Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Evaluation of Failure Mode Using Fuzzy AHP and Variable Weight Synthesizing

Aiming at the most critically debated disadvantage of risk evaluation of failure mode using traditional RPN (Risk Priority Number), that the degree of failure severity isn’t accurately evaluated and the three risk factors are assumed to be equally important. The method, risk evaluation of failure mode using fuzzy Analytic Hierarchy Process (AHP) and variable weight synthesizing is put forward. The model is separated into two layers. Firstly, according to the production loss, repair costs, safety affection and environment impact, fuzzy mathematics, AHP and encouragement variable weight synthesizing are introduced into the quantitative evaluation of failure severity to improve its accuracy and objectiveness. Then, via severity, occurrence, and detection, encouragement variable weight and AHP are used for failure mode risk evaluation to make evaluation results closer to the reality. At last, an example is provided to illustrate the potential applications of the proposed model and the detailed computational process is presented. And the result shows the accuracy and objectiveness can be improved by the proposed model.
Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production

Recent approaches to Sign Language Production (SLP) have adopted spoken language Neural Machine Translation (NMT) architectures, applied without sign-specific modifications. In addition, these works represent sign language as a sequence of skeleton pose vectors, projected to an abstract representation with no inherent skeletal structure. In this paper, we represent sign language sequences as a skeletal graph structure, with joints as nodes and both spatial and temporal connections as edges. To operate on this graphical structure, we propose Skeletal Graph Self-Attention (SGSA), a novel graphical attention layer that embeds a skeleton inductive bias into the SLP model. Retaining the skeletal feature representation throughout, we directly apply a spatio-temporal adjacency matrix into the self-attention formulation. This provides structure and context to each skeletal joint that is not possible when using a non-graphical abstract representation, enabling fluid and expressive sign language production. We evaluate our Skeletal Graph Self-Attention architecture on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset, achieving state-of-the-art back translation performance with an 8% and 7% improvement over competing methods for the dev and test sets.
[C ii] Map of the Molecular Ring and Arms of the Spiral Galaxy NGC 7331

We present the [C ii] 157.7 μm map of galaxy NGC 7331 obtained with the Field-Imaging Far-Infrared Line Spectrometer on board the Stratospheric Observatory for Infrared Astronomy (SOFIA). This map extends an existent Herschel/Photodetector Array Camera and Spectrometer observation of the central strip of the galaxy to encompass the entire molecular ring and much of the disk, including multiple spiral arms with intense far-IR emission. We also present Herschel archival data of the [N ii] 205 μm line, which covers a substantial part of the [C ii] SOFIA observations and allows us to estimate the neutral fraction of the [C ii] emission along the ring and disk of the galaxy. We find that the neutral fraction rises with the distance from the center. In addition, by tracing the azimuthal variation of the neutral fraction, we are able to see how our observing perspective affects this measurement. The high inclination of NGC 7331 allows us to glimpse the internal walls of the molecular ring. There, young bright stars emit UV radiation causing more [C ii] emission to be produced in the ionized gas. On the outer walls, opaque dust shrouds the rest of the ring, making the neutral medium the dominant source of [C ii] emission. Through spatial analysis comparing the [C ii] emission to tracers of gas heating, we are able to investigate how the photoelectric heating efficiency varies throughout NGC 7331 and extend global measurements of the [C ii] deficit to local environments. Since the origin of [C ii] emission has typically been studied in face-on galaxies, our results shed a new light on the interpretation of [C ii] emission, especially when studying distant galaxies with unknown inclination.
How to Avoid Trivial Solutions in Physics-Informed Neural Networks

The advent of scientific machine learning (SciML) has opened up a new field with many promises and challenges in the field of simulation science by developing approaches at the interface of physics- and data-based modelling. To this end, physics-informed neural networks (PINNs) have been introduced in recent years, which cope for the scarcity in training data by incorporating physics knowledge of the problem at so-called collocation points. In this work, we investigate the prediction performance of PINNs with respect to the number of collocation points used to enforce the physics-based penalty terms. We show that PINNs can fail, learning a trivial solution that fulfills the physics-derived penalty term by definition. We have developed an alternative sampling approach and a new penalty term enabling us to remedy this core problem of PINNs in data-scarce settings with competitive results while reducing the amount of collocation points needed by up to 80 \% for benchmark problems.
Direct 0D-3D coupling of a lattice Boltzmann methodology for fluid-structure hemodynamics simulations

