August 2022 arXiv papers — page 44

Valentin Barriere, Martin Claverie

Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizens. In the last decade, the amount of available data dramatically increased, whether it can come from Remote Sensing (using Copernicus Sentinel-2 data) or directly from the farmers

Wouter Jansen, Dennis Laurijssen, Walter Daems, Jan Steckel

Systems for estimating the six-degrees-of-freedom human body pose have been improving for over two decades. Technologies such as motion capture cameras, advanced gaming peripherals and more recently both deep learning techniques and virtual reality systems have shown impressive results. However, most systems that provide high accuracy and high precision are

Alexander Becker, Thomas Liebig

There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act. Thus, multiple approaches were presented to remove the influence of specific target data points from a trained model. However, when evaluating the success of unlea

Iosif Petrakis

We ascribe to the Euclidean Fifth Postulate a genuine constructive role, which makes it absolutely necessary in the parallel construction. For that, we present a reconstruction of the general principles underlying the Euclidean construction of a geometric property. As a consequence, the epistemological role of Euclidean constructions is revealed. We also exa

Wouter Jansen, Dennis Laurijssen, Jan Steckel

Navigation in varied and dynamic indoor environments remains a complex task for autonomous mobile platforms. Especially when conditions worsen, typical sensor modalities may fail to operate optimally and subsequently provide inapt input for safe navigation control. In this study, we present an approach for the navigation of a dynamic indoor environment with

Wouter Kager

Proving that a finitely generated convex cone is closed is often considered the most difficult part of geometric proofs of Farkas' lemma. We provide a short simple proof of this fact and (for completeness) derive Farkas' lemma from it using well-known arguments.

Hongcheng Guo, Yuhui Guo, Renjie Chen, Jian Yang

Fully supervised log anomaly detection methods suffer the heavy burden of annotating massive unlabeled log data. Recently, many semi-supervised methods have been proposed to reduce annotation costs with the help of parsed templates. However, these methods consider each keyword independently, which disregards the correlation between keywords and the contextua

Yu-Tian Xu, Ji-Ping Dai, Dong Zhao, Jun-Qing Xia

The cosmological principle states that our Universe is statistically homogeneous and isotropic at large scales. However, due to the relative motion of the Solar System, an additional kinematic dipole can be detected in the distribution of galaxies, which should be consistent with the dipole observed in the cosmic microwave background temperature. In this pap

A. V. Yudin

The relativistic hydrodynamics equations are adapted for the spherically symmetric case and the Lagrangian form. They are used to model the explosive disruption of a minimum-mass neutron star: a key ingredient of the stripping model for short gamma-ray bursts. The shock breakout from the neutron star surface accompanied by the acceleration of matter to ultra

Ning Xie, Jian-dong Zhang, Shun-Jia Huang, Yi-Ming Hu

The detection of gravitational wave (GW) has opened a new window to test the theory of gravity in the strong field regime. In general relativity (GR), GW can only possess two tensor polarization modes, which are known as the $+$ and $\times$ modes. However, vector and scalar modes can exist in some modified theories of gravity, and we can test the gravitatio

Ahmet Kerem Aksoy, Pavel Dushev, Eleni Tzirita Zacharatou, Holmer Hemsen

The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To address this limitation, we have recently

Mohamed Maghenem, Masoumeh Ghanbarpour, Adnane Saoud

In this brief paper we introduce a robust-safety notion for differential inclusions, and we propose a general framework to certify such a notion in terms of barrier functions. While existing literature studied only what we designate by uniform robust safety, in this paper, we make a clear distinction between the uniform and the non-uniform robust-safety noti

Pouyan Nasiri, Roozbeh Dargazany

Physics-informed neural networks (PINNs) have recently received much attention due to their capabilities in solving both forward and inverse problems. For training a deep neural network associated with a PINN, one typically constructs a total loss function using a weighted sum of different loss terms and then tries to minimize that. This approach often becom

J. Cai, E. Griffin, V. Guarochico-Moreira, D. Barry

Strong electric fields can accelerate molecular dissociation reactions. The phenomenon known as the Wien effect was previously observed using high-voltage electrolysis cells that produced fields of about 10^7 V m-1, sufficient to accelerate the dissociation of weakly bound molecules (e.g., organics and weak electrolytes). The observation of the Wien effect f

