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We settle the complexity of satisfiability, finite-state satisfiability, and model-checking for several fragments of second-order HyperLTL, which extends HyperLTL with quantification over sets of traces: they are all in the analytical hierarchy and beyond, Comment: arXiv admin note: text overlap with arXiv:2311.15675

In this paper we establish the existence of the extended Dirichlet space for nonlinear Dirichlet forms under mild conditions. We employ it to introduce and characterize criticality (recurrence) and subcriticality (transience) and establish basics of a potential theory.

Pure as well as Ag- and Au-substituted YbInCu$_4$ single crystals were structurally and chemically characterized and investigated by means of heat capacity, magnetization, resistivity and ultrasonic measurements. We studied the influence of different compositions of the initial melt as well as of Au and Ag substitutions on the valence change and investigated whether this change occurs via a first-order phase transition or via crossover. We constructed a phase diagram of YbInCu$_4$ as a function of various substitutions and show that the position of the critical endpoint of the valence transition depends on the substituent and on the conditions under which the samples were grown. Multiple thermal cycles through the first-order transition lead to a significant modification of the physical properties which clearly demonstrated the influence of defects in substituted YbInCu$_4$.

The main goal of this article is to establish H\"older stability estimates for the Calder\'on problem related to a relativistic wave equation. The principal novelty of this article is that the partial differential equation (PDE) under consideration depends on three unknown potentials, namely a temporal dissipative potential $A_0$, a spatial vector potential $A$ and an external potential $\Phi$. Moreover, the PDE is posed in an infinite waveguide geometry $\Omega=\omega\times\mathbb{R}$ and not on a bounded domain. For our proof it is essential that the potentials are time-independent as a key tool in this work are pointwise estimates for the Radon transform of the vector potential $\mathcal{A}=(A_0,\mathrm{i} A)$ and external potential $\Phi$. Furthermore, the demonstrated stability estimates hold for a wide range of $H^s$ Sobolev scales and a main contribution is to explicitly determine the dependence of the involved constants and the H\"older exponent on the Sobolev exponents of the potentials $A_0,A$ and $\Phi$., Comment: 40 pages

Reproducible Builds (R-B) guarantee that rebuilding a software package from source leads to bitwise identical artifacts. R-B is a promising approach to increase the integrity of the software supply chain, when installing open source software built by third parties. Unfortunately, despite success stories like high build reproducibility levels in Debian packages, uncertainty remains among field experts on the scalability of R-B to very large package repositories. In this work, we perform the first large-scale study of bitwise reproducibility, in the context of the Nix functional package manager, rebuilding 709 816 packages from historical snapshots of the nixpkgs repository, the largest cross-ecosystem open source software distribution, sampled in the period 2017-2023. We obtain very high bitwise reproducibility rates, between 69 and 91% with an upward trend, and even higher rebuildability rates, over 99%. We investigate unreproducibility causes, showing that about 15% of failures are due to embedded build dates. We release a novel dataset with all build statuses, logs, as well as full ''diffoscopes'': recursive diffs of where unreproducible build artifacts differ.

In this contribution, we are interested in the analysis of a semi-implicit time discretization scheme for the approximation of a parabolic equation driven by multiplicative colored noise involving a $p$-Laplace operator (with $p\geq 2$), nonlinear source terms and subject to Neumann boundary conditions. Using the Minty-Browder theorem, we are able to prove the well-posedness of such a scheme.

In anomaly detection, methods based on large language models (LLMs) can incorporate expert knowledge by reading professional document, while task-specific small models excel at extracting normal data patterns and detecting value fluctuations from training data of target applications. Inspired by the human nervous system, where the brain stores expert knowledge and the peripheral nervous system and spinal cord handle specific tasks like withdrawal and knee-jerk reflexes, we propose CoLLaTe, a framework designed to facilitate collaboration between LLMs and task-specific models, leveraging the strengths of both models for anomaly detection. In particular, we first formulate the collaboration process and identify two key challenges in the collaboration: (1) the misalignment between the expression domains of the LLMs and task-specific small models, and (2) error accumulation arising from the predictions of both models. To address these challenges, we then introduce two key components in CoLLaTe: a model alignment module and a collaborative loss function. Through theoretical analysis and experimental validation, we demonstrate that these components effectively mitigate the identified challenges and achieve better performance than both LLM-based and task-specific models.

The ATLAS Muon Spectrometer is undergoing a major upgrade for the High-Luminosity LHC (HL-LHC), including the addition of three new thin-gap Resistive Plate Chamber (RPC) layers in the inner barrel region. These RPCs have 1 mm gas gaps between high-pressure phenolic laminate (HPL) electrodes, enhancing their background rate capability and longevity. Nearly 1000 RPC gas gaps will be produced to maximize muon trigger acceptance and efficiency. To reduce reliance on a single supplier and expedite production, the ATLAS muon community formed partnerships with two companies in Germany and the Max Planck Institute for Physics. The gas gap assembly procedure was adapted to the industrial partners' infrastructure and tools, enabling the transfer of technology after prototyping. Manufacturer certification involved constructing multiple small- and full-size gas gap prototypes at each facility. These prototypes underwent extensive testing at CERN's Gamma Irradiation Facility (GIF++), where their efficiency and time resolution were verified under varying gamma backgrounds. They also passed an accelerated aging test, having been exposed to the maximum photon dose anticipated at the HL-LHC. This contribution presents the gas gap production procedures, certification test results, and a comparison of the manufacturing methods adopted by the different external companies. These outcomes confirm that the new facilities can reliably produce high-quality RPCs meeting ATLAS standards for HL-LHC operations.

If a crash between two vehicles is imminent, an Automatic Emergency Brake (AEB) is activated to avoid or mitigate the accident. However, the trigger mechanism of the AEB relies on the vehicle's onboard sensors, such as radar and cameras, that require a line of sight to detect the crash opponent. If the line of sight is impaired, for example by bad weather or an obstruction, the AEB cannot be activated in time to avoid the crash. To deal with these cases, a 2-stage braking system is proposed, where the first stage consists of a partial brake that is triggered by Vehicle-to-everything (V2X) communication. The second stage is composed of the standard AEB that is triggered exclusively by an onboard sensor detection. The performance of this V2X-enhanced 2-stage braking system is analysed in obstructed crossing use cases and the results are compared against the use of an AEB-only system. The benefit is quantitatively assessed by determination of the crash avoidance rate and, if the crash cannot be avoided, by estimation of the crash severity mitigation.

Rapid advances in Natural Language Processing (NLP) have revolutionized many fields, including healthcare. However, these advances raise significant privacy concerns, especially when models specialized on sensitive data can memorize and then expose and regurgitate confidential information. This paper presents a privacy-by-design language modeling approach to address the problem of language models anonymization, and thus promote their sharing. Specifically, we propose both a Masking Language Modeling (MLM) methodology to specialize a BERT-like language model, and a Causal Language Modeling (CLM) methodology to specialize a GPT-like model that avoids the model from memorizing direct and indirect identifying information present in the training data. We have comprehensively evaluated our approaches using medical datasets and compared them against different baselines. Our results indicate that by avoiding memorizing both direct and indirect identifiers during model specialization, our masking and causal language modeling schemes offer the best tradeoff for maintaining high privacy while retaining high utility.

Analysis of covariance is a crucial method for improving precision of statistical tests for factor effects in randomized experiments. However, existing solutions suffer from one or more of the following limitations: (i) they are not suitable for ordinal data (as endpoints or explanatory variables); (ii) they require semiparametric model assumptions; (iii) they are inapplicable to small data scenarios due to often poor type-I error control; or (iv) they provide only approximate testing procedures and (asymptotically) exact test are missing. In this paper, we investigate a resampling approach to the NANCOVA framework, which is a fully nonparametric model based on relative effects that allows for an arbitrary number of covariates and groups, where both outcome variable (endpoint) and covariates can be metric or ordinal. Thereby, we evaluate novel NANCOVA tests and a nonparametric competitor test without covariate adjustment in extensive simulations. Unlike approximate tests in the NANCOVA framework, our resampling version showed good performance in small sample scenarios and maintained the nominal type-I error well. Resampling NANCOVA also provided consistently high power: up to 26% higher than the test without covariate adjustment in a small sample scenario with 4 groups and two covariates. Moreover, we prove that resampling NANCOVA provides an asymptotically exact testing procedure, which makes it the first one in the NANCOVA framework. In summary, resampling NANCOVA can be considered a viable tool for analysis of covariance that overcomes issues (i) - (iv).

The adoption of Large Language Models (LLMs) is reshaping software development as developers integrate these LLMs into their applications. In such applications, prompts serve as the primary means of interacting with LLMs. Despite the widespread use of LLM-integrated applications, there is limited understanding of how developers manage and evolve prompts. This study presents the first empirical analysis of prompt evolution in LLM-integrated software development. We analyzed 1,262 prompt changes across 243 GitHub repositories to investigate the patterns and frequencies of prompt changes, their relationship with code changes, documentation practices, and their impact on system behavior. Our findings show that developers primarily evolve prompts through additions and modifications, with most changes occurring during feature development. We identified key challenges in prompt engineering: only 21.9% of prompt changes are documented in commit messages, changes can introduce logical inconsistencies, and misalignment often occurs between prompt changes and LLM responses. These insights emphasize the need for specialized testing frameworks, automated validation tools, and improved documentation practices to enhance the reliability of LLM-integrated applications., Comment: Accepted at MSR 2025

We study a nonlinear, pseudomonotone, stochastic diffusion-convection evolution problem on a bounded spatial domain, in any space dimension, with homogeneous boundary conditions and reflection. The additive noise term is given by a stochastic It\^{o} integral with respect to a Hilbert space valued $Q$-Wiener process. We show existence of a solution to the pseudomonotone stochastic diffusion-convection equation with non-negative initial value as well as the existence of a reflection measure which prevents the solution from taking negative values. In order to show a minimality condition of the measure, we study the properties of quasi everywhere defined representatives of the solution with respect to parabolic capacity., Comment: 38 pages

Let $K$ be a field, $Q$ a quiver, and $\mathcal{A}$ the ideal of the path algebra $KQ$ that is generated by the arrows of $Q$. We present old and new results about the representation theories of the truncations $KQ/\mathcal{A}^L$, $L \in \mathbb{N}$, tracking their development as $L$ goes to infinity. The goal is to gain a better understanding of the category of those finite dimensional $KQ$-modules which arise as finitely generated modules over admissible quotients of $KQ$.

Fuzzy (wave) dark matter (FDM), the dynamical model underlying an ultralight bosonic dark matter species, produces a rich set of non-gravitational signatures that distinguishes it markedly from the phenomenologically related warm (particle) dark matter (WDM) scenario. The emergence of extended interference fringes hosted by cosmic filaments is one such phenomenon reported by cosmological simulations, and a detailed understanding of such may strengthen existing limits on the boson mass but also break the degeneracy with WDM, and provide a unique fingerprint of interference in cosmology. In this paper, we provide initial steps towards this goal. In particular, we show in a bottom-up approach, how the presence of interference in an idealised filament population can lead to a non-suppressive feature in the matter power spectrum -- an observation supported by fully-cosmological FDM simulations. To this end, we build on a theoretically motivated and numerically observed steady-state approximation for filaments and express the equilibrium dynamics of such in an expansion of FDM eigenstates. We optimise the size of the expansion by incorporating classical phase-space information. Ellipsoidal collapse considerations are used to construct a fuzzy filament mass function which, together with the reconstructed FDM wave function, allow us to efficiently compute the one-filament power spectrum. We showcase our non-perturbative interference model for a selection of boson masses and confirm our approach is able to produce the matter power boost observed in fully-cosmological FDM simulations. More precisely, we find an excess in correlation between the spatial scale associated with the FDM ground state and the quantum pressure scale. We speculate about applications of this effect in data analysis., Comment: 15+5 pages, 12+1 figures

HyperQPTL and HyperQPTL$^+$ are expressive specification languages for hyperproperties, i.e., properties that relate multiple executions of a system. Tight complexity bounds are known for HyperQPTL finite-state satisfiability and model-checking. Here, we settle the complexity of satisfiability for HyperQPTL as well as satisfiability, finite-state satisfiability, and model-checking for HyperQPTL$^+$: the former is equivalent to truth in second-order arithmetic, the latter are all equivalent to truth in third-order arithmetic, i.e., they are all four very undecidable., Comment: arXiv admin note: text overlap with arXiv:2311.15675

We define and characterise completely dg-separable dg-extensions $\varphi:(A,d_A)\rightarrow (B,d_B)$. We completely characterise the case of graded commutative dg-division algebras in characteristic different from $2$. We prove that for a dg-separable extension a short exact sequence of dg-modules over $(B,d_B)$ splits if and only if the restriction to $(A,d_A)$ splits, giving that $(B,d_B)$ is acyclic and $\ker(d_B)$ graded-semisimple in case $(A,d_A)$ is a graded commutative dg-division algebra with $d_A=0$.

The main purpose of this article is to establish new uniqueness results for Calder\'on type inverse problems related to damped nonlocal wave equations. To achieve this goal we extend the theory of very weak solutions to our setting, which allows to deduce an optimal Runge approximation theorem. With this result at our disposal, we can prove simultaneous determination results in the linear and semilinear regime., Comment: 21 pages

This paper presents GPSM4K, a comprehensive geometry multimodal dataset tailored to augment the problem-solving capabilities of Large Vision Language Models (LVLMs). GPSM4K encompasses 2157 multimodal question-answer pairs manually extracted from mathematics textbooks spanning grades 7-12 and is further augmented to 5340 problems, consisting of both numerical and theorem-proving questions. In contrast to PGPS9k, Geometry3K, and Geo170K which feature only objective-type questions, GPSM4K offers detailed step-by-step solutions in a consistent format, facilitating a comprehensive evaluation of problem-solving approaches. This dataset serves as an excellent benchmark for assessing the geometric reasoning capabilities of LVLMs. Evaluation of our test set shows that there is scope for improvement needed in open-source language models in geometry problem-solving. Finetuning on our training set increases the geometry problem-solving capabilities of models. Further, We also evaluate the effectiveness of techniques such as image captioning and Retrieval Augmentation generation (RAG) on model performance. We leveraged LLM to automate the task of final answer evaluation by providing ground truth and predicted solutions. This research will help to assess and improve the geometric reasoning capabilities of LVLMs., Comment: 15 pages

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: Updating author information, the submission remains largely unchanged. 98 pages total

We present a lattice QCD calculation of the hadronic light-by-light scattering contribution to the anomalous magnetic moment of the muon using $N_f=2+1+1$ flavors of staggered quarks with masses tuned to their physical values. Our final result, in the continuum limit, reads $a_{\mu}^{\mathrm{hlbl}} = 125.5(11.6)_{\mathrm{stat}}(0.4)_{\mathrm{syst}} \times 10^{-11}$ where the first error is statistical and the second is systematic. Light, strange and charm-quark contributions are considered. In addition to the connected and leading disconnected contributions, we also include an estimate of the sub-leading disconnected diagrams. Our result is compatible with previous lattice QCD and data-driven dispersive determinations., Comment: 28 pages, 18 figures

The FCC-ee offers the potential to measure the electron Yukawa coupling via direct s-channel Higgs production, e+ e- -> H, at a centre-of-mass (CM) energy of 125 GeV. This measurement is significantly facilitated if the CM energy spread of e+ e- collisions can be reduced to a level comparable to the natural width of the Higgs boson, {\Gamma}_H = 4.1 MeV, without substantial loss in luminosity. Achieving this reduction in collision-energy spread is possible through the "monochromatization" concept. The basic idea is to create opposite correlations between spatial position and energy deviation within the colliding beams, which can be accomplished in beam optics by introducing a nonzero dispersion function with opposite signs for the two beams at the interaction point. Since the first proposal in 2016, the implementation of monochromatization at the FCC-ee has been continuously improved, starting from preliminary parametric studies. In this paper, we present a detailed study of the interaction region optics design for this newly proposed collision mode, exploring different potential configurations and their implementation in the FCC-ee global lattice, along with beam dynamics simulations and performance evaluations including the impact of "beamstrahlung."

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging., Comment: Part 3 of 3 in "Considerations and recommendations for preclinical diffusion MRI"

Advancements in light modulator technology have been driving discoveries and progress across various fields. The problem of large-scale coherent optical control of atomic quantum systems-including cold atoms, ions, and solid-state color centers-presents among the most stringent requirements. This motivates a new generation of high-speed large-scale modulator technology with the following requirements: (R1) operation at a design wavelength of choice in the visible (VIS) to near-infrared (NIR) spectrum, (R2) a scalable technology with a high channel density (> 100mm-2 ), (R3) a high modulation speed (> 100MHz), and (R4) a high extinction ratio (> 20 dB). To fulfill these requirements, we introduce a modulator technology based on piezoelectrically actuated silicon nitride resonant waveguide gratings fabricated on 200mm diameter silicon wafers with CMOS compatible processes. We present a proof-of-concept device with 4 x 4 individually addressable 50 {mu}m x 50 {mu}m pixels or channels, each containing a resonant waveguide grating with a ~ 780 nm design wavelength, supporting > 100MHz modulation speeds, and a spectral response with > 20 dB extinction., Comment: 9 pages + 2 pages supplementary

Hadronic contributions dominate the uncertainty of the Standard Model prediction for the anomalous magnetic moment of the muon. In this work, we present results on the hadronic light-by-light contribution obtained from the evaluation of the hadronic four-point function of electromagnetic currents using the position-space formalism developed by the Mainz group. The simulations are performed with staggered fermions directly at the physical point. Several physical volumes are used to estimate finite volume effects. This direct lattice study is supplemented by considering the contribution of the light pseudoscalar pole in both finite and infinite volumes, where we reuse the pseudoscalar transition form factors that have been evaluated in previous simulations on the same ensembles., Comment: 7 pages, 6 figures, LATTICE2024 conference proceedings, submitted to PoS

Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers. Unique characteristics of LLMs force developers, who are accustomed to traditional software development and evaluation, out of their comfort zones as the LLM components shatter standard assumptions about software systems. This study explores the emerging solutions that software developers are adopting to navigate the encountered challenges. Leveraging a mixed-method research, including 26 interviews and a survey with 332 responses, the study identifies 19 emerging solutions regarding quality assurance that practitioners across several product teams at Microsoft are exploring. The findings provide valuable insights that can guide the development and evaluation of LLM-based products more broadly in the face of these challenges., Comment: 10 pages, 2 tables

Time series foundation models have demonstrated impressive performance as zero-shot forecasters. However, achieving effectively unified training on time series remains an open challenge. Existing approaches introduce some level of model specialization to account for the highly heterogeneous nature of time series data. For instance, Moirai pursues unified training by employing multiple input/output projection layers, each tailored to handle time series at a specific frequency. Similarly, TimesFM maintains a frequency embedding dictionary for this purpose. We identify two major drawbacks to this human-imposed frequency-level model specialization: (1) Frequency is not a reliable indicator of the underlying patterns in time series. For example, time series with different frequencies can display similar patterns, while those with the same frequency may exhibit varied patterns. (2) Non-stationarity is an inherent property of real-world time series, leading to varied distributions even within a short context window of a single time series. Frequency-level specialization is too coarse-grained to capture this level of diversity. To address these limitations, this paper introduces Moirai-MoE, using a single input/output projection layer while delegating the modeling of diverse time series patterns to the sparse mixture of experts (MoE) within Transformers. With these designs, Moirai-MoE reduces reliance on human-defined heuristics and enables automatic token-level specialization. Extensive experiments on 39 datasets demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios. Furthermore, this study conducts comprehensive model analyses to explore the inner workings of time series MoE foundation models and provides valuable insights for future research.

Achieving precise medical image segmentation is vital for effective treatment planning and accurate disease diagnosis. Traditional fully-supervised deep learning methods, though highly precise, are heavily reliant on large volumes of labeled data, which are often difficult to obtain due to the expertise required for medical annotations. This has led to the rise of semi-supervised learning approaches that utilize both labeled and unlabeled data to mitigate the label scarcity issue. In this paper, we introduce the Manifold-Aware Local Feature Modeling Network (MANet), which enhances the U-Net architecture by incorporating manifold supervision signals. This approach focuses on improving boundary accuracy, which is crucial for reliable medical diagnosis. To further extend the versatility of our method, we propose two variants: MA-Sobel and MA-Canny. The MA-Sobel variant employs the Sobel operator, which is effective for both 2D and 3D data, while the MA-Canny variant utilizes the Canny operator, specifically designed for 2D images, to refine boundary detection. These variants allow our method to adapt to various medical image modalities and dimensionalities, ensuring broader applicability. Our extensive experiments on datasets such as ACDC, LA, and Pancreas-NIH demonstrate that MANet consistently surpasses state-of-the-art methods in performance metrics like Dice and Jaccard scores. The proposed method also shows improved generalization across various semi-supervised segmentation networks, highlighting its robustness and effectiveness. Visual analysis of segmentation results confirms that MANet offers clearer and more accurate class boundaries, underscoring the value of manifold information in medical image segmentation., Comment: 11 pages

Out-of-Domain (OOD) generalization is the ability of a model trained on one or more domains to generalize to unseen domains. In the ImageNet era of computer vision, evaluation sets for measuring a model's OOD performance were designed to be strictly OOD with respect to style. However, the emergence of foundation models and expansive web-scale datasets has obfuscated this evaluation process, as datasets cover a broad range of domains and risk test domain contamination. In search of the forgotten domain generalization, we create large-scale datasets subsampled from LAION -- LAION-Natural and LAION-Rendition -- that are strictly OOD to corresponding ImageNet and DomainNet test sets in terms of style. Training CLIP models on these datasets reveals that a significant portion of their performance is explained by in-domain examples. This indicates that the OOD generalization challenges from the ImageNet era still prevail and that training on web-scale data merely creates the illusion of OOD generalization. Furthermore, through a systematic exploration of combining natural and rendition datasets in varying proportions, we identify optimal mixing ratios for model generalization across these domains. Our datasets and results re-enable meaningful assessment of OOD robustness at scale -- a crucial prerequisite for improving model robustness.

In the realm of video dialog response generation, the understanding of video content and the temporal nuances of conversation history are paramount. While a segment of current research leans heavily on large-scale pretrained visual-language models and often overlooks temporal dynamics, another delves deep into spatial-temporal relationships within videos but demands intricate object trajectory pre-extractions and sidelines dialog temporal dynamics. This paper introduces the Dual Temporal Grounding-enhanced Video Dialog model (DTGVD), strategically designed to merge the strengths of both dominant approaches. It emphasizes dual temporal relationships by predicting dialog turn-specific temporal regions, filtering video content accordingly, and grounding responses in both video and dialog contexts. One standout feature of DTGVD is its heightened attention to chronological interplay. By recognizing and acting upon the dependencies between different dialog turns, it captures more nuanced conversational dynamics. To further bolster the alignment between video and dialog temporal dynamics, we've implemented a list-wise contrastive learning strategy. Within this framework, accurately grounded turn-clip pairings are designated as positive samples, while less precise pairings are categorized as negative. This refined classification is then funneled into our holistic end-to-end response generation mechanism. Evaluations using AVSD@DSTC-7 and AVSD@DSTC-8 datasets underscore the superiority of our methodology.

Despite its promise of openness and inclusiveness, the development of free and open source software (FOSS) remains significantly unbalanced in terms of gender representation among contributors. To assist open source project maintainers and communities in addressing this imbalance, it is crucial to understand the causes of this inequality.In this study, we aim to establish how the COVID-19 pandemic has influenced the ability of women to contribute to public code. To do so, we use the Software Heritage archive, which holds the largest dataset of commits to public code, and the difference in differences (DID) methodology from econometrics that enables the derivation of causality from historical data.Our findings show that the COVID-19 pandemic has disproportionately impacted women's ability to contribute to the development of public code, relatively to men. Further, our observations of specific contributor subgroups indicate that COVID-19 particularly affected women hobbyists, identified using contribution patterns and email address domains., Comment: Empirical Software Engineering, In press

Across domains, metrics and measurements are fundamental to identifying challenges, informing decisions, and resolving conflicts. Despite the abundance of data available in this information age, not only can it be challenging for a single expert to work across multi-disciplinary data, but non-experts can also find it unintuitive to create effective measures or transform theories into context-specific metrics that are chosen appropriately. This technical report addresses this challenge by examining software communities within large software corporations, where different measures are used as proxies to locate counterparts within the organization to transfer tacit knowledge. We propose a prompt-engineering framework inspired by neural activities, demonstrating that generative models can extract and summarize theories and perform basic reasoning, thereby transforming concepts into context-aware metrics to support software communities given software repository data. While this research zoomed in on software communities, we believe the framework's applicability extends across various fields, showcasing expert-theory-inspired metrics that aid in triaging complex challenges., Comment: 29 pages, 3 figures

We present team semantics for two of the most important linear and branching time specification languages, Linear Temporal Logic (LTL) and Computation Tree Logic (CTL). With team semantics, LTL is able to express hyperproperties, which have in the last decade been identified as a key concept in the verification of information flow properties. We study basic properties of the logic and classify the computational complexity of its satisfiability, path, and model checking problem. Further, we examine how extensions of the basic logic react to adding additional atomic operators. Finally, we compare its expressivity to the one of HyperLTL, another recently introduced logic for hyperproperties. Our results show that LTL with team semantics is a viable alternative to HyperLTL, which complements the expressivity of HyperLTL and has partially better algorithmic properties. For CTL with team semantics, we investigate the computational complexity of the satisfiability and model checking problem. The satisfiability problem is shown to be EXPTIME-complete while we show that model checking is PSPACE-complete.

Coherent Diffraction Imaging (CDI) is an experimental technique to gain images of isolated structures by recording the light scattered off the sample. In principle, the sample density can be recovered from the scattered light field through a straightforward Fourier Transform operation. However, only the amplitude of the field is recorded, while the phase is lost during the measurement process and has to be retrieved by means of suitable, well-established, phase retrieval algorithms. In this work we present SPRING, an analysis framework tailored on X-ray Free Electron Laser (XFEL) diffraction data that implements the Memetic Phase Retrieval method to mitigate the shortcomings of conventional algorithms. We benchmark the approach on experimental data acquired in two experimental campaigns at SwissFEL and European XFEL. Imaging results on isolated nanostructures reveal unprecedented stability and resilience of the algorithm's behavior on the input parameters, as well as the capability of identifying the solution in conditions hardly treatable so far with conventional methods. A user-friendly implementation of SPRING is released as open-source software, aiming at being a reference tool for the coherent diffraction imaging community at XFEL and synchrotron facilities., Comment: 30 pages, 13 figures. Authors list updated

We establish the global-in-time well-posedness of the two-dimensional incompressible Navier-Stokes equations with freely transported viscosity coefficient, under a scaling-invariant smallness condition on the initial data. The viscosity coefficient is allowed to exhibit large jumps across $W^{2,2+\epsilon}$-interfaces. The viscous stress tensor $\mu Su$ is carefully analyzed. Specifically, $(R^\perp\otimes R):(\mu Su)$, where $R$ denotes the Riesz operator, defines a ``good unknown'' that satisfies time-weighted $H^1$-energy estimates. Combined with tangential regularity, this leads to the $W^{1,2+\epsilon}$-regularity of another ``good unknown'', $(\bar{\tau}\otimes n):(\mu Su)$, where $\bar{\tau}$ and $n$ denote the unit tangential and normal vectors of the interfaces, respectively. These results collectively provide a Lipschitz estimate for the velocity field, even in the presence of significant discontinuities in $\mu$. As applications, we investigate the well-posedness of the Boussinesq equations without heat conduction and the density-dependent incompressible Navier-Stokes equations in two spatial dimensions.

Compact optical atomic clocks have become increasingly important in field applications and clock networks. Systems based on Ramsey-Borde interferometry (RBI) with a thermal atomic beam seem promising to fill a technology gap in optical atomic clocks, as they offer higher stability than optical vapour cell clocks while being less complex than cold atomic clocks. Here, we demonstrate RBI with strontium atoms, utilizing the narrow 1S0 -> 3P1 intercombination line at 689 nm, yielding a 60 kHz broad spectral feature. The obtained Ramsey fringes for varying laser power are analyzed and compared with a numerical model. The 1S0 -> 1P1 transition at 461 nm is used for fluorescence detection. Analyzing the slope of the RBI signal and the fluorescence detection noise yields an estimated short-term stability of 4x10-14 / sqrt{tau}. We present our experimental setup in detail, including the atomic beam source, frequency-modulation spectroscopy to lock the 461 nm laser, laser power stabilization and the high-finesse cavity pre-stabilization of the 689 nm laser. Our system serves as a ground testbed for future clock systems in mobile and space applications., Comment: 14+8 pages, 9 figures, submitted to EPJ Quantum Technology

Runtime verification of temporal properties over timed sequences of observations is crucial in various applications within cyber-physical systems ranging from autonomous vehicles over smart grids to medical devices. In this paper, we are addressing the challenge of effectively predicting the failure or success of properties in a continuous real-time setting. Our approach allows predictions to exploit assumptions on the system being monitored and supports predictions of non-observable system behaviour (e.g. internal faults). More concretely, in our approach properties are expressed in Metric Interval Temporal Logic (MITL), assumptions on the monitored system are specified in terms of Timed Automata, and observations are to be provided in terms of sequences of timed constraints. We present an assumption-based runtime verification algorithm and its implementation on top of the real-time verification tool UPPAAL. We show experimentally that assumptions can be effective in anticipating the satisfaction/violation of timed properties and in handling monitoring properties that predicate over unobservable events.

For d/Deaf and hard of hearing (DHH) people, captioning is an essential accessibility tool. Significant developments in artificial intelligence (AI) mean that Automatic Speech Recognition (ASR) is now a part of many popular applications. This makes creating captions easy and broadly available - but transcription needs high levels of accuracy to be accessible. Scientific publications and industry report very low error rates, claiming AI has reached human parity or even outperforms manual transcription. At the same time the DHH community reports serious issues with the accuracy and reliability of ASR. There seems to be a mismatch between technical innovations and the real-life experience for people who depend on transcription. Independent and comprehensive data is needed to capture the state of ASR. We measured the performance of eleven common ASR services with recordings of Higher Education lectures. We evaluated the influence of technical conditions like streaming, the use of vocabularies, and differences between languages. Our results show that accuracy ranges widely between vendors and for the individual audio samples. We also measured a significant lower quality for streaming ASR, which is used for live events. Our study shows that despite the recent improvements of ASR, common services lack reliability in accuracy.

The Word Error Rate (WER) is the common measure of accuracy for Automatic Speech Recognition (ASR). Transcripts are usually pre-processed by substituting specific characters to account for non-semantic differences. As a result of this normalisation, information on the accuracy of punctuation or capitalisation is lost. We present a non-destructive, token-based approach using an extended Levenshtein distance algorithm to compute a robust WER and additional orthographic metrics. Transcription errors are also classified more granularly by existing string similarity and phonetic algorithms. An evaluation on several datasets demonstrates the practical equivalence of our approach compared to common WER computations. We also provide an exemplary analysis of derived use cases, such as a punctuation error rate, and a web application for interactive use and visualisation of our implementation. The code is available open-source., Comment: Accepted in INTERSPEECH 2024

The main purpose of this article is to establish the Runge-type approximation in $L^2(0,T;\widetilde{H}^s(\Omega))$ for solutions of linear nonlocal wave equations. To achieve this, we extend the theory of very weak solutions for classical wave equations to our nonlocal framework. This strengthened Runge approximation property allows us to extend the existing uniqueness results for Calder\'on problems of linear and nonlinear nonlocal wave equations in our earlier works. Furthermore, we prove unique determination results for the Calder\'on problem of nonlocal wave equations with polyhomogeneous nonlinearities., Comment: 38 pages

We study the partial data Calder\'on problem for the anisotropic Schr\"{o}dinger equation \begin{equation} \label{eq: a1} (-\Delta_{\widetilde{g}}+V)u=0\text{ in }\Omega\times (0,\infty), \end{equation} where $\Omega\subset\mathbb{R}^n$ is a bounded smooth domain, $\widetilde{g}=g_{ij}(x)dx^{i}\otimes dx^j+dy\otimes dy$ and $V$ is translationally invariant in the $y$ direction. Our goal is to recover both the metric $g$ and the potential $V$ from the (partial) Neumann-to-Dirichlet (ND) map on $\Gamma\times \{0\}$ with $\Gamma\Subset \Omega$. Our approach can be divided into three steps: Step 1. Boundary determination. We establish a novel boundary determination to identify $(g,V)$ on $\Gamma$ with help of suitable approximate solutions for the Schr\"odinger equation with inhomogeneous Neumann boundary condition. Step 2. Relation to a nonlocal elliptic inverse problem. We relate inverse problems for the Schr\"odinger equation with the nonlocal elliptic equation \begin{equation} \label{eq: a2} (-\Delta_g+V)^{1/2}v=f\text{ in }\Omega, \end{equation} via the Caffarelli--Silvestre type extension, where the measurements are encoded in the source-to-solution map. The nonlocality of this inverse problem allows us to recover the associated heat kernel. Step 3. Reduction to an inverse problem for a wave equation. Combining the knowledge of the heat kernel with the Kannai type transmutation formula, we transfer the inverse problem for the nonlocal equation to an inverse problem for the wave equation \begin{equation} \label{eq: a3} (\partial_t^2-\Delta_g+V)w=F\text{ in }\Omega\times (0,\infty), \end{equation} where the measurement operator is also the source-to-solution map. We can finally determine $(g,V)$ on $\Omega\setminus\Gamma$ by solving the inverse problem for the wave equation., Comment: 54 pages. All comments are welcome

Quantum technologies extensively use laser light for state preparation, manipulation, and readout. For field applications, these systems must be robust and compact, driving the need for miniaturized and highly stable optical setups and system integration. In this work, we present a micro-integrated crossed-beam optical dipole trap setup, the $\mu$XODT, designed for trapping and cooling $^{87}\text{Rb}$. This fiber-coupled setup operates at $1064\,\text{nm}$ wavelength with up to $2.5\,\text{W}$ optical power and realizes a free-space crossed beam geometry. The $\mu$XODT precisely overlaps two focused beams ($w_0 \approx 33\,\mu\text{m}$) at their waists in a $45^\circ$ crossing angle, achieving a position difference $\leq 3.4\,\mu\text{m}$ and 0.998 power ratio between both beams with long-term stability. We describe the design and assembly process in detail, along with optical and thermal tests with temperatures of up to $65\,^\circ C$. The system's volume of $25\,\text{ml}$ represents a reduction of more than two orders of magnitude compared to typically used macroscopic setups, while demonstrating exceptional mechanical robustness and thermal stability. The $\mu$XODT is integrated with a $^{87}\text{Rb}$ 3D MOT setup, trapping $3 \times 10^5$ atoms from a laser-cooled atomic cloud, and has shown no signs of degradation after two years of operation.

Photonic microring resonators are used in a variety of chip-integrated sensing applications where they allow to measure transmission intensity changes upon external signals with a sensitivity that scales linearly with the Q-factor. In this work, we suggest to exploit the nonlinear self-phase-modulation effect to increase the overall sensitivity by an additional gain factor appearing when the operational point of the nonlinear resonator is chosen just at the cross-over from the mono- to the bistable regime. We present the theoretical idea together with a first proof of concept experiment displaying a gain factor of 22 on a chip-integrated silicon-nitride resonator.

We give the definition of a dg-division algebra, that is a concept of a differential graded algebra which may serve as an analogue of a division algebra. We classify them completely, and show that they are either acyclic or have differential $0$. Further, we prove that the graded centre of dg-simple dg-algebras is a dg-division algebra, and also the dg-endomorphism ring of a dg-simple module is a dg-division algebra. We also shall give a Jacobson-Chevalley density theorem for acyclic dg-algebras., Comment: Substantial revision