This work introduces a numerical approach and implementation for the direct coupling of arbitrary complex ordinary differential equation- (ODE-)governed zero-dimensional (0D) boundary conditions to three-dimensional (3D) lattice Boltzmann-based fluid-structure systems for hemodynamics studies. In particular, a most complex configuration is treated by considering a dynamic left ventricle- (LV-)elastance heart model which is governed by (and applied as) a nonlinear, non-stationary hybrid ODE-Dirichlet system. Other ODE-based boundary conditions, such as lumped parameter Windkessel models for truncated vasculature, are also considered. Performance studies of the complete 0D-3D solver, including its treatment of the lattice Boltzmann fluid equations and elastodynamics equations as well as their interactions, is conducted through a variety of benchmark and convergence studies that demonstrate the ability of the coupled 0D-3D methodology in generating physiological pressure and flow waveforms -- ultimately enabling the exploration of various physical and physiological parameters for hemodynamics studies of the coupled LV-arterial system. The methods proposed in this paper can be easily applied to other ODE-based boundary conditions as well as to other fluid problems that are modeled by 3D lattice Boltzmann equations and that require direct coupling of dynamic 0D boundary conditions.
Network Compression via Central Filter

Neural network pruning has remarkable performance for reducing the complexity of deep network models. Recent network pruning methods usually focused on removing unimportant or redundant filters in the network. In this paper, by exploring the similarities between feature maps, we propose a novel filter pruning method, Central Filter (CF), which suggests that a filter is approximately equal to a set of other filters after appropriate adjustments. Our method is based on the discovery that the average similarity between feature maps changes very little, regardless of the number of input images. Based on this finding, we establish similarity graphs on feature maps and calculate the closeness centrality of each node to select the Central Filter. Moreover, we design a method to directly adjust weights in the next layer corresponding to the Central Filter, effectively minimizing the error caused by pruning. Through experiments on various benchmark networks and datasets, CF yields state-of-the-art performance. For example, with ResNet-56, CF reduces approximately 39.7% of FLOPs by removing 47.1% of the parameters, with even 0.33% accuracy improvement on CIFAR-10. With GoogLeNet, CF reduces approximately 63.2% of FLOPs by removing 55.6% of the parameters, with only a small loss of 0.35% in top-1 accuracy on CIFAR-10. With ResNet-50, CF reduces approximately 47.9% of FLOPs by removing 36.9% of the parameters, with only a small loss of 1.07% in top-1 accuracy on ImageNet. The codes can be available at https://github.com/8ubpshLR23/Central-Filter.
A vanishing theorem for quadratic intersection multiplicities

We study intersection theoretic problems in the setting of Chow-Witt groups with coefficients in a fixed Milnor-Witt cycle algebra over a perfect field. We prove that the product maps on such groups satisfy the following property: given two points in a regular local scheme with supports which do not intersect properly, their product vanishes. This gives an analogue of Serre's vanishing result for intersection multiplicities.
Bright and dark states of light: The quantum origin of classical interference

Classical theory asserts that several electromagnetic waves cannot interact with matter if they interfere destructively to zero, whereas quantum mechanics predicts a nontrivial light-matter dynamics even when the average electric field vanishes. Here we show that in quantum optics classical interference emerges from collective bright and dark states of light, \textit{i.e.}, entangled superpositions of multi-mode photon-number states. This makes it possible to explain wave interference using the particle description of light and the superposition principle for linear systems.
Expert elicitation of a navigation service implementation effects on ship groundings and collisions in the Gulf of Finland

When considering the implementation of a novel risk-control option, the estimation of its possible effects often relies on expert elicitation. This article presents an expert-knowledge–based preliminary assessment of how the deployment of Enhanced Navigation Support Information navigation service would affect the ship collisions and groundings in the Gulf of Finland. Experts probabilistically assess the service’s direct effects on various factors, which are then utilized in collision and grounding probability Bayesian network models. The results indicate that implementing the Enhanced Navigation Support Information service could decrease the number of accidents. However, a comparison of the model outcomes to the experts’ qualitative opinions reveals some discrepancies, which suggest that the elicitation procedure or the applied models might require further improvement. Nevertheless, with the proposed Bayesian approach, the model can be updated and uncertainties in the estimates reduced after more evidences are available later from longer and wider use of the service.
Multimedia Datasets for Anomaly Detection: A Survey

Audio or video anomaly datasets play a crucial role in automated surveillance. The range of applications expand from outlier object/ situation detection to the detection of life-threatening events. This research area has been active for more than 1.5 decades, and consequently, more and more datasets dedicated to anomalous actions and object detection have been created. Making use of these public anomaly datasets enable researchers to compare various anomaly detection frameworks with the same input data. This survey aims to address the lack of a comprehensive comparison and analysis of public datasets for audio, video, and audio-visual based anomaly detection. It also assists in selecting the best dataset for bench-marking frameworks. Additionally, we discuss gaps in the existing dataset and future direction insights toward developing multimodal anomaly detection datasets.
Viterbi decoding of CRES signals in Project 8

Cyclotron radiation emission spectroscopy (CRES) is a modern approach for determining charged particle energies via high-precision frequency measurements of the emitted cyclotron radiation. For CRES experiments with gas within the fiducial volume, signal and noise dynamics can be modelled by a hidden Markov model. We introduce a novel application of the Viterbi algorithm in order to derive informational limits on the optimal detection of cyclotron radiation signals in this class of gas-filled CRES experiments, thereby providing concrete limits from which future reconstruction algorithms, as well as detector designs, can be constrained. The validity of the resultant decision rules is confirmed using both Monte Carlo and Project 8 data.
The Kepler IRIS Catalog: Image Subtraction Light Curves for 9150 Stars in and around the Open Clusters NGC 6791 and NGC 6819

The four-year Kepler mission collected long-cadence images of the open clusters NGC 6791 and NGC 6819, known as “superstamps”. Each superstamp region is a 200 pixel square that captures thousands of cluster members, plus foreground and background stars, of which only the brightest were targeted for long- or short-cadence photometry during the Kepler mission. Using image subtraction photometry, we have produced light curves for every object in the Kepler Input Catalog that falls on the superstamps. The Increased Resolution Image Subtraction (IRIS) catalog includes light curves for 9150 stars, and contains a wealth of new data: 8427 of these stars were not targeted at all by Kepler, and we have increased the number of available quarters of long-cadence data for 382 stars. The catalog is available as a high-level science product on MAST, with both raw photometric data for each quarter and corrected light curves for all available quarters for each star. We also present an introduction to our implementation of image subtraction photometry and the open-source IRIS pipeline, alongside an overview of the data products, systematics, and catalog statistics.
Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation.

Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate eight missing data methods in this context: complete-case analysis, extended TMLE incorporating outcome-missingness model, missing covariate missing indicator method, five multiple imputation (MI) approaches using parametric or machine-learning models. Six scenarios were considered, varying in exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/non-linear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a non-linear term. When choosing a method to handle missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and non-linearities is expected to perform well.
Applying the Shuffle Model of Differential Privacy to Vector Aggregation

In this work we introduce a new protocol for vector aggregation in the context of the Shuffle Model, a recent model within Differential Privacy (DP). It sits between the Centralized Model, which prioritizes the level of accuracy over the secrecy of the data, and the Local Model, for which an improvement in trust is counteracted by a much higher noise requirement. The Shuffle Model was developed to provide a good balance between these two models through the addition of a shuffling step, which unbinds the users from their data whilst maintaining a moderate noise requirement. We provide a single message protocol for the summation of real vectors in the Shuffle Model, using advanced composition results. Our contribution provides a mechanism to enable private aggregation and analysis across more sophisticated structures such as matrices and higher-dimensional tensors, both of which are reliant on the functionality of the vector case.
Option pricing with state‐dependent pricing kernel

We introduce a new volatility model for option pricing that combines Markov switching with the Realized GARCH framework. This leads to a novel pricing kernel with a state-dependent variance risk premium and a pricing formula for European options, which is derived with an analytical approximation method. We apply the Markov switching Realized GARCH model to S&P 500 index options from 1990 to 2019 and ﬁnd that investors’ aversion to volatility-speciﬁc risk is time-varying. The proposed framework outperforms competing models and reduces (in-sample and out-of-sample) option pricing errors by 15% or more.
Electronic structure of monolayer and bilayer black phosphorus with charged defects

