Reviewerly/exHarmony huggingface dataset:
3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks

Standard 3D convolution operations usually require larger amounts of memory and computation cost than 2D convolution operations. The fact increases the difficulty of the development of deep neural nets in many 3D vision tasks. In this paper, we investigate the possibility of applying depthwise separable convolutions in 3D scenario and introduce the use of 3D depthwise convolution. A 3D depthwise convolution splits a single standard 3D convolution into two separate steps, which would drastically reduce the number of parameters in 3D convolutions with more than one order of magnitude. We experiment with 3D depthwise convolution on popular CNN architectures and also compare it with a similar structure called pseudo-3D convolution. The results demonstrate that, with 3D depthwise convolutions, 3D vision tasks like classification and reconstruction can be carried out with more light-weighted neural networks while still delivering comparable performances.
A multistage protocol for aggregated queries in distributed cloud databases with privacy protection

This article is devoted to the novel situation, where a large distributed cloud database is a union of several separate databases belonging to individual database owners who are not allowed to transfer their data for storage in locations different from their already chosen separate cloud service providers. For example, a very large number of medical records may be stored in a distributed cloud database, which is a union of several separate databases from different hospitals, or even from different countries. The owners of the databases may need to provide answers to certain common aggregated queries using all information available without sharing or transferring all data. It is necessary to minimize the communication costs, improve efficiency, and comply with the legal requirements protecting the privacy of confidential data. In this situation, it is impossible to aggregate the whole database in one location, but effective methods for answers to the aggregated queries with privacy protection are required. To solve this important problem, the present article proposes a Multistage Separate Query Processing (MSQP) protocol employing homomorphic encryption with split keys. We show that our protocol can answer a large class of natural queries of practical significance. The running time of the MSQP protocol isO(d+md), whered is the number of database owners andm is the total number of records in the whole database. In practice,d is small,m can be very large, and so the running time isO(m). This means that the protocol is very efficient for large databases. It dramatically reduces the communication costs of computation and completely eliminates the need for exchange of confidential data. We define a new generalized additive homomorphic property and introduce a Multipart ElGamal Cryptosystem (MEC) with split keys, which enjoys this property. MEC is a novel modification of the ElGamal cryptosystem with split keys. This paper presents the results of extensive experiments evaluating the effectiveness of the MSQP protocol employing MEC and comparing it with MSQP employing the ElGamal cryptosystem, for a collection of publicly available medical datasets. The experiments evaluating our protocol on 11 real-life databases and a synthetic database demonstrate that the MSQP protocol employing MEC is more efficient than other options and can be recommended for practical implementations.
A new EDAS-based in-sample-out-of-sample classifier for risk-class prediction

Purpose Nowadays, the field of data analytics is witnessing an unprecedented interest from a variety of stakeholders. The purpose of this paper is to contribute to the subfield of predictive analytics by proposing a new non-parametric classifier. Design/methodology/approach The proposed new non-parametric classifier performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new Evaluation Based on Distance from Average Solution (EDAS)-based classifier, and out-of-sample predictions are devised with a CBR-based classifier trained on the class predictions provided by the proposed EDAS-based classifier. Findings The performance of the proposed new non-parametric classification framework is tested on a data set of UK firms in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to the implementation decisions’ choices. Practical implications The exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders. Originality/value Over and above the design elements of the new integrated in-sample-out-of-sample classification framework and its non-parametric nature, it delivers an outstanding predictive performance for a bankruptcy prediction application.
Multitask Feature Selection by Graph-Clustered Feature Sharing

Multitask feature selection (MTFS) methods have become more important for many real world applications, especially in a high-dimensional setting. The most widely used assumption is that all tasks share the same features, and the l2,1 regularization method is usually applied. However, this assumption may not hold when the correlations among tasks are not obvious. Learning with unrelated tasks together may result in negative transfer and degrade the performance. In this paper, we present a flexible MTFS by graph-clustered feature sharing approach. To avoid the above limitation, we adopt a graph to represent the relevance among tasks instead of adopting a hard task set partition. Furthermore, we propose a graph-guided regularization approach such that the sparsity of the solution can be achieved on both the task level and the feature level, and a variant of the smooth proximal gradient method is developed to solve the corresponding optimization problem. An evaluation of the proposed method on multitask regression and multitask binary classification problem has been performed. Extensive experiments on synthetic datasets and real-world datasets demonstrate the effectiveness of the proposed approach to capture task structure.
Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities

Ensemble sensitivity analysis (ESA) has been established in the atmospheric sciences as a correlation-based approach to determine the sensitivity of a scalar forecast quantity computed by a numerical weather prediction model to changes in another model variable at a different model state. Its applications include determining the origin of forecast errors and placing targeted observations to improve future forecasts. We-a team of visualization scientists and meteorologists-present a visual analysis framework to improve upon current practice of ESA. We support the user in selecting regions to compute a meaningful target forecast quantity by embedding correlation-based grid-point clustering to obtain statistically coherent regions. The evolution of sensitivity features computed via ESA are then traced through time, by integrating a quantitative measure of feature matching into optical-flow-based feature assignment, and displayed by means of a swipe-path showing the geo-spatial evolution of the sensitivities. Visualization of the internal correlation structure of computed features guides the user towards those features robustly predicting a certain weather event. We demonstrate the use of our method by application to real-world 2D and 3D cases that occurred during the 2016 NAWDEX field campaign, showing the interactive generation of hypothesis chains to explore how atmospheric processes sensitive to each other are interrelated.
Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming

This paper addresses the time-optimal trajectory planning for autonomous underwater vehicles. A detailed mixed integer nonlinear programming (MINLP) model is presented, explicitly taking into account vehicle kinematic constraints, obstacle avoidance, and a nonlinear flow field to represent the ocean current. MINLP problems pose great challenges because of the combinatorial complexity and nonconvexities introduced by the nature of the flow field. A novel solution approach in an optimization framework is developed to address associated difficulties. The main benefit of the proposed methodology is the ability to find multiple local minima. The contribution of the paper is fourfold: (1) a novel approach to integrate the flow field into the MINLP model; (2) a diversified initialization strategy using multiple waypoints, different solvers and approximated models, namely, a mixed integer linear programming model and the MINLP model with and without the flow field; (3) an algorithm that forces the solver to seek improved solutions; and (4) a parallel computing approach capitalizing on diversified initialization. The performance of the resulting methodology is illustrated on idealized case studies, and the results are used to gain insight into trajectory planning in the presence of flow fields.
Combining virtualization and containerization to support interactive games and simulations on the cloud

Interactive training through the use of game-based and virtual software simulators pose challenges on traditional networks of computing assets. A variety of organizations and domains, including the entertainment industry, energy and financial sectors, military, and video gaming, have turned to high performance computing (HPC) as a powerful enough platform to run these interactive simulations. This work explores the feasibility of extending a traditional HPC environment into a cloud-based service that is capable of supporting multiple simultaneous interactive simulations, while continuing to solve compute-intensive tasks. The goal of this work is to extend the scope of software that can be applied to an HPC environment while preserving performance efficacy. To accomplish the proposed HPC goal, this work investigates four HPC load-balancing techniques through virtualization, software containers, and clustering to simultaneously and optimally analyze, schedule, and execute game-based simulation applications. Our experimentation compares these different techniques for performance and to determine the feasibility of extending HPC capability. The selection of which deployment technique to employ should depend on the availability of cluster resources, the number of competing software jobs, and the type of software to be scheduled.
Session-ocaml: A session-based library with polarities and lenses

We propose session-ocaml, a novel library for session-typed concurrent/distributed programming in OCaml. Our technique solely relies on parametric polymorphism, which can encode core session type structures with strong static guarantees. Our key ideas are: (1) polarised session types, which give an alternative formulation of duality enabling OCaml to automatically infer an appropriate session type in a session with a reasonable notational overhead; and (2) a parameterised monad with a data structure called ‘slots’ manipulated with lenses, which can statically enforce session linearity including delegations. We introduce a notational extension to enhance the session linearity for integrating the session types into the functional programming style. We show applications of session-ocaml to a travel agency use case and an SMTP protocol implementation. Furthermore, we evaluate the performance of on a number of benchmarks.
Quantum Rabi Model with Two-Photon Relaxation

