October 2022 arXiv papers — page 107

Ze-Wei Zhao, Ling-Feng Wang, Ji-Guo Zhang, Jing-Fei Zhang

Interacting dark energy (IDE) scenario assumes that there exists a direct interaction between dark energy and cold dark matter, but this interaction is hard to be tightly constrained by the current data. Fast radio bursts (FRBs) will be seen in large numbers by future radio telescopes, and thus they have potential to become a promising low-redshift cosmologi

Xinghan Liu, Emiliano Lorini

Binary classifiers are traditionally studied by propositional logic (PL). PL can only represent them as white boxes, under the assumption that the underlying Boolean function is fully known. Binary classifiers used in practical applications and trained by machine learning are however opaque. They are usually described as black boxes. In this paper, we provid

Dongdong Ge, Tianhao Liu, Jinsong Liu, Jiyuan Tan

SOLNP+ is a derivative-free solver for constrained nonlinear optimization. It starts from SOLNP proposed in 1989 by Ye Ye with the main idea that uses finite difference to approximate the gradient. We incorporate the techniques of implicit filtering, new restart mechanism and modern quadratic programming solver into this new version with an ANSI C implementa

Kazuma Kobayashi, Shoaib Usman, Carlos Castano, Dinesh Kumar

It is important to accurately model materials' properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can significantly reduce the amount of experimentation required to develop new technologies. Robustness analysis of fuel and structural performance for harsh environments (such as pow

Kyle Boone, Matthew McQuinn

The light from a source at a distance d will arrive at detectors separated by 100 AU at times that differ by as much as 120 (d/100 Mpc)^{-1} nanoseconds because of the curvature of the wavefront. At gigahertz frequencies, the arrival time difference can be determined to better than a nanosecond with interferometry. If the space-time positions of the detector

Qing Li, Yu-Shen Liu, Jin-San Cheng, Cheng Wang

We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Previous methods focus on learning point weights to fit neighborhoods into a geometric surface approximated by a polynomial function with a predefined order, based on which normals are estimated. However, fit

Pia Bredt, Jürgen Reuter, Pascal Stienemeier

We summarize the status of automated NLO SM corrections for hadron and lepton collider processes in the multi-purpose event generator WHIZARD. The focus will be on NLO EW and QCD-EW mixed corrections at the LHC. Also, recent progress on the inclusion of EW corrections in future lepton collider processes and on the POWHEG-matched event generation in the NLO a

Abu Farzan Mitul, Bohan Zhou, Huiyu Zhao, Ming Han

Fiber bragg gratings (FBG) has wide variety of applications in sensor and laser devices. In this work, we have fabricated FBG using point by point (PbP) technique utilizing fs laser. FBGs spectral characteristics were explored through the variation of grating length, fiber holder position, laser power and coated layer. Fs FBG is fabricated on small mode fiel

Medeu Abishev, Aigerim Abylayeva, Andrea Addazi, Yermek Aldabergenov

We further analyze phenomenological implications of double axion monodromy inflation proposed in Ref. Phys. Rev. D 104, L081302 (2021), in gravitational wave physics. We show that in addition to chiral gravitational waves (GW) originating from gauge field instability, the model also predicts significant amount of non-chiral, scalar-induced gravitational wave

Ramis Khabibullin, Sergei Seleznev

This paper presents a fast algorithm for estimating hidden states of Bayesian state space models. The algorithm is a variation of amortized simulation-based inference algorithms, where a large number of artificial datasets are generated at the first stage, and then a flexible model is trained to predict the variables of interest. In contrast to those propose

Kai Melde, Minghui Shi, Heiner Kremer, Senne Seneca

The creation of whole 3D objects in one shot is an ultimate goal for rapid prototyping, most notably biofabrication, where conventional methods are typically slow and apply mechanical or chemical stress on biological cells. Here, we demonstrate one-step assembly of matter to form compact 3D shapes using acoustic forces, which is enabled by the superposition

Dean P. Foster, Sergiu Hart

We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomized forecasting procedures, we show that smooth calibration can be guaranteed by deterministic procedures. As a consequence, it does not matter if the forecasts are leaked, i.e., ma

