Miracle-dz/UniFAR-FTUs huggingface dataset:
EPOC Emotiv EEG Basics

This document provides some basic guidance to start working with the EPOC Emotiv neuroheadset device and describes how to use it to perform basic Brain-Computer Interface (BCI) research. A brief tutorial on how to set up the device, from its electrophysiological point of view, as well as a description and practical code to perform some basic analysis, is explained. A basic experiment is introduced to detect one of the oldest and, indeed, quite still valuable electrophysiological correlate, visual occipital alpha waves, or Berger Rhythm. An additional experiment is expounded where the power spectrum of alpha waves is reduced when a subject is affected by background cognitive disturbances. This document also briefs about the extraction of information by using the EPOC Emotiv library and also with python Emokit package. This report presents a basic guide on how to use EEGLAB and MATLAB, as well as python stack to perform the neurophysiological analysis. Finally, a basic analysis on different feature extraction and classification methods is provided.
A CHANDRA OBSERVATION OF THE ECLIPSING WOLF–RAYET BINARY CQ Cep

The short-period (1.64 d) near-contact eclipsing WN6+O9 binary system CQ Cep provides an ideal laboratory for testing the predictions of X-ray colliding wind shock theory at close separation where the winds may not have reached terminal speeds before colliding. We present results of a Chandra X-ray observation of CQ Cep spanning ∼1 day during which a simultaneous Chandra optical light curve was acquired. Our primary objective was to compare the observed X-ray properties with colliding wind shock theory, which predicts that the hottest shock plasma (T ≳ 20 MK) will form on or near the line-of-centers between the stars. The X-ray spectrum is strikingly similar to apparently single WN6 stars such as WR 134 and spectral lines reveal plasma over a broad range of temperatures T ∼ 4–40 MK. A deep optical eclipse was seen as the O star passed in front of the Wolf–Rayet star and we determine an orbital period Porb = 1.6412400 d. Somewhat surprisingly, no significant X-ray variability was detected. This implies that the hottest X-ray plasma is not confined to the region between the stars, at odds with the colliding wind picture and suggesting that other X-ray production mechanisms may be at work. Hydrodynamic simulations that account for such effects as radiative cooling and orbital motion will be needed to determine if the new Chandra results can be reconciled with the colliding wind picture.
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement

Object detection through LiDAR-based point cloud has recently been important in autonomous driving. Although achieving high accuracy on public benchmarks, the state-of-the-art detectors may still go wrong and cause a heavy loss due to the widespread corruptions in the real world like rain, snow, sensor noise, etc. Nevertheless, there is a lack of a large-scale dataset covering diverse scenes and realistic corruption types with different severities to develop practical and robust point cloud detectors, which is challenging due to the heavy collection costs. To alleviate the challenge and start the first step for robust point cloud detection, we propose the physical-aware simulation methods to generate degraded point clouds under different real-world common corruptions. Then, for the first attempt, we construct a benchmark based on the physical-aware common corruptions for point cloud detectors, which contains a total of 1,122,150 examples covering 7,481 scenes, 25 common corruption types, and 6 severities. With such a novel benchmark, we conduct extensive empirical studies on 8 state-of-the-art detectors that contain 6 different detection frameworks. Thus we get several insight observations revealing the vulnerabilities of the detectors and indicating the enhancement directions. Moreover, we further study the effectiveness of existing robustness enhancement methods based on data augmentation and data denoising. The benchmark can potentially be a new platform for evaluating point cloud detectors, opening a door for developing novel robustness enhancement methods.
Trading Off Resource Budgets for Improved Regret Bounds

In this work we consider a variant of adversarial online learning where in each round one picks $B$ out of $N$ arms and incurs cost equal to the $\textit{minimum}$ of the costs of each arm chosen. We propose an algorithm called Follow the Perturbed Multiple Leaders (FPML) for this problem, which we show (by adapting the techniques of Kalai and Vempala [2005]) achieves expected regret $\mathcal{O}(T^{\frac{1}{B+1}}\ln(N)^{\frac{B}{B+1}})$ over time horizon $T$ relative to the $\textit{single}$ best arm in hindsight. This introduces a trade-off between the budget $B$ and the single-best-arm regret, and we proceed to investigate several applications of this trade-off. First, we observe that algorithms which use standard regret minimizers as subroutines can sometimes be adapted by replacing these subroutines with FPML, and we use this to generalize existing algorithms for Online Submodular Function Maximization [Streeter and Golovin, 2008] in both the full feedback and semi-bandit feedback settings. Next, we empirically evaluate our new algorithms on an online black-box hyperparameter optimization problem. Finally, we show how FPML can lead to new algorithms for Linear Programming which require stronger oracles at the benefit of fewer oracle calls.
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching

In this work, we present a novel non-rigid shape matching framework based on multi-resolution functional maps with spectral attention. Existing functional map learning methods all rely on the critical choice of the spectral resolution hyperparameter, which can severely affect the overall accuracy or lead to overfitting, if not chosen carefully. In this paper, we show that spectral resolution tuning can be alleviated by introducing spectral attention. Our framework is applicable in both supervised and unsupervised settings, and we show that it is possible to train the network so that it can adapt the spectral resolution, depending on the given shape input. More specifically, we propose to compute multi-resolution functional maps that characterize correspondence across a range of spectral resolutions, and introduce a spectral attention network that helps to combine this representation into a single coherent final correspondence. Our approach is not only accurate with near-isometric input, for which a high spectral resolution is typically preferred, but also robust and able to produce reasonable matching even in the presence of significant non-isometric distortion, which poses great challenges to existing methods. We demonstrate the superior performance of our approach through experiments on a suite of challenging near-isometric and non-isometric shape matching benchmarks.
The $p$-adic limits of class numbers in $\mathbb{Z}_p$-towers

This article discusses variants of Weber's class number problem in the spirit of arithmetic topology to connect the results of Sinnott--Kisilevsky and Kionke. Let $p$ be a prime number. We first prove the $p$-adic convergence of class numbers in a $\mathbb{Z}_p$-extension of a global field and a similar result in a $\mathbb{Z}_p$-cover of a compact 3-manifold. Secondly, we establish an explicit formula for the $p$-adic limit of the $p$-power-th cyclic resultants of a polynomial using roots of unity of orders prime to $p$, the $p$-adic logarithm, and the Iwasawa invariants. Finally, we give thorough investigations of torus knots, twist knots, and elliptic curves; we complete the list of the cases with $p$-adic limits being in $\mathbb{Z}$ and find the cases such that the base $p$-class numbers are small and $\nu$'s are arbitrarily large.
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks

Spiking neural networks (SNNs), particularly the single-spike variant in which neurons spike at most once, are considerably more energy efficient than standard artificial neural networks (ANNs). However, single-spike SSNs are difficult to train due to their dynamic and non-differentiable nature, where current solutions are either slow or suffer from training instabilities. These networks have also been critiqued for their limited computational applicability such as being unsuitable for time-series datasets. We propose a new model for training single-spike SNNs which mitigates the aforementioned training issues and obtains competitive results across various image and neuromorphic datasets, with up to a $13.98\times$ training speedup and up to an $81\%$ reduction in spikes compared to the multi-spike SNN. Notably, our model performs on par with multi-spike SNNs in challenging tasks involving neuromorphic time-series datasets, demonstrating a broader computational role for single-spike SNNs than previously believed.
Contrastive introspection (ConSpec) to rapidly identify invariant steps for success

Reinforcement learning (RL) algorithms have achieved notable success in recent years, but still struggle with fundamental issues in long-term credit assignment. It remains diﬃcult to learn in situations where success is contingent upon multiple critical steps that are distant in time from each other and from a sparse reward; as is often the case in real life. Moreover, how RL algorithms assign credit in these diﬃcult situations is typically not coded in a way that can rapidly generalize to new situations. Here, we present an approach using oﬄine contrastive learning, which we call contrastive introspection (ConSpec), that can be added to any existing RL algorithm and addresses both issues. In ConSpec, a contrastive loss is used during oﬄine replay to identify invariances among successful episodes. This takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon than it is to prospectively predict reward at every step taken in the environment. ConSpec stores this knowledge in a collection of prototypes summarizing the intermediate states required for success. During training, arrival at any state that matches these prototypes generates an intrinsic reward that is added to any external rewards. As well, the reward shaping provided by ConSpec can be made to preserve the optimal policy of the underlying RL agent. The prototypes in ConSpec provide two key beneﬁts for credit assignment: (1) They enable rapid identiﬁcation of all the critical states. (2) They do so in a readily interpretable manner, enabling out of distribution generalization when sensory features are altered. In summary, ConSpec is a modular system that can be added to any existing RL algorithm to improve its long-term credit assignment.
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding

Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding. However, most existing methods lack the systematic mining and utilization of layout-centered knowledge, leading to sub-optimal performances. In this paper, we propose ERNIE-Layout, a novel document pre-training solution with layout knowledge enhancement in the whole workflow, to learn better representations that combine the features from text, layout, and image. Specifically, we first rearrange input sequences in the serialization stage, and then present a correlative pre-training task, reading order prediction, to learn the proper reading order of documents. To improve the layout awareness of the model, we integrate a spatial-aware disentangled attention into the multi-modal transformer and a replaced regions prediction task into the pre-training phase. Experimental results show that ERNIE-Layout achieves superior performance on various downstream tasks, setting new state-of-the-art on key information extraction, document image classification, and document question answering datasets. The code and models are publicly available at http://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-layout.
Effectiveness of the Recent Advances in Capsule Networks

