Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Forwarding of Coded Packets on Networks

We consider a scenario of broadcasting information over a network of nodes connected by noiseless communication links. A source node in the network has some data packets to broadcast. It encodes these data packets into <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> coded packets in such a way that any node in the network that receives any <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> out of the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> coded packets will be able to retrieve all the original data packets. The source transmits the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> coded packets to its one-hop neighbours. Every other node in the network follows a probabilistic forwarding protocol, in which it forwards a previously unreceived packet to all its neighbours with a certain probability <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>. We say that the information from the source undergoes a “near-broadcast” if the expected fraction of nodes that receive at least <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> of the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> coded packets is close to 1. The forwarding probability <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is chosen so as to minimize the expected total number of transmissions needed for a near-broadcast. We study how, for a given <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, this minimum forwarding probability and the associated expected total number of packet transmissions varies with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. We specifically analyze the probabilistic forwarding of coded packets on two network topologies: binary trees and square grids. For trees, our analysis shows that for fixed <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, the expected total number of transmissions increases with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. On the other hand, on grids, a judicious choice of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> significantly reduces the expected total number of transmissions needed for a near-broadcast. Behaviour similar to that of the grid is also observed in other well-connected network topologies such as random geometric graphs and random regular graphs.
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target

Multi-step methods such as Retrace($\lambda$) and $n$-step $Q$-learning have become a crucial component of modern deep reinforcement learning agents. These methods are often evaluated as a part of bigger architectures and their evaluations rarely include enough samples to draw statistically significant conclusions about their performance. This type of methodology makes it difficult to understand how particular algorithmic details of multi-step methods influence learning. In this paper we combine the $n$-step action-value algorithms Retrace, $Q$-learning, Tree Backup, Sarsa, and $Q(\sigma)$ with an architecture analogous to DQN. We test the performance of all these algorithms in the mountain car environment; this choice of environment allows for faster training times and larger sample sizes. We present statistical analyses on the effects of the off-policy correction, the backup length parameter $n$, and the update frequency of the target network on the performance of these algorithms. Our results show that (1) using off-policy correction can have an adverse effect on the performance of Sarsa and $Q(\sigma)$; (2) increasing the backup length $n$ consistently improved performance across all the different algorithms; and (3) the performance of Sarsa and $Q$-learning was more robust to the effect of the target network update frequency than the performance of Tree Backup, $Q(\sigma)$, and Retrace in this particular task.
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization

Recent studies on diffusion-based sampling methods have shown that Langevin Monte Carlo (LMC) algorithms can be beneficial for non-convex optimization, and rigorous theoretical guarantees have been proven for both asymp-totic and finite-time regimes. Algorithmically, LMC-based algorithms resemble the well-known gradient descent (GD) algorithm, where the GD recursion is perturbed by an additive Gaussian noise whose variance has a particular form. Fractional Langevin Monte Carlo (FLMC) is a recently proposed extension of LMC, where the Gaussian noise is replaced by a heavy-tailed α-stable noise. As opposed to its Gaussian counterpart , these heavy-tailed perturbations can incur large jumps and it has been empirically demonstrated that the choice of α-stable noise can provide several advantages in modern machine learning problems, both in optimization and sampling contexts. However, as opposed to LMC, only asymptotic convergence properties of FLMC have been yet established. In this study, we analyze the non-asymptotic behavior of FLMC for non-convex optimization and prove finite-time bounds for its expected suboptimality. Our results show that the weak-error of FLMC increases faster than LMC, which suggests using smaller step-sizes in FLMC. We finally extend our results to the case where the exact gradients are replaced by stochas-tic gradients and show that similar results hold in this setting as well.
Skeleton-Based Action Recognition of People Handling Objects

In visual surveillance systems, it is necessary to recognize the behavior of people handling objects such as a phone, a cup, or a plastic bag. In this paper, to address this problem, we propose a new framework for recognizing object-related human actions by graph convolutional networks using human and object poses. In this framework, we construct skeletal graphs of reliable human poses by selectively sampling the informative frames in a video, which include human joints with high confidence scores obtained in pose estimation. The skeletal graphs generated from the sampled frames represent human poses related to the object position in both the spatial and temporal domains, and these graphs are used as inputs to the graph convolutional networks. Through experiments over an open benchmark and our own data sets, we verify the validity of our framework in that our method outperforms the state-of-the-art method for skeleton-based action recognition.
In Search of Lost QoS

