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This paper introduces a thermal infrared detector utilizing a nano-optomechanical silicon nitride (SiN) resonator, equipped with a free-space impedance-matched (FSIM) absorber composed of a platinum (Pt) thin film, offering a broadband spectral absorptance on average of 47%. To reduce photothermal back-action caused by intensity fluctuations of the readout laser, the FSIM absorber incorporates a circular clearance for the laser. The study provides a comprehensive characterization of the thermal time constant, power responsivity, and frequency stability of the resonators, with experimental results compared to analytical models and finite element method (FEM) simulations. The fastest thermal response is observed for the smallest 1 mm resonators, with a thermal time constant tau_th = 14 ms. The noise equivalent power (NEP) of the resonators is assessed, showing that the smallest 1 mm resonators exhibit the best sensitivity, with NEP = 27 pW/sqrt(Hz) and a respective specific detectivity of D* = 3.8e9 cm sqrt(Hz)/W. This is less than three times below the theoretical maximum for an ideal IR detector with 50% absorptance. This places our resonators among the most sensitive room-temperature IR detectors reported to date offering an extended spectral range from the near-IR to far-IR. This work underscores the potential of nano-optomechanical resonators for high-performance IR sensing applications., Comment: main text: 8 pages, 5 figures; supplementary: 3 pages, 2 figures

Large-scale structure surveys measure the shape and position of millions of galaxies in order to constrain the cosmological model with high precision. The resulting large data volume poses a challenge for the analysis of the data, from the estimation of photometric redshifts to the calibration of shape measurements. We present GalSBI, a model for the galaxy population, to address these challenges. This phenomenological model is constrained by observational data using simulation-based inference (SBI). The $\texttt{galsbi}$ Python package provides an easy interface to generate catalogs of galaxies based on the GalSBI model, including their photometric properties, and to simulate realistic images of these galaxies using the $\texttt{UFig}$ package., Comment: 10 pages, 5 figures, submitted to JOSS

With the rise of simulation-based inference (SBI) methods, simulations need to be fast as well as realistic. $\texttt{UFig v1}$ is a public Python package that generates simulated astronomical images with exceptional speed - taking approximately the same time as source extraction. This makes it particularly well-suited for simulation-based inference (SBI) methods where computational efficiency is crucial. To render an image, $\texttt{UFig}$ requires a galaxy catalog and a description of the point spread function (PSF). It can also add background noise, sample stars using the Besan\c{c}on model of the Milky Way, and run $\texttt{SExtractor}$ to extract sources from the rendered image. The extracted sources can be matched to the intrinsic catalog, flagged based on $\texttt{SExtractor}$ output and survey masks, and emulators can be used to bypass the image simulation and extraction steps. A first version of $\texttt{UFig}$ was presented in Berg\'e et al. (2013) and the software has since been used and further developed in a variety of forward modelling applications., Comment: 8 pages, 4 figures, submitted to JOSS

We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizations of galaxy luminosity functions, morphologies and spectral energy distributions. Model constraints are derived through iterative Approximate Bayesian Computation, by comparing Hyper Suprime-Cam deep field images with simulations which include a forward model of instrumental, observational and source extraction effects. We developed an emulator trained on image simulations using a normalizing flow. We use it to accelerate the inference by predicting detection probabilities, including blending effects and photometric properties of each object, while accounting for background and PSF variations. This enables robustness tests for all elements of the forward model and the inference. The model demonstrates excellent performance when comparing photometric properties from simulations with observed imaging data for key parameters such as magnitudes, colors and sizes. The redshift distribution of simulated galaxies agrees well with high-precision photometric redshifts in the COSMOS field within $1.5\sigma$ for all magnitude cuts. Additionally, we demonstrate how GalSBI's redshifts can be utilized for splitting galaxy catalogs into tomographic bins, highlighting its potential for current and upcoming surveys. GalSBI is fully open-source, with the accompanying Python package, $\texttt{galsbi}$, offering an easy interface to quickly generate realistic, survey-independent galaxy catalogs., Comment: 29 pages, 14 figures, submitted to Phys. Rev. D

Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictiveness, however, depend directly on the accuracy of the underlying potential energy surface (PES). Guided by the principle of parsimony this work introduces KerNN, a combined kernel/neural network-based approach to represent molecular PESs. Compared to state-of-the-art neural network PESs the number of learnable parameters of KerNN is significantly reduced. This speeds up training and evaluation times by several orders of magnitude while retaining high prediction accuracy. Importantly, using kernels as the features also improves the extrapolation capabilities of KerNN far beyond the coverage provided by the training data which solves a general problem of NN-based PESs. KerNN applied to spectroscopy and reaction dynamics shows excellent performance on test set statistics and observables including vibrational bands computed from classical and quantum simulations.