Convolutional neural networks (CNNs) have revolutionized the ﬁeld of deep neural networks. However, recent research has shown that CNNs fail to generalize under various conditions and hence the idea of capsules was introduced in 2011, though the real surge of research started from 2017. In this paper, we present an overview of the recent advances in capsule architecture and routing mechanisms. In addition, we ﬁnd that the relative focus in recent literature is on modifying routing procedure or architecture as a whole but the study of other ﬁner components, speciﬁcally, squash function is wanting. Thus, we also present some new insights regarding the effect of squash functions in performance of the capsule networks. Finally, we conclude by discussing and proposing possible opportunities in the ﬁeld of capsule networks.
BORA: Bayesian Optimization for Resource Allocation

Optimal resource allocation is gaining a renewed interest due its relevance as a core problem in managing, over time, cloud and high-performance computing facilities. Semi-Bandit Feedback (SBF) is the reference method for eﬃciently solving this problem. In this paper we propose ( i ) an extension of the optimal resource allocation to a more general class of problems, speciﬁcally with resources availability changing over time, and ( ii ) Bayesian Optimization as a more eﬃcient alternative to SBF. Three algorithms for Bayesian Optimization for Resource Allocation, namely BORA, are presented, working on allocation decisions represented as numerical vectors or distributions. The second option required to consider the Wasserstein distance as a more suitable metric to use into one of the BORA algorithms. Results on ( i ) the original SBF case study proposed in the literature, and ( ii ) a real-life application (i.e., the optimization of multi-channel marketing) empirically prove that BORA is a more eﬃcient and eﬀective learning-and-optimization framework than SBF.
Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments

—Homodyned K (HK) distribution has been widely used to describe the scattering phenomena arising in various research ﬁelds, such as ultrasound imaging or optics. In this work, we propose a machine learning based approach to the estimation of the HK distribution parameters. We develop neural networks that can estimate the HK distribution parameters based on the signal-to-noise ratio, skewness and kurtosis calculated using fractional-order moments. Compared to the previous approaches, we consider the orders of the moments as trainable variables that can be optimized along with the network weights using the back-propagation algorithm. Networks are trained based on samples generated from the HK distribution. Obtained results demonstrate that the proposed method can be used to accurately estimate the HK distribution parameters.
Seeing the faces is so important—Experiences from online team meetings on commercial virtual reality platforms

During the COVID-19 pandemic, online meetings became common for daily teamwork in the home office. To understand the opportunities and challenges of meeting in virtual reality (VR) compared to videoconferences, we conducted the weekly team meetings of our human-computer interaction research lab on five off-the-shelf online meeting platforms over 4 months. After each of the 12 meetings, we asked the participants (N = 32) to share their experiences, resulting in 200 completed online questionnaires. We evaluated the ratings of the overall meeting experience and conducted an exploratory factor analysis of the quantitative data to compare VR meetings and video calls in terms of meeting involvement and co-presence. In addition, a thematic analysis of the qualitative data revealed genuine insights covering five themes: spatial aspects, meeting atmosphere, expression of emotions, meeting productivity, and user needs. We reflect on our findings gained under authentic working conditions, derive lessons learned for running successful team meetings in VR supporting different kinds of meeting formats, and discuss the team’s long-term platform choice.
Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs

—Human-robot interaction will play an essential role in many future industries and daily life tasks, enabling robots to collaborate with humans and reduce their workload effectively. Most existing approaches for human-robot physical collaboration focus on collaboration between humans and grounded robots. In recent years, very little progress has been made in this area when considering aerial robots, which present increased versatility and mobility compared to their grounded counterparts. This paper proposes a novel approach for safe human-robot collaborative transportation and manipulation of a cable-suspended payload with multiple aerial robots. We leverage the proposed method to enable seamless and transparent interaction between the transported objects and a human worker while considering safety constraints during operations by exploiting the redundancy of the internal transportation system. The critical system components are (a) a distributed force-sensor-free payload external wrench estimator; (b) a 6D admittance controller for human-aerial-robot collaborative transportation and manipulation; (c) a safety-aware controller that exploits the internal system redundancy to guar- antee the execution of additional tasks devoted to preserving the human or robot safety without affecting the payload trajectory tracking or quality of interaction. We validate the approach
Unitary property testing lower bounds by polynomials

