October 2022 arXiv papers — page 115

Tobias Fischer, Thomas E. Huang, Jiangmiao Pang, Linlu Qiu

Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions in images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object

Vasily V. Klimov

The review presents an analysis and generalization of classical and most modern approaches to the description and development of operation of open optical nanoresonators, that is, resonators all sizes of which are smaller than the resonant wavelength of radiation in a vacuum. Particular attention is paid to the physics of such phenomena as bound states in a

N. J. Miller, P. F. L. Maxted, D. Graczyk, T. G. Tan

CPD-54 810 is a double-lined detached eclipsing binary containing two mid-F type dwarfs on an eccentric 26-day orbit. We perform a combined analysis of the extensive photometry obtained by the TESS space mission along with previously published observations to obtain a full orbital and physical solution for the system. We measure the following model-independe

Thibault Sellam, Ankur Bapna, Joshua Camp, Diana Mackinnon

Much of text-to-speech research relies on human evaluation, which incurs heavy costs and slows down the development process. The problem is particularly acute in heavily multilingual applications, where recruiting and polling judges can take weeks. We introduce SQuId (Speech Quality Identification), a multilingual naturalness prediction model trained on over

Minh Tran, Khoa Vo, Kashu Yamazaki, Arthur Fernandes

Amodal Instance Segmentation (AIS) aims to segment the region of both visible and possible occluded parts of an object instance. While Mask R-CNN-based AIS approaches have shown promising results, they are unable to model high-level features coherence due to the limited receptive field. The most recent transformer-based models show impressive performance on

Jean-Baptiste Bouhiron, Aurélien Fabre, Qi Liu, Quentin Redon

Modern condensed matter physics relies on the concept of topology to classify matter, from quantum Hall systems to topological insulators. Engineered systems, benefiting from synthetic dimensions, can potentially give access to novel topological states predicted in dimensions $D > 3$. We report the realization of an atomic quantum Hall system evolving in fou

Weiwei Shi, Xiao Tang

Imposing some conditions on derivatives of the known functions, using the Fiber Contraction Theorem we prove the existence of $C^1$ solutions of a class of iterative functional equations which involves iterates of the unknown functions and a nonlinear term.

Giuseppe Gaetano Luciano

We study the cosmological evolution of a nonflat Friedmann-Robertson-Walker Universe filled by pressureless dark matter and Barrow Holographic Dark Energy (BHDE). The latter is a dark energy model based on the holographic principle with Barrow entropy instead of the standard Bekenstein-Hawking one. By assuming the apparent horizon of the Universe as IR cutof

Andrés Anabalón, Antonio Gallerati, Simon Ross, Mario Trigiante

We consider soliton solutions in AdS$_{4}$ with a flat slicing and Wilson loops around one cycle. We study the phase structure and find the ground state and identify supersymmetric solutions as a function of the Wilson loops. We work in the context of a scalar field truncation of gauged $\mathcal{N}=8$ supergravity, where all the dilatons are equal and all t

Carlo Benassi, Michele Caselli

We prove Lipschitz continuity results for solutions to a class of obstacle problems under standard growth conditions of $p$-type, $p \geq 2$. The main novelty is the use of a linearization technique going back to [28] in order to interpret our constrained minimizer as a solution to a nonlinear elliptic equation, with a bounded right-hand side. This leads us

Francesc Fité, Antonella Perucca

We say that two abelian varieties $A$ and $A'$ defined over a field $F$ are polyquadratic twists if they are isogenous over a Galois extension of $F$ whose Galois group has exponent dividing $2$. Let $A$ and $A'$ be abelian varieties defined over a number field $K$ of dimension $g\geq 1$. In this article we prove that, if $g\leq 2$, then $A$ and $A'$ are pol

Kotaro Funakoshi

This paper presents Non-Axiomatic Term Logic (NATL) as a theoretical computational framework of humanlike symbolic reasoning in artificial intelligence. NATL unites a discrete syntactic system inspired from Aristotle's term logic and a continuous semantic system based on the modern idea of distributed representations, or embeddings. This paper positions the

