Miracle-dz/UniFAR-FTUs huggingface dataset:
Scattering of Dirac electrons from a skyrmion: Emergence of robust skew scattering

We study electron scattering from a closed magnetic structure embedded in the top surface of a topological insulator (TI). Outside the structure there is a uniform layer of ferromagnetic insulator (FMI), leading to a positive effective mass for the Dirac electrons. The mass inside can be engineered to be negative, leading to a skyrmion structure. The geometric shape of the structure can be circular or deformed, leading to integrable or chaotic dynamics, respectively, in the classical limit. For a circular structure, the relativistic quantum scattering characteristics can be calculated analytically. For a deformed structure, we develop an efficient numerical method, the multiple multipole method, to solve the scattering wavefunctions. We find that anomalous Hall effect as characterized by strong skew scattering can arise, which is robust against structural deformation due to the resonant modes. In the short (long) wavelength regime, the resonant modes manifest themselves as confined vortices (excited edge states). The origin of the resonant states is the spin phase factor of massive Dirac electrons at the skyrmion boundary. Further, in the short wavelength regime, for a circular skyrmion, a large number of angular momentum channels contribute to the resonant modes. In this regime, in principle, classical dynamics are relevant, but we find that geometric deformations, even those as severe as leading to fully developed chaos, have little effect on the resonant modes. The vortex structure of the resonant states makes it possible to electrically ``charge'' the skyrmion, rendering feasible to manipulate its motion electrically. In the long wavelength regime, only the lowest angular momentum channels contribute to the resonant modes, making the skew scattering sharply directional. These phenomena may find applications for information storage and in Hall devices based on dynamic skyrmion.
Monitoring Platelet Inhibition by Clopidogrel Using Rapid Platelet Function Assay in Patients With Ischemic Stroke

Background: Clopidogrel inhibits platelet P2Y12 adenosine diphosphate (ADP) receptors and has been widely used in patients with ischemic stroke. However, a considerable number of patients suffer from cerebrovascular events despite the use of clopidogrel. The rapid platelet funct ion assay (RPFA) has been used for monitoring the antiplatelet effects on the P2Y12 ADP receptor. This study was performed to measure the platelet response to clopidogrel using RPFA in patients with ischemic stroke, and to identify the clinical factor related with clopidogrel resistance. Methods: A total of 86 patients taking clopidogrel (75 mg/day) were enrolled. Demographic data, vascular risk factors, the presence of obesity and metabolic syndrome, drug history, hemoglobin, platelet counts, and stroke subtypes were recorded. RPFA presented the results as P2Y12 Reaction Units (PRU), base PRU (BASE), and Inhibition (%). Inhibition was calculated as (1-PRU/BASE)×100. The patients showing ineffective aggregationinhibition (percentage of Inhibition < 20) on RPFA were defined as non-responders to clopidogrel. Results: The response of platelet aggregation-inhibition to clopidogrel showed a variable distribution with mean and standard deviation of 32.2± 22.3%. Twenty four (27.9%) patients showed the inhibition below 20%. There was no difference between responders and non-responders regarding t he clinical factors above. We found no influence of co-medication with the statins on platelet response to clopidogrel. Conclusions: There is a patient variability in response to clopidogrel and a considerable portion of stroke patients have clopidogrel resistance on the platelet function test. The clinical usefulness of routine platelet function test requires further validation. J Korean Neurol Assoc 26(4):301-307, 2008
Constructing a variational family for nonlinear state-space models

We consider the problem of maximum likelihood parameter estimation for nonlinear state-space models. This is an important, but challenging problem. This challenge stems from the intractable multidimensional integrals that must be solved in order to compute, and maximise, the likelihood. Here we present a new variational family where variational inference is used in combination with tractable approximations of these integrals resulting in a deterministic optimisation problem. Our developments also include a novel means for approximating the smoothed state distributions. We demonstrate our construction on several examples and show that they perform well compared to state of the art methods on real data-sets.
Efficient Topological Layer based on Persistent Landscapes

We propose a novel topological layer for general deep learning models based on persistent landscapes, in which we can efficiently exploit underlying topological features of the input data structure. We use the robust DTM function and show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration. Thus, our proposed layer can be placed anywhere in the network architecture and feed critical information on the topological features of input data into subsequent layers to improve the learnability of the networks toward a given task. A task-optimal structure of the topological layer is learned during training via backpropagation, without requiring any input featurization or data preprocessing. We provide a tight stability theorem, and show that the proposed layer is robust towards noise and outliers. We demonstrate the effectiveness of our approach by classification experiments on various datasets.
Statistical Inference in Heterogeneous Block Model

