Search

Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption: that the network is trained on a \textit{stationary} data distribution. In settings where this assumption is violated, e.g.\ deep reinforcement learning, learning algorithms become unstable and brittle with respect to hyperparameters and even random seeds. One factor driving this instability is the loss of plasticity, meaning that updating the network's predictions in response to new information becomes more difficult as training progresses. While many recent works provide analyses and partial solutions to this phenomenon, a fundamental question remains unanswered: to what extent do known mechanisms of plasticity loss overlap, and how can mitigation strategies be combined to best maintain the trainability of a network? This paper addresses these questions, showing that loss of plasticity can be decomposed into multiple independent mechanisms and that, while intervening on any single mechanism is insufficient to avoid the loss of plasticity in all cases, intervening on multiple mechanisms in conjunction results in highly robust learning algorithms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks, and further demonstrate its effectiveness on naturally arising nonstationarities, including reinforcement learning in the Arcade Learning Environment.

The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The XENONnT experiment is the latest detector in the program, planned to be an upgrade of its predecessor XENON1T. It features an active target of 5.9 tonnes of cryogenic liquid xenon (8.5 tonnes total mass in cryostat). The experiment is expected to extend the sensitivity to WIMP dark matter by more than an order of magnitude compared to XENON1T, thanks to the larger active mass and the significantly reduced background, improved by novel systems such as a radon removal plant and a neutron veto. This article describes the XENONnT experiment and its sub-systems in detail and reports on the detector performance during the first science run., Comment: 32 pages, 19 figures

Introduction of a dielectric material in an NMR probe head modifies the frequency response of the probe circuit, a phenomenon revealed by the detuning of the probe. For NMR spectroscopy, this detuning is corrected for by tuning and matching the probe head prior to the NMR measurement. The magnitude of the probe detuning - the dielectric shift - provides direct access to the dielectric properties of the sample, enabling NMR spectrometers to simultaneously perform both dielectric and NMR spectroscopy. By measuring sample permittivity as function of frequency, permittivity spectroscopy can be performed using the new methodology. As a proof concept, this was evaluated on methanol, ethanol, 1-propanol, 1-pentanol and 1-octanol using a commercial CPMAS NMR probe head. The results accurately match literature data collected by standard dielectric spectroscopy techniques. Subsequently, the method was also applied to investigate the solvent-surface interactions of water confined in the micropores of an MFI-type, hydrophilic zeolite with Si/Al ratio of 11.5. In the micropores, water adsorbs to Br{\o}nsted acid sites and defect sites, resulting in a drastically decreased dielectric permittivity of the nano-confined water. A theoretical background for the new methodology is provided using an effective electric circuit model of a CPMAS probe head with solenoid coil, describing the detuning resulting from insertion of dielectric samples in the probe head.

The Laser Interferometer Space Antenna (LISA) is the first scientific endeavour to detect and study gravitational waves from space. LISA will survey the sky for Gravitational Waves in the 0.1 mHz to 1 Hz frequency band which will enable the study of a vast number of objects ranging from Galactic binaries and stellar mass black holes in the Milky Way, to distant massive black-hole mergers and the expansion of the Universe. This definition study report, or Red Book, presents a summary of the very large body of work that has been undertaken on the LISA mission over the LISA definition phase., Comment: 155 pages, with executive summary and table of contents

This study explores the behavior of machine learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University's Space Weather Analytics for Solar Flares benchmark dataset (Angryk et al. 2020a,b), we examine the impacts of training methodology and the solar cycle on decision tree, support vector machine, and multilayer perceptron performance. We implement our classifiers using three temporal training windows: stationary, rolling, and expanding. The stationary window trains models using a single set of data available before the first forecasting instance, which remains constant throughout the solar cycle. The rolling window trains models using data from a constant time interval before the forecasting instance, which moves with the solar cycle. Finally, the expanding window trains models using all available data before the forecasting instance. For each window, a number of input features (1, 5, 10, 25, 50, 120) and temporal sizes (5, 8, 11, 14, 17, 20 months) were tested. To our surprise, we found that for a 20-month window, skill scores were comparable regardless of the window type, feature count, and classifier selected. Furthermore, reducing the size of this window only marginally decreased stationary and rolling window performance. This implies that, given enough data, a stationary window can be chosen over other window types, eliminating the need for model retraining. Lastly, a moderately strong positive correlation was found to exist between a model's false positive rate and the solar X-ray background flux. This suggests that the solar cycle phase has a considerable influence on forecasting., Comment: Accepted for publication in ApJ; 25 pages, 11 figures, 2 tables

