Miracle-dz/UniFAR-FTUs huggingface dataset:
Disease and Its Control: The Shaping of Modern Thought

Preface Acknowledgments Abbreviations of Journals The Birth and Death of Diseases Disease and History Disease as Supernatural Disease as Natural and Generalized The Anatomical Idea Disease as Localized The Concept of Experimental Medicine Specific Causation Specific Prevention Specific Therapy Health, Disease, and Illness Appendix: The Hippocratic Oath Bibliography Index
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used in practice. What has been achieved to-date are rather sectorial models that address a limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as dawn/dusk, day and night for time detection, glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used but are not limited to autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.
The cover time of a sparse random intersection graph

Random intersection graphs model real affiliation networks, where each of $n$ actors selects an attribute set from a given collection of $m$ attributes and two actors establish adjacency relation whenever they share a common attribute. In the random intersection graph ${\cal G}(n,m,p)$ actors select attributes independently at random and with probability $p$. We establish the cover time of the simple random walk on ${\cal G}(n,m,p)$ at the connectivity threshold and above it. We consider the range of $n,m,p$ where the typical attribute is shared by (stochastically) bounded number of actors.
Toward the Detection of Relativistic Image Doubling in Imaging Atmospheric Cherenkov Telescopes

Cosmic gamma-ray photons incident on the upper atmosphere create air showers that move to the Earth’s surface with superluminal speed, relative to the air. Even though many of these air showers remain superluminal all along their trajectories, the shower’s velocity component toward a single Imaging Atmospheric Cherenkov Telescope (IACT) may drop from superluminal to subluminal. When this happens, an IACT that is able to resolve the air shower both in time and angle should be able to document an unusual optical effect known as relativistic image doubling (RID). The logic of RID is that the shower appears to precede its own Cherenkov radiation when its speed component toward the IACT is superluminal, but appears to trail its own Cherenkov radiation when its speed component toward the IACT is subluminal. The result is that the IACT will see the shower start not at the top of the atmosphere but in the middle—at the point along the shower’s path where its radial velocity component drops to subluminal. Images of the shower would then be seen by the IACT to go both up and down simultaneously. A simple simulation demonstrating this effect is presented. Clear identification of RID would confirm in the atmosphere a novel optical imaging effect caused not by lenses but solely by relativistic kinematics, and may aid in the accuracy of path and speed reconstructions of the relativistic air shower.
On Fairness in Committee-based Blockchains

Committee-based blockchains are among the most popular alternatives of proof-of-work based blockchains, such as Bitcoin. They provide strong consistency (no fork) under classical assumptions, and avoid using energy-consuming mechanisms to add new blocks in the blockchain. For each block, these blockchains use a committee that executes Byzantine-fault tolerant distributed consensus to decide the next block they will add in the blockchain. Unlike Bitcoin, where there is only one creator per block with high probability, in committee-based blockchain any block is cooperatively created. In order to incentivize committee members to participate to the creation of new blocks rewarding schemes have to be designed. In this paper, we study the fairness of rewarding in committee-based blockchains and we provide necessary and sufficient conditions on the system communication under which it is possible to have a fair reward mechanism.
Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening

Emotion recognition based on EEG has become an active research area. As one of the machine learning models, CNN has been utilized to solve diverse problems including issues in this domain. In this work, a study of CNN and its spatiotemporal feature extraction has been conducted in order to explore capabilities of the model in varied window sizes and electrode orders. Our investigation was conducted in subject-independent fashion. Results have shown that temporal information in distinct window sizes significantly affects recognition performance in both 10-fold and leave-one-subject-out cross validation. Spatial information from varying electrode order has modicum effect on classification. SVM classifier depending on spatiotemporal knowledge on the same dataset was previously employed and compared to these empirical results. Even though CNN and SVM have a homologous trend in window size effect, CNN outperformed SVM using leave-one-subject-out cross validation. This could be caused by different extracted features in the elicitation process.
A physicist-friendly reformulation of the Atiyah-Patodi-Singer index (on a lattice)

