Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweight and high angular resolution x-ray optics for astronomical missions

X-ray optics of both high angular resolution and light weight are essential for advancing x-ray astrophysics. High angular resolution is important for avoiding source confusion and reducing background, thus allowing observation of the most distant objects in the early Universe. It is also important in enabling gratings to achieve high spectral resolution, to study the myriad plasmas in planetary, stellar, and galactic environments, as well as inter-planetary, inter-stellar, and inter-galactic media. Light weight is essential for further increasing photon collection area: X-ray observations must be performed from space, where mass available for a telescope has always been and is expected to continue to be quite limited. This paper reports on a program to develop x-ray optics satisfying these two requirements. The objective of this technology program is to enable Explorer-class missions in the near term and facility-class missions in the long term.
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation

Deep learning networks have demonstrated high performance in a large variety of applications, such as image classification, speech recognition, and natural language processing. However, there exists a major vulnerability exploited by the use of adversarial attacks. An adversarial attack imputes images by altering the input image very slightly, making it nearly undetectable to the naked eye, but results in a very different classification by the network. This paper explores the projected gradient descent (PGD) attack and the Adaptive Mask Segmentation Attack (ASMA) on the image segmentation DeepLabV3 model using two types of architectures: MobileNetV3 and ResNet50, It was found that PGD was very consistent in changing the segmentation to be its target while the generalization of ASMA to a multiclass target was not as effective. The existence of such attack however puts all of image classification deep learning networks in danger of exploitation.
A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO With Mobility

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users’ velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.
Layered Binary Templating: Efficient Detection of Compiler- and Linker-introduced Leakage

Cache template attacks demonstrated automated leakage of user input in shared libraries. However, for large binaries, the runtime is prohibitively high. Other automated approaches focused on cryptographic implementations and media software but are not directly applicable to user input. Hence, discovering and eliminating all user input side-channel leakage on a cache-line granularity within huge code bases are impractical. In this paper, we present a new generic cache template attack technique, LBTA, layered binary templating attacks. LBTA uses multiple coarser-grained side channel layers as an extension to cache-line granularity templating to speed up the runtime of cache templating attacks. We describe LBTA with a variable number of layers with concrete side channels of different granularity, ranging from 64 B to 2MB in practice and in theory beyond. In particular the software-level page cache side channel in combination with the hardware-level L3 cache side channel, already reduces the templating runtime by three orders of magnitude. We apply LBTAs to different software projects and thereby discover data deduplication and dead-stripping during compilation and linking as novel security issues. We show that these mechanisms introduce large spatial distances in binaries for data accessed during a keystroke, enabling reliable leakage of keystrokes. Using LBTA on Chromium-based applications, we can build a full unprivileged cache-based keylogger. Our findings show that all user input to Chromium-based apps is affected and we demonstrate this on a selection of popular apps including Signal, Threema, Discord, and password manager apps like passky. As this is not a flaw of individual apps but the framework, we conclude that all apps that use the framework will also be affected, i.e., hundreds of apps.
Online Decentralized Tracking for Nonlinear Time-Varying Optimal Power Flow of Coupled Transmission–Distribution Grids

The coordinated transmission–distribution alternating current optimal power flow (TD-ACOPF) has become crucial to handle problems related to high penetration of renewable energy sources (RESs). However, obtaining all system details and solving TD-ACOPF centrally is sometimes less preferable in practice due to privacy concerns. Intermittent RESs and uncontrollable loads can swiftly change the operating condition of the power grid. Existing decentralized optimization methods can seldom track the optimal solutions of time-varying TD-ACOPFs. Here, we develop a continuous-time dynamic approach based on the primal-dual interior point method to online track the saddle points of the Lagrangian for time-varying ACOPFs from a control system perspective. To improve tracking accuracy, a prediction term denoted by the partial derivative with respect to time is added to the dynamic system. Next, a decentralized implementation for solving the dynamic system is designed based on only a few information exchanges with respect to boundary variables. Moreover, the proposed continuous-time dynamic approach is derived from the first-order optimality condition of the ACOPF model (without approximation) for coping with the nonlinear power flow equation model without the need for real-time measurements. Numerical test results reveal the effectiveness and fast-tracking performance of the proposed algorithm.
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud

Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud. To train our model without the time consuming and tedious data labeling process, we first generate synthetic primitives for the basic appearance of target lines, and build an iterative line auto-labeling process to gradually refine line labels on real LiDAR scans. Our segmentation model can extract lines under arbitrary scale perturbations, and we use shared EdgeConv encoder layers to train the two segmentation and descriptor heads jointly. Base on the model, we can build a highly-available global registration module for point cloud registration, in conditions without initial transformation hints. Experiments have demonstrated that our line-based registration method is highly competitive to state-of-the-art point-based approaches. Our code is available at https://github.com/zxrzju/SuperLine3D.git.
Compositional Synthesis for Linear Systems via Convex Optimization of Assume-Guarantee Contracts

We take a divide and conquer approach to design controllers for reachability problems given large-scale linear systems with polyhedral constraints on states, controls, and disturbances. Such systems are made of small subsystems with coupled dynamics. We treat the couplings as additional disturbances and use assume-guarantee (AG) contracts to characterize these disturbance sets. For each subsystem, we design and implement a robust controller locally, subject to its own constraints and contracts. The main contribution of this paper is a method to derive the contracts via a novel parameterization and a corresponding potential function that characterizes the distance to the correct composition of controllers and contracts, where all contracts are held. We show that the potential function is convex in the contract parameters. This enables the subsystems to negotiate the contracts with the gradient information from the dual of their local synthesis optimization problems in a distributed way, facilitating compositional control synthesis that scales to large systems. We present numerical examples, including a scalability study on a system with tens of thousands of dimensions, and a case study on applying our method to a distributed Model Predictive Control (MPC) problem in a power system.
A Nonparametric Framework for Online Stochastic Matching with Correlated Arrivals

The design of online policies for stochastic matching and revenue management settings is usually bound by the Bayesian prior that the demand process is formed by a fixed-length sequence of queries with unknown types, each drawn independently. This assumption of serial independence implies that the demand of each type, i.e., the number of queries of a given type, has low variance and is approximately Poisson-distributed. Thus, matching policies are often based on "fluid" LPs that only use the expectations of these distributions.
Solving Inverse PDE Problems using Grid-Free Monte Carlo Estimators

Modeling physical phenomena like heat transport and diffusion is crucially dependent on the numerical solution of partial differential equations (PDEs). A PDE solver finds the solution given coefficients and a boundary condition, whereas an inverse PDE solver goes the opposite way and reconstructs these inputs from an existing solution. In this article, we investigate techniques for solving inverse PDE problems using a gradient-based methodology. Conventional PDE solvers based on the finite element method require a domain meshing step that can be fragile and costly. Grid-free Monte Carlo methods instead stochastically sample paths using variations of the walk on spheres algorithm to construct an unbiased estimator of the solution. The uncanny similarity of these methods to physically-based rendering algorithms has been observed by several recent works. In the area of rendering, recent progress has led to the development of efficient unbiased derivative estimators. They solve an adjoint form of the problem and exploit arithmetic invertibility to compute gradients using a constant amount of memory and linear time complexity. Could these two lines of work be combined to compute cheap parametric derivatives of a grid-free PDE solver? We investigate this question and present preliminary results.
SGEM: stochastic gradient with energy and momentum

In this paper, we propose SGEM, Stochastic Gradient with Energy and Momentum, to solve a large class of general non-convex stochastic optimization problems, based on the AEGD method that originated in the work [AEGD: Adaptive Gradient Descent with Energy. arXiv: 2010.05109]. SGEM incorporates both energy and momentum at the same time so as to inherit their dual advantages. We show that SGEM features an unconditional energy stability property, and derive energy-dependent convergence rates in the general nonconvex stochastic setting, as well as a regret bound in the online convex setting. A lower threshold for the energy variable is also provided. Our experimental results show that SGEM converges faster than AEGD and generalizes better or at least as well as SGDM in training some deep neural networks.
On the Computational Complexity of the Strong Geodetic Recognition Problem

