Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning

The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics and finance. An increasingly popular and effective approach to realizing sequential decision-making in multi-agent systems is through multi-agent reinforcement learning, as it allows for an automatic and model-free analysis of highly complex systems. However, the key issue of scalability complicates the design of control and reinforcement learning algorithms particularly in systems with large populations of agents. While reinforcement learning has found resounding empirical success in many scenarios with few agents, problems with many agents quickly become intractable and necessitate special consideration. In this survey, we will shed light on current approaches to tractably understanding and analyzing large-population systems, both through multi-agent reinforcement learning and through adjacent areas of research such as mean-field games, collective intelligence, or complex network theory. These classically independent subject areas offer a variety of approaches to understanding or modeling large-population systems, which may be of great use for the formulation of tractable MARL algorithms in the future. Finally, we survey potential areas of application for large-scale control and identify fruitful future applications of learning algorithms in practical systems. We hope that our survey could provide insight and future directions to junior and senior researchers in theoretical and applied sciences alike.
Effects of rf current and bias-field direction on the transition from linear to nonlinear gyrotropic dynamics in magnetic vortex structures

We present a frequency-domain study of the dynamic behavior of a magnetic vortex core within a single Permalloy disk by means of electrical detection and micromagnetic simulations. When exciting the vortex core dynamics in a non-linear regime, the lineshape of the rectiﬁed dc signal reveals a resonance peak splitting which depends on the excitation amplitude. Using micromagnetic simulations, we show that at high excitation power the peak splitting originates from the nanosecond time scale quasi-periodic switching of the vortex core polarity. Using lock-in detection, the rectiﬁed voltage is integrated over a ms time scale, so that the net signal detected between the two resonant peaks for a given range of parameters cancels out. The results are in agreement with the reported eﬀects of the in-plane static ﬁeld magnitude on the gyration dynamics, and complement them by detailed analysis of the eﬀects of the rf current amplitude and the azimuthal angle of the in-plane bias magnetic ﬁeld. Systematic characterization shows that a transition from linear to nonlinear dynamical regime can be controlled by rf current as well as by varying the magnitude and the direction of the bias magnetic ﬁeld.
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning

Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), has recently gained increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to build better machine learning models while preserving user privacy. Current works in VFL concentrate on developing a specific protection or attack mechanism for a particular VFL algorithm. In this work, we propose an evaluation framework that formulates the privacy-utility evaluation problem. We then use this framework as a guide to comprehensively evaluate a broad range of protection mechanisms against most of the state-of-the-art privacy attacks for three widely-deployed VFL algorithms. These evaluations may help FL practitioners select appropriate protection mechanisms given specific requirements. Our evaluation results demonstrate that: the model inversion and most of the label inference attacks can be thwarted by existing protection mechanisms; the model completion (MC) attack is difficult to be prevented, which calls for more advanced MC-targeted protection mechanisms. Based on our evaluation results, we offer concrete advice on improving the privacy-preserving capability of VFL systems.
R3LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator

Simultaneous localization and mapping (SLAM) are crucial for autonomous robots (e.g., self-driving cars, autonomous drones), 3D mapping systems, and AR/VR applications. This work proposed a novel LiDAR-inertial-visual fusion framework termed R$^3$LIVE++ to achieve robust and accurate state estimation while simultaneously reconstructing the radiance map on the fly. R$^3$LIVE++ consists of a LiDAR-inertial odometry (LIO) and a visual-inertial odometry (VIO), both running in real-time. The LIO subsystem utilizes the measurements from a LiDAR for reconstructing the geometric structure (i.e., the positions of 3D points), while the VIO subsystem simultaneously recovers the radiance information of the geometric structure from the input images. R$^3$LIVE++ is developed based on R$^3$LIVE and further improves the accuracy in localization and mapping by accounting for the camera photometric calibration (e.g., non-linear response function and lens vignetting) and the online estimation of camera exposure time. We conduct more extensive experiments on both public and our private datasets to compare our proposed system against other state-of-the-art SLAM systems. Quantitative and qualitative results show that our proposed system has significant improvements over others in both accuracy and robustness. In addition, to demonstrate the extendability of our work, {we developed several applications based on our reconstructed radiance maps, such as high dynamic range (HDR) imaging, virtual environment exploration, and 3D video gaming. } Lastly, to share our findings and make contributions to the community, we make our codes, hardware design, and dataset publicly available on our Github: github.com/hku-mars/r3live
A derivation of the electric field inside MAPS detectors from beam-test data and limited TCAD simulations

