Miracle-dz/UniFAR-FTUs huggingface dataset:
0/1 Deep Neural Networks via Block Coordinate Descent

The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsic characteristics (e.g., discontinuity and no viable information of subgradients) impede its development for several decades. Even if there is an impressive body of work on designing DNNs with continuous activation functions that can be deemed as surrogates of the step function, it is still in the possession of some advantageous properties, such as complete robustness to outliers and being capable of attaining the best learning-theoretic guarantee of predictive accuracy. Hence, in this paper, we aim to train DNNs with the step function used as an activation function (dubbed as 0/1 DNNs). We first reformulate 0/1 DNNs as an unconstrained optimization problem and then solve it by a block coordinate descend (BCD) method. Moreover, we acquire closed-form solutions for sub-problems of BCD as well as its convergence properties. Furthermore, we also integrate $\ell_{2,0}$-regularization into 0/1 DNN to accelerate the training process and compress the network scale. As a result, the proposed algorithm has a high performance on classifying MNIST and Fashion-MNIST datasets. As a result, the proposed algorithm has a desirable performance on classifying MNIST, FashionMNIST, Cifar10, and Cifar100 datasets.
Global Contentious Politics Database (GLOCON) Annotation Manuals

The database creation utilized automated text processing tools that detect if a news article contains a protest event, locate protest information within the article, and extract pieces of information regarding the detected protest events. The basis of training and testing the automated tools is the GLOCON Gold Standard Corpus (GSC), which contains news articles from multiple sources from each focus country. The articles in the GSC were manually coded by skilled annotators in both classification and extraction tasks with the utmost accuracy and consistency that automated tool development demands. In order to assure these, the annotation manuals in this document lay out the rules according to which annotators code the news articles. Annotators refer to the manuals at all times for all annotation tasks and apply the rules that they contain. The content of the annotation manual is built on the general principles and standards of linguistic annotation laid out in other prominent annotation manuals such as ACE, CAMEO, and TimeML. These principles, however, have been adapted or rather modified heavily to accommodate the social scientific concepts and variables employed in the EMW project. The manual has been molded throughout a long trial and error process that accompanied the annotation of the GSC. It owes much of its current shape to the meticulous work and invaluable feedback provided by highly specialized teams of annotators, whose diligence and expertise greatly increased the quality of the corpus.
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming

Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications. A natural consequence is the adoption of this paradigm for safety-critical tasks, where human safety and expensive hardware can be involved. In this context, it is crucial to optimize the performance of DRL-based agents while providing guarantees about their behavior. This paper presents a novel technique for incorporating domain-expert knowledge into a constrained DRL training loop. Our technique exploits the scenario-based programming paradigm, which is designed to allow specifying such knowledge in a simple and intuitive way. We validated our method on the popular robotic mapless navigation problem, in simulation, and on the actual platform. Our experiments demonstrate that using our approach to leverage expert knowledge dramatically improves the safety and the performance of the agent.
Asymptotic analysis for hydrodynamic force acting on stiff particles

A three-dimensional mathematical model of a viscous incompressible ﬂuid with two stiﬀ particles is investigated in the near-contact regime. When one of the particles approaches the other motionless particle with pre-scribed translational and angular velocities, there always appears blow-up of hydrodynamic force exerted on the moving particle. In this paper, we construct explicit singular functions corresponding to the ﬂuid velocity and pres- sure to establish precise asymptotic formulas for hydrodynamic force with respect to small interparticle distance, which show that its largest singularity is determined by squeeze motion between two particles. Finally, the primal-dual variational principle is employed to give a complete justiﬁcation for these asymptotics.
Actively Learning Deep Neural Networks with Uncertainty Sampling Based on Sum-Product Networks

Active learning is a popular approach for reducing the amount of data in training deep neural network models. Its success hinges on the choice of an effective acquisition function, which ranks not yet labeled data points according to their expected informativeness. In uncertainty sampling, the uncertainty that the current model has about a point’s class label is the main criterion for this type of ranking. This paper proposes a new approach to uncertainty sampling in training a Convolutional Neural Network (CNN). The main idea is to use the feature representation extracted by the CNN as data for training a Sum-Product Network (SPN). Since SPNs are typically used for estimating the distribution of a dataset, they are well suited to the task of estimating class probabilities that can be used directly by standard acquisition functions such as max entropy and variational ratio. Moreover, we enhance these acquisition functions by weights calculated with the help of the SPN model; these weights make the acquisition function more sensitive to the diversity of conceivable class labels for a data point. The effectiveness of our method is demonstrated in an experimental study on the MNIST, Fashion-MNIST and CIFAR-10 datasets, where we compare it to the state-of-the-art methods MC Dropout and Bayesian Batch. This document contains the supplementary information for Submission 2473. 1
Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk

Given a Lipschitz or smooth convex function $\, f:K \to \mathbb{R}$ for a bounded polytope $K \subseteq \mathbb{R}^d$ defined by $m$ inequalities, we consider the problem of sampling from the log-concave distribution $\pi(\theta) \propto e^{-f(\theta)}$ constrained to $K$. Interest in this problem derives from its applications to Bayesian inference and differentially private learning. Our main result is a generalization of the Dikin walk Markov chain to this setting that requires at most $O((md + d L^2 R^2) \times md^{\omega-1}) \log(\frac{w}{\delta}))$ arithmetic operations to sample from $\pi$ within error $\delta>0$ in the total variation distance from a $w$-warm start. Here $L$ is the Lipschitz-constant of $f$, $K$ is contained in a ball of radius $R$ and contains a ball of smaller radius $r$, and $\omega$ is the matrix-multiplication constant. Our algorithm improves on the running time of prior works for a range of parameter settings important for the aforementioned learning applications. Technically, we depart from previous Dikin walks by adding a"soft-threshold"regularizer derived from the Lipschitz or smoothness properties of $f$ to the log-barrier function for $K$ that allows our version of the Dikin walk to propose updates that have a high Metropolis acceptance ratio for $f$, while at the same time remaining inside the polytope $K$.
Efficient Aggregated Kernel Tests using Incomplete U-statistics

We propose a series of computationally efficient nonparametric tests for the two-sample, independence, and goodness-of-fit problems, using the Maximum Mean Discrepancy (MMD), Hilbert Schmidt Independence Criterion (HSIC), and Kernel Stein Discrepancy (KSD), respectively. Our test statistics are incomplete $U$-statistics, with a computational cost that interpolates between linear time in the number of samples, and quadratic time, as associated with classical $U$-statistic tests. The three proposed tests aggregate over several kernel bandwidths to detect departures from the null on various scales: we call the resulting tests MMDAggInc, HSICAggInc and KSDAggInc. This procedure provides a solution to the fundamental kernel selection problem as we can aggregate a large number of kernels with several bandwidths without incurring a significant loss of test power. For the test thresholds, we derive a quantile bound for wild bootstrapped incomplete $U$-statistics, which is of independent interest. We derive non-asymptotic uniform separation rates for MMDAggInc and HSICAggInc, and quantify exactly the trade-off between computational efficiency and the attainable rates: this result is novel for tests based on incomplete $U$-statistics, to our knowledge. We further show that in the quadratic-time case, the wild bootstrap incurs no penalty to test power over the more widespread permutation-based approach, since both attain the same minimax optimal rates (which in turn match the rates that use oracle quantiles). We support our claims with numerical experiments on the trade-off between computational efficiency and test power. In all three testing frameworks, the linear-time versions of our proposed tests perform at least as well as the current linear-time state-of-the-art tests.
SAMPLE-HD: Simultaneous Action and Motion Planning Learning Environment

Humans exhibit incredibly high levels of multi-modal understanding - combining visual cues with read, or heard knowledge comes easy to us and allows for very accurate interaction with the surrounding environment. Various simulation environments focus on providing data for tasks related to scene understanding, question answering, space exploration, visual navigation. In this work, we are providing a solution to encompass both, visual and behavioural aspects of simulation in a new environment for learning interactive reasoning in manipulation setup. SAMPLE-HD environment allows to generate various scenes composed of small household objects, to procedurally generate language instructions for manipulation, and to generate ground truth paths serving as training data.
Object Localization Assistive System Based on CV and Vibrotactile Encoding