Swapna Sindhu Mishra, Reza Loloee, Norman O. Birge

Ferromagnetic Josephson junctions exhibit fascinating physics and the potential for applications in superconducting logic and memory. The junctions in a demonstrated superconducting memory prototype contain a magnetic spin-valve structure with Ni as the fixed layer and NiFe (Permalloy) as the free layer. However, NiFe exhibits poor supercurrent transmission,

Tom Bachmann, Elden Elmanto, Jeremiah Heller

We use motivic colimits to construct power operations on the homotopy groups of normed motivic spectra admitting a (normed) map from HF_2. We establish enough of their standard properties to prove that the motivic dual Steenrod algebra is generated by one element under ring and power operations, establishing a motivic analog of Steinberger's theorem.

G. Banyard, L. Mahy, H. Sana, J. Bodensteiner

Recent evolutionary computations predict that a few percent of massive OB stars in binary systems should have a dormant BH companion. Despite several reported X-ray quiet OB+BH systems over the last couple of years, finding them with certainty remains challenging. These have great importance as they can be gravitational wave (GW) source progenitors, and are

Alessio Martini, Federico Santagati, Maria Vallarino

Let $\mathbb T_{q+1}$ denote the homogeneous tree of degree $q+1$ with the standard graph distance $d$ and the canonical flow measure $\mu$. The metric measure space $(\mathbb T_{q+1},d,\mu)$ is of exponential growth. Let $\mathcal{L}$ denote the flow Laplacian, which is a probabilistic Laplacian self-adjoint on $L^2(\mu)$. In this note, we prove some weight

Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò

While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging. Existing solutions either simply list local explanations for a given class, or generate a synthetic prototypical

Bowei Zhang

We study different restricted variations of the obnoxious facility location problem on a plane. The first is the constrained obnoxious facility location on a line segment (COFL-Line) problem. We provide an efficient algorithm for this problem that executes in $O(n ^ 2 \log k + n \log k \log (n^2 + k))$ time. Our result improves on the best known result of $O

Sifeddine Benahmed, Romain Postoyan, Mathieu Granzotto, Lucian Buşoniu

We present stability conditions for deterministic time-varying nonlinear discrete-time systems whose inputs aim to minimize an infinite-horizon time-dependent cost. Global asymptotic and exponential stability properties for general attractors are established. This work covers and generalizes the related results on discounted optimal control problems to more

Courtney McBeth, James Motes, Diane Uwacu, Marco Morales

Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In single-robo

Jian Wang, Xiang Long, Guowei Chen, Zewu Wu

High resolution and advanced semantic representation are both vital for dense prediction. Empirically, low-resolution feature maps often achieve stronger semantic representation, and high-resolution feature maps generally can better identify local features such as edges, but contains weaker semantic information. Existing state-of-the-art frameworks such as H

Sabrina Lato

Fiol, Garriga, and Yebra introduced the notion of pseudo-distance-regular vertices, which they used to develop a new characterization of distance-regular graphs. Building on that work, Fiol and Garriga developed the spectral excess theorem for distance-regular graphs. We extend both these characterizations to distancebiregular graphs and show how these chara

Wangzhen Guo, Qinkang Gong, Hanjiang Lai

Multi-hop QA requires reasoning over multiple supporting facts to answer the question. However, the existing QA models always rely on shortcuts, e.g., providing the true answer by only one fact, rather than multi-hop reasoning, which is referred as $\textit{disconnected reasoning}$ problem. To alleviate this issue, we propose a novel counterfactual multihop

Tom Bachmann, Elden Elmanto, Jeremiah Heller

We prove that the universal normed motivic spectrum of characteristic 2 over a scheme on which 2 is a unit, splits into a sum of motivic Eilenberg--MacLane spectra.

