July 2022 arXiv papers — page 124

Lang Nie, Chunyu Lin, Kang Liao, Shuaicheng Liu

Not everybody can be equipped with professional photography skills and sufficient shooting time, and there can be some tilts in the captured images occasionally. In this paper, we propose a new and practical task, named Rotation Correction, to automatically correct the tilt with high content fidelity in the condition that the rotated angle is unknown. This t

Alexander K. Y. Tam, Matthew J. Simpson

We investigate pattern formation in a two-dimensional (2D) Fisher--Stefan model, which involves solving the Fisher--KPP equation on a compactly-supported region with a moving boundary. By combining the Fisher--KPP and classical Stefan theory, the Fisher--Stefan model alleviates two limitations of the Fisher--KPP equation for biological populations. In this w

Yuan Fang, Yi Huang, Chuan Ma, Yinghao Jin

This letter studies the energy-efficient design in a downlink multi-antenna multi-user system consisting of a multi-antenna base station (BS) and multiple single-antenna users, by considering the practical non-linear power amplifier (PA) efficiency and the on-off power consumption of radio frequency (RF) chain at each transmit antenna. Under this setup, we j

Lixin Zou, Haitao Mao, Xiaokai Chu, Jiliang Tang

The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms. However, promising results on the existing benchmark datasets may not be extended to the practical scenario due to the following disadvantages observed from those popular benchmark datasets: (1) outdated semantic feat

Chen-Han Tsai, Yu-Shao Peng

Lung nodules can be an alarming precursor to potential lung cancer. Missed nodule detections during chest radiograph analysis remains a common challenge among thoracic radiologists. In this work, we present a multi-task lung nodule detection algorithm for chest radiograph analysis. Unlike past approaches, our algorithm predicts a global-level label indicatin

Benjamin Carleton, Michael C. Chavrimootoo, Lane A. Hemaspaandra, David E. Narváez

Electoral control types are ways of trying to change the outcome of elections by altering aspects of their composition and structure [BTT92]. We say two compatible (i.e., having the same input types) control types that are about the same election system E form a collapsing pair if for every possible input (which typically consists of a candidate set, a vote

Shreya Ghosh, Abhinav Dhall, Munawar Hayat, Jarrod Knibbe

In challenging real-life conditions such as extreme head-pose, occlusions, and low-resolution images where the visual information fails to estimate visual attention/gaze direction, audio signals could provide important and complementary information. In this paper, we explore if audio-guided coarse head-pose can further enhance visual attention estimation per

Qian Ye, Masanori Suganuma, Takayuki Okatani

In this paper, we consider the problem in defocus image deblurring. Previous classical methods follow two-steps approaches, i.e., first defocus map estimation and then the non-blind deblurring. In the era of deep learning, some researchers have tried to address these two problems by CNN. However, the simple concatenation of defocus map, which represents the

Kemal Davaslioglu, Serdar Boztas, Mehmet Can Ertem, Yalin E. Sagduyu

Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other domains in the form of transfer learning without accounting for the unique characteristics of wireless signals. Self-supervised learning (S

Xiaona Fang, Lihua You

Let $G$ be a graph of size $m$ and $\rho(G)$ be the spectral radius of its adjacency matrix. A graph is said to be $F$-free if it does not contain a subgraph isomorphic to $F$. In this paper, we prove that if $G$ is a $K_{2,r+1}$-free non-star graph with $m\geq (4r+2)^2+1$, then $\rho(G)\leq \rho(S_m^1)$, with equality if and only if $G\cong S_m^1$. Recently

Xinlei Zhao, Fengjie Ma, Peng-Jie Guo, Zhong-Yi Lu

Unconventional Weyl semimetals have attracted intensive research interest in condensed matter physics and materials science, but they are very rare in two dimensions. In this work, based on symmetry analysis and the first-principles electronic structure calculations, we predict that the Si/Bi van der Waals heterostructure is a two-dimensional unconventional

Károly J. Böröczky, Ádám Sagmeister

We prove a stability version of the isodiametric inequality on the sphere and in the hyperbolic space.

