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Reachable set computation is an important tool for analyzing control systems. Simulating a control system can show that the system is generally functioning as desired, but a formal tool like reachability analysis can provide a guarantee of correctness. For linear systems, reachability analysis is straightforward and fast, but as more complex components are added to the control system such as nonlinear dynamics or a neural network controller, reachability analysis may slow down or become overly conservative. To address these challenges, much literature has focused on spatial refinement, e.g., tuning the discretization of the input sets and intermediate reachable sets. However, this paper addresses a different dimension: temporal refinement. The basic idea of temporal refinement is to automatically choose when along the horizon of the reachability problem to execute slow symbolic queries which incur less approximation error versus fast concrete queries which incur more approximation error. Temporal refinement can be combined with other refinement approaches and offers an additional ``tuning knob'' with which to trade off tractability and tightness in approximate reachable set computation. Here, we introduce an automatic framework for performing temporal refinement and we demonstrate the effectiveness of this technique on computing approximate reachable sets for nonlinear systems with neural network control policies. We demonstrate the calculation of reachable sets of varying approximation error under varying computational budget and show that our algorithm is able to generate approximate reachable sets with a similar amount of error to the baseline approach in 20-70% less time.

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

We consider hyperbolic system with nonlinear viscosity such that the viscosity matrix $B(u)$ is commutating with $A(u)$ the matrix associated to the convective term. The drift matrix is assumed to be Temple class. First, we prove the global existence of smooth solutions for initial data with small total variation. We show that the solution to the parabolic equation converges to a semi-group solution of the hyperbolic system as viscosity goes to zero. Furthermore, we prove that the zero diffusion limit coincides with the one obtained in [Bianchini and Bressan, Indiana Univ. Math. J. 2000].

Useful robot control algorithms should not only achieve performance objectives but also adhere to hard safety constraints. Control Barrier Functions (CBFs) have been developed to provably ensure system safety through forward invariance. However, they often unnecessarily sacrifice performance for safety since they are purely reactive. Receding horizon control (RHC), on the other hand, consider planned trajectories to account for the future evolution of a system. This work provides a new perspective on safety-critical control by introducing Forward Invariance in Trajectory Spaces (FITS). We lift the problem of safe RHC into the trajectory space and describe the evolution of planned trajectories as a controlled dynamical system. Safety constraints defined over states can be converted into sets in the trajectory space which we render forward invariant via a CBF framework. We derive an efficient quadratic program (QP) to synthesize trajectories that provably satisfy safety constraints. Our experiments support that FITS improves the adherence to safety specifications without sacrificing performance over alternative CBF and NMPC methods., Comment: 8 pages, 4 figures

We prove fractional boundary Hardy's inequality in dimension one for the critical case $sp =1$. Optimality of the inequality is obtained for any $p$. The extra logarithmic correction term appears in usual fashion. We also provide a concrete (workable) example of a sequence of smooth functions that converges to constant function in $W^{s,p}((0,1))$ for $sp=1$ and $p=2$.

We prove effective equidistribution of expanding horocycles in $\mathrm{SL}_2(\mathbb{Z})\backslash\mathrm{SL}_2(\mathbb{R})$ with respect to various classes of Borel probability measures on $\mathbb{R}$ having certain Fourier asymptotics. Our proof involves new techniques combining tools from automorphic forms and harmonic analysis. In particular, for any Borel probability measure $\mu$, satisfying $\sum_{\mathbb{Z}\ni|m|\leq X}|\widehat{\mu}(m)| = O\left(X^{1/2-\theta}\right)$ with $\theta>7/64,$ our result holds. This class of measures contains convolutions of $s$-Ahlfors regular measures for $s>39/64$, and as well as, a sub-class of self-similar measures. Moreover, our result is sharp upon the Ramanujan--Petersson Conjecture (upon which the above $\theta$ can be chosen arbitrarily small): there are measures $\mu$ with $\widehat\mu(\xi) = O\left(|\xi|^{-1/2+\epsilon}\right)$ for which equidistribution fails., Comment: 28 pages, Improved exposition

Relativistic effects strongly influence the electronic structures of the heaviest elements, thereby shaping their chemical and physical properties. Studying ion mobility within a noble gas environment reveals how the ion-neutral interactions depend on the ion's electronic configurations, thus providing an avenue for exploring these effects. An ion mobility spectrometer with a cryogenic drift tube was developed to precisely measure the low-field reduced mobility of heavy lanthanide and actinide cations. The apparatus was characterized by optimizing the bunching operation of ions with a miniature RF coulomb buncher and evaluating the chromatography performance of the drift tube operated with helium buffer gas at a temperature of 298K. Systematic ion mobility measurements of lutetium cations (Lu$^{+}$) drifting in helium gas were carried out as a case study. The electronic state chromatography of Lu$^{+}$ has been demonstrated. The low-field reduced ion mobility for the ground and lowest meta-stable state of Lu$^{+}$ have been examined. In addition, the variation of both states' reduced mobility and the quenching of meta-stable population has been investigated under different reduced electric fields ($E/n_0$), the ratio of an electric field to neutral gas number density., Comment: 9 pages, 10 figures

We report the propagation of fast wavepackets in classical non-Hermitian lattices, where the group velocity is controlled by the non-Hermiticity parameters, and can be made higher than in the Hermitian counterpart. Specifically, we obtain a square root dependence of the group velocity on the gain/loss parameter, similarly to the dependence of quantum wavepackets in stretched graphene-like lattices subjected to gain and loss. We derive a targeted mapping from the quantum to the classical Hamiltonian to realize this phenomenon in a dynamically stable form. As a result, fast wavepackets of any frequency supported by the lattice are propagating in time domain with a non-growing amplitude. We demonstrate the system experimentally in a topoelectrical metamaterial, where the non-Hermiticity is generated by embedded operational amplifiers in a feedback setup. Our design paves the way to realize increased group velocities, and other wave phenomena inspired by quantum systems in a form that preserves the original system properties, while supporting an inherently stable dynamics.