There exist various types of network block models such as the Stochastic Block Model (SBM), the Degree Corrected Block Model (DCBM), and the Popularity Adjusted Block Model (PABM). While this leads to a variety of choices, the block models do not have a nested structure. In addition, there is a substantial jump in the number of parameters from the DCBM to the PABM. The objective of this paper is formulation of a hierarchy of block model which does not rely on arbitrary identifiability conditions, treats the SBM, the DCBM and the PABM as its particular cases with specific parameter values and, in addition, allows a multitude of versions that are more complicated than DCBM but have fewer unknown parameters than the PABM. The latter allows one to carry out clustering and estimation without preliminary testing to see which block model is really true.
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks

Cardiovascular diseases are the most common cause of mortality worldwide. Detection of atrial fibrillation (AF) in the asymptomatic stage can help prevent strokes. It also improves clinical decision making through the delivery of suitable treatment such as, anticoagulant therapy, in a timely manner. The clinical significance of such early detection of AF in electrocardiogram (ECG) signals has inspired numerous studies in recent years, of which many aim to solve this task by leveraging machine learning algorithms. ECG datasets containing AF samples, however, usually suffer from severe class imbalance, which if unaccounted for, affects the performance of classification algorithms. Data augmentation is a popular solution to tackle this problem.In this study, we investigate the impact of various data augmentation algorithms, e.g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance problem. These algorithms are quantitatively and qualitatively evaluated, compared and discussed in detail. The results show that deep learning-based AF signal classification methods benefit more from data augmentation using GANs and GMMs, than oversampling. Furthermore, the GAN results in circa 3% better AF classification accuracy in average while performing comparably to the GMM in terms of f1-score.
Multiple Access in Dynamic Cell-Free Networks: Outage Performance and Deep Reinforcement Learning-Based Design

In future cell-free (or cell-less) wireless networks, a large number of devices in a geographical area will be served simultaneously in non-orthogonal multiple access scenarios by a large number of distributed access points (APs), which coordinate with a centralized processing pool. For such a centralized cell-free network with static predefined beamforming design, we first derive a closed-form expression of the uplink per-user probability of outage. To significantly reduce the complexity of joint processing of users' signals in presence of a large number of devices and APs, we propose a novel dynamic cell-free network architecture. In this architecture, the distributed APs are partitioned (i.e. clustered) among a set of subgroups with each subgroup acting as a virtual AP equipped with a distributed antenna system (DAS). The conventional static cell-free network is a special case of this dynamic cell-free network when the cluster size is one. For this dynamic cell-free network, we propose a successive interference cancellation (SIC)-enabled signal detection method and an inter-user-interference (IUI)-aware DAS's receive diversity combining scheme. We then formulate the general problem of clustering APs and designing the beamforming vectors with an objective to maximizing the sum rate or maximizing the minimum rate. To this end, we propose a hybrid deep reinforcement learning (DRL) model, namely, a deep deterministic policy gradient (DDPG)-deep double Q-network (DDQN) model, to solve the optimization problem for online implementation with low complexity. The DRL model for sum-rate optimization significantly outperforms that for maximizing the minimum rate in terms of average per-user rate performance. Also, in our system setting, the proposed DDPG-DDQN scheme is found to achieve around $78\%$ of the rate achievable through an exhaustive search-based design.
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network

The Hyperspectral image (HSI) classification is a standard remote sensing task, in which each image pixel is given a label indicating the physical land-cover on the earth's surface. The achievements of image semantic segmentation and deep learning approaches on ordinary images have accelerated the research on hyperspectral image classification. Moreover, the utilization of both the spectral and spatial cues in hyperspectral images has shown improved classification accuracy in hyperspectral image classification. The use of only 3D Convolutional Neural Networks (3D-CNN) to extract both spatial and spectral cues from Hyperspectral images results in an explosion of parameters hence high computational cost. We propose network architecture called the MixedSN that utilizes the 3D convolutions to modeling spectral-spatial information in the early layers of the architecture and the 2D convolutions at the top layers which majorly deal with semantic abstraction. We constrain our architecture to ResNeXt block because of their performance and simplicity. Our model drastically reduced the number of parameters and achieved comparable classification performance with state-of-the-art methods on Indian Pine (IP) scene dataset, Pavia University scene (PU) dataset, Salinas (SA) Scene dataset, and Botswana (BW) dataset.
A Deep Learning Approach for the Computation of Curvature in the Level-Set Method