Juan Michael Sargado, Michael Welch, Michael Lüthje

Rock formations are very often characterized by the presence of fractures that have grown subcritically over geological time scales and under evolving stress fields. In mechanically layered systems, such fractures can either become layer-bound or penetrate into adjacent strata. The growth of fractures in brittle materials is generally dependent on the energy

Stéphane Poulain, Andreas Carlson

We study experimentally the dynamics of a water droplet on a tilted and vertically oscillating rigid fibre. As we vary the frequency and amplitude of the oscillations the droplet transitions between different modes: harmonic pumping, subharmonic pumping, a combination of rocking and pumping modes, and a combination of pumping and swinging modes. We character

Ken Roberts, Najeh Jisrawi, J. Jeyasitharam, Shreyas Suresh

The bound state energies of a 1-dimensional finite quantum square well (FSW) can be determined using a geometric method, involving a smooth mapping between two copies of the complex plane. The method allows one to identify particular strengths of the FSW at which the system can become unusually sensitive to changes in the well depth or geometry. In the prese

Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li

This paper studies the curious phenomenon for machine learning models with Transformer architectures that their activation maps are sparse. By activation map we refer to the intermediate output of the multi-layer perceptrons (MLPs) after a ReLU activation function, and by sparse we mean that on average very few entries (e.g., 3.0% for T5-Base and 6.3% for Vi

Bin Xiao, Chien-Liang Liu, Wen-Hoar Hsaio

Few-shot learning (FSL) has attracted considerable attention recently. Among existing approaches, the metric-based method aims to train an embedding network that can make similar samples close while dissimilar samples as far as possible and achieves promising results. FSL is characterized by using only a few images to train a model that can generalize to nov

Paul Henderson, Areg Ghazaryan, Alexander A. Zibrov, Andrea F. Young

The development of two-dimensional materials has resulted in a diverse range of novel, high-quality compounds with increasing complexity. A key requirement for a comprehensive quantitative theory is the accurate determination of these materials' band structure parameters. However, this task is challenging due to the intricate band structures and the indirect

Connor M. Holland, Yukai Lu, Lawrence W. Cheuk

Entanglement is crucial to many quantum applications including quantum information processing, simulation of quantum many-body systems, and quantum-enhanced sensing. Molecules, because of their rich internal structure and interactions, have been proposed as a promising platform for quantum science. Deterministic entanglement of individually controlled molecu

V. E. Valiulin, N. M. Chtchelkatchev, A. V. Mikheyenkov, V. M. Vinokur

Magnetic systems governed by exchange interactions between magnetic moments harbor frustration that leads to ground state degeneracy and results in the new topological state often referred to as a frustrated state of matter (FSM). The frustration in the commonly discussed magnetic systems has a spatial origin. Here we demonstrate that an array of nanomagnets

Yu Zhao, Brent Harrison, Tingting Yu

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effec

Danyel Cavazos-Cavazos, Ruwan Senaratne, Aashish Kafle, Randall G. Hulet

The Tomonaga-Luttinger liquid (TLL) theory describes the low-energy excitations of strongly correlated one-dimensional (1D) fermions. In the past years, a number of studies have provided a detailed understanding of this universality class. More recently, theoretical investigations that go beyond the standard low-temperature, linear-response, TLL regime have

Raktim Haldar, Robert Johanning, Philip Rübeling, Anahita Khodadad Kashi

Quantum walks are central to a wide range of applications such as quantum search, quantum information processing, and entanglement transport. Gaining control over the duration and the direction of quantum walks (QWs) is crucial to implementing dedicated processing. However, in current systems, it is cumbersome to achieve in a scalable format. High-dimensiona

Pakorn Uttayopas, Xiaoxiao Cheng, Udaya Bhaskar Rongala, Henrik Jörntell

Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional

Adam Elwood, Marco Leonardi, Ashraf Mohamed, Alessandro Rozza

Contextual bandits can solve a huge range of real-world problems. However, current popular algorithms to solve them either rely on linear models, or unreliable uncertainty estimation in non-linear models, which are required to deal with the exploration-exploitation trade-off. Inspired by theories of human cognition, we introduce novel techniques that use max

