Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization

—Reinforcement learning (RL) has made remark- able progress in many decision-making tasks, such as Go, game playing, and robotics control. However, classic RL approaches often presume that all actions can be executed an inﬁnite number of times, which is inconsistent with many decision-making scenarios in which actions have limited budgets or execution opportunities. Imagine an agent playing a gunﬁghting game with limited ammunition. It only ﬁres when the enemy appears in the correct position, making shooting a sparse-executing action. Such sparse-executing action has not been considered by classic RL algorithms in problem formulation or effective algorithms design. This paper attempts to address sparse-executing action issues by ﬁrst formalizing the problem as a Sparse Action Markov Decision Process (SA-MDP), in which certain actions in the action space can only be executed for limited amounts of time. Then, we propose a policy optimization algorithm called Action Sparsity REgularization (ASRE) that gives each action a distinct preference. ASRE evaluates action sparsity through constrained action sampling and regularizes policy training based on the evaluated action sparsity, represented by action distribution. Experiments on tasks with known sparse-executing actions, where classical RL algorithms struggle to train policy efﬁciently, ASRE effectively constrains the action sampling and outperforms baselines. Moreover, we present that ASRE can generally improve the performance in Atari games, demonstrating its broad applicability.
ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions – XV. Steady accretion from global collapse to core feeding in massive hub-filament system SDC335

We present ALMA Band-3/7 observations towards ‘the Heart’ of a massive hub-filament system (HFS) SDC335, to investigate its fragmentation and accretion. At a resolution of ∼0.03 pc, 3 mm continuum emission resolves two massive dense cores MM1 and MM2, with $383(^{\scriptscriptstyle +234}_{\scriptscriptstyle -120})$ M⊙ (10–24 % mass of ‘the Heart’) and $74(^{\scriptscriptstyle +47}_{\scriptscriptstyle -24})$ M⊙, respectively. With a resolution down to 0.01 pc, 0.87 mm continuum emission shows MM1 further fragments into six condensations and multi-transition lines of H2CS provide temperature estimation. The relation between separation and mass of condensations at a scale of 0.01 pc favors turbulent Jeans fragmentation where the turbulence seems to be scale-free rather than scale-dependent. We use the H13CO+ J = 1 − 0 emission line to resolve the complex gas motion inside ‘the Heart’ in position-position-velocity space. We identify four major gas streams connected to large-scale filaments, inheriting the anti-clockwise spiral pattern. Along these streams, gas feeds the central massive core MM1. Assuming an inclination angle of 45(± 15)° and a H13CO+ abundance of 5(± 3) × 10−11, the total mass infall rate is estimated to be 2.40(± 0.78) × 10−3 M⊙ yr−1, numerically consistent with the accretion rates derived from the clump-scale spherical infall model and the core-scale outflows. The consistency suggests a continuous, near steady-state, and efficient accretion from global collapse, therefore ensuring core feeding. Our comprehensive study of SDC335 showcases the detailed gas kinematics in a prototypical massive infalling clump, and calls for further systematic and statistical studies in a large sample.
Competitive learning to generate sparse representations for associative memory

One of the most well established brain principles, Hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned. Palm's work implements this concept through multiple binary Willshaw associative memories, in a model that not only has a wide cognitive explanatory power but also makes neuroscientific predictions. Yet, Willshaw's associative memory can only achieve top capacity when the stored vectors are extremely sparse (number of active bits can grow logarithmically with the vector's length). This strict requirement makes it difficult to apply any model that uses this associative memory, like Palm's, to real data. Hence the fact that most works apply the memory to optimal randomly generated codes that do not represent any information. This issue creates the need for encoders that can take real data, and produce sparse representations - a problem which is also raised following Barlow's efficient coding principle. In this work, we propose a biologically-constrained network that encodes images into codes that are suitable for Willshaw's associative memory. The network is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme. After conducting auto- and hetero-association experiments on two visual data sets, we can conclude that our network not only beats sparse coding baselines, but also that it comes close to the performance achieved using optimal random codes.
Online Decomposition of Surface Electromyogram Into Individual Motor Unit Activities Using Progressive FastICA Peel-Off