We study unitary property testing, where a quantum algorithm is given query access to a black-box unitary and has to decide whether it satisfies some property. In addition to containing the standard quantum query complexity model (where the unitary encodes a binary string) as a special case, this model contains"inherently quantum"problems that have no classical analogue. Characterizing the query complexity of these problems requires new algorithmic techniques and lower bound methods. Our main contribution is a generalized polynomial method for unitary property testing problems. By leveraging connections with invariant theory, we apply this method to obtain lower bounds on problems such as determining recurrence times of unitaries, approximating the dimension of a marked subspace, and approximating the entanglement entropy of a marked state. We also present a unitary property testing-based approach towards an oracle separation between $\mathsf{QMA}$ and $\mathsf{QMA(2)}$, a long standing question in quantum complexity theory.
Road Slope Prediction and Vehicle Dynamics Control for Autonomous Vehicles

— Autonomous vehicles can enhance overall performance and implement safety measures in ways that are impossible with conventional automobiles. These functions are executed through vehicle control systems, which have been the subject of considerable research. Autonomous cars have a distinct advantage as they possess various perception sensors that can predict road surface conditions and other phenomena ahead of time. Many modern automotive control systems treat the road slope as a constant and don’t account for changes in the road profile in their vehicle models. As a result, vehicle states may be miscalculated, which, in the worst-case scenario, may result in accidents. This is particularly true for high center-of-gravity vehicles like trailers and delivery trucks. With the help of perception sensors in autonomous vehicles, a road slope estimation system can be developed to aid these control systems in making informed decisions regarding the vehicle’s state. The current review is divided into three logical steps that can be discussed in the following manner: the first section describes and reviews the individual steps for developing a road slope estimation system. The second one provides a detailed review of previous investigations that implemented different methods that employ this prediction system to improve overall vehicle performance. Finally, a roll control system is presented as an innovative idea that builds on the whole discussion. A rollover prevention system with prediction abilities is presented because (1) it proves to be a critical safety feature, especially for heavy vehicles like buses, trucks, delivery trailers, etc., and (2) not enough research has been conducted on technologies that integrate a roll stability controller with a slope estimation system.
The link between molecular cloud structure and turbulence

We aim to better understand how the spatial structure of molecular clouds is governed by turbulence. For that, we study the large-scale spatial distribution of low density molecular gas and search for characteristic length scales. We employ a 35 square degrees 13CO 1-0 molecular line survey of Cygnus X and visual extinction (Av) maps of 17 Galactic clouds to analyse the spatial structure using the Delta-variance method. The Delta-variance spectra obtained from the Av maps show differences between low-mass star forming (SF) clouds and massive giant molecular clouds (GMC) in terms of shape of the spectrum and values of the slope beta. Low-mass SF clouds have a double-peak structure with characteristic size scales around 1 pc and 4 pc. These scales may represent SF molecular clumps (1 pc) and/or the width (1 pc) and length (4 pc) of filaments. GMCs show no characteristic scale in the Av-maps which can partly be ascribed to a distance effect due to a larger line-of-sight (LOS) confusion. The Delta-variance for Cygnus, determined from the 13CO survey, shows characteristic scales at 4 pc and 40 pc, either reflecting the filament structure and large-scale turbulence forcing or -- for the 4 pc scale -- the treshhold scale when the 13CO 1-0 line becomes optically thick. Though there are different reasons for characteristic scales (geometry, decaying turbulence, LOS-effects and energy injection due to expanding supernava shells, outflows, etc.), and the relative conribution of these effects strongly varies from cloud to cloud, it is remarkable that the resulting turbulent structure of molecular clouds shows similar characteristics.
Exploration via Elliptical Episodic Bonuses

In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes. In this work, we show that the effectiveness of these methods critically relies on a count-based episodic term in their exploration bonus. As a result, despite their success in relatively simple, noise-free settings, these methods fall short in more realistic scenarios where the state space is vast and prone to noise. To address this limitation, we introduce Exploration via Elliptical Episodic Bonuses (E3B), a new method which extends count-based episodic bonuses to continuous state spaces and encourages an agent to explore states that are diverse under a learned embedding within each episode. The embedding is learned using an inverse dynamics model in order to capture controllable aspects of the environment. Our method sets a new state-of-the-art across 16 challenging tasks from the MiniHack suite, without requiring task-specific inductive biases. E3B also matches existing methods on sparse reward, pixel-based VizDoom environments, and outperforms existing methods in reward-free exploration on Habitat, demonstrating that it can scale to high-dimensional pixel-based observations and realistic environments.