Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of the noise knowledge uncertainty for the science exploitation of cosmological and astrophysical stochastic gravitational wave background with LISA

This paper investigates the impact of a lack of knowledge of the instrumental noise on the characterisation of stochastic gravitational wave backgrounds with the Laser Interferometer Space Antenna (LISA). We focus on constraints on modelled backgrounds that represent the possible backgrounds from the mergers of binary black holes of stellar origin, from primordial black hole generation, from non-standard inflation, and from sound wave production during cosmic fluid phase transitions. We use splines to model generic, slowly varying, uncertainties in the auto and cross-spectral densities of the LISA time delay interferometry channels. We find that allowing for noise knowledge uncertainty in this way leads to one to two orders of magnitude degradation in our ability to constrain stochastic backgrounds, and a corresponding increase in the background energy density required for a confident detection. We also find that to avoid this degradation, the LISA noise would have to be known at the sub-percent level, which is unlikely to be achievable in practice.
An Ensemble of Online Estimation Methods for One Degree-of-Freedom Models of Unmanned Surface Vehicles: Applied Theory and Preliminary Field Results with Eight Vehicles

In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing.
A Survey on Popularity Bias in Recommender Systems

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.
Approximate Model-Based Shielding for Safe Reinforcement Learning

Reinforcement learning (RL) has shown great potential for solving complex tasks in a variety of domains. However, applying RL to safety-critical systems in the real-world is not easy as many algorithms are sample-inefficient and maximising the standard RL objective comes with no guarantees on worst-case performance. In this paper we propose approximate model-based shielding (AMBS), a principled look-ahead shielding algorithm for verifying the performance of learned RL policies w.r. t. a set of given safety constraints. Our algorithm differs from other shielding approaches in that it does not require prior knowledge of the safety-relevant dynamics of the system. We provide a strong theoretical justification for AMBS and demonstrate superior performance to other safety-aware approaches on a set of Atari games with state-dependent safety-labels.
High-efficiency inverted metamorphic 1.7/1.1 eV GaInAsP/GaInAs dual-junction solar cells

Photovoltaic conversion efficiencies of 32.6 ± 1.4% under the AM1.5 G173 global spectrum, and 35.5% ± 1.2% at 38-suns concentration under the direct spectrum, are demonstrated for a monolithic, dual-junction 1.7/1.1 eV solar cell. The tandem cell consists of a 1.7 eV GaInAsP top-junction grown lattice-matched to a GaAs substrate, followed by a metamorphic 1.1 eV GaInAs junction grown on a transparent, compositionally graded metamorphic AlGaInAs buffer. This bandgap combination is much closer to the dual-junction optimum and offers headroom for absolute 3% improvement in efficiency, in comparison to the incumbent lattice-matched GaInP/GaAs (∼1.86/1.41 eV) solar cells. The challenge of growing a high-quality 1.7 eV GaInAsP solar cell is the propensity for phase separation in the GaInAsP alloy. The challenge of lattice-mismatched GaInAs solar cell growth is that it requires minimizing the residual dislocation density during the growth of a transparent compositionally graded buffer to enable efficient metamor.
Correlated noise enhances coherence and fidelity in coupled qubits

It is generally assumed that environmental noise arising from thermal fluctuations is detrimental to preserving coherence and entanglement in a quantum system. In the simplest sense, dephasing and decoherence are tied to energy fluctuations driven by coupling between the system and the normal modes of the bath. Here, we explore the role of noise correlation in an open-loop model quantum communication system whereby the ``sender'' and the ``receiver'' are subject to local environments with various degrees of correlation or anticorrelation. We introduce correlation within the spectral density by solving a multidimensional stochastic differential equations and introduce these into the Redfield equations of motion for the system density matrix. We find that correlation can enhance both the fidelity and purity of a maximally entangled (Bell) state. Moreover, by comparing the evolution of different initial Bell states, we show that one can effectively probe the correlation between two local environments. These observations may be useful in the design of high-fidelity quantum gates and communication protocols.
Height Change Feature Based Free Space Detection