Surface electromyogram (SEMG) decomposition provides a promising tool for decoding and understanding neural drive information non-invasively. In contrast to previous SEMG decomposition methods mainly developed in offline conditions, there are few studies on online SEMG decomposition. A novel method for online decomposition of SEMG data is presented using the progressive FastICA peel-off (PFP) method. The proposed online method utilized a two-stage approach, consisting of an offline prework stage for initializing high-quality separation vectors through the offline PFP algorithm, and an online decomposition stage for estimating source signals of different motor units by applying these vectors to the input SEMG data stream. Specifically, a new successive multi-threshold Otsu algorithm was developed in the online stage for determining each motor unit spike train (MUST) precisely with fast and simple computations (to replace a time-consuming iterative threshold setting in the original PFP method). The performance of the proposed online SEMG decomposition method was evaluated by both simulation and experimental approaches. When processing simulated SEMG data, the online PFP method achieved a decomposition accuracy of 97.37%, superior to that (95.1%) of an online method with a traditional k-means clustering algorithm for MUST extraction. Our method was also found to achieve superior performance at higher noise levels. For decomposing experimental SEMG data, the online PFP method was able to extract an average of 12.00 ± 3.46 MUs per trial, with a matching rate of 90.38%, with respect to the expert-guided offline decomposition results. Our study provides a valuable way of online decomposition of SEMG data with advanced applications in movement control and health.
Optimal Lower Bounds for Quantum Learning via Information Theory

Although a concept class may be learnt more efficiently using quantum samples as compared with classical samples in certain scenarios, quantum learners are asymptotically no more efficient than classical ones in the quantum PAC and Agnostic learning models. Lower bounds on sample complexity in these models were previously established via quantum state identification and Fourier analysis. In this paper, we derive optimal lower bounds for quantum sample complexity in both models via an information-theoretic approach. The proofs are arguably simpler, and the same ideas can potentially be used to derive optimal bounds for other problems in quantum learning theory. We then turn to a quantum analogue of the Coupon Collector problem, a classic problem from probability theory also of importance in the study of PAC learning. The quantum sample complexity of this problem has been characterised up to constant factors. First, we show that the information-theoretic approach mentioned above provably does not yield the optimal lower bound. As a by-product, we get a natural ensemble of pure states in arbitrarily high dimensions which are not easily (simultaneously) distinguishable, whereas the ensemble has close to maximal Holevo information. Second, we discover that the information-theoretic approach yields an asymptotically optimal bound for an approximation variant of the problem. Finally, we derive a sharper lower bound for the Quantum Coupon Collector problem via the generalised Holevo-Curlander bounds. All the aspects of the problem we study rest on properties of the spectrum of the associated Gram matrix, which may be of independent interest.
FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion

Fashion-image editing represents a challenging computer vision task, where the goal is to incorporate selected apparel into a given input image. Most existing techniques, known as Virtual Try-On methods, deal with this task by first selecting an example image of the desired apparel and then transferring the clothing onto the target person. Conversely, in this paper, we consider editing fashion images with text descriptions. Such an approach has several advantages over example-based virtual try-on techniques, e.g.: (i) it does not require an image of the target fashion item, and (ii) it allows the expression of a wide variety of visual concepts through the use of natural language. Existing image-editing methods that work with language inputs are heavily constrained by their requirement for training sets with rich attribute annotations or they are only able to handle simple text descriptions. We address these constraints by proposing a novel text-conditioned editing model, called FICE (Fashion Image CLIP Editing), capable of handling a wide variety of diverse text descriptions to guide the editing procedure. Specifically with FICE, we augment the common GAN inversion process by including semantic, pose-related, and image-level constraints when generating images. We leverage the capabilities of the CLIP model to enforce the semantics, due to its impressive image-text association capabilities. We furthermore propose a latent-code regularization technique that provides the means to better control the fidelity of the synthesized images. We validate FICE through rigorous experiments on a combination of VITON images and Fashion-Gen text descriptions and in comparison with several state-of-the-art text-conditioned image editing approaches. Experimental results demonstrate FICE generates highly realistic fashion images and leads to stronger editing performance than existing competing approaches.
Inference on the intraday spot volatility from high-frequency order prices with irregular microstructure noise

We consider estimation of the spot volatility in a stochastic boundary model with one-sided microstructure noise for high-frequency limit order prices. Based on discrete, noisy observations of an Itô semimartingale with jumps and general stochastic volatility, we present a simple and explicit estimator using local order statistics. We establish consistency and stable central limit theorems as asymptotic properties. The asymptotic analysis builds upon an expansion of tail probabilities for the order statistics based on a generalized arcsine law. In order to use the involved distribution of local order statistics for a bias correction, an efficient numerical algorithm is developed. We demonstrate the finite-sample performance of the estimation in a Monte Carlo simulation.
Event Camera Data Pre-training