The Atiyah-Singer index theorem on a closed manifold is well understood and appreciated in physics. On the other hand, the Atiyah-Patodi-Singer index, which is an extension to a manifold with boundary, is physicist-unfriendly, in that it is formulated with a nonlocal boundary condition. Recently we proved that the same index as APS is obtained from the domain-wall fermion Dirac operator. Our theorem indicates that the index can be expressed without any nonlocal conditions, in such a physicist-friendly way that application to the lattice gauge theory is straightforward. The domain-wall fermion provides a natural mathematical foundation for understanding the bulk-edge correspondence of the anomaly inflow.
Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework

Motivated by the benefits of multi-energy integration, this paper establishes a bi-level two-stage framework based on transactive control, to achieve the optimal energy provision among interconnected multi-energy systems (MESs). At the lower level, each MES autonomously determines the optimal setpoints of its controllable assets by solving a cost minimization problem, in which rolling horizon optimization is adopted to deal with the load and renewable energies’ stochastic features. A technique is further implemented for optimization model convexification by relaxing storages’ complementarity constraints, and its mathematical proof verifies the exactness of the relaxation. At the upper level, a coordinator is responsible for minimizing total costs of interconnected MESs while preventing transformer overloading. This collaborative problem is solved iteratively in a proposed two-stage transactive control framework that is compatible with operational time requirement while retaining scalability, information privacy and operation authority of each MES. The effectiveness of the proposed framework is verified by simulation cases that conduct a detailed analysis of the collaborative autonomous optimization mechanism.
Regulating Queueing Delay in 802.11ac WLANs: Nonlinear Controller Analysis and Design

In this paper we consider the analysis and design of a feedback controller to regulate queueing delay in a next generation edge transport architecture for 802.11ac WLANs. We begin by developing a simplified system model suited to control analysis and design, validated against both simulation and experimental measurements. Note that modelling of aggregation behaviour in WLANs is of course challenging in its own right. Using this model we develop a novel nonlinear control design inspired by the solution to an associated proportional fair optimisation problem. The controller compensates for plant nonlinearities and so can be used for the full envelope of operation. The robust stability of the closed-loop system is analysed and the selection of control design parameters discussed. We develop an implementation of the nonlinear control design and use this to present a performance evaluation using both simulations and experimental measurements.
Representing and Reasoning about Dynamic Code

Dynamic code, i.e., code that is created or modified at runtime, is ubiquitous in today's world. The behavior of dynamic code can depend on the logic of the dynamic code generator in subtle and nonobvious ways, e.g., JIT compiler bugs can lead to exploitable vulnerabilities in the resulting JIT-compiled code. Existing approaches to program analysis do not provide adequate support for reasoning about such behavioral relationships. This paper takes a first step in addressing this problem by describing a program representation and a new notion of dependency that allows us to reason about dependency and information flow relationships between the dynamic code generator and the generated dynamic code. Experimental results show that analyses based on these concepts are able to capture properties of dynamic code that cannot be identified using traditional program analyses.
Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation

Various schemes for adapting batch size have been recently proposed to accelerate stochastic algorithms. However, existing schemes either apply prescribed batch size adaption or require additional backtracking and condition verification steps to exploit the information along optimization path. In this paper, we propose an easy-to-implement scheme for adapting batch size by exploiting history stochastic gradients, based on which we propose the Adaptive batch size SGD (AbaSGD), AbaSVRG, and AbaSPIDER algorithms. To handle the dependence of the batch size on history stochastic gradients, we develop a new convergence analysis technique, and show that these algorithms achieve improved overall complexity over their vanilla counterparts. Moreover, their convergence rates are adaptive to the optimization landscape that the iterate experiences. Extensive experiments demonstrate that our algorithms substantially outperform existing competitive algorithms.
Status of ProtoDUNE Dual Phase

