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X-ray radioscopy was used to measure the 2D projected dynamic void fraction in a zero/narrow gap alkaline water electrolyzer at a spatial resolution of 15 $\mu$m, for narrow gap sizes up to 300 $\mu$m and current densities up to 0.54 A/cm$^2$. As expected, the void fraction in the bulk was found to increase along the cell height and with increasing current density. The void fraction measured in the gap region (the space between the diaphragm and the electrode and its holes) was always larger than in the bulk. It hardly depended on the gap size at current densities below 0.3 A/cm$^2$. The lowest cell potential was measured for zero gap. No evidence of isolating gas pockets/films in the gaps was found. Liquid crossover and oxygen void fraction exceeding the hydrogen void fraction occurred for porous plate electrodes, but these phenomena were suppressed for perforated foil electrodes.

Saturn has long been the only giant planet in our solar system without any known Trojan members. In this paper, with serendipitous archival observations and refined orbit determination, we report that 2019 UO$_{14}$ is a Trojan of the gas giant. However, the object is only a transient Trojan currently librating around the leading Lagrange point $L_4$ of the Sun-Saturn system in a period of $\sim\!0.7$ kyr. Our N-body numerical simulation shows that 2019 UO$_{14}$ was likely captured as a Centaur and became trapped around $L_4$ $\sim\!2$ kyr ago from a horseshoe coorbital. The current Trojan state will be maintained for another millennium or thereabouts before transitioning back to a horseshoe state. Additionally, we characterize the physical properties of 2019 UO$_{14}$. Assuming a linear phase slope of $0.06 \pm 0.01$ mag/deg, the mean $r$-band absolute magnitude of the object was determined to be $H_r = 13.11 \pm 0.07$, with its color measured to be consistent with those of Jupiter and Neptune Trojans and not statistically different from Centaurs. Although the short-lived Saturn Trojan exhibited no compelling evidence of activity in the observations, we favour the possibility that it could be an active Trojan. If confirmed, 2019 UO$_{14}$ would be marked as the first active Trojan in our solar system. We conservatively determine the optical depth of dust within our photometric aperture to be $\lesssim\!10^{-7}$, corresponding to a dust mass-loss rate to be $\lesssim\!1$ kg s$^{-1}$, provided that the physical properties of dust grains resemble Centaur 29P/Schwassmann-Wachmann 1.

Millimeter wave sensing provides people with the capability of sensing the surrounding crowds in a non-invasive and privacy-preserving manner, which holds huge application potential. However, detecting stationary crowds remains challenging due to several factors such as minimal movements (like breathing or casual fidgets), which can be easily treated as noise clusters during data collection and consequently filtered in the following processing procedures. Additionally, the uneven distribution of signal power due to signal power attenuation and interferences resulting from external reflectors or absorbers further complicates accurate detection. To address these challenges and enable stationary crowd detection across various application scenarios requiring specialized domain adaption, we introduce LLMCount, the first system to harness the capabilities of large-language models (LLMs) to enhance crowd detection performance. By exploiting the decision-making capability of LLM, we can successfully compensate the signal power to acquire a uniform distribution and thereby achieve a detection with higher accuracy. To assess the system's performance, comprehensive evaluations are conducted under diversified scenarios like hall, meeting room, and cinema. The evaluation results show that our proposed approach reaches high detection accuracy with lower overall latency compared with previous methods.

Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms first identify common features across images and then minimize reconstruction errors after estimating the shape of the terrain. This bundle adjustment (BA) step optimizes around a single, simplifying scalar value that obfuscates many possible causes of reconstruction errors (e.g., initial estimate of the position and orientation of the camera, lighting conditions, ease of feature detection in the terrain). Reconstruction errors can lead to inaccurate scientific inferences or endanger a spacecraft exploring a remote environment. To address this challenge, we present VECTOR, a visual analysis tool that improves error inspection for stereo reconstruction BA. VECTOR provides analysts with previously unavailable visibility into feature locations, camera pose, and computed 3D points. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission.

We consider the problem of estimating log-determinants of large, sparse, positive definite matrices. A key focus of our algorithm is to reduce computational cost, and it is based on sparse approximate inverses. The algorithm can be implemented to be adaptive, and it uses graph spline approximation to improve accuracy. We illustrate our approach on classes of large sparse matrices., Comment: 5 pages, python code included as an ancillary file