Solid semiconductor sensors are used as detectors in high-energy physics experiments, in medical applications, in space missions and elsewhere. Minimal knowledge of the electric field inside the elementary cells of these sensors is highly important for their performance understanding. The field governs the charge propagation processes and ultimately determines the size and quality of the electronic signal of the cell. Hence, the simulation of these sensors as detectors in different analyses relies strongly on the field knowledge. For a certain voltage applied to the cell, the field depends on the specifics of the device's growth and fabrication. The information about these is often commercially protected or otherwise very difficult to encode in state-of-the-art technology computer-aided-design (TCAD) software. In this work, we show that by taking the top-down approach, combining public beam-test data and a very limited public TCAD knowledge, we are able to effectively approximate the 3D electric field function in the pixel cell of one important and widely used example, namely the ALPIDE sensor, for simulating the charge propagation processes. Despite its broad usage worldwide, the ALPIDE field is not available to the community. We provide an effective field function, that adequately describes the sensor behaviour without trying to reconstruct further details about the device or the details behind its processing. We comment on the process by which the effective field function is derived with the help of the Allpix2 software, and on how similar work can be performed for other devices, starting from the same grounds.
CAP: instance complexity-aware network pruning

Existing differentiable channel pruning methods often attach scaling factors or masks behind channels to prune ﬁlters with less importance, and assume uniform contribution of input samples to ﬁlter importance. Speciﬁcally, the effects of instance complexity on pruning performance are not yet fully investigated. In this paper, we propose a simple yet effective differentiable network pruning method CAP based on instance complexity-aware ﬁlter importance scores. We deﬁne instance complexity related weight for each sample by giving higher weights to hard samples, and measure the weighted sum of sample-speciﬁc soft masks to model non-uniform contribution of different inputs, which encourages hard samples to dominate the pruning process and the model performance to be well preserved. In addition, we introduce a new regularizer to encourage polarization of the masks, such that a sweet spot can be easily found to identify the ﬁlters to be pruned. Performance evaluations on various network architectures and datasets demonstrate CAP has advantages over the state-of-the-arts in pruning large networks. For instance, CAP improves the accuracy of ResNet56 on CIFAR-10 dataset by 0.33% aftering removing 65.64% FLOPs, and prunes 87.75% FLOPs of ResNet50 on ImageNet dataset with only 0.89% Top-1 accuracy loss.
Learning Sparse Graphon Mean Field Games

Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard challenge. Graphon mean field games (GMFGs) enable the scalable analysis of MARL problems that are otherwise intractable. By the mathematical structure of graphons, this approach is limited to dense graphs which are insufficient to describe many real-world networks such as power law graphs. Our paper introduces a novel formulation of GMFGs, called LPGMFGs, which leverages the graph theoretical concept of $L^p$ graphons and provides a machine learning tool to efficiently and accurately approximate solutions for sparse network problems. This especially includes power law networks which are empirically observed in various application areas and cannot be captured by standard graphons. We derive theoretical existence and convergence guarantees and give empirical examples that demonstrate the accuracy of our learning approach for systems with many agents. Furthermore, we extend the Online Mirror Descent (OMD) learning algorithm to our setup to accelerate learning speed, empirically show its capabilities, and conduct a theoretical analysis using the novel concept of smoothed step graphons. In general, we provide a scalable, mathematically well-founded machine learning approach to a large class of otherwise intractable problems of great relevance in numerous research fields.
Black-Box Audits for Group Distribution Shifts

When a model informs decisions about people, distribution shifts can create undue disparities. However, it is hard for external entities to check for distribution shift, as the model and its training set are often proprietary. In this paper, we introduce and study a black-box auditing method to detect cases of distribution shift that lead to a performance disparity of the model across demographic groups. By extending techniques used in membership and property inference attacks -- which are designed to expose private information from learned models -- we demonstrate that an external auditor can gain the information needed to identify these distribution shifts solely by querying the model. Our experimental results on real-world datasets show that this approach is effective, achieving 80--100% AUC-ROC in detecting shifts involving the underrepresentation of a demographic group in the training set. Researchers and investigative journalists can use our tools to perform non-collaborative audits of proprietary models and expose cases of underrepresentation in the training datasets.
SOM-Net: Unrolling the Subspace-Based Optimization for Solving Full-Wave Inverse Scattering Problems