In the context of autonomous forklifts, ensuring non-collision during travel, pick, and place operations is crucial. To accomplish this, the forklift must be able to detect and locate areas of free space and potential obstacles in its environment. However, this is particularly challenging in highly dynamic environments, such as factory sites and production halls, due to numerous industrial trucks and workers moving throughout the area. In this paper, we present a novel method for free space detection, which consists of the following steps. We introduce a novel technique for surface normal estimation relying on spherical projected LiDAR data. Subsequently, we employ the estimated surface normals to detect free space. The presented method is a heuristic approach that does not require labeling and can ensure real-time application due to high processing speed. The effectiveness of the proposed method is demonstrated through its application to a real-world dataset obtained on a factory site both indoors and outdoors, and its evaluation on the Semantic KITTI dataset [2]. We achieved a mean Intersection over Union (mIoU) score of 50.90 % on the benchmark dataset, with a processing speed of 105 Hz. In addition, we evaluated our approach on our factory site dataset. Our method achieved a mIoU score of 63.30 % at 54 Hz.
A statistical framework for recovering intensity mapping autocorrelations from crosscorrelations

Intensity mapping experiments will soon have surveyed large swathes of the sky, providing information about the underlying matter distribution of the early universe. The resulting maps can be used to recover statistical information, such as the power spectrum, about the measured spectral lines (for example, HI, [CII], and [OIII]). However precise power spectrum measurements, such as the 21 cm autocorrelation, continue to be challenged by the presence of bright foregrounds and non-trivial systematics. By crosscorrelating different data sets, it may be possible to mitigate the effects of both foreground uncertainty and uncorrelated instrumental systematics. Beyond their own merit, crosscorrelations could also be used to recover autocorrelation information. Such a technique was proposed in Beane et al. (2019) for recovering the 21 cm power spectrum. Generalizing their result, we develop a statistical framework for combining multiple crosscorrelation signals in order to infer information about the corresponding autocorrelations. We do this first within the Least Squares Estimator (LSE) framework, and show how one can derive their estimator, along with several alternative estimators. We also investigate the posterior distribution of recovered autocorrelation and associated model parameters. We find that for certain noise regimes and cosmological signal modeling assumptions this procedure is effective at recovering autospectra from a set of crosscorrelations. Finally, we showcase our framework in the context of several near-future line intensity mapping experiments.
A Novel Cross-Perturbation for Single Domain Generalization

Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. However, the limited diversity in the training data hampers the learning of domain-invariant features, resulting in compromised generalization performance. To address this, data perturbation (augmentation) has emerged as a crucial method to increase data diversity. Nevertheless, existing perturbation methods often focus on either image-level or feature-level perturbations independently, neglecting their synergistic effects. To overcome these limitations, we propose CPerb, a simple yet effective cross-perturbation method. Specifically, CPerb utilizes both horizontal and vertical operations. Horizontally, it applies image-level and feature-level perturbations to enhance the diversity of the training data, mitigating the issue of limited diversity in single-source domains. Vertically, it introduces multi-route perturbation to learn domain-invariant features from different perspectives of samples with the same semantic category, thereby enhancing the generalization capability of the model. Additionally, we propose MixPatch, a novel feature-level perturbation method that exploits local image style information to further diversify the training data. Extensive experiments on various benchmark datasets validate the effectiveness of our method.
Energy Correlators Taking Charge

The confining transition from asymptotically free partons to hadrons remains one of the most mysterious aspects of Quantum Chromodynamics. With the wealth of high quality jet substructure data we can hope to gain new experimental insights into the details of its dynamics. Jet substructure has traditionally focused on correlations, $\langle \mathcal{E}(n_1) \mathcal{E}(n_2) \cdots \mathcal{E}(n_k) \rangle$, in the energy flux of hadrons. However, significantly more information about the confinement transition is encoded in how energy is correlated between hadrons with different quantum numbers, for example electric charge. In this Letter we develop the field theoretic formalism to compute general correlations, $\langle \mathcal{E}_{R_1}(n_1) \mathcal{E}_{R_2}(n_2) \cdots\mathcal{E}_{R_k}(n_k) \rangle$, between the energy flux carried by hadrons with quantum numbers $R_i$, by introducing new universal non-perturbative functions, which we term joint track functions. Using this formalism we show that the strong interactions introduce enhanced small angle correlations between opposite-sign hadrons, relative to like-sign hadrons, identifiable as an enhanced scaling of $\langle \mathcal{E}_+(n_1) \mathcal{E}_-(n_2) \rangle$ relative to $\langle \mathcal{E}_+(n_1) \mathcal{E}_+(n_2) \rangle$. We are also able to compute the scaling of a $C$-odd three-point function, $\langle \mathcal{E}_\mathcal{Q}(n_1) \mathcal{E}_\mathcal{Q}(n_2) \mathcal{E}_\mathcal{Q}(n_3) \rangle$. Our results greatly extend the class of systematically computable jet substructure observables, pushing perturbation theory deeper into the parton to hadron transition, and providing new observables to understand the dynamics of confinement.
Spatio-Temporal Branching for Motion Prediction using Motion Increments

Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications. Traditional methods rely on hand-crafted features and machine learning techniques, which often struggle to model the complex dynamics of human motion. Recent deep learning-based methods have achieved success by learning spatio-temporal representations of motion, but these models often overlook the reliability of motion data. Additionally, the temporal and spatial dependencies of skeleton nodes are distinct. The temporal relationship captures motion information over time, while the spatial relationship describes body structure and the relationships between different nodes. In this paper, we propose a novel spatio-temporal branching network using incremental information for HMP, which decouples the learning of temporal-domain and spatial-domain features, extracts more motion information, and achieves complementary cross-domain knowledge learning through knowledge distillation. Our approach effectively reduces noise interference and provides more expressive information for characterizing motion by separately extracting temporal and spatial features. We evaluate our approach on standard HMP benchmarks and outperform state-of-the-art methods in terms of prediction accuracy. Code is available at https://github.com/JasonWang959/STPMP.
Advancing microdata privacy protection: A review of synthetic data methods

Synthetic data generation is a powerful tool for privacy protection when considering public release of record‐level data files. Initially proposed about three decades ago, it has generated significant research and application interest. To meet the pressing demand of data privacy protection in a variety of contexts, the field needs more researchers and practitioners. This review provides a comprehensive introduction to synthetic data, including technical details of their generation and evaluation. Our review also addresses the challenges and limitations of synthetic data, discusses practical applications, and provides thoughts for future work.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms
Bogoliubov transformation and Schrödinger representation on curved space

It is usually accepted that quantum dynamics described by Schrodinger equation that determines the evolution of states from one Cauchy surface to another is unitary. However, it has been known for some time that this expectation is not borne out in the conventional setting in which one envisages the dynamics on a fixed Hilbert space. Indeed it is not even true for linear quantum field theory on Minkowski space if the chosen Cauchy surfaces are not preserved by the flow of a timelike Killing vector. This issue was elegantly addressed and resolved by Agullo and Ashtekar who showed that in a general setting quantum dynamics in the Schrodinger picture does not take place in a fixed Hilbert space. Instead, it takes place on a non-trivial bundle over time, the Hilbert bundle, whose fibre at a given time is a Hilbert space at that time. In this article, we postulate a Schrodinger equation that incorporates the effect of change in vacuum during time evolution by including the Bogoliubov transformation explicitly in the Schrodinger equation. More precisely, for a linear (real) Klein-Gordon field on a globally hyperbolic spacetime we write down a Schrodinger equation that propagates states between arbitrary chosen Cauchy surfaces, thus describing the quantum dynamics on a Hilbert bundle. We show that this dynamics is unitary if a specific tensor on the canonical phase space satisfies the Hilbert-Schmidt condition. Generalized unitarity condition of Agullo-Ashtekar follows quite naturally from our construction.
Consideration on the Future Major Upgrades of the SSRF Storage Ring

The SSRF storage ring began its user operation in 2009, currently it is operating at the energy of 3.5GeV, the natural emittance of 3.9 nm-rad and the beam current of 240 mA, serving for 13 beamlines with 9 IDs. Around 2020, there will be close to 40 operational beamlines at SSRF, which demand to further improve the performance of the existing storage ring, such as implementing the new lattice with superbends and installing 18 more new IDs. Looking for the future beyond Phase-II beamline project, a major upgrade towards a diffraction limited storage ring is under consideration. This paper presents the initial proposal on the ultimate storage ring upgrade for SSRF.
Bulk viscous cosmological model in f(R,T) theory of gravity