Stephen Cantrell, Eduardo Reyes

For any non-elementary hyperbolic group $\Gamma$, we find an outer automorphism invariant geodesic bicombing for the space of metric structures on $\Gamma$ equipped with a symmetrized version of the Thurston metric on Techim\"uller space. We construct and study a boundary for this space and show that it contains many well-known pseudo metrics including those

Tomasz Limisiewicz, Dan Malkin, Gabriel Stanovsky

Multilingual models have been widely used for cross-lingual transfer to low-resource languages. However, the performance on these languages is hindered by their underrepresentation in the pretraining data. To alleviate this problem, we propose a novel multilingual training technique based on teacher-student knowledge distillation. In this setting, we utilize

Yann-Meing Law, Daniel Appelö

The Hermite-Taylor method, introduced in 2005 by Goodrich, Hagstrom and Lorenz, is highly efficient and accurate when applied to linear hyperbolic systems on periodic domains. Unfortunately its widespread use has been prevented by the lack of a systematic approach to implementing boundary conditions. In this paper we present the Hermite-Taylor Correction Fun

Márton Erdélyi, Will Sawin, Árpád Tóth

We establish the exact structure of the cohomology associated to a certain matrix exponential sum investigated in prior work (arXiv:2109.00762) of the first and last author.

Magda Amiridi, Nicholas D. Sidiropoulos

Learning the multivariate distribution of data is a core challenge in statistics and machine learning. Traditional methods aim for the probability density function (PDF) and are limited by the curse of dimensionality. Modern neural methods are mostly based on black-box models, lacking identifiability guarantees. In this work, we aim to learn multivariate cum

Giacomo Cacciapaglia, Antimo Cagnotta, Roberta Calabrese, Francesco Carnevali

The Standard Model of Particle Physics and its description of Nature have been recently challenged by a series of precision measurements performed via different accelerator machines. Statistically significant anomalies emerged in the heavy meson physics sector, when measuring the muon magnetic momentum, and very recently when deducing the mass of the W boson

Tim Adamo, Tung Tran

The existence of interacting higher-spin theories is tightly constrained by many no-go theorems. In this paper, we construct a chiral, higher-spin generalization of Yang-Mills theory in flat space which avoids these no-go theorems and has non-trivial tree-level scattering amplitudes with some higher-spin external legs. The fields and action are complex, so t

E. Ruben van Beesten, Daan Hulshof

We consider a zonal international power market and investigate potential economic incentives for short-term reductions of transmission capacities on existing interconnectors by the responsible transmission system operators (TSOs). We show that if a TSO aims to maximize domestic total welfare, it often has an incentive to reduce the capacity on the interconne

Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang

We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event -- or a reasoning-graph. To employ large language models (LMs) for this task, existing approaches ``serialize'' the output graph as a flat list of nodes and edges. Although feasible, these serialized graphs strongl

María Lorente, Francisco J. Martín-Reyes, Israel P. Rivera-Ríos

In this paper we provide some quantitative one-sided estimates that recover the dependences in the classical setting. Among them we provide estimates for the one-sided maximal function in Lorentz spaces and we show that the conjugation method for commutators works as well in this setting.

Hendrik Schuff, Heike Adel, Peng Qi, Ngoc Thang Vu

While much research focused on producing explanations, it is still unclear how the produced explanations' quality can be evaluated in a meaningful way. Today's predominant approach is to quantify explanations using proxy scores which compare explanations to (human-annotated) gold explanations. This approach assumes that explanations which reach higher proxy

Filippo Francesco Favale, Juan Carlos Naranjo, Gian Pietro Pirola, Sara Torelli

In this paper we consider unramified coverings of the moduli space $\mathcal{M}_g$ of smooth projective complex curves of genus $g$. Under some hypothesis on the branch locus of the finite extended map to the Deligne-Mumford compactification, we prove the vanishing of the vector space of holomorphic 1-forms on the preimage of the smooth locus of $\mathcal{M}

Jian Wang, Chenhui Gou, Qiman Wu, Haocheng Feng

Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in this field, due to the time-consuming computation mechanism of transformer. We propose RTFormer, an efficient dual-resolution transformer for real-time semantic segmenation, which ac

Zhi-qiang Bao, Ju-wen Ding, Junjie Qi

The real magnetic fields (MFs) acting on the graphene can induce flat real Landau levels (LLs). As an analogy, strains in graphene can produce significant pseudo MFs, triggering the appearance of dispersive pseudo LLs. By analyzing the low-energy effective Hamiltonian, we introduce the concept of the effective orbital MFs to integrate the real MFs and pseudo