H. Augustin, R. Diener, S. Dittmeier, P. M. Freeman

The DESY II Test Beam Facility is a key infrastructure for modern high energy physics detector development, providing particles with a small momentum spread in a range from 1 to 6 GeV to user groups e.g. from the LHC experiments and Belle II as well as generic detector R&D. Beam telescopes are provided in all three test beam areas as precise tracking referen

Antoine Sandoz, Verena Ducret, Georg A. Gottwald, Gilles Vilmart

We extend the data-driven method of Sparse Identification of Nonlinear Dynamics (SINDy) developed by Brunton et al, Proc. Natl. Acad. Sci USA 113 (2016) to the case of delay differential equations (DDEs). This is achieved in a bilevel optimization procedure by first applying SINDy for fixed delay and then subsequently optimizing the error of the reconstructe

Belle Collaboration, S. X. Li, C. P. Shen, I. Adachi

We report a study of $\Lambda_c^+ \to \Sigma^+ \pi^0$, $\Lambda_c^+ \to \Sigma^+ \eta$, and $\Lambda_c^+ \to \Sigma^+ \eta'$ using the data sample corresponding to an integrated luminosity of 980 $\rm fb^{-1}$ collected with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider. The branching fractions relative to $\Lambda_c^+ \to \Sigma^+ \pi^0

Gregor Gantner, Rob Stevenson

We consider the first-order system space-time formulation of the heat equation introduced in [Bochev, Gunzburger, Springer, New York (2009)], and analyzed in [F\"uhrer, Karkulik, Comput. Math. Appl. 92 (2021)] and [Gantner, Stevenson, ESAIM Math. Model. Numer. Anal.} 55 (2021)], with solution components $(u_1,{\bf u}_2)=(u,-\nabla_{\bf x} u)$. The correspond

Wouter Jansen, Dennis Laurijssen, Jan Steckel

Navigating spatially varied and dynamic environments is one of the key tasks for autonomous agents. In this paper we present a novel method of navigating a mobile platform with one or multiple 3D-sonar sensors. Moving a mobile platform and subsequently any 3D-sonar sensor on it, will create signature variations over time of the echoed reflections in the sens

Francesco Tonini, Cigdem Beyan, Elisa Ricci

This paper addresses the gaze target detection problem in single images captured from the third-person perspective. We present a multimodal deep architecture to infer where a person in a scene is looking. This spatial model is trained on the head images of the person-of- interest, scene and depth maps representing rich context information. Our model, unlike

Dawid Kucharski, Maciej Obst, Jarosław Adamiec

A self-built laser projection system for optical transverse strain measurements in tensile testing is presented. The setup based on laser diode modules, CCD (Charge-Coupled Device) array detectors, and standard optomechanical components is proposed to be a low-cost system for testing polymer samples. The optical setup is mobile and can easily be mounted on a

Daniel Franzen, Saskia Nuñez von Voigt, Peter Sörries, Florian Tschorsch

Decisions about sharing personal information are not trivial, since there are many legitimate and important purposes for such data collection, but often the collected data can reveal sensitive information about individuals. Privacy-preserving technologies, such as differential privacy (DP), can be employed to protect the privacy of individuals and, furthermo

Aymeric Duigou-Majumdar, Pierre-Philippe Cortet, Christophe Poulard

The debonding criterion of fibrils of soft adhesive materials is a key element regarding the quantitative modelisation of pressure sensitive adhesive tapes peeling energy. We present in this article an experimental study of the detachment of a commercial acrylic adhesive tape from the top surface of a single micrometric pillar of PDMS elastomer. During an ex

Nidhi Gahlian, Garima Pant

We study Hayman conjecture for different paired complex polynomials under certain conditions. In 2021, the zeros distribution of $f^{n}(z)L(g)-a(z)$ and $g^{n}(z)L(f)-a(z)$ was studied by Gao and Liu for $n\geq 3$. In this paper, we work on the zeros distribution of $f^{2}(z)L(g)-a(z)$ and $g^{2}(z)L(f)-a(z)$, where $a(z)$ is a non-zero small function of bot

Hsien-Chin Lin, Christian Geishauser, Shutong Feng, Nurul Lubis

User simulators (USs) are commonly used to train task-oriented dialogue systems (DSs) via reinforcement learning. The interactions often take place on semantic level for efficiency, but there is still a gap from semantic actions to natural language, which causes a mismatch between training and deployment environment. Incorporating a natural language generati