A. J. Barclay, A. R. W. McKellar, N. Moazzen-Ahmadi

High resolution spectra of (CO$_2$)$_2$-Rg trimers (Rg = Ne, Ar, Kr, and Xe) in the region of the CO2 nu3 fundamental (~2350 cm$^{-1}$) are reported, using a tunable OPO laser source to probe a pulsed supersonic slit jet expansion. These (CO$_2$)$_2$-Rg transitions tend to be hidden among stronger spectra due to other species, such as CO$_2$-Rg and (CO$_2$)$

Bo-Kai Ruan, Hong-Han Shuai, Wen-Huang Cheng

Transformers have achieved great success in natural language processing. Due to the powerful capability of self-attention mechanism in transformers, researchers develop the vision transformers for a variety of computer vision tasks, such as image recognition, object detection, image segmentation, pose estimation, and 3D reconstruction. This paper presents a

Alec Hannaford-Gunn, Edward Sankey, Kadir Utku Can, Roger Horsley

Theoretical predictions of the proton--neutron mass difference and measurements of the proton's charge radius require inputs from the Compton amplitude subtraction function. Model-dependent and non-relativistic calculations of this subtraction function vary significantly, and hence it contributes sizeable uncertainties to the aforementioned physical quantiti

Yongtao Liu, Rama K. Vasudevan, Kyle P. Kelley, Hiroshi Funakubo

We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for

Xin Gu, Hanhua Ye, Guang Chen, Yufei Wang

This paper describes our champion solution for the CVPR2022 Generic Event Boundary Captioning (GEBC) competition. GEBC requires the captioning model to have a comprehension of instantaneous status changes around the given video boundary, which makes it much more challenging than conventional video captioning task. In this paper, a Dual-Stream Transformer wit

Stefan Johann Hofmeister, Michael Hahn, Daniel Wolf Savin

The point-spread function (PSF) of an imaging system describes the response of the system to a point source. Accurately determining the PSF enables one to correct for the combined effects of focussing and scattering within the imaging system, and thereby enhance the spatial resolution and dynamic contrast of the resulting images. We present a semi-empirical

Tracy Qian, Andy Xie, Camille Bruckmann

The explosion in novel NLP word embedding and deep learning techniques has induced significant endeavors into potential applications. One of these directions is in the financial sector. Although there is a lot of work done in state-of-the-art models like GPT and BERT, there are relatively few works on how well these methods perform through fine-tuning after

Wenqi Shao, Xun Zhao, Yixiao Ge, Zhaoyang Zhang

This paper addresses an important problem of ranking the pre-trained deep neural networks and screening the most transferable ones for downstream tasks. It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive. R

Yiqi Lin, Frank Windmeijer, Xinyuan Song, Qingliang Fan

We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the "sparsest rule", which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators ba

Lu Gan, Jessy W. Grizzle, Ryan M. Eustice, Maani Ghaffari

This work reports on developing a deep inverse reinforcement learning method for legged robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive sensory data. Existing works use robot-agnostic exteroceptive environmental features or handcrafted kinematic features; instead, we propose to also learn robot-specific inertial

Anna B. Stephenson, Ming Xiao, Victoria Hwang, Liangliang Qu

Photonic balls are spheres tens of micrometers in diameter containing assemblies of nanoparticles or nanopores with a spacing comparable to the wavelength of light. When these nanoscale features are disordered, but still correlated, the photonic balls can show structural color with low angle-dependence. Their colors, combined with the ability to add them to

Junxi Zhang, Yaozhong Hu

Normalized random measures with independent increments represent a large class of Bayesian nonaprametric priors and are widely used in the Bayesian nonparametric framework. In this paper, we provide the posterior consistency analysis for normalized random measures with independent increments (NRMIs) through the corresponding Levy intensities used to characte

Durmus Alp Emre Acar, Venkatesh Saligrama

We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures. We introduce training with a side objective to the devices of higher complexities to jointly train different architectures in a federated setting. We empirically show that our approach improves the performance of different

Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani

This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks. We propose to clean the training set by utilizing a distinct property of knowledge-intensive generation: The connection of query-answer pairs to items in the knowledge base. We filter training examples via a threshold of confidence on the relevance

