Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Fusion with Latent Map Gaussian Processes

Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in engineering design. In this paper, we introduce a novel approach based on latent-map Gaussian processes (LMGPs) that enables efficient and accurate data fusion. In our approach, we convert data fusion into a latent space learning problem where the relations among different data sources are automatically learned. This conversion endows our approach with attractive advantages such as increased accuracy, reduced costs, flexibility to jointly fuse any number of data sources, and ability to visualize correlations between data sources. This visualization allows the user to detect model form errors or determine the optimum strategy for high-fidelity emulation by fitting LMGP only to the subset of the data sources that are well-correlated. We also develop a new kernel function that enables LMGPs to not only build a probabilistic multi-fidelity surrogate but also estimate calibration parameters with high accuracy and consistency. The implementation and use of our approach are considerably simpler and less prone to numerical issues compared to existing technologies. We demonstrate the benefits of LMGP-based data fusion by comparing its performance against competing methods on a wide range of examples.
Wireless monitoring of structures including acoustic emission techniques

The inspection of building structures is currently made by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to damage, and time consuming to install. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques can be used with such a network, but acoustic emission techniques have been rarely utilized. With the use of Acoustic Emission (AE) techniques it is possible to detect internal structural damage from cracks propagating during the routine use of a structure or the break of wires of pre-stressed elements for example. Most of the existing AE data analysis techniques are not appropriate for the requirements of a wireless network, especially power consump- tion. Sensors with low price are required for AE systems to be accepted. To fully utilize the power of the acoustic emission technique on large, extended structures, recording and analysis techniques need more powerful algorithms to handle and reduce the immense amount of data generated. In particular, this paper deals with the optimization of the network to record different type of data including AE data. The basic principles of a wireless monitoring system equipped with MEMS sensors is presented along with a first prototype able to record temperature, moisture, strain and other data continuously.
Quantum entanglement between excitons in two-dimensional materials

The quantum entanglement between two excitons in two-dimensional materials, embedded in an optical microcavity, was investigated. The energy eigenstates of a Jaynes-Cummings like Hamiltonian for two qubits coupled to a single cavity mode have been calculated. The quantum entanglement between such states was estimated by calculating the concurrence between two qubits in each of these eigenstates. According to the results of our calculations, if the system is allowed to decay only through the emission of cavity photons at low temperatures, there is a maximally entangled eigenstate, protected from decay. We demonstrated that the existence of such a state results in the counter-intuitive conclusion that, for some initial states of the system, the fact that the cavity is leaky can actually lead to an increase in the average concurrence on the timescales of the average photonic lifetime. In addition, we calculated the time evolution of the concurrence between a pair of excitons in a strained graphene monolayer.
STRONG MOTION NETWORK OPERATED BY BUILDING RESEARCH INSTITUTE

Building Research Institute (BRI) is operating a nationwide strong motion network. Total 75 stations are disposed in major cities throughout Japan. The observation project aims to contribute to advance in seismic design technology for buildings. Therefore seismic response of buildings is the main target of the observation. A great number of strong motion records have been obtained and utilized to study local site and geological effects, soil-structure interaction and dynamic behavior of buildings. The paper introduces outline of the BRI strong motion network and its recent topics.
Flexible-Joint Manipulator Trajectory Tracking with Learned Two-Stage Model employing One-Step Future Prediction

Flexible-joint manipulators are frequently used for increased safety during human-robot collaboration and shared workspace tasks. However, joint flexibility significantly reduces the accuracy of motion, especially at high velocities and with inexpensive actuators. In this paper, we present a learning-based approach to identify the unknown dynamics of a flexible-joint manipulator and improve the trajectory tracking at high velocities. We propose a two-stage model which is composed of a one-step forward dynamics future predictor and an inverse dynamics estimator. The second part is based on linear time-invariant dynamical operators to approximate the feed-forward joint position and velocity commands. We train the model end-to-end on real-world data and evaluate it on the Baxter robot. Our experiments indicate that augmenting the input with one-step future state prediction improves the performance, compared to the same model without prediction. We compare joint position, joint velocity and end-effector position tracking accuracy against the classical baseline controller and several simpler models.
Quadrupolar active stress induces exotic phases of defect motion in active nematics