We propose a deep learning strategy to compute the mean curvature of an implicit level-set representation of an interface. Our approach is based on fitting neural networks to synthetic datasets of pairs of nodal $\phi$ values and curvatures obtained from circular interfaces immersed in different uniform resolutions. These neural networks are multilayer perceptrons that ingest sample level-set values of grid points along a free boundary and output the dimensionless curvature at the center vertices of each sampled neighborhood. Evaluations with irregular (smooth and sharp) interfaces, in both uniform and adaptive meshes, show that our deep learning approach is systematically superior to conventional numerical approximation in the $L^2$ and $L^\infty$ norms. Our methodology is also less sensitive to steep curvatures and approximates them well with samples collected with fewer iterations of the reinitialization equation, often needed to regularize the underlying implicit function. Additionally, we show that an application-dependent map of local resolutions to neural networks can be constructed and employed to estimate interface curvatures more efficiently than using typically expensive numerical schemes while still attaining comparable or higher precision.
Differentiable Fixed-Point Iteration Layer

Recently, several studies proposed methods to utilize some restricted classes of optimization problems as layers of deep neural networks. However, these methods are still in their infancy and require special treatments, i.e., analyzing the KKT condition, etc., for deriving the backpropagation formula. Instead, in this paper, we propose a method to utilize fixed-point iteration (FPI), a generalization of many types of numerical algorithms, as a network layer. We show that the derivative of an FPI layer depends only on the fixed point, and then we present a method to calculate it efficiently using another FPI which we call the backward FPI. The proposed method can be easily implemented based on the autograd functionalities in existing deep learning tools. Since FPI covers vast different types of numerical algorithms in machine learning and other fields, it has a lot of potential applications. In the experiments, the differentiable FPI layer is applied to two scenarios, i.e., gradient descent iterations for differentiable optimization problems and FPI with arbitrary neural network modules, of which the results demonstrate the simplicity and the effectiveness.
Continuous Geodesic Convolutions for Learning on 3D Shapes

The majority of descriptor-based methods for geometric processing of non-rigid shape rely on hand-crafted descriptors. Recently, learning-based techniques have been shown effective, achieving state-of-the-art results in a variety of tasks. Yet, even though these methods can in principle work directly on raw data, most methods still rely on handcrafted descriptors at the input layer. In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh. To this end, we introduce two modules into our neural architecture. The first is a local reference frame (LRF) used to explicitly make the features invariant to rigid transformations. The second is continuous convolution kernels that provide robustness to sampling. We show the efficacy of our proposed network in learning on raw meshes using two cornerstone tasks: shape matching, and human body parts segmentation. Our results show superior results over baseline methods that use handcrafted descriptors.
First order convergence of weak Wong--Zakai approximations of Lévy driven Marcus SDEs

For solutions $X=(X_t)_{t\in[0,T]}$ of Levy-driven Marcus stochastic differential equations we study the Wong--Zakai type time discrete approximations $\bar X=(\bar X_{kh})_{0\leq k\leq T/h}$, $h>0$, and establish the first order convergence $|E f(X_T)-E f(X^h_T)|\leq C h$ for $f\in C_b^4$.
SMA: Eliminate Memory Spatial Errors via Saturation Memory Access

Memory spatial error, i.e., buffer overflow, has been a well-known issue in computer security for a long time and remains one of the root causes of exploitable vulnerabilities. Existing tools focus on the detection of memory spatial errors and prevent intrusion by terminating the execution of the victim program. However, such tools cannot eliminate the vulnerabilities without patching the program. Unfortunately, in the increasingly popular embedded environment, deploying patches becomes harder because of the enormous number of devices. The limited resource in the embedded environment also prevents many existing tools to be used in the real world. This paper proposes the Saturation Memory Access (SMA), a memory spatial error elimination tool that prevents out-of-bound access without terminating the execution of a program. We use the tagged pointer scheme to store the boundary metadata of a memory object in the pointer itself, and correct the address to the object boundary upon detecting out-of-bound access. This method is based on a key observation that developers generally do not rely on out-of-bounds access to implement the program logic, so the correction of the address does not interfere with the execution of a program. We have implemented the prototype of SMA on LLVM 4.0. 1 with two pointer encoding schemes designed for different tradeoff decisions between performance and memory usage. Experiments show that our prototype can stop nearly all attack forms in the RIPE benchmark and incurs 64\%-84\% overhead on SPEC CPU2017.
Prevalence of Complex Organic Molecules in Starless and Prestellar Cores within the Taurus Molecular Cloud