Yifan Liu, Wei Wei, Jiayi Liu, Xianling Mao

Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions. However, existing personalized approaches commonly generate responses in light of static predefined personas depicted with textual description, which may severely restrict the interactivity of human and the chatbot, especially when the agent need

Yanyan Tang, Jie Xiong

We study the optimal control problem for a weighted mean-field system. A new feature of the control problem is that the coefficients depend on the state process as well as its weighted measure and the control variable. By applying variational technique, we establish a stochastic maximum principle. As an application, we investigate the optimal premium policy

Martin Lambers

Environment maps with high dynamic range lighting, such as daylight sky maps, require importance sampling to keep the balance between noise and number of samples per pixel manageable. Typically, importance sampling schemes for environment maps are based directly on the map parameterization, e.g. equirectangular maps, and do not work with alternative paramete

Jana Lasser, Segun Taofeek Aroyehun, Fabio Carrella, Almog Simchon

The spread of online misinformation on social media is increasingly perceived as a problem for societal cohesion and democracy. The role of political leaders in this process has attracted less research attention, even though politicians who "speak their mind" are perceived by segments of the public as authentic and honest even if their statements are unsuppo

Yuxiang Nie, Heyan Huang, Zewen Chi, Xian-Ling Mao

Unsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models. However, most of these works regard named entity (NE) as the only answer type, which ignores the high diversity of answers in the real world. To

Giovanni E. Comi, Graziano Crasta, Virginia De Cicco, Annalisa Malusa

The purpose of this paper is to find pointwise representation formulas for the density of the pairing between divergence-measure fields and BV functions, in this way continuing the research started in [17,20]. In particular, we extend a representation formula from an unpublished paper of Anzellotti [7] involving the limit of cylindrical averages for normal t

Anup Kanale, Feng Ling, Hanliang Guo, Sebastian Fuerthauer

Ciliated tissues such as in the mammalian lungs, brains, and reproductive tracts, are specialized to pump fluid. They generate flows by the collective activity of hundreds of thousands of individual cilia that beat in a striking metachronal wave pattern. Despite progress in analyzing cilia coordination, a general theory that links coordination and fluid pump

Pinak Mandal, Shashank Kumar Roy, Amit Apte

Using the recently developed Sinkhorn algorithm for approximating the Wasserstein distance between probability distributions represented by Monte Carlo samples, we demonstrate exponential filter stability of two commonly used nonlinear filtering algorithms, namely, the particle filter and the ensemble Kalman filter, for deterministic dynamical systems. We al

Shishir Khandelwal, Martí Perarnau-Llobet, Stella Seah, Nicolas Brunner

A physical system connected to two thermal reservoirs at different temperatures is said to act as a heat rectifier when it is able to bias the heat current in a given direction, similarly to an electronic diode. We propose to quantify the performance of a heat rectifier by mapping out the trade-off between heat currents and rectification. By optimizing over

Hui Li, Zidong Guo, Seon-Min Rhee, Seungju Han

Accurate facial landmarks are essential prerequisites for many tasks related to human faces. In this paper, an accurate facial landmark detector is proposed based on cascaded transformers. We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end. With self-attention in transformers, our model can in

P. D. Sacramento, M. F. Madeira

We consider the influence of topological phases, or their vicinity, on the spin density and spin polarization through a chiral chain. We show the quantization of the Berry phase in a one-dimensional polarization helix structure, under the presence of an external magnetic field, and show its influence on the spin density. The polar angle of the momentum space

Dongjie Yang, Zhuosheng Zhang, Hai Zhao

Masked Language Modeling (MLM) has been widely used as the denoising objective in pre-training language models (PrLMs). Existing PrLMs commonly adopt a Random-Token Masking strategy where a fixed masking ratio is applied and different contents are masked by an equal probability throughout the entire training. However, the model may receive complicated impact

Kaïs Ammari, Mostafa Sabri

In this short note we prove a sharp dispersive estimate $\|\mathrm{e}^{\mathrm{i} tH} f\|_\infty < t^{-d/3}\|f\|_1$ for any Cartesian product $\mathbb{Z}^d\mathop\square G_F$ of the integer lattice and a finite graph. This includes the infinite ladder, $k$-strips and infinite cylinders, which can be endowed with certain potentials.