In this paper, we have presented bulk viscous cosmological model of the universe in the modified gravity theory in which the Lagragian of the gravitational action contains a general function [Formula: see text], where [Formula: see text] and [Formula: see text] denote the curvature scalar and the trace of the energy–momentum tensor, respectively, in the framework of a flat Friedmann–Robertson–Walker model with isotropic fluid. We obtain cosmological solution in [Formula: see text] theory of gravity, specially of particular choice [Formula: see text], where [Formula: see text] is a constant, in the presence of bulk viscosity that are permitted in Eckart theory, Truncated Israel Stewart (TIS) theory and Full Israel Stewart (FIS) theory. The physical and geometrical properties of the models in Eckart, TIS theory and FIS theory are studied in detail. The analysis of the variation of bulk viscous pressure, energy density, scale factor, Hubble parameter and deceleration parameter with cosmic evolution is done in the respective theories. The models are analyzed by comparison with recent observational data. The cosmological models are compatible with observations.
A Neutrino Detector Design for Safeguarding Small Modular Reactors

Nuclear reactors have long been a favored source for antineutrino measurements for estimates of power and burnup. With appropriate detector parameters and background rejection, an estimate of the reactor power can be derived from the measured antineutrino event rate. Antineutrino detectors are potentially attractive as a safeguards technology that can monitor reactor operations and thermal power from a distance. Advanced reactors have diverse features that may present challenges for current safeguards methods. By comparison, neutrino detectors offer complementary features, including a remote, continuous, unattended, and near-real-time monitoring capability, that may make them useful for safeguarding certain classes of advanced reactors. This study investigates the minimum depth and size of an antineutrino detector for a SMR to meet safeguards needs for advanced reactors. Extrapolating performance from several prior reactor antineutrino experiments, this study uses an analytical approach to develop a possible design for a remote antineutrino-based monitoring device.
Evaluating Spiking Neural Network on Neuromorphic Platform For Human Activity Recognition

Energy efficiency and low latency are crucial requirements for designing wearable AI-empowered human activity recognition systems, due to the hard constraints of battery operations and closed-loop feedback. While neural network models have been extensively compressed to match the stringent edge requirements, spiking neural networks and event-based sensing are recently emerging as promising solutions to further improve performance due to their inherent energy efficiency and capacity to process spatiotemporal data in very low latency. This work aims to evaluate the effectiveness of spiking neural networks on neuromorphic processors in human activity recognition for wearable applications. The case of workout recognition with wrist-worn wearable motion sensors is used as a study. A multi-threshold delta modulation approach is utilized for encoding the input sensor data into spike trains to move the pipeline into the event-based approach. The spikes trains are then fed to a spiking neural network with direct-event training, and the trained model is deployed on the research neuromorphic platform from Intel, Loihi, to evaluate energy and latency efficiency. Test results show that the spike-based workouts recognition system can achieve a comparable accuracy (87.5%) comparable to the popular milliwatt RISC-V bases multi-core processor GAP8 with a traditional neural network (88.1%) while achieving two times better energy-delay product (0.66 vs. 1.32 ).
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases

Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system's learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://github.com/Magnomic/CNST.
Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN

The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods. Computed Tomography (CT) scan images have emerged as a valuable tool for detecting the disease. In this article, we present a novel deep learning approach for automated COVID-19 CT scan classification where a modified Xception model is proposed which incorporates a newly designed channel attention mechanism and weighted global average pooling to enhance feature extraction thereby improving classification accuracy. The channel attention module selectively focuses on informative regions within each channel, enabling the model to learn discriminative features for COVID-19 detection. Experiments on a widely used COVID-19 CT scan dataset demonstrate a very good accuracy of 96.99% and show its superiority to other state-of-the-art techniques. This research can contribute to the ongoing efforts in using artificial intelligence to combat current and future pandemics and can offer promising and timely solutions for efficient medical image analysis tasks.