We demonstrate sub-Doppler cooling of calcium atoms using a two-photon transition from the ${^1}S_0$ ground state to the upper $4s5s~{^1}S_0$ state via the ${^1}P_1$ intermediate state. We achieve temperatures as low as $260~\mu\text{K}$ in a magneto-optical trap (MOT), well below the Doppler limit ($T_{\text{D}} = 0.8~\text{mK}$) of the ${^1}P_1$ state. We characterize temperature, lifetime and confinement of the MOT over a range of experimental parameters, observing no reduction in lifetime due to coupling to the higher state. We perform theoretical simulations of the cooling scheme and observe good agreement with the experimental results. The two-photon cooling scheme presented in this work provides an alternative to the standard Doppler cooling applied to alkaline-earth atoms, based on a sequence of two magneto-optical traps. The advantages of our scheme are the possibility of varying the effective linewidth of the ${^1}P_1$ state, a higher transfer efficiency (close to 100$\%$), and a more straightforward experimental implementation., Comment: 8 pages 4+1 figures

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 performance of neural networks scales with both their size and the amount of data they have been trained on. This is shown in both language and image generation. However, this requires scaling-friendly network architectures as well as large-scale datasets. Even though scaling-friendly architectures like transformers have emerged for 3D vision tasks, the GPT-moment of 3D vision remains distant due to the lack of training data. In this paper, we introduce ARKit LabelMaker, the first large-scale, real-world 3D dataset with dense semantic annotations. Specifically, we complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. To this end, we extend LabelMaker, a recent automatic annotation pipeline, to serve the needs of large-scale pre-training. This involves extending the pipeline with cutting-edge segmentation models as well as making it robust to the challenges of large-scale processing. Further, we push forward the state-of-the-art performance on ScanNet and ScanNet200 dataset with prevalent 3D semantic segmentation models, demonstrating the efficacy of our generated dataset.

Dust particle impacts on the Wind spacecraft were detected with its plasma wave instrument Wind/WAVES. Frequency analysis on this dust impact time series revealed spectral peaks indicative of a solar rotation signature. We investigated whether this solar rotation signature is embedded in the interplanetary or interstellar dust (ISD) and whether it is caused by co-rotating interaction regions (CIRs), by the sector structure of the interplanetary magnetic field (IMF), or by external effects. We performed frequency analysis on subsets of the data to investigate the origin of these spectral peaks, comparing segments of Wind's orbit when the spacecraft moved against or with the ISD inflow direction and comparing the time periods of the ISD focusing and defocusing phases of the solar magnetic cycle. A superposed epoch analysis of the number of dust impacts during CIRs was used to investigate the systematic effect of CIRs. Case studies of time periods with frequent or infrequent occurrences of CIRs were compared to synthetic data of dust impacts affected by CIRs. We performed similar case studies for time periods with a stable or chaotic IMF sector structure. The superposed epoch analysis was repeated for a time series of the spacecraft floating potential. Spectral peaks were found at the solar rotation period of ~27d and its harmonics at 13.5d and 9d. This solar rotation signature may affect both interplanetary and ISD. The appearance of this signature correlates with the occurrence of CIRs but not with the stability of the IMF sector structure. The CIRs cause, on average, a reduction in the number of dust impact detections. Periodic changes of the spacecraft's floating potential were found to partially counteract this reduction by enhancing the instrument's sensitivity to dust impacts; these changes of the floating potential are thus unlikely to be the cause of the solar rotation signature.