We present the Citizen Science program Active Asteroids and describe discoveries stemming from our ongoing project. Our NASA Partner program is hosted on the Zooniverse online platform and launched on 2021 August 31, with the goal of engaging the community in the search for active asteroids -- asteroids with comet-like tails or comae. We also set out to identify other unusual active solar system objects, such as active Centaurs, active quasi-Hilda asteroids, and Jupiter-family comets (JFCs). Active objects are rare in large part because they are difficult to identify, so we ask volunteers to assist us in searching for active bodies in our collection of millions of images of known minor planets. We produced these cutout images with our project pipeline that makes use of publicly available Dark Energy Camera (DECam) data. Since the project launch, roughly 8,300 volunteers have scrutinized some 430,000 images to great effect, which we describe in this work. In total we have identified previously unknown activity on 15 asteroids, plus one Centaur, that were thought to be asteroidal (i.e., inactive). Of the asteroids, we classify four as active quasi-Hilda asteroids, seven as JFCs, and four as active asteroids, consisting of one Main-belt comet (MBC) and three MBC candidates. We also include our findings concerning known active objects that our program facilitated, an unanticipated avenue of scientific discovery. These include discovering activity occurring during an orbital epoch for which objects were not known to be active, and the reclassification of objects based on our dynamical analyses., Comment: 35 pages, 5 figures, 3 tables

We extend previous inelastic neutron scattering results on the geometrically frustrated antiferromagnet hexagonal-YMnO$_3$, which has been suggested to belong to the class of classical spin liquids. We extend the energy transfer coverage of the diffuse signal up to 6.9 meV within a wide temperature range around the ordering temperature, $T_\mathrm{N}$. The two distinct diffuse signals in the a-b plane, the signal localized at $\Gamma$' and the scattering intensity connecting $\Gamma$' points over the M', are shown to be only weakly energy dependent. In addition, an external magnetic field of up to 10.5 T applied along c is shown to have no effect on the diffuse signal. In the orthogonal scattering plane, the signals are shown to be dependent on l only through the magnetic form factor, showing that the correlations are purely two-dimensional, and supporting its origin to be the frustrated Mn$^{3+}$ triangles. This result is corroborated by atomistic spin dynamics simulations showing similar scattering vector and temperature behaviours. Lastly, data for the spin wave scattering in the (h, 0, l) plane allow for a discussion of the magnetic ground state where better agreement is found between the data and an ordered structure of the $\Gamma_1$ or $\Gamma_3$ symmetry, albeit crystal electric field arguments dismisses the $\Gamma_1$ as possibility.

This short paper introduces a novel approach to global sensitivity analysis, grounded in the variance-covariance structure of random variables derived from random measures. The proposed methodology facilitates the application of information-theoretic rules for uncertainty quantification, offering several advantages. Specifically, the approach provides valuable insights into the decomposition of variance within discrete subspaces, similar to the standard ANOVA analysis. To illustrate this point, the method is applied to datasets obtained from the analysis of randomized controlled trials on evaluating the efficacy of the COVID-19 vaccine and assessing clinical endpoints in a lung cancer study., Comment: 10 pages

A complete accounting of nearby objects -- from the highest-mass white dwarf progenitors down to low-mass brown dwarfs -- is now possible, thanks to an almost complete set of trigonometric parallax determinations from Gaia, ground-based surveys, and Spitzer follow-up. We create a census of objects within a Sun-centered sphere of 20-pc radius and check published literature to decompose each binary or higher-order system into its separate components. The result is a volume-limited census of $\sim$3,600 individual star formation products useful in measuring the initial mass function across the stellar ($<8 M_\odot$) and substellar ($\gtrsim 5 M_{Jup}$) regimes. Comparing our resulting initial mass function to previous measurements shows good agreement above 0.8$M_\odot$ and a divergence at lower masses. Our 20-pc space densities are best fit with a quadripartite power law, $\xi(M) = dN/dM \propto M^{-\alpha}$ with long-established values of $\alpha = 2.3$ at high masses ($0.55 < M < 8.00 M_\odot$) and $\alpha = 1.3$ at intermediate masses ($0.22 < M < 0.55 M_\odot$), but at lower masses we find $\alpha = 0.25$ for $0.05 < M <0.22 M_\odot$ and $\alpha = 0.6$ for $0.01 < M < 0.05 M_\odot$. This implies that the rate of production as a function of decreasing mass diminishes in the low-mass star/high-mass brown dwarf regime before increasing again in the low-mass brown dwarf regime. Correcting for completeness, we find a star to brown dwarf number ratio of, currently, 4:1, and an average mass per object of 0.41 $M_\odot$., Comment: Accepted for publication in The Astrophysical Journal Supplement Series. 123 pages with four ancillary files

Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised deep-learning applications, these methods tend to be less adequate when there is large intra-class variance and low inter-class variance in input data distribution. Deep Metric Learning seeks to develop methods that aim to measure the similarity between data samples by learning a representation function that maps these data samples into a representative embedding space. It leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminative embedding space even for distributions having low inter-class and high intra-class variances. In this chapter, we will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches., Comment: Book Chapter Published In Handbook of Statistics, Special Issue - Deep Learning 48, 59

The European Spallation Source, currently under construction in Lund, Sweden, is a multidisciplinary international laboratory. Once completed to full specifications, it will operate the world's most powerful pulsed neutron source. Supported by a 3 million Euro Research and Innovation Action within the EU Horizon 2020 program, a design study (HighNESS) has been completed to develop a second neutron source located below the spallation target. Compared to the first source, designed for high cold and thermal brightness, the new source has been optimized to deliver higher intensity, and a shift to longer wavelengths in the spectral regions of cold (CN, 2--20\,\AA), very cold (VCN, 10--120\,\AA), and ultracold (UCN, ${>}\,{500}$\,\AA) neutrons. The second source comprises a large liquid deuterium moderator designed to produce CN and support secondary VCN and UCN sources. Various options have been explored in the proposed designs, aiming for world-leading performance in neutronics. These designs will enable the development of several new instrument concepts and facilitate the implementation of a high-sensitivity neutron-antineutron oscillation experiment (NNBAR). This document serves as the Conceptual Design Report for the HighNESS project, representing its final deliverable., Comment: 269 pages, 255 figures. Volume I of the final deliverable of the HighNESS Project (HORIZON 2020 grant agreement ID: 951782)

This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD). Arabic is considered a resource-poor language, meaning that there are few data sets available, which leads to limited availability of NLP methods. To overcome this limitation, we create a dedicated data set from publicly available resources. Subsequently, transformer-based machine learning models are being trained and evaluated. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches, when we apply linguistically informed pre-training methods such as Named Entity Recognition (NER). To our knowledge, this is the first large-scale evaluation for this task in Arabic, as well as the first application of multi-task pre-training in this context., Comment: submitted to IEEE SSCI 2023

The optimization of network performance is vital for the delivery of services using standard cellular technologies for mobile communications. Call setup delay and User Equipment (UE) battery savings significantly influence network performance. Improving these factors is vital for ensuring optimal service delivery. In comparison to traditional circuit-switched voice calls, VoLTE (Voice over LTE) technology offers faster call setup durations and better battery-saving performance. To validate these claims, a drive test was carried out using the XCAL drive test tool to collect real-time network parameter details in VoLTE and non-VoLTE voice calls. The findings highlight the analysis of real-time network characteristics, such as the call setup delay calculation, battery-saving performance, and DRX mechanism. The study contributes to the understanding of network optimization strategies and provides insights for enhancing the quality of service (QoS) in mobile communication networks. Examining VoLTE and non-VoLTE operations, this research highlights the substantial energy savings obtained by VoLTE. Specifically, VoLTE saves approximately 60.76% of energy before the Service Request and approximately 38.97% of energy after the Service Request. Moreover, VoLTE to VoLTE calls have a 72.6% faster call setup delay than non-VoLTE-based LTE to LTE calls, because of fewer signaling messages required. Furthermore, as compared to non-VoLTE to non-VoLTE calls, VoLTE to non-VoLTE calls offer an 18.6% faster call setup delay. These results showcase the performance advantages of VoLTE and reinforce its potential for offering better services in wireless communication networks., Comment: Accepted for presentation and Publication on the IEEE 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI 2023)

Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation strategy., Comment: Paper accepted at IJCB 2023

Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks. However, development of practical and robust contactless fingerprint matching techniques is constrained by the limited availability of large scale real-world datasets. To motivate further advances in contactless fingerprint matching across sensors, we introduce the RidgeBase benchmark dataset. RidgeBase consists of more than 15,000 contactless and contact-based fingerprint image pairs acquired from 88 individuals under different background and lighting conditions using two smartphone cameras and one flatbed contact sensor. Unlike existing datasets, RidgeBase is designed to promote research under different matching scenarios that include Single Finger Matching and Multi-Finger Matching for both contactless- to-contactless (CL2CL) and contact-to-contactless (C2CL) verification and identification. Furthermore, due to the high intra-sample variance in contactless fingerprints belonging to the same finger, we propose a set-based matching protocol inspired by the advances in facial recognition datasets. This protocol is specifically designed for pragmatic contactless fingerprint matching that can account for variances in focus, polarity and finger-angles. We report qualitative and quantitative baseline results for different protocols using a COTS fingerprint matcher (Verifinger) and a Deep CNN based approach on the RidgeBase dataset. The dataset can be downloaded here: https://www.buffalo.edu/cubs/research/datasets/ridgebase-benchmark-dataset.html, Comment: Paper accepted at IJCB 2022

We describe and benchmark a new quantum charge-coupled device (QCCD) trapped-ion quantum computer based on a linear trap with periodic boundary conditions, which resembles a race track. The new system successfully incorporates several technologies crucial to future scalability, including electrode broadcasting, multi-layer RF routing, and magneto-optical trap (MOT) loading, while maintaining, and in some cases exceeding, the gate fidelities of previous QCCD systems. The system is initially operated with 32 qubits, but future upgrades will allow for more. We benchmark the performance of primitive operations, including an average state preparation and measurement error of 1.6(1)$\times 10^{-3}$, an average single-qubit gate infidelity of $2.5(3)\times 10^{-5}$, and an average two-qubit gate infidelity of $1.84(5)\times 10^{-3}$. The system-level performance of the quantum processor is assessed with mirror benchmarking, linear cross-entropy benchmarking, a quantum volume measurement of $\mathrm{QV}=2^{16}$, and the creation of 32-qubit entanglement in a GHZ state. We also tested application benchmarks including Hamiltonian simulation, QAOA, error correction on a repetition code, and dynamics simulations using qubit reuse. We also discuss future upgrades to the new system aimed at adding more qubits and capabilities., Comment: 24 pages, 24 figures. Made some minor edits and added several more authors

Fine-tuning the Natural Language Processing (NLP) models for each new data set requires higher computational time associated with increased carbon footprint and cost. However, fine-tuning helps the pre-trained models adapt to the latest data sets; what if we avoid the fine-tuning steps and attempt to generate summaries using just the pre-trained models to reduce computational time and cost. In this paper, we tried to omit the fine-tuning steps and investigate whether the Marginal Maximum Relevance (MMR)-based approach can help the pre-trained models to obtain query-focused summaries directly from a new data set that was not used to pre-train the models. First, we used topic modelling on Wikipedia Current Events Portal (WCEP) and Debatepedia datasets to generate queries for summarization tasks. Then, using MMR, we ranked the sentences of the documents according to the queries. Next, we passed the ranked sentences to seven transformer-based pre-trained models to perform the summarization tasks. Finally, we used the MMR approach again to select the query relevant sentences from the generated summaries of individual pre-trained models and constructed the final summary. As indicated by the experimental results, our MMR-based approach successfully ranked and selected the most relevant sentences as summaries and showed better performance than the individual pre-trained models.

We report statistically significant detections of non-radial nongravitational accelerations based on astrometric data in the photometrically inactive objects 1998 KY$_{26}$, 2005 VL$_1$, 2016 NJ$_{33}$, 2010 VL$_{65}$, 2016 RH$_{120}$, and 2010 RF$_{12}$. The magnitudes of the nongravitational accelerations are greater than those typically induced by the Yarkovsky effect and there is no radiation-based, non-radial effect that can be so large. Therefore, we hypothesize that the accelerations are driven by outgassing, and calculate implied H$_2$O production rates for each object. We attempt to reconcile outgassing induced acceleration with the lack of visible comae or photometric activity via the absence of surface dust and low levels of gas production. Although these objects are small and some are rapidly rotating, surface cohesive forces are stronger than the rotational forces and rapid rotation alone cannot explain the lack of surface debris. It is possible that surface dust was removed previously, perhaps via outgassing activity that increased the rotation rates to their present day value. We calculate dust production rates of order $\sim10^{-4}$ g s$^{-1}$ in each object assuming that the nuclei are bare, within the upper limits of dust production from a sample stacked image of 1998 KY$_{26}$ of $\dot{M}_{\rm Dust}<0.2$ g s$^{-1}$. This production corresponds to brightness variations of order $\sim0.0025\%$, which are undetectable in extant photometric data. We assess the future observability of each of these targets, and find that the orbit of 1998 KY$_{26}$ -- which is also the target for the extended Hayabusa2 mission -- exhibits favorable viewing geometry before 2025., Comment: 21 pages, 9 figures, 3 tables, accepted for publication in the Planetary Science Journal

Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow., Comment: Accepted to WACV 2023

The maximum parsimony distance $d_{\textrm{MP}}(T_1,T_2)$ and the bounded-state maximum parsimony distance $d_{\textrm{MP}}^t(T_1,T_2)$ measure the difference between two phylogenetic trees $T_1,T_2$ in terms of the maximum difference between their parsimony scores for any character (with $t$ a bound on the number of states in the character, in the case of $d_{\textrm{MP}}^t(T_1,T_2)$). While computing $d_{\textrm{MP}}(T_1, T_2)$ was previously shown to be fixed-parameter tractable with a linear kernel, no such result was known for $d_{\textrm{MP}}^t(T_1,T_2)$. In this paper, we prove that computing $d_{\textrm{MP}}^t(T_1, T_2)$ is fixed-parameter tractable for all~$t$. Specifically, we prove that this problem has a kernel of size $O(k \lg k)$, where $k = d_{\textrm{MP}}^t(T_1, T_2)$. As the primary analysis tool, we introduce the concept of leg-disjoint incompatible quartets, which may be of independent interest.

Deep Neural Networks miss a principled model of their operation. A novel framework for supervised learning based on Topological Quantum Field Theory that looks particularly well suited for implementation on quantum processors has been recently explored. We propose using this framework to understand the problem of generalisation in Deep Neural Networks. More specifically, in this approach, Deep Neural Networks are viewed as the semi-classical limit of Topological Quantum Neural Networks. A framework of this kind explains the overfitting behavior of Deep Neural Networks during the training step and the corresponding generalisation capabilities. We explore the paradigmatic case of the perceptron, which we implement as the semiclassical limit of Topological Quantum Neural Networks. We apply a novel algorithm we developed, showing that it obtains similar results to standard neural networks, but without the need for training (optimisation)., Comment: 22 pages (two columns), 9 figures. v2: Several parts rewritten, and computational results added

Induction-transduction of activating-deactivating points are fundamental mechanisms of action that underlie innumerable systems and phenomena, mathematical, natural, and anthropogenic, and can exhibit complex behaviors such as self-excitation, phase transitions, hysteresis, polarization, periodicity, chaos, wave behavior, geometry, and energy transfer. We describe a class of primitives for induction-transduction based on dynamics on images of marked random counting measures under graphical random transformations. We derive a field equation for the law of the activation-deactivation (Bernoulli) process on an arbitrary measurable space and describe some mechanisms of action on the unit interval., Comment: 15 pages main (4 figures, 1 table), 80 page appendix (76 figures, 1 table), comments welcome

Here we present the effect of uniaxial stress on the magnetic stripes in the cuprate system La$_{2-x}$Ba$_{x}$CuO$_4$ with $x = 0.115$, previously found to have a stress-induced enhancement in the superconducting transition temperature. By means of neutron scattering, we show that the static stripes are suppressed by stress, pointing towards a trade-off between superconductivity and static magnetism, in direct agreement with previously reported $\mu$SR measurements. Additionally, we show that some of the reduced weight in the elastic channel appears to have moved to the inelastic channel. Moreover, a stress-induced momentum shift of the fluctuations towards the typical 1/8 value of commensurability is observed. These results impose a strong constraint on the theoretical interpretation of stress-enhanced superconductivity in cuprate systems.

We use modulation calorimetry to study the heat capacity of small samples (30 nano gram-10 micro gram) of the classical spin-ice compounds Dy2Ti2O7 and Ho2Ti2O7 at low temperature (0.5-3 K). Using modulation frequencies of 0.1-200 Hz we find a strong frequency dependence in the measured heat capacity and are able to study thermal relaxation effects on the corresponding timescales. Performing dynamic Monte Carlo simulations we verify that the specific heat frequency response has its origin in the slow magnetic monopole dynamics indigenous to spin ice. We find a timescale of 20ms per Monte Carlo step at 4K in contrast to 2.5ms mentioned in previous studies by other 1 techniques. Our study establishes modulation calorimetry as a versatile experimental probe of dynamic processes in frustrated magnetic materials., Comment: 7 pages, 5 figures

We report photometric follow-up observations of thirteen exoplanets (HATS-1 b, HATS2 b, HATS-3 b, HAT-P-18 b, HAT-P-27 b, HAT-P-30 b, HAT-P-55 b, KELT-4A b, WASP-25 b, WASP-42 b, WASP-57 b, WASP-61 b and WASP-123 b), as part of the Original Research By Young Twinkle Students (ORBYTS) programme. All these planets are potentially viable targets for atmospheric characterisation and our data, which were taken using the LCOGT network of ground-based telescopes, will be combined with observations from other users of ExoClock to ensure that the transit times of these planets continue to be well-known, far into the future., Comment: Accepted for publication in the Astronomy Theory, Observations and Methods Journal. Secondary school students (16-17 y/o) performed the majority of the analysis, as well as writing much of the paper, as part of the ORBYTS programme

We form the Jacobi theta distribution through discrete integration of exponential random variables over an infinite inverse square law surface. It is continuous, supported on the positive reals, has a single positive parameter, is unimodal, positively skewed, and leptokurtic. Its cumulative distribution and density functions are expressed in terms of the Jacobi theta function. We describe asymptotic and log-normal approximations, inference, and a few applications of such distributions to modeling., Comment: 16 pages, 8 figures

We study the use of machine learning for finding numerical hermitian Yang-Mills connections on line bundles over Calabi-Yau manifolds. Defining an appropriate loss function and focusing on the examples of an elliptic curve, a K3 surface and a quintic threefold, we show that neural networks can be trained to give a close approximation to hermitian Yang-Mills connections.

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, finding the optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-and-error process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms for each component. To optimize the workflow per application, we employ automated machine learning using a random search and ensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1) liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77); 5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis (0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer's disease (0.87); and 12) head and neck cancer (0.84). We show that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performs similar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automatically optimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications. To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework, and the code to reproduce this study., Comment: 33 pages, 4 figures, 4 tables, 2 supplementary figures, 3 supplementary table, submitted to Medical Image Analysis; revision

CSPEC is the cold chopper spectrometer of the European Spallation Source (ESS) and will come on-line with the ESS beam on target. CSPEC will be the first cold chopper spectrometer on a long pulsed spallation source which provides great opportunities in terms of signal to noise and novel measuring schemes. We provide a detailed overview of the instrument, scientific design considerations and engineering requirements.

With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. To improve the performance of the detection model, a series of techniques have been developed. However, existing work still leaves much to be desired, such as privacy disclosure and communication cost. To address these issues, we propose FedSup, a client-edge-cloud framework for privacy and efficient fatigue detection. Inspired by the federated learning technique, FedSup intelligently utilizes the collaboration between client, edge, and cloud server to realizing dynamic model optimization while protecting edge data privacy. Moreover, to reduce the unnecessary system communication overhead, we further propose a Bayesian convolutional neural network (BCNN) approximation strategy on the clients and an uncertainty weighted aggregation algorithm on the cloud to enhance the central model training efficiency. Extensive experiments demonstrate that the FedSup framework is suitable for IoV scenarios and outperforms other mainstream methods.

We present an experimental study of the magnetic structure and dynamics of two frustrated hyperkagome compounds, Gd3Ga5O12 and Gd3Al5O12. It has previously been shown that Gd3Ga5O12 exhibits long-range correlations of multipolar directors, that are formed from antiferromagnetic spins on loops of ten ions. Using neutron diffraction and Reverse Monte Carlo simulations we prove the existence of similar magnetic correlations in Gd3Al5O12, showing the ubiquity of these complex structures in frustrated hyperkagome materials. Using inelastic neutron scattering we shed further light on the director state and the associated low lying magnetic excitations. In addition we have measured quasielastic dynamics that show evidence of spin diffusion. Finally, we present AC susceptibility measurements on both Gd3Ga5O12 and Gd3Al5O12, revealing a large difference in the low frequency dynamics between the two otherwise similar compounds., Comment: 13 pages, 12 figures, Accepted by Physical Review B

We present a general framework for uncertainty quantification that is a mosaic of interconnected models. We define global first and second order structural and correlative sensitivity analyses for random counting measures acting on risk functionals of input-output maps. These are the ANOVA decomposition of the intensity measure and the decomposition of the random measure variance, each into subspaces. Orthogonal random measures furnish sensitivity distributions. We show that the random counting measure may be used to construct positive random fields, which admit decompositions of covariance and sensitivity indices and may be used to represent interacting particle systems. The first and second order global sensitivity analyses conveyed through random counting measures elucidate and integrate different notions of uncertainty quantification, and the global sensitivity analysis of random fields conveys the proportionate functional contributions to covariance. This framework complements others when used in conjunction with for instance algorithmic uncertainty and model selection uncertainty frameworks., Comment: 65 pages, 20 figures, 14 tables

We show that the sequence of thinned uniform random counting measures converges weakly to the Poisson random measure. We call such measures 'orthogonal dice' due to their natural connection with certain counting problems and link the sizes of orthogonal dice with the primes by constructing the largest known prime orthogonal die. We give many examples of possible use of the construct of orthogonal dice in various areas of applications including gambling, cosmology, random matrices, approximation theory and circuits. The gambling example reveals for instance that fair six-sided die numbered $\{1,2,3,4,5,6\}$ generate negative covariance in point representations of hands across players, in contrast to seven-sided dice numbered $\{1,2,3,4,5,6,7\}$ that generate zero covariance and are orthogonal. The cosmology example suggests that a 'supermassive' orthogonal die underlies the galaxy point patterns of the Universe. The random matrix application reveals that the variance of the spectral gap of the Gaussian orthogonal ensemble exhibits non-monotone scaling with thinning for the Dirac (empirical) random measure, in contrast to the orthogonal dice where the spectral gap variance is monotone. Finally, the approximation application shows that the discrete Legendre polynomials converge to the Charlier polynomials in $L^p$ for integers $p\ge1$ whereas the electronic circuit/shot noise application identifies electric current as the Ornstein-Uhlenbeck process driven by an orthogonal die random measure., Comment: 31 pages, 6 figures, 1 table

Our work intends to show that: (1) Quantum Neural Networks (QNN) can be mapped onto spinnetworks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theories (TQFT); (2) Deep Neural Networks (DNN) are a subcase of QNN, in the sense that they emerge as the semiclassical limit of QNN; (3) A number of Machine Learning (ML) key-concepts can be rephrased by using the terminology of TQFT. Our framework provides as well a working hypothesis for understanding the generalization behavior of DNN, relating it to the topological features of the graphs structures involved., Comment: 25 pages, 10 figures

This article describes the operation of the near-infrared wavefront sensing based Adaptive Optics (AO) system CIAO. The Coud\'e Infrared Adaptive Optics (CIAO) system is a central auxiliary component of the Very Large Telescope (VLT) interferometer (VLTI). It enables in particular the observations of the Galactic Center (GC) using the GRAVITY instrument. GRAVITY is a highly specialized beam combiner, a device that coherently combines the light of the four 8-m telescopes and finally records interferometric measurements in the K-band on 6 baselines simultaneously. CIAO compensates for phase disturbances caused by atmospheric turbulence, which all four 8 m Unit Telescopes (UT) experience during observation. Each of the four CIAO units generates an almost diffraction-limited image quality at its UT, which ensures that maximum flux of the observed stellar object enters the fibers of the GRAVITY beam combiner. We present CIAO performance data obtained in the first 3 years of operation as a function of weather conditions. We describe how CIAO is configured and used for observations with GRAVITY. In addition, we focus on the outstanding features of the near-infrared sensitive Saphira detector, which is used for the first time on Paranal, and show how it works as a wavefront sensor detector., Comment: 12 pages, 8 figures, accepted for publication in Instruments (open access journal from mdpi)

Inelastic neutron experiments on the classical triangular-lattice geometrically frustrated antiferromagnet h-YMnO$_3$ reveal diffuse, gapless magnetic excitations present both below and far above the ordering temperature, $T_N$. The correlation length of the excitations increases as the temperature approaches zero, bearing strong resemblance to critical scattering. We model the scattering as critical spin-spin correlations in a two-dimensional magnetic ground state, and we speculate that this may provide a general framework to understand features typically attributed to classical spin liquids.

The spin ice materials Ho2Ti2O7 and Dy2Ti2O7 are experimental and theoretical exemplars of highly frustrated magnetic materials. However, the effects of an applied uniaxial pressure are not well studied, and here we report magnetization measurements of Ho2Ti2O7 under uniaxial pressure applied in the [001], [111] and [110] crystalline directions. The basic features are captured by an extension of the dipolar spin ice model. We find a good match between our model and measurements with pressures applied along two of the three directions, and extend the framework to discuss the influence of crystal misalignment for the third direction. The parameters determined from the magnetization measurements reproduce neutron scattering measurements we perform under uniaxial pressure applied along the [110] crystalline direction. In the detailed analysis we include the recently verified susceptibility dependence of the demagnetizing factor. Our work demonstrates the application of a moderate applied pressure to modify the magnetic interaction parameters. The knowledge can be used to predict critical pressures needed to induce new phases and transitions in frustrated materials, and in the case of Ho2Ti2O7 we expect a transition to a ferromagnetic ground state for uniaxial pressures above 3.3 GPa., Comment: 13 pages, 14 figures

The boron-10 based Multi-Grid detector is being developed as an alternative to helium-3 based neutron detectors. At the European Spallation Source, the detector will be used for time-of-flight neutron spectroscopy at cold to thermal neutron energies. The objective of this work is to investigate fine time- and energy-resolved effects of the Multi-Grid detector, down to a few $\mu$eV, while comparing it to the performance of a typical helium-3 tube. Furthermore, it is to characterize differences between the detector technologies in terms of internal scattering, as well as the time reconstruction of ~ $\mu$s short neutron pulses. The data were taken at the Helmholtz Zentrum Berlin, where the Multi-Grid detector and a helium-3 tube were installed at the ESS test beamline, V20. Using a Fermi-chopper, the neutron beam of the reactor was chopped into a few tens of $\mu$s wide pulses before reaching the detector, located a few tens of cm downstream. The data of the measurements show an agreement between the derived and calculated neutron detection efficiency curve. The data also provide fine details on the effect of internal scattering, and how it can be reduced. For the first time, the chopper resolution was comparable to the timing resolution of the Multi-Grid detector. This allowed a detailed study of time- and energy resolved effects, as well as a comparison with a typical helium-3 tube., Comment: 37 pages, 21 figures

We report neutron scattering, magnetic susceptibility and Monte Carlo theoretical analysis to verify the short range nature of the magnetic structure and spin-spin correlations in a Yb$_3$Ga$_5$O$_{12}$ single crystal. The quantum spin state of Yb$^{3+}$ in Yb$_3$Ga$_5$O$_{12}$ is verified. The quantum spins organise into a short ranged emergent director state for T $<$ 0.4 K derived from anisotropy and near neighbour exchange. We derive the magnitude of the near neighbour exchange interactions $0.6\; {\rm K} < J_1 < 0.7\; {\rm K}, J_2 = 0.12$~K and the magnitude of the dipolar exchange interaction, $D$, in the range $0.18 < D < 0.21$ K. Certain aspects of the broad experimental dataset can be modelled using a $J_1D$ model with ferromagnetic near neighbour spin-spin correlations while other aspects of the data can be accurately reproduced using a $J_1J_2D$ model with antiferromagnetic near neighbour spin-spin correlation. As such, although we do not quantify all the relevant exchange interactions we nevertheless provide a strong basis for the understanding of the complex Hamiltonian required to fully describe the magnetic state of Yb$_3$Ga$_5$O$_{12}$., Comment: Main paper 10 pages, appendix 6 pages