The Deep Underground Neutrino Experiment (DUNE) is a dual-site experiment for long baseline neutrino oscillation studies, and for neutrino astrophysics and nucleon decay searches. DUNE will comprise four 10 kton fiducial liquid argon time-projection-chamber (LAr TPC) modules placed at the Sanford Underground Research Facility (South Dakota, USA). One of these modules will profit form the dual phase (DP) technology where the charge is extracted, amplified, and detected in gaseous argon above the liquid surface allowing a fine readout pitch, a low energy threshold, and good pattern reconstruction of the events. To gain experience in building and operating such a large-scale DP LAr detector, a prototype is currently being assembled at the CERN Neutrino Platform. The ProtoDUNE-DP detector consists of a 6x6x6 m3 LAr TPC and commissioning started in Summer 2019.
Multiple Sample Clustering

The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades. Many clustering algorithms: such as k-means and spectral clustering are proposed based on the single sample assumption. However, in real life, each input object can usually be the multiple samples drawn from a certain hidden distribution. The traditional clustering algorithms cannot handle such a situation. This calls for the multiple sample clustering algorithm. But the traditional multiple sample clustering algorithms can only handle scalar samples or samples from Gaussian distribution. This constrains the application field of multiple sample clustering algorithms. In this paper, we purpose a general framework for multiple sample clustering. Various algorithms can be generated by this framework. We apply two specific cases of this framework: Wasserstein distance version and Bhattacharyya distance version on both synthetic data and stock price data. The simulation results show that the sufficient statistic can greatly improve the clustering accuracy and stability.
Kernelized Wasserstein Natural Gradient

Many machine learning problems can be expressed as the optimization of some cost functional over a parametric family of probability distributions. It is often beneficial to solve such optimization problems using natural gradient methods. These methods are invariant to the parametrization of the family, and thus can yield more effective optimization. Unfortunately, computing the natural gradient is challenging as it requires inverting a high dimensional matrix at each iteration. We propose a general framework to approximate the natural gradient for the Wasserstein metric, by leveraging a dual formulation of the metric restricted to a Reproducing Kernel Hilbert Space. Our approach leads to an estimator for gradient direction that can trade-off accuracy and computational cost, with theoretical guarantees. We verify its accuracy on simple examples, and show the advantage of using such an estimator in classification tasks on Cifar10 and Cifar100 empirically.
Vanishing Nodes: Another Phenomenon That Makes Training Deep Neural Networks Difficult

It is well known that the problem of vanishing/exploding gradients is a challenge when training deep networks. In this paper, we describe another phenomenon, called vanishing nodes, that also increases the difficulty of training deep neural networks. As the depth of a neural network increases, the network's hidden nodes have more highly correlated behavior. This results in great similarities between these nodes. The redundancy of hidden nodes thus increases as the network becomes deeper. We call this problem vanishing nodes, and we propose the metric vanishing node indicator (VNI) for quantitatively measuring the degree of vanishing nodes. The VNI can be characterized by the network parameters, which is shown analytically to be proportional to the depth of the network and inversely proportional to the network width. The theoretical results show that the effective number of nodes vanishes to one when the VNI increases to one (its maximal value), and that vanishing/exploding gradients and vanishing nodes are two different challenges that increase the difficulty of training deep neural networks. The numerical results from the experiments suggest that the degree of vanishing nodes will become more evident during back-propagation training, and that when the VNI is equal to 1, the network cannot learn simple tasks (e.g. the XOR problem) even when the gradients are neither vanishing nor exploding. We refer to this kind of gradients as the walking dead gradients, which cannot help the network converge when having a relatively large enough scale. Finally, the experiments show that the likelihood of failed training increases as the depth of the network increases. The training will become much more difficult due to the lack of network representation capability.
A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs

Objective: We propose an end-to-end CNN-based locating model for pulmonary tuberculosis (TB) diagnosis in radiographs. This model makes full use of chest radiograph (X-ray) for its improved accessibility, reduced cost and high accuracy for TB disease. Methods: Several specialized improvements are proposed for detection task in medical field. A false positive (FP) restrictor head is introduced for FP reduction. Anchor-oriented network heads is proposed in the position regression section. An optimization of loss function is designed for hard example mining. Results: The experimental results show that when the threshold of intersection over union (IoU) is set to 0.3, the average precision (AP) of two test data sets provided by different hospitals reaches 0.9023 and 0.9332. Ablation experiments shows that hard example mining and change of regressor heads contribute most in this work, but FP restriction is necessary in a CAD diagnose system. Conclusion: The results prove the high precision and good generalization ability of our proposed model comparing to previous works. Significance: We first make full use of the feature extraction ability of CNNs in TB diagnostic field and make exploration in localization of TB, when the previous works focus on the weaker task of healthy-sick subject classification.
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems

We propose an efficient inference method for switching nonlinear dynamical systems. The key idea is to learn an inference network which can be used as a proposal distribution for the continuous latent variables, while performing exact marginalization of the discrete latent variables. This allows us to use the reparameterization trick, and apply end-to-end training with stochastic gradient descent. We show that the proposed method can successfully segment time series data, including videos and 3D human pose, into meaningful ``regimes'' by using the piece-wise nonlinear dynamics.
Discretization of flux-limited gradient flows: Γ-convergence and numerical schemes

We study a discretization in space and time for a class of nonlinear diffusion equations with flux limitation. That class contains the so-called relativistic heat equation, as well as other gradient flows of Renyi entropies with respect to transportation metrics with finite maximal velocity. Discretization in time is performed with the JKO method, thus preserving the variational structure of the gradient flow. This is combined with an entropic regularization of the transport distance, which allows for an efficient numerical calculation of the JKO minimizers. Solutions to the fully discrete equations are entropy dissipating, mass conserving, and respect the finite speed of propagation of support. First, we give a proof of $\Gamma$-convergence of the infinite chain of JKO steps in the joint limit of infinitely refined spatial discretization and vanishing entropic regularization. The singularity of the cost function makes the construction of the recovery sequence significantly more difficult than in the $L^p$-Wasserstein case. Second, we define a practical numerical method by combining the JKO time discretization with a "light speed" solver for the spatially discrete minimization problem using Dykstra's algorithm, and demonstrate its efficiency in a series of experiments.
Stochastic Feedforward Neural Networks: Universal Approximation

In this chapter we take a look at the universal approximation question for stochastic feedforward neural networks. In contrast to deterministic networks, which represent mappings from a set of inputs to a set of outputs, stochastic networks represent mappings from a set of inputs to a set of probability distributions over the set of outputs. In particular, even if the sets of inputs and outputs are finite, the class of stochastic mappings in question is not finite. Moreover, while for a deterministic function the values of all output variables can be computed independently of each other given the values of the inputs, in the stochastic setting the values of the output variables may need to be correlated, which requires that their values are computed jointly. A prominent class of stochastic feedforward networks which has played a key role in the resurgence of deep learning are deep belief networks. The representational power of these networks has been studied mainly in the generative setting, as models of probability distributions without an input, or in the discriminative setting for the special case of deterministic mappings. We study the representational power of deep sigmoid belief networks in terms of compositions of linear transformations of probability distributions, Markov kernels, that can be expressed by the layers of the network. We investigate different types of shallow and deep architectures, and the minimal number of layers and units per layer that are sufficient and necessary in order for the network to be able to approximate any given stochastic mapping from the set of inputs to the set of outputs arbitrarily well.
Joint spatial filter and time-varying MCLP for dereverberation and interference suppression of a dynamic/static speech source

Dereverberation of a moving speech source in the presence of other directional interferers, is a harder problem than that of stationary source and interference cancellation. We explore joint multi channel linear prediction (MCLP) and relative transfer function (RTF) formulation in a stochastic framework and maximum likelihood estimation. We found that the combination of spatial filtering with distortion-less response constraint, and time-varying complex Gaussian model for the desired source signal at a reference microphone does provide better signal estimation. For a stationary source, we consider batch estimation, and obtain an iterative solution. Extending to a moving source, we formulate a linear time-varying dynamic system model for the MCLP coefficients and RTF based online adaptive spatial filter. For the case of tracking a desired source in the presence of interfering sources, the same formulation is used by specifying the RTF. Simulated experimental results show that the proposed scheme provides better spatial selectivity and dereverberation than the traditional methods, for both stationary and dynamic sources even in the presence of interfering sources.
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension

We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the rest were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT.