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This paper deals with the question of how to calculate the volume of a body in the three-dimensional Euclidean space when it is cut into slices perpendicular to a given curve. The answer is provided by a formula that can be considered as a generalized version of the second Pappus-Guldin theorem. It turns out that the computation becomes very simple if the curve passes directly through the centroids of the perpendicular cross-sections. In this context, the question arises whether a curve with this centroid property exists. We investigate this problem for a convex body $K$ by using the volume distance and certain features of the so-called floating bodies of $K$. As an example, we further determine the non-trivial centroid curves of a triaxial ellipsoid, and finally we apply our results to derive a rather simple formula for determining the centroid of a bent rod., Comment: 14 pages, 4 figures

PDS 70 is a unique system as it hosts a protoplanetary disk with two confirmed forming planets, making it an ideal target for characterizing dust in such disks. We present new high-contrast polarimetric differential imaging of PDS 70 using the $N\_R$ filter on SPHERE/ZIMPOL, combined with archival VLT/SPHERE data across five wavelengths ($N\_R$, $VBB$, $J$, $H$, and $Ks$) spanning seven epochs over eight years. For each epoch, we corrected smearing effects from instrument resolution, analyzed azimuthal brightness profiles, and derived intrinsic disk-integrated polarized reflectivity and brightness contrasts. Our analysis reveals significant temporal variability in both integrated polarized reflectivity and azimuthal brightness profiles, suggesting variable shadowing on the outer disk from unresolved inner disk structures. Nonetheless, we observe a systematic wavelength-dependent contrast between the near and far sides of the inclined disk, highlighting the need to consider shadowing from the inner disk and surface geometry of the reflecting disk in data interpretation., Comment: The main text includes 6 pages and 4 figures, appendix has 4 pages; accepted by A&A Letters

External disturbances exciting a mechanical resonator can be exploited to gain information on the environment. Many of these interactions manifest as momentum kicks, such as the recoil of residual gas, radioactive decay, or even hypothetical interactions with dark matter. These disturbances are often rare enough that they can be resolved as singular events rather than cumulated as force noise. While high-Q resonators with low masses are particularly sensitive to such momentum kicks, they will strongly excite the resonator, leading to nonlinear effects that deteriorate the sensing performance. Hence, this paper utilizes optimal estimation methods to extract individual momentum kicks from measured stochastic trajectories of a mechanical resonator kept in the linear regime through feedback control. The developed scheme is illustrated and tested experimentally using a pre-stressed SiN trampoline resonator. Apart from enhancing a wide range of sensing scenarios mentioned above, our results indicate the feasibility of novel single-molecule mass spectrometry approaches., Comment: 8 pages, 7 figures

The restricted mean survival time (RMST) has become a popular measure to summarize event times in longitudinal studies. Defined as the area under the survival function up to a time horizon $\tau$ > 0, the RMST can be interpreted as the life expectancy within the time interval [0, $\tau$]. In addition to its straightforward interpretation, the RMST also allows for the definition of valid estimands for the causal analysis of treatment contrasts in medical studies. In this work, we introduce a non-parametric approach to model the RMST conditional on a set of baseline variables (including, e.g., treatment variables and confounders). Our method is based on a direct modeling strategy for the RMST, using leave-one-out jackknife pseudo-values within a random forest regression framework. In this way, it can be employed to obtain precise estimates of both patient-specific RMST values and confounder-adjusted treatment contrasts. Since our method (termed "pseudo-value random forest", PVRF) is model-free, RMST estimates are not affected by restrictive assumptions like the proportional hazards assumption. Particularly, PVRF offers a high flexibility in detecting relevant covariate effects from higher-dimensional data, thereby expanding the range of existing pseudo-value modeling techniques for RMST estimation. We investigate the properties of our method using simulations and illustrate its use by an application to data from the SUCCESS-A breast cancer trial. Our numerical experiments demonstrate that PVRF yields accurate estimates of both patient-specific RMST values and RMST-based treatment contrasts.

Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge due to an intrinsic trade-off between targeting specific chemical properties and generating meaningful samples from the data distribution. In this work, we present Time-Aware Conditional Synthesis (TACS), a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties while maintaining validity and stability. A key component of our algorithm is our new type of diffusion sampler, Time Correction Sampler (TCS), which is used to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process at the same time. Our proposed method demonstrates significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields., Comment: NeurIPS 2024

Web-scale visual entity recognition, the task of associating images with their corresponding entities within vast knowledge bases like Wikipedia, presents significant challenges due to the lack of clean, large-scale training data. In this paper, we propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Instead of relying on the multimodal LLM to directly annotate data, which we found to be suboptimal, we prompt it to reason about potential candidate entity labels by accessing additional contextually relevant information (such as Wikipedia), resulting in more accurate annotations. We further use the multimodal LLM to enrich the dataset by generating question-answer pairs and a grounded finegrained textual description (referred to as "rationale") that explains the connection between images and their assigned entities. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks (e.g. +6.9% improvement in OVEN entity task), underscoring the importance of high-quality training data in this domain., Comment: NeurIPS 2024

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: \textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at \url{https://github.com/pluskal-lab/MassSpecGym}.

We analyze data from multiple flybys by the Solar Orbiter, BepiColombo, and Parker Solar Probe (PSP) missions to study the interaction between Venus' plasma environment and the solar wind forming the induced magnetosphere. Through examination of magnetic field and plasma density signatures we characterize the spatial extent and dynamics of Venus' magnetotail, focusing mainly on boundary crossings. Notably, we observe significant differences in boundary crossing location and appearance between flybys, highlighting the dynamic nature of Venus' magnetotail. In particular, during Solar Orbiter's third flyby, extreme solar wind conditions led to significant variations in the magnetosheath plasma density and magnetic field properties, but the increased dynamic pressure did not compress the magnetotail. Instead, it is possible that the increased EUV flux at this time rather caused it to expand in size. Key findings also include the identification of several far downstream bow shock (BS), or bow wave, crossings to at least 60 Rv (1 Rv = 6,052 km is the radius of Venus), and the induced magnetospheric boundary to at least 20 Rv. These crossings provide insight into the extent of the induced magnetosphere. Pre-existing models from Venus Express were only constrained to within ~5 Rv of the planet, and we provide modifications to better fit the far-downstream crossings. The new model BS is now significantly closer to the central tail than previously suggested, by about 10 Rv at 60 Rv downstream.

Strongly correlated insulators, such as Mott or charge-transfer insulators, exhibit a strong temperature dependence in their resistivity. Consequently, self-heating effects can lead to electrothermal instabilities in planar thin film devices of these materials. When the electrical bias current exceeds a device-specific threshold, the device can switch from a high- to a low-resistance state through the formation of metallic filaments. However, since the current and temperature redistribution effects that create these filaments are sustained by local Joule heating, a reduction of the bias current below a second (lower) threshold leads to the disappearance of filaments, and the device switches back into the high-resistance state. Hence, electrothermal resistive switching is usually volatile. Here, on the contrary, we report on non-volatile resistive switching in a planar $\mathrm{NdNiO}_3$ thin-film device. By combining electrical transport measurements with optical wide-field microscopy, we provide evidence for a metallic filament that persists even after returning the bias current to zero. We attribute this effect to the pronounced hysteresis between the cooling and heating branches in the resistance vs. temperature dependence of the device. At least one hundred intermediate resistance states can be prepared, which are persistent as long as the base temperature is kept constant. Further, the switching process is non-destructive, and thermal cycling can reset the device to its pristine state., Comment: 10 pages, 5 figures

We study a stroboscopic quantum Ising model with Fibonacci dynamics. Focusing on boundary spin correlation functions in long but finite chains, our simulations as well as analytical arguments reveal a self-similar phase diagram exhibiting regions with Majorana zero modes (MZM) as well as Majorana golden-ratio modes (MGM). We identify the self-similarity transform which governs the evolution of the phase diagram with increasing simulation time. Integrability-breaking perturbations lead to a temporal decay of the boundary spin correlations, ultimaltely limiting the self-similarity of the phase diagram. Our predictions are testable with current quantum information processors., Comment: 5+4 pages, 5 figures

Resource allocation in distributed and networked systems such as the Cloud is becoming increasingly flexible, allowing these systems to dynamically adjust toward the workloads they serve, in a demand-aware manner. Online balanced partitioning is a fundamental optimization problem underlying such self-adjusting systems. We are given a set of $\ell$ servers. On each server we can schedule up to $k$ processes simultaneously. The demand is described as a sequence of requests $\sigma_t=\{p_i, p_{j}\}$, which means that the two processes $p_i,p_{j}$ communicate. A process can be migrated from one server to another which costs 1 unit per process move. If the communicating processes are on different servers, it further incurs a communication cost of 1 unit for this request. The objective is to minimize the competitive ratio: the cost of serving such a request sequence compared to the cost incurred by an optimal offline algorithm. Henzinger et al. (at SIGMETRICS 2019) introduced a learning variant of this problem where the cost of an online algorithm is compared to the cost of a static offline algorithm that does not perform any communication, but which simply learns the communication graph and keeps the discovered connected components together. This problem variant was recently also studied at SODA 2021. In this paper, we consider a more general learning model (i.e., stronger adversary), where the offline algorithm is not restricted to keep connected components together. Our main contribution are tight bounds for this problem. In particular, we present two deterministic online algorithms: (1) an online algorithm with competitive ratio $O(\max(\sqrt{k\ell \log k}, \ell \log k))$ and augmentation $1+\epsilon$; (2) an online algorithm with competitive ratio $O(\sqrt{k})$ and augmentation $2+\epsilon$. We further present lower bounds showing optimality of these bounds.

Despite the remarkable success of deep neural networks (DNNs) in computer vision, they fail to remain high-performing when facing distribution shifts between training and testing data. In this paper, we propose Knowledge-Guided Visual representation learning (KGV), a distribution-based learning approach leveraging multi-modal prior knowledge, to improve generalization under distribution shift. We use prior knowledge from two distinct modalities: 1) a knowledge graph (KG) with hierarchical and association relationships; and 2) generated synthetic images of visual elements semantically represented in the KG. The respective embeddings are generated from the given modalities in a common latent space, i.e., visual embeddings from original and synthetic images as well as knowledge graph embeddings (KGEs). These embeddings are aligned via a novel variant of translation-based KGE methods, where the node and relation embeddings of the KG are modeled as Gaussian distributions and translations respectively. We claim that incorporating multi-model prior knowledge enables more regularized learning of image representations. Thus, the models are able to better generalize across different data distributions. We evaluate KGV on different image classification tasks with major or minor distribution shifts, namely road sign classification across datasets from Germany, China, and Russia, image classification with the mini-ImageNet dataset and its variants, as well as the DVM-CAR dataset. The results demonstrate that KGV consistently exhibits higher accuracy and data efficiency than the baselines across all experiments.

We consider the analytical control design for a pair of switched linear multiple-input multiple-output (MIMO) systems that are subject to arbitrary switching signals. A state feedback controller design method is proposed to obtain an eigenstructure assignment that ensures that the closed-loop switched system is globally asymptotically stable, and the outputs achieve the non-overshooting tracking of a step reference. Our analysis indicates whether non-overshooting or even monotonic tracking is achievable for the given system and considered outputs and provides a choice of possible eigenstructures to be assigned to the constituent subsystems. We derive a structural condition that verifies the feasibility of the chosen assignment. A constructive algorithm to obtain suitable feedback matrices is provided, and the method is illustrated with numerical examples.

Serverless computing has emerged as a prominent paradigm, with a significant adoption rate among cloud customers. While this model offers advantages such as abstraction from the deployment and resource scheduling, it also poses limitations in handling complex use cases due to the restricted nature of individual functions. Serverless workflows address this limitation by orchestrating multiple functions into a cohesive application. However, existing serverless workflow platforms exhibit significant differences in their programming models and infrastructure, making fair and consistent performance evaluations difficult in practice. To address this gap, we propose the first serverless workflow benchmarking suite SeBS-Flow, providing a platform-agnostic workflow model that enables consistent benchmarking across various platforms. SeBS-Flow includes six real-world application benchmarks and four microbenchmarks representing different computational patterns. We conduct comprehensive evaluations on three major cloud platforms, assessing performance, cost, scalability, and runtime deviations. We make our benchmark suite open-source, enabling rigorous and comparable evaluations of serverless workflows over time.

The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural networks on high-order domains such as simplicial complexes. However, progress in this field is hindered by the scarcity of datasets for benchmarking these architectures. To address this gap, we introduce MANTRA, the first large-scale, diverse, and intrinsically high order dataset for benchmarking high-order models, comprising over 43,000 and 249,000 triangulations of surfaces and three-dimensional manifolds, respectively. With MANTRA, we assess several graph- and simplicial complex-based models on three topological classification tasks. We demonstrate that while simplicial complex-based neural networks generally outperform their graph-based counterparts in capturing simple topological invariants, they also struggle, suggesting a rethink of TDL. Thus, MANTRA serves as a benchmark for assessing and advancing topological methods, leading the way for more effective high-order models., Comment: 26 pages, 2 figures, 22 tables

Generalizing language-conditioned robotic policies to new tasks remains a significant challenge, hampered by the lack of suitable simulation benchmarks. In this paper, we address this gap by introducing GemBench, a novel benchmark to assess generalization capabilities of vision-language robotic manipulation policies. GemBench incorporates seven general action primitives and four levels of generalization, spanning novel placements, rigid and articulated objects, and complex long-horizon tasks. We evaluate state-of-the-art approaches on GemBench and also introduce a new method. Our approach 3D-LOTUS leverages rich 3D information for action prediction conditioned on language. While 3D-LOTUS excels in both efficiency and performance on seen tasks, it struggles with novel tasks. To address this, we present 3D-LOTUS++, a framework that integrates 3D-LOTUS's motion planning capabilities with the task planning capabilities of LLMs and the object grounding accuracy of VLMs. 3D-LOTUS++ achieves state-of-the-art performance on novel tasks of GemBench, setting a new standard for generalization in robotic manipulation. The benchmark, codes and trained models are available at \url{https://www.di.ens.fr/willow/research/gembench/}.

Modern communication networks feature local fast failover mechanisms in the data plane, to swiftly respond to link failures with pre-installed rerouting rules. This paper explores resilient routing meant to tolerate $\leq k$ simultaneous link failures, ensuring packet delivery, provided that the source and destination remain connected. While past theoretical works studied failover routing under static link failures, i.e., links which permanently and simultaneously fail, real-world networks often face link flapping--dynamic down states caused by, e.g., numerous short-lived software-related faults. Thus, in this initial work, we re-investigate the resilience of failover routing against link flapping, by categorizing link failures into static, semi-dynamic (removing the assumption that links fail simultaneously), and dynamic (removing the assumption that links fail permanently) types, shedding light on the capabilities and limitations of failover routing under these scenarios. We show that $k$-edge-connected graphs exhibit $(k-1)$-resilient routing against dynamic failures for $k \leq 5$. We further show that this result extends to arbitrary $k$ if it is possible to rewrite $\log k$ bits in the packet header. Rewriting $3$ bits suffices to cope with $k$ semi-dynamic failures. However, on general graphs, tolerating $2$ dynamic failures becomes impossible without bit-rewriting. Even by rewriting $\log k$ bits, resilient routing cannot resolve $k$ dynamic failures, demonstrating the limitation of local fast rerouting.

Being able to recognise defects in industrial objects is a key element of quality assurance in production lines. Our research focuses on visual anomaly detection in RGB images. Although Convolutional Neural Networks (CNNs) achieve high accuracies in this task, end users in industrial environments receive the model's decisions without additional explanations. Therefore, it is of interest to enrich the model's outputs with further explanations to increase confidence in the model and speed up anomaly detection. In our work, we focus on (1) CNN-based classification models and (2) the further development of a model-agnostic explanation algorithm for black-box classifiers. Additionally, (3) we demonstrate how we can establish an interactive interface that allows users to further correct the model's output. We present our NearCAIPI Interaction Framework, which improves AI through user interaction, and show how this approach increases the system's trustworthiness. We also illustrate how NearCAIPI can integrate human feedback into an interactive process chain.

Skid-steered wheel mobile robots (SSWMRs) operate in a variety of outdoor environments exhibiting motion behaviors dominated by the effects of complex wheel-ground interactions. Characterizing these interactions is crucial both from the immediate robot autonomy perspective (for motion prediction and control) as well as a long-term predictive maintenance and diagnostics perspective. An ideal solution entails capturing precise state measurements for decisions and controls, which is considerably difficult, especially in increasingly unstructured outdoor regimes of operations for these robots. In this milieu, a framework to identify pre-determined discrete modes of operation can considerably simplify the motion model identification process. To this end, we propose an interactive multiple model (IMM) based filtering framework to probabilistically identify predefined robot operation modes that could arise due to traversal in different terrains or loss of wheel traction.

This work leverages laser vibrometry and the weak form of the sparse identification of nonlinear dynamics (WSINDy) for partial differential equations to learn macroscale governing equations from full-field experimental data. In the experiments, two beam-like specimens, one aluminum and one IDOX/Estane composite, are subjected to shear wave excitation in the low frequency regime and the response is measured in the form of particle velocity on the specimen surface. The WSINDy for PDEs algorithm is applied to the resulting spatio-temporal data to discover the effective dynamics of the specimens from a family of potential PDEs. The discovered PDE is of the recognizable Euler-Bernoulli beam model form, from which the Young's modulus for the two materials are estimated. An ensemble version of the WSINDy algorithm is also used which results in information about the uncertainty in the PDE coefficients and Young's moduli. The discovered PDEs are also simulated with a finite element code to compare against the experimental data with reasonable accuracy. Using full-field experimental data and WSINDy together is a powerful non-destructive approach for learning unknown governing equations and gaining insights about mechanical systems in the dynamic regime., Comment: 25 pages, 10 figures

Information extraction from textual data, where the query is represented by a finite transducer and the task is to enumerate all results without repetition, and its extension to the weighted case, where each output element has a weight and the output elements are to be enumerated sorted by their weights, are important and well studied problems in database theory. On the one hand, the first framework already covers the well-known case of regular document spanners, while the latter setting covers several practically relevant tasks that cannot be described in the unweighted setting. It is known that in the unweighted case this problem can be solved with linear time preprocessing $O(|D|)$ and output-linear delay $O(|s|)$ in data complexity, where $D$ is the input data and $s$ is the current output element. For the weighted case, Bourhis, Grez, Jachiet, and Riveros [ICDT 2021] recently designed an algorithm with linear time preprocessing, but the delay of $O(|s| \cdot \log|\mathsf{D}|)$ depends on the size of the data. We first show how to leverage the existing results on enumerating shortest paths to obtain a simple alternative algorithm with linear preprocessing and a delay of $O(|s_i| + \min\{ \log i, \log|\mathsf{D}|\})$ for the $i^{\text{th}}$ output element $s_i$ (in data complexity); thus, substantially improving the previous algorithm. Next, we develop a technically involved rounding technique that allows us to devise an algorithm with linear time preprocessing and output-linear delay $O(|s|)$ with high probability. To this end, we combine tools from algebra, high-dimensional geometry, and linear programming.

Understanding and predicting recreational fishing activity is important for sustainable fisheries management. However, traditional methods of measuring fishing pressure, such as surveys, can be costly and limited in both time and spatial extent. Predictive models that relate fishing activity to environmental or economic factors typically rely on historical data, which often restricts their spatial applicability due to data scarcity. In this study, high-resolution angler-generated data from an online platform and easily accessible auxiliary data were tested to predict daily boat presence and aerial counts of boats at almost 200 lakes over five years in Ontario, Canada. Lake-information website visits alone enabled predicting daily angler boat presence with 78% accuracy. While incorporating additional environmental, socio-ecological, weather and angler-generated features into machine learning models did not remarkably improve prediction performance of boat presence, they were substantial for the prediction of boat counts. Models achieved an R2 of up to 0.77 at known lakes included in the model training, but they performed poorly for unknown lakes (R2 = 0.21). The results demonstrate the value of integrating angler-generated data from online platforms into predictive models and highlight the potential of machine learning models to enhance fisheries management., Comment: 31 pages

Informed learning is an emerging field in machine learning that aims to compensate for insufficient data with prior knowledge. Shape knowledge covers many types of prior knowledge concerning the relationship of a function's output with respect to input variables, for example, monotonicity, convexity, etc. This shape knowledge -- when formalized into algebraic inequalities (shape constraints) -- can then be incorporated into the training of regression models via a constraint problem formulation. The defined shape-constrained regression problem is, mathematically speaking, a semi-infinite program (SIP). Although off-the-shelf algorithms can be used at this point to solve the SIP, we recommend an adaptive feasible-point algorithm that guarantees optimality up to arbitrary precision and strict fulfillment of the shape constraints. We apply this semi-infinite approach for shape-constrained regression (SIASCOR) to three application examples from manufacturing and one artificial example. One application example has not been considered in a shape-constrained regression setting before, so we used a methodology (ISI) to capture the shape knowledge and define corresponding shape constraints. Finally, we compare the SIASCOR method with a purely data-driven automated machine learning method (AutoML) and another approach for shape-constrained regression (SIAMOR) that uses a different solution algorithm.

