Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact convex structure of measurements and its applications to simulability, incompatibility, and convex resource theory of continuous-outcome measurements

We introduce the post-processing preorder and equivalence relations for general measurements on a possibly infinite-dimensional general probabilistic theory described by an order unit Banach space $E$ with a Banach predual. We define the measurement space $\mathfrak{M}(E)$ as the set of post-processing equivalence classes of continuous measurements on $E . $ We define the weak topology on $\mathfrak{M} (E)$ as the weakest topology in which the state discrimination probabilities for any finite-label ensembles are continuous and show that $\mathfrak{M}(E)$ equipped with the convex operation corresponding to the probabilistic mixture of measurements can be regarded as a compact convex set regularly embedded in a locally convex Hausdorff space. We also prove that the measurement space $\mathfrak{M}(E) $ is infinite-dimensional except when the system is $1$-dimensional and give a characterization of the post-processing monotone affine functional. We apply these general results to the problems of simulability and incompatibility of measurements. We show that the robustness measures of unsimulability and incompatibility coincide with the optimal ratio of the state discrimination probability of measurement(s) relative to that of simulable or compatible measurements, respectively. The latter result for incompatible measurements generalizes the recent result for finite-dimensional quantum measurements. Throughout the paper, the fact that any weakly$\ast$ continuous measurement can be arbitrarily approximated in the weak topology by a post-processing increasing net of finite-outcome measurements is systematically used to reduce the discussions to finite-outcome cases.
Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks

Optical character recognition (OCR) is widely applied in real applications serving as a key preprocessing tool. The adoption of deep neural network (DNN) in OCR results in the vulnerability against adversarial examples which are crafted to mislead the output of the threat model. Different from vanilla colorful images, images of printed text have clear backgrounds usually. However, adversarial examples generated by most of the existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose a watermark attack method to produce natural distortion that is in the disguise of watermarks and evade human eyes' detection. Experimental results show that watermark attacks can yield a set of natural adversarial examples attached with watermarks and attain similar attack performance to the state-of-the-art methods in different attack scenarios.
Progressive Stochastic Greedy Sparse Reconstruction and Support Selection

Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary $m$-dimensional sparse vector $\mathbf{x}$ having $k$ nonzero entries from $n$ measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing. Existing greedy-based algorithms achieve optimal $n = \mathcal{O}(k\log\frac{m}{k})$ sampling complexity with computational complexity that is linear in the size of the data $m$ and cardinality constraint $k$. However, the ${\mathcal{O}}(mk)$ computational complexity is still prohibitive for large-scale datasets. In this paper, we present the first sparse support selection algorithm for arbitrary sparse vectors that achieves exact identification of the optimal subset from $n = \mathcal{O}(k\log\frac{m}{k})$ measurements with $\tilde{\mathcal{O}}(m)$ computational complexity. The proposed scheme utilizes the idea of randomly restricting search space of the greedy method in a progressive manner to reduce the computational cost while maintaining the same order of sampling complexity as the existing greedy schemes. Simulation results including an application of the algorithm to the task of column subset selection demonstrate efficacy of the proposed algorithm.
Segmented Graph-Bert for Graph Instance Modeling

In graph instance representation learning, both the diverse graph instance sizes and the graph node orderless property have been the major obstacles that render existing representation learning models fail to work. In this paper, we will examine the effectiveness of GRAPH-BERT on graph instance representation learning, which was designed for node representation learning tasks originally. To adapt GRAPH-BERT to the new problem settings, we re-design it with a segmented architecture instead, which is also named as SEG-BERT (Segmented GRAPH-BERT) for reference simplicity in this paper. SEG-BERT involves no node-order-variant inputs or functional components anymore, and it can handle the graph node orderless property naturally. What's more, SEG-BERT has a segmented architecture and introduces three different strategies to unify the graph instance sizes, i.e., full-input, padding/pruning and segment shifting, respectively. SEG-BERT is pre-trainable in an unsupervised manner, which can be further transferred to new tasks directly or with necessary fine-tuning. We have tested the effectiveness of SEG-BERT with experiments on seven graph instance benchmark datasets, and SEG-BERT can out-perform the comparison methods on six out of them with significant performance advantages.
Nonlinear Canonical Correlation Analysis:A Compressed Representation Approach

