, Yue Chen, Guoqiang Hu, Costas J. Spanos

Peer-to-peer (P2P) energy trading is a promising market scheme to accommodate the increasing distributed energy resources (DERs). However, how P2P to be integrated into the existing power systems remains to be investigated. In this paper, we apply network charge as a means for the grid operator to attribute transmission loss and ensure network constraints fo

Elisa Soave, Vincent Corre, Cornelis Ravensbergen, Jeong Ho Han

We report on high-resolution Feshbach spectroscopy on a degenerate, spin-polarized Fermi gas of $^{161}$Dy atoms, measuring three-body recombination losses at low magnetic field. For field strength up to 1\,G, we identify as much as 44 resonance features and observe plateaus of very low losses. For four selected typical resonances, we study the dependence of

Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan

This paper aims to theoretically analyze the complexity of feature transformations encoded in piecewise linear DNNs with ReLU layers. We propose metrics to measure three types of complexities of transformations based on the information theory. We further discover and prove the strong correlation between the complexity and the disentanglement of transformatio

Sergey Bravyi, Isaac Kim, Alexander Kliesch, Robert Koenig

We consider Kitaev's quantum double model based on a finite group $G$ and describe quantum circuits for (a) preparation of the ground state, (b) creation of anyon pairs separated by an arbitrary distance, and (c) non-destructive topological charge measurement. We show that for any solvable group $G$ all above tasks can be realized by constant-depth adapt

Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton, Xinyu Yang

Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists. These images contain complex information requiring time-consuming expert human interpretation that is prone to human bias. Supervised deep learning approaches have proven powerful for classification tasks, but they are inherently limit

Sami Ede, Serop Baghdadlian, Leander Weber, An Nguyen

The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, the traditional optimization algorithms often require large amounts of data available during training time and updates wrt. new data are difficult after the training process has been

Sophia Sun, Robin Walters, Jinxi Li, Rose Yu

Learning multi-agent dynamics is a core AI problem with broad applications in robotics and autonomous driving. While most existing works focus on deterministic prediction, producing probabilistic forecasts to quantify uncertainty and assess risks is critical for downstream decision-making tasks such as motion planning and collision avoidance. Multi-agent dyn

Johanna Barzen, Frank Leymann

The algorithm of Shor for prime factorization is a hybrid algorithm consisting of a quantum part and a classical part. The main focus of the classical part is a continued fraction analysis. The presentation of this is often short, pointing to text books on number theory. In this contribution, we present the relevant results and proofs from the theory of cont

Alessandro Bosisio, Francesca Soldan, Andrea Morotti, Gaetano Iannarelli

COVID-19 pandemic has been a disruptive event from health, social, and economic points of view. Besides that, changes in people's lifestyles, especially during the 2020 lockdowns, also affected energy networks. COVID-19 pandemic has resulted in a significant decline in electricity demand. The lockdown measures applied to handle the health crisis have cau

Dheeraj Baby, Yu-Xiang Wang

We consider the problem of universal dynamic regret minimization under exp-concave and smooth losses. We show that appropriately designed Strongly Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5} \vee d^2)$, where $n$ is the time horizon and $C_n$ a path variational based on second order differences of the comparator sequence.

Jun Yu, Hao Chang, Keda Lu, Liwen Zhang

Multi-modal aerial view object classification (MAVOC) in Automatic target recognition (ATR), although an important and challenging problem, has been under studied. This paper firstly finds that fine-grained data, class imbalance and various shooting conditions preclude the representational ability of general image classification. Moreover, the MAVOC dataset

Kinjal Banerjee, Aditya Sharma

We present the quantum field description of Galilean electrodynamics minimally coupled to massless Galilean fermion in (3 + 1) dimensions. At the classical level, the Lagrangian is obtained as a null reduction of a relativistic theory in one higher dimension. We use functional techniques to develop the quantum field description of the theory. Quantum correct

Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung

Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby s

Quentin Bramas, Jean-Romain Luttringer, Sébastien Tixeuil

We consider transportation networks that are modeled by dynamic graphs, and introduce the possibility for traveling agents to use Backward Time-Travel (BTT) devices at any node to go back in time (to some extent, and with some appropriate fee) before resuming their trip. We focus on dynamic line graphs. In more detail, we propose exact algorithms to compute

A. W. Thomas, X. G. Wang

We present an analysis of the experimental data for parity-violating electron scattering (PVES) and atomic parity-violation, including the effects of a dark photon. We derive the favored region of dark photon parameter space, which provides a good description of the experimental data from the Qweak Collaboration and the Jefferson Lab PVDIS Collaboration and

Jürgen Herzog, Vijaylaxmi Trivedi

Let $p>3$ be a prime number. We compute the rings of invariants of the elementary abelian $p$-group $(\mathbb Z/p\mathbb Z)^r$ for $3$-dimensional generic representations. Furthermore we show that these rings of invariants are complete intersections rings with embedding dimension $\lceil r/2\rceil +3$. This proves a conjecture of Campbell, Shank and Wehlau i

Chandni Saxena, Mudit Chaudhary, Helen Meng

Cross-lingual word embeddings can be applied to several natural language processing applications across multiple languages. Unlike prior works that use word embeddings based on the Euclidean space, this short paper presents a simple and effective cross-lingual Word2Vec model that adapts to the Poincaré ball model of hyperbolic space to learn unsupervised cro

Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine

The incredible feats of athleticism demonstrated by humans are made possible in part by a vast repertoire of general-purpose motor skills, acquired through years of practice and experience. These skills not only enable humans to perform complex tasks, but also provide powerful priors for guiding their behaviors when learning new tasks. This is in stark contr

Marios Papamichalopoulos, George Papadakis, George Mandilaras, Maria Despoina Siampou

Geospatial data constitutes a considerable part of (Semantic) Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with topological relations like those of the Dimensionally Extended 9-Intersection Model. Due to its quadratic time complexity, var

Ayush Tripathi, Arnab Kumar Mondal, Lalan Kumar, Prathosh A. P

The problem of Airwriting Recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in H

Runsheng Xu, Zhengzhong Tu, Yuanqi Du, Xiaoyu Dong

Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer from severe and commingled degradation such as cracks, defocus, and color-fading, which are difficult to treat individually and harder to repair when they interact. Deep learning p

Yuting Sun, Tong Chen, Hongzhi Yin

Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to prevent crimes. With the increasing availability of both fine-grained urban and public service data, there is a recent surge in fu

L. Q. Lai, Y. B. Yu, Erich J. Mueller

Motivated by recent experiments, we investigate particle emission from a Bose-Einstein condensate in a one-dimensional lattice, where the interaction strength is periodically modulated. The modulated interactions parametrically excite a collective mode, leading to density oscillations. These collective oscillations in turn drive particle emission. This multi

Sara Ayman Metwalli, Rodney Van Meter

As the scale of quantum programs grows to match that of classical software, the nascent field of quantum software engineering must mature and tools such as debuggers will become increasingly important. However, developing a quantum debugger is challenging due to the nature of a quantum computer; sneaking a peek at the value of a quantum state will cause eith

Jan Wilczek, Alec Wright, Vesa Välimäki, Emanuël Habets

Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ordinary differential equations (ODEs) governing the first-order and the second-order diode clipper. The proposed models achieve performance comparable

Denis Spiridonov, Sergei Stepanov, Vasil`ev Vasiliy

In this paper, we present an Online Generalized Multiscale Finite Element Method(Online GMsFEM) for heat and mass transfer problem in heterogeneous media with artificial ground freezing pipes. The mathematical model of the process is based on the classical Stefan model, which describes heat transfer with a phase transition and takes into account filtration i

Bernard Field, Agustin Schiffrin, Nikhil V. Medhekar

Two-dimensional (2D) metal-organic frameworks (MOFs) in a kagome lattice can exhibit strong electron-electron interactions, which can lead to tunable quantum phases including many exotic magnetic phases. While technological developments of 2D MOFs typically take advantage of substrates for growth, support, and electrical contacts, investigations often ignore