Yitian Liu, Zhouhui Lian

Automatic generation of high-quality Chinese fonts from a few online training samples is a challenging task, especially when the amount of samples is very small. Existing few-shot font generation methods can only synthesize low-resolution glyph images that often possess incorrect topological structures or/and incomplete strokes. To address the problem, this

Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron, Warith Harchaoui

In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely discussed for improvements, little attention has been dedicated to the relevance of MI as a clustering object

Marawan Gamal Abdel Hameed, Ali Mosleh, Marzieh S. Tahaei, Vahid Partovi Nia

While convolutional neural networks (CNNs) have become the de facto standard for most image processing and computer vision applications, their deployment on edge devices remains challenging. Tensor decomposition methods provide a means of compressing CNNs to meet the wide range of device constraints by imposing certain factorization structures on their convo

Zhiying Song, Fuxi Wen, Hailiang Zhang, Jun Li

Cooperative perception is challenging for safety-critical autonomous driving applications.The errors in the shared position and pose cause an inaccurate relative transform estimation and disrupt the robust mapping of the Ego vehicle. We propose a distributed object-level cooperative perception system called OptiMatch, in which the detected 3D bounding boxes

Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen

In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time. Compared to conventional adversarial defenses, VP allows us to design universal (i.e., data-agnostic) input prompting templates, which have plug-and-play capabilities at testing time to achieve desired model performance without int

George M. Seidel, Christian Enss

The quasiparticle propagation away from the track of a highly ionizing particle in superfluid helium at low temperatures has previously been shown to exhibit anisotropy. We discuss the mechanism responsible for this behavior and show that it occurs for nuclear scattering by dark matter for recoil energies down to a few keV, and perhaps lower. This makes it p

Suvodip Dey, Maunendra Sankar Desarkar, Asif Ekbal, P. K. Srijith

Long-range context modeling is crucial to both dialogue understanding and generation. The most popular method for dialogue context representation is to concatenate the last-$k$ utterances in chronological order. However, this method may not be ideal for conversations containing long-range dependencies, i.e., when there is a need to look beyond last-$k$ utter

Aditya Sharma, Apoorv Saxena, Chitrank Gupta, Seyed Mehran Kazemi

Recent years have witnessed much interest in temporal reasoning over knowledge graphs (KG) for complex question answering (QA), but there remains a substantial gap in human capabilities. We explore how to generalize relational graph convolutional networks (RGCN) for temporal KGQA. Specifically, we propose a novel, intuitive and interpretable scheme to modula

Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk

Tabular data is among the oldest and most ubiquitous forms of data. However, the generation of synthetic samples with the original data's characteristics remains a significant challenge for tabular data. While many generative models from the computer vision domain, such as variational autoencoders or generative adversarial networks, have been adapted for tab

Will Sawin, Melanie Matchett Wood

The moment problem in probability theory asks for criteria for when there exists a unique measure with a given tuple of moments. We study a variant of this problem for random objects in a category, where a moment is given by the average number of epimorphisms to a fixed object. When the moments do not grow too fast, we give a necessary and sufficient conditi

Stella Civelli, Emanuele Parente, Enrico Forestieri, Marco Secondini

The performance of different probabilistic amplitude shaping (PAS) techniques in the nonlinear regime is investigated, highlighting its dependence on the PAS block length and the interaction with carrier phase recovery (CPR). Different PAS implementations are considered, based on different distribution matching (DM) techniques-namely, sphere shaping, shell m

Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang

Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models. Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. To tackle the challenge, we propose a task prefix

T. Rees-Crockford, C. J. Nelson, M. Mathioudakis

Solar active regions (ARs) play a fundamental role in driving many of the geo-effective eruptions which propagate into the Solar System. However, we are still unable to consistently predict where and when ARs will occur across the solar disk by identifying pre-emergence signatures in observables such as the Doppler velocity (without using Helioseismic method

