Miracle-dz/UniFAR-FTUs huggingface dataset:
Signatures of a sampling quantum advantage in driven quantum many-body systems

A crucial milestone in the field of quantum simulation and computation is to demonstrate that a quantum device can perform a computation task that is classically intractable. A key question is to identify setups that can achieve such goal within current technologies. In this work, we provide formal evidence that sampling bit-strings from a periodic evolution of a unitary drawn from the circular orthogonal ensemble (COE) cannot be efficiently simulated with classical computers. As the statistical properties of COE coincide with a large class of driven analog quantum systems thanks to the Floquet eigenstate thermalization hypothesis, our results indicate the possibility that those driven systems could constitute practical candidates for a sampling quantum advantage. To further support this, we give numerical examples of driven disordered Ising chains and 1D driven Bose–Hubbard model.
Optimization-based modal decomposition for systems with multiple transports

Mode-based model-reduction is used to reduce the degrees of freedom of high dimensional systems, often by describing the system state by a linear combination of spatial modes. Transport dominated phenomena, ubiquitous in technical and scientific applications, often require a large number of linear modes to obtain a small representation error. This difficulty, even for the most simple transports, originates from the inappropriateness of the decomposition structure in time dependent amplitudes of purely spatial modes. In this article an approach is discussed, which decomposes a flow field into several fields of co-moving frames, where each one can be approximated by a few modes. The method of decomposition is formulated as an optimization problem. Different singular-value-based objective functions are discussed and connected to former formulations. A boundary treatment is provided. The decomposition is applied to generic cases and to a technically relevant flow configuration of combustion physics.
Max-Affine Spline Insights into Deep Generative Networks

We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities. By characterizing the latent space partition, dimension and angularity of the generated manifold, we relate the manifold dimension and approximation error to the sample size. The manifold-per-region affine subspace defines a local coordinate basis; we provide necessary and sufficient conditions relating those basis vectors with disentanglement. We also derive the output probability density mapped onto the generated manifold in terms of the latent space density, which enables the computation of key statistics such as its Shannon entropy. This finding also enables the computation of the GDN likelihood, which provides a new mechanism for model comparison as well as providing a quality measure for (generated) samples under the learned distribution. We demonstrate how low entropy and/or multimodal distributions are not naturally modeled by DGNs and are a cause of training instabilities.
Learning to Shadow Hand-Drawn Sketches

We present a fully automatic method to generate detailed and accurate artistic shadows from pairs of line drawing sketches and lighting directions. We also contribute a new dataset of one thousand examples of pairs of line drawings and shadows that are tagged with lighting directions. Remarkably, the generated shadows quickly communicate the underlying 3D structure of the sketched scene. Consequently, the shadows generated by our approach can be used directly or as an excellent starting point for artists. We demonstrate that the deep learning network we propose takes a hand-drawn sketch, builds a 3D model in latent space, and renders the resulting shadows. The generated shadows respect the hand-drawn lines and underlying 3D space and contain sophisticated and accurate details, such as self-shadowing effects. Moreover, the generated shadows contain artistic effects, such as rim lighting or halos appearing from backlighting, that would be achievable with traditional 3D rendering methods.
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning

Structured convex optimization on weighted graphs finds numerous applications in machine learning and computer vision. In this work, we propose a novel adaptive preconditioning strategy for proximal algorithms on this problem class. Our preconditioner is driven by a sharp analysis of the local linear convergence rate depending on the "active set" at the current iterate. We show that nested-forest decomposition of the inactive edges yields a guaranteed local linear convergence rate. Further, we propose a practical greedy heuristic which realizes such nested decompositions and show in several numerical experiments that our reconditioning strategy, when applied to proximal gradient or primal-dual hybrid gradient algorithm, achieves competitive performances. Our results suggest that local convergence analysis can serve as a guideline for selecting variable metrics in proximal algorithms.
SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization.

We propose novel stochastic proximal alternating linearized minimization (PALM) algorithms for solving a class of non-smooth and non-convex optimization problems which arise in many statistical machine learning, computer vision, and imaging applications. We provide a theoretical analysis, showing that our proposed method with variance-reduced stochastic gradient estimators such as SAGA and SARAH achieves state-of-the-art oracle complexities. We also demonstrate the efficiency of our algorithm via numerical experiments including sparse non-negative matrix factorization, sparse principal component analysis, and blind image deconvolution.
Thermal and pressure ionization in warm, dense MgSiO3 studied with first-principles computer simulations