Yuxin Mao, Zhexiong Wan, Yuchao Dai, Xin Yu

Single image blind deblurring is highly ill-posed as neither the latent sharp image nor the blur kernel is known. Even though considerable progress has been made, several major difficulties remain for blind deblurring, including the trade-off between high-performance deblurring and real-time processing. Besides, we observe that current single image blind deb

Zhu-Bo Wang, Chen-yue Gu, Xin-Xin Hu, Ya-Ting Zhang

Single atoms are interesting candidates for studying quantum optics and quantum information processing. Recently, trapping and manipulation of single atoms using tight optical dipole traps have generated considerable interest. Here we report an experimental investigation of the dynamics of atoms in a modified optical dipole trap with a backward propagating d

Joan Bagaria, Philipp Lücke

We unveil new patterns of Structural Reflection in the large-cardinal hierarchy below the first measurable cardinal. Namely, we give two different characterizations of strongly unfoldable and subtle cardinals in terms of a weak form of the principle of Structural Reflection, and also in terms of weak product structural reflection. Our analysis prompts the in

Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various use

Ludovic Berthier, Elijah Flenner, Grzegorz Szamel

Comment on `Fickian Non-Gaussian Diffusion in Glass-Forming Liquids', by Rusciano et al., Phys. Rev. Lett. 128, 168001 (2022). In a recent Letter, Rusciano et al. examined the statistics of individual particles displacements in two-dimensional glass-formers and concluded that the corresponding probability distribution is non-Gaussian in a time regime where t

Maria Lomaeva, Ilse De Looze, Amélie Saintonge, Marjorie Decleir

Star formation histories (SFHs) are integral to our understanding of galaxy evolution. We can study recent SFHs by comparing the star formation rate (SFR) calculated using different tracers, as each probes a different timescale. We aim to calibrate a proxy for the present-day rate of change in SFR, dSFR/dt, which does not require full spectral energy distrib

Breno W. Carvalho, Artur D'Avilla Garcez, Luis C. Lamb

Encoder-decoder architectures are prominent building blocks of state-of-the-art solutions for tasks across multiple fields where deep learning (DL) or foundation models play a key role. Although there is a growing community working on the provision of interpretation for DL models as well as considerable work in the neuro-symbolic community seeking to integra

Hongfei Shu, Peng Zhao, Rui-Dong Zhu, Hao Zou

We count the Bethe states of quantum integrable models with twisted boundary conditions using the Witten index of 2d supersymmetric gauge theories. For multi-component models solvable by the nested Bethe ansatz, the result is a novel restricted occupancy problem. For the SU(3) spin chain and the t-J model, we propose formulae for the solution count on singul

Ryan Moodie

In this thesis, we present recent advances at the precision frontier of higher-order quantum chromodynamics (QCD) calculations. We consider massless two-loop five-point amplitudes, with a particular focus on diphoton-plus-jet production through gluon fusion. We build a library of infrared functions up to at most next-to-next-to-leading order (NNLO) in QCD, w

Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Zhao

In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. Existing works generally utilize two independent modules to approach this problem's two successive sub-tasks: first with a hard-label decision making and second with a question generati

Ian Cavey

We introduce the $q,t$-Catalan measures, a sequence of piece-wise polynomial measures on $\mathbb{R}^2$. These measures are defined in terms of suitable area, dinv, and bounce statistics on continuous families of paths in the plane, and have many combinatorial similarities to the $q,t$-Catalan numbers. Our main result realizes the $q,t$-Catalan measures as a

Jimin Sun, Patrick Fernandes, Xinyi Wang, Graham Neubig

Recent work on tokenizer-free multilingual pretrained models show promising results in improving cross-lingual transfer and reducing engineering overhead (Clark et al., 2022; Xue et al., 2022). However, these works mainly focus on reporting accuracy on a limited set of tasks and data settings, placing less emphasis on other important factors when tuning and