Xiaomeng Hu, Shi Yu, Chenyan Xiong, Zhenghao Liu

Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task

Si-wen Li, Sen-kai Luo, Hao-qian Li

According to the gauge-gravity duality, we systematically study the Schwinger effect and electric instability with anisotropy in a top-down holographic approach. The anisotropic black brane and bubble (soliton) background in IIB supergravity are employed and the dual theories in these backgrounds are expected to be anisotropic theory at finite temperature an

Yogesh Kumar, VPS Awana

We report, an experimental evidence of surface states (SS) driven magneto-transport in a Bi0.85Sb0.15 single crystal. Detailed high field (up to 12T) and low temperature (down to 2K) magneto-transport measurements are been carried out on the studied Bi0.85Sb0.15 single crystal. The phase, composition and Raman modes are studied through X-ray diffraction, Ene

Ravi Kumar, Shahin Boluki, Karl Isler, Jonas Rauch

We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Developing practical algorithms that can estimate price elasticities robustly, especially when information about no purchases (losses) is not available, to drive such automated pricing systems is a challenge faced by many industries. Based on the P

Saeed Ranjbar Alvar, Mateen Ulhaq, Hyomin Choi, Ivan V. Bajić

When it comes to image compression in digital cameras, denoising is traditionally performed prior to compression. However, there are applications where image noise may be necessary to demonstrate the trustworthiness of the image, such as court evidence and image forensics. This means that noise itself needs to be coded, in addition to the clean image itself.

Michael Novack, Xiaodong Yan

We consider the asymptotic behavior as $\varepsilon $ goes to zero of the 2D smectics model in the periodic setting given by \begin{equation*} \mathcal{E}_{\varepsilon }( w) =\frac{1}{2}\int_{\mathbb{T}^{2}}\frac{1}{ \varepsilon }\left( \left\vert \partial_{1}\right\vert ^{-1}\left( \partial_{2}w-\partial_{1}\frac{1}{2}w^{2}\right) \right) ^{2}+\varepsilon \

Nicholas R. Buttle, Ravindra Pethiyagoda, Timothy J. Moroney, Brian Winship

Recent research suggests that studying the time-frequency response of ship wave signals has potential to shed light on a range of applications, such as inferring the dynamical and geometric properties of a moving vessel based on the surface elevation data detected at a single point in space. We continue this line of research here with a study of mathematical

Max Kapur, Sung-Pil Hong

This paper considers the maximization of the expected maximum value of a portfolio of random variables subject to a budget constraint. We refer to this as the optimal college application problem. When each variable's cost, or each college's application fee, is identical, we show that the optimal portfolios are nested in the budget constraint, yieldin

Chia-Fu Liu, Ali Mostafavi

We present a latent characteristic in socio-spatial networks, hazard-exposure heterophily, to capture the extent to which populations with similar hazard exposure could assist each other through social ties. Heterophily is the tendency of unlike individuals to form social ties. Conversely, populations in spatial areas with significant hazard exposure similar

Colin Scheibner, Michel Fruchart, Vincenzo Vitelli

We provide a perspective on soft metamaterials and biomatter with a special emphasis on future research directions at the interface between material intelligence and adaptation.

Xuandong Zhao, Lei Li, Yu-Xiang Wang

Large language models are shown to memorize privacy information such as social security numbers in training data. Given the sheer scale of the training corpus, it is challenging to screen and filter these privacy data, either manually or automatically. In this paper, we propose Confidentially Redacted Training (CRT), a method to train language generation mod

Xiaofan Jia, Sadeed Bin Sayed, Nahian Ibn Hasan, Luis J. Gomez

Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly evaluate the tDCS-induced current density in near real-time, this paper proposes a deep learning-based emulator, named DeeptDCS. Methods: The emulator leverages Attent