Giulia Meglioli, Alberto Roncoroni

We investigate the uniqueness, in suitable weighted Lebesgue spaces, of solutions to a class of elliptic equations with a drift posed on a complete, noncompact, Riemannian manifold $M$ of infinite volume and dimension $N\ge2$. Furthermore, in the special case of a model manifold with polynomial volume growth, we show that the conditions on the drift term are

Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha

We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no commun

F. Ming, X. Wu, C. Chen, K. D. Wang

Sn adatoms on a Si(111) substrate with 1/3 monolayer coverage form a two-dimensional triangular adatom lattice with one unpaired electron per site and an antiferromagnetic Mott insulating state. The Sn layers can be modulation hole-doped and metallized using heavily-doped $p$-type Si(111) substrates, and become superconducting at low temperatures. While the

Wenjian Hao, Bowen Huang, Wei Pan, Di Wu

This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS

Nina Javerzat, Mehdi Bouzid

The rigidity transition occurs when, as the density of microscopic components is increased, a disordered medium becomes able to transmit and ensure macroscopic mechanical stability, owing to the appearance of a space-spanning rigid connected component, or cluster. As a continuous phase transition it exhibits a scale invariant critical point, at which the rig

Yuxuan Xue, Haolong Li, Stefan Leutenegger, Jörg Stückler

Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras. In this paper, we propose a novel approach for reconstructing such deformations using measurements from event-based cameras. Under the assumption of a static background, where all events are generated by the motion, our approach estimate

Luke Baker, Saif R. Kazi, Rodrigo B. Platte, Anatoly Zlotnik

We formulate a control system model for the distributed flow of mixtures of highly heterogeneous gases through large-scale pipeline networks with time-varying injections of constituents, withdrawals, and control actions of compressors. This study is motivated by the proposed blending of clean hydrogen into natural gas pipelines as an interim means to reducin

Tom R. V. Steentjes, Mircea Lazar, Paul M. J. Van den Hof

Control in a classical transfer function or state-space setting typically views a controller as a signal processor: sensor outputs are mapped to actuator inputs. In behavioral system theory, control is simply viewed as interconnection; the interconnection of a plant with a controller. In this paper we consider the problem of control of interconnected systems

Nama Premsai

The stability of GFET devices is a major problem and needs a good passivation layer for the same. Low cost and low-temperature sol-gel alumina passivation layer-based GFETs is studied here with the goals to improve stability of the device and achieve high process yield. The process yield and device stability are explored for two different molarities of 0.1 M

Georgios Margazoglou, Luca Magri

The prediction of the temporal dynamics of chaotic systems is challenging because infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal perturbations is the subject of stability analysis. In stability analysis, we linearize the equations of the dynamical system around a reference point, and compute the properties of th

Maximilian Schleich, Amir Shaikhha, Dan Suciu

Tensor programs often need to process large tensors (vectors, matrices, or higher order tensors) that require a specialized storage format for their memory layout. Several such layouts have been proposed in the literature, such as the Coordinate Format, the Compressed Sparse Row format, and many others, that were especially designed to optimally store tensor

Clement Gauchy, Cyril Feau, Josselin Garnier

Seismic fragility curves have been introduced as key components of Seismic Probabilistic Risk Assessment studies. They express the probability of failure of mechanical structures conditional to a seismic intensity measure and must take into account the inherent uncertainties in such studies, the so-called epistemic uncertainties (i.e. coming from the uncerta

Aleksey Andreev

We propose to take a look at a new approach to the study of integral polyhedra. The main idea is to give an integral representation, or matrix model representation, for the key combinatorial characteristics of integral polytopes. Based on the well-known geometric interpretations of matrix model digram techniques, we construct a new model that enumerates tria

Jun Wang, Fei Xue, Chuanming Zong

In 1933, K. Borsuk proposed the following problem: Can every bounded set in $\mathbb{E}^n$ be divided into $n+1$ subsets of smaller diameters? In 1965, V. G. Boltyanski and I. T. Gohberg made the following conjecture: Every bounded set in an $n$-dimensional metric space can be divided into $2^n$ subsets of smaller diameters. In this paper, we prove the follo