Prakruthi Prabhakar, Yiping Yuan, Guangyu Yang, Wensheng Sun

Mobile notification systems play a major role in a variety of applications to communicate, send alerts and reminders to the users to inform them about news, events or messages. In this paper, we formulate the near-real-time notification decision problem as a Markov Decision Process where we optimize for multiple objectives in the rewards. We propose an end-t

Rasoul Ghadimi, Takanori Sugimoto, Takami Tohyama

Quasicrystal is now open to search for novel topological phenomena enhanced by its peculiar structure characterized by an irrational number and high-dimensional primitive vectors. Here we extend the concept of a topological insulator with an emerging staggered local magnetic flux (i.e., without external fields), similar to the Haldane's honeycomb model, to t

Ruihong Wang, Jianguo Wang, Stratos Idreos, M. Tamer Özsu

Memory disaggregation (MD) allows for scalable and elastic data center design by separating compute (CPU) from memory. With MD, compute and memory are no longer coupled into the same server box. Instead, they are connected to each other via ultra-fast networking such as RDMA. MD can bring many advantages, e.g., higher memory utilization, better independent s

Qi Zhao, Wenjing Liu, Qizhi Fang, Qingqin Nong

In this paper, we propose a constrained heterogeneous facility location model where a set of alternative locations are feasible for building facilities and the number of facilities built at each location is limited. Supposing that a set of agents on the real line can strategically report their locations and each agent's cost is her distance to the further fa

Mehak Maniktala, Min Chi, Tiffany Barnes

Research on intelligent tutoring systems has been exploring data-driven methods to deliver effective adaptive assistance. While much work has been done to provide adaptive assistance when students seek help, they may not seek help optimally. This had led to the growing interest in proactive adaptive assistance, where the tutor provides unsolicited assistance

James Thornton, Michael Hutchinson, Emile Mathieu, Valentin De Bortoli

Score-based generative models exhibit state of the art performance on density estimation and generative modeling tasks. These models typically assume that the data geometry is flat, yet recent extensions have been developed to synthesize data living on Riemannian manifolds. Existing methods to accelerate sampling of diffusion models are typically not applica

Komal Chawla, Abhishek Gupta, Abhijeet S. Bhardwaj, Ramathasan Thevamaran

Protective applications in extreme environments demand thermally stable materials with superior modulus, strength, and specific energy absorption (SEA) at lightweight. However, these properties typically have a trade-off. Hierarchically architected materials--such as the architected vertically aligned carbon nanotube (VACNT) foams--offer the potential to ove

K. Azizi, Y. Sarac, H. Sundu

The developments in the experimental facilities and analyses techniques have recently lead to the observation of many hadronic states ranging from excitations of conventional hadrons to various exotic states. The baryons with single heavy quark are among these states providing an attractive field of research to get a better understanding of the nonperturbati

Masato Fujita

We consider an almost o-minimal expansion of an ordered group $\mathcal M=(M,<,+,0,\ldots)$ and its tame extension $\mathcal N=(N,<,+,0,\ldots)$. We demonstrate that the subset $\{x \in M^n\;|\; \mathcal N \models \Phi(x,a)\}$ of $M^n$ defined by a formula $\Phi(x,y)$ with $\mathcal M$-bounded parameters $a$ in $\mathcal N$ is $\mathcal M$-definable. We also

Nicolas Pröllochs, Stefan Feuerriegel

Social media platforms disseminate extensive volumes of online content, including true and, in particular, false rumors. Previous literature has studied the diffusion of offline rumors, yet more research is needed to understand the diffusion of online rumors. In this paper, we examine the role of lifetime and crowd effects in social media sharing behavior fo

Filipe Pereira

Driven largely by multiple ground-based radial-velocity (RV) surveys and photometric space missions such as Kepler and K2, the discovery of new exoplanets has increased rapidly since the early 2000s. However, due to a target selection bias in favor of main-sequence stars, only a handful of transiting planets have been found orbiting evolved hosts. These plan

David Bensaïd, Amit Bracha, Ron Kimmel

Evaluating the similarity of non-rigid shapes with significant partiality is a fundamental task in numerous computer vision applications. Here, we propose a novel axiomatic method to match similar regions across shapes. Matching similar regions is formulated as the alignment of the spectra of operators closely related to the Laplace-Beltrami operator (LBO).

Riccardo Doyle

Several novel frameworks for hyperparameter search have emerged in the last decade, but most rely on strict, often normal, distributional assumptions, limiting search model flexibility. This paper proposes a novel optimization framework based on upper confidence bound sampling of conformal confidence intervals, whose weaker assumption of exchangeability enab

Marco Ripà

Given any $n \in \mathbb{Z}^{+}$, we constructively prove the existence of covering paths and circuits in the plane which are characterized by the same link length of the minimum-link covering trails for the two-dimensional grid $G_n^2 := \{0,1, \ldots, n-1\} \times \{0, 1, \ldots, n-1\}$. Furthermore, we introduce a general algorithm that returns a covering

Lisa Hartung, Michèle Lehnen

We study the height of the maximal particle at time $t$ of a one dimensional branching Brownian motion with a space-dependent branching rate. The branching rate is set to zero in finitely many intervals (obstacles) of order $t$. We obtain almost sure asymptotics of the first order of the maximum, describe the path of a particle reaching this height and descr

Sanjana Das

For primes $p$, we study the maximal possible size of a $p$-core $p'$-partition (a partition with no hook lengths or parts divisible by $p$). McDowell recently proved that the maximum is attained by a unique partition, say $\Lambda_p$. Using his graph theoretic description of $\Lambda_p$, we prove for $p > 10^6$ that \[\frac{1}{24}p^6 - p^5\sqrt{p} < |\Lambd

Gennadiy Feldman

According to the well-known Heyde theorem the Gaussian distribution on the real line is characterized by the symmetry of the conditional distribution of one linear form of independent random variables given another. We study analogues of this theorem for some locally compact Abelian groups. We consider linear forms of two independent random variables with va

Vasileios Chousionis, Sean Li, Robert Young

We prove that the Heisenberg Riesz transform is $L_2$--unbounded on a family of intrinsic Lipschitz graphs in the first Heisenberg group $\mathbb{H}$. We construct this family by combining a method from \cite{NY2} with a stopping time argument, and we establish the $L_2$--unboundedness of the Riesz transform by introducing several new techniques to analyze s

Hua-Sheng Shao, David d'Enterria

The automated generation of arbitrary exclusive final states produced via photon fusion in ultraperipheral high-energy collisions of protons and/or nuclei is implemented in the MadGraph5_aMC@NLO and HelacOnia Monte Carlo codes. Cross sections are calculated in the equivalent photon approximation using $\gamma$ fluxes derived from electric dipole and charge f

Rafael Haenel, Tarun Tummuru, Marcel Franz

Twisting two monolayers of a high-$T_c$ cuprate superconductor can engender a chiral topological state with spontaneously broken time reversal symmetry $\mathcal{T}$. A crucial ingredient required for the emergence of a gapped topological phase is electron tunneling between the CuO$_2$ planes, whose explicit form (in an ideal clean sample) is dictated by the

Shailesh Chaudhari, HyukJoon Kwon

We address the problem of computing the interference-plus-noise covariance matrix from a sparsely located demodulation reference signal (DMRS) for spatial domain interference whitening (IW). The IW procedure is critical at the user equipment (UE) to mitigate the co-channel interference in 5G new radio (NR) systems. A supervised learning based algorithm is pr

Wei Ding, Sansit Patnaik, Fabio Semperlotti

This study presents a comprehensive theoretical framework to simulate the response of multiscale nonlocal elastic beams. By employing distributed-order (DO) fractional operators with a fourth-order tensor as the strength-function, the framework can accurately capture anisotropic behavior of 2D heterogeneous beams with nonlocal effects localized across multip

Mauricio Valenzuela, Jorge Zanelli

We analyze the Rarita--Schwinger (RS) massless theory in the Lagrangian and Hamiltonian approaches. At the Lagrangian level, the standard gamma-trace gauge fixing constraint leaves a spin-1/2 and a spin-3/2 propagating Poincar\'e group helicities. At the Hamiltonian level, the result depends on whether the Dirac conjecture--that all first class constraints g