The area of quality of service (QoS) in communications networks has been the target of research for already several decades with tens of thousands of published journal and conference papers. However, the practical introduction of QoS systems in commercial networks has been limited (with a preference for simple overprovisioning). Despite this dissonance, most influential QoS papers do not discuss this lack of penetration or challenge any of the common assumptions used to argue for QoS systems. So far, the few critical QoS papers have had only a minor effect on QoS research and standardization. Therefore, there is a serious risk that QoS will remain an academic research topic without significant practical relevance. To help elucidate these issues, in this work, we first perform a comprehensive review of QoS including a general overview and an analysis of both influential and critical work from the past 30 years. We examine properties such as citations, keywords, and author traits to show that QoS has passed through several distinct phases with different topics while maintaining the overall attitude towards the role and objective of QoS systems. We then discuss QoS as a social phenomenon and in the context of current networking standards. Finally, we propose a QoS scheme based on incentives that avoids some of the problems identified in critical work, and we provide simple recommendations for network operators. Overall, we hope to spark the community to take a fresh look at QoS.
Fine-grained ECG Classification Based on Deep CNN and Online Decision Fusion

Early recognition of abnormal rhythm in ECG signals is crucial for monitoring or diagnosing patients' cardiac conditions and increasing the success rate of the treatment. Classifying abnormal rhythms into fine-grained categories is very challenging due to the the broad taxonomy of rhythms, noises and lack of real-world data and annotations from large number of patients. This paper presents a new ECG classification method based on Deep Convolutional Neural Networks (DCNN) and online decision fusion. Different from previous methods which utilize hand-crafted features or learn features from the original signal domain, the proposed DCNN based method learns features and classifiers from the time-frequency domain in an end-to-end manner. First, the ECG wave signal is transformed to time-frequency domain by using Short-Time Fourier Transform. Next, specific DCNN models are trained on ECG samples of specific length. Finally, an online decision fusion method is proposed to fuse past and current decisions from different models into a more accurate one. Experimental results on both synthetic and real-world ECG datasets convince the effectiveness and efficiency of the proposed method.
Perception-in-the-Loop Adversarial Examples

We present a scalable, black box, perception-in-the-loop technique to find adversarial examples for deep neural network classifiers. Black box means that our procedure only has input-output access to the classifier, and not to the internal structure, parameters, or intermediate confidence values. Perception-in-the-loop means that the notion of proximity between inputs can be directly queried from human participants rather than an arbitrarily chosen metric. Our technique is based on covariance matrix adaptation evolution strategy (CMA-ES), a black box optimization approach. CMA-ES explores the search space iteratively in a black box manner, by generating populations of candidates according to a distribution, choosing the best candidates according to a cost function, and updating the posterior distribution to favor the best candidates. We run CMA-ES using human participants to provide the fitness function, using the insight that the choice of best candidates in CMA-ES can be naturally modeled as a perception task: pick the top $k$ inputs perceptually closest to a fixed input. We empirically demonstrate that finding adversarial examples is feasible using small populations and few iterations. We compare the performance of CMA-ES on the MNIST benchmark with other black-box approaches using $L_p$ norms as a cost function, and show that it performs favorably both in terms of success in finding adversarial examples and in minimizing the distance between the original and the adversarial input. In experiments on the MNIST, CIFAR10, and GTSRB benchmarks, we demonstrate that CMA-ES can find perceptually similar adversarial inputs with a small number of iterations and small population sizes when using perception-in-the-loop. Finally, we show that networks trained specifically to be robust against $L_\infty$ norm can still be susceptible to perceptually similar adversarial examples.
Hybrid Precoder Design for Cache-Enabled Millimeter-Wave Radio Access Networks