In this paper, we introduce HEADS-UP, the first egocentric dataset collected from head-mounted cameras, designed specifically for trajectory prediction in blind assistance systems. With the growing population of blind and visually impaired individuals, the need for intelligent assistive tools that provide real-time warnings about potential collisions with dynamic obstacles is becoming critical. These systems rely on algorithms capable of predicting the trajectories of moving objects, such as pedestrians, to issue timely hazard alerts. However, existing datasets fail to capture the necessary information from the perspective of a blind individual. To address this gap, HEADS-UP offers a novel dataset focused on trajectory prediction in this context. Leveraging this dataset, we propose a semi-local trajectory prediction approach to assess collision risks between blind individuals and pedestrians in dynamic environments. Unlike conventional methods that separately predict the trajectories of both the blind individual (ego agent) and pedestrians, our approach operates within a semi-local coordinate system, a rotated version of the camera's coordinate system, facilitating the prediction process. We validate our method on the HEADS-UP dataset and implement the proposed solution in ROS, performing real-time tests on an NVIDIA Jetson GPU through a user study. Results from both dataset evaluations and live tests demonstrate the robustness and efficiency of our approach.

Students often drift in and out of focus during class. Effective teachers recognize this and re-engage them when necessary. With the shift to remote learning, teachers have lost the visual feedback needed to adapt to varying student engagement. We propose using readily available front-facing video to infer attention levels based on movements of the eyes, head, and face. We train a deep learning model to predict a measure of attention based on overt eye movements. Specifically, we measure Inter-Subject Correlation of eye movements in ten-second intervals while students watch the same educational videos. In 3 different experiments (N=83) we show that the trained model predicts this objective metric of attention on unseen data with $R^2$=0.38, and on unseen subjects with $R^2$=0.26-0.30. The deep network relies mostly on a student's eye movements, but to some extent also on movements of the brows, cheeks, and head. In contrast to Inter-Subject Correlation of the eyes, the model can estimate attentional engagement from individual students' movements without needing reference data from an attentive group. This enables a much broader set of online applications. The solution is lightweight and can operate on the client side, which mitigates some of the privacy concerns associated with online attention monitoring. GitHub implementation is available at https://github.com/asortubay/timeISC, Comment: 10 pages, 3 figures

Data rates and volume for mobile communication are ever-increasing with the growing number of users and connected devices. With the deployment of 5G and 6G on the horizon, wireless communication is advancing to higher frequencies and larger bandwidths enabling higher speeds and throughput. Current micro-acoustic resonator technology, a key component in radiofrequency front end filters, is struggling to keep pace with these developments. This work presents a novel acoustic resonator architecture enabling multi-frequency, low-loss, and wideband filtering for the 5G and future 6G bands located above 3 GHz. Thanks to the exceptional performance of these resonators, filters for the 5G n77 and n79 bands are demonstrated, exhibiting fractional bandwidths of 13% and 25% respectively with low insertion loss of around 1 dB. With its unique frequency scalability and wideband capabilities, the reported architecture offers a promising option for filtering and multiplexing in future mobile devices., Comment: 23 pages, 11 figures

Modern TTS systems are capable of creating highly realistic and natural-sounding speech. Despite these developments, the process of customizing TTS voices remains a complex task, mostly requiring the expertise of specialists within the field. One reason for this is the utilization of deep learning models, which are characterized by their expansive, non-interpretable parameter spaces, restricting the feasibility of manual customization. In this paper, we present a novel human-in-the-loop paradigm based on an evolutionary algorithm for directly interacting with the parameter space of a neural TTS model. We integrated our approach into a user-friendly graphical user interface that allows users to efficiently create original voices. Those voices can then be used with the backbone TTS model, for which we provide a Python API. Further, we present the results of a user study exploring the capabilities of VoiceX. We show that VoiceX is an appropriate tool for creating individual, custom voices.

Note alignment refers to the task of matching individual notes of two versions of the same symbolically encoded piece. Methods addressing this task commonly rely on sequence alignment algorithms such as Hidden Markov Models or Dynamic Time Warping (DTW) applied directly to note or onset sequences. While successful in many cases, such methods struggle with large mismatches between the versions. In this work, we learn note-wise representations from data augmented with various complex mismatch cases, e.g. repeats, skips, block insertions, and long trills. At the heart of our approach lies a transformer encoder network - TheGlueNote - which predicts pairwise note similarities for two 512 note subsequences. We postprocess the predicted similarities using flavors of weightedDTW and pitch-separated onsetDTW to retrieve note matches for two sequences of arbitrary length. Our approach performs on par with the state of the art in terms of note alignment accuracy, is considerably more robust to version mismatches, and works directly on any pair of MIDI files., Comment: to be published in Proceedings of the 25th International Society for Music Information Retrieval Conference (ISMIR), 2024

