Miracle-dz/UniFAR-FTUs huggingface dataset:
A multiscale scheme accurately simulates macroscale shocks in an equation-free framework

Scientists and engineers often create accurate, trustworthy, computational simulation schemes - but all too often these are too computationally expensive to execute over the time or spatial domain of interest. The equation-free approach is to marry such trusted simulations to a framework for numerical macroscale reduction - the patch dynamics scheme. This article extends the patch scheme to scenarios in which the trusted simulation resolves abrupt state changes on the microscale that appear as shocks on the macroscale. Accurate simulation for problems in these scenarios requires extending the patch scheme by capturing the shock within a novel patch, and also modifying the patch coupling rules in the vicinity in order to maintain accuracy. With these two extensions to the patch scheme, straightforward arguments derive consistency conditions that match the usual order of accuracy for patch schemes. The new scheme is successfully tested on four archetypal problems. This technique will empower scientists and engineers to accurately and efficiently simulate, over large spatial domains, multiscale multiphysics systems that have rapid transition layers on the microscale.
Analysis of Attack via Grounding and Countermeasures in Discrete-Time Consensus Networks

We investigate the disruption of discrete-time consensus problems via grounding. Loosely speaking, grounding a network occurs if the state of one agent no longer responds to inputs from other agents and/or changes its dynamics. Then, the agent becomes a leader or a so called stubborn agent. The disruption of the agent can be caused by internal faults, safety protocols or externally due to a malicious attack. In this paper we investigate how the grounding affects the eigenratio of expander graph families that usually exhibit good scaling properties with increasing network size. It is shown that the algebraic connectivity and eigenratio of the network will decrease due to the grounding causing the performance and scalability of the network to deteriorate, even to the point of losing consensusability. We then present countermeasures to such interruptions both in a passive and active manner. Our findings are supported by numerical simulations given within the paper.
Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges

Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$ flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow Jones Industrial Average lose $9\%$ of its value within minutes, to automated order "spoofing" algorithms. In this paper, we build a set of methodologies to characterize and empirically measure different algorithmic trading strategies in Binance, a large centralized cryptocurrency exchange, using a complete data set of historical trades. We find that a sub-strategy of triangular arbitrage is widespread, where bots convert between two coins through an intermediary coin, and obtain a favorable exchange rate compared to the direct one. We measure the profitability of this strategy, characterize its risks, and outline two strategies that algorithmic trading bots use to mitigate their losses. We find that this strategy yields an exchange ratio that is $0.144\%$, or $14.4$ basis points (bps) better than the direct exchange ratio. $2.71\%$ of all trades on Binance are attributable to this strategy.
Memory-Constrained No-Regret Learning in Adversarial Bandits

An adversarial bandit problem with memory constraints is studied where only the statistics of a subset of arms can be stored. A hierarchical learning policy that requires only a sublinear order of memory space in terms of the number of arms is developed. Its sublinear regret orders with respect to the time horizon are established for both weak regret and shifting regret. This work appears to be the first on memory-constrained bandit problems under the adversarial setting.
Light curves from highly compact neutron stars with spot size effect

We systematically examine light curves from a single hot spot on a slowly rotating neutron star with very high compactness, where the so-called invisible zone does not exist. In particular, we adopt three different shapes of hot spot and take into account the finite size effect of hot spot on the light curves. Then, we find that the brightening of flux occurs when the hot spot with small area crosses the opposite side to the observer, where the brightening becomes stronger as the area of hot spot decreases. Since this brightening happens only when a part of hot spot crosses the opposite side to the observer, one may constrain the neutron star geometry, i.e., the combination of an inclination angle, angle between the rotational axis and normal vector at the center of hot spot, and the opening angle of hot spot, if the brightening would be observed. In addition, by counting such a brightening in the light curves, one may know how many narrow bands of hot spot and/or spots with small area cross the opposite side to the observer.
Advances in Collaborative Filtering and Ranking

