Miracle-dz/UniFAR-FTUs huggingface dataset:
Revealed Multi-Objective Utility Aggregation in Human Driving

A central design problem in game theoretic analysis is the estimation of the players' utilities. In many real-world interactive situations of human decision making, including human driving, the utilities are multi-objective in nature; therefore, estimating the parameters of aggregation, i.e., mapping of multi-objective utilities to a scalar value, becomes an essential part of game construction. However, estimating this parameter from observational data introduces several challenges due to a host of unobservable factors, including the underlying modality of aggregation and the possibly boundedly rational behaviour model that generated the observation. Based on the concept of rationalisability, we develop algorithms for estimating multi-objective aggregation parameters for two common aggregation methods, weighted and satisficing aggregation, and for both strategic and non-strategic reasoning models. Based on three different datasets, we provide insights into how human drivers aggregate the utilities of safety and progress, as well as the situational dependence of the aggregation process. Additionally, we show that irrespective of the specific solution concept used for solving the games, a data-driven estimation of utility aggregation significantly improves the predictive accuracy of behaviour models with respect to observed human behaviour.
Clustering with Simplicial Complexes

In this work, we propose a new clustering algorithm to group nodes in networks based on second-order simplices (aka filled triangles) to leverage higher-order network interactions. We define a simplicial conductance function, which on minimizing, yields an optimal partition with a higher density of filled triangles within the set while the density of filled triangles is smaller across the sets. To this end, we propose a simplicial adjacency operator that captures the relation between the nodes through second-order simplices. This allows us to extend the well-known Cheeger inequality to cluster a simplicial complex. Then, leveraging the Cheeger inequality, we propose the simplicial spectral clustering algorithm. We report results from numerical experiments on synthetic and real-world network data to demonstrate the efficacy of the proposed approach. *
VR Haptics at Home: Repurposing Everyday Objects and Environment for Casual and On-Demand VR Haptic Experiences

This paper introduces VR Haptics at Home, a method of repurposing everyday objects in the home to provide casual and on-demand haptic experiences. Current VR haptic devices are often expensive, complex, and unreliable, which limits the opportunities for rich haptic experiences outside research labs. In contrast, we envision that, by repurposing everyday objects as passive haptics props, we can create engaging VR experiences for casual uses with minimal cost and setup. To explore and evaluate this idea, we conducted an in-the-wild study with eight participants, in which they used our proof-of-concept system to turn their surrounding objects such as chairs, tables, and pillows at their own homes into haptic props. The study results show that our method can be adapted to different homes and environments, enabling more engaging VR experiences without the need for complex setup process. Based on our findings, we propose a possible design space to showcase the potential for future investigation.
Sequential Spatial Network for Collision Avoidance in Autonomous Driving

Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area. The purpose of collision avoidance is achieved by adjusting the trajectory of autonomous vehicles (AV) to avoid intersection or overlap with the trajectory of surrounding vehicles. A large number of sophisticated vision algorithms have been designed for target inspection, classification, and other tasks, such as ResNet, YOLO, etc., which have achieved excellent performance in vision tasks because of their ability to accurately and quickly capture regional features. However, due to the variability of different tasks, the above models achieve good performance in capturing small regions but are still insufficient in correlating the regional features of the input image with each other. In this paper, we aim to solve this problem and develop an algorithm that takes into account the advantages of CNN in capturing regional features while establishing feature correlation between regions using variants of attention. Finally, our model achieves better performance in the test set of L5Kit compared to the other vision models. The average number of collisions is 19.4 per 10000 frames of driving distance, which greatly improves the success rate of collision avoidance.
Solving the Dirichlet problem via Brownian motion

Consider the Dirichlet problem of the following form: Let D be a bounded, connected open set in R and ∂D its boundary. Given any continuous function f defined on the boundary ∂D, one needs to find a function u which is continuous on D = D ∪ ∂D, equal to f on ∂D, and harmonic in D. The problem may be interpreted as that of finding the steady-state temperature distribution u in the heat-conducting region D, given the temperature function on the boundary ∂D. Provided that the boundary ∂D is sufficiently “nice”, the problem can be solved in a variety of ways. One of the most intriguing methods (if not the shortest) is via Brownian motion. This method perhaps comes the closest to modeling the physics of heat diffusion. Here we give an outline of this approach which will be greatly amplified in the sections to follow. For simplicity, we temporarily restrict ourselves to the simplest nontrivial case where the dimension d = 2. Before introducing the notion of a standard Brownian motion process, let us review the mean-value property and harmonicity. Recall that a function u : R → C (or identifying R with C, we may write u : C → C) has the mean-value property if its average on any circle equals its value at the center of the circle. In other words, for any (x1, x2) ∈ R and r ≥ 0, we have
Fast Rates for Maximum Entropy Exploration

