Miracle-dz/UniFAR-FTUs huggingface dataset:
Force tracing: a method to sculpt the optical force

A brief description of the long-standing problem of the optical momentum in media and its resolution is given. The method of force tracing to trace optical force fields along the trajectories of light rays is reviewed and a few illustrative examples are shown. Then, based on the method of force tracing, several graded-index devices performing interesting optical manipulations are reviewed.
3D Confocal Raman Tomography to Probe Field Enhancements inside Supercluster Metamaterials

Spherical colloidal superclusters, composed from sub-100 nm plasmonic nanoparticles, have been proposed to possess collective plasmonic modes imbued with large field enhancements and tunable spectral response extending from the visible to infrared regions. Here, we report the experimental verification of collective near-IR plasmonic modes inside single superclusters, with dimensions ranging from 0.77 μm up to 2 μm. Raman reporters, coated onto the nanoparticle building blocks, were used as local probes of the electric field enhancement inside the metamaterial. By performing diffraction-limited 3D Raman tomography we were able to build up the electric field intensity distribution within the superclusters. We demonstrate that plasmonic responses of superclusters vary according to their size and excitation wavelength, in accordance with theoretical predictions of their tunable optical properties. The existence of three-dimensional internal collective modes in these superclusters enables the excitation of a l.
Generalization Properties and Implicit Regularization for Multiple Passes SGM

We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size or the number of passes over the data. In this view, these parameters can be seen to control a form of implicit regularization. Numerical results complement the theoretical ndings.
Gradual convergence for Langevin dynamics on a degenerate potential

In this paper, we study an ordinary differential equation with a degenerate global attractor at the origin, to which we add a white noise with a small parameter that regulates its intensity. Under general conditions, for any fixed intensity, as time tends to infinity, the solution of this stochastic dynamics converges exponentially fast in total variation distance to a unique equilibrium distribution. We suitably accelerate the random dynamics and show that the preceding convergence is gradual, that is, the total variation distance of the accelerated random dynamics and its equilibrium distribution converges to a decreasing function, which corresponds to the total variation distance between the marginal of a stochastic differential equation that comes down from infinity and its corresponding equilibrium distribution. This completes the classification of all possible behaviors of the total variation distance between the time marginal of the aforementioned stochastic dynamics and its invariant measure for one dimensional smooth convex potentials. In addition, there is no cut-off phenomenon for this one-parameter family of random processes and asymptotics of the mixing times are derived.
Bypassing the Simulation-to-Reality Gap: Online Reinforcement Learning Using a Supervisor

Deep reinforcement learning (DRL) is a promising method to learn control policies for robots only from demonstration and experience. To cover the whole dynamic behaviour of the robot, DRL training is an active exploration process typically performed in simulation environments. Although this simulation training is cheap and fast, applying DRL algorithms to real-world settings is difficult. If agents are trained until they perform safely in simulation, transferring them to physical systems is difficult due to the sim-to-real gap caused by the difference between the simulation dynamics and the physical robot. In this paper, we present a method of online training a DRL agent to drive autonomously on a physical vehicle by using a model-based safety supervisor. Our solution uses a supervisory system to check if the action selected by the agent is safe or unsafe and ensure that a safe action is always implemented on the vehicle. With this, we can bypass the sim-to-real problem while training the DRL algorithm safely, quickly, and efficiently. We compare our method with conventional learning in simulation and on a physical vehicle. We provide a variety of real-world experiments where we train online a small-scale vehicle to drive autonomously with no prior simulation training. The evaluation results show that our method trains agents with improved sample efficiency while never crashing, and the trained agents demonstrate better driving performance than those trained in simulation.
The Effectiveness of World Models for Continual Reinforcement Learning

World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the agent faces changing environments. World models typically employ a replay buffer for training, which can be naturally extended to continual learning. We systematically study how different selective experience replay methods affect performance, forgetting, and transfer. We also provide recommendations regarding various modeling options for using world models. The best set of choices is called Continual-Dreamer, it is task-agnostic and utilizes the world model for continual exploration. Continual-Dreamer is sample efficient and outperforms state-of-the-art task-agnostic continual reinforcement learning methods on Minigrid and Minihack benchmarks.
Numerical solution of Poisson partial differential equations in high dimension using two-layer neural networks

