Miracle-dz/UniFAR-FTUs huggingface dataset:
Failure Analysis of Automotive Components

The automotive industry constantly faces new challenges as the demand for inexpensive and high quality components increases. Extreme diligence must be imparted to ensure that such components are designed and manufactured with sufficient quality to withstand a variety of service environments. In the event that such a component fails, it is imperative that the root cause of the failure be determined quickly and accurately, and that proper corrective measures be taken in order to prevent future failures. A systematic analysis is the key to finding the root cause of any failure. This article will describe the necessary steps for performing a proper failure investigation, and will illustrate the use of various techniques to identify the root cause of failure. Several automotive component examples are cited for illustration of these techniques.
Anytime Optimal PSRO for Two-Player Zero-Sum Games

Policy space response oracles (PSRO) is a multi-agent reinforcement learning algorithm that has achieved state-of-the-art performance in very large two-player zero-sum games. PSRO is based on the tabular double oracle (DO) method, an algorithm that is guaranteed to converge to a Nash equilibrium, but may increase exploitability from one iteration to the next. We propose anytime double oracle (ADO) , a tabular double oracle algorithm for 2-player zero-sum games that is guaranteed to converge to a Nash equilibrium while decreasing exploitability from one iteration to the next. Unlike DO, in which the restricted distribution is based on the restricted game formed by each player’s strategy sets, ADO finds the restricted distribution for each player that minimizes its exploitability against any policy in the full, unrestricted game. We also propose a method of finding this restricted distribution via a no-regret algorithm updated against best responses, called RM-BR DO . Finally, we propose anytime PSRO (APSRO) , a version of ADO that calculates best responses via reinforcement learning. In experiments on Leduc poker and random normal form games, we show that our methods achieve far lower exploitability than DO and PSRO and decrease exploitability monotonically.
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale

The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck. In this paper, inspired by our experience when deploying hyper-parameter tuning in a real-world application in production and the limitations of existing systems, we propose Hyper-Tune, an efficient and robust distributed hyper-parameter tuning framework. Compared with existing systems, Hyper-Tune highlights multiple system optimizations, including (1) automatic resource allocation, (2) asynchronous scheduling, and (3) multi-fidelity optimizer. We conduct extensive evaluations on benchmark datasets and a large-scale real-world dataset in production. Empirically, with the aid of these optimizations, Hyper-Tune outperforms competitive hyper-parameter tuning systems on a wide range of scenarios, including XGBoost, CNN, RNN, and some architectural hyper-parameters for neural networks. Compared with the state-of-the-art BOHB and A-BOHB, Hyper-Tune achieves up to 11.2X and 5.1X speedups, respectively.
Performance Estimations of a Pulse Detonation Engine with Exit Nozzle

The PDE exit nozzle maintains operating pressure and also controls operating frequency. Two parametric studies were performed for two flight Mach number (Minf) conditions of 0.8 and 3.0 and an altitude of 30 kft, in which the exit nozzle contraction ratio Rnc, was systematically varied from 1.6 to 6.4. For the supersonic flight Mach number of 3.0, fuel-specific impulse, Ispf, increases with increasing values of Rnc and attains a constant value of 1950 s for Rnc > 5.0. For the subsonic flight conditions of Minf = 0.8, Ispf increases with increasing values of the exit nozzle contraction ratio and attains a constant value of 1600 for an exit nozzle contraction ratio of 3.0. These parametric studies also quantified PDE operation parameters such as mass flowrates of air and fuel, frequency, and fill velocity. Two additional parametric studies were performed in which purge fraction and fill fraction were systematically varied and they show opposite effect on Ispf and thrust generated. The estimated Ispf was found to have a maximum of 1980 s at a flight Mach number of 3.0, and a minimum value of 1580 s, at a flight Mach number of 0.8 and an altitude of 30 kft.
Compressed Smooth Sparse Decomposition