This paper proposes a pre-trained neural network for handling event camera data. Our model is a self-supervised learning framework, and uses paired event camera data and natural RGB images for training. Our method contains three modules connected in a sequence: i) a family of event data augmentations, generating meaningful event images for self-supervised training; ii) a conditional masking strategy to sample informative event patches from event images, encouraging our model to capture the spatial layout of a scene and accelerating training; iii) a contrastive learning approach, enforcing the similarity of embeddings between matching event images, and between paired event and RGB images. An embedding projection loss is proposed to avoid the model collapse when enforcing the event image embedding similarities. A probability distribution alignment loss is proposed to encourage the event image to be consistent with its paired RGB image in the feature space. Transfer learning performance on downstream tasks shows the superiority of our method over state-of-the-art methods. For example, we achieve top-1 accuracy at 64.83% on the N-ImageNet dataset. Our code is available at https://github.com/Yan98/Event-Camera-Data-Pre-training.
Evidence of coherence in strong-field electron photoemission from a semiconductor

Strong-field quantum electronics is emerging as a potential candidate in information processing but still coherence vs decoherence is a primary concern of the concept. Strong-field coherent processes in band gap materials have led during the last decade to the emergence of high harmonic generation in semiconductors, petahertz electronics, or strong-field quantum states. However, the coherent behavior of the sub-optical cycle-driven electrons has never been directly observed. We report here on the experimental evidence of coherent ultrashort emission of hot electrons from a nanostructured semiconductor. Our method uses sub-wavelength electric field enhancement to localize the electron emission within a nanometer-scale spot. We found similarities with the electron emission from metallic nanotips in the strong-field regime, a topic that has opened a vast domain of applications during the last decade. The electron spectra display both odd and even harmonic orders of the driving femtosecond laser frequency, a signature of the coherent nature of the electron emission and their attosecond timing. Our findings complete our knowledge of phenomena governing coherent strong-field processes in semiconductors and open perspectives for the generation of future quantum devices operating in the strong-field regime.
Explain to Me: Towards Understanding Privacy Decisions

Privacy assistants help users manage their privacy online. Their tasks could vary from detecting privacy violations to recommending sharing actions for content that the user intends to share. Recent work on these tasks are promising and show that privacy assistants can successfully tackle them. However, for such privacy assistants to be employed by users, it is important that these assistants can explain their decisions to users. Accordingly, this paper develops a methodology to create explanations of privacy. The methodology is based on identifying important topics in a domain of interest, providing explanation schemes for decisions, and generating them automatically. We apply our proposed methodology on a real-world privacy data set, which contains images labeled as private or public to explain the labels. We evaluate our approach on a user study that depicts what factors are influential for users to find explanations useful.
Algorithms and Complexity for Computing Nash Equilibria in Adversarial Team Games

Adversarial team games model multiplayer strategic interactions in which a team of identically-interested players is competing against an adversarial player in a zero-sum game. Such games capture many well-studied settings in algorithmic game theory, unifying two-player zero-sum games and potential games, but go well-beyond to environments wherein the cooperation of one team---in the absence of explicit communication---is obstructed by competing entities; the latter setting remains poorly understood despite its numerous applications, and serves as an important step towards understanding more realistic strategic interactions that feature both competing and shared interests. Since the seminal work of Von Stengel and Koller (GEB '97), different solution concepts have received attention from an algorithmic standpoint. Yet, the complexity of the standard Nash equilibrium has remained open.
Beyond spectral gap (extended): The role of the topology in decentralized learning

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers communicate over a sparse graph, current theory fails to capture important aspects of real-world behavior. First, the `spectral gap' of the communication graph is not predictive of its empirical performance in (deep) learning. Second, current theory does not explain that collaboration enables larger learning rates than training alone. In fact, it prescribes smaller learning rates, which further decrease as graphs become larger, failing to explain convergence dynamics in infinite graphs. This paper aims to paint an accurate picture of sparsely-connected distributed optimization. We quantify how the graph topology influences convergence in a quadratic toy problem and provide theoretical results for general smooth and (strongly) convex objectives. Our theory matches empirical observations in deep learning, and accurately describes the relative merits of different graph topologies. This paper is an extension of the conference paper by Vogels et. al. (2022). Code: https://github.com/epfml/topology-in-decentralized-learning.
Sign-Changing Bubble Tower Solutions for Sinh-Poisson Type Equations on Pierced Domains

For asymmetric sinh-Poisson type problems with Dirichlet boundary condition arising as a mean field equation of equilibrium turbulence vortices with variable intensities of interest in hydrodynamic turbulence, we address the existence of sign-changing bubble tower solutions on a pierced domain $\Omega_\epsilon:=\Omega\setminus \displaystyle \overline{B(\xi,\epsilon)}$, where $\Omega$ is a smooth bounded domain in $\mathbb{R}^2$ and $B(\xi,\epsilon)$ is a ball centered at $\xi\in \Omega$ with radius $\epsilon>0$. Precisely, given a small parameter $\rho>0$ and any integer $m\ge 2$, there exist a radius $\epsilon=\epsilon(\rho)>0$ small enough such that each sinh-Poisson type equation, either in Liouville form or mean field form, has a solution $u_\rho$ with an asymptotic profile as a sign-changing tower of $m$ singular Liouville bubbles centered at the same $\xi$ and with $\epsilon(\rho)\to 0^+$ as $\rho$ approaches to zero.
A Streamline upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations under Optimal Control