We use an atomistic approach to study the electronic properties of monolayer and bilayer black phosphorus in the vicinity of a charged defect. In particular, we combine screened defect potentials obtained from first-principles linear response theory with large-scale tight-binding simulations to calculate the wavefunctions and energies of bound acceptor and donor states. As a consequence of the anisotropic band structure, the defect states in these systems form distorted hydrogenic orbitals with a different ordering than in isotropic materials. For the monolayer, we study the dependence of the binding energies of charged adsorbates on the defect height and the dielectric constant of a substrate in an experimental setup. We also compare our results to an anisotropic effective mass model and find quantitative and qualitative differences when the charged defect is close to the black phosphorus or when the screening from the substrate is weak. For the bilayer, we compare results for charged adsorbates and charged intercalants and find that intercalants induce more prominent secondary peaks in the local density of states because they interact strongly with electronic states on both layers. These new insights can be directly tested in scanning tunneling spectroscopy measurements and enable a detailed understanding of the role of Coulomb impurities in electronic devices.
A Large-Scale Benchmark for the Incompressible Navier-Stokes Equations

We introduce a collection of benchmark problems in 2D and 3D (geometry description and boundary conditions), including simple cases with known analytic solution, classical experimental setups, and complex geometries with fabricated solutions for evaluation of numerical schemes for incompressible Navier-Stokes equations in laminar flow regime. We compare the performance of a representative selection of most broadly used algorithms for Navier-Stokes equations on this set of problems. Where applicable, we compare the most common spatial discretization choices (unstructured triangle/tetrahedral meshes and structured or semi-structured quadrilateral/hexahedral meshes). The study shows that while the type of spatial discretization used has a minor impact on the accuracy of the solutions, the choice of time integration method, spatial discretization order, and the choice of solving the coupled equations or reducing them to simpler subproblems have very different properties. Methods that are directly solving the original equations tend to be more accurate than splitting approaches for the same number of degrees of freedom, but numerical or computational difficulty arise when they are scaled to larger problem sizes. Low-order splitting methods are less accurate, but scale more easily to large problems, while higher-order splitting methods are accurate but require dense time discretizations to be stable. We release the description of the experiments and an implementation of our benchmark, which we believe will enable statistically significant comparisons with the state of the art as new approaches for solving the incompressible Navier-Stokes equations are introduced.
Couplformer: Rethinking Vision Transformer with Coupling Attention Map

With the development of the self-attention mechanism, the Transformer model has demonstrated its outstanding performance in the computer vision domain. However, the massive computation brought from the full attention mechanism became a heavy burden for memory consumption. Sequentially, the limitation of memory reduces the possibility of improving the Transformer model. To remedy this problem, we propose a novel memory economy attention mechanism named Couplformer, which decouples the attention map into two sub-matrices and generates the alignment scores from spatial information. A series of different scale image classification tasks are applied to evaluate the effectiveness of our model. The result of experiments shows that on the ImageNet-1k classification task, the Couplformer can significantly decrease 28% memory consumption compared with regular Transformer while accessing sufficient accuracy requirements and outperforming 0.92% on Top-1 accuracy while occupying the same memory footprint. As a result, the Couplformer can serve as an efficient backbone in visual tasks, and provide a novel perspective on the attention mechanism for researchers.
Unrolling Palm for Sparse Semi-blind Source Separation

Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications - for instance, in astrophysics and remote sensing. Classical sparse BSS methods, such as the Proximal Alternating Linearized Minimization (PALM) algorithm, nevertheless often suffer from a difficult hyperparameter choice, which undermines their results. To bypass this pitfall, we propose in this work to build on the thriving field of algorithm unfolding/unrolling. Unrolling PALM enables to leverage the data-driven knowledge stemming from realistic simulations or ground-truth data by learning both PALM hyperparameters and variables. In contrast to most existing unrolled algorithms, which assume a fixed known dictionary during the training and testing phases, this article further emphasizes on the ability to deal with variable mixing matrices (a.k.a. dictionaries). The proposed Learned PALM (LPALM) algorithm thus enables to perform semi-blind source separation, which is key to increase the generalization of the learnt model in real-world applications. We illustrate the relevance of LPALM in astrophysical multispectral imaging: the algorithm not only needs up to $10^4-10^5$ times fewer iterations than PALM, but also improves the separation quality, while avoiding the cumbersome hyperparameter and initialization choice of PALM. We further show that LPALM outperforms other unrolled source separation methods in the semi-blind setting.