Image-based anomaly detection systems are of vital importance in various manufacturing applications. The resolution and acquisition rate of such systems are increasing significant in recent years under the fast development of image sensing technology. This enables the detection of tiny anomalies in real time. However, such a high resolution and a high acquisition rate of image data not only slow down the speed of image processing algorithms but also, increase data storage and transmission cost. To tackle this problem, we propose a fast and data-efficient method with theoretical performance guarantee that is suitable for sparse anomaly detection in images with a smooth background (smooth plus sparse signal). The proposed method, named compressed smooth sparse decomposition (CSSD), is a one-step method that unifies the compressive image acquisition- and decomposition-based image processing techniques. To further enhance its performance in a high-dimensional scenario, a Kronecker compressed smooth sparse decomposition (KronCSSD) method is proposed. Compared with traditional smooth and sparse decomposition algorithms, significant transmission cost reduction and computational speed boost can be achieved with negligible performance loss. Simulation examples and several case studies in various applications illustrate the effectiveness of the proposed framework.
Sandbox Sample Classification Using Behavioral Indicators of Compromise

Behavioral Indicators of Compromise are associated with various automated methods used to extract the sample behavior by observing the system function calls performed in a virtual execution environment. Thus, every sample is described by a set of BICs triggered by the sample behavior in the sandbox environment. Here we discuss a Machine Learning approach to the classification of the sandbox samples as MALICIOUS or BENIGN, based on the list of triggered BICs. Besides the more traditional methods like Logistic Regression and Naive Bayes Classification we also discuss a different approach inspired by the statistical Monte Carlo methods. The numerical results are illustrated using ThreatGRID and ReversingLabs data.
The Family of V1311 Ori: a Young Sextuple System or a Minicluster?

A compact bound group of four active M-type dwarfs containing V1311 Ori is identified in the Gaia catalog of nearby stars. Located at a distance of 39 pc, it is likely related to the β Pictoris and 32 Ori moving groups by kinematics, isochronal age, and other indicators of youth (Hα emission, presence of lithium, and fast rotation). The brightest star A is a known close binary, for which a preliminary 80 yr visual-spectroscopic orbit is determined. Star B is resolved here into a 0. ″08 pair, and the faintest stars C and D are probably single. Considering the nonhierarchical configuration with projected separations of ∼10 kau, this could be either a young sextuple system or a bound but dynamically unstable minicluster (trapezium) that avoided disruption so far. This pre-main-sequence system bridges the gap between moving groups and wide hierarchies.
Klexikon: A German Dataset for Joint Summarization and Simplification

Traditionally, Text Simplification is treated as a monolingual translation task where sentences between source texts and their simplified counterparts are aligned for training. However, especially for longer input documents, summarizing the text (or dropping less relevant content altogether) plays an important role in the simplification process, which is currently not reflected in existing datasets. Simultaneously, resources for non-English languages are scarce in general and prohibitive for training new solutions. To tackle this problem, we pose core requirements for a system that can jointly summarize and simplify long source documents. We further describe the creation of a new dataset for joint Text Simplification and Summarization based on German Wikipedia and the German children’s encyclopedia “Klexikon”, consisting of almost 2,900 documents. We release a document-aligned version that particularly highlights the summarization aspect, and provide statistical evidence that this resource is well suited to simplification as well. Code and data are available on Github: https://github.com/dennlinger/klexikon
On the origin of supertetragonality in BaTiO3

Understanding ferroelectricity is of both fundamental and technological importance to further stimulate the development of new materials designs and manipulations. Here, we perform an in-depth first-principle study on the well-known ferroelectric barium titanate BaTiO$_{3}$ under a hydrostatic negative pressure, showing an isosymmetric phase transition to a supertetragonal phase with high $c/a$ ratio of $\sim1.3$. The microscopic origin and driving mechanisms of this phase transition are identified as a drastic change of the covalently $\pi$-bonded electrons. These findings provide guidance in the search for new supertetragonal phases, with great opportunities for novel multiferroic materials; and can be generalized in the understanding of other isosymmetric phase transitions.
Implicit Tracking-Based Distributed Constraint-Coupled Optimization

A class of distributed optimization problem with a globally coupled equality constraint and local constrained sets is studied in this article. For its special case where local constrained sets are absent, an augmented primal-dual gradient dynamics is proposed and analyzed, but it cannot be implemented distributedly since the violation of the coupled constraint needs to be used. Benefiting from the brand-new comprehending of a classical distributed unconstrained optimization algorithm, the novel implicit tracking approach is proposed to track the violation distributedly, which leads to the birth of the implicit tracking-based distributed augmented primal-dual gradient dynamics (IDEA). A projected variant of IDEA, i.e., Proj-IDEA, is further designed to deal with the general case where local constrained sets exist. With the aid of the Lyapunov stability theory, the convergences of IDEA and Pro-IDEA over undigraphs and digraphs are analyzed respectively. As far as we know, Proj-IDEA is the first constant step-size distributed algorithm which can solve the studied problem without the need of the strict convexity of local cost functions. Besides, if local cost functions are strongly convex and smooth, IDEA can achieve exponential convergence with a weaker condition about the coupled constraint. Finally, numerical experiments are taken to corroborate our theoretical results.
Variable Augmented Network for Invertible MR Coil Compression

A large number of coils are able to provide enhanced signal-to-noise ratio and improve imaging performance in parallel imaging. Nevertheless, the increasing growth of coil number simultaneously aggravates the drawbacks of data storage and reconstruction speed, especially in some iterative reconstructions. Coil compression addresses these issues by generating fewer virtual coils. In this work, a novel variable augmentation network for invertible coil compression termed VAN-ICC is presented. It utilizes inherent reversibility of normalizing flow-based models for high-precision compression and invertible recovery. By employing the variable augmentation technology to image/k-space variables from multi-coils, VAN-ICC trains invertible networks by finding an invertible and bijective function, which can map the original data to the compressed counterpart and vice versa. Experiments conducted on both fully-sampled and under-sampled data verified the effectiveness and flexibility of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep learning-based approaches demonstrated that VAN-ICC can carry much higher compression effects. Additionally, its performance is not susceptible to different number of virtual coils.
Synchronization of Complex Network Systems with Stochastic Disturbances

We study the robustness of the synchronization of coupled phase oscillators. When fluctuations of phase differences in lines caused by disturbance exceed a certain threshold, the state cannot return to synchrony thus leading to desynchronization. Our main result is the deviation of explicit formulas of a variance matrix that characterizes the severity of these fluctuations. We highlight the utility of these results in two general problems: vulnerable line identification and network design. We find that the vulnerability of lines can be encoded by the cycle space of graphs. It is analytically shown that a line in large-size cycles is more vulnerable than those in small-size cycles and adding a new line or increasing coupling strength of a line reduces the vulnerability of the lines in any cycle including this line, while it does not affect the vulnerability of the other lines.
Nonequispaced Fast Fourier Transform Boost for the Sinkhorn Algorithm

This contribution features an accelerated computation of the Sinkhorn's algorithm, which approximates the Wasserstein transportation distance, by employing nonequispaced fast Fourier transforms (NFFT). The algorithm proposed allows approximations of the Wasserstein distance by involving not more than $\mathcal O(n\log n)$ operations for probability measures supported by~$n$ points. Furthermore, the proposed method avoids expensive allocations of the characterizing matrices. With this numerical acceleration, the transportation distance is accessible to probability measures out of reach so far. Numerical experiments using synthetic and real data affirm the computational advantage and superiority.
Radiative properties of Cu-isoelectronic As, Se and Br ions for astrophysical applications

We present precise radiative data of line strengths, transition probabilities and oscillator strengths for the allowed transitions among the nS1/2, nP1/2,3/2, n ′D3/2,5/2 and n ′F5/2,7/2 states with n = 4 to 6 and n ′ = 4, 5 of the Cuisoelectronic As, Se and Br ions. Due to unavailability of precise observations of these spectroscopic data, their accurate estimations are of great interest and useful in analyzing various astrophysical phenomena undergoing inside the heavenly bodies that contain As, Se and Br elements. An all-order perturbative many-body method in the relativistic theory framework has been employed to determine the atomic wave functions, which are further used to estimate the above quantities with the uncertainties. We found significant differences between some of our results and results that are available earlier.
Learning to Rank For Push Notifications Using Pairwise Expected Regret

Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we contribute an analysis of learning to rank for personalized mobile push notifications and discuss the unique challenges this presents compared to traditional ranking problems. To address these challenges, we introduce a novel ranking loss based on weighting the pairwise loss between candidates by the expected regret incurred for misordering the pair. We demonstrate that the proposed method can outperform prior methods both in a simulated environment and in a production experiment on a major social network.
Detecting Stance in Tweets : A Signed Network based Approach