Large Language Models (LLMs) show remarkable performance on a wide variety of tasks. Most LLMs split text into multi-character tokens and process them as atomic units without direct access to individual characters. This raises the question: To what extent can LLMs learn orthographic information? To answer this, we propose a new benchmark, CUTE, which features a collection of tasks designed to test the orthographic knowledge of LLMs. We evaluate popular LLMs on CUTE, finding that most of them seem to know the spelling of their tokens, yet fail to use this information effectively to manipulate text, calling into question how much of this knowledge is generalizable., Comment: Accepted to EMNLP 2024 main conference

It is commonly believed that failures of tomographic completeness undermine assessments of nonclassicality in noncontextuality experiments. In this work, we study how such failures can indeed lead to mistaken assessments of nonclassicality. We then show that proofs of the failure of noncontextuality are robust to a very broad class of failures of tomographic completeness, including the kinds of failures that are likely to occur in real experiments. We do so by showing that such proofs actually rely on a much weaker assumption that we term relative tomographic completeness: namely, that one's experimental procedures are tomographic for each other. Thus, the failure of noncontextuality can be established even with coarse-grained, effective, emergent, or virtual degrees of freedom. This also implies that the existence of a deeper theory of nature (beyond that being probed in one's experiment) does not in and of itself pose any challenge to proofs of nonclassicality. To prove these results, we first introduce a number of useful new concepts within the framework of generalized probabilistic theories (GPTs). Most notably, we introduce the notion of a GPT subsystem, generalizing a range of preexisting notions of subsystems (including those arising from tensor products, direct sums, decoherence processes, virtual encodings, and more). We also introduce the notion of a shadow of a GPT fragment, which captures the information lost when one's states and effects are unknowingly not tomographic for one another., Comment: 21 pages plus appendices; Many figures and diagrams

We propose a pre-training pipeline for audio spectrogram transformers for frame-level sound event detection tasks. On top of common pre-training steps, we add a meticulously designed training routine on AudioSet frame-level annotations. This includes a balanced sampler, aggressive data augmentation, and ensemble knowledge distillation. For five transformers, we obtain a substantial performance improvement over previously available checkpoints both on AudioSet frame-level predictions and on frame-level sound event detection downstream tasks, confirming our pipeline's effectiveness. We publish the resulting checkpoints that researchers can directly fine-tune to build high-performance models for sound event detection tasks., Comment: Submitted to ICASSP'25. Source code available: https://github.com/fschmid56/PretrainedSED

Calibrating model parameters to measured data by minimizing loss functions is an important step in obtaining realistic predictions from model-based approaches, e.g., for process optimization. This is applicable to both knowledge-driven and data-driven model setups. Due to measurement errors, the calibrated model parameters also carry uncertainty. In this contribution, we use cubature formulas based on sparse grids to calculate the variance of the regression results. The number of cubature points is close to the theoretical minimum required for a given level of exactness. We present exact benchmark results, which we also compare to other cubatures. This scheme is then applied to estimate the prediction uncertainty of the NRTL model, calibrated to observations from different experimental designs., Comment: 44 pages, 21 figures

We analyse the convergence to equilibrium of the Bernoulli--Laplace urn model: initially, one urn contains $k$ red balls and a second $n-k$ blue balls; in each step, a pair of balls is chosen uniform and their locations are switched. Cutoff is known to occur at $\tfrac12 n \log \min\{k, \sqrt n\}$ with window order $n$ whenever $1 \ll k \le \tfrac12 n$. We refine this by determining the limit profile: a function $\Phi$ such that \[ d_\mathsf{TV}\bigl( \tfrac12 n \log \min\{k, \sqrt n\} + \theta n \bigr) \to \Phi(\theta) \quad\text{as}\quad n \to \infty \quad\text{for all}\quad \theta \in \mathbb R. \] Our main technical contribution, of independent interest, approximates a rescaled chain by a diffusion on $\mathbb R$ when $k \gg \sqrt n$, and uses its explicit law as a Gaussian process., Comment: 19 pages

The Local Friendliness argument is an extended Wigner's friend no-go theorem that provides strong constraints on the nature of reality -- stronger even than those imposed by Bell's theorem or by noncontextuality arguments. In this work, we prove a variety of connections between Local Friendliness scenarios and Kochen-Specker noncontextuality. Specifically, we first show how one can derive new Local Friendliness inequalities using known tools and results from the literature on Kochen-Specker noncontextuality. In doing so, we provide a new derivation for some of the facets of the Local Friendliness polytope, and we prove that this polytope is equal to the Bell polytope in a wide range of extended Wigner's friend scenarios with multipartite agents and sequential measurements. We then show how any possibilistic Kochen-Specker argument can be mathematically translated into a related proof of the Local Friendliness no-go theorem. In particular, we construct a novel kind of Local Friendliness scenario where a friend implements several compatible measurements (or joint measurements of these) in between the superobserver's operations on them. We illustrate this with the well-known 5-cycle and Peres-Mermin contextuality arguments., Comment: 22 pages + appendix, 16 figures, all comments welcome! The second half of this work is a follow-up work to arXiv:2310.06976v2

Science is founded on the benchmarking of theoretical models against experimental measurements, with the challenge that for all but the simplest systems, the calculations required for high precision become extremely challenging. $\mathrm{H}_2^+$ is the simplest stable molecule, and its internal structure is calculable to high precision from first principles. This allows tests of theoretical models and the determination of fundamental constants. However, studying $\mathrm{H}_2^+$ experimentally presents significant challenges. Standard control methods such as laser cooling, fluorescence detection and optical pumping are not applicable to $\mathrm{H}_2^+$ due to the very long lifetimes of its excited rotational and vibrational states. Here we solve this issue by using Quantum Logic Spectroscopy techniques to demonstrate full quantum control of a single $\mathrm{H}_2^+$ molecule by co-trapping it with an atomic 'helper' ion and performing quantum operations between the two ions. This enables us to perform pure quantum state preparation, coherent control and non-destructive readout, which we use to perform high-resolution microwave spectroscopy of $\mathrm{H}_2^+$. Our results pave the way for high precision spectroscopy of $\mathrm{H}_2^+$ in both the microwave and optical domains, while offering techniques which are transferable to other molecular ions.

The increasing demand for energy storage solutions has spurred intensive research into next-generation battery technologies. Oxygen-ion batteries (OIBs), which leverage mixed ionic-electronic conducting (MIEC) oxides, have emerged as promising candidates due to their solid, non-flammable nature and potential for high power densities. This study investigates the use of over-stoichiometric La2NiO4+delta (L2NO4) as a cathode material for OIBs, exploring its capacity for electrochemical energy storage. Half-cell measurements reveal that L2NO4 with a closed-pore microstructure can store oxygen, achieving a volumetric charge of 63 mA.h.cm-3 at 400 {\deg}C with a current density of 3.6 uA.cm-2 and potentials up to 0.75 V vs. 1 bar O2. Additionally, a functional full cell combining over-stoichiometric L2NO4 and under-stoichiometric La0.5Sr0.5Cr0.2Mn0.8O3-delta (LSCrMn) has been successfully developed, demonstrating excellent cyclability and coulomb efficiency. The full cell reaches a maximum volumetric charge of 90 mA.h.cm-3 at 400 {\deg}C, 17.8 uA.cm-2, and a cut-off voltage of 1.8 V. This proof of concept underscores the viability of combining over- and under-stoichiometric MIEC materials in OIBs and provides critical insights into optimizing electrode materials and tuning oxygen content for improved performance. This research lays the groundwork for future advancements in OIB technology, aiming to develop materials with lower resistance and higher efficiency.

Fishing is a valuable recreational activity in our society. To assess future fishing activity, identifying variables related to differences in fishing activity, such as gender or Covid-19, is helpful. We conducted a Canada-wide email survey of users of an online fishing platform and analyzed responses with a focus on gender, the impact of Covid-19, and variables directly related to fisher effort. Genders (90.1% male and 9.9% female respondents) significantly differed in demographics, socioeconomic status, and fishing skills but were similar in fishing preferences, fisher effort in terms of trip frequency, and travel distance. For almost half of the fishers, Covid-19 caused a change in trip frequency, determined by the activity level and gender of the fisher. A Bayesian network revealed that travel distance was the main determinant of trip frequency and negatively impacted the fishing activity of 61% of the fishers. Fisher effort was also directly related to fishing expertise. The study shows how online surveys and Bayesian networks can help understand the relationship between fishers' characteristics and activity and predict future fishing trends., Comment: 50 pages

The simulation of fluid flow problems, specifically incompressible flows governed by the Navier-Stokes equations (NSE), holds fundamental significance in a range of scientific and engineering applications. Traditional numerical methods employed for solving these equations on three-dimensional (3D) meshes are commonly known for their moderate conservation properties, high computational intensity and substantial resource demands. Relying on its ability to capture the intrinsic geometric and topological properties of simplicial meshes, discrete exterior calculus (DEC) provides a discrete analog to differential forms and enables the discretization of partial differential equations (PDEs) on meshes.We present a hybrid discretization approach for the 3D incompressible Navier-Stokes equations based on DEC and Fourier transform (FT). An existing conservative primitive variable DEC discretization of incompressible Navier-Stokes equations over surface simplicial meshes developed by Jagad et al. [1] is considered in the planar dimension while the Fourier expansion is applied in the third dimension. The test cases of three-dimensional lid-driven cavity and viscous Taylor-Green three-dimensional vortex (TGV) flows show that the simulation results using this hybrid approach are comparable to literature., Comment: 29 pages, 17 figures

