Miracle-dz/UniFAR-FTUs huggingface dataset:
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective

Adversarial machine learning (AML) studies the adversarial phenomenon of machine learning, which may make inconsistent or unexpected predictions with humans. Some paradigms have been recently developed to explore this adversarial phenomenon occurring at different stages of a machine learning system, such as backdoor attack occurring at the pre-training, in-training and inference stage; weight attack occurring at the post-training, deployment and inference stage; adversarial attack occurring at the inference stage. However, although these adversarial paradigms share a common goal, their developments are almost independent, and there is still no big picture of AML. In this work, we aim to provide a unified perspective to the AML community to systematically review the overall progress of this field. We firstly provide a general definition about AML, and then propose a unified mathematical framework to covering existing attack paradigms. According to the proposed unified framework, we build a full taxonomy to systematically categorize and review existing representative methods for each paradigm. Besides, using this unified framework, it is easy to figure out the connections and differences among different attack paradigms, which may inspire future researchers to develop more advanced attack paradigms. Finally, to facilitate the viewing of the built taxonomy and the related literature in adversarial machine learning, we further provide a website, \ie, \url{http://adversarial-ml.com}, where the taxonomies and literature will be continuously updated.
Online Continuous Hyperparameter Optimization for Contextual Bandits

In stochastic contextual bandits, an agent sequentially makes actions from a time-dependent action set based on past experience to minimize the cumulative regret. Like many other machine learning algorithms, the performance of bandits heavily depends on their multiple hyperparameters, and theoretically derived parameter values may lead to unsatisfactory results in practice. Moreover, it is infeasible to use offline tuning methods like cross-validation to choose hyperparameters under the bandit environment, as the decisions should be made in real time. To address this challenge, we propose the first online continuous hyperparameter tuning framework for contextual bandits to learn the optimal parameter configuration within a search space on the fly. Specifically, we use a double-layer bandit framework named CDT (Continuous Dynamic Tuning) and formulate the hyperparameter optimization as a non-stationary continuum-armed bandit, where each arm represents a combination of hyperparameters, and the corresponding reward is the algorithmic result. For the top layer, we propose the Zooming TS algorithm that utilizes Thompson Sampling (TS) for exploration and a restart technique to get around the switching environment. The proposed CDT framework can be easily used to tune contextual bandit algorithms without any pre-specified candidate set for hyperparameters. We further show that it could achieve sublinear regret in theory and performs consistently better on both synthetic and real datasets in practice.
Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions

Stochastic gradient descent is a workhorse for training deep neural networks due to its excellent generalization performance. Several studies demonstrated this success is attributed to the implicit bias of the method that prefers a flat minimum and developed new methods based on this perspective. Recently, Izmailov et al. (2018) empirically observed that an averaged stochastic gradient descent with a large step size can bring out the implicit bias more effectively and can converge more stably to a flat minimum than the vanilla stochastic gradient descent. In our work, we theoretically justify this observation by showing that the averaging scheme improves the bias-optimization tradeoff coming from the stochastic gradient noise: a large step size amplifies the bias but makes convergence unstable, and vice versa. Specifically, we show that the averaged stochastic gradient descent can get closer to a solution of a penalized objective on the sharpness than the vanilla stochastic gradient descent using the same step size under certain conditions. In experiments, we verify our theory and show this learning scheme significantly improves performance.
Infrared ellipsometry study of the charge dynamics in 
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-terphenyl

We report an infrared ellipsometry study of the charge carrier dynamics in polycrystalline Kxp-terphenyl samples with nominal $x=3$, for which signatures of high-temperature superconductivity were previously reported. The infrared spectra are dominated by two Lorentzian bands with maxima around 4 000 cm$^{-1}$ and 12 000 cm$^{-1}$ which, from a comparison with calculations based on a H\"uckel model are assigned to intra-molecular excitations of $\pi$ electrons of the anionic p-terphenyl molecules. The inter-molecular electronic excitations are much weaker and give rise to a Drude peak and a similarly weak Lorentzian band around 220 cm$^{-1}$. A dc resistivity of about 0.3 $\Omega$ cm at 300 K is deduced from the IR data, comparable to values measured by electrical resistivity on a twin sample. The analysis of the temperature dependence of the low-frequency response reveals a gradual decrease of the plasma frequency and the scattering rate of the Drude peak below 300 K that gets anomalously enhanced below 90 K. The corresponding missing spectral weight of the Drude peak appears blue-shifted towards the Lorentz-band at 220 cm$^{-1}$. This characteristic blue-shift signifies an enhanced localization of the charge carriers at low temperatures and contrasts the behavior expected for a bulk superconducting state for which the missing spectral weight would be redshifted to a delta-function at zero frequency that accounts for the loss-free response of the superconducting condensate. Our data might still be compatible with a filamentary superconducting state with a volume fraction well below the percolation limit for which the spatial confinement of the condensate can result in a plasmonic resonance at finite frequency.
High Flux Heat Removal with Microchannels—A Roadmap of Challenges and Opportunities

Heat fluxes in IC chips and other electronics equipment have reached the current limits of air-cooling technology. Some of the applications require heat fluxes well beyond the limit of 100 W/cm 2 , thus demanding advanced cooling solutions. Liquid cooling technology has also received attention as the advances in single-phase liquid cooling in microchannels have shown considerable promise. The extension of compact heat exchanger technology to microscale applications offers many new possibilities. The liquid cooling technology is expected to reach heat dissipation rates as high as 10 MW/m 2 (1 kW/cm 2 ) with enhanced microchannels and a junction-to-air temperature difference of 50°C. The challenges facing flow boiling systems are also evaluated. This paper reviews the fundamental technological developments in liquid cooling as well as flow boiling and presents possible solutions in integrating the cooling system with a building's HVAC unit in a large server-type application. The opportunities and challenges are described in an attempt to provide the roadmap of cooling technology for cooling high flux devices in the next decade.
Canonical Cointegrating Regression and Testing for Cointegration in the Presence of I(1) and I(2) Variables

This paper introduces tests for the null of cointegration in the presence of I(1) and I(2) variables. These tests use residuals from Park's (1992, Econometrica 60,119–143) canonical cointegrating regression (CCR) and the leads-and-lags regression of Saikkonen (1991, Econometric Theory 9,1–21) and Stock and Watson (1993, Econometrica 61, 783–820). Asymptotic theory for CCR in the presence of I(1) and I(2) variables is also introduced. The distributions of the cointegration tests are nonstandard, and hence their percentiles are tabulated by using simulation. Monte Carlo simulation results to study the finite sample performance of the CCR estimates and the cointegration tests are also reported.
Computational study of defect complexes in β-LiGaO2 and their relation to the donor–acceptor-pair recombination

Hybrid functional calculations are presented for defects in LiGaO2 with the fraction of non-local exchange adjusted to reproduce the recently reported exciton gap of 6.0 eV. We study how the defect transition levels of the main native defects change with respect to the band edges compared to earlier calculations which assumed a smaller bandgap near 5.1 eV. In addition, we consider defect complexes formed by combining the main native donor GaLi with the main acceptors, VLi and LiGa antisites as a function of their relative position. These results are used to tentatively identify the photoluminescence bands previously assigned to donor–acceptor-pair recombination.
Generative Ornstein-Uhlenbeck Markets via Geometric Deep Learning

We consider the problem of simultaneously approximating the conditional distribution of market prices and their log returns with a single machine learning model. We show that an instance of the GDN model of Kratsios and Papon (2022) solves this problem without having prior assumptions on the market's"clipped"log returns, other than that they follow a generalized Ornstein-Uhlenbeck process with a priori unknown dynamics. We provide universal approximation guarantees for these conditional distributions and contingent claims with a Lipschitz payoff function.
Formal computational framework for the study of molecular evolution

Over the past 10 years, multiple executable modelling formalisms for molecular biology have been developed in order to address the growing need for a system-level understanding of complex biological phenomena. An important class of these formalisms are biology-inspired process algebras, which offer—among other desirable properties—an almost complete separation of model specification (syntax) from model dynamics (semantics). In this thesis, the similarity between this separation and the genotype-phenotype duality in evolutionary biology is exploited to develop a process-algebraic approach to the study of evolution of biochemical systems. The main technical contribution of this thesis is the continuous π-calculus (cπ), a novel process algebra based on the classical π-calculus of Milner et. al. Its two defining characteristics are: continuous, compositional, computationally inexpensive semantics, and a flexible interaction structure of processes (molecules). Both these features are conductive to evolutionary analysis of biochemical systems by, respectively, enabling many variants of a given model to be evaluated, and facilitating in silico evolution of new functional connections. A further major contribution is a collection of variation operators, syntactic model transformation schemes corresponding to common evolutionary events. When applied to a cπ model of a biochemical system, variation operators produce its evolutionary neighbours, yielding insights into the local fitness landscape and neutral neighbourhood. Two well-known biochemical systems are modelled in this dissertation to validate the developed theory. One is the KaiABC circadian clock in the cyanobacterium S. elongatus, the other is a mitogen-activated protein kinase cascade. In each case we study the system itself as well as its predicted evolutionary variants. Simpler examples, particularly that of a generic enzymatic reaction, are used throughout the thesis to illustrate important concepts as they are introduced.
A Statistical Analysis of Polyak-Ruppert Averaged Q-Learning