We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards. In this work, we study the maximum entropy exploration problem of two different types. The first type is visitation entropy maximization previously considered by Hazan et al.(2019) in the discounted setting. For this type of exploration, we propose a game-theoretic algorithm that has $\widetilde{\mathcal{O}}(H^3S^2A/\varepsilon^2)$ sample complexity thus improving the $\varepsilon$-dependence upon existing results, where $S$ is a number of states, $A$ is a number of actions, $H$ is an episode length, and $\varepsilon$ is a desired accuracy. The second type of entropy we study is the trajectory entropy. This objective function is closely related to the entropy-regularized MDPs, and we propose a simple algorithm that has a sample complexity of order $\widetilde{\mathcal{O}}(\mathrm{poly}(S,A,H)/\varepsilon)$. Interestingly, it is the first theoretical result in RL literature that establishes the potential statistical advantage of regularized MDPs for exploration. Finally, we apply developed regularization techniques to reduce sample complexity of visitation entropy maximization to $\widetilde{\mathcal{O}}(H^2SA/\varepsilon^2)$, yielding a statistical separation between maximum entropy exploration and reward-free exploration.
Toric Geometry in OSCAR

We report on the computer implementation for toric geometry in the computer algebra system $\texttt{OSCAR}$. The main architectural feature of $\texttt{OSCAR}$ is that its four fundamental tools $\texttt{Antic}$ (Hecke, Nemo), $\texttt{GAP}$, $\texttt{Polymake}$ and $\texttt{Singular}$ are $\mathit{integral~components}$, rather than external software. Toric geometry benefits greatly from this architecture. $\texttt{Julia}$ is a high-performance programming language designed for numerical and scientific computing. The growing ecosystem of $\texttt{Julia}$ packages ensures its continued viability for scientific computing and data analysis. Indeed, $\texttt{OSCAR}$ is written in $\texttt{Julia}$. This implies that the performance of $\texttt{OSCAR}$ should be comparable or even better than many other implementations.
Object-based SLAM utilizing unambiguous pose parameters considering general symmetry types

Existence of symmetric objects, whose observation at different viewpoints can be identical, can deteriorate the performance of simultaneous localization and mapping (SLAM). This work proposes a system for robustly optimizing the pose of cameras and objects even in the presence of symmetric objects. We classify objects into three categories depending on their symmetry characteristics, which is efficient and effective in that it allows to deal with general objects and the objects in the same category can be associated with the same type of ambiguity. Then we extract only the unambiguous parameters corresponding to each category and use them in data association and joint optimization of the camera and object pose. The proposed approach provides significant robustness to the SLAM performance by removing the ambiguous parameters and utilizing as much useful geometric information as possible. Comparison with baseline algorithms confirms the superior performance of the proposed system in terms of object tracking and pose estimation, even in challenging scenarios where the baseline fails.
Learning Adaptable Risk-Sensitive Policies to Coordinate in Multi-Agent General-Sum Games

In general-sum games, the interaction of self-interested learning agents commonly leads to socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH). Previous works address this challenge by sharing rewards or shaping their opponents' learning process, which require too strong assumptions. In this paper, we demonstrate that agents trained to optimize expected returns are more likely to choose a safe action that leads to guaranteed but lower rewards. However, there typically exists a risky action that leads to higher rewards in the long run only if agents cooperate, e.g., cooperate-cooperate in ISH. To overcome this, we propose using action value distribution to characterize the decision's risk and corresponding potential payoffs. Specifically, we present Adaptable Risk-Sensitive Policy (ARSP). ARSP learns the distributions over agent's return and estimates a dynamic risk-seeking bonus to discover risky coordination strategies. Furthermore, to avoid overfitting training opponents, ARSP learns an auxiliary opponent modeling task to infer opponents' types and dynamically alter corresponding strategies during execution. Empirically, agents trained via ARSP can achieve stable coordination during training without accessing opponent's rewards or learning process, and can adapt to non-cooperative opponents during execution. To the best of our knowledge, it is the first method to learn coordination strategies between agents both in iterated prisoner's dilemma (IPD) and iterated stag hunt (ISH) without shaping opponents or rewards, and can adapt to opponents with distinct strategies during execution. Furthermore, we show that ARSP can be scaled to high-dimensional settings.
High-mass Starless Clumps: Dynamical State and Correlation between Physical Parameters