Mendel Nguyen, Yuya Tanizaki, Mithat Ünsal

While the $\theta$ dependence of field theories is $2\pi$ periodic, the ground-state wavefunctions at $\theta$ and $\theta+2\pi$ often belong to different classes of symmetry-protected topological states. When this is the case, a continuous change of the $\theta$ parameter can introduce an interface that supports a nontrivial field theory localized on the wa

S. Moretti, S. Semlali, C. H. Shepherd-Themistocleous

We present a reinterpretation study of existing results from the CMS Collaboration, specifically, searches for light Beyond the Standard Model (BSM) Higgs pairs produced in the chain decay $pp\to H_{\rm SM}\to hh(aa)$ into a variety of final states, in the context of the CP-conserving 2-Higgs Doublet Model (2HDM) Type-I. Through this, we test the Large Hadro

Ling Li, Ali Shafiee Ardestani, Joseph Hassoun

Though image transformers have shown competitive results with convolutional neural networks in computer vision tasks, lacking inductive biases such as locality still poses problems in terms of model efficiency especially for embedded applications. In this work, we address this issue by introducing attention masks to incorporate spatial locality into self-att

Henry Helvajian, Alan Rosenthal, John Poklemba, Thomas A. Battista

We present initial results of an ongoing engineering study on the feasibility of a space mission to the focal region of the solar gravitational lens (SGL). The mission goal is to conduct exoplanet imaging operations at heliocentric distances in the range ~548-900 astronomical units (AU). Starting at 548 AU from the Sun, light from an exoplanet located behind

Sudipta Das

In this paper, we work with certain families of ideals called $p$-families in rings of prime characteristic. This family of ideals is present in the theories of tight closure, Hilbert-Kunz multiplicity, and $F$-signature. For each $p$-family of ideals, we attach a Euclidean object called $p$-body, which is analogous to the Newton Okounkov body associated wit

Rafael Prieto Curiel, Jorge E. Patino, Brilé Anderson

A large proportion of Africa's infrastructure is yet to be built. Where and how these new buildings are constructed matters since today's decisions will last for decades. The resulting morphology of cities has lasting implications for a city's energy needs. Estimating and projecting these needs has always been challenging in Africa due to the lack of data. Y

Fabio Bernasconi, Liam Stigant

In this note we prove the semiampleness conjecture for klt Calabi--Yau surface pairs over an excellent base ring. As applications we deduce that generalised abundance and Serrano's conjecture hold for surfaces. Finally, we study the semiampleness conjecture for Calabi--Yau threefolds over a mixed characteristic DVR.

Guanxiong Shen, Junqing Zhang, Alan Marshall, Mikko Valkama

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by the intrinsic hardware impairments. State-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. I

Markus Millinger, Lina Reichenberg, Fredrik Hedenus, Göran Berndes

Abatement options for the hard-to-electrify parts of the transport sector are needed to achieve ambitious emissions targets. Biofuels based on biomass, electrofuels based on renewable hydrogen and a carbon source, as well as fossil fuels compensated by carbon dioxide removal (CDR) are the main options. Currently, biofuels are the only renewable fuels availab

Guanxiong Shen, Junqing Zhang, Alan Marshall, Roger Woods

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. RFFI is implemented in the wireless receiver and acts to extract the transmitter impairments and then perform classification. The receiver hardware impairments will actually i

Vladan Majerech

100 prisoners and a light bulb is a long standing mathematical puzzle. The problem was studied mostly in 2002 [5], 2003 [1], and 2004 [3]. Solutions in published articles had average number of visits above 3850, but best solutions on forums had (declared) average number of visits around 3500. I spent some time in 2007-2009 to optimize the communication strat

Warren R. Brown, Mukremin Kilic, Alekzander Kosakowski, A. Gianninas

We present the discovery of 17 double white dwarf (WD) binaries from our on-going search for extremely low mass (ELM) <0.3 Msun WDs, objects that form from binary evolution. Gaia parallax provides a new means of target selection that we use to evaluate our original ELM Survey selection criteria. Cross-matching the Gaia and Sloan Digital Sky Survey (SDSS) cat

Hantao Cui

It is a general notion that, in transient stability simulations, reducing the number of algebraic variables for the differential-algebraic equations (DAE) can improve the simulation performance. Many simulation programs split algebraic variables internal to a dynamic model from the full DAE and evaluate them outside each iterative step, using results from th