Michael Greinecker, Christoph Kuzmics

A range of empirical puzzles in finance has been explained as a consequence of traders being averse to ambiguity. Ambiguity averse traders can behave in financial portfolio problems in ways that cannot be rationalized as maximizing subjective expected utility. However, this paper shows that when traders have access to limit orders, all investment behavior of

Adrian Wiltz, Dimos V. Dimarogonas

For a class of spatio-temporal tasks defined by a fragment of Signal Temporal Logic (STL), we construct a nonsmooth time-varying control barrier function (CBF) and develop a controller based on a set of simple optimization problems. Each of the optimization problems invokes constraints that allow to exploit the piece-wise smoothness of the CBF for optimizati

Md Sadman Sakib Rahman, Aydogan Ozcan

Diffractive deep neural networks (D2NNs) define an all-optical computing framework comprised of spatially engineered passive surfaces that collectively process optical input information by modulating the amplitude and/or the phase of the propagating light. Diffractive optical networks complete their computational tasks at the speed of light propagation throu

Yash Raj, Bhavesh Laddagiri

Transliteration is a task in the domain of NLP where the output word is a similar-sounding word written using the letters of any foreign language. Today this system has been developed for several language pairs that involve English as either the source or target word and deployed in several places like Google Translate and chatbots. However, there is very li

Wenhe Cai, Sizheng Cao, Xian-Hui Ge, Masataka Matsumoto

We show that a non-Hermitian two coupled Sachdev-Ye-Kitaev (SYK) model can provide thermodynamic structure equivalent to Hermitian two coupled SYK model. The energy spectrum, the entanglement degree of the ground states and the low energy effective action of this model are not influenced by the non-Hermiticity. The novel biorthogonal ground states demonstrat

Elena Issoglio, Francesco Russo

We consider SDEs with (distributional) drift in negative Besov spaces and random initial condition and investigate them from two different viewpoints. In the first part we set up a martingale problem and show its well-posedness.We then prove further properties of the martingale problem, like continuity with respect to the drift and the link with the Fokker-P

Dominik Lips, Eric Cereceda-López, Antonio Ortiz-Ambriz, Pietro Tierno

The flow-driven transport of interacting micron-sized particles occurs in many soft matter systems spanning from the translocation of proteins to moving emulsions in microfluidic devices. Here we combine experiments and theory to investigate the collective transport properties of colloidal particles along a rotating ring of optical traps. In the co-rotating

Hisaichi Shibata, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi

From birth to death, we all experience surprisingly ubiquitous changes over time due to aging. If we can predict aging in the digital domain, that is, the digital twin of the human body, we would be able to detect lesions in their very early stages, thereby enhancing the quality of life and extending the life span. We observed that none of the previously dev

Côme Butin, Damien Chablat, Yannick Aoustin, David Gouaillier

This work proposes a new kinematics of a myoelectric hand prosthesis with a single actuator, allowing to realize the tridigital grip but also the lateral grip. Inspired by tridigital prostheses, which are simpler, more robust and less expensive than polydigital prostheses, this new kinematics aims at proposing an accessible prosthesis (affordable, easy-to-us

Barbara McGillivray, Martina Astrid Rodda

Distributional semantics, the quantitative study of meaning variation and change through corpus collocations, is currently one of the most productive research areas in computational linguistics. The wider availability of big data and of reproducible algorithms for analysis has boosted its application to living languages in recent years. But can we use distri

Anna Maria Candela, Addolorata Salvatore, Caterina Sportelli

The aim of this paper is investigating the existence of one or more weak solutions of the coupled quasilinear elliptic system of gradient type \[ (P)\qquad \left\{ \begin{array}{ll} - {\rm div} (A(x, u)\vert\nabla u\vert^{p_1 -2} \nabla u) + \frac{1}{p_1}A_u (x, u)\vert\nabla u\vert^{p_1} = G_u(x, u, v) &\hbox{ in $\Omega$,}\\[5pt] - {\rm div} (B(x, v)\vert\

Minam Moon, Injo Hur, Sunghwan Moon

Photoacoustic tomography (PAT) is a novel and promising technology in hybrid medical imaging that involves generating acoustic waves in the object of interest by stimulating electromagnetic energy. The acoustic wave is measured outside the object. One of the key mathematical problems in PAT is the reconstruction of the initial function that contains diagnost