The aim of this article is to analyze numerical schemes using two-layer neural networks with infinite width for the resolution of the high-dimensional Poisson-Neumann partial differential equations (PDEs) with Neumann boundary conditions. Using Barron's representation of the solution with a measure of probability, the energy is minimized thanks to a gradient curve dynamic on the $2$ Wasserstein space of parameters defining the neural network. Inspired by the work from Bach and Chizat, we prove that if the gradient curve converges, then the represented function is the solution of the elliptic equation considered. Numerical experiments are given to show the potential of the method.
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations

Deep neural networks have proven to be vulnerable to adversarial attacks in the form of adding specific perturbations on images to make wrong outputs. Designing stronger adversarial attack methods can help more reliably evaluate the robustness of DNN models. To release the harbor burden and improve the attack performance, auto machine learning (AutoML) has recently emerged as one successful technique to help automatically find the near-optimal adversarial attack strategy. However, existing works about AutoML for adversarial attacks only focus on $L_{\infty}$-norm-based perturbations. In fact, semantic perturbations attract increasing attention due to their naturalnesses and physical realizability. To bridge the gap between AutoML and semantic adversarial attacks, we propose a novel method called multi-objective evolutionary search of variable-length composite semantic perturbations (MES-VCSP). Specifically, we construct the mathematical model of variable-length composite semantic perturbations, which provides five gradient-based semantic attack methods. The same type of perturbation in an attack sequence is allowed to be performed multiple times. Besides, we introduce the multi-objective evolutionary search consisting of NSGA-II and neighborhood search to find near-optimal variable-length attack sequences. Experimental results on CIFAR10 and ImageNet datasets show that compared with existing methods, MES-VCSP can obtain adversarial examples with a higher attack success rate, more naturalness, and less time cost.
Agreement Tracking for Multi-Issue Negotiation Dialogues

Automated negotiation support systems aim to help human negotiators reach more favorable outcomes in multi-issue negotiations (e.g., an employer and a candidate negotiating over issues such as salary, hours, and promotions before a job offer). To be successful, these systems must accurately track agreements reached by participants in real-time. Existing approaches either focus on task-oriented dialogues or produce unstructured outputs, rendering them unsuitable for this objective. Our work introduces the novel task of agreement tracking for two-party multi-issue negotiations, which requires continuous monitoring of agreements within a structured state space. To address the scarcity of annotated corpora with realistic multi-issue negotiation dialogues, we use GPT-3 to build GPT-Negochat, a synthesized dataset that we make publicly available. We present a strong initial baseline for our task by transfer-learning a T5 model trained on the MultiWOZ 2.4 corpus. Pre-training T5-small and T5-base on MultiWOZ 2.4's DST task enhances results by 21% and 9% respectively over training solely on GPT-Negochat. We validate our method's sample-efficiency via smaller training subset experiments. By releasing GPT-Negochat and our baseline models, we aim to encourage further research in multi-issue negotiation dialogue agreement tracking.
Trajectory-Aware Rate Adaptation for Flying Networks

Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios. We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes' movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT.
Research Explosion: More Effort to Climb onto Shoulders of the Giant

Fast-growing scientific publications present challenges to the scientific community. In this paper, we describe their implications to researchers. As references form explicit foundations for researchers to conduct a study, we investigate the evolution in reference patterns based on 60.8 million papers published from 1960 to 2015. The results demonstrate that recent papers contain more references than older ones, especially the well-cited papers compared with other papers. Well-cited papers receive 10 or more citations within 5 years of publication. Their references cover a longer period from classic research to very recent studies. Authors of well-cited papers are also farsighted to discover the reference papers with good potential to receive high citation numbers in near future. We also discover that the number of accumulative publications has a negative impact on next-5-year citation count for most fields except Chemistry, Materials science, Environmental science, Biology, and Engineering. Our findings suggest that researchers are expected to devote more effort to producing impactful research. Based on all these findings, we strongly advise against judging researchers simply based on the number of their publications. On the other hand, authors and reviewers should ensure that published papers contain adequate contributions. The code for our analysis is on GitHub: https://github.com/ECNU-Text-Computing/Research-Explosion.
Designing Behavior Trees from Goal-Oriented LTLf Formulas