In order to study the initial conditions of massive star formation, we have previously built a sample of 463 high-mass starless clumps (HMSCs) across the inner Galactic plane covered by multiple continuum surveys. Here, we use 13CO(2–1) line data from the SEDIGISM survey, which covers 78° in longitude (−60° < l < 18°, ∣b∣ < 0.°5) with 30″ resolution, to investigate the global dynamical state of these parsec-scale HMSCs (207 sources with good-quality data, mass 102–105 M ⊙, size 0.1–3.6 pc). We find that most HMSCs are highly turbulent with a median Mach number ∼8.2 , and 44%–55% of them are gravitationally bound (with virial parameter α vir ≲ 2) if no magnetic fields are present. A median magnetic field strength of 0.33–0.37 mG would be needed to support these bound clumps against collapse, in agreement with previous observations of magnetic fields in regions of massive star formation. Luminosity-to-mass ratio, an important tracer of evolutionary stage, is strongly correlated with dust temperature. Magnetic field strength is also correlated with density. The Larson line width–size scaling does not hold in HMSCs. This study advances our understanding of the global properties of HMSCs, and our high-resolution observations with the Atacama Large Millimeter/submillimeter Array are in progress to study the resolved properties.
BlinkFlow: A Dataset to Push the Limits of Event-Based Optical Flow Estimation

Event cameras provide high temporal precision, low data rates, and high dynamic range visual perception, which are well-suited for optical flow estimation. While data-driven optical flow estimation has obtained great success in RGB cameras, its generalization performance is seriously hindered in event cameras mainly due to the limited and biased training data. In this paper, we present a novel simulator, BlinkSim, for the fast generation of large-scale data for event-based optical flow. BlinkSim incorporates a configurable rendering engine alongside an event simulation suite. By leveraging the wealth of current 3D assets, the rendering engine enables us to automatically build up thousands of scenes with different objects, textures, and motion patterns and render very high-frequency images for realistic event data simulation. Based on BlinkSim, we construct a large training dataset and evaluation benchmark BlinkFlow that contains sufficient, diversiform, and challenging event data with optical flow ground truth. Experiments show that BlinkFlow improves the generalization performance of state-of-the-art methods by more than 40% on average and up to 90%. Moreover, we further propose an Event-based optical Flow transFormer (E-FlowFormer) architecture. Powered by our BlinkFlow, E-FlowFormer outperforms the SOTA methods by up to 91% on the MVSEC dataset and 14% on the DSEC dataset and presents the best generalization performance. The source code and data are available at https://zju3dv.github. io/blinkflow/.
Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation

Existing approaches focus on using class-level features to improve semantic segmentation performance. How to characterize the relationships of intra-class pixels and inter-class pixels is the key to extract the discriminative representative class-level features. In this paper, we introduce for the first time to describe intra-class variations by multiple distributions. Then, multiple distributions representation learning(\textbf{MDRL}) is proposed to augment the pixel representations for semantic segmentation. Meanwhile, we design a class multiple distributions consistency strategy to construct discriminative multiple distribution representations of embedded pixels. Moreover, we put forward a multiple distribution semantic aggregation module to aggregate multiple distributions of the corresponding class to enhance pixel semantic information. Our approach can be seamlessly integrated into popular segmentation frameworks FCN/PSPNet/CCNet and achieve 5.61\%/1.75\%/0.75\% mIoU improvements on ADE20K. Extensive experiments on the Cityscapes, ADE20K datasets have proved that our method can bring significant performance improvement.
Optimal bifurcation control to improve small-signal stability of power systems

Combined with the conception of small signal stability region,it is suggested to define the small signal stability control as the optimal bifurcation control— "With the uncontrollable bifurcation parameter μ ≥ μref,the optimal configuration of controllable parameters is searched in the small signal stability region to ensure the minimum control costs" . The mathematic models of optimal saddle-node bifurcation control and optimal Hopf bifurcation control are presented including all kinds of unequal constraint conditions,which strictly ensure the optimal control in both security and economy of power systems. An optimal saddle-node bifurcation control approach,which takes AVR reference voltages as control parameters,is tested in New England 39-bus test system. The initial value prediction and the control parameter selection are proposed for the optimal Hopf bifurcation control of large system. Simulation results show the effectiveness of proposed small-signal stability control strategy.
Online to Offline Crossover of White Supremacist Propaganda

White supremacist extremist groups are a significant domestic terror threat in many Western nations. These groups harness the Internet to spread their ideology via online platforms: blogs, chat rooms, forums, and social media, which can inspire violence offline. In this work, we study the persistence and reach of white supremacist propaganda in both online and offline environments. We also study patterns in narratives that crossover from online to offline environments, or vice versa. From a geospatial analysis, we find that offline propaganda is geographically widespread in the United States, with a slight tendency toward Northeastern states. Propaganda that spreads the farthest and lasts the longest has a patriotic framing and is short, memorable, and repeatable. Through text comparison methods, we illustrate that online propaganda typically leads the appearance of the same propaganda in offline flyers, banners, and graffiti. We hope that this study sheds light on the characteristics of persistent white supremacist narratives both online and offline.
Oscillon formation from preheating in asymmetric inflationary potentials