Identifying user stance related to a political event has several applications, like determination of individual stance, shaping of public opinion, identifying popularity of government measures and many others. The huge volume of political discussions on social media platforms, like, Twitter, provide opportunities in developing automated mechanisms to identify individual stance and subsequently, scale to a large volume of users. However, issues like short text and huge variance in the vocabulary of the tweets make such exercise enormously difficult. Existing stance detection algorithms require either event specific training data or annotated twitter handles and therefore, are difficult to adapt to new events. In this paper, we propose a sign network based framework that use external information sources, like news articles to create a signed network of relevant entities with respect to a news event and subsequently use the same to detect stance of any tweet towards the event. Validation on 5,000 tweets related to 10 events indicates that the proposed approach can ensure over 6.5% increase in average F1 score compared to the existing stance detection approaches.
Overton: A bibliometric database of policy document citations

Abstract This paper presents an analysis of the Overton policy document database, describing the makeup of materials indexed and the nature in which they cite academic literature. We report on various aspects of the data, including growth, geographic spread, language representation, the range of policy source types included, and the availability of citation links in documents. Longitudinal analysis over established journal category schemes is used to reveal the scale and disciplinary focus of citations and determine the feasibility of developing field-normalized citation indicators. To corroborate the data indexed, we also examine how well self-reported funding outcomes collected by UK funders correspond to data indexed in the Overton database. Finally, to test the data in an experimental setting, we assess whether peer-review assessment of impact as measured by the UK Research Excellence Framework (REF) 2014 correlates with derived policy citation metrics. Our findings show that for some research topics, such as health, economics, social care, and the environment, Overton contains a core set of policy documents with sufficient citation linkage to academic literature to support various citation analyses that may be informative in research evaluation, impact assessment, and policy review.
Q-ViT: Fully Differentiable Quantization for Vision Transformer

In this paper, we propose a fully differentiable quantization method for vision transformer (ViT) named as Q-ViT, in which both of the quantization scales and bit-widths are learnable parameters. Specifically, based on our observation that heads in ViT display different quantization robustness, we leverage head-wise bit-width to squeeze the size of Q-ViT while preserving performance. In addition, we propose a novel technique named switchable scale to resolve the convergence problem in the joint training of quantization scales and bit-widths. In this way, Q-ViT pushes the limits of ViT quantization to 3-bit without heavy performance drop. Moreover, we analyze the quantization robustness of every architecture component of ViT and show that the Multi-head Self-Attention (MSA) and the Gaussian Error Linear Units (GELU) are the key aspects for ViT quantization. This study provides some insights for further research about ViT quantization. Extensive experiments on different ViT models, such as DeiT and Swin Transformer show the effectiveness of our quantization method. In particular, our method outperforms the state-of-the-art uniform quantization method by 1.5% on DeiT-Tiny.
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes

Progress in 3D object understanding has relied on manually “canonicalized” shape datasets that contain instances with consistent position and orientation (3D pose). This has made it hard to generalize these methods to in-the-wild shapes, e.g., from internet model collections or depth sensors. ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds. We build on top of Tensor Field Networks (TFNs), a class of permutation- and rotation-equivariant, and translation-invariant 3D networks. During inference, our method takes an unseen full or partial 3D point cloud at an arbitrary pose and outputs an equivariant canonical pose. During training, this network uses self-supervision losses to learn the canonical pose from an un-canonicalized collection of full and partial 3D point clouds. ConDor can also learn to consistently co-segment object parts without any supervision. Extensive quantitative results on four new metrics show that our approach out-performs existing methods while enabling new applications such as operation on depth images and annotation transfer.
Collective electrical response of simulated memristive arrays using SPICE

Self-assembled structures are possible solutions to the problem of increasing the density and connectivity of memristive units in massive arrays. Although they would allow surpassing the limit imposed by the lithographic feature size, the spontaneous formation of highly interconnected networks poses a new challenge: how to characterize and control the obtained assemblies. In view of a flourishing field of such experimental realizations, this study explores the collective electrical response of simulated memristive units when assembled in geometrically organized and progressively distorted configurations. We show that highly idealized memristive arrays already display a degree of complexity that needs to be taken into account when characterizing self-assemblies to be technologically exploited. Moreover, the introduction of simple distortions has a considerable impact on the available resistance states and their evolution upon cycling. Considering arrays of a limited size, we also demonstrate that the collective response resembles aspects of the individual model while also revealing its own phenomenology.
BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning

Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear model predictive control (MPC) framework, the BiConMP which can generate whole body trajectories online by efficiently exploiting the structure of the robot dynamics. BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes, and transitioning online between different gaits. Furthermore, the ability of BiConMP to generate nontrivial acyclic whole-body dynamic motions on the robot is presented. The same approach is also used to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. Finally, an extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.