The detection of complex organic molecules (COMs) toward dense, collapsing prestellar cores has sparked interest in the fields of astrochemistry and astrobiology, yet the mechanisms for COM formation are still debated. It was originally believed that COMs first form in ices, only to be irradiated by UV radiation from the surrounding interstellar radiation field as well as forming protostars, and subsequently photodesorbed into the gas phase. However, starless and prestellar cores do not have internal protostars to heat up and sublimate the ices. Alternative models using chemical energy have been developed to explain the desorption of COMs, yet in order to test these models, robust measurements of COM abundances are needed toward representative samples of cores. We have conducted a large sample survey of 31 starless and prestellar cores in the Taurus molecular cloud, detecting methanol (CH3OH) in 100% of the cores targeted and acetaldehyde (CH3CHO) in 70%. At least two transition lines of each molecule were measured, allowing us to place tight constraints on excitation temperature, column density, and abundance. Additional mapping of methanol revealed extended emission detected down to AV as low as ∼3 mag. We find that COMs are detectable in the gas phase and are being formed early, at least hundreds of thousands of years prior to star and planet formation. The precursor molecule, CH3OH, may be chemically linked to the more complex CH3CHO; however, higher spatial resolution maps are needed to further test chemical models.
Context Aware Image Annotation in Active Learning

Image annotation for active learning is labor-intensive. Various automatic and semi-automatic labeling methods are proposed to save the labeling cost, but a reduction in the number of labeled instances does not guarantee a reduction in cost because the queries that are most valuable to the learner may be the most difficult or ambiguous cases, and therefore the most expensive for an oracle to label accurately. In this paper, we try to solve this problem by using image metadata to offer the oracle more clues about the image during annotation process. We propose a Context Aware Image Annotation Framework (CAIAF) that uses image metadata as similarity metric to cluster images into groups for annotation. We also present useful metadata information as context for each image on the annotation interface. Experiments show that it reduces that annotation cost with CAIAF compared to the conventional framework, while maintaining a high classification performance.
Structural Language Models of Code

We address the problem of any-code completion - generating a missing piece of source code in a given program without any restriction on the vocabulary or structure. We introduce a new approach to any-code completion that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM). SLM estimates the probability of the program's abstract syntax tree (AST) by decomposing it into a product of conditional probabilities over its nodes. We present a neural model that computes these conditional probabilities by considering all AST paths leading to a target node. Unlike previous techniques that have severely restricted the kinds of expressions that can be generated in this task, our approach can generate arbitrary code in any programming language. Our model significantly outperforms both seq2seq and a variety of structured approaches in generating Java and C# code. We make our code, datasets, and models publicly available.
Jointly Sparse Signal Recovery via Deep Auto-encoder and Parallel Coordinate Descent Unrolling

In this paper, combining techniques in compressed sensing, parallel optimization and deep learning, an autoencoder-based approach is proposed to jointly design the common measurement matrix and jointly sparse signal recovery method for complex sparse signals. The encoder achieves noisy linear compression for jointly sparse signals, with a common measurement matrix. The decoder realizes jointly sparse signal recovery based on an iterative parallel-coordinate descent algorithm which is proposed to solve GROUP LASSO in a parallel manner. In particular, the decoder consists of an approximation part which unfolds (several iterations of) the proposed iterative algorithm to obtain an approximate solution of GROUP LASSO and a correction part which reduces the difference between the approximate solution and the actual jointly sparse signals. To our knowledge, this is the first time that an optimization-based jointly sparse signal recovery method is implemented using a neural network. The proposed approach achieves higher recovery accuracy with less computation time than the classic GROUP LASSO method, and the gain significantly increases in the presence of extra structures in sparse patterns. The common measurement matrix obtained by the proposed approach is also suitable for the classic GROUP LASSO method. We consider an application example, i.e., channel estimation in Multiple-Input Multiple-Output (MIMO)-based grant-free massive access for massive machine-type communications (mMTC). By numerical results, we demonstrate the substantial gains of the proposed approach over GROUP LASSO and AMP when the number of jointly sparse signals is not very large.
Memory-Augmented Generative Adversarial Networks for Anomaly Detection

We propose a memory-augmented deep learning model for semisupervised anomaly detection (AD). While many traditional AD methods focus on modeling the distribution of normal data, additional constraints in the modeling process are needed to distinguish between normal and abnormal data. The proposed model, named memory augmented generative adversarial networks (MEMGAN), is coupled with external memory units through attentional operations. One property of MEMGAN in the latent space is such that encoded normal data are expected to reside in the convex hull of the memory units, while the abnormal ones are separated outside. This property makes the AD process of MEMGAN more robust and reliable. Experiments on AD datasets adapted from MVTec, MNIST, CIFAR10, and Arrhythmia demonstrate that MEMGAN notably improves over previous AD models. We also find that the decoded memory units in MEMGAN are more diverse and interpretable than those in previous memory-augmented models.