Tommaso Frassetto, Patrick Jauernig, David Koisser, David Kretzler

Smart contracts enable users to execute payments depending on complex program logic. Ethereum is the most notable example of a blockchain that supports smart contracts leveraged for countless applications including games, auctions and financial products. Unfortunately, the traditional method of running contract code on-chain is very expensive, for instance,

Chris Callison-Burch, Gaurav Singh Tomar, Lara J. Martin, Daphne Ippolito

AI researchers have posited Dungeons and Dragons (D&D) as a challenge problem to test systems on various language-related capabilities. In this paper, we frame D&D specifically as a dialogue system challenge, where the tasks are to both generate the next conversational turn in the game and predict the state of the game given the dialogue history. We create a

Daniel C. M Palumbo, Zachary Gelles, Paul Tiede, Dominic O. Chang

The Event Horizon Telescope (EHT) has produced images of two supermassive black holes, Messier~87* (M 87*) and Sagittarius~A* (Sgr A*). The EHT collaboration used these images to indirectly constrain black hole parameters by calibrating measurements of the sky-plane emission morphology to images of general relativistic magnetohydrodynamic (GRMHD) simulations

Mohd Ali, Vardarajan Suneeta

In this paper, we discuss the quantization of an interesting model of Carlip which appeared recently. It shows a way to associate boundary degrees of freedom to the stretched horizon of a stationary non-extremal black hole, as has been done in JT gravity for near-extremal black holes. The path integral now contains an integral over the boundary degrees of fr

Nathaniel Bristow, Jiaqi Li, Peter Hartford, Michele Guala

A particle tracking velocimetry apparatus is presented that is capable of measuring three-dimensional particle trajectories across large volumes, of the order of several meters, during natural snowfall events. Field experiments, aimed at understanding snow settling kinematics in atmospheric flows, were conducted during the 2021/2022 winter season using this

Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov

CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms. It emphasizes a simple developing experience with a straightforward codebase and a modern analysis tracking tool. In CORL, we isolate methods implementation into separate single files, m

Colin Leclercq, Denis Sipp

This paper introduces a new operator relevant to input-output analysis of flows in a statistically steady regime far from the steady base flow: the mean resolvent $\mathbf{R}_0$. It is defined as the operator predicting, in the frequency domain, the mean linear response to forcing of the time-varying base flow. As such, it provides the statistically optimal

Viacheslav A. Emelyanov

We propose a model describing spin-half quantum particles in curved spacetime in the framework of quantum field theory. Our model is based on embodying Einstein's equivalence principle and general covariance in the definition of quantum-particle states. With this model at hand, we compute several observables which characterise spin-half quantum particles in

Juan S. Sierra, Jesus Pineda, Daniela Rueda, Alejandro Tello

Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dy

Fran Llopis-Cardona, Carmen Armero, Gabriel Sanfélix-Gimeno

Illness-death models are a class of stochastic models inside the multi-state framework. In those models, individuals are allowed to move over time between different states related to illness and death. They are of special interest when working with non-terminal diseases, as they not only consider the competing risk of death but also allow to study progressio

Viktor Reshniak

We propose an unsupervised technique for implicit parameterization of data manifolds. In our approach, the data is assumed to belong to a lower dimensional manifold in a higher dimensional space, and the data points are viewed as the endpoints of the trajectories originating outside the manifold. Under this assumption, the data manifold is an attractive mani

Richard Dengler

We examine the statistics of conformations of a linear polymer in a solvent. The polymer is allowed to form double polymers. We closely follow a classical technique to derive a field theory for the problem from an $O\left(n\right)$ symmetric spin model. The field theory is a model for RNA or DNA with constant binding energy per monomer. It is shown that ther

Kuo Han, Jinlei Zhang, Chunqi Zhu, Lixing Yang

Accurate short-term passenger flow prediction in urban rail transit stations has great benefits for reasonably allocating resources, easing congestion, and reducing operational risks. However, compared with data-rich stations, the passenger flow prediction in newly-operated stations is limited by passenger flow data volume, which would reduce the prediction

J. Nathan Kutz, Andre Nachbin, Peter J. Baddoo, John W. M. Bush

We develop a data-driven characterization of the pilot-wave hydrodynamic system in which a bouncing droplet self-propels along the surface of a vibrating bath. We consider drop motion in a confined one-dimensional geometry, and apply the {\em Dynamic mode decomposition} (DMD) in order to characterize the evolution of the wave field as the bath's vibrational

Enrico Priola

We show uniqueness in law for the critical SPDE \begin{eqnarray} \label{qq1} dX_t = AX_t dt + (-A)^{1/2}F(X(t))dt + dW_t,\;\; X_0 =x \in H, \end{eqnarray} where $A$ $ : \text{dom}(A) \subset H \to H$ is a negative definite self-adjoint operator on a separable Hilbert space $H$ having $A^{-1}$ of trace class and $W$ is a cylindrical Wiener process on $H$. Her

Shaked Yehezkel, Yuval Pinter

Most current popular subword tokenizers are trained based on word frequency statistics over a corpus, without considering information about co-occurrence or context. Nevertheless, the resulting vocabularies are used in language models' highly contextualized settings. We present SaGe, a tokenizer that tailors subwords for their downstream use by baking in the

Amílcar Branquinho, Ana Foulquié-Moreno, Manuel Mañas

Recently a spectral Favard theorem for bounded banded lower Hessenberg matrices that admit a positive bidiagonal factorization was presented. These type of matrices are oscillatory. In this paper the Lima-Loureiro hypergeometric multiple orthogonal polynomials and the Jacobi-Pi\~neiro multiple orthogonal polynomials are discussed at the light of this bidiago

Runze Mao, Minqi Lin, Yan Zhang, Tianhan Zhang

In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity o

Linqing Liu, Minghan Li, Jimmy Lin, Sebastian Riedel

Query expansion is an effective approach for mitigating vocabulary mismatch between queries and documents in information retrieval. One recent line of research uses language models to generate query-related contexts for expansion. Along this line, we argue that expansion terms from these contexts should balance two key aspects: diversity and relevance. The o

Vladislav Pankratov, Dmitry Skryabin

Frequency conversion in microresonators has revolutionised modern-day nonlinear and quantum optics. Here, we present a theory of the multimode second harmonic generation in microresonators under conditions when the parametric conversion back to the pump spectrum dominates through the large domain in the resonator parameter space. We demonstrate that the spec

Toshiya Ueta, Masaaki Otsuka

Plasma diagnostics are the bases of investigation into the physical and chemical properties of line-emitting gaseous systems. To perform plasma diagnostics properly, it is essential to correct the input spectrum for extinction properly. This is simply because determining the degree of extinction is dependent on the physical properties of the line-emitting ga

Kerianne L. Hobbs, Joseph B. Lyons, Martin S. Feather, Benjamen P Bycroft

Technology Readiness Levels are a mainstay for organizations that fund, develop, test, acquire, or use technologies. Technology Readiness Levels provide a standardized assessment of a technology's maturity and enable consistent comparison among technologies. They inform decisions throughout a technology's development life cycle, from concept, through develop

Björn Eichmann, Michael Kachelrieß

The observational data on ultrahigh energy cosmic rays (UHECR), in particular their mass composition, show strong indications for extremely hard spectra of individual mass groups of CR nuclei at Earth. In this work, we show that such hard spectra can be the result of the finite life-time of UHECR sources, if a few individual sources dominate the UHECR flux a

Amílcar Branquinho, Ana Foulquié-Moreno, Manuel Mañas

Recently a spectral Favard theorem for bounded banded lower Hessenberg matrices that admit a positive bidiagonal factorization was presented. In this paper conditions, in terms of continued fractions, for an oscillatory tetradiagonal Hessenberg matrix to have such positive bidiagonal factorization are found. Oscillatory tetradiagonal Toeplitz matrices are ta

Gizem Gezici

This work first presents our attempts to establish an automated model using state-of-the-art approaches for analysing bias in search results of Bing and Google. Experimental results indicate that the current class-wise F1-scores of our best model are not sufficient to establish an automated model for bias analysis. Thus, we decided not to continue with this

Gordon Blower, Ian Doust

Let $(-A,B,C)$ be a linear system in continuous time $t>0$ with input and output space ${\mathbb C}^2$ and state space $H$. The scattering functions $\phi_{(x)}(t)=Ce^{-(t+2x)A}B$ determines a Hankel integral operator $\Gamma_{\phi_{(x)}}$; if $\Gamma_{\phi_{(x)}}$ is trace class, then the Fredholm determinant $\tau (x)=\det (I+\Gamma_{\phi_{(x)}})$ determin

Leroy Chew, Friedrich Slivovsky

We pioneer a new technique that allows us to prove a multitude of previously open simulations in QBF proof complexity. In particular, we show that extended QBF Frege p-simulates clausal proof systems such as IR-Calculus, IRM-Calculus, Long-Distance Q-Resolution, and Merge Resolution. These results are obtained by taking a technique of Beyersdorff et al. (JAC

Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko

Calibrated probabilistic classifiers are models whose predicted probabilities can directly be interpreted as uncertainty estimates. It has been shown recently that deep neural networks are poorly calibrated and tend to output overconfident predictions. As a remedy, we propose a low-bias, trainable calibration error estimator based on Dirichlet kernel density

Tomonori Totani

Searching for extrasolar biosignatures is important to understand life on Earth and its origin. Astronomical observations of exoplanets may find such signatures, but it is difficult and may be impossible to claim unambiguous detection of life by remote sensing of exoplanet atmospheres. Here, another approach is considered: collecting grains ejected by astero

Mirko Spasić, Milena Vujošević Janičić

Tool SPECS implements an efficient automated approach for reasoning about the SPARQL query containment problem. In this paper, we prove the correctness of this approach. We give precise semantics of the core subset of SPARQL language. We briefly discuss the procedure used for reducing the query containment problem into a formal logical framework. We prove th

Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro

The implicit biases of gradient-based optimization algorithms are conjectured to be a major factor in the success of modern deep learning. In this work, we investigate the implicit bias of gradient flow and gradient descent in two-layer fully-connected neural networks with leaky ReLU activations when the training data are nearly-orthogonal, a common property

Ignasi Mundet i Riera

We study properties of continuous finite group actions on topological manifolds that hold true, for any finite group action, after possibly passing to a subgroup of index bounded above by a constant depending only on the manifold. These include the Jordan property, the almost fixed point property, as well as bounds on the discrete symmetry group. Most of our

Danny C. Price, Cheuk-Yu Edward Tong, Adrian T. Sutinjo, Lincoln J. Greenhill

Noise parameters are a set of four measurable quantities which determine the noise performance of a radio-frequency device under test. The noise parameters of a 2-port device can be extracted by connecting a set of 4 or more source impedances at the device's input, measuring the noise power of the device with each source connected, and then solving a matrix

Rakshith S Srinivasa, Seonho Kim, Kiryung Lee

In many practical applications including remote sensing, multi-task learning, and multi-spectrum imaging, data are described as a set of matrices sharing a common column space. We consider the joint estimation of such matrices from their noisy linear measurements. We study a convex estimator regularized by a pair of matrix norms. The measurement model corres

Anurag Roy, David Johnson Ekka, Saptarshi Ghosh, Abir Das

Generating natural language questions from visual scenes, known as Visual Question Generation (VQG), has been explored in the recent past where large amounts of meticulously labeled data provide the training corpus. However, in practice, it is not uncommon to have only a few images with question annotations corresponding to a few types of answers. In this pa

Maciej Kierkla, Alexandros Karam, Bogumila Swiezewska

We present an updated analysis of the first-order phase transition associated with symmetry breaking in the early Universe in a classically scale-invariant model extended with a new SU(2) gauge group. Including recent developments in understanding supercooled phase transitions, we compute all of its characteristics and significantly constrain the parameter s

Andy Rosenbaum, Saleh Soltan, Wael Hamza, Amir Saffari

A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data. Given the complexity and cost of human annotation for SP, labeled data is often scarce, particularly in multilingual settings. Large Language Models (LLMs) excel at SP given only a few examples, however LLMs are unsuitable for runtime system

Mitja Jančič, Gregor Kosec

We present an algorithm for $hp$-adaptive collocation-based mesh-free numerical analysis of partial differential equations. Our solution procedure follows a well-established iterative solve-estimate-mark-refine paradigm. The solve phase relies on the Radial Basis Function-generated Finite Differences (RBF-FD) using point clouds generated by advancing front n