In this article, an unrolling algorithm of the iterative subspace-based optimization method (SOM) is proposed for solving full-wave inverse scattering problems (ISPs). The unrolling network, named SOM-Net, inherently embeds the Lippmann–Schwinger physical model into the design of network structures. The SOM-Net takes the deterministic induced current and the raw permittivity image obtained from backpropagation (BP) as the input. It then updates the induced current and the permittivity successively in subnetwork blocks of the SOM-Net by imitating iterations of the SOM. The final output of the SOM-Net is the full predicted induced current, from which the scattered field and the permittivity image can also be deduced analytically. The parameters of the SOM-Net are optimized in a supervised manner with the total loss to simultaneously ensure the consistency of the induced current, the scattered field, and the permittivity in the governing equations. Numerical tests on both synthetic and experimental data verify the superior performance of the proposed SOM-Net over typical ones. The results on challenging examples, such as scatterers with tough profiles or high permittivity, demonstrate the good generalization ability of the SOM-Net. With the use of deep unrolling technology, this work builds a bridge between traditional iterative methods and deep learning methods for solving ISPs.
Quantum-based solution of complex time-dependent Riccati equations

We use BCH-like relations to solve the time evolution operator of quantum systems described by time-dependent (TD) hamiltonians given in terms of the generators of the su (1 , 1) , su (2) and so (2 , 1) Lie algebras and establish the necessary and sufﬁcient conditions for its unitarity in the factorized representation. Then, we use this solution to solve a TD complex Riccati equation (CRE) which is derived from the Schr¨odinger equation using the Wei-Norman theory. This solution is given as continued generalized fractions, being optimal for numerical implementations, and covers the wide branch of the CRE that heritage the symmetries corresponding to the aforementioned Lie algebras. Moreover, our formalism allows to straightforwardly obtain effective quantum hamiltonians for systems described by this CRE, as we show for the Bloch-Riccati equation (BRE) whose hamiltonian can be realized with a TD-qubit, for instance. Finally, as an application but also as a consistency test, we compare our solution with the analytical one for the BRE with the Rabi frequency driven by a complex hyperbolic secant pulse that generates spin inversion, showing an excellent agreement.
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach

In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a few annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM tasks to directly generate textual responses to CRI-specific prompts.We compare the performance of this method against ensemble techniques trained on the entire dataset.Our best-performing submission was fine-tuned with only 256 instances per class, 15.7% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).
Image Feature Information Extraction for Interest Point Detection: A Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.
Frame-Subtitle Self-Supervision for Multi-Modal Video Question Answering

Multi-modal video question answering aims to predict correct answer and localize the temporal boundary relevant to the question. The temporal annotations of questions improve QA performance and interpretability of recent works, but they are usually empirical and costly. To avoid the temporal annotations, we devise a weakly supervised question grounding (WSQG) setting, where only QA annotations are used and the relevant temporal boundaries are generated according to the temporal attention scores. To substitute the temporal annotations, we transform the correspondence between frames and subtitles to Frame-Subtitle (FS) self-supervision, which helps to optimize the temporal attention scores and hence improve the video-language understanding in VideoQA model. The extensive experiments on TVQA and TVQA+ datasets demonstrate that the proposed WSQG strategy gets comparable performance on question grounding, and the FS self-supervision helps improve the question answering and grounding performance on both QA-supervision only and full-supervision settings.
Superquadric-based scene description

This paper is concerned with describing scenes consisting of piles of simple but unknown objects from dense range image. What differentiates this work from other approaches is that it is purely data driven, using concepts such as symmetry, viewpoint independence, and solidity to hypothesize the unknown shape and size of the objects. We use the superquadric model for scene description so that the method is applicable for general 3-D objects. The unknown shapes and sizes of the objects in the scene are verified by an algorithm which uses the enclosing boundings and the central moments for initial estimate of sizes and poses, and then the refined estimations are obtained by a nonlinear least squares technique. The final scene description is based on a relational graph of primitives. This techniques may be applied in situations where dimensioned CAD models are unavailable. <<ETX>>
Resonance transmission through a single atom