In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss; chapter 5 is about the new regularization technique Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE, which plays an important role in preventing our personalized model from overfitting to the training data; chapter 7, we summarize what we have achieved so far and predict what the future directions can be; chapter 8 is the appendix to all the chapters.
Avoided quantum criticality in exact numerical simulations of a single disordered Weyl cone

Existing theoretical works differ on whether three-dimensional Dirac and Weyl semimetals are stable to a short-range-correlated random potential. Numerical evidence suggests the semimetal to be unstable, while some field-theoretic instanton calculations have found it to be stable. The differences go beyond method: the continuum field-theoretic works use a single, perfectly linear Weyl cone, while numerical works use tight-binding lattice models which inherently have band curvature and multiple Weyl cones. In this work, we bridge this gap by performing exact numerics on the same model used in analytic treatments, and we find that all phenomena associated with rare regions near the Weyl node energy found in lattice models persist in the continuum theory: The density of states is nonzero and exhibits an avoided transition. In addition to characterizing this transition, we find rare states and show that they have the expected behavior. The simulations utilize sparse matrix techniques with formally dense matrices; doing so allows us to reach Hilbert space sizes upwards of ${10}^{7}$ states, substantially larger than anything achieved before.
Lipschitz and Comparator-Norm Adaptivity in Online Learning

We study Online Convex Optimization in the unbounded setting where neither predictions nor gradient are constrained. The goal is to simultaneously adapt to both the sequence of gradients and the comparator. We first develop parameter-free and scale-free algorithms for a simplified setting with hints. We present two versions: the first adapts to the squared norms of both comparator and gradients separately using $O(d)$ time per round, the second adapts to their squared inner products (which measure variance only in the comparator direction) in time $O(d^3)$ per round. We then generalize two prior reductions to the unbounded setting; one to not need hints, and a second to deal with the range ratio problem (which already arises in prior work). We discuss their optimality in light of prior and new lower bounds. We apply our methods to obtain sharper regret bounds for scale-invariant online prediction with linear models.
Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.
Have hierarchical three-body mergers been detected by LIGO/Virgo?

One of the proposed channels of binary black hole mergers involves dynamical interactions of three black holes. In such scenarios, it is possible that all three black holes merge in a so-called hierarchical merger chain, where two of the black holes merge first and then their remnant subsequently merges with the remaining single black hole. Depending on the dynamical environment, it is possible that both mergers will appear within the observable time window. Here, we perform a search for such merger pairs in the public available LIGO and Virgo data from the O1/O2 runs. Using a frequentist p-value assignment statistics, we do not find any significant merger pair candidates, the most significant being GW170809-GW151012 pair. Assuming no observed candidates in O3/O4, we derive upper limits on merger pairs to be ∼11–110 yr−1 Gpc−3, corresponding to a rate that relative to the total merger rate is ∼0.1−1.0. From this, we argue that both a detection and a non-detection within the next few years can be used to put useful constraints on some dynamical progenitor models.
Reducing Geographic Performance Differentials for Face Recognition

As face recognition algorithms become more accurate and get deployed more widely, it becomes increasingly important to ensure that the algorithms work equally well for everyone. We study the geographic performance differentials—differences in false acceptance and false rejection rates across different countries—when comparing selfies against photos from ID documents. We show how to mitigate geographic performance differentials using sampling strategies despite large imbalances in the dataset. Using vanilla domain adaptation strategies to fine-tune a face recognition CNN on domain-specific doc-selfie data improves the performance of the model on such data, but, in the presence of imbalanced training data, also significantly increases the demographic bias. We then show how to mitigate this effect by employing sampling strategies to balance the training procedure.
Towards a complete description of spectra and polarization of black hole accretion discs: albedo profiles and returning radiation