Dirk König, Michael Frentzen, Daniel Hiller, Noël Wilck

The electronic structure of low nanoscale (LNS) intrinsic silicon (i-Si) embedded in SiO2 vs. Si3N4 shifts away from vs. towards the vacuum level Evac, as described by the Nanoscale Electronic Structure Shift Induced by Anions at Surfaces (NESSIAS). Here, we fully explain the NESSIAS based on the quantum chemical properties of the elements involved. Deriving

Fabian Isensee, Constantin Ulrich, Tassilo Wald, Klaus H. Maier-Hein

Semantic segmentation is one of the most popular research areas in medical image computing. Perhaps surprisingly, despite its conceptualization dating back to 2018, nnU-Net continues to provide competitive out-of-the-box solutions for a broad variety of segmentation problems and is regularly used as a development framework for challenge-winning algorithms. H

Paul Brunzema, Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe

We consider the problem of sequentially optimizing a time-varying objective function using time-varying Bayesian optimization (TVBO). Current approaches to TVBO require prior knowledge of a constant rate of change to cope with stale data arising from time variations. However, in practice, the rate of change is usually unknown. We propose an event-triggered a

Shubham Raghuvanshi, Jayanta Dutta

It has been argued that the low-mass primordial stars ($m_{\rm Pop III}\,\leq 0.8\,M_\odot$) are likely to enter the main sequence and hence possibly be found in the present-day Galaxy. However, due to limitations in existing numerical capabilities, current three-dimensional (3D) simulations of disk fragmentation are capable of following only a few thousands

Ido Cohen, Dan Valsky, Ronen Talmon

During Deep Brain Stimulation(DBS) surgery for treating Parkinson's disease, one vital task is to detect a specific brain area called the Subthalamic Nucleus(STN) and a sub-territory within the STN called the Dorsolateral Oscillatory Region(DLOR). Accurate detection of the STN borders is crucial for adequate clinical outcomes. Currently, the detection is bas

Abhishek Joshi, Sathish Chalasani, Kiran Nanjunda Iyer

Detecting Out-of-Distribution (OOD) samples in real world visual applications like classification or object detection has become a necessary precondition in today's deployment of Deep Learning systems. Many techniques have been proposed, of which Energy based OOD methods have proved to be promising and achieved impressive performance. We propose semantic dri

Takashi Miyagawa

The functional equations of the Riemann zeta function, the Hurwitz zeta function, and the Lerch zeta function have been well known for a long time, and there is great importance in studying these zeta functions. For example, fundamental properties such as the upper bounds, the distribution of zeros, and the zero-free regions in the Riemann zeta function deri

Mengjie Yang, Luojia Wang, Xiaoxiong Wu, Han Xiao

Non-Hermitian systems have recently attracted broad interest and exhibited intriguing physical phenomena, in which the non-Hermitian skin effect is one of the most remarkable quantum phenomena desiring detailed investigations and has been widely studied in various fermionic and bosonic systems. Here we propose a non-Hermitian atom-waveguide system composed o

Nikhil V. S. Avula, Shivanand K. Veesam, Sudarshan Behera, Sundaram Balasubramanian

Shear viscosity, though being a fundamental property of all liquids, is computationally expensive to estimate from equilibrium molecular dynamics simulations. Recently, Machine Learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a relatively inexpensive manner. However, ML

David M T Kuo, Yia-Chung Chang

Transport and thermoelectric properties of finite textured graphene nanoribbons (t-GNRs) connected to electrodes with various coupling strengths are theoretically studied in the framework of the tight-binding model and Green's function approach. Due to quantum constriction induced by the indented edges, such t-GNRs behave like serially-coupled graphene quant

C. Royon

We describe the most recent results from the TOTEM collaboration on elastic, inelastic and total cross sections as well as the odderon discovery by the D0 and TOTEM collaborations.

