Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Driven Macroscopic Modeling across Knudsen Numbers for Rarefied Gas Dynamics and Application to Rayleigh Scattering

Macroscopic modeling of the gas dynamics across Knudsen numbers from dense gas region to rarefied gas region remains a great challenge. The reason is macroscopic models lack accurate constitutive relations valid across different Knudsen numbers. To address this problem, we proposed a Data-driven, KnUdsen number Adaptive Linear constitutive relation model named DUAL. The DUAL model is accurate across a range of Knudsen numbers, from dense to rarefied, through learning to adapt Knudsen number change from observed data. It is consistent with the Navier-Stokes equation under the hydrodynamic limit, by utilizing a constrained neural network. In addition, it naturally satisfies the second law of thermodynamics and is robust to noisy data. We test the DUAL model on the calculation of Rayleigh scattering spectra. The DUAL model gives accurate spectra for various Knudsen numbers and is superior to traditional perturbation and moment expansion methods.
Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs

N-ary relationships, which relate N entities where N is not necessarily two, can be visually represented as polygons whose vertices are the entities of the relationships. Manually generating a high-quality layout using this representation is labor-intensive. In this paper, we provide an automatic polygon layout generation algorithm for the visualization of N-ary relationships. At the core of our algorithm is a set of objective functions motivated by a number of design principles that we have identified. These objective functions are then used in an optimization framework that we develop to achieve high-quality layouts. Recognizing the duality between entities and relationships in the data, we provide a second visualization in which the roles of entities and relationships in the original data are reversed. This can lead to additional insight about the data. Furthermore, we enhance our framework for a joint optimization on the primal layout (original data) and the dual layout (where the roles of entities and relationships are reversed). This allows users to inspect their data using two complementary views. We apply our visualization approach to a number of datasets that include co-authorship data and social contact pattern data.
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification

The electroencephalography (EEG) signal is a non-stationary, stochastic, and highly non-linear bioelectric signal for which achieving high classification accuracy is challenging, especially when the number of subjects is limited. As frequently used solution, classifiers based on multilayer neural networks has to be implemented without large training data sets and careful tuning. This paper proposes a manifold learning framework to classify two types of EEG data from motor imagery (MI) tasks by discovering lower dimensional geometric structures. For feature extraction, it is implemented by Common Spatial Pattern (CSP) from the preprocessed EEG signals. In the neighborhoods of the features for classification, the local approximation to the support of the data is obtained, and then the features are assigned to the classes with the closest support. A spherical approximation (SPA) classifier is created using spherelets for local approximation, and the extracted features are classified with this manifold-based method. The SPA classifier achieves high accuracy in the 2008 BCI competition data, and the analysis shows that this method can significantly improve the decoding accuracy of MI tasks and exhibit strong robustness for small sample datasets. It would be simple and efficient to tune the two-parameters classifier for the online brain-computer interface(BCI)system.
Unified analysis of finite-size error for periodic Hartree-Fock and second order Møller-Plesset perturbation theory

Despite decades of practice, finite-size errors in many widely used electronic structure theories for periodic systems remain poorly understood. For periodic systems using a general Monkhorst-Pack grid, there has been no comprehensive and rigorous analysis of the finite-size error in the Hartree-Fock theory (HF) and the second order M{\o}ller-Plesset perturbation theory (MP2), which are the simplest wavefunction based method, and the simplest post-Hartree-Fock method, respectively. Such calculations can be viewed as a multi-dimensional integral discretized with certain trapezoidal rules. Due to the Coulomb singularity, the integrand has many points of discontinuity in general, and standard error analysis based on the Euler-Maclaurin formula gives overly pessimistic results. The lack of analytic understanding of finite-size errors also impedes the development of effective finite-size correction schemes. We propose a unified analysis to obtain sharp convergence rates of finite-size errors for the periodic HF and MP2 theories. Our main technical advancement is a generalization of the result of [Lyness, 1976] for obtaining sharp convergence rates of the trapezoidal rule for a class of non-smooth integrands. Our result is applicable to three-dimensional bulk systems as well as low dimensional systems (such as nanowires and 2D materials). Our unified analysis also allows us to prove the effectiveness of the Madelung-constant correction to the Fock exchange energy, and the effectiveness of a recently proposed staggered mesh method for periodic MP2 calculations [Xing, Li, Lin, J. Chem. Theory Comput. 2021]. Our analysis connects the effectiveness of the staggered mesh method with integrands with removable singularities, and suggests a new staggered mesh method for reducing finite-size errors of periodic HF calculations.
Learning Domain-Specific Edit Operations from Model Repositories with Frequent Subgraph Mining