A wide range of living and artificial active matter exists in close contact with substrates and under strong confinement, where in addition to dipolar active stresses, quadrupolar active stresses can become important. Here, we numerically investigate the impact of quadrupolar non-equilibrium forces on the emergent patterns of self-organisation in non-momentum conserving active nematics. Our results reveal that beyond having stabilising effects, the quadrupolar active forces can induce various modes of topological defect motion in active nematics. In particular, we find the emergence of both polar and nematic ordering of the defects, as well as new phases of self-organisation that comprise topological defect chains and topological defect asters. The results contribute to further understanding of emergent patterns of collective motion and non-equilibrium self-organisation in active matter.
Dirac-type results for tilings and coverings in ordered graphs

Abstract A recent paper of Balogh, Li and Treglown [3] initiated the study of Dirac-type problems for ordered graphs. In this paper, we prove a number of results in this area. In particular, we determine asymptotically the minimum degree threshold for forcing (i) a perfect H-tiling in an ordered graph, for any fixed ordered graph H of interval chromatic number at least $3$ ; (ii) an H-tiling in an ordered graph G covering a fixed proportion of the vertices of G (for any fixed ordered graph H); (iii) an H-cover in an ordered graph (for any fixed ordered graph H). The first two of these results resolve questions of Balogh, Li and Treglown, whilst (iii) resolves a question of Falgas-Ravry. Note that (i) combined with a result from [3] completely determines the asymptotic minimum degree threshold for forcing a perfect H-tiling. Additionally, we prove a result that, combined with a theorem of Balogh, Li and Treglown, asymptotically determines the minimum degree threshold for forcing an almost perfect H-tiling in an ordered graph (for any fixed ordered graph H). Our work therefore provides ordered graph analogues of the seminal tiling theorems of Kühn and Osthus [Combinatorica 2009] and of Komlós [Combinatorica 2000]. Each of our results exhibits some curious, and perhaps unexpected, behaviour. Our solution to (i) makes use of a novel absorbing argument.
Deep-Dive Analysis of Selfish and Stubborn Mining in Bitcoin and Ethereum

Bitcoin and Ethereum are the top two blockchain-based cryptocurrencies whether from cryptocurrency market cap or popularity. However, they are vulnerable to selfish mining and stubborn mining due to that both of them adopt Proof-of-Work consensus mechanism. In this paper, we develop a novel Markov model, which can study selfish mining and seven kinds of stubborn mining in both Bitcoin and Ethereum. The formulas are derived to calculate several key metrics, including relative revenue of miners, blockchain performance in terms of stale block ratio and transactions per second, and blockchain security in terms of resistance against double-spending attacks. Numerical analysis is conducted to investigate the quantitative relationship between the relative-revenue-optimal mining strategy for malicious miners and two miner features in Bitcoin and Ethereum, respectively. The quantitative analysis results can assist honest miners in detecting whether there is any malicious miner in the system and setting the threshold of mining node's hash power in order to prevent malicious miners from making profit through selfish and stubborn mining.
Projective Clustering Product Quantization

This paper suggests the use of projective clustering based product quantization for improving nearest neighbor and max-inner-product vector search (MIPS) algorithms. We provide anisotropic and quantized variants of projective clustering which outperform previous clustering methods used for this problem such as ScaNN. We show that even with comparable running time complexity, in terms of lookup-multiply-adds, projective clustering produces more quantization centers resulting in more accurate dot-product estimates. We provide thorough experimentation to support our claims.
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease

We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimation of the most discriminant direction while controlling for its complexity with appropriate differential regularization. Our approach does not require prior estimation of the covariance structure of the functional predictors, which is computationally prohibitive in our application setting. We provide a theoretical analysis of the out-of-sample prediction error of the proposed model and explore the finite sample performance in a simulation setting. We apply the proposed method to a pooled dataset from the Alzheimer's Disease Neuroimaging Initiative and the Parkinson's Progression Markers Initiative. Through this application, we identify discriminant directions that capture both cortical geometric and thickness predictive features of Alzheimer's disease that are consistent with the existing neuroscience literature.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robustness analysis results are available publicly on the NL-Augmenter repository (https://github.com/GEM-benchmark/NL-Augmenter).
Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis.
DRAN: Detailed Region-Adaptive Normalization for Conditional Image Synthesis

In recent years, conditional image synthesis has attracted growing attention due to its controllability in the image generation process. Although recent works have achieved realistic results, most of them have difficulty handling fine-grained styles with subtle details. To address this problem, a novel normalization module, named Detailed Region-Adaptive Normalization (DRAN), is proposed. It adaptively learns both fine-grained and coarse-grained style representations. Specifically, we first introduce a multi-level structure, Spatiality-aware Pyramid Pooling, to guide the model to learn coarse-to-fine features. Then, to adaptively fuse different levels of styles, we propose Dynamic Gating, making it possible to adaptively fuse different levels of styles according to different spatial regions. Finally, we collect a new makeup dataset (Makeup-Complex dataset) that contains a wide range of complex makeup styles with diverse poses and expressions. To evaluate the effectiveness and show the general use of our method, we conduct a set of experiments on makeup transfer and semantic image synthesis. Quantitative and qualitative experiments show that equipped with DRAN, simple baseline models are able to achieve promising improvements in complex style transfer and detailed texture synthesis.
Multiple Interest and Fine Granularity Network for User Modeling

User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue. How to extract users' multiple interests effectively from their historical behavior sequences to improve the relevance and personalization of the recommend results remains an open problem for user modeling.Most existing deep-learning based approaches exploit item-ids and category-ids but neglect fine-grained features like color and mate-rial, which hinders modeling the fine granularity of users' interests.In the paper, we present Multiple interest and Fine granularity Net-work (MFN), which tackle users' multiple and fine-grained interests and construct the model from both the similarity relationship and the combination relationship among the users' multiple interests.Specifically, for modeling the similarity relationship, we leverage two sets of embeddings, where one is the fixed embedding from pre-trained models (e.g. Glove) to give the attention weights and the other is trainable embedding to be trained with MFN together.For modeling the combination relationship, self-attentive layers are exploited to build the higher order combinations of different interest representations. In the construction of network, we design an interest-extract module using attention mechanism to capture multiple interest representations from user historical behavior sequences and leverage an auxiliary loss to boost the distinction of the interest representations. Then a hierarchical network is applied to model the attention relation between the multiple interest vectors of different granularities and the target item. We evaluate MFNon both public and industrial datasets. The experimental results demonstrate that the proposed MFN achieves superior performance than other existed representing methods.
Using Convolutional Neural Networks for fault analysis and alleviation in accelerator systems

Today, Neural Networks are the basis of breakthroughs in virtually every technical domain. Their application to accelerators has recently resulted in better performance and efficiency in these systems. At the same time, the increasing hardware failures due to the latest (shrinked) semiconductor technology needs to be addressed. Since accelerator systems are often used to back time-critical applications such as self-driving cars or medical diagnosis applications, these hardware failures must be eliminated. Our research evaluates these failures from a systemic point of view. Based on our results, we find critical results for the system reliability enhancement and we further put forth an efficient method to avoid these failures with minimal hardware overhead.
The mirror Lagrangian cobordism for the Euler exact sequence

For X = P the Euler sequence is given by 0→ ΩPn → O Pn (−1)→ OPn → 0 We describe the Lagrangian cobordism corresponding to this sequence via mirror symmetry, in the sense of Biran-Cornea. In particular, we describe the mirror Lagrangian of the cotangent sheaf ΩPn ∈ D(P) in the mirror Fukaya category Fuk(U∆).
Bayesian Optimal Two-sample Tests in High-dimension

