Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle

—This paper addresses the problem of optimizing charging/discharging schedules of electric vehicles (EVs) when participate in demand response (DR). As there exist uncertainties in EVs’ remaining energy, arrival and departure time, and future electricity prices, it is quite difficult to make charging decisions to minimize charging cost while guarantee that the EV’s battery state-of-charge (SOC) is within certain range. To handle with this dilemma, this paper formulates the EV charging scheduling problem as a constrained Markov decision process (CMDP). By synergistically combining the augmented Lagrangian method and soft actor critic algorithm, a novel safe off-policy reinforcement learning (RL) approach is proposed in this paper to solve the CMDP. The actor network is updated in a policy gradient manner with the Lagrangian value function. A double-critics network is adopted to synchronously estimate the action-value function to avoid overestimation bias. The proposed algorithm does not require strong convexity guarantee of examined problems and is sample efficient. Comprehensive numerical experiments with real-world electricity price demonstrate that our proposed algorithm can achieve high solution optimality and constraints compliance.
Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty

Among attempts at giving a theoretical account of the success of deep neural networks, a recent line of work has identified a so-called lazy training regime in which the network can be well approximated by its linearization around initialization. Here we investigate the comparative effect of the lazy (linear) and feature learning (non-linear) regimes on subgroups of examples based on their difficulty. Specifically, we show that easier examples are given more weight in feature learning mode, resulting in faster training compared to more difficult ones. In other words, the non-linear dynamics tends to sequentialize the learning of examples of increasing difficulty. We illustrate this phenomenon across different ways to quantify example difficulty, including c-score, label noise, and in the presence of easy-to-learn spurious correlations. Our results reveal a new understanding of how deep networks prioritize resources across example difficulty.
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning

The paper presents a joint beamforming algorithm using statistical channel state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for multiuser MISO wireless communications. We used S-CSI, which is a long-term average of the cascaded channel as opposed to instantaneous CSI utilized in most existing works. Through this method, the overhead of channel estimation is dramatically reduced. We propose a proximal policy optimization (PPO) algorithm which is a well-known actor-critic based reinforcement learning (RL) algorithm to solve the optimization problem. To test the efficacy of this algorithm, simulation results are presented along with evaluations of key system parameters, including the Rician factor and RIS location, on the achievable sum rate of the users.
Adversarial Color Projection: A Projector-Based Physical Attack to DNNs

Recent research has demonstrated that deep neural networks (DNNs) are vulnerable to adversarial perturbations. Therefore, it is imperative to evaluate the resilience of advanced DNNs to adversarial attacks. However, traditional methods that use stickers as physical perturbations to deceive classifiers face challenges in achieving stealthiness and are susceptible to printing loss. Recently, advancements in physical attacks have utilized light beams, such as lasers, to perform attacks, where the optical patterns generated are artificial rather than natural. In this work, we propose a black-box projector-based physical attack, referred to as adversarial color projection (AdvCP), which manipulates the physical parameters of color projection to perform an adversarial attack. We evaluate our approach on three crucial criteria: effectiveness, stealthiness, and robustness. In the digital environment, we achieve an attack success rate of 97.60% on a subset of ImageNet, while in the physical environment, we attain an attack success rate of 100% in the indoor test and 82.14% in the outdoor test. The adversarial samples generated by AdvCP are compared with baseline samples to demonstrate the stealthiness of our approach. When attacking advanced DNNs, experimental results show that our method can achieve more than 85% attack success rate in all cases, which verifies the robustness of AdvCP. Finally, we consider the potential threats posed by AdvCP to future vision-based systems and applications and suggest some ideas for light-based physical attacks.
Asymptotic capacity of the range of random walks on free products of graphs

In this article we prove existence of the asymptotic capacity of the range of random walks on free products of graphs. In particular, we will show that the asymptotic capacity of the range is almost surely constant and strictly positive. Furthermore, we provide a central limit theorem for the capacity of the range and show that it varies real-analytically in terms of finitely supported probability measures of constant support.
On the Correlation Gap of Matroids

A set function can be extended to the unit cube in various ways; the correlation gap measures the ratio between two natural extensions. This quantity has been identified as the performance guarantee in a range of approximation algorithms and mechanism design settings. It is known that the correlation gap of a monotone submodular function is at least $1-1/e$, and this is tight for simple matroid rank functions. We initiate a fine-grained study of the correlation gap of matroid rank functions. In particular, we present an improved lower bound on the correlation gap as parametrized by the rank and girth of the matroid. We also show that for any matroid, the correlation gap of its weighted matroid rank function is minimized under uniform weights. Such improved lower bounds have direct applications for submodular maximization under matroid constraints, mechanism design, and contention resolution schemes.
The survey of planetary nebulae in Andromeda (M31). IV. Radial oxygen and argon abundance gradients of the thin and thicker disc