$\omega$ Centauri is considered the most massive globular cluster of the Milky Way and likely the former nuclear star cluster of a galaxy accreted by the Milky Way. It is speculated to contain an intermediate-mass black hole (IMBH) from several dynamical models. However, uncertainties regarding the location of the cluster center or the retention of stellar remnants limit the robustness of the IMBH detections reported so far. In this paper, we derive and study the stellar kinematics from the highest-resolution spectroscopic data yet, using the Multi Unit Spectroscopic Explorer (MUSE) in the narrow field mode (NFM) and wide field mode (WFM). Our exceptional data near the center reveal for the first time that stars within the inner 20" ($\sim$0.5 pc) counter-rotate relative to the bulk rotation of the cluster. Using this dataset, we measure the rotation and line-of-sight velocity dispersion (LOSVD) profile out to 120$''$ with different centers proposed in the literature. We find that the velocity dispersion profiles using different centers match well with those previously published. Based on the counter--rotation, we determine a kinematic center and look for any signs of an IMBH using the high-velocity stars close to the center. We do not find any significant outliers $>$60 km/s within the central 20$''$, consistent with no IMBH being present at the center of $\omega$ Centauri. A detailed analysis of Jeans' modeling of the putative IMBH will be presented in the next paper of the series., Comment: Accepted for publication in MNRAS, 16 pages, 9 figures

Spherosilicate, consisting of a double 4-ring cyclosilicate core (D4R; Si8O20) with every corner functionalized with a dimethylsilyl chloride group (-SiMe2Cl), was used as node to construct an iso-reticular series of porous expanded network materials. Interconnecting the nodes with linear, aliphatic {\alpha},{\omega}-alkanediol linker molecules yields PolySilicate Porous Organic Polymers (PSiPOPs), a new type of ordered reticular material related to the well-known metal-organic and covalent organic frameworks (MOFs & COFs). In the synthesis, sacrificial hydrogen-bonded Si8O20 cyclosilicate crystals are first converted into silyl chloride terminated spherosilicate. In a second step, these nodes are linked up by alkanediol units via the intermediate formation of a Si-N bond with catalytic amines such as pyridine and dimethylformamide. Overall, the presented synthesis converts D4R cyclosilicate into an ordered reticular framework with [Si8O20]-[Si(CH3)2-]8 nodes and O-(CH2)n-O linkers. Example materials with ethylene glycol, 1,5-pentanediol, and 1,7-heptanediol as linker (n = 2, 5, and 7) were produced and characterized. On a macroscopic level, the synthesis yields porous frameworks exhibiting a thermal stability up to 400{\deg}C and a chemical stability between pH 1 and 12. N2 physisorption revealed a secondary mesopore structure, indicating future options to produce hierarchical materials using soft templates. The molecular level structure of these reticular PSiPOP materials was elucidated using an NMR crystallography approach implementing a combination of 1D and 2D 1H and 29Si solid-state MAS NMR spectroscopy experiments. Previously reported reticular COF/POP materials implementing D4R-based nodes, used Si8 octakis (phenyl) D4R POSS as a node, connecting it to the linker via a Si-C bond instead of a Si-O-C linkage.