Uichin Lee, Gyuwon Jung, Eun-Yeol Ma, Jin San Kim

With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement a

Hui Xu, Robert Tibshirani

In supervised learning, the estimation of prediction error on unlabeled test data is an important task. Existing methods are usually built on the assumption that the training and test data are sampled from the same distribution, which is often violated in practice. As a result, traditional estimators like cross-validation (CV) will be biased and this may res

Ferdinand Kossmann, Zhihao Jia, Alex Aiken

The Mixture of Experts architecture allows for outrageously large neural networks by scaling model parameter size independently from computational demand (FLOPs). However, current DNN frameworks cannot effectively support the dynamic data flow in Mixture of Experts, and implementations on top of these frameworks need to use workarounds that introduce signifi

Zehao Dou, Zhou Fan, Harrison Zhou

We study the continuous multi-reference alignment model of estimating a periodic function on the circle from noisy and circularly-rotated observations. Motivated by analogous high-dimensional problems that arise in cryo-electron microscopy, we establish minimax rates for estimating generic signals that are explicit in the dimension $K$. In a high-noise regim

Yu Zhang, Yu Meng, Xuan Wang, Sheng Wang

Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users' particular interests due to their inability of leveraging user guidance. Although there exist seed-guided topic discovery approaches that leverage user-provided seeds to d

Camelia R. Walker, Roslyn I. Hickson, Edmond Chang, Pengby Ngor

Plasmodium (P.) falciparum and P. vivax are the two most common causes of malaria. While the majority of deaths and severe morbidity are due to P. falciparum, P. vivax poses a greater challenge to eliminating malaria outside of Africa due to its ability to form latent liver stage parasites (hypnozoites), which can cause relapsing episodes within an individua

CMS Collaboration

A search is performed for narrow resonances decaying to final states of two jets, with at least one jet originating from a b quark, in proton-proton collisions at $\sqrt{s}$ = 13 TeV. The data set corresponds to an integrated luminosity of 138 fb$^{-1}$ collected with the CMS detector at the LHC. Jets originating from energetic b hadrons are identified throu

Jason Priem, Heather Piwowar, Richard Orr

OpenAlex is a new, fully-open scientific knowledge graph (SKG), launched to replace the discontinued Microsoft Academic Graph (MAG). It contains metadata for 209M works (journal articles, books, etc); 2013M disambiguated authors; 124k venues (places that host works, such as journals and online repositories); 109k institutions; and 65k Wikidata concepts (link

Yacine Jernite, Huu Nguyen, Stella Biderman, Anna Rogers

The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data. This work proposes an approach to global language data governance that attempts to organize data management amongst stakeholders, values, and rights. Our propo

Sanjay Kumar, Joseph M. Melby

In this paper, we study the variation of the Turaev--Viro invariants for $3$-manifolds with toroidal boundary under the operation of attaching a $(p,q)$-cable space. We apply our results to a conjecture of Chen and Yang which relates the asymptotics of the Turaev--Viro invariants to the simplicial volume of a compact oriented $3$-manifold. For $p$ and $q$ co

Nathaniel Merrill, Yuliang Guo, Xingxing Zuo, Xinyu Huang

We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our knowledge, our system is among the first to utilize the camera pose information from SLAM to provide prior knowledge for tracking keypoints on symmetric objects -- ensuring that new me

Ping Li, Qian Liu, Jianbei Liu, Ming Shao

Super tau-Charm facility(STCF) is a future electron-position collider operating at tau-Charm energy region aimed to study hadron structure and spectroscopy. The baseline design of the STCF barrel particle identification(PID) detector in the momentum range up to 2 GeV/c is provided by a Ring Imaging Cherenkov Counter(RICH). The architecture of the RICH is an

Petr Kostka, Bruno Rossi, Mouzhi Ge

The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluatio

Ivan Arraut, Joao Alexandre Lobo Marques, Sergio Gomes

The Spontaneous Symmetry breaking in Quantum Finance considers the martingale condition in the stock market as a vacuum state if we express the financial equations in the Hamiltonian form. The original analysis for this phenomena ignores completely the kinetic terms in the neighborhood of the minimal of the potential terms. This is correct in most of the cas