Accretion disks around stellar-mass black holes (BHs) emit radiation peaking in the soft X-rays when the source is in the thermal state. The emerging photons are polarized and, for symmetry reasons, the polarization integrated over the source is expected to be either parallel or perpendicular to the (projected) disk symmetry axis, because of electron scattering in the disk. However, due to General Relativity effects photon polarization vectors will rotate with respect to their original orientation, by an amount depending on both the BH spin and the observer's inclination. Hence, X-ray polarization measurements may provide important information about strong gravity effects around these sources. Along with the spectral and polarization properties of radiation which reaches directly the observer once emitted from the disk, in this paper we also include the contribution of returning radiation, i.e. photons that are bent by the strong BH gravity to return again on the disk, where they scatter until eventually escaping to infinity. A comparison between our results and those obtained in previous works by different authors show an overall good agreement, despite the use of different code architectures. We finally consider the effects of absorption in the disk material by including more realistic albedo profiles for the disk surface. Our findings in this respect show that considering also the ionization state of the disk may deeply modify the behavior of polarization observables.
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference

In this contribution, we propose a new computationally efficient method to combine Variational Inference (VI) with Markov Chain Monte Carlo (MCMC). This approach can be used with generic MCMC kernels, but is especially well suited to \textit{MetFlow}, a novel family of MCMC algorithms we introduce, in which proposals are obtained using Normalizing Flows. The marginal distribution produced by such MCMC algorithms is a mixture of flow-based distributions, thus drastically increasing the expressivity of the variational family. Unlike previous methods following this direction, our approach is amenable to the reparametrization trick and does not rely on computationally expensive reverse kernels. Extensive numerical experiments show clear computational and performance improvements over state-of-the-art methods.
H i asymmetries in LVHIS, VIVA, and HALOGAS galaxies

We present an analysis of morphological, kinematic, and spectral asymmetries in observations of atomic neutral hydrogen (H i) gas from the Local Volume H i Survey (LVHIS), the VLA Imaging of Virgo in Atomic Gas (VIVA) survey, and the Hydrogen Accretion in Local Galaxies Survey. With the aim of investigating the impact of the local environment density and stellar mass on the measured H i asymmetries in future large H i surveys, we provide recommendations for the most meaningful measures of asymmetry for use in future analysis. After controlling for stellar mass, we find signs of statistically significant trends of increasing asymmetries with local density. The most significant trend we measure is for the normalized flipped spectrum residual (Aspec), with mean LVHIS and VIVA values of 0.204 ± 0.011 and 0.615 ± 0.068 at average weighted 10th nearest-neighbour galaxy number densities of log (ρ10/Mpc−3) = −1.64 and 0.88, respectively. Looking ahead to the Widefield ASKAP L-band Legacy All-sky Blind survey on the Australian Square Kilometre Array Pathfinder, we estimate that the number of detections will be sufficient to provide coverage over 5 orders of magnitude in both local density and stellar mass increasing the dynamic range and accuracy with which we can probe the effect of these properties on the asymmetry in the distribution of atomic gas in galaxies.
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image

For a robotic grasping task in which diverse unseen target objects exist in a cluttered environment, some deep learning-based methods have achieved state-of-the-art results using visual input directly. In contrast, actor-critic deep reinforcement learning (RL) methods typically perform very poorly when grasping diverse objects, especially when learning from raw images and sparse rewards. To make these RL techniques feasible for vision-based grasping tasks, we employ state representation learning (SRL), where we encode essential information first for subsequent use in RL. However, typical representation learning procedures are unsuitable for extracting pertinent information for learning the grasping skill, because the visual inputs for representation learning, where a robot attempts to grasp a target object in clutter, are extremely complex. We found that preprocessing based on the disentanglement of a raw input image is the key to effectively capturing a compact representation. This enables deep RL to learn robotic grasping skills from highly varied and diverse visual inputs. We demonstrate the effectiveness of this approach with varying levels of disentanglement in a realistic simulated environment.
Uncertainty Quantification for Sparse Deep Learning