Jongha Kim, Jinheon Baek, Sung Ju Hwang

In this work, we propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects and their spatial-semantic similarities, respectively. Specifically, we aim to consider relationships among objects for effectively contextualizing them. To achieve this, we first detect objects and then measure

Gilles P. Leborgne

A covariant formulation of the virtual power principle based on Lie derivatives is proposed. The Lie covariant approach does not require an inner product and the Cauchy deformation tensor to start, but, at first order in a Galilean Euclidean setting, gives the usual linear results classically obtained with the Cauchy deformation tensor. The Lie approach may

Nicklas Boserup, Raghavendra Selvan

Learning discriminative representations of unlabelled data is a challenging task. Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks. In this work, we propose a simple and efficient framework for self-supervised image segmentation using contrasti

Giacomo Oliveri, Marco Salucci, Andrea Massa

The ability of passive flat patterned electromagnetic skins (EMSs) to overcome the asymptotic limit of the total path attenuation (TPA) of flat metallic reflectors of arbitrary size in non-line-of-sight (NLOS) specular wireless links is assessed. Closed-form expressions for the achievable TPA in EMS-powered NLOS links as well as the condition on the panel si

Lukas Achatz, Lukas Bulla, Evelyn A. Ortega, Michael Bartokos

Entanglement distribution is at the heart of most quantum communication protocols. Inevitable loss of photons along quantum channels is a major obstacle for distributing entangled photons over long distances, as the no-cloning theorem forbids the information to simply be amplified along the way as is done in classical communication. It is therefore desirable

Iñigo Liberal, J. Enrique Vázquez-Lozano

Temporal metamaterials empower novel forms of wave manipulation with direct applications to quantum state transformations. In this work, we investigate vacuum amplification effects in anisotropic temporal boundaries. Our results theoretically demonstrate that the anisotropy of the temporal boundary provides control over the angular distribution of the genera

Martin de Wit, Luciano Gottardi, Marcel L. Ridder, Kenichiro Nagayoshi

Transition edge sensor (TES) microcalorimeters and bolometers are used for a variety of applications. The sensors are based on the steep temperature-dependent resistance of the normal-to-superconducting transition, and are thus intrinsically sensitive to magnetic fields. Conventionally the detectors are shielded from stray magnetic fields using external magn

Joey Beauvais-Feisthauer, Yatin Patel, Andrew Salch

Over fields of characteristic zero, we construct equivalences between certain categories of bialgebras which are generated by grouplikes and generalized primitives, and certain categories of structured Lie algebras. The relevant families of bialgebras include many which are not connected, and which fail to admit antipodes.

Kexuan Xin, Zequn Sun, Wen Hua, Wei Hu

Entity alignment is a crucial task in knowledge graph fusion. However, most entity alignment approaches have the scalability problem. Recent methods address this issue by dividing large KGs into small blocks for embedding and alignment learning in each. However, such a partitioning and learning process results in an excessive loss of structure and alignment.

Svetlana Pavlitskaya, Nikolai Polley, Michael Weber, J. Marius Zöllner

Taking into account information across the temporal domain helps to improve environment perception in autonomous driving. However, it has not been studied so far whether temporally fused neural networks are vulnerable to deliberately generated perturbations, i.e. adversarial attacks, or whether temporal history is an inherent defense against them. In this wo

Xiaoyan Yang, Jiaojiao Lu

Let $\mathfrak{a}$ be an ideal of a commutative noetherian ring $R$ and $M, N$ two finitely generated $R$-modules. By using a spectral sequence argument, it is shown that if either $\mathrm{dim}_RM\leq2$ and $\mathrm{H}^{i}_\mathfrak{a}(N)$ are $\mathfrak{a}$-cofinite for all $i\geq0$, or $\mathrm{H}^{i}_\mathfrak{a}(N)$ is an $\mathfrak{a}$-cofinite module

Dewang Hou, Yuanyuan Du, Kai Zhao, Yang Zhao

With the wide application of sparse ToF sensors in mobile devices, RGB image-guided sparse depth completion has attracted extensive attention recently, but still faces some problems. First, the fusion of multimodal information requires more network modules to process different modalities. But the application scenarios of sparse ToF measurements usually deman

Xingyu Tian, Zhifan Ye, Alec Lu, Licheng Guo

Stencil computation is one of the fundamental computing patterns in many application domains such as scientific computing and image processing. While there are promising studies that accelerate stencils on FPGAs, there lacks an automated acceleration framework to systematically explore both spatial and temporal parallelisms for iterative stencils that could

Zhangyang Xiong, Dong Du, Yushuang Wu, Jingqi Dong

It is very challenging to accurately reconstruct sophisticated human geometry caused by various poses and garments from a single image. Recently, works based on pixel-aligned implicit function (PIFu) have made a big step and achieved state-of-the-art fidelity on image-based 3D human digitization. However, the training of PIFu relies heavily on expensive and