Model transformations play a fundamental role in model-driven software development. They can be used to solve or support central tasks, such as creating models, handling model co-evolution, and model merging. In the past, various (semi-)automatic approaches have been proposed to derive model transformations from meta-models or from examples. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful to practitioners in an industrial setting.
QED Mesons, the QED Neutron, and the Dark Matter

Schwinger’s boson solution for massless fermions in QED in 1+1D has been applied and generalized to quarks interacting in QED and QCD interactions, leading to stable and confined open-string QED and QCD boson excitations of the quark-QCD-QED system in 1+1D. Just as the open-string QCD excitations in 1+1D can be the idealization of QCD mesons with a flux tube in 3+1D, so the open-string QED excitations in 1+1D may likewise be the idealization of QED mesons with masses in the tens of MeV region, corresponding possibly to the anomalous X17 and E38 particles observed recently. A further search for bound states of quarks interacting in the QED interaction alone leads to the examination on the stability of the QED neutron, consisting of two d quarks and one u quark. Theoretically, the QED neutron has been found to be stable and estimated to have a mass of 44.5 MeV, whereas the analogous QED proton is unstable, leading to a long-lived QED neutron that may be a good candidate for the dark matter.
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning

This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities which are intractable, costly, and/or noisy. This type of problem can be found in numerous real-world scenarios, including stochastic optimization and reinforcement learning, where each evaluation of a density function may incur some computationally-expensive or even physical (real-world activity) cost, likely to give different results each time. The surrogate model does not incur this cost, but there are important trade-offs and considerations involved in the choice and design of such methodologies. We classify the different methodologies into three main classes and describe specific instances of algorithms under a unified notation. A modular scheme which encompasses the considered methods is also presented. A range of application scenarios is discussed, with special attention to the likelihood-free setting and reinforcement learning. Several numerical comparisons are also provided.
Zonotope-based Controller Synthesis for LTL Specifications

This paper studies the controller synthesis problem for Linear Temporal Logic (LTL) specifications using (constrained) zonotope techniques. First, we implement (constrained) zonotope techniques to partition the state space and further to verify whether the LTL specification can be satisfied. Once the LTL specification can be satisfied, the next step is to design a controller to guarantee the satisfaction of the LTL specification for dynamic systems. Based on the verification of the LTL specification, an abstraction-based control design approach is proposed in this paper: a novel abstraction construction is developed first, then finite local abstract controllers are designed to achieve the LTL specification, and finally the designed abstract controllers are combined and refined as the controller for the original system. The proposed control strategy is illustrated via a numerical example from autonomous robots.
Learning to Learn to Demodulate with Uncertainty Quantification via Bayesian Meta-Learning

—Meta-learning, or learning to learn, offers a principled framework for few-shot learning. It leverages data from multiple related learning tasks to infer an inductive bias that enables fast adaptation on a new task. The application of meta-learning was recently proposed for learning how to demodulate from few pilots. The idea is to use pilots received and stored for ofﬂine use from multiple devices in order to meta-learn an adaptation procedure with the aim of speeding up online training on new devices. Standard frequentist learning, which can yield relatively accurate “hard” classiﬁcation decisions, is known to be poorly calibrated, particularly in the small-data regime. Poor calibration implies that the soft scores output by the demodulator are inaccurate estimates of the true probability of correct demodulation. In this work, we introduce the use of Bayesian meta-learning via variational inference for the purpose of obtaining well-calibrated few-pilot demodulators. In a Bayesian framework, each neural network weight is represented by a distribution, capturing epistemic uncertainty. Bayesian meta-learning optimizes over the prior distribution of the weights. The resulting Bayesian ensembles offer better calibrated soft decisions, at the computational cost of running multiple instances of the neural network for demodulation. Numerical results for single-input single-output Rayleigh fading channels with transmitter’s non-linearities are provided that compare symbol error rate and expected calibration error for both frequentist and Bayesian meta-learning, illustrating how the latter is both more accurate and better-calibrated.
Tensor-Train Density Estimation

Estimation of probability density function from samples is one of the central problems in statistics and machine learning. Modern neural network-based models can learn high dimensional distributions but have problems with hyperparameter selection and are often prone to instabilities during training and inference. We propose a new efficient tensor train-based model for density estimation (TTDE). Such density parametrization allows exact sampling, calculation of cumulative and marginal density functions, and partition function. It also has very intuitive hyperparameters. We develop an efficient non-adversarial training procedure for TTDE based on the Riemannian optimization. Experimental results demonstrate the competitive performance of the proposed method in density estimation and sampling tasks, while TTDE significantly outperforms competitors in training speed.
A Survey on Audio Synthesis and Audio-Visual Multimodal Processing

With the development of deep learning and artificial intelligence, audio synthesis has a pivotal role in the area of machine learning and shows strong applicability in the industry. Meanwhile, significant efforts have been dedicated by researchers to handle multimodal tasks at present such as audio-visual multimodal processing. In this paper, we conduct a survey on audio synthesis and audio-visual multimodal processing, which helps understand current research and future trends. This review focuses on text to speech(TTS), music generation and some tasks that combine visual and acoustic information. The corresponding technical methods are comprehensively classified and introduced, and their future development trends are prospected. This survey can provide some guidance for researchers who are interested in the areas like audio synthesis and audio-visual multimodal processing.
Repdigits As Sums Of Two Associated Pell Numbers∗

In this paper, we show that 2, 4, 6, 8 and 44 are the only repdigits that are sums of two associated Pell numbers, which again confirms that the sum of two Lucas-balancing numbers can never be a repdigit with at least two digits. As a consequence of our main result, we show that 4, 8 and 88 are the only repdigits which are sums of two Pell-Lucas numbers.
Combined approach with second-order optimality conditions for bilevel programming problems

In this paper, we propose a combined approach with second-order optimality conditions of the lower level problem to study constraint qualifications and optimality conditions for bilevel programming problems. The new method is inspired by the combined approach developed by Ye and Zhu in 2010, where the authors combined the classical first-order and the value function approaches to derive new necessary optimality conditions. In our approach, we add a second-order optimality condition to the combined program as a new constraint. We show that when all known approaches fail, adding the second-order optimality condition as a constraint makes the corresponding partial calmness condition and the resulting necessary optimality condition easier to hold. We also give some discussions on advantages and disadvantages of the combined approaches with the first-order and the second-order information.
Prescribing net demand for electricity market clearing

We consider a two-stage electricity market comprising a forward and a realtime settlement. The former pre-dispatches the power system following a least-cost merit order and facing an uncertain net demand, while the latter copes with the plausible deviations with respect to the forward schedule by making use of power regulation during the actual operation of the system. Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation (usually referred to as a point forecast), so as to minimize the need for power regulation in real time. However, it is well known that the cost structure of a power system is highly asymmetric and dependent on its operating point, with the result that minimizing the amount of power imbalances is not necessarily aligned with minimizing operating costs. In this paper, we propose a mixed-integer program to construct, from the available historical data, an alternative estimate of the net demand that accounts for the power system’s cost asymmetry. Furthermore, to accommodate the strong dependence of this cost on the power system’s operating point, we use clustering to tailor the proposed estimate to the foreseen net-demand regime. By way of an ∗Corresponding author Email addresses: juan.morales@uma.es (J. M. Morales), miguelangeljmd@uma.es (M. A. Muñoz), spinedamorente@gmail.com (S. Pineda) Preprint submitted to Elsevier August 3, 2021 illustrative example and a more realistic case study based on the European power system, we show that our approach leads to substantial cost savings compared to the customary way of doing.
On properties of positive solutions to nonlinear tri-harmonic and bi-harmonic equations with negative exponents

In this paper, we investigate various properties (e.g., nonexistence, asymptotic behavior, uniqueness and integral representation formula) of positive solutions to nonlinear triharmonic equations in R (n ≥ 2) and bi-harmonic equations in R with negative exponents. Such kind of equations arise from conformal geometry.
BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images

Background: Delineating the lesion area is an important task in image-based diagnosis. Pixel-wise classification is a popular approach to segmenting the region of interest. However, at fuzzy boundaries, such methods usually result in glitches, discontinuity or disconnection, inconsistent with the fact that lesions are solid and smooth. Methods: To overcome these problems and to provide an efficient, accurate, robust and concise solution that simplifies the whole segmentation pipeline in AI-assisted applications, we propose the BézierSeg model which outputs Bézier curves encompassing the region of interest. Results: Directly modeling the contour with analytic equations ensures that the segmentation is connected and continuous, and that the boundary is smooth. In addition, it offers sub-pixel accuracy. Without loss of precision, the Bézier contour can be resampled and overlaid with images of any resolution. Moreover, clinicians can conveniently adjust the curve’s control points to refine the result. Conclusions: Our experiments show that the proposed method runs in real time and achieves accuracy competitive with pixel-wise segmentation models.
Skeena: Efficient and Consistent Cross-Engine Transactions

Database systems are becoming increasingly multi-engine. In particular, a main-memory database engine may coexist with a traditional storage-centric engine in a system to support various applications. It is desirable to allow applications to access data in both engines using cross-engine transactions. But existing systems are either only designed for single-engine accesses, or impose many restrictions by limiting cross-engine transactions to certain isolation levels and table operations. The result is inadequate cross-engine support in terms of correctness, performance and programmability. This paper describes Skeena, a holistic approach to cross-engine transactions. We propose a lightweight snapshot tracking structure and an atomic commit protocol to efficiently ensure correctness and support various isolation levels. Evaluation results show that Seena maintains high performance for single-engine transactions and enables cross-engine transactions which can improve throughput by up to 30x by judiciously placing tables in different engines.
Threat of Adversarial Attacks on Deep Learning in Computer Vision: Survey II

—Deep Learning (DL) is the most widely used tool in the contemporary ﬁeld of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical applications. However, it is now known that DL is vulnerable to adversarial attacks that can manipulate its predictions by introducing visually imperceptible perturbations in images and videos. Since the discovery of this phenomenon in 2013 [1], it has attracted signiﬁcant attention of researchers from multiple sub-ﬁelds of machine intelligence. In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018. Many of those contributions have inspired new directions in this area, which has matured signiﬁcantly since witnessing the ﬁrst generation methods. Hence, as a legacy sequel of [2], this literature review focuses on the advances in this area since 2018. To ensure authenticity, we mainly consider peer-reviewed contributions published in the prestigious sources of computer vision and machine learning research. Besides a comprehensive literature review, the article also provides concise deﬁnitions of technical terminologies for non-experts in this domain. Finally, this article discusses challenges and future outlook of this direction based on the literature reviewed herein and [2].
A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs

Along with climate change, more frequent extreme events, such as flooding and tropical cyclones, threaten the livelihoods and wellbeing of poor and vulnerable populations. One of the most immediate needs of people affected by a disaster is finding shelter. While the proliferation of data on disasters is already helping to save lives, identifying damages in buildings, assessing shelter needs, and finding appropriate places to establish emergency shelters or settlements require a wide range of data to be combined rapidly. To address this gap and make a headway in comprehensive assessments, this paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. This workflow is built around open and online data to ensure scalability and broad accessibility. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used. This technique allows the collected multimodal data to share a common semantic space that facilitates the prediction of individual variables. Each fused numerical vector was fed into an unsupervised clustering algorithm called Self-Organizing-Maps (SOM). The trained SOM serves as a predictor for future cases, allowing predicting consequences such as total deaths, total people affected, and total damage, and provides specific recommendations for assessments in the shelter and housing sector. To achieve such prediction, a satellite image from before the disaster and the geographic and demographic conditions are shown to the trained model, which achieved a prediction accuracy of 62 %
Learning Maritime Obstacle Detection from Weak Annotations by Scaffolding

Coastal water autonomous boats rely on robust perception methods for obstacle detection and timely collision avoidance. The current state-of-the-art is based on deep segmentation networks trained on large datasets. Per-pixel ground truth labeling of such datasets, however, is labor-intensive and expensive. We observe that far less information is required for practical obstacle avoidance - the location of water edge on static obstacles like shore and approximate location and bounds of dynamic obstacles in the water is sufficient to plan a reaction. We propose a new scaffolding learning regime (SLR) that allows training obstacle detection segmentation networks only from such weak annotations, thus significantly reducing the cost of ground-truth labeling. Experiments show that maritime obstacle segmentation networks trained using SLR substantially outperform the same networks trained with dense ground truth labels, despite a significant reduction in labelling effort. Thus accuracy is not sacrificed for labelling simplicity but is in fact improved, which is a remarkable result.