Zhifeng Peng, Zhicheng Wang

In \cite{JZ1}, D. Jiang and L. Zhang proposed a conjecture which related the wavefront sets and the descent method in the local fields case. Recently, in \cite{JLZ}, they and D. Liu define the arithmetic wavefront set of certain irreducible admissible representation $\pi$ of a classical group $G(k)$ defined over local field $k$, which is a subset of $k$-rati

Kevin Xu, Hieu Nguyen

The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine

Alpay Sabuncuoglu, T. Metin Sezgin

Defining abstract algorithmic structures like functions and variables using self-made tangibles can enhance the usability and affordability of the tangible programming experience by maintaining the input modality and physical interaction throughout the activity and reducing the dependence on electronic devices. However, existing tangible programming environm

Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic, Nassir Navab

Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can translate images from one imaging modality to another at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite its effectiveness in learning the underlying data

S. Kirusheva, H. Huang, T. S. Salisbury

In this paper we consider the problem of optimizing lifetime consumption under a habit formation model. Our work differs from previous results, because we incorporate mortality and pension income. Lifetime utility of consumption makes the problem time inhomogeneous, because of the effect of ageing. Considering habit formation means increasing the dimension o

Rotem Dror, Haoyu Wang, Dan Roth

What are the events involved in a pandemic outbreak? What steps should be taken when planning a wedding? The answers to these questions can be found by collecting many documents on the complex event of interest, extracting relevant information, and analyzing it. We present a new approach in which large language models are utilized to generate source document

Xiao-Jie Zhu

We give all possible holomorphic Eisenstein series on $\Gamma_0(p)$, of rational weights greater than $2$, and with multiplier systems the same as certain rational-weight eta-quotients at all cusps. We prove they are modular forms and give their Fourier expansions. We establish four sorts of identities that equate such series to rational-weight eta-quotients

M. Panessa, D. Seifried, S. Walch, B. Gaches

Modeling the chemistry of molecular clouds is critical to accurately simulating their evolution. To reduce computational cost, 3D simulations generally restrict their chemistry to species with strong heating and cooling effects. Time-dependent information about the evolution of other species is therefore often neglected. We address this gap by post-processin

Hui Lei, Siyan Li, Xiaopan Lian, Susu Wang

The dichromatic number of $D$, denoted by $\overrightarrow{\chi}(D)$, is the smallest integer $k$ such that $D$ admits an acyclic $k$-coloring. We use $mader_{\overrightarrow{\chi}}(F)$ to denote the smallest integer $k$ such that if $\overrightarrow{\chi}(D)\ge k$, then $D$ contains a subdivision of $F$. A digraph $F$ is called Mader-perfect if for every su

Hongming Zhang, Yintong Huo, Yanai Elazar, Yangqiu Song

Recently, the community has achieved substantial progress on many commonsense reasoning benchmarks. However, it is still unclear what is learned from the training process: the knowledge, inference capability, or both? We argue that due to the large scale of commonsense knowledge, it is infeasible to annotate a large enough training set for each task to cover

Zeerak Talat, Anne Lauscher

Machine learning and NLP require the construction of datasets to train and fine-tune models. In this context, previous work has demonstrated the sensitivity of these data sets. For instance, potential societal biases in this data are likely to be encoded and to be amplified in the models we deploy. In this work, we draw from developments in the field of hist

Ke-Han Lu, Kuan-Yu Chen

Non-autoregressive automatic speech recognition (ASR) modeling has received increasing attention recently because of its fast decoding speed and superior performance. Among representatives, methods based on the connectionist temporal classification (CTC) are still a dominating stream. However, the theoretically inherent flaw, the assumption of independence b

Sang-Hyun Je

Knowledge graph (KG) embedding is widely used in many downstream applications using KGs. Generally, since KGs contain only ground truth triples, it is necessary to construct arbitrary negative samples for representation learning of KGs. Recently, various methods for sampling high-quality negatives have been studied because the quality of negative triples has

Minjia Shi, Shitao Li, Jon-Lark Kim

The purpose of this paper is to solve the two conjectures on the largest minimum distance $d_{so}(n,5)$ of a binary self-orthogonal $[n,5]$ code proposed by Kim and Choi (IEEE Trans. Inf. Theory, 2022). The determination of $d_{so}(n,k)$ has been a fundamental and difficult problem in coding theory because there are too many binary self-orthogonal codes as t

Yuanyuan Liu, Chengjiang Long, Zhaoxuan Zhang, Bokai Liu

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring conte

Zilong Zhao, Robert Birke, Lydia Y. Chen

Synthetic tabular data emerges as an alternative for sharing knowledge while adhering to restrictive data access regulations, e.g., European General Data Protection Regulation (GDPR). Mainstream state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GANs), which are composed of a generator and a discriminator. Whi

Minxi He, Kazunori Kohri, Kyohei Mukaida, Masaki Yamada

In this paper, we investigate the thermalization of Hawking radiation from primordial black holes (PBHs) in the early Universe, taking into account the interference effect on thermalization of high energy particles, known as Landau-Pomeranchuk-Migdal (LPM) effect. Small PBHs with masses $ \lesssim 10^9 \, \mathrm{g} $ completely evaporate before the big bang

Matteo Bellitti, Garry Goldstein, Chris R. Laumann

Recent experimental progress has produced Molecular Superfluids (MSF) in thermal equilibrium; this opens the door to a new class of experiments investigating the associated thermodynamic and dynamical responses. We review the theoretical picture of the phase diagram and quasiparticle spectrum in the Atomic Superfluid (ASF) and MSF phases. We further compute

Hauke Petersen, János Brodbeck, Thomas C. Schmidt, Matthias Wählisch

The IPv6 over Bluetooth Low Energy (BLE) standard defines the transfer of IP data via BLE connections. This connection-oriented approach provides high reliability but increases packet delays and requires substantial overhead to manage BLE connections. To overcome these drawbacks we present the design and implementation of IPv6 over BLE advertisements, a stan

Cuiyun Gao, Yaoxian Li, Shuhan Qi, Yang Liu

App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. There exist prior explorations on app review mining for release planning, however, most of the studies strongly rely on pre-defined classes or manually-annotated reviews. A

Oliver Goertsches, Panagiotis Konstantis, Leopold Zoller

GKM theory is a powerful tool in equivariant topology and geometry that can be used to generalize classical ideas from (quasi)toric manifolds to more general torus actions. After an introduction to the topic this survey focuses on recent results in low dimensions, where the interaction between geometry and combinatorics turns out to be particularly fruitful.

Qiaochu Guo, Anthony D'Addario, Yang Cheng, Jeremy Kline

Electrical switching of N\'eel order in an antiferromagnetic insulator is desirable as a basis for memory applications. Unlike electrically-driven switching of ferromagnetic order via spin-orbit torques, electrical switching of antiferromagnetic order remains poorly understood. Here we investigate the low-field magnetic properties of 30 nm thick, c-axis orie

Bin Wang, Yaolin Jiang

Maxwell equations describe the propagation of electromagnetic waves and are therefore fundamental to understanding many problems encountered in the study of antennas and electromagnetics. The aim of this paper is to propose and analyse an efficient fully discrete scheme for solving three-dimensional Maxwell's equations. This is accomplished by combining time

G. A. Ummarino, D. Torsello

We calculated the critical temperature in the framework of s+-+d-wave multiband Eliashberg theory. We have solved these equations numerically to see at what values of the input parameters there is a solution with a non-zero critical temperature and what is the symmetry of the order parameter of this solution. For our model we consider the pnictide case with

Yingji Zhang, Danilo S. Carvalho, André Freitas

Formal/symbolic semantics can provide canonical, rigid controllability and interpretability to sentence representations due to their \textit{localisation} or \textit{composition} property. How can we deliver such property to the current distributional sentence representations to control and interpret the generation of language models (LMs)? In this work, we

Leanne Nortje, Herman Kamper

Imagine being able to show a system a visual depiction of a keyword and finding spoken utterances that contain this keyword from a zero-resource speech corpus. We formalise this task and call it visually prompted keyword localisation (VPKL): given an image of a keyword, detect and predict where in an utterance the keyword occurs. To do VPKL, we propose a spe

Elias Laurin Meyer, Peter Mesenbrink, Nicholas A. Di Prospero, Juan M. Pericàs

Non-alcoholic steatohepatitis (NASH) is the progressive form of nonalcoholic fatty liver disease (NAFLD) and a disease with high unmet medical need. Platform trials provide great benefits for sponsors and trial participants in terms of accelerating drug development programs. In this article, we describe some of the activities of the EU-PEARL consortium (EU P

Edric Matwiejew, Jason Pye, Jingbo B. Wang

Solving optimisation problems is a promising near-term application of quantum computers. Quantum variational algorithms leverage quantum superposition and entanglement to optimise over exponentially large solution spaces using an alternating sequence of classically tunable unitaries. However, prior work has primarily addressed discrete optimisation problems.

Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet

Several algorithms involving the Variational R\'enyi (VR) bound have been proposed to minimize an alpha-divergence between a target posterior distribution and a variational distribution. Despite promising empirical results, those algorithms resort to biased stochastic gradient descent procedures and thus lack theoretical guarantees. In this paper, we formali

Pooja Prajod, Elisabeth André

Stress is prevalent in many aspects of everyday life including work, healthcare, and social interactions. Many works have studied handcrafted features from various bio-signals that are indicators of stress. Recently, deep learning models have also been proposed to detect stress. Typically, stress models are trained and validated on the same dataset, often in

S. K. Sounda, K. Mandal

The essential of this monograph is to reproduce the results of Sudarshans paper Equivalence of semiclassical and quantum mechanical descriptions of statistical light beams published in 1963. To go in that direction we also describe the linear harmonic oscillator and its algebraic equivalence with a single-mode quantized beam of light. Coherent states and the

Yizeng Han, Zhihang Yuan, Yifan Pu, Chenhao Xue

Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixels, such an adaptive inference paradigm reduces the spatial redundancy in image features and saves a considerable amount of unnecessary computation. However, the theoretical effici

Zahra Askarpour, Hosein Cheraghchi

We investigate transport properties through nano-ribbons of thin topological insulators irradiated by high frequency light with circular polarization. By using high frequency regime, a coherent and quantized transport through the nano-ribbon is guaranteed and then Lanadauer formalism is applicable. It is demonstrated that the pseudo-spin edge modes inside th

Keisuke Teramoto

We study focal surfaces of (wave) fronts associated to unbounded principal curvatures near non-degenerate singular points of initial fronts. We give characterizations of singularities of those focal surfaces in terms of types of singularities and geometrical properties of initial fronts. Moreover, we investigate behavior of the Gaussian curvature of the foca

E. Carretti, S. O'Sullivan, V. Vacca, F. Vazza

Measuring the magnetic field in cosmic filaments reveals how the Universe is magnetised and the process that magnetised it. Using the Rotation Measures (RM) at 144-MHz from the LoTSS DR2 data, we analyse the rms of the RM extragalactic component as a function of redshift to investigate the evolution with redshift of the magnetic field in filaments. From prev

Yingjie Luo, Bin Chen, Sijie Yu, Marina Battaglia

Flare-associated quasiperiodic pulsations (QPPs) in radio and X-ray wavelengths, particularly those related to nonthermal electrons, contain important information about the energy release and transport processes during flares. However, the paucity of spatially resolved observations of such QPPs with a fast time cadence has been an obstacle for us to further

Jaeyun Moon, Simon Thébaud, Lucas Lindsay, Takeshi Egami

Understanding thermodynamics in liquids at the atomic level is challenging because of strong atomic interactions and lack of symmetry. Recent prior theoretical works have focused on describing heat capacity of liquids in terms of phonon-like excitations but often rely on fitting parameters and ad hoc assumptions. In this work, we perform microscopic analysis

H. Peter Boswijk, Roger J. A. Laeven, Evgenii Vladimirov

We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied conditional log-characteristic function, which is functionally a

Edoardo Arnaudo, Antonio Tavera, Fabrizio Dominici, Carlo Masone

We investigate the task of unsupervised domain adaptation in aerial semantic segmentation and discover that the current state-of-the-art algorithms designed for autonomous driving based on domain mixing do not translate well to the aerial setting. This is due to two factors: (i) a large disparity in the extension of the semantic categories, which causes a do

Hong-Wei Ke, Yi-Fan Shi, Xiao-Hai Liu, Xue-Qian Li

Two iso-singlet hadron states $X_0(2900)$ and $X_1(2900)$ with $J=0$ and 1 respectively, discovered by the LHCb collaboration in 2020, were identified as molecular bound states of $\bar D^*K^*$. Recently two structures $T^a_{cs0}(2900)^0$ and $T^a_{cs0}(2900)^{++}$ have been observed at the hadron spectra, one would suspect if they also are molecular states

Yan Liu, Jianguo Lei

The existence of large sets of Kirkman triple systems (LKTSs) is one of the best-known open problems in combinatorial design theory. Steiner quadruple systems with resolvable derived designs (RDSQSs) play an important role in the recursive constructions of LKTSs. In this paper, we introduce a special combinatorial structure RDSQS*(v) and use it to present a

Somya Sharma, Rahul Ghosh, Arvind Renganathan, Xiang Li

The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow prediction. For solving inverse problems in such applications, ensuring explainability is pivotal for tackling issues relating to data bias and

Adam Kinos, Klaus Mølmer

We consider a multi-qubit system of atoms or ions with two computational ground states and an interacting excited state in the so-called blockade regime, such that only one qubit can be excited at any one time. Examples of such systems are rare-earth-ion-doped crystals and neutral atoms trapped in tweezer arrays. We present a simple laser excitation protocol

Yuta Kimura, Hiroyuki Minamoto, Kota Yamaura

In representation theory of graded Iwanaga-Gorenstein algebras, tilting theory of the stable category $\underline{\mathsf{CM}}^{\mathbb{Z}} A$ of graded Cohen-Macaulay modules plays a prominent role. In this paper we study the following two central problems of tilting theory of $\underline{\mathsf{CM}}^{\mathbb{Z}} A$ in the case where $A$ is finite dimensio

Ting Jiang, Deqing Wang, Fuzhen Zhuang, Ruobing Xie

To overcome the overparameterized problem in Pre-trained Language Models (PLMs), pruning is widely used as a simple and straightforward compression method by directly removing unimportant weights. Previous first-order methods successfully compress PLMs to extremely high sparsity with little performance drop. These methods, such as movement pruning, use first

Yannick Herfray, Kirill Krasnov, Evgeny Skvortsov

We lift the recently proposed theories of higher-spin self-dual Yang-Mills (SDYM) and gravity (SDGR) to the twistor space. We find that the most natural room for the twistor formulation of these theories is not in the projective, but in the full twistor space, which is the total space of the spinor bundle over the 4-dimensional manifold. In the case of highe

André Fuchs, Wided Medjroubi, Hannes Hochstein, Gerd Gülker

Fractal grids generate turbulence by exciting many length scales of different sizes simultaneously rather than using the nonlinear cascade mechanism to obtain multi-scale structures, as it is the case for regular grids. The interest in these grids has been further building up since the surprising findings stemming from the experimental and computational stud

Abdullatif Köksal, Silvia Severini, Hinrich Schütze

Word alignments are essential for a variety of NLP tasks. Therefore, choosing the best approaches for their creation is crucial. However, the scarce availability of gold evaluation data makes the choice difficult. We propose SilverAlign, a new method to automatically create silver data for the evaluation of word aligners by exploiting machine translation and