Zhendi Gong, Andrew P. French, Guoping Qiu, Xin Chen

Convolutional neural networks (CNNs) achieved the state-of-the-art performance in medical image segmentation due to their ability to extract highly complex feature representations. However, it is argued in recent studies that traditional CNNs lack the intelligence to capture long-term dependencies of different image regions. Following the success of applying

Yunhua Zhou, Pengyu Wang, Peiju Liu, Yuxin Wang

Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms. However, they are underdeveloped in the underlying principle that the models should have differentiated confidence in In- and Out-of-domain intent. In this work, we shed light on the fundamental cause of model overconfide

Jonathan Gorard

This article aims to provide a novel formalization of the concept of computational irreducibility in terms of the exactness of functorial correspondence between a category of data structures and elementary computations and a corresponding category of (1-dimensional) cobordisms. We proceed to demonstrate that, by equipping both categories with a symmetric mon

T. Shang, Y. Chen, W. Xie, D. J. Gawryluk

The CuIr$_{2-x}$Ru$_x$Te$_4$ superconductors (with a $T_c$ around 2.8 K) can host charge-density waves, whose onset and interplay with superconductivity are not well known at a microscopic level. Here, we report a comprehensive study of the $x$ = 0 and 0.05 cases, whose superconductivity was characterized via electrical-resistivity-, magnetization-, and heat

Veronika Koren, Stefano Panzeri

Understanding how the dynamics of neural networks is shaped by the computations they perform is a fundamental question in neuroscience. Recently, the framework of efficient coding proposed a theory of how spiking neural networks can compute low-dimensional stimulus signals with high efficiency. Efficient spiking networks are based on time-dependent minimizat

Uta Isabella Meyer, Frédéric Grosshans, Damian Markham

Standard Bell inequalities hold when distant parties are not allowed to communicate. Barrett et al. found correlations from Pauli measurements on certain network graphs refute a local hidden variable (LHV) description even allowing some communication along the graph. This has recently found applications in proving separation between classical and quantum com

Diane Guignard, Peter Jantsch

Motivated by nonlinear approximation results for classes of parametric partial differential equations (PDEs), we seek to better understand so-called library approximations to analytic functions of countably infinite number of variables. Rather than approximating a function of interest in a single space, a library approximation uses a collection of spaces and

Paul Fearnhead, Piotr Fryzlewicz

This chapter overviews some of the work on detecting and estimating the location of a single change. We first consider the most common change-point problem, namely that of detecting a change in mean, before looking at extensions to detecting other types of change. The intuition from the problem of detecting a single change-point is helpful for understanding

Jonas Krook, Yuvaraj Selvaraj, Wolfgang Ahrendt, Martin Fabian

The safety of automated driving systems must be justified by convincing arguments and supported by compelling evidence to persuade certification agencies, regulatory entities, and the general public to allow the systems on public roads. This persuasion is typically facilitated by compiling the arguments and the compelling evidence into a safety case. Reviews

Kangwei Li, Sheldy Ombrosi, Israel P. Rivera-Ríos

In this note we revisit the upper endpoint estimates for commutators following the line by Harboure, Segovia and Torrea. Relying upon the suitable BMO subspace suited for the commutator that was introduced by Accomazzo, we obtain a counterpart for commutators of the upper endpoint extrapolation result by Harboure, Mac\'ias and Segovia. Multilinear counterpar

Lukas Miklautz, Martin Teuffenbach, Pascal Weber, Rona Perjuci

The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clustering method. Most existing deep clustering methods are designed for a single clustering method, e.g., k-means, spectral clustering, or Gaussian mixture models, but it is well know

Akash Biswas, Bidya Binay Karak, Robert Cameron

A striking feature of the solar cycle is that at the beginning, sunspots appear around mid-latitudes, and over time the latitudes of emergences migrate towards the equator.The maximum level of activity (e.g., sunspot number) varies from cycle to cycle.For strong cycles, the activity begins early and at higher latitudes with wider sunspot distributions than f