Canonical Correlation Analysis (CCA) is a linear representation learning method that seeks maximally correlated variables in multi-view data. Nonlinear CCA extends this notion to a broader family of transformations, which are more powerful in many real-world applications. Given the joint probability, the Alternating Conditional Expectation (ACE) algorithm provides an optimal solution to the nonlinear CCA problem. However, it suffers from limited performance and an increasing computational burden when only a finite number of samples is available. In this work, we introduce an information-theoretic compressed representation framework for the nonlinear CCA problem (CRCCA), which extends the classical ACE approach. Our suggested framework seeks compact representations of the data that allow a maximal level of correlation. This way, we control the trade-off between the flexibility and the complexity of the model. CRCCA provides theoretical bounds and optimality conditions, as we establish fundamental connections to rate-distortion theory, the information bottleneck and remote source coding. In addition, it allows a soft dimensionality reduction, as the compression level is determined by the mutual information between the original noisy data and the extracted signals. Finally, we introduce a simple implementation of the CRCCA framework, based on lattice quantization.
Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning

Abstract. Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning based methods have shown convincing results. However, most of these methods only model the uncertainty contained in the data, while ignoring the uncertainty of the employed dense stereo matching procedure. Additionally modelling the latter, however, is particularly beneficial if the domain of the training data varies from that of the data to be processed. For this purpose, in the present work the idea of probabilistic deep learning is applied to the task of dense stereo matching for the first time. Based on the well-known and commonly employed GC-Net architecture, a novel probabilistic neural network is presented, for the task of joint depth and uncertainty estimation from epipolar rectified stereo image pairs. Instead of learning the network parameters directly, the proposed probabilistic neural network learns a probability distribution from which parameters are sampled for every prediction. The variations between multiple such predictions on the same image pair allow to approximate the model uncertainty. The quality of the estimated depth and uncertainty information is assessed in an extensive evaluation on three different datasets.
Interpreting the Spitzer/IRAC colours of 7 ≤ z ≤ 9 galaxies: distinguishing between line emission and starlight using ALMA

Prior to the launch of JWST, Spitzer/IRAC photometry offers the only means of studying the rest-frame optical properties of z >7 galaxies. Many such high-redshift galaxies display a red [3.6]−[4.5] micron colour, often referred to as the ‘IRAC excess’, which has conventionally been interpreted as arising from intense [O iii]+H β emission within the [4.5] micron bandpass. An appealing aspect of this interpretation is similarly intense line emission seen in star-forming galaxies at lower redshift as well as the redshift-dependent behaviour of the IRAC colours beyond z ∼ 7 modelled as the various nebular lines move through the two bandpasses. In this paper, we demonstrate that, given the photometric uncertainties, established stellar populations with Balmer (4000 Å rest frame) breaks, such as those inferred at z > 9 where line emission does not contaminate the IRAC bands, can equally well explain the redshift-dependent behaviour of the IRAC colours in 7 ≲ z ≲ 9 galaxies. We discuss possible ways of distinguishing between the two hypotheses using ALMA measures of [O iii] λ88 micron and dust continuum fluxes. Prior to further studies with JWST, we show that the distinction is important in determining the assembly history of galaxies in the first 500 Myr.
Moduli spaces of semiorthogonal decompositions in families

For a smooth projective family of schemes we prove that a semiorthogonal decomposition of the bounded derived category of coherent sheaves of the fiber of a point uniquely deforms over an etale neighborhood of the point. We do this using a comparison theorem between semiorthogonal decompositions and decomposition triangles of the structure sheaf of the diagonal. We then apply to the latter a deformation theory for morphisms with a fixed lift of the target, which is developed in the appendix. Using this as a key ingredient we introduce a moduli space which classifies semiorthogonal decompositions of the category of perfect complexes of a smooth projective family. We show that this is an etale algebraic space over the base scheme of the family, which can be non-quasicompact and non-separated. This is done using Artin's criterion for a functor to be an etale algebraic space over the base. We generalize this to families of geometric noncommutative schemes in the sense of Orlov. We also define the open subspace classifying non-trivial semiorthogonal decompositions only, which is used in a companion paper to study indecomposability of derived categories in various examples.
Convolutional Neural Network Pruning Using Filter Attenuation

Filters are the essential elements in convolutional neural networks (CNNs). Filters generate feature maps and form the main part of the computational and memory requirements of the convolutional networks. In filter pruning methods, a filter with all of its components, including channels and connections, are removed. The removal of a filter can cause a drastic change in the network’s performance. Also, the removed filters cannot come back to the network structure. We want to address these problems in this paper. We propose a CNN pruning method based on filter attenuation in which weak filters are not abruptly removed. Instead, weak filters are attenuated and gradually removed. In the proposed attenuation approach, there is a chance for weak filters to return to the network. The filter attenuation method is assessed using the VGG model for the Cifar10 image classification task. Simulation results show that the filter attenuation works well based on different pruning criteria, and better results are obtained in comparison with the conventional pruning methods.
Facilitation into Drug Treatment or Self-Help among out-of-Treatment IDUS in Portland: You Can Lead a Horse to Water, But…

This study's purpose was to encourage out-of-treatment injection drug users (IDUs) to participate in drug treatment or self-help, and thereby reduce their injection drug use and HIV risk. In Portland, Oregon, IDUs were recruited for HIV counseling and testing and were randomly assigned to a standard intervention (SI) or one of two enhanced intervention (EI) groups. SI subjects received two sessions of counseling and testing only. In addition to counseling and testing, EI subjects received social support and incentives designed to facilitate their entrance into either drug treatment (DT) or self-help (SH). Compared to SI subjects, a higher proportion of DT subjects initiated drug treatment, and more SH subjects initiated self-help. There was, however, rapid dropout from DT and SH among EI participants, and no difference was found between SI and EI subjects in injecting at a 6-month follow-up. Subjects retained in DT or SH had reduced injection drug use, suggesting the need to determine factors that make DT and SH more intrinsically appealing.
NGC 7469 as seen by MEGARA: new results from high-resolution IFU spectroscopy

We present our analysis of high-resolution (R ∼ 20 000) GTC/MEGARA integral-field unit spectroscopic observations, obtained during the commissioning run, in the inner region (12.5 arcsec × 11.3 arcsec) of the active galaxy NGC 7469, at spatial scales of 0.62 arcsec. We explore the kinematics, dynamics, ionization mechanisms, and oxygen abundances of the ionized gas, by modelling the H α-[N ii] emission lines at high signal-to-noise (> 15) with multiple Gaussian components. MEGARA observations reveal, for the first time for NGC 7469, the presence of a very thin (20 pc) ionized gas disc supported by rotation (V/σ = 4.3), embedded in a thicker (222 pc), dynamically hotter (V/σ = 1.3) one. These discs nearly corotate with similar peak-to-peak velocities (163 versus 137 km s−1), but with different average velocity dispersion (38 ± 1 versus 108 ± 4 km s−1). The kinematics of both discs could be possibly perturbed by star-forming regions. We interpret the morphology and the kinematics of a third (broader) component (σ > 250 km s−1) as suggestive of the presence of non-rotational turbulent motions possibly associated either to an outflow or to the lense. For the narrow component, the [N ii]/H α ratios point to the star-formation as the dominant mechanism of ionization, being consistent with ionization from shocks in the case of the intermediate component. All components have roughly solar metallicity. In the nuclear region of NGC 7469, at r ≤ 1.85 arcsec, a very broad (FWHM = 2590 km s−1) H α component is contributing (41 per cent) to the global H α-[N ii] profile, being originated in the (unresolved) broad line region of the Seyfert 1.5 nucleus of NGC 7469.
Wasserstein Proximal Gradient