The yielding transition in athermal complex fluids can be interpreted as an absorbing phase transition between an elastic, absorbing state with high mesoscopic degeneracy and a flowing, active state. We characterize quantitatively this phase transition in an elastoplastic model under fixed applied shear stress, using a finite-size scaling analysis. We find vanishing critical fluctuations of the order parameter (i.e., the shear rate), and relate this property to the convex character of the phase transition ($\beta >1$). We show explicitly that the CDP class is recovered when both properties are relaxed. We locate yielding within a family of models akin to fixed-energy sandpile (FES) models, only with long-range redistribution kernels with zero-modes that result from mechanical equilibrium. For redistribution kernels with sufficiently fast decay, this family of models belong to a short-range universality class distinct from the Conserved Directed Percolation class of usual FES, which is induced by zero modes., Comment: 4 pages + Supplementary material

We give a survey of the implosion construction, extending some of its aspects relating to hypertoric geometry from type $A$ to a general reductive group, and interpret it in the context of the Moore-Tachikawa category. We use these ideas to discuss how the contraction construction in symplectic geometry can be generalised to the hyperk\"ahler or complex symplectic situation., Comment: 23 Pages. To appear in Int. J. Math. (special issue in honour of Oscar Garcia Prada)

In this paper, we present Coyote C++, a fully automated white-box unit testing tool for C and C++. Whereas existing tools have struggled to realize unit test generation for C++, Coyote C++ is able to produce high coverage results from unit test generation at a testing speed of over 10,000 statements per hour. This impressive feat is made possible by the combination of a powerful concolic execution engine with sophisticated automated test harness generation. Additionally, the GUI of Coyote C++ displays detailed code coverage visualizations and provides various configuration features for users seeking to manually optimize their coverage results. Combining potent one-click automated testing with rich support for manual tweaking, Coyote C++ is the first automated testing tool that is practical enough to make automated testing of C++ code truly viable in industrial applications.

Our online lives generate a wealth of behavioral records -'digital footprints'- which are stored and leveraged by technology platforms. This data can be used to create value for users by personalizing services. At the same time, however, it also poses a threat to people's privacy by offering a highly intimate window into their private traits (e.g., their personality, political ideology, sexual orientation). Prior work has proposed a potential remedy: The cloaking of users' footprints. That is, platforms could allow users to hide portions of their digital footprints from predictive algorithms to avoid undesired inferences. While such an approach has been shown to offer privacy protection in the moment, there are two open questions. First, it remains unclear how well cloaking performs over time. As people constantly leave new digital footprints, the algorithm might regain the ability to predict previously cloaked traits. Second, cloaking digital footprints to avoid one undesirable inference may degrade the performance of models for other, desirable inferences (e.g., those driving desired personalized content). In the light of these research gaps, our contributions are twofold: 1) We propose a novel cloaking strategy that conceals 'metafeatures' (automatically generated higher-level categories) and compares its effectiveness against existing cloaking approaches, and 2) we test the spill-over effects of cloaking one trait on the accuracy of inferences on other traits. A key finding is that the effectiveness of cloaking degrades over times, but the rate at which it degrades is significantly smaller when cloaking metafeatures rather than individual footprints. In addition, our findings reveal the expected trade-off between privacy and personalization: Cloaking an undesired trait also partially conceals other desirable traits.

Soft amorphous materials are viscoelastic solids ubiquitously found around us, from clays and cementitious pastes to emulsions and physical gels encountered in food or biomedical engineering. Under an external deformation, these materials undergo a noteworthy transition from a solid to a liquid state that reshapes the material microstructure. This yielding transition was the main theme of a workshop held from January 9 to 13, 2023 at the Lorentz Center in Leiden. The manuscript presented here offers a critical perspective on the subject, synthesizing insights from the various brainstorming sessions and informal discussions that unfolded during this week of vibrant exchange of ideas. The result of these exchanges takes the form of a series of open questions that represent outstanding experimental, numerical, and theoretical challenges to be tackled in the near future., Comment: 21 pages, 7 figures, perspective on the workshop 'Yield stress and fluidization in brittle and ductile amorphous systems' held from January 9 to 13, 2023 at the Lorentz Center in Leiden

An analysis of solar neutrino data from the fourth phase of Super-Kamiokande~(SK-IV) from October 2008 to May 2018 is performed and the results are presented. The observation time of the data set of SK-IV corresponds to $2970$~days and the total live time for all four phases is $5805$~days. For more precise solar neutrino measurements, several improvements are applied in this analysis: lowering the data acquisition threshold in May 2015, further reduction of the spallation background using neutron clustering events, precise energy reconstruction considering the time variation of the PMT gain. The observed number of solar neutrino events in $3.49$--$19.49$ MeV electron kinetic energy region during SK-IV is $65,443^{+390}_{-388}\,(\mathrm{stat.})\pm 925\,(\mathrm{syst.})$ events. Corresponding $\mathrm{^{8}B}$ solar neutrino flux is $(2.314 \pm 0.014\, \rm{(stat.)} \pm 0.040 \, \rm{(syst.)}) \times 10^{6}~\mathrm{cm^{-2}\,s^{-1}}$, assuming a pure electron-neutrino flavor component without neutrino oscillations. The flux combined with all SK phases up to SK-IV is $(2.336 \pm 0.011\, \rm{(stat.)} \pm 0.043 \, \rm{(syst.)}) \times 10^{6}~\mathrm{cm^{-2}\,s^{-1}}$. Based on the neutrino oscillation analysis from all solar experiments, including the SK $5805$~days data set, the best-fit neutrino oscillation parameters are $\rm{sin^{2} \theta_{12,\,solar}} = 0.306 \pm 0.013 $ and $\Delta m^{2}_{21,\,\mathrm{solar}} = (6.10^{+ 0.95}_{-0.81}) \times 10^{-5}~\rm{eV}^{2}$, with a deviation of about 1.5$\sigma$ from the $\Delta m^{2}_{21}$ parameter obtained by KamLAND. The best-fit neutrino oscillation parameters obtained from all solar experiments and KamLAND are $\sin^{2} \theta_{12,\,\mathrm{global}} = 0.307 \pm 0.012 $ and $\Delta m^{2}_{21,\,\mathrm{global}} = (7.50^{+ 0.19}_{-0.18}) \times 10^{-5}~\rm{eV}^{2}$., Comment: 47 pages, 61 figures

We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251\deg $\le l \le$ 358\deg and 2\deg $\le l \le$ 61\deg at $|b| \le 1.5\deg $). SMGPS is the largest, most sensitive and highest angular resolution 1 GHz survey of the Plane yet carried out, with an angular resolution of 8" and a broadband RMS sensitivity of $\sim$10--20 $\mu$ Jy/beam. Here we describe the first publicly available data release from SMGPS which comprises data cubes of frequency-resolved images over 908--1656 MHz, power law fits to the images, and broadband zeroth moment integrated intensity images. A thorough assessment of the data quality and guidance for future usage of the data products are given. Finally, we discuss the tremendous potential of SMGPS by showcasing highlights of the Galactic and extragalactic science that it permits. These highlights include the discovery of a new population of non-thermal radio filaments; identification of new candidate supernova remnants, pulsar wind nebulae and planetary nebulae; improved radio/mid-IR classification of rare Luminous Blue Variables and discovery of associated extended radio nebulae; new radio stars identified by Bayesian cross-matching techniques; the realisation that many of the largest radio-quiet WISE HII region candidates are not true HII regions; and a large sample of previously undiscovered background HI galaxies in the Zone of Avoidance., Comment: Accepted for publication in MNRAS. The data release is live and links can be found in the Data Availability Statement in the paper

Background: Asymmetric fission is known to occur in two regions, the actinides and sub-lead, and is dependent on the fissioning system excitation energy. Experimental evidence in the sub-lead region show that this mode is surprisingly persistent with increasing energy and its origin is not fully understood. Purpose: To experimentally study the fusion-fission reaction of $^{215}$Fr at moderate excitation energy and determine previously unknown independent fission yields and other properties. Method: The compound nucleus was formed in the reaction $^{18}$O + $^{197}$Au. The prompt gamma-rays emitted during the reaction were measured with the high efficiency and high granularity $\nu$-ball2 spectrometer. Independent fission yields of even-even nuclei were determined by detecting triple-gamma cascades in the fission fragments. Results: The observed yields, although dominated by a symmetric peak, show maxima for heavy fragment of $Z \approx 54-56$, which is consistent with the known results in the actinide region but unexpected for the nuclide of interest, and at the studied excitation energy. Conclusions: The mode of asymmetric fission is present even at relatively high excitation energies in the system studied. This observation matches experimental findings in the sub-lead region, contrary to the actinides, and so far there is no well-developed explanation of this phenomenon.

The reciprocity approach is a powerful method to determine the expected signal power of axion haloscopes in a model-independent way. Especially for open and broadband setups like the MADMAX dielectric haloscope the sensitivity to the axion field is difficult to calibrate since they do not allow discrete eigenmode analysis and are optically too large to fully simulate. The central idea of the reciprocity approach is to measure a reflection-induced test field in the setup instead of trying to simulate the axion-induced field. In this article, the reciprocity approach is used to determine the expected signal power of a dish antenna and a minimal dielectric haloscope directly from measurements. The results match expectations from simulation but also include important systematic effects that are too difficult to simulate. In particular, the effect of antenna standing waves and higher order mode perturbations can be quantified for the first time in a dielectric haloscope., Comment: v2, updated to match journal version, 18 pages, 10 figures

Metaproteomics has become a crucial omics technology for studying microbiomes. In this area, the Unipept ecosystem, accessible at https://unipept.ugent.be, has emerged as an invaluable resource for analyzing metaproteomic data. It offers in-depth insights into both taxonomic distributions and functional characteristics of complex ecosystems. This tutorial explains essential concepts like Lowest Common Ancestor (LCA) determination and the handling of peptides with missed cleavages. It also provides a detailed, step-by-step guide on using the Unipept Web application and Unipept Desktop for thorough metaproteomics analyses. By integrating theoretical principles with practical methodologies, this tutorial empowers researchers with the essential knowledge and tools needed to fully utilize metaproteomics in their microbiome studies.

We present a measurement of neutrino oscillation parameters with the Super-Kamiokande detector using atmospheric neutrinos from the complete pure-water SK I-V (April 1996-July 2020) data set, including events from an expanded fiducial volume. The data set corresponds to 6511.3 live days and an exposure of 484.2 kiloton-years. Measurements of the neutrino oscillation parameters $\Delta m^2_{32}$, $\sin^2\theta_{23}$, $\sin^2 \theta_{13}$, $\delta_{CP}$, and the preference for the neutrino mass ordering are presented with atmospheric neutrino data alone, and with constraints on $\sin^2 \theta_{13}$ from reactor neutrino experiments. Our analysis including constraints on $\sin^2 \theta_{13}$ favors the normal mass ordering at the 92.3% level., Comment: 24 pages, 18 figures

We report the first measurement of the atmospheric neutrino-oxygen neutral-current quasielastic (NCQE) cross section in the gadolinium-loaded Super-Kamiokande (SK) water Cherenkov detector. In June 2020, SK began a new experimental phase, named SK-Gd, by loading 0.011% by mass of gadolinium into the ultrapure water of the SK detector. The introduction of gadolinium to ultrapure water has the effect of improving the neutron-tagging efficiency. Using a 552.2 day data set from August 2020 to June 2022, we measure the NCQE cross section to be 0.74 $\pm$ 0.22(stat.) $^{+0.85}_{-0.15}$ (syst.) $\times$ 10$^{-38}$ cm$^{2}$/oxygen in the energy range from 160 MeV to 10 GeV, which is consistent with the atmospheric neutrino-flux-averaged theoretical NCQE cross section and the measurement in the SK pure-water phase within the uncertainties. Furthermore, we compare the models of the nucleon-nucleus interactions in water and find that the Binary Cascade model and the Liege Intranuclear Cascade model provide a somewhat better fit to the observed data than the Bertini Cascade model. Since the atmospheric neutrino-oxygen NCQE reactions are one of the main backgrounds in the search for diffuse supernova neutrino background (DSNB), these new results will contribute to future studies - and the potential discovery - of the DSNB in SK., Comment: 8 pages, 3 figures

We report a search for time variations of the solar $^8$B neutrino flux using 5804 live days of Super-Kamiokande data collected between May 31, 1996, and May 30, 2018. Super-Kamiokande measured the precise time of each solar neutrino interaction over 22 calendar years to search for solar neutrino flux modulations with unprecedented precision. Periodic modulations are searched for in a dataset comprising five-day interval solar neutrino flux measurements with a maximum likelihood method. We also applied the Lomb-Scargle method to this dataset to compare it with previous reports. The only significant modulation found is due to the elliptic orbit of the Earth around the Sun. The observed modulation is consistent with astronomical data: we measured an eccentricity of (1.53$\pm$0.35)\%, and a perihelion shift of ($-$1.5$\pm$13.5) days., Comment: 8 pages, 5 figures, 2 tables, and data file: "sksolartimevariation5804d.txt" (the data file updated with additional 3 columns -- R^2 correction, upper-error, lower-error)

Coyote C++ is an automated testing tool that uses a sophisticated concolic-execution-based approach to realize fully automated unit testing for C and C++. While concolic testing has proven effective for languages such as C and Java, tools have struggled to achieve a practical level of automation for C++ due to its many syntactical intricacies and overall complexity. Coyote C++ is the first automated testing tool to breach the barrier and bring automated unit testing for C++ to a practical level suitable for industrial adoption, consistently reaching around 90% code coverage. Notably, this testing process requires no user involvement and performs test harness generation, test case generation and test execution with "one-click" automation. In this paper, we introduce Coyote C++ by outlining its high-level structure and discussing the core design decisions that shaped the implementation of its concolic execution engine. Finally, we demonstrate that Coyote C++ is capable of achieving high coverage results within a reasonable timespan by presenting the results from experiments on both open-source and industrial software.

Confinement of water and methane in mesopores of hydrophobized SBA-15 is demonstrated to promote methane hydrate formation. In comparison to as-synthesized SBA-15, hydrophobization by C8 grafting accelerates the kinetics of methane storage in and delivery from the hydrate. C8 grafting density was determined at 0.5 groups nm-2 based on TGA and quantitative NMR spectroscopy. Multinuclear 1H-1H DQSQ and 1H-1H RFDR NMR provided spectroscopic evidence for the occurrence of C8 chains inside the mesopores of SBA-15, by showcasing close spatial proximity between the grafted C8 chains and pore-intruded water species. X-ray diffraction demonstrates formation of Structure I hydrate on SBA-15 C8. At 7.0 MPa and 248 K, the water-to-hydrate conversion on hydrophobized SBA-15 C8 reaches 96 pct. as compared to only 71 pct. on a pristine SBA-15 sample with comparable pore size, pore volume and surface area. The clathrate loading amounted to 14.8 g g-1. 2D correlation NMR spectroscopy (1H-13C CP-HETCOR, 1H-1H RFDR) reveals hydrate formation occurs within pores of SBA-15 C8 as well as in interparticle volumes. Following the initial crystallization of SBA-15 C8-supported methane hydrate taking several hours, a pressure swing process at 248 K allows to desorb and re-adsorb methane from the structure within minutes and without thawing the frozen water structure. Fast loading and unloading of methane was achieved in 19 subsequent cycles without losses in kinetics. The ability to harvest the gas and regenerate the structure without the need to re-freeze the water represents a 50 pct. energy gain with respect to melting and subsequently recrystallizing the hydrate at 298 K and 248 K, respectively. After methane desorption, a small amount of residual methane hydrate in combination with an amorphous yet locally ordered ice phase is observed using 13C and 2H NMR spectroscopy.

In modern scientific research, the objective is often to identify which variables are associated with an outcome among a large class of potential predictors. This goal can be achieved by selecting variables in a manner that controls the the false discovery rate (FDR), the proportion of irrelevant predictors among the selections. Knockoff filtering is a cutting-edge approach to variable selection that provides FDR control. Existing knockoff statistics frequently employ linear models to assess relationships between features and the response, but the linearity assumption is often violated in real world applications. This may result in poor power to detect truly prognostic variables. We introduce a knockoff statistic based on the conditional prediction function (CPF), which can pair with state-of-art machine learning predictive models, such as deep neural networks. The CPF statistics can capture the nonlinear relationships between predictors and outcomes while also accounting for correlation between features. We illustrate the capability of the CPF statistics to provide superior power over common knockoff statistics with continuous, categorical, and survival outcomes using repeated simulations. Knockoff filtering with the CPF statistics is demonstrated using (1) a residential building dataset to select predictors for the actual sales prices and (2) the TCGA dataset to select genes that are correlated with disease staging in lung cancer patients.