Temporal logic can be used to formally specify autonomous agent goals, but synthesizing planners that guarantee goal satisfaction can be computationally prohibitive. This paper shows how to turn goals specified using a subset of finite trace Linear Temporal Logic (LTL) into a behavior tree (BT) that guarantees that successful traces satisfy the LTL goal. Useful LTL formulas for achievement goals can be derived using achievement-oriented task mission grammars, leading to missions made up of tasks combined using LTL operators. Constructing BTs from LTL formulas leads to a relaxed behavior synthesis problem in which a wide range of planners can implement the action nodes in the BT. Importantly, any successful trace induced by the planners satisfies the corresponding LTL formula. The usefulness of the approach is demonstrated in two ways: a) exploring the alignment between two planners and LTL goals, and b) solving a sequential key-door problem for a Fetch robot.
Free-Form Composition Networks for Egocentric Action Recognition

Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognition from a compositional generalization perspective. To tackle this problem, we propose a free-form composition network (FFCN) that can simultaneously learn disentangled verb, preposition, and noun representations, and then use them to compose new samples in the feature space for rare classes of action videos. First, we use a graph to capture the spatial-temporal relations among different hand/object instances in each action video. We thus decompose each action into a set of verb and preposition spatial-temporal representations using the edge features in the graph. The temporal decomposition extracts verb and preposition representations from different video frames, while the spatial decomposition adaptively learns verb and preposition representations from action-related instances in each frame. With these spatial-temporal representations of verbs and prepositions, we can compose new samples for those rare classes in a free-form manner, which is not restricted to a rigid form of a verb and a noun. The proposed FFCN can directly generate new training data samples for rare classes, hence significantly improve action recognition performance. We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition.
Spectral analysis for elastica 3-dimensional dynamics in a shear flow

We present the spectral analysis of three-dimensional dynamics of an elastic filament in a shear flow of a viscous fluid at a low Reynolds number in the absence of Brownian motion. The elastica model is used. The fiber initially is almost straight at an arbitrary orientation, with small perpendicular perturbations in the shear plane and out-of-plane. To analyze the stability of both perturbations, equations for the eigenvalues and eigenfunctions are derived and solved by the Chebyshev spectral collocation method. It is shown that their crucial features are the same as in the case of the two-dimensional elastica dynamics in shear flow [Becker and Shelley, Phys. Rev. Lett. 2001] and the three-dimensional elastica dynamics in the compressional flow [Chakrabarti et al., Nat. Phys., 2020]. We find a similar dependence of the buckled shapes on the ratio of bending to hydrodynamic forces as in the simulations for elastic fibers of a nonzero thickness [Slowicka et al., New J. Phys., 2022].
Caustic analysis of partially coherent self-accelerating beams: Investigating self-healing property

We employed caustic theory to analyze the propagation dynamics of partially coherent self-accelerating beams such as self-healing of partially coherent Airy beams. Our findings revealed that as the spatial coherence decreases, the self-healing ability of beams increases. This result have been demonstrated both in simulation and experiment. This is an innovative application of the caustic theory to the field of partially coherent structured beams, and provides a comprehensive understanding of self-healing property. Our results have significant implications for practical applications of partially coherent beams in fields such as optical communication, encryption, and imaging.
Gas-Kinetic Scheme for Partially Ionized Plasma in Hydrodynamic Regime

Most plasmas are only partially ionized. To better understand the dynamics of these plasmas, the behaviors of a mixture of neutral species and plasma in ideal magnetohydrodynamic states are investigated. The current approach is about the construction of coupled kinetic models for the neutral gas, electron, and proton, and the development of the corresponding gas-kinetic scheme (GKS) for the solution in the continuum flow regime. The scheme is validated in the 1D Riemann problem for an enlarged system with the interaction from the Euler waves of the neutral gas and magnetohydrodynamic ones of the plasma. Additionally, the Orszag-Tang vortex problem across different ionized states is tested to examine the influence of neutrals on the MHD wave evolution. These tests demonstrate that the proposed scheme can capture the fundamental features of ideal partially ionized plasma, and a transition in the wave structure from the ideal MHD solution of the fully ionized plasma to the Euler solution of the neutral gas is obtained.
Stochastic Reaction-Diffusion Systems in Biophysics: Towards a Toolbox for Quantitative Model Evaluation

We develop a statistical toolbox for a quantitative model evaluation of stochastic reaction-diffusion systems modeling space-time evolution of biophysical quantities on the intracellular level. Starting from space-time data $X_N(t,x)$, as, e.g., provided in fluorescence microscopy recordings, we discuss basic modelling principles for conditional mean trend and fluctuations in the class of stochastic reaction-diffusion systems, and subsequently develop statistical inference methods for parameter estimation. With a view towards application to real data, we discuss estimation errors and confidence intervals, in particular in dependence of spatial resolution of measurements, and investigate the impact of misspecified reaction terms and noise coefficients. We also briefly touch implementation issues of the statistical estimators. As a proof of concept we apply our toolbox to the statistical inference on intracellular actin concentration in the social amoeba Dictyostelium discoideum.
Machine Learning practices and infrastructures

Machine Learning (ML) systems, particularly when deployed in high-stakes domains, are deeply consequential. They can exacerbate existing inequities, create new modes of discrimination, and reify outdated social constructs. Accordingly, the social context (i.e. organisations, teams, cultures) in which ML systems are developed is a site of active research for the field of AI ethics, and intervention for policymakers. This paper focuses on one aspect of social context that is often overlooked: interactions between practitioners and the tools they rely on, and the role these interactions play in shaping ML practices and the development of ML systems. In particular, through an empirical study of questions asked on the Stack Exchange forums, the use of interactive computing platforms (e.g. Jupyter Notebook and Google Colab) in ML practices is explored. I find that interactive computing platforms are used in a host of learning and coordination practices, which constitutes an infrastructural relationship between interactive computing platforms and ML practitioners. I describe how ML practices are co-evolving alongside the development of interactive computing platforms, and highlight how this risks making invisible aspects of the ML life cycle that AI ethics researchers’ have demonstrated to be particularly salient for the societal impact of deployed ML systems.
Design of Antiferromagnetic Second-order Band Topology with Rotation Topological Invariants in Two Dimensions

The existence of fractionally quantized topological corner states serves as a key indicator for two-dimensional second-order topological insulators (SOTIs), yet has not been experimentally observed in realistic materials. Here, based on effective model analysis and symmetry arguments, we propose a strategy for achieving SOTI phases with in-gap corner states in two dimensional systems with antiferromagnetic (AFM) order. We uncover by a minimum lattice model that the band topology originates from the interplay between intrinsic spin-orbital coupling and interlayer AFM exchange interactions. Using first principles calculations, we show that the 2D AFM SOTI phases can be realized in (MnBi$_2$Te$_4$)(Bi$_2$Te$_3$)$_{m}$ films. Moreover, we demonstrate that the nontrivial corner states are linked to rotation topological invariants under three-fold rotation symmetry $C_3$, resulting in $C_3$-symmetric SOTIs with corner charges fractionally quantized to $\frac{n}{3} \lvert e \rvert $ (mod $e$). Due to the great recent achievements in (MnBi$_2$Te$_4$)(Bi$_2$Te$_3$)$_{m}$ systems, our results providing reliable material candidates for experimentally accessible AFM higher-order band topology would draw intense attentions.
Autoparametric resonance extending the bit-flip time of a cat qubit up to 0.3 s

Cat qubits, for which logical $|0\rangle$ and $|1\rangle$ are coherent states $|\pm\alpha\rangle$ of a harmonic mode, offer a promising route towards quantum error correction. Using dissipation to our advantage so that photon pairs of the harmonic mode are exchanged with single photons of its environment, it is possible to stabilize the logical states and exponentially increase the bit-flip time of the cat qubit with the photon number $|\alpha|^2$. Large two-photon dissipation rate $\kappa_2$ ensures fast qubit manipulation and short error correction cycles, which are instrumental to correct the remaining phase-flip errors in a repetition code of cat qubits. Here we introduce and operate an autoparametric superconducting circuit that couples a mode containing the cat qubit to a lossy mode whose frequency is set at twice that of the cat mode. This passive coupling does not require a parametric pump and reaches a rate $\kappa_2/2\pi\approx 2~\mathrm{MHz}$. With such a strong two-photon dissipation, bit-flip errors of the autoparametric cat qubit are prevented for a characteristic time up to 0.3~s with only a mild impact on phase-flip errors. Besides, we illustrate how the phase of a quantum superposition between $|\alpha\rangle$ and $|-\alpha\rangle$ can be arbitrarily changed by driving the harmonic mode while keeping the engineered dissipation active.