We consider the task of sampling from a log-concave probability distribution. This target distribution can be seen as a minimizer of the relative entropy functional defined on the space of probability distributions. The relative entropy can be decomposed as the sum of a functional called the potential energy, assumed to be smooth, and a nonsmooth functional called the entropy. We adopt a Forward Backward (FB) Euler scheme for the discretization of the gradient flow of the relative entropy. This FB algorithm can be seen as a proximal gradient algorithm to minimize the relative entropy over the space of probability measures. Using techniques from convex optimization and optimal transport, we provide a non-asymptotic analysis of the FB algorithm. The convergence rate of the FB algorithm matches the convergence rate of the classical proximal gradient algorithm in Euclidean spaces. The practical implementation of the FB algorithm can be challenging. In practice, the user may choose to discretize the space and work with empirical measures. In this case, we provide a closed form formula for the proximity operator of the entropy.
Bilinear Graph Neural Network with Node Interactions

Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co-occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well-known GCN and GAT, respectively. Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN --- BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy.
Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning

Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time. In this paper, we propose Droidetec, a deep learning based method for android malware detection and malicious code localization, to model an application program as a natural language sequence. Droidetec adopts a novel feature extraction method to derive behavior sequences from Android applications. Based on that, the bi-directional Long Short Term Memory network is utilized for malware detection. Each unit in the extracted behavior sequence is inventively represented as a vector, which allows Droidetec to automatically analyze the semantics of sequence segments and eventually find out the malicious code. Experiments with 9616 malicious and 11982 benign programs show that Droidetec reaches an accuracy of 97.22% and an F1-score of 98.21%. In all, Droidetec has a hit rate of 91% to properly find out malicious code segments.
Reed–Muller Codes: Theory and Algorithms

Reed-Muller (RM) codes are among the oldest, simplest and perhaps most ubiquitous family of codes. They are used in many areas of coding theory in both electrical engineering and computer science. Yet, many of their important properties are still under investigation. This paper covers some of the recent developments regarding the weight enumerator and the capacity-achieving properties of RM codes, as well as some of the algorithmic developments. In particular, the paper discusses the recent connections established between RM codes, thresholds of Boolean functions, polarization theory, hypercontractivity, and the techniques of approximating low weight codewords using lower degree polynomials (when codewords are viewed as evaluation vectors of degree <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> polynomials in <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> variables). It then overviews some of the algorithms for decoding RM codes. It covers both algorithms with provable performance guarantees for every block length, as well as algorithms with state-of-the-art performances in practical regimes, which do not perform as well for large block length. Finally, the paper concludes with a few open problems.
Entropy‐Bounded Solutions to the One‐Dimensional Heat Conductive Compressible Navier‐Stokes Equations with Far Field Vacuum

In the presence of vacuum, the physical entropy for polytropic gases behave singularly, and it is thus a challenge to study its dynamics. It is shown in this paper that the boundedness of the entropy can be propagated up to any finite time provided that the initial vacuum presents only at far fields with sufficiently slow decay of the initial density. More precisely, for the Cauchy problem of the one‐dimensional heat conductive compressible Navier‐Stokes equations, the global well‐posedness of strong solutions and uniform boundedness of the corresponding entropy are established, as long as the initial density vanishes only at far fields with a rate no more than O1x2 . The main tools of proving the uniform boundedness of the entropy are some singularly weighted energy estimates carefully designed for the heat conductive compressible Navier‐Stokes equations and an elaborate De Giorgi type iteration technique for some classes of degenerate parabolic equations. The De Giorgi–type iterations are carried out to different equations in establishing the lower and upper bounds of the entropy. © 2021 Wiley Periodicals LLC.
Pointhop++: A Lightweight Learning Model on Point Sets for 3D Classification

The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. It has an extremely low training complexity while achieving state-of-the-art classification performance. In this work, we improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. The resulting method is called PointHop++. The first improvement is essential for wearable and mobile computing while the second improvement bridges statistics-based and optimization-based machine learning methodologies. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.