A strong geodetic set of a graph~$G=(V,E)$ is a vertex set~$S \subseteq V(G)$ in which it is possible to cover all the remaining vertices of~$V(G) \setminus S$ by assigning a unique shortest path between each vertex pair of~$S$. In the Strong Geodetic problem (SG) a graph~$G$ and a positive integer~$k$ are given as input and one has to decide whether~$G$ has a strong geodetic set of cardinality at most~$k$. This problem is known to be NP-hard for general graphs. In this work we introduce the Strong Geodetic Recognition problem (SGR), which consists in determining whether even a given vertex set~$S \subseteq V(G)$ is strong geodetic. We demonstrate that this version is NP-complete. We investigate and compare the computational complexity of both decision problems restricted to some graph classes, deriving polynomial-time algorithms, NP-completeness proofs, and initial parameterized complexity results, including an answer to an open question in the literature for the complexity of SG for chordal graphs.
Character Generation through Self-Supervised Vectorization

The prevalent approach in self-supervised image generation is to operate on pixel level representations. While this approach can produce high quality images, it cannot benefit from the simplicity and innate quality of vectorization. Here we present a drawing agent that operates on stroke-level representation of images. At each time step, the agent first assesses the current canvas and decides whether to stop or keep drawing. When a 'draw' decision is made, the agent outputs a program indicating the stroke to be drawn. As a result, it produces a final raster image by drawing the strokes on a canvas, using a minimal number of strokes and dynamically deciding when to stop. We train our agent through reinforcement learning on MNIST and Omniglot datasets for unconditional generation and parsing (reconstruction) tasks. We utilize our parsing agent for exemplar generation and type conditioned concept generation in Omniglot challenge without any further training. We present successful results on all three generation tasks and the parsing task. Crucially, we do not need any stroke-level or vector supervision; we only use raster images for training.
YOLO-FaceV2: A Scale and Occlusion Aware Face Detector

In recent years, face detection algorithms based on deep learning have made great progress. These algorithms can be generally divided into two categories, i.e. two-stage detector like Faster R-CNN and one-stage detector like YOLO. Because of the better balance between accuracy and speed, one-stage detectors have been widely used in many applications. In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2. We design a Receptive Field Enhancement module called RFE to enhance receptive field of small face, and use NWD Loss to make up for the sensitivity of IoU to the location deviation of tiny objects. For face occlusion, we present an attention module named SEAM and introduce Repulsion Loss to solve it. Moreover, we use a weight function Slide to solve the imbalance between easy and hard samples and use the information of the effective receptive field to design the anchor. The experimental results on WiderFace dataset show that our face detector outperforms YOLO and its variants can be find in all easy, medium and hard subsets. Source code in https://github.com/Krasjet-Yu/YOLO-FaceV2
Differentiable Subdivision Surface Fitting

In this paper, we present a powerful differentiable surface fitting technique to derive a compact surface representation for a given dense point cloud or mesh, with application in the domains of graphics and CAD/CAM. We have chosen the Loop subdivision surface, which in the limit yields the smooth surface underlying the point cloud, and can handle complex surface topology better than other popular compact representations, such as NURBS. The principal idea is to fit the Loop subdivision surface not directly to the point cloud, but to the IMLS (implicit moving least squares) surface defined over the point cloud. As both Loop subdivision and IMLS have analytical expressions, we are able to formulate the problem as an unconstrained minimization problem of a completely differentiable function that can be solved with standard numerical solvers. Differentiability enables us to integrate the subdivision surface into any deep learning method for point clouds or meshes. We demonstrate the versatility and potential of this approach by using it in conjunction with a differentiable renderer to robustly reconstruct compact surface representations of spatial-temporal sequences of dense meshes.
Distinct viscoelastic scaling for isostatic spring networks of the same fractal dimension

Fractal structure emerges spontaneously from the chemical crosslinking of monomers into hydrogels, and has been directly linked to power law viscoelasticity at the gel transition, as recently demonstrated for isostatic (marginally–rigid) spring networks based on the Sierpinski triangle. Here we generalize the Sierpinski triangle generation rules to produce 4 fractals, all with the same dimension d f = log 3 / log 2, with the Sierpinski triangle being one case. We show that spring networks derived from these fractals are all isostatic, but exhibit one of two distinct exponents for their power–law viscoelasticity. We conclude that, even for networks with ﬁxed connectivity, power–law viscoelasticity cannot generally be a function of the fractal dimension alone.
PolarFly: A Cost-Effective and Flexible Low-Diameter Topology