Paul Kremer, Hamed Rahimi Nohooji, Jose Luis Sanchez-Lopez, Holger Voos

This work introduces TRIGGER, the first lighTweight univeRsal jammInG Gripper for aErial gRasping. TRIGGER is an omnidirectional, landing-capable aerial grasping system with resilience and robustness to collisions and inherent passive compliance. In particular, this work presents the design, fabrication, and experimental validation of a novel, intelligent, m

Stefania Barsanti, Matthew Colless, Charlotte Welker, Sree Oh

We study the alignments of galaxy spin axes with respect to cosmic web filaments as a function of various properties of the galaxies and their constituent bulges and discs. We exploit the SAMI Galaxy Survey to identify 3D spin axes from spatially-resolved stellar kinematics and to decompose the galaxy into the kinematic bulge and disc components. The GAMA su

MeiXing Dong, Ruixuan Sun, Laura Biester, Rada Mihalcea

The COVID-19 pandemic disrupted everyone's life across the world. In this work, we characterize the subjective wellbeing patterns of 112 cities across the United States during the pandemic prior to vaccine availability, as exhibited in subreddits corresponding to the cities. We quantify subjective wellbeing using positive and negative affect. We then measure

Archit Gupta, Arvind Easwaran

Duckiebots are low-cost mobile robots that are widely used in the fields of research and education. Although there are existing self-driving algorithms for the Duckietown platform, they are either too complex or perform too poorly to navigate a multi-lane track. Moreover, it is essential to give memory and computational resources to a Duckiebot so it can per

Tomonari Mizoguchi, Yasuhiro Hatsugai

We propose random tight-binding models that host macroscopically degenerate zero energy modes and belong to the unitary class. Specifically, we employ the molecular-orbital representation, where a Hamiltonian is constructed by a set of non-orthogonal orbitals composed of linear combinations of atomic orbitals. By setting the coefficients appearing in molecul

Marie Trin

In [Bon88], Bonahon gave a construction of Thurston's compactification of Teichm{\"u}ller space using geodesic currents. His argument only applies in the case of closed surfaces, and there are good reasons for that. We present a variant which applies to surfaces of finite area.

Jinyoung Jun, Jae-Han Lee, Chul Lee, Chang-Su Kim

We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features. The proposed network is composed of a shared encoder and three decoders, called G-Net, N-Net, and M-Net, which estimate gradient maps, a normalized depth map, and a metric depth map, respectively. M-Net learns to esti

Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu

Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and Local-Global Conditional Networks (CRCNet) for few-shot segmentation. Unlike previous works that only predict the query image's mask, our proposed model concurrently makes predictio

Ali Borji

My goal in this paper is twofold: to study how well deep models can understand the images generated by DALL-E 2 and Midjourney, and to quantitatively evaluate these generative models. Two sets of generated images are collected for object recognition and visual question answering (VQA) tasks. On object recognition, the best model, out of 10 state-of-the-art o

Xintian Han, Mark Goldstein, Rajesh Ranganath

Survival analysis, the art of time-to-event modeling, plays an important role in clinical treatment decisions. Recently, continuous time models built from neural ODEs have been proposed for survival analysis. However, the training of neural ODEs is slow due to the high computational complexity of neural ODE solvers. Here, we propose an efficient alternative

Tianwei Chen, Noa Garcia, Mayu Otani, Chenhui Chu

Is more data always better to train vision-and-language models? We study knowledge transferability in multi-modal tasks. The current tendency in machine learning is to assume that by joining multiple datasets from different tasks their overall performance will improve. However, we show that not all the knowledge transfers well or has a positive impact on rel

Parviz Goodarzi

We study anisotropic inflation in non-minimal derivative coupling model where the scalar field non-minimally coupled to the $U(1)$ gauge fields and derivative of the scalar field non-minimally coupled to the Einstein tensor. Within the framework we find power-law anisotropic solutions in this model when both the inflaton potential and the gauge kinetic funct

Andrei T. Patrascu

Neural networks are being used to improve the probing of the state spaces of many particle systems as approximations to wavefunctions and in order to avoid the recurring sign problem of quantum monte-carlo. One may ask whether the usual classical neural networks have some actual hidden quantum properties that make them such suitable tools for a highly couple