We obtain a magnitude-limited sample of Andromeda (M 31) disc PNe with chemical abundance estimated through the direct detection of the [OIII] 4363 $\mathring{\mathrm A}$ line. This leads to $205$ and $200$ PNe with oxygen and argon abundances respectively. We find that high- and low-extinction M 31 disc PNe have statistically distinct argon and oxygen abundance distributions. In the radial range $2-30$ kpc, the older low-extinction disc PNe are metal-poorer on average with a slightly positive radial oxygen abundance gradient ($0.006 \pm 0.003$ dex/kpc) and slightly negative for argon ($-0.005 \pm 0.003$ dex/kpc), while the younger high-extinction disc PNe are metal-richer on average with steeper radial abundance gradients for both oxygen ($-0.013 \pm 0.006$ dex/kpc) and argon ($-0.018 \pm 0.006$ dex/kpc), similar to the gradients computed for the M 31 HII regions. The M 31 disc abundance gradients are consistent with values computed from major merger simulations, with the majority of the low-extinction PNe being the older pre-merger disc stars in the thicker disc, and the majority of the high-extinction PNe being younger stars in the thin disc, formed during and after the merger event. The chemical abundance of the M 31 thicker disc has been radially homogenized because of the major merger. Accounting for disc scale-lengths, the positive radial oxygen abundance gradient of the M 31 thicker disc is in sharp contrast to the negative one of the MW thick disc. However, the thin discs of the MW and M 31 have remarkably similar negative oxygen abundance gradients.
MAN: Multi-Action Networks Learning

Learning control policies with large discrete action spaces is a challenging problem in the field of reinforcement learning due to present inefficiencies in exploration. With high dimensional action spaces, there are a large number of potential actions in each individual dimension over which policies would be learned. In this work, we introduce a Deep Reinforcement Learning (DRL) algorithm call Multi-Action Networks (MAN) Learning that addresses the challenge of high-dimensional large discrete action spaces. We propose factorizing the N-dimension action space into N 1-dimensional components, known as sub-actions, creating a Value Neural Network for each sub-action. Then, MAN uses temporal-difference learning to train the networks synchronously, which is simpler than training a single network with a large action output directly. To evaluate the proposed method, we test MAN on three scenarios: an n-dimension maze task, a block stacking task, and then extend MAN to handle 12 games from the Atari Arcade Learning environment with 18 action spaces. Our results indicate that MAN learns faster than both Deep Q-Learning and Double Deep Q-Learning, implying our method is a better performing synchronous temporal difference algorithm than those currently available for large discrete action spaces.
Nonlinear approximation spaces for inverse problems

This paper is concerned with the ubiquitous inverse problem of recovering an unknown function u from finitely many measurements possibly affected by noise. In recent years, inversion methods based on linear approximation spaces were introduced in [MPPY15, BCDDPW17] with certified recovery bounds. It is however known that linear spaces become ineffective for approximating simple and relevant families of functions, such as piecewise smooth functions that typically occur in hyperbolic PDEs (shocks) or images (edges). For such families, nonlinear spaces [Devore98] are known to significantly improve the approximation performance. The first contribution of this paper is to provide with certified recovery bounds for inversion procedures based on nonlinear approximation spaces. The second contribution is the application of this framework to the recovery of general bidimensional shapes from cell-average data. We also discuss how the application of our results to n-term approximation relates to classical results in compressed sensing.
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks

While significant theoretical progress has been achieved, unveiling the generalization mystery of overparameterized neural networks still remains largely elusive. In this paper, we study the generalization behavior of shallow neural networks (SNNs) by leveraging the concept of algorithmic stability. We consider gradient descent (GD) and stochastic gradient descent (SGD) to train SNNs, for both of which we develop consistent excess risk bounds by balancing the optimization and generalization via early-stopping. As compared to existing analysis on GD, our new analysis requires a relaxed overparameterization assumption and also applies to SGD. The key for the improvement is a better estimation of the smallest eigenvalues of the Hessian matrices of the empirical risks and the loss function along the trajectories of GD and SGD by providing a refined estimation of their iterates.
Boosting the Discriminant Power of Naive Bayes

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. The proposed stack auto-encoder consists of two auto-encoders for different purposes. The first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. The second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. The proposed method is evaluated on a set of machine-learning benchmark datasets. The experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.
Essentially entangled component of multipartite mixed quantum states, its properties, and an efficient algorithm for its extraction

Using geometric means, we first consider a density matrix decomposition of a multipartite quantum system of a finite dimension into two density matrices: a separable one, also known as the best separable approximation, and an essentially entangled one, which contains no product state components. We show that this convex decomposition can be achieved in practice with the help of a linear programming algorithm that scales in the general case polynomially with the system dimension. We illustrate the algorithm implementation with an example of a composite system of dimension 12 that undergoes a loss of coherence due to classical noise and we trace the time evolution of its essentially entangled component. We suggest a ``geometric'' description of entanglement dynamics and demonstrate how it explains the well-known phenomena of sudden death and revival of multipartite entanglements. For a statistical weight loss of the essentially entangled component with time, its average entanglement content is not affected by the coherence loss.
Forward modelling and the quest for mode identification in rapidly rotating stars

Asteroseismology has opened a window on the internal physics of thousands of stars, by relating oscillation spectra properties to the internal physics of stars. Mode identification, namely the process of associating a measured oscillation frequency to the corresponding mode geometry and properties, is the cornerstone of this analysis of seismic spectra. In rapidly rotating stars this identification is a challenging task that remains incomplete, as modes assume complex geometries and regular patterns in frequencies get scrambled under the influence of the Coriolis force and centrifugal flattening. In this article, I will first discuss the various classes of mode geometries that emerge in rapidly rotating stars and the related frequency and period patterns, as predicted by ray dynamics, complete (non-)adiabatic calculations, or using the traditional approximation of rotation. These patterns scale with structural quantities and help us derive crucial constraints on the structure and evolution of these stars. I will summarize the amazing progress accomplished over the last few years for the deciphering of gravity-mode pulsator oscillation spectra, and recent developments based on machine-learning classification techniques to distinguish oscillation modes and pattern analysis strategies that let us access the underlying physics of pressure-mode pulsators. These approaches pave the way to ensemble asteroseismology of classical pulsators. Finally, I will highlight how these recent progress can be combined to improve forward seismic modelling. I will focus on the example of Rasalhague, a well-known rapid rotator, to illustrate the process and the needed advances to obtain à-la-carte modelling of such stars.
An Experimental and Theoretical Investigation of HCN Production in the Hadean Earth Atmosphere

A critical early stage for the origin of life on Earth may have involved the production of hydrogen cyanide (HCN) in a reducing, predominantly H$_2$ atmosphere. HCN is crucial for the origin of life as it is a possible precursor to several biomolecules that make up RNA and proteins including nucleobases, nucleotides, amino acids, and ribose. In this work, we perform an in depth experimental and theoretical investigation of HCN production in reducing atmospheric conditions (89-95% H$_2$) possibly representing the earliest stages of the Hadean eon, ~4.5-4.3 billion years ago. We make use of cold plasma discharges - a laboratory analog to shortwave UV radiation - to simulate HCN production in the upper layers of the atmosphere for CH$_4$ abundances ranging from 0.1-6.5%. We then combine experimental mass spectrum measurements with our theoretical plasma models to estimate the HCN concentrations produced in our experiments. We find that upper atmospheric HCN production scales linearly with CH$_4$ abundance with the relation [HCN] = 0.13 $\pm$ 0.01[CH$_4$]. Concentrations of HCN near the surface of the Hadean Earth are expected to be about 2-3 orders of magnitude lower. The addition of 1% water to our experiments results in a ~50% reduction in HCN production. We find that four reactions are primarily responsible for HCN production in our experiments: (i) $^4$N + CH$_3$ ->H$_2$CN + H ->HCN + H$_2$, (ii) $^4$N + CH ->CN + H followed by CN + CH$_4$ ->HCN + CH$_3$, (iii) C$_2$H$_4$ + $^4$N ->HCN + CH$_3$, and (iv) $^4$N + $^3$CH$_2$ ->HCN + H. The most prebiotically favorable Hadean atmosphere would have been very rich in CH$_4$ (>5%), and as a result of greenhouse effects the surface would be likely very hot. In such a prebiotic scenario, it may have been important to incorporate HCN into organic hazes that could later release biomolecules and precursors into the first ponds.
Predicting Performances of Mutual Funds using Deep Learning and Ensemble Techniques

Predicting fund performance is beneﬁcial to both investors and fund managers, and yet is a challenging task. In this paper, we have tested whether deep learning models can predict fund performance more accurately than traditional statistical techniques. Fund performance is typically evaluated by the Sharpe ratio, which represents the risk-adjusted performance to ensure meaningful comparability across funds. We calculated the annualised Sharpe ratios based on the monthly returns time series data for more than 600 open-end mutual funds investing in listed large-cap equities in the United States. We ﬁnd that long short-term memory (LSTM) and gated recurrent units (GRUs) deep learning methods, both trained with modern Bayesian optimization, provide higher accuracy in forecasting funds’ Sharpe ratios than traditional statistical ones. An ensemble method, which combines forecasts from LSTM and GRUs, achieves the best performance of all models. There is evidence to say that deep learning and ensembling oﬀer promising solutions in addressing the challenge of fund performance forecasting.
Yet Another Format of Universal Dependencies for Korean

In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies. We present the linguistic rationale that illustrates the motivation and the necessity of adopting the morpheme-based format, and develop scripts that convert between the original format used by Universal Dependencies and the proposed morpheme-based format automatically. The effectiveness of the proposed format for Korean dependency parsing is then testified by both statistical and neural models, including UDPipe and Stanza, with our carefully constructed morpheme-based word embedding for Korean. morphUD outperforms parsing results for all Korean UD treebanks, and we also present detailed error analysis.
Calculating interface transport parameters at finite temperatures: Nonmagnetic interfaces

First-principles scattering calculations are used to investigate spin transport through interfaces between diffusive nonmagnetic metals where the symmetry lowering leads to an enhancement of the effect of spin-orbit coupling (SOC) and to a discontinuity of the spin currents passing through the interfaces. From the conductance and local spin currents calculated for nonmagnetic bilayers, we extract values of the room temperature interface resistance $R_{\rm I}$, of the spin memory loss parameter $\delta$ and of the interface spin Hall angle $\Theta_{\rm I}$ for nonmagnetic Au$|$Pt and Au$|$Pd interfaces using a frozen thermal disorder scheme to model finite temperatures. Substantial values of all three parameters are found with important consequences for experiments involving nonmagnetic spacer and capping layers. The temperature dependence of the interface parameters is determined for Au$|$Pt.
A Framework for Preserving Privacy and Cybersecurity in Brain-Computer Interfacing Applications

Brain-Computer Interfaces (BCIs) comprise a rapidly evolving field of technology with the potential of far-reaching impact in domains ranging from medical over industrial to artistic, gaming, and military. BCIs provide technical interfaces with recording and/or stimulation functionality to connect the brain with computer systems running "decoders" for online analysis of the recorded brain signals. This online analysis in turn can inform various effectors such as robots, vehicles (brain-to-vehicle interfaces), brain stimulation devices, or computer games (neurogaming). Today, these emerging BCI applications are typically still at early technology readiness levels, but because BCIs create novel, technical communication channels for the human brain, they have raised privacy and security concerns. In particular, as brain data contain personal information, adversaries may utilize BCIs to compromise brain privacy. There are first empirical proofs-of-principle that such attacks are indeed possible, possibly foreshadowing a next level of privacy and cybersecurity threats targeting the brain by neurotechnological means. To mitigate such risks, a large body of countermeasures has been proposed in the literature, but a general framework is lacking which would describe how privacy and security of BCI applications can be protected by design, i.e., already as an integral part of the early BCI design process, in a systematic manner, and allowing suitable depth of analysis for different contexts such as commercial BCI product development vs. academic research and lab prototypes. Here we propose the adoption of recent systems-engineering methodologies for privacy threat modeling, risk assessment, and privacy engineering to the BCI field. These methodologies address privacy and security concerns in a more systematic and holistic way than previous approaches, and provide reusable patterns on how to move from principles to actions. We apply these methodologies to BCI processes and data flows and derive a generic, extensible, and actionable framework for brain-privacy-preserving cybersecurity in BCI applications. This framework is designed for flexible application to the wide range of current and future BCI applications. We also propose a range of novel privacy-by-design features for BCIs, with an emphasis on features promoting BCI transparency as a prerequisite for informational self-determination of BCI users, as well as design features for ensuring BCI user autonomy. We anticipate that our framework will contribute to the development of privacy-respecting, trustworthy BCI technologies.