Frederic Gmeiner, Kenneth Holstein, Nikolas Martelaro

Generative, ML-driven interactive systems have the potential to change how people interact with computers in creative processes - turning tools into co-creators. However, it is still unclear how we might achieve effective human-AI collaboration in open-ended task domains. There are several known challenges around communication in the interaction with ML-driv

Gabriel Hale, Bjorn Vogen, Matthew Wright

Magic squares are well-known arrangements of integers with common row, column, and diagonal sums. Various other magic shapes have been proposed, but triangles have been somewhat overlooked. We introduce certain triangular arrangements of integers with common sums in three directions, which we call magic triangles. For small sizes of these triangles, we count

Nada Sahlab, Nasser Jazdi, Michael Weyrich

Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradig

Yuan Gao, Siman Yang

Locally repairable codes(LRCs) play important roles in distributed storage systems(DSS). LRCs with small locality have their own advantages since fewer available symbols are needed in the recovery of erased symbols. In this paper, we prove an upper bound on the dimension of LRCs with minimum distance $d\geq 7$. An upper bound on the length of almost optimal

Yang Pan, Mahfujur Rahaman, Lu He, Ilya Milekhin

We report on large exciton tuning in WSe$_2$ monolayers via substrate induced non-degenerate doping. We observe a redshift of $\sim$62 meV for the $A$ exciton together with a 1-2 orders of magnitude photoluminescence (PL) quenching when the monolayer WSe$_2$ is brought in contact with highly oriented pyrolytic graphite (HOPG) compared to the dielectric subst

Avishek Ghosh, Sayak Ray Chowdhury

We consider model selection for classic Reinforcement Learning (RL) environments -- Multi Armed Bandits (MABs) and Markov Decision Processes (MDPs) -- under general function approximations. In the model selection framework, we do not know the function classes, denoted by $\mathcal{F}$ and $\mathcal{M}$, where the true models -- reward generating function for

Nada Sahlab, Hesham Kahoul, Nasser Jazdi, Michael Weyrich

Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research activity, from which new concepts stem. However, with the massive availability of publications at a rapid growing rate, especially digitally, it becomes challenging to efficiently

Xingyu Liao, Hyowon Park

While ultra-thin layers of the LaNiO$_3$ film exhibit a remarkable metal-insulator transition as the film thickness becomes smaller than a few unit cell (u.c.), the formation of oxygen vacancies and their effects on the correlated electronic structure have been rarely studied. Here, we investigate the effects of the surface termination and the oxygen vacancy

Anway De, Thong Vo, Matthew Wright

Persistent homology, an algebraic method for discerning structure in abstract data, relies on the construction of a sequence of nested topological spaces known as a filtration. Two-parameter persistent homology allows the analysis of data simultaneously filtered by two parameters, but requires a bifiltration -- a sequence of topological spaces simultaneously

Susmit Jha, John Rushby

Shared intentionality is a critical component in developing conscious AI agents capable of collaboration, self-reflection, deliberation, and reasoning. We formulate inference of shared intentionality as an inverse reinforcement learning problem with logical reward specifications. We show how the approach can infer task descriptions from demonstrations. We al

Mnacho Echenim, Emmanuel Gobet, Anne-Claire Maurice

We design a novel calibration procedure that is designed to handle the specific characteristics of options on cryptocurrency markets, namely large bid-ask spreads and the possibility of missing or incoherent prices in the considered data sets. We show that this calibration procedure is significantly more robust and accurate than the standard one based on tra

Tomas Cerny, Amr S. Abdelfattah, Vincent Bushong, Abdullah Al Maruf

Microservice system solutions are driving digital transformation; however, fundamental tools and system perspectives are missing to better observe, understand, and manage these systems, their properties, and their dependencies. Microservices architecture leads towards decentralization, which implies many advantages to system operation; it, however, brings ch

Marius Beceanu, Michael Goldberg

We extend several fundamental estimates regarding spectral multipliers for the free Laplacian on $\mathbb R^3$ to the case of perturbed Hamiltonians of the form $-\Delta+V$, where $V$ is a scalar real-valued potential. In this paper, we prove resolvent estimates, a dispersive bound for the perturbed wave propagator, Mihlin multiplier and fractional integrati

Martin Ondrus, Ivor Cribben

Change point detection is a commonly used technique in time series analysis, capturing the dynamic nature in which many real-world processes function. With the ever increasing troves of multivariate high-dimensional time series data, especially in neuroimaging and finance, there is a clear need for scalable and data-driven change point detection methods. Cur

Shuai Huang, Mona Zehni, Ivan Dokmanić, Zhizhen Zhao

Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. A line of work starting with Kam (1980) employs the method of moments (MoM) with rotation-invariant Fourier features to solve UVT in the frequency domain, assuming that the orientations are uniformly distributed. This line of work includes the

David Jorrin, Martin Schvellinger

Symmetric and antisymmetric structure functions from electromagnetic deep inelastic scattering of charged leptons off spin-1/2 hadrons are investigated in the framework of a top-down holographic dual description. We consider the BPST Pomeron, type IIB superstring theory scattering amplitudes, and type IIB supergravity on AdS$_5 \times S^5$. In all cases it i

Aleksei Aleksandrov, Vladimir Peller

For a pair $(A,B)$ of not necessarily bounded and not necessarily commuting self-adjoint operators and for a function $f$ on the Euclidean space ${\Bbb R}^2$ that belongs to the inhomogeneous Besov class $B_{\infty,1}^1({\Bbb R}^2)$, we define the function $f(A,B)$ of these operators as a densely defined operator. We consider the problem of estimating the fu

Aviad Etzion, Itzik Klein

Mobile robots are used in industrial, leisure, and military applications. In some situations, a robot navigation solution relies only on inertial sensors and as a consequence, the navigation solution drifts in time. In this paper, we propose the MoRPI framework, a mobile robot pure inertial approach. Instead of travelling in a straight line trajectory, the r

Kenneth A. Jung, Thomas E. Markland

The third-order response lies at the heart of simulating and interpreting nonlinear spectroscopies ranging from two dimensional infrared (2D-IR) to 2D electronic (2D-ES), and 2D sum frequency generation (2D-SFG). The extra time and frequency dimensions in these spectroscopies provides access to rich information on the electronic and vibrational states presen

Gennady Voronov, Rose Lightheart, Joe Davison, Christoph A. Krettler

Small molecules in biological samples are studied to provide information about disease states, environmental toxins, natural product drug discovery, and many other applications. The primary window into the composition of small molecule mixtures is tandem mass spectrometry (MS2), which produces data that are of high sensitivity and part per million resolution

Devarshi Mukherjee, Guillermo Cortiñas

In this note, we construct a closed model structure on the category of $\mathbb{Z}/2\mathbb{Z}$-graded complexes of projective systems of ind-Banach spaces. When the base field is the fraction field $F$ of a complete discrete valuation ring $V$, the homotopy category of this model structure is the derived category of the quasi-abelian category $\overleftarro

Aidan Evans, Jorge Blanco

Logical Neural Networks (LNNs) are a type of architecture which combine a neural network's abilities to learn and systems of formal logic's abilities to perform symbolic reasoning. LLNs provide programmers the ability to implicitly modify the underlying structure of the neural network via logical formulae. In this paper, we take advantage of this abstraction

Aymeric Baradat, Elias Ventre

The Sinkhorn algorithm is the most popular method for solving the entropy minimization problem called the Schr\"odinger problem: in the non-degenerate cases, the latter admits a unique solution towards which the algorithm converges linearly. Here, motivated by recent applications of the Schr\"odinger problem with respect to structured stochastic processes (s

Matthias Springstein, Stefanie Schneider, Christian Althaus, Ralph Ewerth

Gesture as language of non-verbal communication has been theoretically established since the 17th century. However, its relevance for the visual arts has been expressed only sporadically. This may be primarily due to the sheer overwhelming amount of data that traditionally had to be processed by hand. With the steady progress of digitization, though, a growi

Aleksei Aleksandrov, Vladimir Peller

In this paper we study properties of the triangular projection ${\mathcal P}_n$ on the space of $n\times n$ matrices. The projection ${\mathcal P}_n$ annihilates the entries of an $n\times n$ matrix below the main diagonal and leaves the remaining entries unchanged. We estimate the $p$-norms of ${\mathcal P}_n$ as an operator on the Schatten--von Neumann cla

Tomas Cerny, Amr S. Abdelfattah, Vincent Bushong, Abdullah Al Maruf

Microservices are supporting digital transformation; however, fundamental tools and system perspectives are missing to better observe, understand, and manage these systems, their properties, and their dependencies. Microservices architecture leans toward decentralization, which yields many advantages to system operation; it, however, brings challenges to the

Wei Chen

In two-dimensional time-reversal symmetric topological insulators described by Dirac models, the ${\mathbb Z}_{2}$ topological invariant can be described by the spin Chern number. We present a linear response theory for the spin Berry curvature that integrates to the spin Chern number, and introduce its spectral function that can be measured at finite temper

Avi Ziskind, Sujeong Kim, Giedrius T. Burachas

Self-supervised learning methods overcome the key bottleneck for building more capable AI: limited availability of labeled data. However, one of the drawbacks of self-supervised architectures is that the representations that they learn are implicit and it is hard to extract meaningful information about the encoded world states, such as 3D structure of the vi

Yifan Peng, Siddharth Dalmia, Ian Lane, Shinji Watanabe

Conformer has proven to be effective in many speech processing tasks. It combines the benefits of extracting local dependencies using convolutions and global dependencies using self-attention. Inspired by this, we propose a more flexible, interpretable and customizable encoder alternative, Branchformer, with parallel branches for modeling various ranged depe

Yuzhang Shang, Dan Xu, Ziliang Zong, Liqiang Nie

Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit. However, there is still a huge performance gap between Binary Neural Networks (BNNs) and their full-precision (FP) counterparts. As the quantization error caused by weights binarization has been reduced in earlier works, the activations binarization be

Federico Fallucca, Christian Gleissner

In this note we present examples of complex algebraic surfaces of general type with canonical maps of degree $10$, $11$ and $14$. They are constructed as quotients of a product of two Fermat septics using certain free actions of the group $\mathbb Z_7^2$.

Dexiong Chen, Bowen Fan, Carlos Oliver, Karsten Borgwardt

We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional Euclidean space. Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis

Pierre Germain, Simon L. Rydin Myerson

We investigate norms of spectral projectors on thin spherical shells for the Laplacian on generic tori, including generic rectangular tori. We state a conjecture and partially prove it, improving on previous results concerning arbitrary tori.

Sree Harsha Bhimineni, Tianhang Zhou, Saeed Mahmoodpour, Mrityunjay Singh

Deep saline aquifers are one of the best options for large-scale and long-term hydrogen storage. Predicting the diffusion coefficient of hydrogen molecules at the conditions of saline aquifers is critical for modelling hydrogen storage. The diffusion coefficient of hydrogen molecules in chloride brine with different cations ($\mathrm{Na}^+$, $\mathrm{K}^+$,

César D. Fosco, Fernando C. Lombardo, Francisco D. Mazzitelli

We evaluate the probability of (de-)excitation and photon emission from a neutral, moving, non-relativistic atom, coupled to the quantum electromagnetic field and in the presence of a thin, perfectly conducting plane ("mirror"). These results extend, to a more realistic model, the ones we had presented for a scalar model, where the would-be electron was desc

Xuyang Yan, Shabnam Nazmi, Biniam Gebru, Mohd Anwar

In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data. ALCS employs a density-based clustering approach to explore the cluster structure from the data without requiring exhaustive parameter tuning. A bi-cluster boundary-based sample

Andrea Bevilacqua

We will briefly describe how to build a field theory of a complex scalar field in the $\kappa$-Minkowski spacetime. After introducing the action, we will shortly describe its properties under both continuous and deformed symmetry transformations. We will then describe how to compute the charges and describe their non-trivial properties due to $\kappa$-deform

Adam Van Etten

Machine learning is increasingly critical for analysis of the ever-growing corpora of overhead imagery. Advanced computer vision object detection techniques have demonstrated great success in identifying objects of interest such as ships, automobiles, and aircraft from satellite and drone imagery. Yet relying on computer vision opens up significant vulnerabi