Intelligent assistive systems can navigate blind people, but most of them could only give non-intuitive cues or inefficient guidance. Based on computer vision and vibrotactile encoding, this paper presents an interactive system that provides blind people with intuitive spatial cognition. Different from the traditional auditory feedback strategy based on speech cues, this paper firstly introduces a vibration-encoded feedback method that leverages the haptic neural pathway and enables the users to interact with objects other than manipulating an assistance device. Based on this strategy, a wearable visual module based on an RGB-D camera is adopted for 3D spatial object localization, which contributes to accurate perception and quick object localization in the real environment. The experimental results on target blind individuals indicate that vibrotactile feedback reduces the task completion time by over 25% compared with the mainstream voice prompt feedback scheme. The proposed object localization system provides a more intuitive spatial navigation and comfortable wearability for blindness assistance.
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal–Oxide–Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. We parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low Analog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck $R_{\text{ON}}/R_{\text{OFF}}$ memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791 W of power while occupying an area of 31.255 mm2 in a 22 nm FDSOI CMOS process.
QuDASH: Quantum-Inspired Rate Adaptation Approach for DASH Video Streaming

Internet traffic is dramatically increasing with the development of network technologies and video streaming traffic accounts for large amount within the total traffic, which reveals the importance to guarantee the quality of content delivery service. Based on the network conditions, adaptive bitrate (ABR) control is utilized as a common technique which can choose the proper bitrate to ensure the video streaming quality. In this paper, new bitrate control method, QuDASH is proposed by taking advantage of the emerging quantum technology. In QuDASH, the adaptive control model is developed using the quadratic unconstrained binary optimization (QUBO), which aims at increasing the average bitrate and decreasing the video rebuffering events to maximize the user quality of experience (QoE). In order to formulate the video control model, first the QUBO terms of different factors are defined regarding video quality, bitrate change, and buffer condition. Then, all the individual QUBO terms are merged to generate an objective function. By minimizing the QUBO objective function, the bitrate choice is determined from the solution. The control model is solved by Digital Annealer, which is a quantum-inspired computing technology. The evaluation of the proposed method is carried out by simulation with the throughput traces obtained in real world under different scenarios and the comparison with other methods is conducted. Experiment results demonstrated that the proposed QuDASH method has better performance in terms of QoE compared with other advanced ABR methods. In 68.2% of the examined cases, QuDASH achieves the highest QoE results, which shows the superiority of the QuDASH over conventional methods.
Multi-criteria optimization and automated network restructuring to mitigate construction projects delays on-the-run

250 words) Purpose (limit 100 words) Construction project management requires dynamic mitigation control ensuring the project's timely completion by a best fit for common purpose strategy for all stakeholders. Current mitigation approaches are usually performed by an iterative Monte Carlo (MC) analysis focussing on lowest-cost strategies which do not include (1) the project manager's goal-oriented behaviour, (2) automated network restructuring potential, and (3) multi-dimensional optimization criteria for best fitting mitigation strategies-criteria. Therefore, the development statement within this paper is to design a method and implementation tool that properly dissolves all the aforementioned shortcomings ensuring the project's completion date by finding the most effective and efficient mitigation strategy. Design/methodology/approach (limit 100 words) To fulfill the purpose of this paper, the Mitigation Controller (MitC) has been developed using an integrative approach of non-linear optimization techniques, probabilistic Monte Carlo simulation, and preference function modeling. Findings (limit 100 words) Compared to the conventional way of mitigating project delays. The developed MitC allows mitigating potential delays with the least negative consequences on several project criteria, such as cost, environmental impact, etc. The application of the model to the demonstrative case study shows the ability of the model to significantly increase the probability of completing the project in the given target duration. Embedding the multi-criteria evaluation in the optimization model ensures that other interests are also represented in finding the optimal strategy for project delays. Originality/value (limit 100 words) 1 Faculty of Civil Engineering and Geosciences, Delft University of Technology, Netherlands. 2 Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, Netherlands, Corresponding author Email: o.kammouh@tudelft.nl 3 Faculty of Civil Engineering and Geosciences, Delft University of Technology, Netherlands. The MitC is a state-of-the-art decision support tool that a-priori automates and optimizes the search for the best set of mitigation strategies for common purpose rather than a-posteriori evaluating the potentially sub-optimal and over-designed mitigation strategies. Combining simulation and optimization of the project network schedule to systematically change the network structure to mitigate the effects of risks and uncertainties is something that was not yet developed. The inclusion of a multicriteria evaluation within optimization is also a novel perspective in the field. These two aspects prove this development to be a relevant addition to the scientific knowledge.
A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms

Measuring the influence of users in social networks is key for numerous applications. A recently proposed influence metric, coined as Ψ-score, allows to go beyond traditional centrality metrics, which only assess structural graph importance, by further incorporating the rich information provided by the posting and re-posting activity of users. The Ψ-score is shown in fact to generalize PageRank for non-homogeneous node activity. Despite its significance, it scales poorly to large datasets; for a network of $N$ users, it requires to solve $N$ linear systems of equations of size $N$. To address this problem, this work introduces a novel scalable algorithm for the fast approximation of Ψ- score, named Power-Ψ. The proposed algorithm is based on a novel equation indicating that it suffices to solve one system of equations of size $N$ to compute the Ψ-score. Then, our algorithm exploits the fact that such a system can be recursively and distributedly approximated to any desired error. This permits the Ψ-score, summarizing both structural and behavioral information for the nodes, to run as fast as PageRank. We validate the effectiveness of the proposed algorithm, which we release as an open source Python library, on several real-world datasets.
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks

Node classiﬁcation on attributed networks is a semi-supervised task that is crucial for network analysis. By decoupling two critical operations in Graph Convolutional Networks (GCNs), namely feature transformation and neighborhood aggregation, some recent works of decoupled GCNs could support the information to propagate deeper and achieve advanced performance. However, they follow the traditional structure-aware propagation strategy of GCNs, making it hard to capture the attribute correlation of nodes and sensitive to the structure noise described by edges whose two endpoints belong to diﬀerent categories. To address these issues, we propose a new method called the Propagation with Adaptive Mask then Training (PAMT). The key idea is to integrate the attribute similarity mask into the structure-aware propagation process. In this way, PAMT could preserve the attribute correlation of adjacent nodes during the propagation and eﬀectively reduce the inﬂuence of structure noise. Moreover, we develop an iterative reﬁnement mechanism to update the similarity mask during the training process for improving the training performance. Extensive experiments on four real-world datasets demonstrate the superior performance and robustness of PAMT.
A new generation of reduction methods for networks of neurons with complex dynamic phenotypes

Collective dynamics of spiking networks of neurons has been of central interest to both computation neuroscience and network science. Over the past years a new generation of neural population models based on exact reductions (ER) of spiking networks have been developed. However, most of these efforts have been limited to networks of neurons with simple dynamics (e.g. the quadratic integrate and fire models). Here, we present an extension of ER to conductance-based networks of two-dimensional Izhikevich neuron models. We employ an adiabatic approximation, which allows us to analytically solve the continuity equation describing the evolution of the state of the neural population and thus to reduce model dimensionality. We validate our results by showing that the reduced mean-field description we derived can qualitatively and quantitatively describe the macroscopic behaviour of populations of two-dimensional QIF neurons with different electrophysiological profiles (regular firing, adapting, resonator and type III excitable). Most notably, we apply this technique to develop an ER for networks of neurons with bursting dynamics.
Responsibility-associated Multi-agent Collision Avoidance with Social Preferences

This paper introduces a novel social preference-aware decentralized safe control framework to address the responsibility allocation problem in multi-agent collision avoidance. Considering that agents do not necessarily cooperate in symmetric ways, this paper focuses on semi-cooperative behavior among heterogeneous agents with varying cooperation levels. Drawing upon the idea of Social Value Orientation (SVO) for quantifying the individual selfishness, we propose a novel concept of Responsibility-associated Social Value Orientation (R-SVO) to express the intended relative social implications between pairwise agents. This is used to redefine each agent's social preferences or personalities in terms of corresponding responsibility shares in contributing to the coordination scenario, such as semi-cooperative collision avoidance where all agents interact in an asymmetric way. By incorporating such relative social implications through proposed Local Pairwise Responsibility Weights, we develop a Responsibility-associated Control Barrier Function-based safe control framework for individual agents, and multi-agent collision avoidance is achieved with formally provable safety guarantees. Simulations are provided to demonstrate the effectiveness and efficiency of the proposed framework in several multi-agent navigation tasks, such as a position-swapping game, a self-driving car highway ramp merging scenario, and a circular position swapping game.