Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming

There is a vast body of literature related to methods for detecting changepoints (CP). However, less attention has been paid to assessing the statistical reliability of the detected CPs. In this paper, we introduce a novel method to perform statistical inference on the significance of the CPs, estimated by a Dynamic Programming (DP)-based optimal CP detection algorithm. Based on the selective inference (SI) framework, we propose an exact (non-asymptotic) approach to compute valid p-values for testing the significance of the CPs. Although it is well-known that SI has low statistical power because of over-conditioning, we address this disadvantage by introducing parametric programming techniques. Then, we propose an efficient method to conduct SI with the minimum amount of conditioning, leading to high statistical power. We conduct experiments on both synthetic and real-world datasets, through which we offer evidence that our proposed method is more powerful than existing methods, has decent performance in terms of computational efficiency, and provides good results in many practical applications.
A Joint Bayesian Framework for Causal Inference and Bipartite Matching for Record Linkage

The recent proliferation in the use of digital health data has opened possibilities for gathering information on a common set of entities from various government and non-government sources and make causal inferences about important health outcomes. In such scenarios, the response may be obtained from a source different than the one from which the treatment assignment and covariates are obtained. In absence of error free direct identifiers (e.g., SSN), straightforward merging of separate files based on these identifiers is not feasible, giving rise to need for matching on imperfect linking variables (e.g., names, birth years). Causal inference in such situations generally follows using a two-stage procedure, wherein the first stage involves linking two files using a probabilistic linkage technique with imperfect linking variables common to both files, followed by causal inference on the linked dataset in the second stage. Rather than sequentially performing record linkage and causal inference, this article proposes a novel framework for simultaneous Bayesian inference on probabilistic linkage and the causal effect. In contrast with the two-stage approach, our proposed methodology facilitates borrowing of information between the models employed for causal inference and record linkage, thus improving accuracy of inference in both models. Importantly, the joint modeling framework offers characterization of uncertainty, both in causal inference and in record linkage. An efficient computational template using Markov chain Monte Carlo (MCMC) is developed for the joint model. Simulation studies and real data analysis provide evidence of both improved accuracy in estimates of treatment effects, as well as more accurate linking of two files in the joint modeling framework over the two-stage modeling option. The conclusion is further buttressed by theoretical insights presented in this article.
Design and Analysis of Communication Protocols using Quantum Resources

This thesis is focused on the design and analysis of quantum communication protocols. Several schemes for quantum communication have been introduced in the recent past. For example, quantum teleportation, dense coding, quantum key distribution, quantum secure direct communication, etc., have been rigorously studied in the last 2-3 decades. Specifically, a specific attention of the present thesis is to study the quantum teleportation schemes with entangled orthogonal and nonorthogonal states and their experimental realization, but not limited to it. We have also studied some aspects of quantum cryptography.
Learning Optimal Classification Trees: Strong Max-Flow Formulations

We consider the problem of learning optimal binary classification trees. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality of heuristic approaches and the tremendous improvements in mixed-integer programming (MIP) technology. Yet, existing approaches from the literature do not leverage the power of MIP to its full extent. Indeed, they rely on weak formulations, resulting in slow convergence and large optimality gaps. To fill this gap in the literature, we propose a flow-based MIP formulation for optimal binary classification trees that has a stronger linear programming relaxation. Our formulation presents an attractive decomposable structure. We exploit this structure and max-flow/min-cut duality to derive a Benders' decomposition method, which scales to larger instances. We conduct extensive computational experiments on standard benchmark datasets on which we show that our proposed approaches are 50 times faster than state-of-the art MIP-based techniques and improve out of sample performance up to 13.8%.
Parameterized Objectives and Algorithms for Clustering Bipartite Graphs and Hypergraphs

Motivated by applications in community detection and dense subgraph discovery, we consider new clustering objectives in hypergraphs and bipartite graphs. These objectives are parameterized by one or more resolution parameters in order to enable diverse knowledge discovery in complex data. For both hypergraph and bipartite objectives, we identify parameter regimes that are equivalent to existing objectives and share their (polynomial-time) approximation algorithms. We first show that our parameterized hypergraph correlation clustering objective is related to higher-order notions of normalized cut and modularity in hypergraphs. It is further amenable to approximation algorithms via hyperedge expansion techniques. Our parameterized bipartite correlation clustering objective generalizes standard unweighted bipartite correlation clustering, as well as bicluster deletion. For a certain choice of parameters it is also related to our hypergraph objective. Although in general it is NP-hard, we highlight a parameter regime for the bipartite objective where the problem reduces to the bipartite matching problem and thus can be solved in polynomial time. For other parameter settings, we present approximation algorithms using linear program rounding techniques. These results allow us to introduce the first constant-factor approximation for bicluster deletion, the task of removing a minimum number of edges to partition a bipartite graph into disjoint bi-cliques. In several experimental results, we highlight the flexibility of our framework and the diversity of results that can be obtained in different parameter settings. This includes clustering bipartite graphs across a range of parameters, detecting motif-rich clusters in an email network and a food web, and forming clusters of retail products in a product review hypergraph, that are highly correlated with known product categories.
Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing

Serverless computing is an excellent fit for big data processing because it can scale quickly and cheaply to thousands of parallel functions. Existing serverless platforms, however, isolate functions in ephemeral, stateless containers. This means that functions cannot efficiently share memory, forcing users to serialise data repeatedly when composing functions. We observe that container-based isolation is ill-suited for serverless big data processing. Instead it requires a lightweight isolation approach that allows for efficient state sharing. We introduce Faaslets, a new isolation abstraction for serverless big data computing. Faaslets isolate the memory of executed functions using software-fault isolation (SFI), as provided by WebAssembly, while allowing memory regions to be shared between functions in the same address space. Faaslets can thus avoid expensive data movement when functions are co-located on the same machine. Our runtime for Faaslets, Faasm, isolates other resources, e.g. CPU and network, using standard Linux cgroups, and provides a low-level POSIX host interface for networking, file system access and dynamic loading. To reduce initialisation times, Faasm restores Faaslets from already-initialised snapshots. We compare Faasm to a standard container-based platform and show that, when training a machine learning model, it achieves a 2x speed-up with 10x less memory; for serving machine learning inference, Faasm doubles the throughput and reduces tail latency by 90%.
Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems With a Kalman‐Inspired Proposal Distribution

Bayesian analysis is widely used in science and engineering for real‐time forecasting, decision making, and to help unravel the processes that explain the observed data. These data are some deterministic and/or stochastic transformations of the underlying parameters. A key task is then to summarize the posterior distribution of these parameters. When models become too difficult to analyze analytically, Monte Carlo methods can be used to approximate the target distribution. Of these, Markov chain Monte Carlo (MCMC) methods are particularly powerful. Such methods generate a random walk through the parameter space and, under strict conditions of reversibility and ergodicity, will successively visit solutions with frequency proportional to the underlying target density. This requires a proposal distribution that generates candidate solutions starting from an arbitrary initial state. The speed of the sampled chains converging to the target distribution deteriorates rapidly, however, with increasing parameter dimensionality. In this paper, we introduce a new proposal distribution that enhances significantly the efficiency of MCMC simulation for highly parameterized models. This proposal distribution exploits the cross covariance of model parameters, measurements, and model outputs and generates candidate states much alike the analysis step in the Kalman filter. We embed the Kalman‐inspired proposal distribution in the DiffeRential Evolution Adaptive Metropolis algorithm during burn‐in and present several numerical experiments with complex, high‐dimensional, or multimodal target distributions. Results demonstrate that this new proposal distribution can greatly improve simulation efficiency of MCMC. Specifically, we observe a speedup on the order of 10–30 times for groundwater models with more than 100 parameters.
Bidirectional Generative Modeling Using Adversarial Gradient Estimation

This paper considers the general $f$-divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases. We present a new optimization method for this formulation, where the gradient is computed using an adversarially learned discriminator. In our framework, we show that different divergences induce similar algorithms in terms of gradient evaluation, except with different scaling. Therefore this paper gives a general recipe for a class of principled $f$-divergence based generative modeling methods. Theoretical justifications and extensive empirical studies are provided to demonstrate the advantage of our approach over existing methods.
An Investigation of Interpretability Techniques for Deep Learning in Predictive Process Analytics