We propose optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including genomics and medical imaging, it is natural to assume that only a few entries of two mean vectors or covariance matrices are different. Many existing tests that rely on aggregating the difference between empirical means or covariance matrices are not optimal or yield low power under such setups. Motivated by this, we develop Bayesian two-sample tests employing a divide-and-conquer idea, which is powerful especially when the difference between two populations is sparse but large. The proposed two-sample tests manifest closed forms of Bayes factors and allow scalable computations even in high-dimensions. We prove that the proposed tests are consistent under relatively mild conditions compared to existing tests in the literature. Furthermore, the testable regions from the proposed tests turn out to be optimal in terms of rates. Simulation studies show clear advantages of the proposed tests over other state-of-the-art methods in various scenarios. Our tests are also applied to the analysis of the gene expression data of two cancer data sets.
Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

This letter presents an enhanced design of multi-dimensional (MD) constellations which play a pivotal role in many communication systems such as code-domain non-orthogonal multiple access (CD-NOMA). MD constellations are attractive as their structural properties, if properly designed, lead to signal space diversity and hence improved error rate performance. Unlike the existing works which mostly focus on MD constellations with large minimum Euclidean distance (MED), we look for new MD constellations with additional feature that the minimum product distance (MPD) is also large. To this end, a non-convex optimization problem is formulated and then solved by the convex-concave procedure (CCCP). Compared with the state-of-the-art literature, our proposed MD constellations lead to significant error performance enhancement over Rayleigh fading channels whilst maintaining almost the same performance over the Gaussian channels. To demonstrate their application, we also show that these MD constellations give rise to good codebooks in sparse code multiple access systems.
HHF: Hashing-Guided Hinge Function for Deep Hashing Retrieval

Deep hashing has shown promising performance in large-scale image retrieval. The hashing process utilizes Deep Neural Networks (DNNs) to embed images into compact continuous latent codes, then map them into binary codes by hashing function for efficient retrieval. Recent approaches perform metric loss and quantization loss to supervise the two procedures that cluster samples with the same categories and alleviate semantic information loss after binarization in the end-to-end training framework. However, we observe the incompatible conflict that the optimal cluster positions are not identical to the ideal hash positions because of the different objectives of the two loss terms, which lead to severe ambiguity and error-hashing after the binarization process. To address the problem, we borrow the Theory of Minimum-Distance Bounds for Binary Linear Codes to design the inflection point that depends on the hash bit length and category numbers and thereby propose Hashing-guided Hinge Function (HHF) to explicitly enforce the termination of metric loss to prevent the negative pairs unlimited alienated. Such modification is proven effective and essential for training, which contributes to proper intra- and inter-distances for clusters and better hash positions for accurate image retrieval simultaneously. Extensive experiments in CIFAR-10, CIFAR-100, ImageNet, and MS-COCO justify that HHF consistently outperforms existing techniques and is robust and flexible to transplant into other methods. Code is available at https://github.com/JerryXu0129/HHF.
Exclusive hadronic $\tau$ decays, within $\&$ beyond the Standard Model

Semileptonic exclusive tau decays in the Standard Model are reviewed. As it is well-known, they are a privileged arena to learn about low-energy hadronization. They also allow for a number of clean new physics tests, which have attracted much attention recently, probing scales as high as a few TeV. In light of forthcoming Belle-II data, perspectives on this area are bright.
Extended model for optimizing high-order harmonic generation in absorbing gases

We report on an extended version of the one-dimensional model proposed by Constant et al (1999 Phys. Rev. Lett. 82 1668) to study phase matching of high-order harmonic generation in absorbing and dispersive medium. The model—expanded from zeroth to first order—can be used with media having a pressure profile varying linearly with propagation length. Based on the new formulas, the importance of having a generation medium that ends abruptly with a steep pressure gradient for achieving high flux is highlighted. In addition to further rule-of-thumb guidelines for harmonic-flux optimization, it is shown that having a steep increase of pressure in the beginning of the medium increases harmonic flux, while it also decreases the required medium length to reach the absorption-limited maximum.