Deep learning methods continue to have a decided impact on machine learning, both in theory and in practice. Statistical theoretical developments have been mostly concerned with approximability or rates of estimation when recovering infinite dimensional objects (curves or densities). Despite the impressive array of available theoretical results, the literature has been largely silent about uncertainty quantification for deep learning. This paper takes a step forward in this important direction by taking a Bayesian point of view. We study Gaussian approximability of certain aspects of posterior distributions of sparse deep ReLU architectures in non-parametric regression. Building on tools from Bayesian non-parametrics, we provide semi-parametric Bernstein-von Mises theorems for linear and quadratic functionals, which guarantee that implied Bayesian credible regions have valid frequentist coverage. Our results provide new theoretical justifications for (Bayesian) deep learning with ReLU activation functions, highlighting their inferential potential.
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Neurophysiological laboratory studies are often constraint to immediate geographical surroundings and access to equipment may be temporally restricted. Limitations of ecological validity, scalability, and generalizability of findings pose a significant challenge for the development of brain-computer interfaces (BCIs), which ultimately need to function in any context, on consumer-grade hardware. We introduce MYND: An open-source framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies. Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires at home. As a use case, thirty subjects evaluated two BCI control strategies "Positive memories" and "Music imagery" by using a four-channel electroencephalogram (EEG) with MYND. Neural activity in both control strategies could be decoded with an average offline accuracy of 68.5% and 64.0% across all days.
Assortativity and bidegree distributions on Bernoulli random graph superpositions

Abstract A probabilistic generative network model with $n$ nodes and $m$ overlapping layers is obtained as a superposition of $m$ mutually independent Bernoulli random graphs of varying size and strength. When $n$ and $m$ are large and of the same order of magnitude, the model admits a sparse limiting regime with a tunable power-law degree distribution and nonvanishing clustering coefficient. In this article, we prove an asymptotic formula for the joint degree distribution of adjacent nodes. This yields a simple analytical formula for the model assortativity and opens up ways to analyze rank correlation coefficients suitable for random graphs with heavy-tailed degree distributions. We also study the effects of power laws on the asymptotic joint degree distributions.
Maximising Manipulability During Resolved-Rate Motion Control

Resolved-rate motion control of redundant serial-link manipulators is commonly achieved using the Moore-Penrose pseudoinverse in which the norm of the control input is minimized. However, as kinematic singularities are a significant issue for robotic manipulators, we propose a Manipulability Motion Controller which chooses joint velocities which will also increase the manipulability of the robot. The manipulability measure has a complex non-linear relationship with the robot's joint configuration and in this paper we derive the manipulability Jacobian which directly relates joint velocities to the rate of change of manipulability. Furthermore, we use the manipulability Jacobian within a constrained quadratic program to create an improved resolved-rate motion controller for redundant robots. The resulting real-time controller provides joint velocities which achieve a desired Cartesian end-effector velocity while also maximising the robot's manipulability. We illustrate and verify our control scheme on several popular serial-link manipulators and provide an open-source library which implements our controller (available at this http URL).
Open Set Modulation Recognition Based on Dual-Channel LSTM Model.

Deep neural networks have achieved great success in computer vision, speech recognition and many other areas. The potential of recurrent neural networks especially the Long Short-Term Memory (LSTM) for open set communication signal modulation recognition is investigated in this letter. Time-domain sampled signals are first converted to two normalized matrices which will be fed into a four layer Dual-Channel LSTM network tailored for open set modulation recognition. With two cascaded Dual-Channel LSTM layers, the designed network can automatically learn sequence-correlated features from the raw data. With center loss and weibull distribution, proposed algorithm can recognize partial open set modulations. Experiments on the public RadioML dataset indicates that different analog and digital modulations can be effectively classified by the proposed model, while partial open set modulations can be recognized. Quantitative analysis on the dataset shows that the proposed method can achieve an average accuracy of 90.2% at varying SNR ranging from 0dB to 18dB in classifying the considered 11 classes, while accuracy of open set experiment dramatically improved by 14.2%.
New Test of the Gravitational 1/r^{2} Law at Separations down to 52  μm.

We tested the gravitational 1/r^{2} law using a stationary torsion-balance detector and a rotating attractor containing test bodies with both 18-fold and 120-fold azimuthal symmetries that simultaneously tests the 1/r^{2} law at two different length scales. We took data at detector-attractor separations between 52 μm and 3.0 mm. Newtonian gravity gave an excellent fit to our data, limiting with 95% confidence any gravitational-strength Yukawa interactions to ranges <38.6 μm.