Using path integral Monte Carlo and density functional molecular dynamics (DFT-MD) simulations, we study the properties of MgSiO$_3$ enstatite in the regime of warm dense matter. We generate a consistent equation of state (EOS) that spans across a wide range of temperatures and densities (10$^4$--10$^7$ K and 6.42--64.16 g cm$^{-3}$). We derive the shock Hugoniot curve, that is in good agreement with the experiments. We identify the boundary between the regimes of thermal ionization and pressure ionization by locating where the internal energy at constant temperature attains a minimum as a function of density or pressure. At low density, the internal energy decreases with increasing density as the weight of free states changes. Thermal ionization dominates. Conversely, at high density, in the regime of pressure ionization, the internal energy increases with density. We determine the boundary between the two regimes and show that the compression maximum along the shock Hugoniot curve occurs because K shell electrons become thermally ionized rather than pressure ionized.
XSepConv: Extremely Separated Convolution

Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes ($\ge5$) have been applied to it recently. In this paper, we propose a novel extremely separated convolutional block (XSepConv), which fuses spatially separable convolutions into depthwise convolution to further reduce both the computational cost and parameter size of large kernels. Furthermore, an extra $2\times2$ depthwise convolution coupled with improved symmetric padding strategy is employed to compensate for the side effect brought by spatially separable convolutions. XSepConv is designed to be an efficient alternative to vanilla depthwise convolution with large kernel sizes. To verify this, we use XSepConv for the state-of-the-art architecture MobileNetV3-Small and carry out extensive experiments on four highly competitive benchmark datasets (CIFAR-10, CIFAR-100, SVHN and Tiny-ImageNet) to demonstrate that XSepConv can indeed strike a better trade-off between accuracy and efficiency.
Fast implicit difference schemes for time‐space fractional diffusion equations with the integral fractional Laplacian

In this paper, we develop two fast implicit difference schemes for solving a class of variable‐coefficient time–space fractional diffusion equations with integral fractional Laplacian (IFL). The proposed schemes utilize the graded L1 formula for the Caputo fractional derivative and a special finite difference discretization for IFL, where the graded mesh can capture the model problem with a weak singularity at initial time. The stability and convergence are rigorously proved via the M‐matrix analysis, which is from the spatial discretized matrix of IFL. Moreover, the proposed schemes use the fast sum‐of‐exponential approximation and Toeplitz matrix algorithms to reduce the computational cost for the nonlocal property of time and space fractional derivatives, respectively. The fast schemes greatly reduce the computational work of solving the discretized linear systems from O(MN3+M2N) by a direct solver to O(MN(logN+Nexp)) per preconditioned Krylov subspace iteration and a memory requirement from 𝒪(MN2) to 𝒪(NNexp), where N and (Nexp ≪) M are the number of spatial and temporal grid nodes, respectively. The spectrum of preconditioned matrix is also given for ensuring the acceleration benefit of circulant preconditioners. Finally, numerical results are presented to show the utility of the proposed methods.
Exploiting Variable Impedance for Energy Efficient Sequential Movements

Compliant robotics have seen successful applications in energy efficient locomotion and cyclic manipulation. However, fully exploitation of variable physical impedance for energy efficient sequential movements has not been extensively addressed. This work employs a hierarchical approach to encapsulate low-level optimal control for sub-movement generation into an outer loop of iterative policy improvement, thereby benefits of both optimal control and reinforcement learning are leveraged. The framework enables optimizing efficiency trade-off for minimal energy expenses in a model-free manner, by taking account of cost function weighting, variable impedance exploitation, and transition timing, which are associated with the skill of compliance. The effectiveness of the proposed method is evaluated using two consecutive reaching tasks on a variable impedance actuator. The results demonstrate significant energy saving by improving the skill of compliance, with a 30% electrical consumption reduction measured on hardware.
Self-Supervised Deep Pose Corrections for Robust Visual Odometry

We present a self-supervised deep pose correction (DPC) network that applies pose corrections to a visual odom-etry estimator to improve its accuracy. Instead of regressing inter-frame pose changes directly, we build on prior work that uses data-driven learning to regress pose corrections that account for systematic errors due to violations of modelling assumptions. Our self-supervised formulation removes any requirement for six-degrees-of-freedom ground truth and, in contrast to expectations, often improves overall navigation accuracy compared to a supervised approach. Through extensive experiments, we show that our self-supervised DPC network can significantly enhance the performance of classical monocular and stereo odometry estimators and substantially out-performs state-of-the-art learning-only approaches.
Practicing Safe Browsing: Understanding How and Why University Students Use Virtual Private Networks

Recent studies have shown that some commercial VPNs leak user traffic to third parties. In this paper, we aim to understand how and why a population of VPN users---university students---chooses and uses VPNs, and whether they are aware of the security and privacy risks that VPNs pose. To answer these questions, we conducted 32 in-person interviews and a survey with 349 respondents, all university students in the United States. We find students are mostly concerned with access to content and privacy concerns were often secondary. They made tradeoffs to achieve a particular goal, such as using a free commercial VPN that may collect their online activities to access an online service in a geographic area. Many users expected that their VPNs were collecting data about them, although they did not understand how VPNs work. We conclude with a discussion of ways to help users make choices about VPNs.
Notes on the lattice of fuzzy rough sets

Since the theory of rough sets was introduced by Zdzislaw Pawlak, several approaches have been proposed to combine rough set theory with fuzzy set theory. In this paper, we examine one of these approaches, namely fuzzy rough sets, from a lattice theoretic point of view. We connect the lower and upper approximations of a fuzzy relation $R$ to the approximations of the core and support of $R$. We also show that the lattice of fuzzy rough sets corresponding to a fuzzy equivalence relation $R$ and the crisp subsets of its universe is isomorphic to the lattice of rough sets for the (crisp) equivalence relation $E$, where $E$ is the core of $R$. We establish a connection between the exact (fuzzy) sets of $R$ and the exact (crisp) sets of the support of $R$. Additionally, we examine some properties of a special case of a fuzzy relation.
Fair Adversarial Networks

The influence of human judgement is ubiquitous in datasets used across the analytics industry, yet humans are known to be sub-optimal decision makers prone to various biases. Analysing biased datasets then leads to biased outcomes of the analysis. Bias by protected characteristics (e.g. race) is of particular interest as it may not only make the output of analytical process sub-optimal, but also illegal. Countering the bias by constraining the analytical outcomes to be fair is problematic because A) fairness lacks a universally accepted definition, while at the same time some definitions are mutually exclusive, and B) the use of optimisation constraints ensuring fairness is incompatible with most analytical pipelines. Both problems are solved by methods which remove bias from the data and returning an altered dataset. This approach aims to not only remove the actual bias variable (e.g. race), but also alter all proxy variables (e.g. postcode) so the bias variable is not detectable from the rest of the data. The advantage of using this approach is that the definition of fairness as a lack of detectable bias in the data (as opposed to the output of analysis) is universal and therefore solves problem (A). Furthermore, as the data is altered to remove bias the problem (B) disappears because the analytical pipelines can remain unchanged. This approach has been adopted by several technical solutions. None of them, however, seems to be satisfactory in terms of ability to remove multivariate, non-linear and non-binary biases. Therefore, in this paper I propose the concept of Fair Adversarial Networks as an easy-to-implement general method for removing bias from data. This paper demonstrates that Fair Adversarial Networks achieve this aim.
Classification of radial solutions for elliptic systems driven by the $k$-Hessian operator.

We are concerned with non-constant positive radial solutions of the system $$ \left\{ \begin{aligned} S_k(D^2 u)&=|\nabla u|^{m} v^{p}&&\quad\mbox{ in }\Omega,\\ S_k(D^2 v)&=|\nabla u|^{q} v^{s} &&\quad\mbox{ in }\Omega, \end{aligned} \right. $$ where $S_k(D^2u)$ is the $k$-Hessian operator of $u\in C^2(\Omega)$ ($1\leq k\leq N$) and $\Omega\subset\mathbb{R}^N$ $(N\geq 2)$ is either a ball or the whole space. The exponents satisfy $q>0$, $m,s\geq 0$, $p\geq s\geq 0$ and $(k-m)(k-s)\neq pq$. In the case where $\Omega$ is a ball, we classify all the positive radial solutions according to their behavior at the boundary. Further, we consider the case $\Omega=\mathbb{R}^N$ and find that the above system admits non-constant positive radial solutions if and only if $0\leq m<k$ and $pq < (k-m)(k-s)$. Using arguments from three component cooperative and irreducible dynamical systems we deduce the behavior at infinity of such solutions.
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

The success of deep learning has been widely recognized in many machine learning tasks during the last decades, ranging from image classification and speech recognition to natural language understanding. As an extension of deep learning, Graph neural networks (GNNs) are designed to solve the non-Euclidean problems on graph-structured data which can hardly be handled by general deep learning techniques. Existing GNNs under various mechanisms, such as random walk, PageRank, graph convolution, and heat diffusion, are designed for different types of graphs and problems, which makes it difficult to compare them directly. Previous GNN surveys focus on categorizing current models into independent groups, lacking analysis regarding their internal connection. This paper proposes a unified framework and provides a novel perspective that can widely fit existing GNNs into our framework methodologically. Specifically, we survey and categorize existing GNN models into the spatial and spectral domains, and reveal connections among subcategories in each domain. Further analysis establishes a strong link across the spatial and spectral domains.
The Chiral Puzzle of Life