In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the P\'eclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Streamline Upwind Petrov-Galerkin technique is used in the optimality system to overcome these unpleasant effects. We will apply a finite element method discretization in a optimize-then-discretize approach. For the parabolic case, a space-time framework will be considered and stabilization will also occur in the bilinear forms involving time derivatives. Then we will build Reduced Order Models on this discretization procedure and two possible settings can be analyzed: whether or not stabilization is needed in the online phase, too. In order to build the reduced bases for state, control, and adjoint variables we will consider a Proper Orthogonal Decomposition algorithm in a partitioned approach. The discussion is supported by computational experiments, where relative errors between the FEM and ROM solutions are studied together with the respective computational times.
In Search of the Edge: A Bayesian Exploration of the Detectability of Red Edges in Exoplanet Reflection Spectra

Reflection spectroscopy holds great promise for characterizing the atmospheres and surfaces of potentially habitable terrestrial exoplanets. The surface of the modern Earth exhibits a sharp albedo change near 750 nm caused by vegetation—the red edge—which would leave a strong spectral signature if present on an exoplanet. However, the retrieval of wavelength-dependent surface properties from reflection spectra has seen relatively little study. Here, we propose a new surface albedo parameterization capable of retrieving the wavelength location of a priori unknown “edge-like” features. We demonstrate that a wavelength-dependent surface albedo model achieves higher accuracy in retrieving atmospheric composition than a uniform albedo model. Wavelength-dependent surfaces are also generally preferred over a uniform albedo model when retrieving simulated reflection spectra for a modern Earth analog, even for moderate signal-to-noise ratios (S/N = 10) and Earth-like clouds. Further, the location of the modern Earth’s red edge can be robustly and precisely constrained (within 70 nm for S/N = 10). Our results suggest that future space-based direct-imaging missions have the potential to infer surface compositions for rocky exoplanets, including spectral edges similar to those caused by life on the modern Earth.
Hubble Space Telescope Proper Motion Measurements of Supernova Remnant N132D: Center of Expansion and Age

We present proper motion measurements of the oxygen-rich ejecta of the LMC supernova remnant N132D using two epochs of Hubble Space Telescope Advanced Camera for Surveys data spanning 16 years. The proper motions of 120 individual knots of oxygen-rich gas were measured and used to calculate a center of expansion (CoE) of α = 5h25m01. ˢ71 and δ = −69°38′41. ″64 (J2000) with a 1σ uncertainty of 2. ″90. This new CoE measurement is 9. ″2 and 10. ″8 from two previous CoE estimates based on the geometry of the optically emitting ejecta. We also derive an explosion age of 2770 ± 500 yr, which is consistent with recent age estimates of ≈2500 yr made from 3D ejecta reconstructions. We verified our estimates of the CoE and age using a new automated procedure that detected and tracked the proper motions of 137 knots, with 73 knots that overlap with the visually identified knots. We find that the proper motions of the ejecta are still ballistic, despite the remnant’s age, and are consistent with the notion that the ejecta are expanding into an interstellar medium cavity. Evidence for explosion asymmetry from the parent supernova is also observed. Using the visually measured proper motion measurements and corresponding CoE and age, we compare N132D to other supernova remnants with proper motion ejecta studies.
Quantum machine learning: from physics to software engineering

ABSTRACT Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key approaches that can offer advancements in both the development of quantum technologies and the power of artificial intelligence. Among these approaches are quantum-enhanced algorithms, which apply quantum software engineering to classical information processing to improve keystone machine learning solutions. In this context, we explore the capability of hybrid quantum-classical neural networks to improve model generalization and increase accuracy while reducing computational resources. We also illustrate how machine learning can be used both to mitigate the effects of errors on presently available noisy intermediate-scale quantum devices, and to understand quantum advantage via an automatic study of quantum walk processes on graphs. In addition, we review how quantum hardware can be enhanced by applying machine learning to fundamental and applied physics problems as well as quantum tomography and photonics. We aim to demonstrate how concepts in physics can be translated into practical engineering of machine learning solutions using quantum software. GRAPHICAL ABSTRACT AI-generated image of a quantum robotic Schrödinger's cat that reads a quantum machine learning review paper.