We study Q-learning with Polyak-Ruppert averaging in a discounted Markov decision process in synchronous and tabular settings. Under a Lipschitz condition, we establish a functional central limit theorem for the averaged iteration $\bar{\boldsymbol{Q}}_T$ and show that its standardized partial-sum process converges weakly to a rescaled Brownian motion. The functional central limit theorem implies a fully online inference method for reinforcement learning. Furthermore, we show that $\bar{\boldsymbol{Q}}_T$ is the regular asymptotically linear (RAL) estimator for the optimal Q-value function $\boldsymbol{Q}^*$ that has the most efficient influence function. We present a nonasymptotic analysis for the $\ell_{\infty}$ error, $\mathbb{E}\|\bar{\boldsymbol{Q}}_T-\boldsymbol{Q}^*\|_{\infty}$, showing that it matches the instance-dependent lower bound for polynomial step sizes. Similar results are provided for entropy-regularized Q-learning without the Lipschitz condition.
Almost Transitive Actions on Spaces with the Rational Homotopy of Sphere Products

We determine the structure of transitive actions of compact Lie groups on spaces which have the dimension and the (rational) homotopy groups of a product 1 ◊ m of spheres. These homogeneous spaces arise in several geometric contexts and may be considered as 1 -bundles over certain spaces, e.g. over lens spaces and over certain quotients of Stiefel manifolds. Furthermore, we show that if a non-compact simply connected Lie group acts transitively on such a space, then the orbits of the maximal compact sub- groups are all simply connected rational cohomology spheres of codimension one and hence classified. We obtain this by giving a short proof of the existence and structure of the natural bundle of Gorbatsevich under these much less general assumptions. In this special case the proof gets considerably shorter by the use of the homotopy properties of the spaces in question and a theorem of Mostert on the structure of orbit spaces of compact Lie groups on manifolds.
Origin of 
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-shifted three-dimensional charge density waves in the kagomé metal 
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Understanding the nature of charge density wave (CDW) and superconductivity in kagome metal AV$_3$Sb$_5$ (A=Cs,Rb,K) is a recent subject of intensive study. Due to the presence of van Hove singularities, electron-electron interaction has been suggested to play an important role in the formation of such broken symmetry states. Recent experiments show that the CDW order is three-dimensional and it is staggered across different kagome layers. However, the experimental interpretation for the precise structure of CDW varies in terms of whether it is the star of David (SD), inverse star of David (ISD) or the alternation of the two among neighboring layers. In this work, we show that the origin of these distinct CDW orders can be understood in a unified picture by considering intra- and inter-layer electron-electron interactions as well as the coupling between electrons and lattice distortions. Utilizing an effective 9-band model with V $d$ orbitals and out-of-plane Sb $p$ orbitals, it is demonstrated that the repulsive electron-electron interaction favors charge bond order which induces either SD or ISD upon including lattice distortions. As the inter-layer interaction is introduced, $\pi$-shifted CDW develops with the staggered ordering along the $c$-axis. We also find that the phase with alternating SD and ISD can be stabilized as the ground state under strong inter-layer interaction.
Electronic nonequilibrium effect in ultrafast-laser-irradiated solids

This paper describes the effects of electronic nonequilibrium in a simulation of ultrafast laser irradiation of materials. The simulation scheme based on tight-binding molecular dynamics, in which the electronic populations are traced with a combined Monte Carlo and Boltzmann equation, enables the modeling of nonequilibrium, nonthermal, and nonadiabatic (electron-phonon coupling) effects simultaneously. The electron-electron thermalization is described within the relaxation-time approximation, which automatically restores various known limits such as instantaneous thermalization (the thermalization time τe−e→0 ) and Born-Oppenheimer (BO) approximation ( τe−e→∞ ). The results of the simulation suggest that the non-equilibrium state of the electronic system slows down electron-phonon coupling with respect to the electronic equilibrium case in all studied materials: metals, semiconductors, and insulators. In semiconductors and insulators, it also alters the damage threshold of ultrafast nonthermal phase transitions induced by modification of the interatomic potential due to electronic excitation. It is demonstrated that the models that exclude electron-electron thermalization (using the assumption of τe−e→∞, such as BO or Ehrenfest approximations) may produce qualitatively different results, and a reliable model should include all three effects: electronic nonequilibrium, nonadiabatic electron-ion coupling, and nonthermal evolution of interatomic potential.
Best of Both Worlds Policy Optimization