We investigate the flavor-specific properties of leptophilic dark matter in neutrino mass models, where dark matter signals are directly correlated with the neutrino oscillation data, providing complementary insights into the neutrino mass hierarchy and CP phases. Notably, this can be accomplished without introducing a flavor-specific portal to dark matter, imposing any new flavor symmetry, or involving flavon fields. As a case study, we analyze the correlation between the flavor-philic nature of dark matter and neutrino oscillation data in the type-II seesaw and Zee-Babu models, and extend this discussion to other neutrino mass models. We analyze the indirect signatures of such leptophilic dark matter, specifically examining the spectrum of the cosmic ray electron/positron flux resulting from the pair annihilation of dark matter in the Galactic halo, and explore correlated lepton-specific signals at collider experiments sensitive to neutrino oscillation data., Comment: 34 pages, 13 figures, 6 tables

Single-molecule fluorescence spectroscopy is a powerful method that avoids ensemble averaging, but its temporal resolution is limited by the fluorescence lifetime to nanoseconds at most. At the ensemble level, two-dimensional spectroscopy provides insight into ultrafast femtosecond processes such as energy transfer and line broadening, even beyond the Fourier limit, by correlating pump and probe spectra. Here, we combine these two techniques and demonstrate 2D spectroscopy of individual molecules at room temperature using the example of dibenzoterrylene (DBT) in a polymer matrix. We excite the molecule in a confocal microscope with a phase-modulated train of femtosecond pulses and detect the emitted fluorescence with single-photon counting detectors. Using a phase sensitive detection scheme, we were able to measure the nonlinear 2D spectra of most of the DBT molecules we studied. Our method is applicable to a wide range of single emitters and opens new avenues for understanding energy transfer in single quantum objects on ultrafast time scales., Comment: 10 pages, 4 figures

Recent philosophical work has explored how the social identity of knowers influences how their contributions are received, assessed, and credited. However, a critical gap remains regarding the role of technology in mediating and enabling communication within today's epistemic communities. This paper addresses this gap by examining how social media platforms and their recommendation algorithms shape the professional visibility and opportunities of researchers from minority groups. Using agent-based simulations, we investigate this question with respect to components of a widely used recommendation algorithm, and uncover three key patterns: First, these algorithms disproportionately harm the professional visibility of researchers from minority groups, creating systemic patterns of exclusion. Second, within these minority groups, the algorithms result in greater visibility for users who more closely resemble the majority group, incentivizing assimilation at the cost of professional invisibility. Third, even for topics that strongly align with minority identities, content created by minority researchers is less visible to the majority than similar content produced by majority users. Importantly, these patterns emerge, even though individual engagement with professional content is independent of group identity. These findings have significant implications for philosophical discussions on epistemic injustice and exclusion, and for policy proposals aimed at addressing these harms. More broadly, they call for a closer examination of the pervasive, but often neglected role of AI and data-driven technologies in shaping today's epistemic communities.

We show that non-standard neutrino interactions (NSI) can notably modify the pattern of resonant flavor conversion of neutrinos within supernovae and significantly impact the neutronization burst signal in forthcoming experiments such as the Deep Underground Neutrino Experiment (DUNE). The presence of NSI can invert the energy levels of neutrino matter eigenstates and even induce a new resonance in the inner parts close to the proto-neutron star. We demonstrate how DUNE can use these new configurations of energy levels to have sensitivity to NSIs down to $\mathcal{O}(0.1)$. We also elucidate how the effect may result in a puzzling confusion of normal and inverted mass orderings by highlighting the emergence or vanishing of the neutronization peak, which distinguishes between the two mass orderings. Potential implications are analyzed thoroughly., Comment: 7 pages + references, 6 figures

Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., "neutral", to study and address potential gender biases in Large Language Models (LLMs). We evaluate the performance of several foundational and large language models in predicting gender based on first names only. Additionally, we investigate the impact of adding birth years to enhance the accuracy of gender prediction, accounting for shifting associations between names and genders over time. Our findings indicate that most LLMs identify male and female names with high accuracy (over 80%) but struggle with gender-neutral names (under 40%), and the accuracy of gender prediction is higher for English-based first names than non-English names. The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations. We recommend using caution when applying LLMs for gender identification in downstream tasks, particularly when dealing with non-binary gender labels., Comment: Accepted at ACL 2024, GeBNLP Workshop

Unimodality of the normalized coefficients of the characteristic polynomial of distance matrices of trees are known and bounds on the location of its peak (the largest coefficient) are also known. Recently, an extension of these results to distance matrices of block graphs was given. In this work, we extend these results to two additional distance-type matrices associated with trees: the Min-4PC matrix and the 2-Steiner distance matrix. We show that the sequences of coefficients of the characteristic polynomials of these matrices are both unimodal and log-concave. Moreover, we find the peak location for the coefficients of the characteristic polynomials of the Min-4PC matrix of any tree on $n$ vertices. Further, we show that the Min-4PC matrix of any tree on $n$ vertices is isometrically embeddable in $\mathbb{R}^{n-1}$ equipped with the $\ell_1$ norm.

Sign language translation from video to spoken text presents unique challenges owing to the distinct grammar, expression nuances, and high variation of visual appearance across different speakers and contexts. The intermediate gloss annotations of videos aim to guide the translation process. In our work, we focus on {\em Gloss2Text} translation stage and propose several advances by leveraging pre-trained large language models (LLMs), data augmentation, and novel label-smoothing loss function exploiting gloss translation ambiguities improving significantly the performance of state-of-the-art approaches. Through extensive experiments and ablation studies on the PHOENIX Weather 2014T dataset, our approach surpasses state-of-the-art performance in {\em Gloss2Text} translation, indicating its efficacy in addressing sign language translation and suggesting promising avenues for future research and development.