In this paper we present PolarFly, a diameter-2 network topology based on the Erdos-Renyi family of polarity graphs from finite geometry. This is the first known diameter-2 topology that asymptotically reaches the Moore bound on the number of nodes for a given network degree and diameter. PolarFly achieves high Moore bound efficiency even for the moderate radixes commonly seen in current and near-future routers, reaching more than 96% of the theoretical peak. It also offers more feasible router degrees than the state-of-the-art solutions, greatly adding to the selection of scalable diameter-2 networks. PolarFly enjoys many other topological properties highly relevant in practice, such as a modular design and expandability that allow incremental growth in network size without rewiring the whole network. Our evaluation shows that PolarFly outperforms competitive networks in terms of scalability, cost and performance for various traffic patterns.
Universal crossover in quantum circuits governed by a proximate classical error correction transition

We formulate a semi-classical circuit model to clarify the role of quantum entanglement in the recently discovered encoding phase transitions in quantum circuits with measurements. As a starting point we define a random circuit model with nearest neighbor classical gates interrupted by erasure errors. In analogy with the quantum setting, this system undergoes a purification transition at a critical error rate above which the classical information entropy in the output state vanishes. We show that this phase transition is in the directed percolation universality class, consistent with the fact that having zero entropy is an absorbing state of the dynamics; this classical circuit cannot generate entropy. Adding an arbitrarily small density of quantum gates in the presence of errors eliminates the transition by destroying the absorbing state: the quantum gates generate internal entanglement, which can be effectively converted to classical entropy by the errors. We describe the universal properties of this instability in an effective model of the semi-classical circuit. Our model highlights the crucial differences between information dynamics in classical and quantum circuits.
A Fractional Image Inpainting Model Using a Variant of Mumford-Shah Model

In this paper, we propose a fourth order PDE model for image inpainting based on a variant of the famous Mumford-Shah (MS) image segmentation model. Convexity splitting is used to discrtised the time and we replace the Laplacian by its fractional counterpart in the time discretised scheme. Fourier spectral method is used for space discretization. Consistency, stability and convergence of the time discretised model has been carried out. The model is tested on some standard test images and compared them with the result of some models existed in the literature.
Analysis of Br(anti-B ---> X(s gamma)) at NNLO with a cut on photon energy

By combining a recent estimate of the total {bar B} {yields} X{sub s}{gamma} branching fraction at O({alpha}{sub s}{sup 2}) with a detailed analysis of the effects of a cut E{sub {gamma}} {ge} 1.6 GeV on photon energy, a prediction for the partial {bar B} {yields} X{sub s}{gamma} branching fraction at next-to-next-to-leading order in renormalization-group improved perturbation theory is obtained, in which contributions from all relevant scales are properly factorized. The result Br({bar B} {yields} X{sub s}{gamma}) = (2.98 {+-} 0.26) {center_dot} 10{sup -4} is about 1.4{sigma} lower than the experimental world average. This opens a window for significant New Physics contributions in rare radiative B decays.
Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing

Process mining employs event data extracted from diﬀerent types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Diﬀerential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data are released in only one shot, whereas business processes are continuously executed. Hence, event data are published repeatedly, resulting in additional risks. In this paper, we demonstrate that continuously released event data are not independent, and the correlation among diﬀerent releases can result in privacy degradation when the same diﬀerential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.
Harrow-Hassidim-Lloyd algorithm without ancilla postselection

Harrow-Hassidim-Lloyd algorithm (HHL) allows for the exponentially faster solution of a system of linear equations. However, this algorithm requires the postselection of an ancilla qubit to obtain the solution. This postselection makes the algorithm result probabilistic. Here we show conditions when the HHL algorithm can work without postselection of ancilla qubit. We derive expressions for expectation values for an observable $M$ on the HHL outcome state when ancilla qubit is measured in 0 and 1. When a commutator of an input matrix and an observable matrix is zero, the HHL algorithm can give correct expectation values for any outcome of ancilla measurement. We demonstrate this result on a toy 2 by 2 matrix example. We further provide more general examples of matrices $A$ and observables $M$, which allow the postselection-free running of the algorithm. Our work can improve the performance of the HHL-based algorithms.