We investigate the possibility of oscillon formation during the preheating phase of asymmetric inflationary potentials. We analytically establish the existence of oscillon-like solutions for the Klein-Gordon equation for a polynomial potential of the form $V(\phi)=\frac{1}{2}\phi^2+A\phi^3+B\phi^4$ using the small amplitude analysis, which naturally arises as a Taylor expansion of the $\alpha$-attractor E-model for $\phi\ll M_\text{pl}$ and $\alpha\sim\mathcal{O}(1)$. We perform a detailed numerical analysis to study the formation of nonlinear structures in the $\alpha$-attractor E-model using the publicly available lattice simulation code $\mathcal{C}\text{osmo}\mathcal{L}\text{attice}$ for parameters in the range $10^{-5}\lesssim\alpha\lesssim 5\times 10^{-4}$. We find the backreaction of the field fluctuations onto the evolution of the homogeneous inflaton condensate to be significant for $\alpha\lesssim 2\times 10^{-4}$ for which we observe the formation of highly nonlinear structures with average equation of state $w\simeq 0$. These nonlinear structures maybe interpreted as oscillons, providing evidence that they can form during the inflaton oscillations around an asymmetric potential and are found to be present for the entirety of the runtime of our simulations, comprising $\gtrsim 40\%$ of the total energy density.
Automatic summarisation of Instagram social network posts Combining semantic and statistical approaches

The proliferation of data and text documents such as articles, web pages, books, social network posts, etc. on the Internet has created a fundamental challenge in various fields of text processing under the title of"automatic text summarisation". Manual processing and summarisation of large volumes of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text summarisation systems are divided into extractive and abstract categories. In the extractive summarisation method, the final summary of a text document is extracted from the important sentences of the same document without any modification. In this method, it is possible to repeat a series of sentences and to interfere with pronouns. However, in the abstract summarisation method, the final summary of a textual document is extracted from the meaning and significance of the sentences and words of the same document or other documents. Many of the works carried out have used extraction methods or abstracts to summarise the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this work, a crawler has been developed to extract popular text posts from the Instagram social network with appropriate preprocessing, and a set of extraction and abstraction algorithms have been combined to show how each of the abstraction algorithms can be used. Observations made on 820 popular text posts on the social network Instagram show the accuracy (80%) of the proposed system.
Study on the Data Storage Technology of Mini-Airborne Radar Based on Machine Learning

The data rate of airborne radar is much higher than the wireless data transfer rate in many detection applications, so the onboard data storage systems are usually used to store the radar data. Data storage systems with good seismic performance usually use NAND Flash as storage medium, and there is a widespread problem of long file management time, which seriously affects the data storage speed, especially under the limitation of platform miniaturization. To solve this problem, a data storage method based on machine learning is proposed for mini-airborne radar. The storage training model is established based on machine learning, and could process various kinds of radar data. The file management methods are classified and determined using the model, and then are applied to the storage of radar data. To verify the performance of the proposed method, a test was carried out on the data storage system of a mini-airborne radar. The experimental results show that the method based on machine learning can form various data storage methods adapted to different data rates and application scenarios. The ratio of the file management time to the actual data writing time is extremely low.
Happy-GLL: modular, reusable and complete top-down parsers for parameterized nonterminals

Parser generators and parser combinator libraries are the most popular tools for producing parsers. Parser combinators use the host language to provide reusable components in the form of higher-order functions with parsers as parameters. Very few parser generators support this kind of reuse through abstraction and even fewer generate parsers that are as modular and reusable as the parts of the grammar for which they are produced. This paper presents a strategy for generating modular, reusable and complete top-down parsers from syntax descriptions with parameterized nonterminals, based on the FUN-GLL variant of the GLL algorithm. The strategy is discussed and demonstrated as a novel back-end for the Happy parser generator. Happy grammars can contain `parameterized nonterminals' in which parameters abstract over grammar symbols, granting an abstraction mechanism to define reusable grammar operators. However, the existing Happy back-ends do not deliver on the full potential of parameterized nonterminals as parameterized nonterminals cannot be reused across grammars. Moreover, the parser generation process may fail to terminate or may result in exponentially large parsers generated in an exponential amount of time. The GLL back-end presented in this paper implements parameterized nonterminals successfully by generating higher-order functions that resemble parser combinators, inheriting all the advantages of top-down parsing. The back-end is capable of generating parsers for the full class of context-free grammars, generates parsers in linear time and generates parsers that find all derivations of the input string. To our knowledge, the presented GLL back-end makes Happy the first parser generator that combines all these features. This paper describes the translation procedure of the GLL back-end and compares it to the LALR and GLR back-ends of Happy in several experiments.