An unexpected failure of a concrete gravity dam may cause unimaginable human suffering and massive economic losses. An earthquake is the main factor contributing to the concrete gravity dam's failure. In recent years, there has been a rise in efforts globally to make dams safe under dynamic loading. Numerical modeling of dams under earthquake loading yields substantial insights into dams' fracture and damage progression. In the present work, a particle-based computational framework is developed to investigate the failure of the Koyna dam, a concrete gravity dam in India exposed to dynamic loading. The dam-foundation system is considered here. The numerically obtained crack results in the concrete dam are compared with the available experimental results. The findings are consistent with one another.

{\sc Vertex $(s, t)$-Cut} and {\sc Vertex Multiway Cut} are two fundamental graph separation problems in algorithmic graph theory. We study matroidal generalizations of these problems, where in addition to the usual input, we are given a representation $R \in \mathbb{F}^{r \times n}$ of a linear matroid $\mathcal{M} = (V(G), \mathcal{I})$ of rank $r$ in the input, and the goal is to determine whether there exists a vertex subset $S \subseteq V(G)$ that has the required cut properties, as well as is independent in the matroid $\mathcal{M}$. We refer to these problems as {\sc Independent Vertex $(s, t)$-cut}, and {\sc Independent Multiway Cut}, respectively. We show that these problems are fixed-parameter tractable ({\sf FPT}) when parameterized by the solution size (which can be assumed to be equal to the rank of the matroid $\mathcal{M}$). These results are obtained by exploiting the recent technique of flow augmentation [Kim et al.~STOC '22], combined with a dynamic programming algorithm on flow-paths \'a la [Feige and Mahdian,~STOC '06] that maintains a representative family of solutions w.r.t.~the given matroid [Marx, TCS '06; Fomin et al., JACM]. As a corollary, we also obtain {\sf FPT} algorithms for the independent version of {\sc Odd Cycle Transversal}. Further, our results can be generalized to other variants of the problems, e.g., weighted versions, or edge-deletion versions.

Today, it would be difficult for us to live a full life without polymers, especially in medicine, where its applicability is constantly expanding, giving satisfactory results without any harm effects on health. This study focused on the formation of hexagonal domains doped with AgNPs using a KrF excimer laser ({\lambda}=248 nm) on the polyetheretherketone (PEEK) surface that acts as an unfailing source of the antibacterial agent - silver. The hexagonal structure was formed with a grid placed in front of the incident laser beam. Surfaces with immobilized silver nanoparticles (AgNPs) were observed by AFM and SEM. Changes in surface chemistry were studied by XPS. To determine the concentration of released Ag+ ions, ICP-MS analysis was used. The antibacterial tests proved the antibacterial efficacy of Ag-doped PEEK composites against Escherichia coli and Staphylococcus aureus as the most common pathogens. Because AgNPs are also known for their strong toxicity, we also included cytotoxicity tests in this study. The findings presented here contribute to the advancement of materials design in the biomedical field, offering a novel starting point for combating bacterial infections through the innovative integration of AgNPs into inert synthetic polymers.

We propose a simple extension to the Standard Model, wherein neutrinos naturally attain small Majorana masses through a one-loop radiative mechanism featuring particles within the loops characterized by milli-charges. Unlike the conventional scotogenic model, our approach avoids imposing a discrete symmetry or expanding the gauge sector. The minuscule electric charges ensure the stability of the lightest particle within the loop as a viable dark matter candidate. Our investigation systematically scrutinizes the far-reaching phenomenological implications arising from these minuscule charges., Comment: 9 pages + references, 7 figures

We explore both experimental and theoretical aspects of the superconducting properties in the distinctive layered caged compound, Ba$_{3}$Ir$_{4}$Ge$_{16}$. Our approach integrates muon spin rotation and relaxation ($\mu$SR) measurements with magnetization and heat capacity experiments, accompanied by first-principle calculations. The compound's bulk superconductivity is unequivocally established through DC magnetization measurements, revealing a critical temperature ($T_\mathrm{C}$) of 5.7 K. A noteworthy characteristic observed in the low-temperature superfluid density is its saturating behavior, aligning with the features typical of conventional Bardeen-Cooper-Schrieffer (BCS) superconductors. The assessment of moderate electron-phonon coupling superconductivity is conducted through transverse field $\mu$SR measurements, yielding a superconducting gap to $T_\mathrm{C}$ ratio ($2\Delta(0)/k_\mathrm{B}T_\mathrm{C}$) of 4.04, a value corroborated by heat capacity measurements. Crucially, zero field $\mu$SR measurements dismiss the possibility of any spontaneous magnetic field emergence below $T_\mathrm{C}$, highlighting the preservation of time-reversal symmetry. Our experimental results are reinforced by first-principles density functional calculations, underscoring the intricate interplay between crystal structure and superconducting order parameter symmetry in polyhedral caged compounds. This comprehensive investigation enhances our understanding of the nuanced relationship between crystal structure and superconductivity in such unique compounds.

The determination of Lande's g-factor and the damping constant is central to extracting crucial information about the spin-spin and spin-orbit interactions in magnetically ordered systems. In insulating compounds based on 3d elements, the spin-orbit interaction can be effectively probed by investigating the ground state of the corresponding free 3d ion within the crystalline environment. For metallic systems, the problem is non-trivial because of additional band structure effects. Here, we investigate the anisotropic magnetization dynamics in bulk single crystalline Fe5GeTe2, a van der Waals 2D itinerant ferromagnet at room temperature, using broadband ferromagnetic resonance spectroscopy. Strikingly, contrary to the results of ab-initio calculations, we do not observe any intrinsic anisotropy of magnetization damping close to room temperature, suggesting diminished role of spin-orbit interaction. However, there is a sizable anisotropy in the Lande's g factor near room temperature which is attributed to anisotropic critical spin fluctuations.