This paper explores interpretability techniques for two of the most successful learning algorithms in medical decision-making literature: deep neural networks and random forests. We applied these algorithms in a real-world medical data set containing information about patients with cancer,where we learn models that try to predict the type of cancer of the patient, given their set of medical activity records. We explored different algorithms based on neural network architectures using long short term deep neural networks, and random forests. Since there is a growing need to provide decision-makers understandings about the logic of predictions of black boxes, we also explored different techniques that provide interpretations for these classifiers. In one of the techniques, we intercepted some hidden layers of these neural networks and used auto encoders in order to learn what is the representation of the input in the hidden layers. In another, we investigated an interpretable model locally around the random forest’s prediction. Results show learning an interpretable model locally around the model’s prediction leads to a higher understanding of why the algorithm is making some decision. Use of local and linear model helps identify the features used in prediction of a specific instance or data point. We see certain distinct features used for predictions that provide useful insights about the type of cancer, along with features that do not generalize well. In addition, the structured deep learning approach using auto encoders provided meaningful prediction insights, which resulted in the identification of nonlinear clusters correspondent to the patients’ different types of cancer.
Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition

In this paper, we propose an approach for Facial Expressions Recognition (FER) based on a deep multi-facial patches aggregation network. Deep features are learned from facial patches using deep sub-networks and aggregated within one deep architecture for expression classification . Several problems may affect the performance of deep-learning based FER approaches, in particular, the small size of existing FER datasets which might not be sufficient to train large deep learning networks. Moreover, it is extremely time-consuming to collect and annotate a large number of facial images. To account for this, we propose two data augmentation techniques for facial expression generation to expand FER labeled training datasets. We evaluate the proposed framework on three FER datasets. Results show that the proposed approach achieves state-of-art FER deep learning approaches performance when the model is trained and tested on images from the same dataset. Moreover, the proposed data augmentation techniques improve the expression recognition rate, and thus can be a solution for training deep learning FER models using small datasets. The accuracy degrades significantly when testing for dataset bias.
Extending 2D Saliency Models for Head Movement Prediction in 360-Degree Images using CNN-Based Fusion

Saliency prediction can be of great benefit for 360-degree image/video applications, including compression, streaming, rendering and viewpoint guidance. It is therefore quite natural to adapt the 2D saliency prediction methods for 360-degree images. To achieve this, it is necessary to project the 360-degree image to 2D plane. However, the existing projection techniques introduce different distortions, which provides poor results and makes inefficient the direct application of 2D saliency prediction models to 360-degree content. Consequently, in this paper, we propose a new framework for effectively applying any 2D saliency prediction method to 360-degree images. The proposed framework particularly includes a novel convolutional neural network based fusion approach that provides more accurate saliency prediction while avoiding the introduction of distortions. The proposed framework has been evaluated with five 2D saliency prediction methods, and the experimental results showed the superiority of our approach compared to the use of weighted sum or pixel-wise maximum fusion methods.
Nonlinearity Compensation in A Multi-DoF Shoulder Sensing Exosuit For Real-Time Teleoperation

The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi- DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.
General recipe to form input space for deep learning analysis of HEP scattering processes

Deep learning neural network technique (DNN) is one of the most efficient and general approach of multivariate data analysis of the collider experiments. The important step of the analysis is the optimization of the input space for multivariate technique. In the article we propose the general recipe how to form the set of low-level observables sensitive for the differences in hard scattering processes at the colliders. It is shown in the paper that without any sophisticated analysis of the kinematic properties one can achieve close to optimal performance of DNN with the proposed general set of low-level observables.
Coexistence of localized Gibbs measures and delocalized gradient Gibbs measures on trees

We study gradient models for spins taking values in the integers (or an integer lattice), which interact via a general potential depending only on the differences of the spin values at neighboring sites, located on a regular tree with d + 1 neighbors. We first provide general conditions in terms of the relevant p-norms of the associated transfer operator Q which ensure the existence of a countable family of proper Gibbs measures. Next we prove existence of delocalized gradient Gibbs measures, under natural conditions on Q. This implies coexistence of both types of measures for large classes of models including the SOS-model, and heavy-tailed models arising for instance for potentials of logarithmic growth.
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy

The objective of reinforcement learning (RL) is to find an optimal strategy for solving a dynamical control problem. Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, where the underlying dynamical system is only accessible as a black box such that adjoint methods cannot be used. However, existing ES methods have two limitations that hinder its applicability in RL. First, most existing methods rely on Monte Carlo based gradient estimators to generate search directions. Due to low accuracy of Monte Carlo estimators, the RL training suffers from slow convergence and requires more iterations to reach the optimal solution. Second, the landscape of the reward function can be deceptive and may contain many local maxima, causing ES algorithms to prematurely converge and be unable to explore other parts of the parameter space with potentially greater rewards. In this work, we employ a Directional Gaussian Smoothing Evolutionary Strategy (DGS-ES) to accelerate RL training, which is well-suited to address these two challenges with its ability to (i) provide gradient estimates with high accuracy, and (ii) find nonlocal search direction which lays stress on large-scale variation of the reward function and disregards local fluctuation. Through several benchmark RL tasks demonstrated herein, we show that the DGS-ES method is highly scalable, possesses superior wall-clock time, and achieves competitive reward scores to other popular policy gradient and ES approaches.
A Hybrid Algorithm Based Robust Big Data Clustering for Solving Unhealthy Initialization, Dynamic Centroid Selection and Empty clustering Problems with Analysis

Big Data is a massive volume of both structured and unstructured data that is too large and it also difficult to process using traditional techniques. Clustering algorithms have developed as a powerful learning tool that can exactly analyze the volume of data that produced by modern applications. Clustering in data mining is the grouping of a particular set of objects based on their characteristics. The main aim of clustering is to classified data into clusters such that objects are grouped in the same clusters when they are corresponding according to similarities and features mainly. Till now, K-MEANS is the best utilized calculation connected in a wide scope of zones to recognize gatherings where cluster separations are a lot than between gathering separations. Our developed algorithm works with K-MEANS for high quality clustering during clustering from big data. Our proposed algorithm EG K-MEANS : Extended Generation K-MEANS solves mainly three issues of K-MEANS: unhealthy initialization, dynamic centroid selection and empty clustering. It ensures the best way of preventing unhealthy initialization, dynamic centroid selection and empty clustering problems for getting high quality clustering.
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm

Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two Long short-term memory(LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea. Two forecasting horizons including ten-minutes ahead (absolute short term) and one-hour ahead (short term) are considered in our experiments. Our experimental results indicate that the new approach is superior to five other applied machine learning models, i.e., polynomial neural network (PNN), feed-forward neural network (FNN), nonlinear autoregressive neural network (NAR) and adaptive neuro-fuzzy inference system (ANFIS), as measured by five performance criteria.
VMC J021140.41–735320.4—An Extremely Red L Dwarf Candidate Member of the AB Doradus Young Moving Group

We report the discovery of VMC J021140.41-735320.4, a faint ($J=19.9$ mag) and extremely red ($J-K_s=3.0$ mag, $W1-W2=0.45$ mag, $J-W2=4.7$ mag) high proper motion object in front of the Magellanic Bridge region. It is even redder in the near-infrared than the reddest L7 dwarfs ($J-K_s \gtrsim 2.8$ mag) found in the survey of Schneider et al. (2017) and its colours do also not match exactly those of other peculiar red L7.5 dwarfs. Therefore, we consider its photometric distance estimate of about 73 pc as uncertain. A preliminary combined proper motion and parallax solution involving 20 epochs from the Visible and Infrared Survey Telescope for Astronomy (VISTA) survey of the Magellanic Clouds (VMC), the VISTA hemisphere survey (VHS), and the Wide-field Infrared Survey Explorer (WISE) yields $\mu_{\alpha}\cos{\delta}=+74.3\pm3.4$ mas/yr, $\mu_{\delta}=+22.3\pm3.4$ mas/yr, and $\pi=21.0\pm7.1$ mas. Applying the BANYAN tool (this http URL) to these results we find a $\approx$68% membership probability of this object in the AB Doradus young moving group (YMG). With its trignometric distance of $\approx$48 pc, VMC J021140.41-735320.4 has a similar absolute magnitude of $M_{K_s}\approx13.4$ mag as the planetary mass companion of another AB Doradus YMG member candidate, 2MASS J22362452+4751425. Follow-up classification spectroscopy and astrometric monitoring are recommended.