Policy optimization methods are popular reinforcement learning algorithms in practice. Recent works have built theoretical foundation for them by proving $\sqrt{T}$ regret bounds even when the losses are adversarial. Such bounds are tight in the worst case but often overly pessimistic. In this work, we show that in tabular Markov decision processes (MDPs), by properly designing the regularizer, the exploration bonus and the learning rates, one can achieve a more favorable polylog$(T)$ regret when the losses are stochastic, without sacrificing the worst-case guarantee in the adversarial regime. To our knowledge, this is also the first time a gap-dependent polylog$(T)$ regret bound is shown for policy optimization. Specifically, we achieve this by leveraging a Tsallis entropy or a Shannon entropy regularizer in the policy update. Then we show that under known transitions, we can further obtain a first-order regret bound in the adversarial regime by leveraging the log-barrier regularizer.
Normative Framework for Deriving Neural Networks with Multicompartmental Neurons and Non-Hebbian Plasticity

An established normative approach for understanding the algorithmic basis of neural computation is to derive online algorithms from principled computational objectives and evaluate their compatibility with anatomical and physiological observations. Similarity matching objectives have served as successful starting points for deriving online algorithms that map onto neural networks (NNs) with point neurons and Hebbian/anti-Hebbian plasticity. These NN models account for many anatomical and physiological observations; however, the objectives have limited computational power and the derived NNs do not explain multi-compartmental neuronal structures and non-Hebbian forms of plasticity that are prevalent throughout the brain. In this article, we unify and generalize recent extensions of the similarity matching approach to address more complex objectives, including a large class of unsupervised and self-supervised learning tasks that can be formulated as symmetric generalized eigenvalue problems or nonnegative matrix factorization problems. Interestingly, the online algorithms derived from these objectives naturally map onto NNs with multi-compartmental neurons and local, non-Hebbian learning rules. Therefore, this unified extension of the similarity matching approach provides a normative framework that facilitates understanding multi-compartmental neuronal structures and non-Hebbian plasticity found throughout the brain.
Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems

Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of these systems. Redundant controller architectures have been widely adopted for safety assurance in such contexts. These architectures augment LEC "performant" controllers that are difficult to verify with "safety" controllers and the decision logic to switch between them. While these architectures ensure safety, we point out two limitations. First, they are trained offline to learn a conservative policy of always selecting a controller that maintains the system's safety, which limits the system's adaptability to dynamic and non-stationary environments. Second, they do not support reverse switching from the safety controller to the performant controller, even when the threat to safety is no longer present. To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching. We consider switching as a sequential decision-making problem and model it as a semi-Markov decision process. We leverage a combination of a myopic selector using surrogate models (for the forward switch) and a non-myopic planner (for the reverse switch) to balance safety and performance. We evaluate this approach using an autonomous vehicle case study in the CARLA simulator using different driving conditions, locations, and component failures. We show that the proposed approach results in fewer collisions and higher performance than state-of-the-art alternatives.
Additive functions of intervals and Hausdorff measure

Consider bounded sets of points in a Euclidean space Rq of q dimensions. Let h(t) be a continuous increasing function, positive for t>0, and such that h(0) = 0. Then the Hausdroff measure h–mE of a set E in Rq, relative to the function h(t), is defined as follows. Let ε be a small positive number and suppose E is covered by a finite or enumerably infinite sequence of convex sets {Ui} (open or closed) of diameters di less than or equal to ε. Write h–mεE = greatest lower bound for any such sequence {Ui}. Then h–mεE is non-decreasing as ε tends to zero. We define
Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach

In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes. Previous iFSD works achieved the desired results by applying metalearning. However, meta-learning approaches show insufficient performance that is difficult to apply to practical problems. In this light, we propose a simple fine-tuning-based approach, the Incremental Two-stage Fine-tuning Approach (iTFA) for iFSD, which contains three steps: 1) base training using abundant base classes with the class-agnostic box regressor, 2) separation of the RoI feature extractor and classifier into the base and novel class branches for preserving base knowledge, and 3) fine-tuning the novel branch using only a few novel class examples. We evaluate our iTFA on the real-world datasets PASCAL VOC, COCO, and LVIS. iTFA achieves competitive performance in COCO and shows a 30% higher AP accuracy than meta-learning methods in the LVIS dataset. Experimental results show the effectiveness and applicability of our proposed method11Code is available at https://github.com/TMIU/iTFA.