We study machine learning of phenomenologically relevant properties of string compactifications, which arise in the context of heterotic line bundle models. Both supervised and unsupervised learning are considered. We find that, for a fixed compactification manifold, relatively small neural networks are capable of distinguishing consistent line bundle models with the correct gauge group and the correct chiral asymmetry from random models without these properties. The same distinction can also be achieved in the context of unsupervised learning, using an auto-encoder. Learning non-topological properties, specifically the number of Higgs multiplets, turns out to be more difficult, but is possible using sizeable networks and feature-enhanced data sets., Comment: 10 pages

Comprehensive muon spin rotation/relaxation (muSR) and neutron powder diffraction (NPD) studies supported via bulk measurements have been performed on the ordered double perovskite Sr2YbRuO6 to investigate the nature of the magnetic ground state. Two sharp transitions at TN1 ~ 42 K and TN2 ~ 36 K have been observed in the static and dynamic magnetization measurements, coinciding with the heat capacity data. In order to confirm the origin of the observed phase transitions and the magnetic ground state, microscopic evidences are presented here. An initial indication of long-range magnetic ordering comes from a sharp drop in the muon initial asymmetry and a peak in the relaxation rate near TN1. NPD confirms that the magnetic ground state of Sr2YbRuO6 consists of an antiferromagnetic (AFM) structure with interpenetrating lattices of parallel Yb3+ and Ru5+ moments lying in the ab-plane and adopting a A-type AFM structure. Intriguingly, a small but remarkable change is observed in the long-range ordering parameters at TN2 confirming the presence of a weak spin reorientation (i.e. change in spin configuration) transition of Ru and Yb moments, as well as a change in the magnetic moment evolution of the Yb3+ spins at TN2. The temperature dependent behaviour of the Yb3+ and Ru5+ moments suggests that the 4d-electrons of Ru5+ play a dominating role in stabilizing the long range ordered magnetic ground state in the double perovskite Sr2YbRuO6 whereas only the Yb3+ moments show an arrest at TN2. The observed magnetic structure and the presence of a ferromagnetic interaction between Ru- and Yb- ions are explained with use of the Goodenough-Kanamori-Anderson (GKA) rules. Possible reasons for the presence of the second magnetic phase transition and of a compensation point in the magnetization data are linked to competing mechanisms of magnetic anisotropy., Comment: 28 pages, 12 figures

Under shear, a system of particles changes its contact network and becomes unstable as it transitions between mechanically stable states. For hard spheres at zero pressure, contact breaking events necessarily generate an instability, but this is not the case at finite pressure, where we identify two types of contact changes: network events that do not correspond to instabilities and rearrangement events that do. The relative fraction of such events is constant as a function of system size, pressure and interaction potential, consistent with our observation that both nonlinearities obey the same finite-size scaling. Thus, the zero-pressure limit of the nonlinear response is highly singular.

The magnetically frustrated spin ice family of materials is host to numerous exotic phenomena such as magnetic monopole excitations and macroscopic residual entropy extending to low temperature. A finite-temperature ordering transition in the absence of applied fields has not been experimentally observed in the classical spin ice materials Dy2Ti2O7 and Ho2Ti2O7. Such a transition could be induced by the application of pressure, and in this work we consider the effects of uniaxial pressure on classical spin ice systems. Theoretically we find that the pressure induced ordering transition in Dy2Ti2O7 is strongly affected by the dipolar interaction. We also report measurements on the neutron structure factor of Ho2Ti2O7 under pressure, and compare the experimental results to the predictions of our theoretical model., Comment: 8 pages, 9 figures

We conjecture and prove closed-form index expressions for the cohomology dimensions of line bundles on del Pezzo and Hirzebruch surfaces. Further, for all compact toric surfaces we provide a simple algorithm which allows expression of any line bundle cohomology in terms of an index. These formulae follow from general theorems we prove for a wider class of surfaces. In particular, we construct a map that takes any effective line bundle to a nef line bundle while preserving the zeroth cohomology dimension. For complex surfaces, these results explain the appearance of piecewise polynomial equations for cohomology and they are a first step towards understanding similar formulae recently obtained for Calabi-Yau three-folds., Comment: 30 pages

We investigate different approaches to machine learning of line bundle cohomology on complex surfaces as well as on Calabi-Yau three-folds. Standard function learning based on simple fully connected networks with logistic sigmoids is reviewed and its main features and shortcomings are discussed. It has been observed recently that line bundle cohomology can be described by dividing the Picard lattice into certain regions in each of which the cohomology dimension is described by a polynomial formula. Based on this structure, we set up a network capable of identifying the regions and their associated polynomials, thereby effectively generating a conjecture for the correct cohomology formula. For complex surfaces, we also set up a network which learns certain rigid divisors which appear in a recently discovered master formula for cohomology dimensions., Comment: 24 pages, Latex, 19 figures