When a single atom is sandwiched in between two electrodes, an atomic tunneling device may be realized depending on the distance between the atom and the electrodes. We have performed first-principles pesudopotential calculation in conjunction with a three-dimensional evaluation of the quantum scattering matrix for such a device. We predict the quantum conductance and resonance transmission properties of the Si and Al atomic tunneling devices.
Cooperative Path Planning of Unmanned Aerial Vehicles

About the Authors. Series Preface. Preface. Acknowledgements. List of Figures. List of Tables. Nomenclature. 1. Introduction. 1.1 Path Planning Formulation. 1.2 Path Planning Constraints. 1.3 Cooperative Path Planning and Mission Planning. 1.4 Path Planning - An Overview. 1.5 The Road Map Method. 1.6 Probabilistic Methods. 1.7 Potential Field. 1.8 Cell Decomposition. 1.9 Optimal Control. 1.10 Optimization Techniques. 1.11 Trajectories for Path Planning. 1.12 Outline of the Book. References. 2. Path Planning in Two Dimensions. 2.1 Dubins Paths. 2.2 Designing Dubins Path using Analytical Geometry. 2.3 Existence of Dubins Paths. 2.4 Length of Dubins Paths. 2.5 Design of Dubins Paths using Principles of Differential Geometry. 2.6 Path of Continuous Curvature. 2.7 Producing Flyable Clothoid Paths. 28 Producing Flyable Pythagorean Hodograph Paths (2D). References. 3. Path Planning in Three Dimensions. 3.1 Dubins Paths in Three Dimensions Using Differential Geometry. 3.2 Path Length - Dubins 3D. 3.3 Pythagorean Hodograph Paths - 3D. 3.4 Design of Flyable Paths Using PH Curves. References. 4. Collision Avoidance. 4.1 Research into Obstacle Avoidance. 4.2 Obstacle Avoidance for Mapped Obstacles. 4.3 Obstacle Avoidance of Unmapped Static Obstacles. 4.4 Algorithmic Implementation. References. 5. Path-Following Guidance. 5.1 Path Following the Dubins Path. 5.2 Linear Guidance Algorithm. 5.3 Nonlinear Dynamic Inversion Guidance. 5.4 Dynamic Obstacle Avoidance Guidance. References. 6. Path Planning for Multiple UAVs. 6.1 Problem Formulation. 6.2 Simultaneous Arrival. 6.3 Phase I: Producing Flyable Paths. 6.4 Phase II: Producing Feasible Paths. 6.5 Phase III: Equalizing Path Length. 6.6 Multiple Path Algorithm. 6.7 Algorithm Application for Multiple UAVs. 6.8 2D Pythagorean Hodograph Paths. 6.9 3D Dubins Paths. 6.10 3D Pythagorean Hodograph Paths. References. Appendix A Differential Geometry. Appendix B. Pythagorean Hodograph. Index.
Multi-Granularity Prediction for Scene Text Recognition

Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet powerful vision STR model, which is built upon ViT and outperforms previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, i.e. , subword representations (BPE and WordPiece) widely-used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push the performance envelop of STR to an even higher level. Specifically, it achieves an average recognition accuracy of 93.35% on standard benchmarks. Code is available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR.
Quasi-convexity in mixtures for generalized rank-dependent functions

Quasi-convexity in probabilistic mixtures is a common and useful property in decision analysis. We study a general class of non-monotone mappings, called the generalized rank-dependent functions, which include the preference models of expected utilities, dual utilities, and rank-dependent utilities as special cases, as well as signed Choquet integrals used in risk management. As one of our main results, quasi-convex (in mixtures) signed Choquet integrals precisely include two parts: those that are convex (in mixtures) and the class of scaled quantile-spread mixtures, and this result leads to a full characterization of quasi-convexity for generalized rank-dependent functions. Seven equivalent conditions for quasi-convexity in mixtures are obtained for dual utilities and signed Choquet integrals. We also illustrate a conﬂict between convexity in mixtures and convexity in risk pooling among constant-additive mappings.