This work combines state-of-the-art machine learning techniques with highest-level electronic structure calculations and full-dimensional quantum tunneling calculations to obtain a quantitative characterization of tunneling splittings for system sizes that are currently out of reach using traditional approaches. For intramolecular hydrogen transfer in tropolone, the best computed splitting including perturbative corrections in the ring-polymer instanton calculations is 0.94 cm$^{-1}$ and compares with 0.974 cm$^{-1}$ from experiments. On the other hand, for intermolecular double hydrogen transfer in the (propiolic acid)-(formic acid) dimer, the computations yield 0.0147 cm$^{-1}$ which is larger by 40 % compared with experiment (0.0097 cm$^{-1}$) but still in much better agreement than previous attempts (0.63 cm$^{-1}$). The strategy pursued in the present work is applicable to yet larger systems and other properties of interest and provides a rational route for highest-accuracy energy functions for prediction and benchmarking electronic structure methods vis-a-vis experiments.

With the release of ChatGPT and other large language models (LLMs) the discussion about the intelligence, possibilities, and risks, of current and future models have seen large attention. This discussion included much debated scenarios about the imminent rise of so-called "super-human" AI, i.e., AI systems that are orders of magnitude smarter than humans. In the spirit of Alan Turing, there is no doubt that current state-of-the-art language models already pass his famous test. Moreover, current models outperform humans in several benchmark tests, so that publicly available LLMs have already become versatile companions that connect everyday life, industry and science. Despite their impressive capabilities, LLMs sometimes fail completely at tasks that are thought to be trivial for humans. In other cases, the trustworthiness of LLMs becomes much more elusive and difficult to evaluate. Taking the example of academia, language models are capable of writing convincing research articles on a given topic with only little input. Yet, the lack of trustworthiness in terms of factual consistency or the existence of persistent hallucinations in AI-generated text bodies has led to a range of restrictions for AI-based content in many scientific journals. In view of these observations, the question arises as to whether the same metrics that apply to human intelligence can also be applied to computational methods and has been discussed extensively. In fact, the choice of metrics has already been shown to dramatically influence assessments on potential intelligence emergence. Here, we argue that the intelligence of LLMs should not only be assessed by task-specific statistical metrics, but separately in terms of qualitative and quantitative measures., Comment: 3 pages, 1 figure

We report on the fabrication of a Hybrid SAW/BAW resonator made of a thin layer of Sc-doped AlN (AlScN) with a Sc concentration of 40 at% on a 4H-SiC substrate. A Sezawa mode, excited by a vertical electric field, exploits the d31 piezoelectric coefficient to propagate a longitudinal acoustic wave in the AlScN. The resonant frequency is determined via the pitch in the interdigitated transducer (IDT) defined by Deep Ultraviolet (DUV) lithography. The resonant mode travels in the piezoelectric layer without leaking in the substrate thanks to the mismatch in acoustic phase velocities between the piezoelectric and substrate materials. We show the impact of the piezoelectric and IDT layers' thickness on the two found modes. Importantly, we show how thin piezoelectric and electrode layers effectively suppress the Rayleigh mode. While some challenges in the deposition of AlScN remain towards a large coupling coefficient k_eff^2, We show how wave confinement in the IDT obtains a good quality factor. We also show how modifying the IDT reflectivity allows us to engineer a stopband to prevent unwanted modes from being excited between resonance and antiresonance frequencies. Finally, we validate the simulation with fabricated and measured devices and present possible improvements to this resonator architecture., Comment: Submitted to IEEE EDL on 26.07.2024

Strong optomechanical coupling -- a regime where mechanical motion is damped by environmental radiation -- has traditionally required demanding experimental ingredients such as superconducting resonators, high-quality optical cavities, or large magnetic fields. Here we demonstrate a room temperature, cavity-free, all-electric device reaching this regime at radio frequencies, enabled by a mechanically compliant parallel-plate capacitor with a nanoscale plate separation and an aspect ratio exceeding 1,000. The device has four orders of magnitude lower insertion loss than a comparable commercial quartz crystal, and achieves a position imprecision rivaling an optical interferometer. With the help of a back-action isolation scheme, we observe radiative cooling of mechanical motion by a remote cryogenic load. This work provides a technologically accessible route to high-precision sensing, transduction, and signal processing., Comment: 19+13 pages, 5+2 figures