In this paper, we study the design of a hybrid precoder, consisting of an analog and a digital precoder, for the delivery phase of downlink cache-enabled millimeter-wave (mm-wave) radio access networks (CeMm-RANs). In CeMm-RANs, enhanced remote radio heads (eRRHs), which are equipped with local cache and baseband signal processing capabilities in addition to the basic functionalities of conventional RRHs, are connected to the baseband processing unit via fronthaul links. Two different fronthaul information transfer strategies are considered, namely, hard fronthaul information transfer, where hard information of uncached requested files is transmitted via the fronthaul links to a subset of eRRHs, and soft fronthaul information transfer, where the fronthaul links are used to transmit quantized baseband signals of uncached requested files. The hybrid precoder is optimized for maximization of the minimum user rate under a fronthaul capacity constraint, an eRRH transmit power constraint, and a constant-modulus constraint on the analog precoder. The resulting optimization problem is non-convex, and hence, the global optimal solution is difficult to obtain. Therefore, convex approximation methods are employed to tackle the non-convexity of the achievable user rate, the fronthaul capacity constraint, and the constant modulus constraint on the analog precoder. Then, an effective algorithm with provable convergence is developed to solve the approximated optimization problem. The simulation results are provided to evaluate the performance of the proposed algorithms, where fully digital precoding is used as the benchmark. The results reveal that except for the case of a large fronthaul link capacity, soft fronthaul information transfer is preferable for CeMm-RANs. Furthermore, surprisingly, hybrid precoding outperforms fully digital precoding with soft fronthaul information transfer for medium-to-large file sizes and fronthaul capacity limited mm-wave cloud RANs.
Observability-Aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation

External effects such as shocks and temperature variations affect the calibration of visual–inertial sensor systems and thus they cannot fully rely on factory calibrations. Re-calibrations performed on short user-collected datasets might yield poor performance since the observability of certain parameters is highly dependent on the motion. In addition, on resource-constrained systems (e.g., mobile phones), full-batch approaches over longer sessions quickly become prohibitively expensive. In this paper, we approach the self-calibration problem by introducing information theoretic metrics to assess the information content of trajectory segments, thus allowing to select the most informative parts from a dataset for calibration purposes. With this approach, we are able to build compact calibration datasets either: 1) by selecting segments from a long session with limited exciting motion or 2) from multiple short sessions where a single session does not necessarily excite all modes sufficiently. Real-world experiments in four different environments show that the proposed method achieves comparable performance to a batch calibration approach, yet, at a constant computational complexity which is independent of the duration of the session.
Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks

Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions while the conditional probability p(x|y) stays fixed. This is relevant in settings such as medical diagnosis, where a classifier trained to predict disease based on observed symptoms may need to be adapted to a different distribution where the baseline frequency of the disease is higher. Given calibrated estimates of p(y|x), one can apply an EM algorithm to correct for the shift in class imbalance between the training and test distributions without ever needing to calculate p(x|y). Unfortunately, modern neural networks typically fail to produce well-calibrated probabilities, compromising the effectiveness of this approach. Although Temperature Scaling can greatly reduce miscalibration in these networks, it can leave behind a systematic bias in the probabilities that still poses a problem. To address this, we extend Temperature Scaling with class-specific bias parameters, which largely eliminates systematic bias in the calibrated probabilities and allows for effective domain adaptation under label shift. We term our calibration approach "Bias-Corrected Temperature Scaling". On experiments with CIFAR10, we find that EM with Bias-Corrected Temperature Scaling significantly outperforms both EM with Temperature Scaling and the recently-proposed Black-Box Shift Estimation.
Correctness Analysis of IBFT

In this paper we analyse the correctness of Istanbul BFT (IBFT), which is a Byzantine-fault-tolerant (BFT) proof-of-authority (PoA) blockchain consensus protocol that ensures immediate finality. We show that the IBFT protocol does not guarantee Byzantine-fault-tolerant consistency and liveness when operating in an eventually synchronous network, and we propose modifications to the protocol to ensure both Byzantine-fault-tolerant consistency and liveness in eventually synchronous settings.
How to Use Undiscovered Information Inequalities: Direct Applications of the Copy Lemma

We discuss linear programming techniques that help to deduce corollaries of non-classical inequalities for Shannon’s entropy. We focus on direct applications of the copy lemma. These applications involve implicitly some (known or unknown) non-classical universal inequalities for Shannon’s entropy, though we do not derive these inequalities explicitly. To reduce the computational complexity of these problems, we extensively use symmetry considerations. We present two examples of use of these techniques: we provide a reduced size formal inference of the best known bound for the Ingleton score (originally proven by Dougherty et al. with explicitly derived non-Shannon type inequalities), and improve the lower bound for the optimal information ratio of the secret sharing scheme for an access structure based on the Vámos matroid.
Visual Entailment: A Novel Task for Fine-Grained Image Understanding

Existing visual reasoning datasets such as Visual Question Answering (VQA), often suffer from biases conditioned on the question, image or answer distributions. The recently proposed CLEVR dataset addresses these limitations and requires fine-grained reasoning but the dataset is synthetic and consists of similar objects and sentence structures across the dataset. In this paper, we introduce a new inference task, Visual Entailment (VE) - consisting of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. To realize this task, we build a dataset SNLI-VE based on the Stanford Natural Language Inference corpus and Flickr30k dataset. We evaluate various existing VQA baselines and build a model called Explainable Visual Entailment (EVE) system to address the VE task. EVE achieves up to 71% accuracy and outperforms several other state-of-the-art VQA based models. Finally, we demonstrate the explainability of EVE through cross-modal attention visualizations. The SNLI-VE dataset is publicly available at this https URL necla-ml/SNLI-VE.
Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks

Surface Electromyography (sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and High-Density (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.
The distribution of mass within spiral galaxies: unique solutions from gas and stellar kinematics

In dit proefschrift wordt de hoeveelheid en verdeling van de verschillende dynamische componenten, gevormd door de aanwezige sterren, het aanwezige gas en de donkere materie, in 30 spiraalstelsels gelijkend op onze eigen Melkweg onderzocht. Dit onderzoek biedt inzicht in ons algemeen begrip van de vorming en evolutie van sterrenstelsels. Met behulp van optische- en radiospectroscopie heb ik de totale dynamische massa van ieder spiraalstelsel bepaald aan de hand van de rotatiekromme van de aanwezige schijf. De totale massa van de schijf volgt uit de gemeten verticale bewegingen van de sterren in de schijf, terwijl de totale massa van het atomaire en moleculaire gas direct volgt uit hun emissie in het infrarood en radio. Vervolgens heb ik de massa en verdeling van de aanwezige donkere materie bepaald door het verschil te meten tussen de totale dynamische massa en de totale massa van de overige (zichtbare) componenten. In tegenstelling tot wat algemeen wordt aangenomen, heb ik gevonden dat donkere materie een belangrijke, en zelfs dominante, component vormt, zelfs in de centra van de meest heldere spiraalstelsels. De zichtbare componenten dragen gemiddeld slechts ongeveer 30 procent bij aan de totale massa binnen een straal vergelijkbaar met de afstand van onze zon tot het centrum van ons Melkwegstelsel. De overige 70 procent van de massa is dus donker. Hieruit concludeer ik dat de lichtgevende massa van spiraalstelsels ongeveer drie keer kleiner is dan algemeen wordt aangenomen.
Hybrid Task Cascade for Instance Segmentation

Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4% and 1.5% improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task. Code is available at https://github.com/open-mmlab/mmdetection.
Distributed Nesterov Gradient Methods Over Arbitrary Graphs

In this letter, we introduce a distributed Nesterov gradient method, <inline-formula><tex-math notation="LaTeX">$\mathcal {ABN}$</tex-math></inline-formula>, that does not require doubly stochastic weights. Instead, the implementation is based on a simultaneous application of both row- and column-stochastic weights that makes <inline-formula><tex-math notation="LaTeX">$\mathcal {ABN}$</tex-math></inline-formula> applicable to arbitrary (strongly-connected) graphs. Since constructing column-stochastic weights needs additional information (the number of outgoing neighbors), not available in certain communication protocols, we derive a variation, <italic>FROZEN</italic>, that only requires row-stochastic weights, but at the expense of additional iterations for eigenvector estimation. We numerically study these algorithms for various objective functions and network parameters and show that the proposed distributed Nesterov gradient methods achieve acceleration compared to the current state-of-the-art methods for distributed optimization.