Biological molecules chose one of two structurally chiral systems which are related by reflection in a mirror. It is proposed that this choice was made, causally, by cosmic rays, which are known to play a major role in mutagenesis. It is shown that magnetically polarized cosmic rays that dominate at ground level today can impose a small, but persistent, chiral bias in the rate at which they induce structural changes in simple, chiral monomers that are the building blocks of biopolymers. A much larger effect should be present with helical biopolymers, in particular, those that may have been the progenitors of ribonucleic acid and deoxyribonucleic acid. It is shown that the interaction can be both electrostatic, just involving the molecular electric field, and electromagnetic, also involving a magnetic field. It is argued that this bias can lead to the emergence of a single, chiral life form over an evolutionary timescale. If this mechanism dominates, then the handedness of living systems should be universal. Experiments are proposed to assess the efficacy of this process.
Structure formation models weaken limits on WIMP dark matter from dwarf spheroidal galaxies

Dwarf spheroidal galaxies that form in halo substructures provide stringent constraints on dark matter annihilation. Many ultrafaint dwarfs discovered with modern surveys contribute significantly to these constraints. At present, because of the lack of abundant stellar kinematic data for the ultrafaints, noninformative prior assumptions are usually made for the parameters of the density profiles. Based on semianalytic models of dark matter subhalos and their connection to satellite galaxies, we present more informative and realistic satellite priors. We show that our satellite priors lead to constraints on the annihilation rate that are between a factor of 2 and a factor of 7 weaker than under noninformative priors. As a result, the thermal relic cross section can at best only be excluded (with 95% probability) for dark matter masses of $\ensuremath{\lesssim}40\text{ }\text{ }\mathrm{GeV}$ from dwarf spheroidal data, assuming annihilation into $b\overline{b}$.
On gapped continuum resonance spectra

In this work, we study a warped five-dimensional (5D) model with ultraviolet (UV) and infrared (IR) branes, that solves the hierarchy problem with a fundamental 5D Planck scale [Formula: see text], and curvature parameter [Formula: see text], of the order of the 4D Planck mass [Formula: see text] TeV. The model exhibits a continuum of Kaluza–Klein (KK) modes with different mass gaps, at the TeV scale, for all fields. We have computed Green’s functions and spectral densities, and shown how the presence of a continuum KK spectrum can produce an enhancement in the cross-section of some Standard Model processes. The metric is linear near the IR, in conformal coordinates, as in the linear dilaton (LD) and 5D clockwork models, for which [Formula: see text] TeV. We also analyze a pure (continuum) LD scenario, solving the hierarchy problem with more conventional fundamental [Formula: see text] and [Formula: see text] scales of the order of [Formula: see text], and a continuum spectrum.
Defending against Backdoor Attack on Deep Neural Networks

Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks. In this paper, we focus on the so-called \textit{backdoor attack}, which injects a backdoor trigger to a small portion of training data (also known as data poisoning) such that the trained DNN induces misclassification while facing examples with this trigger. To be specific, we carefully study the effect of both real and synthetic backdoor attacks on the internal response of vanilla and backdoored DNNs through the lens of Gard-CAM. Moreover, we show that the backdoor attack induces a significant bias in neuron activation in terms of the $\ell_\infty$ norm of an activation map compared to its $\ell_1$ and $\ell_2$ norm. Spurred by our results, we propose the \textit{$\ell_\infty$-based neuron pruning} to remove the backdoor from the backdoored DNN. Experiments show that our method could effectively decrease the attack success rate, and also hold a high classification accuracy for clean images.
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments

Exploration in sparse reward environments remains one of the key challenges of model-free reinforcement learning. Instead of solely relying on extrinsic rewards provided by the environment, many state-of-the-art methods use intrinsic rewards to encourage exploration. However, we show that existing methods fall short in procedurally-generated environments where an agent is unlikely to visit a state more than once. We propose a novel type of intrinsic reward which encourages the agent to take actions that lead to significant changes in its learned state representation. We evaluate our method on multiple challenging procedurally-generated tasks in MiniGrid, as well as on tasks with high-dimensional observations used in prior work. Our experiments demonstrate that this approach is more sample efficient than existing exploration methods, particularly for procedurally-generated MiniGrid environments. Furthermore, we analyze the learned behavior as well as the intrinsic reward received by our agent. In contrast to previous approaches, our intrinsic reward does not diminish during the course of training and it rewards the agent substantially more for interacting with objects that it can control.
SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech Translation

We propose autoencoding speaker conversion for training data augmentation in automatic speech translation. This technique directly transforms an audio sequence, resulting in audio thesized to resemble another speaker’s voice. Our method compares favorably to SpecAugment on English–French and English–Romanian automatic speech translation (AST) tasks as well as on a low-resource English automatic speech recognition (ASR) task. Further, in ablations, we show the benefits of both quantity and diversity in augmented data. Finally, we show that we can combine our approach with augmentation by machine-translated transcripts to obtain a competitive end-to-end AST model that outperforms a very strong cascade model on an English–French AST task. Our method is sufficiently general that it can be applied to other speech generation and analysis tasks.