The ability to understand and reason about spatial relationships between objects in images is an important component of visual reasoning. This skill rests on the ability to recognize and localize objects of interest and determine their spatial relation. Early vision and language models (VLMs) have been shown to struggle to recognize spatial relations. We extend the previously released What'sUp dataset and propose a novel comprehensive evaluation for spatial relationship understanding that highlights the strengths and weaknesses of 27 different models. In addition to the VLMs evaluated in What'sUp, our extensive evaluation encompasses 3 classes of Multimodal LLMs (MLLMs) that vary in their parameter sizes (ranging from 7B to 110B), training/instruction-tuning methods, and visual resolution to benchmark their performances and scrutinize the scaling laws in this task., Comment: Accepted to NeurIPS 2024 Workshop on Compositional Learning

We present an open-source web service for Czech morphosyntactic analysis. The system combines a deep learning model with rescoring by a high-precision morphological dictionary at inference time. We show that our hybrid method surpasses two competitive baselines: While the deep learning model ensures generalization for out-of-vocabulary words and better disambiguation, an improvement over an existing morphological analyser MorphoDiTa, at the same time, the deep learning model benefits from inference-time guidance of a manually curated morphological dictionary. We achieve 50% error reduction in lemmatization and 58% error reduction in POS tagging over MorphoDiTa, while also offering dependency parsing. The model is trained on one of the currently largest Czech morphosyntactic corpora, the PDT-C 1.0, with the trained models available at https://hdl.handle.net/11234/1-5293. We provide the tool as a web service deployed at https://lindat.mff.cuni.cz/services/udpipe/. The source code is available at GitHub (https://github.com/ufal/udpipe/tree/udpipe-2), along with a Python client for a simple use. The documentation for the models can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model., Comment: Accepted to TSD 2024

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Although interest in synthetic medical data (SMD) for training and testing AI methods is growing, the absence of a standardized framework to evaluate its quality and applicability hinders its wider adoption. Here, we outline an evaluation framework designed to meet the unique requirements of medical applications, and introduce SMD Card, which can serve as comprehensive reports that accompany artificially generated datasets. This card provides a transparent and standardized framework for evaluating and reporting the quality of synthetic data, which can benefit SMD developers, users, and regulators, particularly for AI models using SMD in regulatory submissions., Comment: 7 pages, 2 figures

In this article, we deal with the fine boundary regularity, a weighted H\"{o}lder regularity of weak solutions to the problem involving the fractional $(p,q)$-Laplacian denoted by \begin{eqnarray*} \begin{array}{rll} (-\Delta)_{p}^{s} u + (-\Delta)_{q}^{s} u &= f(x) &\text{ in } \Omega u&=0 &\text{ in } \mathbb{R}^N\setminus\Omega; \end{array} \end{eqnarray*} where $\Omega$ is a $C^{1,1}$ bounded domain and $2 \leq p \leq q <\infty.$ For $0

Numerical simulations are a highly valuable tool to evaluate the impact of the uncertainties of various modelparameters, and to optimize e.g. injection-production scenarios in the context of underground storage (of CO2typically). Finite volume approximations of Darcy's parabolic model for flows in porous media are typically runmany times, for many values of parameters like permeability and porosity, at costly computational efforts.We study the relevance of reduced basis methods as a way to lower the overall simulation cost of finite volumeapproximations to Darcy's parabolic model for flows in porous media for different values of the parameters suchas permeability. In the context of underground gas storage (of CO2 typically) in saline aquifers, our aim isto evaluate quickly, for many parameter values, the flux along some interior boundaries near the well injectionarea-regarded as a quantity of interest-. To this end, we construct reduced bases by a standard POD-Greedyalgorithm. Our POD-Greedy algorithm uses a new goal-oriented error estimator designed from a discrete space-time energy norm independent of the parameter. We provide some numerical experiments that validate theefficiency of the proposed estimator.

For nonlinear operators of fractional $p$-Laplace type, we consider two types of solutions to the nonlocal Dirichlet problem: Sobolev solutions based on fractional Sobolev spaces and Perron solutions based on superharmonic functions. These solutions give rise to two different concepts of regularity for boundary points, namely Sobolev and Perron regularity. We show that these two notions are equivalent and we also provide several characterizations of regular boundary points. Along the way, we give a new definition of Perron solutions, which is applicable to arbitrary exterior Dirichlet data $g: \Omega^c \to [-\infty,\infty]$. We obtain resolutivity and invariance results for these Perron solutions, and show that the Sobolev and Perron solutions coincide for a large class of exterior Dirichlet data. A uniqueness result for the Dirichlet problem is also obtained for the class of bounded solutions taking prescribed continuous exterior data quasieverywhere on the boundary.

Fuzzing is an effective technique for discovering software vulnerabilities by generating random test inputs and executing them against the target program. However, fuzzing large and complex programs remains challenging due to difficulties in uncovering deeply hidden vulnerabilities. This paper addresses the limitations of existing coverage-guided fuzzers, focusing on the scheduler and mutator components. Existing schedulers suffer from information sparsity and the inability to handle fine-grained feedback metrics. The mutators are agnostic of target program branches, leading to wasted computation and slower coverage exploration. To overcome these issues, we propose an end-to-end online stochastic control formulation for coverage-guided fuzzing. Our approach incorporates a novel scheduler and custom mutator that can adapt to branch logic, maximizing aggregate edge coverage achieved over multiple stages. The scheduler utilizes fine-grained branch distance measures to identify frontier branches, where new coverage is likely to be achieved. The mutator leverages branch distance information to perform efficient and targeted seed mutations, leading to robust progress with minimal overhead. We present FOX, a proof-of-concept implementation of our control-theoretic approach, and compare it to industry-standard coverage-guided fuzzers. 6 CPU-years of extensive evaluations on the FuzzBench dataset and complex real-world programs (a total of 38 test programs) demonstrate that FOX outperforms existing state-of-the-art fuzzers, achieving average coverage improvements up to 26.45% in real-world standalone programs and 6.59% in FuzzBench programs over the state-of-the-art AFL++. In addition, it uncovers 20 unique bugs in popular real-world applications including eight that are previously unknown, showcasing real-world security impact., Comment: To Appear in Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24)

A hallmark of biological tissues, viewed as complex cellular materials, is the active generation of mechanical stresses by cellular processes, such as cell divisions. Each cellular event generates a force dipole that deforms the surrounding tissue. Therefore, a quantitative description of these force dipoles, and their consequences on tissue mechanics, is one of the central problems in understanding the overall tissue mechanics. In this work we analyze previously published experimental data on fruit fly \textit{D. melanogaster} wing epithelia to quantitatively describe the deformation fields induced by a cell-scale force dipole. We find that the measured deformation field can be explained by a simple model of fly epithelium as a linearly elastic sheet. This fact allows us to use measurements of the strain field around cellular events, such as cell divisions, to infer the magnitude and dynamics of the mechanical forces they generate. In particular, we find that cell divisions exert a transient isotropic force dipole field, corresponding to the temporary localisation of the cell nucleus to the tissue surface during the division, and traceless-symmetric force dipole field that remains detectable from the tissue strain field for up to about $3.5$ hours after the division. This is the timescale on which elastic strains are erased by other mechanical processes and therefore it corresponds to the tissue fluidization timescale. In summary, we have developed a method to infer force dipoles induced by cell divisions, by observing the strain field in the surrounding tissues. Using this method we quantitatively characterize mechanical forces generated during a cell division, and their effects on the tissue mechanics.

Branch-and-bound (BaB) is among the most effective techniques for neural network (NN) verification. However, existing works on BaB for NN verification have mostly focused on NNs with piecewise linear activations, especially ReLU networks. In this paper, we develop a general framework, named GenBaB, to conduct BaB on general nonlinearities to verify NNs with general architectures, based on linear bound propagation for NN verification. To decide which neuron to branch, we design a new branching heuristic which leverages linear bounds as shortcuts to efficiently estimate the potential improvement after branching. To decide nontrivial branching points for general nonlinear functions, we propose to pre-optimize branching points, which can be efficiently leveraged during verification with a lookup table. We demonstrate the effectiveness of our GenBaB on verifying a wide range of NNs, including NNs with activation functions such as Sigmoid, Tanh, Sine and GeLU, as well as NNs involving multi-dimensional nonlinear operations such as multiplications in LSTMs and Vision Transformers. Our framework also allows the verification of general nonlinear computation graphs and enables verification applications beyond simple NNs, particularly for AC Optimal Power Flow (ACOPF). GenBaB is part of the latest $\alpha,\!\beta$-CROWN, the winner of the 4th and the 5th International Verification of Neural Networks Competition (VNN-COMP 2023 and 2024)., Comment: Preprint

We present CWRCzech, Click Web Ranking dataset for Czech, a 100M query-document Czech click dataset for relevance ranking with user behavior data collected from search engine logs of Seznam$.$cz. To the best of our knowledge, CWRCzech is the largest click dataset with raw text published so far. It provides document positions in the search results as well as information about user behavior: 27.6M clicked documents and 10.8M dwell times. In addition, we also publish a manually annotated Czech test for the relevance task, containing nearly 50k query-document pairs, each annotated by at least 2 annotators. Finally, we analyze how the user behavior data improve relevance ranking and show that models trained on data automatically harnessed at sufficient scale can surpass the performance of models trained on human annotated data. CWRCzech is published under an academic non-commercial license and is available to the research community at https://github.com/seznam/CWRCzech., Comment: Accepted to SIGIR 2024

Graphomotor and handwriting disabilities (GD and HD, respectively) could significantly reduce children's quality of life. Effective remediation depends on proper diagnosis; however, current approaches to diagnosis and assessment of GD and HD have several limitations and knowledge gaps, e.g. they are subjective, they do not facilitate identification of specific manifestations, etc. The aim of this work is to introduce a new scale (GHDRS Graphomotor and Handwriting Disabilities Rating Scale) that will enable experts to perform objective and complex computeraided diagnosis and assessment of GD and HD. The scale supports quantification of 17 manifestations associated with the process/product of drawing/ handwriting. The whole methodology of GHDRS design is made maximally transparent so that it could be adapted for other languages.

We present a detailed exposition of a statistical method for estimating cosmological parameters from the observation of a large number of strongly lensed binary-black-hole (BBH) mergers observable by next (third) generation (XG) gravitational-wave (GW) detectors. This method, first presented in Jana (2023 Phys. Rev. Lett. 130 261401), compares the observed number of strongly lensed GW events and their time delay distribution (between lensed images) with observed events to infer cosmological parameters. We show that the precision of the estimation of the cosmological parameters does not have a strong dependance on the assumed BBH redshift distribution model. Using the large number of unlensed mergers, XG detectors are expected to measure the BBH redshift distribution with sufficient precision for the cosmological inference. However, a biased inference of the BBH redshift distribution will bias the estimation of cosmological parameters. An incorrect model for the distribution of lens properties can also lead to a biased cosmological inference. However, Bayesian model selection can assist in selecting the right model from a set of available parametric models for the lens distribution. We also present a way to incorporate the effect of contamination in the data due to the limited efficiency of lensing identification methods, so that it will not bias the cosmological inference., Comment: 17 pages, 5 figures

Global minimum structures of neutral (Fe2O3)n clusters with n = 1-5 were determined employing the genetic algorithm in combination with ab initio parameterized interatomic potentials and subsequent refinement at the density functional theory level. Systematic investigations of magnetic configurations of the clusters using a broken symmetry approach reveal antiferromagnetic and ferrimagnetic ground states. Whereas (Fe2O3)n clusters with n = 2-5 contain exclusively Fe3+, Fe2O3 was found to be a special case formally containing both Fe2+ and Fe3+. Calculated magnetic coupling constants revealed predominantly strong antiferromagnetic interactions, which exceed bulk values found in hematite. The precise magnetization (spin) state of the clusters has only small influence on their geometric structure. Starting from n = 4 also the relative energies of different cluster isomers are only weakly influenced by their magnetic configuration. These findings are important for simulations of larger (Fe2O3)n clusters and nanoparticles.

Reinforcement Learning with Human Feedback (RLHF) is considered a standard approach to fine-tuning Large Language Models (LLMs). However, such methods often face limitations such as unsound black-box reward models, difficulties in collecting human preference data, and the reliance on sparse scalar rewards. These methods often fall short when applied to tasks that require complex domain-specific understanding. To address these challenges, we propose a new fine-tuning paradigm we refer to as Reinforcement Learning via Symbolic Feedback (RLSF), which aims to improve domain-specific understanding of LLMs more effectively than traditional reward signals. In the RLSF setting, the LLM being fine-tuned is considered an RL agent, while the environment is allowed access to reasoning or domain knowledge tools (e.g., solvers, provers, algebra systems, or knowledge bases). Crucially, in RLSF, these reasoning tools can provide feedback to the LLMs via poly-sized certificates (e.g., proofs), that characterize errors in the LLM-generated object with respect to some correctness specification. As a bonus, our RLSF approach does not require the reasoning systems we use to be differentiable. The ability of RLSF-based fine-tuning to leverage certificate-generating symbolic tools enables sound fine-grained (token-level) reward signals to LLMs, and thus addresses the limitations of traditional reward models mentioned above. Via extensive evaluations, we show that our RLSF-based fine-tuning of LLMs outperforms traditional approaches on five different applications, namely, program synthesis from natural language pseudo-code to programming language, three chemistry tasks, and solving the Game of 24. A takeaway is that fine-tuning via RLSF enables relatively smaller LLMs to significantly outperform closed-source models that are orders of magnitude larger (e.g., GPT-4).

Plasmon-driven reactions on plasmonic nanoparticles (NPs) occur under significantly different conditions from those of classical organic synthesis and provide a promising pathway for enhancing the efficiency of various chemical processes. However, these reactions can also have undesirable effects, such as 4-mercaptophenylboronic acid (MPBA) deboronation. MPBA chemisorbs well to Ag NPs through its thiol group and can subsequently bind to diols, enabling the detection of various biological structures by surface-enhanced Raman scattering (SERS), but not upon its deboronation. To avoid this reaction, we investigated the experimental conditions of MPBA deboronation on Ag NPs by SERS. Our results showed that the level of deboronation strongly depends on both the morphology of the system and the excitation laser wavelength and power. In addition, we detected not only the expected products, namely thiophenol and biphenyl-4,4-dithiol, but also 4-nitrothiophenol (NTP). The crucial reagent for NTP formation was an oxidation product of hydroxylamine hydrochloride, the reduction agent used in Ag NP synthesis. Ultimately, this reaction was replicated by adding NaNO2 to the system, and its progress was monitored as a function of the laser power, thereby identifying a new reaction of plasmon-driven -B(OH)2 substitution for -NO2.

Identifying the causal pathways of unfairness is a critical objective for improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requires knowledge of the causal graph, hindering practical applications in complex or low-knowledge domains. Moreover, global discovery methods that learn causal structure from data can display unstable performance on finite samples, preventing robust fairness conclusions. To mitigate these challenges, we introduce local discovery for direct discrimination (LD3): a method that uncovers structural evidence of direct unfairness by identifying the causal parents of an outcome variable. LD3 performs a linear number of conditional independence tests relative to variable set size, and allows for latent confounding under the sufficient condition that all parents of the outcome are observed. We show that LD3 returns a valid adjustment set (VAS) under a new graphical criterion for the weighted controlled direct effect, a qualitative indicator of direct discrimination. LD3 limits unnecessary adjustment, providing interpretable VAS for assessing unfairness. We use LD3 to analyze causal fairness in two complex decision systems: criminal recidivism prediction and liver transplant allocation. LD3 was more time-efficient and returned more plausible results on real-world data than baselines, which took 46$\times$ to 5870$\times$ longer to execute.

Data assimilation algorithms integrate prior information from numerical model simulations with observed data. Ensemble-based filters, regarded as state-of-the-art, are widely employed for large-scale estimation tasks in disciplines such as geoscience and meteorology. Despite their inability to produce the true posterior distribution for nonlinear systems, their robustness and capacity for state tracking are noteworthy. In contrast, Particle filters yield the correct distribution in the ensemble limit but require substantially larger ensemble sizes than ensemble-based filters to maintain stability in higher-dimensional spaces. It is essential to transcend traditional Gaussian assumptions to achieve realistic quantification of uncertainties. One approach involves the hybridisation of filters, facilitated by tempering, to harness the complementary strengths of different filters. A new adaptive tempering method is proposed to tune the underlying schedule, aiming to systematically surpass the performance previously achieved. Although promising numerical results for certain filter combinations in toy examples exist in the literature, the tuning of hyperparameters presents a considerable challenge. A deeper understanding of these interactions is crucial for practical applications.

Most integrated evanescent-field photonic sensors measure changes in either the real part or the imaginary part of the complex refractive index of the sample, i.e., refraction or absorption. Here we propose and experimentally demonstrate a near-infrared sensor based on a silicon nitride Mach-Zehnder interferometer which provides a direct measurement of the complex refractive index. Our architecture exhibits a high sensitivity, achieving limits of detection below $2\cdot 10^{-6}\,\mathrm{RIU}$ for both the real and imaginary parts of the refractive index. We furthermore show that our sensor can be employed as an integrated dispersion spectrometer.

A drastic variation in thermal conductivity (\k{appa}) for synthesized samples (TiCoSb1+x, x=0.0, 0.01, 0.02, 0.03, 0.04, and 0.06) is observed and ~47% reduction in \k{appa} is reported for TiCoSb1.02 sample. In depth structural analysis is performed, employing mixed-phase Rietveld refinement technique. Embedded phases and vacancy are analyzed from X-ray diffraction (XRD) and Scanning electron microscopy data. Local structures of the synthesized samples are explored for the first time by X-ray absorption spectroscopy measurements for TiCoSb system and corroborated with Rietveld refinement data. Lattice dynamics are revealed using Raman Spectroscopy (RS) measurements in unprecedented attempts for TiCoSb system. XRD and RS data accomplishes that variation in \k{appa} as a function of Sb concentration is observed owing to an alteration in phonon group velocity related to lattice softening. Polar nature of TiCoSb HH sample is revealed. LO-TO splitting (related to polar optical phonon scattering) in phonon vibration is observed due to polar nature of TiCoSb synthesized samples. Tailoring in LO-TO splitting due to screening effect, correlated with Co vacancies is reported for TiCoSb1+x synthesized samples. Lattice softening and LO-TO splitting lead to decreases in \k{appa}~47% for TiCoSb1.02 synthesized sample., Comment: Main article (17 pages, 10 figures), Supplemental article (5 pages, 7 figures), Comments are welcome

In an earlier analysis \href{https://link.aps.org/doi/10.1103/PhysRevD.105.085007}{Phys. Rev. D 105 (2022) 085007}, we have explored the event of acceleration radiation for an atom freely falling into the event horizon of a quantum-corrected Schwarzschild black hole. We want to explore the acceleration-radiation when the atom is freely falling into the event horizon of a charged quantum-corrected black hole. We consider the quantum effects of the electromagnetic field along with the gravitational field in an asymptotic safety regime. Introducing the quantum improved Reisner-Nordstr\"{o}m metric, we have calculated the excitation probability of a two-level atom freely falling into the event horizon of quantum improved charged black hole. Recently, in the case of the braneworld black hole (where the tidal charge has the same dimension as the square of the charge of a Reissner-Nordstr\"{o}m black hole in natural units), we have observed from the form of the transition probability that the temperature will have no contribution in the first order of the tidal charge. We observe that for a quantum corrected Reissner-Nordstr\"{o}m black hole, there is a second-order contribution in the charge parameter in the temperature that can be read off from the transition probability. Next, we calculate the HBAR entropy in this thought experiment and show that this entropy has a leading order Bekenstein-Hawking entropy term along with some higher order correction terms involving logarithmic as well as fractional terms of the black hole area due to infalling photons. We have finally investigated the validity of Wien's displacement law and compared the critical value of the field wavelength with the general Schwarzschild black hole and its corresponding quantum-corrected case., Comment: 12 pages LATEX, comments are welcome

This paper focuses on the runtime verification of hyperproperties expressed in HypermuHML, an expressive yet simple logic for describing properties of sets of traces. To this end, we first consider a simple language of monitors that can observe sets of system executions and report verdicts w.r.t. a given HypermuHML formula. In this setting, a unique omniscient monitor observes all system traces, and, in this sense, it is 'centralized'. However, in a possibly distributed system, having a centralized entity is undesirable; hence, we also provide a language for 'decentralized' monitors, where each trace has its own monitor, and monitors for different traces can yield a unique verdict by communicating their observations. For both the centralized and the decentralized settings, we provide a synthesis procedure that, given a formula, yields a monitor that is correct (i.e., sound and violation complete). A key step in proving the correctness of the synthesis for decentralized monitors is a result showing that, for each formula, the synthesized centralized monitor and its corresponding decentralized one are weakly bisimilar for a suitable notion of weak bisimulation.

Recently, a new kind of collinear magnetism, dubbed altermagnetism, has attracted considerable interests. A key characteristic of altermagnet is the momentum-dependent band and spin splitting without net magnetization. However, finding altermagnetic materials with large splitting near the Fermi level, which necessarily requires three-dimensional k-space mapping and is crucial for spintronic applications and emergent phenomena, remains challenging. Here by employing synchrotron-based angle-resolved photoemission spectroscopy (ARPES), spin-resolved ARPES and model calculations, we uncover a large altermagnetic splitting, up to ~1.0 eV, near the Fermi level in CrSb. We verify its bulk-type g-wave altermagnetism through systematic three-dimensional k-space mapping, which unambiguously reveals the altermagnetic symmetry and associated nodal planes. The ARPES results are well captured by density functional theory calculations. Spin-resolved ARPES measurements further verify the spin polarizations of the split bands near Fermi level. In addition, tight-binding model analysis indicates that the large altermagnetic splitting arises from strong third-nearest-neighbor hopping mediated by Sb ions, which breaks both the space-time reversal symmetry and the translational spin-rotation symmetry. The large band/spin splitting near Fermi level in metallic CrSb, together with its high TN (up to 705 K) and simple spin configuration, paves the way for exploring emergent phenomena and spintronic applications based on altermagnets., Comment: The manuscript contains 19 pages, 5 figures and 1 table. The supplementary information contains 15 pages, 13 figures and 5 tables

The crossover from hadronic to quark matter is understood to be both a deconfinement as well as a chiral symmetry restoring transition. Here, we study observables related to both aspects using lattice simulations: the Polyakov loop and its derivatives and the chiral condensate and its derivatives. At zero baryochemical potential, and infinite volume, the chiral and deconfinement crossover temperatures almost agree. However, chiral and deconfinement related observables have a qualitatively different chemical potential and volume dependence. In general, deconfinement related observables have a milder volume dependence. Furthermore, while the deconfinement transition appears to get broader with increasing $\mu_B$, the width as well as the strength of the chiral transition is approximately constant. Our results are based on simulations at zero and imaginary chemical potentials using 4stout-improved staggered fermions with $N_\tau=12$ time-slices and physical quark masses., Comment: 10 pages, 8 figures

Reliable estimation and approximation of probability density functions is fundamental for their further processing. However, their specific properties, i.e. scale invariance and relative scale, prevent the use of standard methods of spline approximation and have to be considered when building a suitable spline basis. Bayes Hilbert space methodology allows to account for these properties of densities and enables their conversion to a standard Lebesgue space of square integrable functions using the centered log-ratio transformation. As the transformed densities fulfill a zero integral constraint, the constraint should likewise be respected by any spline basis used. Bayes Hilbert space methodology also allows to decompose bivariate densities into their interactive and independent parts with univariate marginals. As this yields a useful framework for studying the dependence structure between random variables, a spline basis ideally should admit a corresponding decomposition. This paper proposes a new spline basis for (transformed) bivariate densities respecting the desired zero integral property. We show that there is a one-to-one correspondence of this basis to a corresponding basis in the Bayes Hilbert space of bivariate densities using tools of this methodology. Furthermore, the spline representation and the resulting decomposition into interactive and independent parts are derived. Finally, this novel spline representation is evaluated in a simulation study and applied to empirical geochemical data.

Our investigation explores the intricate domain of thermoelectric phenomena within silver (Ag)-infused chalcopyrites, focusing on compositions such as AgXTe$_2$ (where X=Ga, In) and the complex quaternary system Ag$_2$ZnSn/GeY$_2$ (with Y=S, Se). Using a sophisticated combination of methodologies, we integrate a non-empirical screened dielectric-dependent hybrid (DDH) functional with semiclassical Boltzmann transport theory. This approach allows us to conduct a detailed analysis of critical thermoelectric properties, including electrical conductivity, Seebeck coefficient, and power factor. Our methodology goes beyond superficial assessments, delving into the intricate interplay of material properties to reveal their true thermoelectric potential. Additionally, we investigate the often-overlooked phenomena of phonon scattering by leveraging both the elastic constant tensor and the deformation potential method. This enables a rigorous examination of electron relaxation time and lattice thermal conductivity, enhancing the robustness of our predictions and demonstrating our commitment to thorough exploration.Through our rigorous investigation, we identify materials with a thermoelectric figure of merit (ZT = $\sigma S^{2}T/ \kappa$) exceeding the critical threshold of unity. This significant achievement signals the discovery of materials capable of revolutionizing efficient thermoelectric systems. Our findings delineate a promising trajectory, laying the groundwork for the emergence of a new class of Ag-based chalcopyrites distinguished by their exceptional thermoelectric characteristics. This research not only contributes to the understanding of materials science principles but also catalyzes transformative advancements in thermoelectric technology.

In a world where in-person context transitions more into remote and hybrid concepts, we should consider new concepts of interaction in health and rehabilitation and what advantages and disadvantages they bring. One of the rising topics is mixed reality, where we can use the advantages of immersive 3D, 360-degree environments. Meanwhile, physical activity is further decreasing and with it negative effects increase through sedentary behaviour or wrong and untrained movements. In this position paper, we discuss these new risks and potential benefits of mixed reality technology when used for rehabilitation and fitness. We conclude with suggesting better feedback and guidance for physical movement and tasks at home. Improving feedback and guidance for participants could be achieved through using new technologies like virtual reality and motion tracking., Comment: workshop paper

Uncovering causal effects at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity. Focusing on selection-on-observables, this paper compares three such approaches, the modified causal forest (mcf), the generalized random forest (grf), and double machine learning (dml). It also provides proven theoretical guarantees for the mcf and compares the theoretical properties of the approaches. The findings indicate that dml-based methods excel for average treatment effects at the population level (ATE) and group level (GATE) with few groups, when selection into treatment is not too strong. However, for finer causal heterogeneity, explicitly outcome-centred forest-based approaches are superior. The mcf has three additional benefits: (i) It is the most robust estimator in cases when dml-based approaches underperform because of substantial selectivity; (ii) it is the best estimator for GATEs when the number of groups gets larger; and (iii), it is the only estimator that is internally consistent, in the sense that low-dimensional causal ATEs and GATEs are obtained as aggregates of finer-grained causal parameters., Comment: arXiv admin note: text overlap with arXiv:2209.03744

Large deformation analysis in geomechanics plays an important role in understanding the nature of post-failure flows and hazards associated with landslides under different natural calamities. In this study, a SPH framework is proposed for large deformation and failure analysis of geomaterials. An adaptive B-spline kernel function in combination with a pressure zone approach is proposed to counteract the numerical issues associated with tensile instability. The proposed algorithm is validated using a soil cylinder drop problem, and the results are compared with FEM. Finally, the effectiveness of the proposed algorithm in the successful removal of tensile instability and stress noise is demonstrated using the well-studied slope failure simulation of a cohesive soil vertical cut.