Deep Ultraviolet (DUV) Photolithography is currently used to fabricate mass-scale integrated circuits (ICs). Its high throughput and resolution could benefit large-scale RF MEMS production for the telecommunication market. We present a process flow to fabricate suspended acoustic resonators using DUV Photolithography. This method allows for scalable production of resonators with critical dimensions of 250 nm and alignment accuracy of less than 100 nm. We show how photoresists and anti-reflective coatings integrate with the process, help with deposition quality and resolution, and how Ion Beam Etching allows for vertical sidewalls of the resonators. We measure resonance frequencies (fr) up to 7.5 GHz and electromechanical couplings up to 8%, and we investigate the uniformity of this process by analyzing the deviation of fs over the wafer surface for four main resonance modes. We show that the deviation of the S0 mode can be kept below 1%. These results indicate the suitability of this process for quick scale-up of Lamb wave resonator technology, bridging the gap from research to industry., Comment: Submitted to IEEE-TUFFC on 21.07.2024

Understanding optical absorption in silicon nitride is crucial for cutting-edge technologies like photonic integrated circuits, nanomechanical photothermal infrared sensing and spectroscopy, and cavity optomechanics. Yet, the origin of its strong dependence on film deposition and fabrication process is not fully understood. This Letter leverages nanomechanical photothermal sensing to investigate optical extinction $\kappa_{\mathrm{ext}}$ at 632.8 nm wavelength in LPCVD SiN strings across a wide range of deposition-related tensile stresses ($200-850$ MPa). Measurements reveal a reduction in $\kappa_{\mathrm{ext}}$ from 10$^3$ to 10$^1$ ppm with increasing stress, correlated to variations in Si/N content ratio. Within the band-fluctuations framework, this trend indicates an increase of the energy bandgap with the stress, ultimately reducing absorption. Overall, this study showcases the power and simplicity of nanomechanical photothermal sensing for low absorption measurements, offering a sensitive, scattering-free platform for material analysis in nanophotonics and nanomechanics., Comment: Main text: 7 pages, 3 figures, 1 table Supporting Information: 4 pages, 4 figures, 1 table

Nanomechanical photothermal sensing has significantly advanced single-molecule/particle microscopy and spectroscopy, and infrared detection through the use of nanomechanical resonators that detect shifts in resonant frequency due to photothermal heating. However, the relationship between resonator design, photothermal sensitivity, and response time remains unclear. This paper compares three resonator types - strings, drumheads, and trampolines - to explore this relationship. Through theoretical modeling, experimental validation, and finite element method simulations, we find that strings offer the highest sensitivity (with a noise equivalent power of 280 fW/Hz$^{1/2}$ for strings made of silicon nitride), while drumheads exhibit the fastest thermal response. The study reveals that photothermal sensitivity correlates with the average temperature rise and not the peak temperature. Finally, the impact of photothermal back-action is discussed, which can be a major source of frequency instability. This work clarifies the performance differences and limits among resonator designs and guides the development of advanced nanomechanical photothermal sensors, benefiting a wide range of applications., Comment: Main text 28 pages, 7 figures. Supplementary Information: 11 pages, 10 figures

We present an advanced thermal response model for micro- and nanomechanical systems in photothermal sensing, designed to balance speed and precision. Our model considers the two time constants of the nanomechanical element and the supporting chip, triggered by photothermal heating, enabling precise photothermal input signal estimation through Kalman filtering. By integrating heat transfer and noise models, we apply an adaptive Kalman filter optimized for FPGA systems in real-time or offline. This method, used for photothermal infrared (IR) spectroscopy with nanomechanical resonators and a quantum cascade laser (QCL) in step-scan mode, enhances response speed beyond standard low-pass filters, reducing the effects of drift and random walk. Analytical calculations show the significant impact of the thermal expansion coefficients' ratio on the frequency response. The adaptive Kalman filter, informed by the QCL's input characteristics, accelerates the system's response, allowing rapid and precise IR spectrum generation. The use of the Levenberg-Marquardt algorithm and PSD analysis for system identification further refines our approach, promising fast and accurate nanomechanical photothermal sensing.

In this paper, we demonstrate the feasibility of alterfactual explanations for black box image classifiers. Traditional explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning that humans are familiar with. However, most common approaches from this field are based on communicating information about features or characteristics that are especially important for an AI's decision. However, to fully understand a decision, not only knowledge about relevant features is needed, but the awareness of irrelevant information also highly contributes to the creation of a user's mental model of an AI system. To this end, a novel approach for explaining AI systems called alterfactual explanations was recently proposed on a conceptual level. It is based on showing an alternative reality where irrelevant features of an AI's input are altered. By doing so, the user directly sees which input data characteristics can change arbitrarily without influencing the AI's decision. In this paper, we show for the first time that it is possible to apply this idea to black box models based on neural networks. To this end, we present a GAN-based approach to generate these alterfactual explanations for binary image classifiers. Further, we present a user study that gives interesting insights on how alterfactual explanations can complement counterfactual explanations., Comment: Accepted at IJCAI 2024. arXiv admin note: text overlap with arXiv:2207.09374

Recent developments in Large Language Models (LLMs) have significantly expanded their applications across various domains. However, the effectiveness of LLMs is often constrained when operating individually in complex environments. This paper introduces a transformative approach by organizing LLMs into community-based structures, aimed at enhancing their collective intelligence and problem-solving capabilities. We investigate different organizational models-hierarchical, flat, dynamic, and federated-each presenting unique benefits and challenges for collaborative AI systems. Within these structured communities, LLMs are designed to specialize in distinct cognitive tasks, employ advanced interaction mechanisms such as direct communication, voting systems, and market-based approaches, and dynamically adjust their governance structures to meet changing demands. The implementation of such communities holds substantial promise for improve problem-solving capabilities in AI, prompting an in-depth examination of their ethical considerations, management strategies, and scalability potential. This position paper seeks to lay the groundwork for future research, advocating a paradigm shift from isolated to synergistic operational frameworks in AI research and application.

Magnonics is an emerging research field that addresses the use of spin waves (magnons), purely magnetic waves, for information transport and processing. Spin waves are a potential replacement for electric current in modern computational devices that would make them more compact and energy efficient. The field is yet little known, even among physicists. Additionally, with the development of new measuring techniques and computational physics, the obtained magnetic data becomes more complex, in some cases including 3D vector fields and time-resolution. This work presents an approach to the audio-visual representation of the spin waves and discusses its use as a tool for science communication exhibits and possible data analysis tool. The work also details an instance of such an exhibit presented at the annual international digital art exhibition Ars Electronica Festival in 2022., Comment: Accepted to The 29th International Conference on Auditory Display (ICAD 2024) conference proceedings

Complex systems, such as brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often challenging. We propose using a Vector Autoregressive model with eXogenous input (VARX) to capture delayed interactions between internal and external variables. While this model aligns with Granger's statistical formalism for testing "causal relations", the connection between the two is not widely understood. Here, we bridge this gap by providing fundamental equations, user-friendly code, and demonstrations using simulated and real-world data from neuroscience, physiology, sociology, and economics. Our examples illustrate how the model avoids spurious correlation by factoring out external influences from internal dynamics, leading to more parsimonious explanations of the systems. We also provide methods for enhancing model efficiency, such as L2 regularization for limited data and basis functions to cope with extended delays. Additionally, we analyze model performance under various scenarios where model assumptions are violated. MATLAB, Python, and R code are provided for easy adoption: https://github.com/lcparra/varx

Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles, Deep Evidential Regression (DER), and Gaussian Mixture Models (GMM) - were applied to the H-transfer reaction between ${\it syn-}$Criegee and vinyl hydroxyperoxide. The results indicate that ensemble models provide the best results for detecting outliers, followed by GMM. For example, from a pool of 1000 structures with the largest uncertainty, the detection quality for outliers is $\sim 90$ \% and $\sim 50$ \%, respectively, if 25 or 1000 structures with large errors are sought. On the contrary, the limitations of the statistical assumptions of DER greatly impacted its prediction capabilities. Finally, a structure-based indicator was found to be correlated with large average error, which may help to rapidly classify new structures into those that provide an advantage for refining the neural network.

Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades., Comment: 7 pages, 6 figures, accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent. In this paper, we present the AffectToolbox, a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers. Aiming to facilitate ease of use, the AffectToolbox requires no programming knowledge and offers its functionality to reliably analyze the affective state of users through an accessible graphical user interface. The architecture encompasses a variety of models for emotion recognition on multiple affective channels and modalities, as well as an elaborate fusion system to merge multi-modal assessments into a unified result. The entire system is open-sourced and will be publicly available to ensure easy integration into more complex applications through a well-structured, Python-based code base - therefore marking a substantial contribution toward advancing affective computing research and fostering a more collaborative and inclusive environment within this interdisciplinary field.

Speech is a natural interface for humans to interact with robots. Yet, aligning a robot's voice to its appearance is challenging due to the rich vocabulary of both modalities. Previous research has explored a few labels to describe robots and tested them on a limited number of robots and existing voices. Here, we develop a robot-voice creation tool followed by large-scale behavioral human experiments (N=2,505). First, participants collectively tune robotic voices to match 175 robot images using an adaptive human-in-the-loop pipeline. Then, participants describe their impression of the robot or their matched voice using another human-in-the-loop paradigm for open-ended labeling. The elicited taxonomy is then used to rate robot attributes and to predict the best voice for an unseen robot. We offer a web interface to aid engineers in customizing robot voices, demonstrating the synergy between cognitive science and machine learning for engineering tools., Comment: Accepted to CHI 2024, May 11 to 16, 2024, Honolulu, HI, USA

Accurate redshift calibration is required to obtain unbiased cosmological information from large-scale galaxy surveys. In a forward modelling approach, the redshift distribution n(z) of a galaxy sample is measured using a parametric galaxy population model constrained by observations. We use a model that captures the redshift evolution of the galaxy luminosity functions, colours, and morphology, for red and blue samples. We constrain this model via simulation-based inference, using factorized Approximate Bayesian Computation (ABC) at the image level. We apply this framework to HSC deep field images, complemented with photometric redshifts from COSMOS2020. The simulated telescope images include realistic observational and instrumental effects. By applying the same processing and selection to real data and simulations, we obtain a sample of n(z) distributions from the ABC posterior. The photometric properties of the simulated galaxies are in good agreement with those from the real data, including magnitude, colour and redshift joint distributions. We compare the posterior n(z) from our simulations to the COSMOS2020 redshift distributions obtained via template fitting photometric data spanning the wavelength range from UV to IR. We mitigate sample variance in COSMOS by applying a reweighting technique. We thus obtain a good agreement between the simulated and observed redshift distributions, with a difference in the mean at the 1$\sigma$ level up to a magnitude of 24 in the i band. We discuss how our forward model can be applied to current and future surveys and be further extended. The ABC posterior and further material will be made publicly available at https://cosmology.ethz.ch/research/software-lab/ufig.html., Comment: Accepted for publication in JCAP. 35 pages, 9 figures

This work introduces a benchmark assessing the performance of clustering German text embeddings in different domains. This benchmark is driven by the increasing use of clustering neural text embeddings in tasks that require the grouping of texts (such as topic modeling) and the need for German resources in existing benchmarks. We provide an initial analysis for a range of pre-trained mono- and multilingual models evaluated on the outcome of different clustering algorithms. Results include strong performing mono- and multilingual models. Reducing the dimensions of embeddings can further improve clustering. Additionally, we conduct experiments with continued pre-training for German BERT models to estimate the benefits of this additional training. Our experiments suggest that significant performance improvements are possible for short text. All code and datasets are publicly available., Comment: 15 pages, 4 figures

Semantic embeddings play a crucial role in natural language-based information retrieval. Embedding models represent words and contexts as vectors whose spatial configuration is derived from the distribution of words in large text corpora. While such representations are generally very powerful, they might fail to account for fine-grained domain-specific nuances. In this article, we investigate this uncertainty for the domain of characterizations of expressive piano performance. Using a music research dataset of free text performance characterizations and a follow-up study sorting the annotations into clusters, we derive a ground truth for a domain-specific semantic similarity structure. We test five embedding models and their similarity structure for correspondence with the ground truth. We further assess the effects of contextualizing prompts, hubness reduction, cross-modal similarity, and k-means clustering. The quality of embedding models shows great variability with respect to this task; more general models perform better than domain-adapted ones and the best model configurations reach human-level agreement.