Fu-Lai Wang, Hong-Yan Zhou, Zhan-Wei Liu, Xiang Liu

Inspired by the observation of the $P_{cs}(4459)$ and $P_{\psi s}^{\Lambda}(4338)$, we systematically investigate the magnetic moments, the transition magnetic moments, and the radiative decay behaviors of the $S$-wave isoscalar $\Xi_c^{(\prime)}\bar D^{(*)}$ molecular pentaquark states in this work. Our quantitative investigation shows that their electromag

Han Wang, Jingzheng Huang, Chaozheng Huang, Hongjing Li

Experiments based on free space platform have demonstrated that the weak value amplification (WVA) technique can provide high sensitivity and precision for optical sensing and metrology. To promote this technique for real-world applications, it is more suitable to implement WVA based on optical fiber platform due to the lower cost, smaller scale and higher s

Wang Yang, Alberto Nocera, Chao Xu, Ian Affleck

Recently, extended gapless phases with emergent SU(2)$_1$ conformal invariance occupying finite regions in the phase diagrams have been found in one-dimensional spin-1/2 models with nonsymmorphic $O_h$ symmetry groups. In this work, we investigate the question of whether the conditions for emergent SU(2)$_1$ invariance can be loosened. We find that besides t

Shen Li, Bryan Hooi

Of particular interest is to discover useful representations solely from observations in an unsupervised generative manner. However, the question of whether existing normalizing flows provide effective representations for downstream tasks remains mostly unanswered despite their strong ability for sample generation and density estimation. This paper investiga

Bai Xue, Naijun Zhan, Martin Fränzle

In this paper we propose a novel semi-definite programming approach that solves reach-avoid problems over open (i.e., not bounded a priori) time horizons for dynamical systems modeled by polynomial stochastic differential equations. The reach-avoid problem in this paper is a probabilistic guarantee: we approximate from the inner a $p$-reach-avoid set, i.e.,

Min Seok Kim

We present an effective way to predict search query-item relationship. We combine pre-trained transformer and LSTM models, and increase model robustness using adversarial training, exponential moving average, multi-sampled dropout, and diversity based ensemble, to tackle an extremely difficult problem of predicting against queries not seen before. All of our

Noé Bárcenas, Manuel Sedano-Mendoza

In this article we propose a metric variation on the C^0-version of the Zimmer program for three manifolds. After a reexamination of the isometry groups of geometric three-manifolds, we consider homomorphisms defined on higher rank lattices to them and establish a dichotomy betweeen finite image or infinite volume of the quotient. Along the way, we enumerate

Lisa Seccia

Closed graphs have been characterized by Herzog et al. as the graphs whose binomial edge ideals have a quadratic Gr\"obner basis with respect to a diagonal term order. In this paper, we focus on a generalization of closed graphs, namely weakly-closed graphs (or co-comparability graphs). Build on some results about Knutson ideals of generic matrices, we chara

Dae-Woong Lee, P. Christopher Staecker

In this article, we develop the basic theory of digital topological groups. The basic definitions directly lead to two separate categories, based on the details of the continuity required of the group multiplication. We define $\NP_1$- and $\NP_2$-digital topological groups, and investigate their properties and algebraic structure. The $\NP_2$ category is ve

Pengcheng Tang, Xuejun Zhang

Let $\mu$ be a positive Borel measure on $[0,1)$. If $f \in H(\mathbb{D})$ and $\alpha>-1$, the generalized integral type Hilbert operator defined as follows: $$\mathcal{I}_{\mu_{\alpha+1}}(f)(z)=\int^1_{0} \frac{f(t)}{(1-tz)^{\alpha+1}}d\mu(t), \ \ \ z\in \mathbb{D} .$$ The operator $\mathcal{I}_{\mu_{1}}$ has been extensively studied recently. In this pape

Pranali Roy Chowdhury, Sergei Petrovskii, Vitaly Volpert, Malay Banerjee

Spatio-temporal complexity of ecological dynamics has been a major focus of research for a few decades. Pattern formation, chaos, regime shifts and long transients are frequently observed in field data but specific factors and mechanisms responsible for the complex dynamics often remain obscure. An elementary building block of ecological population dynamics

Jinkui Hao, Ting Shen, Xueli Zhu, Yonghuai Liu

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation,