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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

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 get images of isolated structures by recording the light scattered off the sample. Thanks to the extremely bright and short coherent light pulses provided by X-ray Free Electron Lasers, CDI makes it possible to study nanostructures in the gas phase and get time-resolved snapshots of their ultrafast dynamics with unprecedented resolution. 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. We present the Memetic Phase Retrieval (MPR) method, an improved approach to the phase retrieval problem, which makes use of a combination of existing phase retrieval algorithms and evolutionary algorithms to mitigate the shortcomings of conventional approaches. We benchmark the method on experimental data acquired in two experimental campaigns at SwissFEL and European XFEL. Imaging results on isolated nanostructures reveal considerable stability of the algorithm's behavior on the input parameters, as well as the capability of identifying the solution in challenging conditions. A user-friendly implementation of the MPR method is released as open-source software, aiming at being a reference tool for the FEL imaging community., Comment: 37 pages, 13 figures. Software not yet released as open source. Software documentation under construction. Authors list up to date

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

Accurate forecasts of the impact of spatial weather and pan-European socio-economic and political risks on hourly electricity demand for the mid-term horizon are crucial for strategic decision-making amidst the inherent uncertainty. Most importantly, these forecasts are essential for the operational management of power plants, ensuring supply security and grid stability, and in guiding energy trading and investment decisions. The primary challenge for this forecasting task lies in disentangling the multifaceted drivers of load, which include national deterministic (daily, weekly, annual, and holiday patterns) and national stochastic weather and autoregressive effects. Additionally, transnational stochastic socio-economic and political effects add further complexity, in particular, due to their non-stationarity. To address this challenge, we present an interpretable probabilistic mid-term forecasting model for the hourly load that captures, besides all deterministic effects, the various uncertainties in load. This model recognizes transnational dependencies across 24 European countries, with multivariate modeled socio-economic and political states and cross-country dependent forecasting. Built from interpretable Generalized Additive Models (GAMs), the model enables an analysis of the transmission of each incorporated effect to the hour-specific load. Our findings highlight the vulnerability of countries reliant on electric heating under extreme weather scenarios. This emphasizes the need for high-resolution forecasting of weather effects on pan-European electricity consumption especially in anticipation of widespread electric heating adoption.

Foundation models are rapidly being developed for computational pathology applications. However, it remains an open question which factors are most important for downstream performance with data scale and diversity, model size, and training algorithm all playing a role. In this work, we propose algorithmic modifications, tailored for pathology, and we present the result of scaling both data and model size, surpassing previous studies in both dimensions. We introduce three new models: Virchow2, a 632 million parameter vision transformer, Virchow2G, a 1.9 billion parameter vision transformer, and Virchow2G Mini, a 22 million parameter distillation of Virchow2G, each trained with 3.1 million histopathology whole slide images, with diverse tissues, originating institutions, and stains. We achieve state of the art performance on 12 tile-level tasks, as compared to the top performing competing models. Our results suggest that data diversity and domain-specific methods can outperform models that only scale in the number of parameters, but, on average, performance benefits from the combination of domain-specific methods, data scale, and model scale.

Direct volume rendering using ray-casting is widely used in practice. By using GPUs and applying acceleration techniques as empty space skipping, high frame rates are possible on modern hardware. This enables performance-critical use-cases such as virtual reality volume rendering. The currently fastest known technique uses volumetric distance maps to skip empty sections of the volume during ray-casting but requires the distance map to be updated per transfer function change. In this paper, we demonstrate a technique for subdividing the volume intensity range into partitions and deriving what we call partitioned distance maps. These can be used to accelerate the distance map computation for a newly changed transfer function by a factor up to 30. This allows the currently fastest known empty space skipping approach to be used while maintaining high frame rates even when the transfer function is changed frequently., Comment: 5 pages, 5 figures, 1 table, To appear in Proc. IEEE VIS 2024, for associated mp4 file, see https://osf.io/n5k6z

This paper presents a natural language processing (NLP) approach to the problem of thoroughly comprehending song lyrics, with particular attention on genre classification, view-based success prediction, and approximate release year. Our tests provide promising results with 65\% accuracy in genre classification and 79\% accuracy in success prediction, leveraging a DistilBERT model for genre classification and BERT embeddings for release year prediction. Support Vector Machines outperformed other models in predicting the release year, achieving the lowest root mean squared error (RMSE) of 14.18. Our study offers insights that have the potential to revolutionize our relationship with music by addressing the shortcomings of current approaches in properly understanding the emotional intricacies of song lyrics.

As the number of scientific publications and preprints is growing exponentially, several attempts have been made to navigate this complex and increasingly detailed landscape. These have almost exclusively taken unsupervised approaches that fail to incorporate domain knowledge and lack the structural organisation required for intuitive interactive human exploration and discovery. Especially in highly interdisciplinary fields, a deep understanding of the connectedness of research works across topics is essential for generating insights. We have developed a unique approach to data navigation that leans on geographical visualisation and uses hierarchically structured domain knowledge to enable end-users to explore knowledge spaces grounded in their desired domains of interest. This can take advantage of existing ontologies, proprietary intelligence schemata, or be directly derived from the underlying data through hierarchical topic modelling. Our approach uses natural language processing techniques to extract named entities from the underlying data and normalise them against relevant domain references and navigational structures. The knowledge is integrated by first calculating similarities between entities based on their shared extracted feature space and then by alignment to the navigational structures. The result is a knowledge graph that allows for full text and semantic graph query and structured topic driven navigation. This allows end-users to identify entities relevant to their needs and access extensive graph analytics. The user interface facilitates graphical interaction with the underlying knowledge graph and mimics a cartographic map to maximise ease of use and widen adoption. We demonstrate an exemplar project using our generalisable and scalable infrastructure for an academic biomedical literature corpus that is grounded against hundreds of different named domain entities.

In autonomous driving, deep models have shown remarkable performance across various visual perception tasks with the demand of high-quality and huge-diversity training datasets. Such datasets are expected to cover various driving scenarios with adverse weather, lighting conditions and diverse moving objects. However, manually collecting these data presents huge challenges and expensive cost. With the rapid development of large generative models, we propose DriveDiTFit, a novel method for efficiently generating autonomous Driving data by Fine-tuning pre-trained Diffusion Transformers (DiTs). Specifically, DriveDiTFit utilizes a gap-driven modulation technique to carefully select and efficiently fine-tune a few parameters in DiTs according to the discrepancy between the pre-trained source data and the target driving data. Additionally, DriveDiTFit develops an effective weather and lighting condition embedding module to ensure diversity in the generated data, which is initialized by a nearest-semantic-similarity initialization approach. Through progressive tuning scheme to refined the process of detail generation in early diffusion process and enlarging the weights corresponding to small objects in training loss, DriveDiTFit ensures high-quality generation of small moving objects in the generated data. Extensive experiments conducted on driving datasets confirm that our method could efficiently produce diverse real driving data. The source codes will be available at https://github.com/TtuHamg/DriveDiTFit.

Enterprise applications are often built as service-oriented architectures, where the individual services are designed to perform specific functions and interact with each other by means of well-defined APIs (Application Programming Interfaces). The architecture of an enterprise application evolves over time, in order to adapt to changing business requirements. This evolution might require changes to the APIs offered by services, which can be achieved through appropriate API refactorings. Previous studies on API refactoring focused on the effects on API definitions, with general considerations on related forces and smells. So far, instead, the development strategy for realising these refactorings has received little attention. This paper addresses exactly this aspect. We introduce a conceptual framework for the implementation of API refactorings. Our framework elicits that there are important trade-offs and choices, which significantly affect the efficiency, maintainability, and isolation properties of the resulting architecture. We validate our framework by implementing several refactorings that introduce established API patterns with different choices, which illustrates the guiding principles offered by our framework. Our work also elicits, for the first time, how certain programming language features can reduce friction in applying API refactoring and open up more architectural choices.

Detecting visual content on language expression has become an emerging topic in the community. However, in the video domain, the existing setting, i.e., spatial-temporal video grounding (STVG), is formulated to only detect one pre-existing object in each frame, ignoring the fact that language descriptions can involve none or multiple entities within a video. In this work, we advance the STVG to a more practical setting called described spatial-temporal video detection (DSTVD) by overcoming the above limitation. To facilitate the exploration of DSTVD, we first introduce a new benchmark, namely DVD-ST. Notably, DVD-ST supports grounding from none to many objects onto the video in response to queries and encompasses a diverse range of over 150 entities, including appearance, actions, locations, and interactions. The extensive breadth and diversity of the DVD-ST dataset make it an exemplary testbed for the investigation of DSTVD. In addition to the new benchmark, we further present two baseline methods for our proposed DSTVD task by extending two representative STVG models, i.e., TubeDETR, and STCAT. These extended models capitalize on tubelet queries to localize and track referred objects across the video sequence. Besides, we adjust the training objectives of these models to optimize spatial and temporal localization accuracy and multi-class classification capabilities. Furthermore, we benchmark the baselines on the introduced DVD-ST dataset and conduct extensive experimental analysis to guide future investigation. Our code and benchmark will be publicly available.

A control system consists of a plant component and a controller which periodically computes a control input for the plant. We consider systems where the controller is implemented by a feedforward neural network with ReLU activations. The reachability problem asks, given a set of initial states, whether a set of target states can be reached. We show that this problem is undecidable even for trivial plants and fixed-depth neural networks with three inputs and outputs. We also show that the problem becomes semi-decidable when the plant as well as the input and target sets are given by automata over infinite words.

The aim of this contribution is to address the convergence study of a time and space approximation scheme for an Allen-Cahn problem with constraint and perturbed by a multiplicative noise of It\^o type. The problem is set in a bounded domain of $\mathbb{R}^d$ (with $d=2$ or $3$) and homogeneous Neumann boundary conditions are considered. The employed strategy consists in building a numerical scheme on a regularized version \`a la Moreau-Yosida of the constrained problem, and passing to the limit simultaneously with respect to the regularization parameter and the time and space steps, denoted respectively by $\epsilon$, $\Delta t$ and $h$. Combining a semi-implicit Euler-Maruyama time discretization with a Two-Point Flux Approximation (TPFA) scheme for the spatial variable, one is able to prove, under the assumption $\Delta t=\mathcal{O}(\epsilon^{2+\theta})$ for a positive $\theta$, the convergence of such a $(\epsilon, \Delta t, h)$ scheme towards the unique weak solution of the initial problem, \textit{ a priori} strongly in $L^2(\Omega;L^2(0,T;L^2(\Lambda)))$ and \textit{a posteriori} also strongly in $L^{p}(0,T; L^2(\Omega\times \Lambda))$ for any finite $p\geq 1$., Comment: arXiv admin note: text overlap with arXiv:2304.02259

Nfer is a Runtime Verification language for the analysis of event traces that applies rules to create hierarchies of time intervals. This work examines the complexity of the evaluation and satisfiability problems for the data-free fragment of nfer. The evaluation problem asks whether a given interval is generated by applying rules to a known input, while the satisfiability problem asks if an input exists that will generate a given interval. By excluding data from the language, we obtain polynomial-time algorithms for the evaluation problem and for satisfiability when only considering inclusive rules. Furthermore, we show decidability for the satisfiability problem for cycle-free specifications and undecidability for satisfiability of full data-free nfer.

The chromatic number of a lattice in n-dimensional Euclidean space is defined as the chromatic number of its Voronoi graph. The Voronoi graph is the Cayley graph on the lattice having the strict Voronoi vectors as generators. In this paper we determine the chromatic number of all 4-dimensional lattices. To achieve this we use the known classification of 52 parallelohedra in dimension 4. These 52 geometric types yield 16 combinatorial types of relevant Voronoi graphs. We discuss a systematic approach to checking for isomorphism of Cayley graphs of lattices. Lower bounds for the chromatic number are obtained from choosing appropriate small finite induced subgraphs of the Voronoi graphs. Matching upper bounds are derived from periodic colorings. To determine the chromatic numbers of these finite graphs, we employ a SAT solver., Comment: (v2) 17 pages, referees comments incorporated

Previous theoretical work on contrastive learning (CL) with InfoNCE showed that, under certain assumptions, the learned representations uncover the ground-truth latent factors. We argue these theories overlook crucial aspects of how CL is deployed in practice. Specifically, they assume that within a positive pair, all latent factors either vary to a similar extent, or that some do not vary at all. However, in practice, positive pairs are often generated using augmentations such as strong cropping to just a few pixels. Hence, a more realistic assumption is that all latent factors change, with a continuum of variability across these factors. We introduce AnInfoNCE, a generalization of InfoNCE that can provably uncover the latent factors in this anisotropic setting, broadly generalizing previous identifiability results in CL. We validate our identifiability results in controlled experiments and show that AnInfoNCE increases the recovery of previously collapsed information in CIFAR10 and ImageNet, albeit at the cost of downstream accuracy. Additionally, we explore and discuss further mismatches between theoretical assumptions and practical implementations, including extensions to hard negative mining and loss ensembles.

A seamless chip-to-world photonic interface enables wide-ranging advancements in optical ranging, display, communication, computation, imaging, and light-matter interaction. An optimal solution allows for 2D scanning of a diffraction-limited beam from anywhere on a photonic chip over a large number of beam-spots in free-space. Currently, devices with direct PIC integration rely on tiled apertures with poor mode qualities, large footprints, and complex control systems. Micro-mechanical beam scanners have good beam quality but lack direct PIC integration and are inertially-limited due to the use of bulk optical components or structures in which the optical aperture and actuator sizes are inextricably linked, resulting in trade-offs among resolution, speed, and footprint. Here, we overcome these limitations with the photonic "ski-jump": a nanoscale optical waveguide monolithically integrated atop a piezoelectrically actuated cantilever which passively curls ~90$^{\circ}$ out-of-plane in a footprint of <0.1 mm$^{2}$, emits sub-micron diffraction-limited optical modes, and exhibits kHz-rate mechanical resonances with quality factors exceeding 10,000. This enables two-dimensional beam-scanning with footprint-adjusted spot-rates of 68.6 mega-spots/(s$\cdot$mm$^{2}$) at CMOS-level voltages, which is equivalent to a 1 megapixel display at 100 Hz from a 1.5 mm$^{2}$ footprint, and exceeds the performance of state-of-the-art MEMS mirrors by >50$\times$. Using this device, we demonstrate image projection, video projection, and the initialization and readout of single photons from silicon vacancy centers in diamond waveguides. Based on current performance, we identify pathways for achieving >1 giga-spots at kHz-rates in a ~1 cm$^{2}$ area to provide a seamless, scalable optical pipeline between integrated photonic processors and the free-space world., Comment: 12 pages main text, 1 page methods, 10 pages supplementary information

Human motion copy is an intriguing yet challenging task in artificial intelligence and computer vision, which strives to generate a fake video of a target person performing the motion of a source person. The problem is inherently challenging due to the subtle human-body texture details to be generated and the temporal consistency to be considered. Existing approaches typically adopt a conventional GAN with an L1 or L2 loss to produce the target fake video, which intrinsically necessitates a large number of training samples that are challenging to acquire. Meanwhile, current methods still have difficulties in attaining realistic image details and temporal consistency, which unfortunately can be easily perceived by human observers. Motivated by this, we try to tackle the issues from three aspects: (1) We constrain pose-to-appearance generation with a perceptual loss and a theoretically motivated Gromov-Wasserstein loss to bridge the gap between pose and appearance. (2) We present an episodic memory module in the pose-to-appearance generation to propel continuous learning that helps the model learn from its past poor generations. We also utilize geometrical cues of the face to optimize facial details and refine each key body part with a dedicated local GAN. (3) We advocate generating the foreground in a sequence-to-sequence manner rather than a single-frame manner, explicitly enforcing temporal inconsistency. Empirical results on five datasets, iPER, ComplexMotion, SoloDance, Fish, and Mouse datasets, demonstrate that our method is capable of generating realistic target videos while precisely copying motion from a source video. Our method significantly outperforms state-of-the-art approaches and gains 7.2% and 12.4% improvements in PSNR and FID respectively.

Microservice architectures have gained popularity as one of the preferred architectural approaches to develop large-scale systems, replacing the monolith architecture approach. Similarly, strategic Domain-Driven Design (DDD) gained traction as the preferred architectural design approach for the development of microservices. However, DDD and its strategic patterns are open-ended by design, leading to a gap between the concepts of DDD and the design of microservices. This gap is especially evident in migration tools that identify microservices from monoliths, where candidate decompositions into microservices provide little in terms of DDD refactoring and visualization. This paper proposes a solution to this problem by extending the operational pipeline of a multi-strategy microservice identification tool, called Mono2Micro, with a DDD modeling tool that provides a language, called Context Mapper DSL (CML), for formalizing the most relevant DDD concepts. The extension maps the content of the candidate decompositions, which include clusters, entities, and functionalities, to CML constructs that represent DDD concepts such as Bounded Context, Aggregate, Entity, and Service, among others. The results are validated with a case study by comparing the candidate decompositions resulting from a real-world monolith application with and without CML translation., Comment: 11 pages, 11 figures, 4 tables

Satellite-based street-view information extraction by cross-view matching refers to a task that extracts the location and orientation information of a given street-view image query by using one or multiple geo-referenced satellite images. Recent work has initiated a new research direction to find accurate information within a local area covered by one satellite image centered at a location prior (e.g., from GPS). It can be used as a standalone solution or complementary step following a large-scale search with multiple satellite candidates. However, these existing works require an accurate initial orientation (angle) prior (e.g., from IMU) and/or do not efficiently search through all possible poses. To allow efficient search and to give accurate prediction regardless of the existence or the accuracy of the angle prior, we present PetalView extractors with multi-scale search. The PetalView extractors give semantically meaningful features that are equivalent across two drastically different views, and the multi-scale search strategy efficiently inspects the satellite image from coarse to fine granularity to provide sub-meter and sub-degree precision extraction. Moreover, when an angle prior is given, we propose a learnable prior angle mixer to utilize this information. Our method obtains the best performance on the VIGOR dataset and successfully improves the performance on KITTI dataset test 1 set with the recall within 1 meter (r@1m) for location estimation to 68.88% and recall within 1 degree (r@1d) 21.10% when no angle prior is available, and with angle prior achieves stable estimations at r@1m and r@1d above 70% and 21%, up to a 40-degree noise level., Comment: This paper has been accepted by ACM Multimedia 2023. This version contains additional supplementary materials

The Cryogenic Underground Observatory for Rare Events (CUORE) is a detector array comprised by 988 5$\;$cm$\times$5$\;$cm$\times$5$\;$cm TeO$_2$ crystals held below 20 mK, primarily searching for neutrinoless double-beta decay in $^{130}$Te. Unprecedented in size amongst cryogenic calorimetric experiments, CUORE provides a promising setting for the study of exotic through-going particles. Using the first tonne-year of CUORE's exposure, we perform a search for hypothesized fractionally charged particles (FCPs), which are well-motivated by various Standard Model extensions and would have suppressed interactions with matter. No excess of FCP candidate tracks is observed over background, setting leading limits on the underground FCP flux with charges between $e/24-e/5$ at 90\% confidence level. Using the low background environment and segmented geometry of CUORE, we establish the sensitivity of tonne-scale sub-Kelvin detectors to diverse signatures of new physics., Comment: 7 pages, 5 figures

We investigate the inverse problem of recovering the diffusion and absorption coefficients $(\sigma,q)$ in the nonlocal diffuse optical tomography equation $(-\text{div}( \sigma \nabla))^s u+q u =0 \text{ in }\Omega$ from the nonlocal Dirichlet-to-Neumann (DN) map $\Lambda^s_{\sigma,q}$. The purpose of this article is to establish the following approximation and uniqueness results. - Approximation: We show that solutions to the conductivity equation $ \text{div}( \sigma \nabla v)=0 \text{ in }\Omega$ can be approximated in $H^1(\Omega)$ by solutions to the nonlocal diffuse optical tomography equation and the DN map $\Lambda_\sigma$ related to conductivity equation can be approximated by the nonlocal DN map $\Lambda_{\sigma,q}^s$. - Local uniqueness: We prove that the absorption coefficient $q$ can be determined in a neighborhood $\mathcal{N}$ of the boundary $\partial\Omega$ provided $\sigma$ is already known in $\mathcal{N}$. - Global uniqueness: Under the same assumptions as for the local uniqueness result, and if one of the potentials vanishes in $\Omega$, then one can turn with the help of \ref{item 1 abstract} the local determination into a global uniqueness result. It is worth mentioning that the approximation result relies on the Caffarelli--Silvestre type extension technique and the geometric form of the Hahn--Banach theorem., Comment: 37 pages

Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare. While deep learning techniques are becoming standard, adapting them to bioacoustics remains difficult. We address this with animal2vec, an interpretable large transformer model, and a self-supervised training scheme tailored for sparse and unbalanced bioacoustic data. It learns from unlabeled audio and then refines its understanding with labeled data. Furthermore, we introduce and publicly release MeerKAT: Meerkat Kalahari Audio Transcripts, a dataset of meerkat (Suricata suricatta) vocalizations with millisecond-resolution annotations, the largest labeled dataset on non-human terrestrial mammals currently available. Our model outperforms existing methods on MeerKAT and the publicly available NIPS4Bplus birdsong dataset. Moreover, animal2vec performs well even with limited labeled data (few-shot learning). animal2vec and MeerKAT provide a new reference point for bioacoustic research, enabling scientists to analyze large amounts of data even with scarce ground truth information., Comment: Code available at: https://github.com/livingingroups/animal2vec | Dataset available at: https://doi.org/10.17617/3.0J0DYB

Folk wisdom dictates that a lower bound on the dark matter particle mass, $m$, can be obtained by demanding that the de Broglie wavelength in a given galaxy must be smaller than the virial radius of the galaxy, leading to $m\gtrsim 10^{-22}\text{ eV}$ when applied to typical dwarf galaxies. This lower limit has never been derived precisely or rigorously. We use stellar kinematical data for the Milky Way satellite galaxy Leo II to self-consistently reconstruct a statistical ensemble of dark matter wavefunctions and corresponding density profiles. By comparison to a data-driven, model-independent reconstruction, and using a variant of the maximum mean discrepancy as a statistical measure, we determine that a self-consistent description of dark matter in the local Universe requires $m>2.2 \times 10^{-21}\,\mathrm{eV}\;\mathrm{(CL>95\%)}$. This lower limit is free of any assumptions pertaining to cosmology, microphysics (including spin), or dynamics of dark matter, and only assumes that it is predominantly composed of a single bosonic particle species., Comment: 5 pages, 3 figures. The jaxsp library is available at https://github.com/timzimm/jaxsp

The increasing number of vehicles highlights the need for efficient parking space management. Predicting real-time Parking Availability (PA) can help mitigate traffic congestion and the corresponding social problems, which is a pressing issue in densely populated cities like Singapore. In this study, we aim to collectively predict future PA across Singapore with complex factors from various domains. The contributions in this paper are listed as follows: (1) A New Dataset: We introduce the \texttt{SINPA} dataset, containing a year's worth of PA data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors. (2) A Data-Driven Approach: We present DeepPA, a novel deep-learning framework, to collectively and efficiently predict future PA across thousands of parking lots. (3) Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. Furthermore, we implement DeepPA in a practical web-based platform to provide real-time PA predictions to aid drivers and inform urban planning for the governors in Singapore. We release the dataset and source code at https://github.com/yoshall/SINPA., Comment: Accepted by IJCAI 2024 (Multi-Year Track On AI And Social Good with ~20% acceptance rate)

We present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable operations, allow for an in-depth exploration of both spatial and time dependence of backgrounds. We achieve high sensitivity to both bulk and surface activities of the materials of the setup, detecting levels as low as 10 nBq kg$^{-1}$ and 0.1 nBq cm$^{-2}$, respectively. We compare the contamination levels we extract from the background model with prior radio-assay data, which informs future background risk mitigation strategies. The results of this background model play a crucial role in constructing the background budget for the CUPID experiment as it will exploit the same CUORE infrastructure.

The intricate relationship between electrons and the crystal lattice is a linchpin in condensed matter, traditionally described by the Fr\"ohlich model encompassing the lowest-order lattice-electron coupling. Recently developed quantum acoustics, emphasizing the wave nature of lattice vibrations, has enabled the exploration of previously uncharted territories of electron-lattice interaction not accessible with conventional tools such as perturbation theory. In this context, our agenda here is two-fold. First, we showcase the application of machine learning methods to categorize various interaction regimes within the subtle interplay of electrons and the dynamical lattice landscape. Second, we shed light on a nebulous region of electron dynamics identified by the machine learning approach and then attribute it to transient localization, where strong lattice vibrations result in a momentary Anderson prison for electronic wavepackets, which are later released by the evolution of the lattice. Overall, our research illuminates the spectrum of dynamics within the Fr\"ohlich model, such as transient localization, which has been suggested as a pivotal factor contributing to the mysteries surrounding strange metals. Furthermore, this paves the way for utilizing time-dependent perspectives in machine learning techniques for designing materials with tailored electron-lattice properties., Comment: 14 pages, 9 figures

Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stability, and energy trading. While numerous models effectively predict short-term (hours to a few days) hourly load, mid-term forecasting solutions remain scarce. In mid-term load forecasting, besides daily, weekly, and annual seasonal and autoregressive effects, capturing weather and holiday effects, as well as socio-economic non-stationarities in the data, poses significant modeling challenges. To address these challenges, we propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines and enhanced with autoregressive post-processing. This model uses smoothed temperatures, Error-Trend-Seasonal (ETS) modeled non-stationary states, a nuanced representation of holiday effects with weekday variations, and seasonal information as input. The proposed model is evaluated on load data from 24 European countries. This analysis demonstrates that the model not only has significantly enhanced forecasting accuracy compared to state-of-the-art methods but also offers valuable insights into the influence of individual components on predicted load, given its full interpretability. Achieving performance akin to day-ahead TSO forecasts in fast computation times of a few seconds for several years of hourly data underscores the model's potential for practical application in the power system industry.

In our increasingly interconnected world, where intelligent devices continually amass copious personalized multi-modal data, a pressing need arises to deliver high-quality, personalized device-aware services. However, this endeavor presents a multifaceted challenge to prevailing artificial intelligence (AI) systems primarily rooted in the cloud. As these systems grapple with shifting data distributions between the cloud and devices, the traditional approach of fine-tuning-based adaptation (FTA) exists the following issues: the costly and time-consuming data annotation required by FTA and the looming risk of model overfitting. To surmount these challenges, we introduce a Universal On-Device Multi-modal Model Adaptation Framework, revolutionizing on-device model adaptation by striking a balance between efficiency and effectiveness. The framework features the Fast Domain Adaptor (FDA) hosted in the cloud, providing tailored parameters for the Lightweight Multi-modal Model on devices. To enhance adaptability across multi-modal tasks, the AnchorFrame Distribution Reasoner (ADR) minimizes communication costs. Our contributions, encapsulated in the Cloud-Device Collaboration Multi-modal Parameter Generation (CDC-MMPG) framework, represent a pioneering solution for on-Device Multi-modal Model Adaptation (DMMA). Extensive experiments validate the efficiency and effectiveness of our method, particularly in video question answering and retrieval tasks, driving forward the integration of intelligent devices into our daily lives.