The ability of a brain or a neural network to efficiently learn depends crucially on both the task structure and the learning rule. Previous works have analyzed the dynamical equations describing learning in the relatively simplified context of the perceptron under assumptions of a student-teacher framework or a linearized output. While these assumptions have facilitated theoretical understanding, they have precluded a detailed understanding of the roles of the nonlinearity and input-data distribution in determining the learning dynamics, limiting the applicability of the theories to real biological or artificial neural networks. Here, we use a stochastic-process approach to derive flow equations describing learning, applying this framework to the case of a nonlinear perceptron performing binary classification. We characterize the effects of the learning rule (supervised or reinforcement learning, SL/RL) and input-data distribution on the perceptron's learning curve and the forgetting curve as subsequent tasks are learned. In particular, we find that the input-data noise differently affects the learning speed under SL vs. RL, as well as determines how quickly learning of a task is overwritten by subsequent learning. Additionally, we verify our approach with real data using the MNIST dataset. This approach points a way toward analyzing learning dynamics for more-complex circuit architectures., Comment: NeurIPS 2024 camera ready version

We consider the stationary measure of the open asymmetric simple exclusion process (ASEP) with light particles. We prove several results on the asymptotic locations of the light particles under the stationary measure. Moreover, in the fan region of the high and low density phases, we study the mixing times for open ASEP with light particles. Our approach involves the matrix product ansatz, Askey-Wilson signed measures, moderate deviation results for second class particles, as well as various coupling arguments. Along the way we obtain the particle densities at single sites for the standard open ASEP without light particles, which are of independent interest., Comment: 30 pages, 5 figures

Radio-frequency interference (RFI) is becoming an increasingly significant problem for most radio telescopes. Working with Green Bank Telescope data from PSR J1730+0747 in the form of complex-valued channelized voltages and their respective high-resolution power spectral densities, we evaluate a variety of statistical measures to characterize RFI. As a baseline for performance comparison, we use median absolute deviation (MAD) in complex channelized voltage data and spectral kurtosis (SK) in power spectral density data to characterize and filter out RFI. From a new perspective, we implement the Shapiro-Wilks (SW) test for normality and two information theoretical measures, spectral entropy (SE) and spectral relative entropy (SRE), and apply them to mitigate RFI. The baseline RFI mitigation algorithms are compared against our novel RFI detection algorithms to determine how effective and robust the performance is. Except for MAD, we find significant improvements in signal-to-noise ratio through the application of SE, symmetrical SRE, asymmetrical SRE, SK, and SW. These algorithms also do a good job of characterizing broadband RFI. Time- and frequency-variable RFI signals are best detected by SK and SW tests., Comment: 13 pages, 26 figures, accepted for publication in RAS Techniques and Instruments

The rapid deployment of robotics technologies requires dedicated optimization algorithms to manage large fleets of autonomous agents. This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. The model maximizes throughput, while managing human workload at the workstations and congestion in the facility. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. The algorithm relies on an offline machine learning procedure to predict objective improvements based on subproblem features, and an online optimization model to generate a new subproblem at each iteration. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches. In particular, our solution enhances the utilization of robotic fleets by coordinating robotic tasks for human operators to pick multiple items at once, and by coordinating robotic routes to avoid congestion in the facility.

With increasingly larger and more complex telecommunication networks, there is a need for improved monitoring and reliability. Requirements increase further when working with mission-critical systems requiring stable operations to meet precise design and client requirements while maintaining high availability. This paper proposes a novel methodology for developing a machine learning model that can assist in maintaining availability (through anomaly detection) for client-server communications in mission-critical systems. To that end, we validate our methodology for training models based on data classified according to client performance. The proposed methodology evaluates the use of machine learning to perform anomaly detection of a single virtualized server loaded with simulated network traffic (using SIPp) with media calls. The collected data for the models are classified based on the round trip time performance experienced on the client side to determine if the trained models can detect anomalous client side performance only using key performance indicators available on the server. We compared the performance of seven different machine learning models by testing different trained and untrained test stressor scenarios. In the comparison, five models achieved an F1-score above 0.99 for the trained test scenarios. Random Forest was the only model able to attain an F1-score above 0.9 for all untrained test scenarios with the lowest being 0.980. The results suggest that it is possible to generate accurate anomaly detection to evaluate degraded client-side performance.

A central ingredient in fault-tolerant quantum algorithms is the initialization of a logical state for a given quantum error-correcting code from a set of noisy qubits. A scheme that has demonstrated promising results for small code instances that are realizable on currently available hardware composes a non-fault-tolerant state preparation step with a verification step that checks for spreading errors. Known circuit constructions of this scheme are mostly obtained manually, and no algorithmic techniques for constructing depth- or gate-optimal circuits exist. As a consequence, the current state of the art exploits this scheme only for specific code instances and mostly for the special case of distance 3 codes. In this work, we propose an automated approach for synthesizing fault-tolerant state preparation circuits for arbitrary CSS codes. We utilize methods based on satisfiability solving (SAT) techniques to construct fault-tolerant state preparation circuits consisting of depth- and gate-optimal preparation and verification circuits. We also provide heuristics that can synthesize fault-tolerant state preparation circuits for code instances where no optimal solution can be obtained in an adequate timeframe. Moreover, we give a general construction for non-deterministic state preparation circuits beyond distance 3. Numerical evaluations using $d=3$ and $d=5$ codes confirm that the generated circuits exhibit the desired scaling of the logical error rates. The resulting methods are publicly available as part of the Munich Quantum Toolkit (MQT) at https://github.com/cda-tum/mqt-qecc. Such methods are an important step in providing fault-tolerant circuit constructions that can aid in near-term demonstration of fault-tolerant quantum computing., Comment: 23 pages, 14 figures Version 2: included some references, fixed error in printed circuits

Dual-encoder-based audio retrieval systems are commonly optimized with contrastive learning on a set of matching and mismatching audio-caption pairs. This leads to a shared embedding space in which corresponding items from the two modalities end up close together. Since audio-caption datasets typically only contain matching pairs of recordings and descriptions, it has become common practice to create mismatching pairs by pairing the audio with a caption randomly drawn from the dataset. This is not ideal because the randomly sampled caption could, just by chance, partly or entirely describe the audio recording. However, correspondence information for all possible pairs is costly to annotate and thus typically unavailable; we, therefore, suggest substituting it with estimated correspondences. To this end, we propose a two-staged training procedure in which multiple retrieval models are first trained as usual, i.e., without estimated correspondences. In the second stage, the audio-caption correspondences predicted by these models then serve as prediction targets. We evaluate our method on the ClothoV2 and the AudioCaps benchmark and show that it improves retrieval performance, even in a restricting self-distillation setting where a single model generates and then learns from the estimated correspondences. We further show that our method outperforms the current state of the art by 1.6 pp. mAP@10 on the ClothoV2 benchmark., Comment: In Proceedings of the 9th Workshop on Detection and Classification of Acoustic Scenes and Events, DCASE, Tokyo, Japan, 2024. Implementation available on GitHub: https://github.com/OptimusPrimus/salsa

Query-by-Vocal Imitation (QBV) is about searching audio files within databases using vocal imitations created by the user's voice. Since most humans can effectively communicate sound concepts through voice, QBV offers the more intuitive and convenient approach compared to text-based search. To fully leverage QBV, developing robust audio feature representations for both the vocal imitation and the original sound is crucial. In this paper, we present a new system for QBV that utilizes the feature extraction capabilities of Convolutional Neural Networks pre-trained with large-scale general-purpose audio datasets. We integrate these pre-trained models into a dual encoder architecture and fine-tune them end-to-end using contrastive learning. A distinctive aspect of our proposed method is the fine-tuning strategy of pre-trained models using an adapted NT-Xent loss for contrastive learning, creating a shared embedding space for reference recordings and vocal imitations. The proposed system significantly enhances audio retrieval performance, establishing a new state of the art on both coarse- and fine-grained QBV tasks., Comment: Accepted to the DCASE Workshop 2024. Source code available: https://github.com/Jonathan-Greif/QBV

Surfaces of cubic perovskite oxides attract significant attention for their physical tunability and high potential for technical applications. Bulk-terminated surfaces are desirable for theoretical modelling and experimental reproducibility, yet there is a lack of methods for preparing such well-defined surfaces. We discuss a method for strain-assisted cleaving of perovskite single crystals, using a setup easily transferable between different experimental systems. The details of the cleaving device and the procedure were optimized in a systematic study on the model SrTiO$_3$. The large-area morphology and typical distribution of surface terminations on cleaved SrTiO$_3$(001) is presented, with specific guidelines on how to distinguish well-cleaved surfaces from conchoidally fractured ones. The cleaving is applicable to other cubic perovskites, as demonstrated on KTaO$_3$(001) and BaTiO$_3$(001). This approach opens up a pathway for obtaining high-quality surfaces of this promising class of materials.

In ground-based astronomy, the ability to couple light into single-mode fibers (SMFs) is limited by atmospheric turbulence, which prohibits the use of many astrophotonic instruments. We propose a silicon-on-insulator photonic chip capable of coherently coupling the out-of-phase beamlets from the subapertures of a telescope pupil into an SMF. The photonic integrated circuit (PIC) consists of an array of grating couplers that are used to inject light from free space into single-mode waveguides on the chip. Metallic heaters modulate the refractive index of a coiled section of the waveguides, facilitating the co-phasing of the propagating modes. The phased beamlets can then be coherently combined to efficiently deliver the light to an output SMF. In an adaptive optics (AO) system, the phase corrector acts as a deformable mirror (DM) commanded by a controller that takes phase measurements from a wavefront sensor (WFS). We present experimental results for the PIC tested on an AO testbed and compare the performance to simulations., Comment: 13 pages, 11 figures, 2 tables, submitted to SPIE Adaptive Optics Systems IX

We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net., Comment: Project Page: https://meyerls.github.io/fruit_nerf/

We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and detection scheme that combines the expectation maximization (EM) algorithm and the ubiquitous belief propagation (BP) algorithm. Interweaving the iterations of both schemes significantly reduces the EM algorithm's computational burden while retaining its excellent performance. To this end, we apply simple yet effective model-based learning methods to find a suitable parameter update schedule by introducing momentum in both the EM parameter updates as well as in the BP message passing. Numerical simulations verify that the proposed method can learn efficient schedules that generalize well and even outperform coherent BP detection in high signal-to-noise scenarios., Comment: Accepted for presentation at Asilomar Conference on Signals, Systems, and Computers 2024