We study a cavity-QED setup consisting of a two-level system coupled to a single cavity mode with two-photon relaxation. The system dynamics is modeled via a Lindblad master equation consisting of the Rabi Hamiltonian and a two-photon dissipator. We show that an even-photon relaxation preserves the $Z_2$ symmetry of the Rabi model, and provide a framework to study the corresponding non-Hermitian dynamics in the number-parity basis. We discuss the role of different terms in the two-photon dissipator and show how one can extend existing results for the closed Rabi spectrum to the open case. Furthermore, we characterize the role of the $Z_2$ symmetry in the excitation-relaxation dynamics of the system as a function of light-matter coupling. Importantly, we observe that initial states with even-odd parity manifest qualitatively distinct transient and steady state behaviors, contrary to the Hermitian dynamics that is only sensitive to whether the initial state is parity-invariant. Moreover, the parity-sensitive dynamical behavior is not a creature of ultrastrong coupling and is present even at weak coupling values.
Integration of feature models: A systematic mapping study

The integration of feature models has been widely investigated in the last decades, given its pivotal role for supporting the evolution of software product lines. Unfortunately, academia and industry have overlooked the production of a thematic analysis of the current literature. Hence, a thorough understanding of the state-of-the-art works remains still limited. This study seeks to create a panoramic view of the current literature to pinpoint gaps and supply insights of this research field. A systematic mapping study was performed based on well-established empirical guidelines for answering six research questions. In total, 47 primary studies were selected by applying a filtering process from a sample of 2874 studies. The main results obtained are: (1) most studies use a generic notation (68.09%, 32/47) for representing feature models; (2) only one study (2%, 1/47) compares feature models based on their syntactic and semantics; (3) there is no preponderant use of a particular integration technique in the selected studies; (4) most studies (70%, 33/47) provide a product-based strategy to evaluate the integrated feature models; (5) majority (70%, 33/47) automates the integration process; and (6) most studies (90%, 42/47) propose techniques, rather than focusing on producing practical knowledge derived from empirical studies. The results were encouraging and suggest that integration of feature models is still an evolving research area. This study provides insightful information for the definition of a more ambitious research agenda. Lastly, empirical studies exploring the required effort to apply the current integration techniques in real-world settings are highly recommended in future work.
Efficient weakly-supervised discrete hashing for large-scale social image retrieval

Hashing has been widely exploited for information retrieval recently, because of its high computation efficiency and low storage cost. However, many existing hashing methods cannot perform well on large-scale social image retrieval, due to the relaxed hash optimization and the lack of supervised semantic labels. In this paper, we propose an efficient Weakly-supervised Discrete Hashing (WDH) to solve the limitations. We formulate a unified weakly-supervised hash learning framework. It could effectively enrich the semantics of image hash codes with the freely obtained user-provided social tags and simultaneously remove their involved adverse noises. Furthermore, instead of relaxed hash optimization, we propose an efficient discrete hash optimization method based on Augmented Lagrangian Multiplier (ALM) to directly solve the hash codes without quantization information loss. Experiments on two standard social image datasets demonstrate the superior performance of the proposed method compared with several state-of-the-art hashing techniques.
Visions in assemblages: Future-making and governance in FabLabs

The impacts of socio-technical future-making on the governance of innovation processes are increasingly important in sociological STS research and technology assessment (TA). Through the case of the emergence of FabLabs, a global network of several hundred of organizations that aim to make digital fabrication machines such as 3D printing accessible to diverse audiences, and their transformation from elite to collective Fab Labs this paper sheds light on the constitutive interplay between socio-technical futures in practices and changes of the governance of an innovation process. To grasp the processes of future-making and its governance we apply a practice-based perspective inspired by assemblage theory of Deleuze and Guattari and combine this theoretical model with the analytical concept of visions as socio-epistemic practices, a new approach in TA. With this approach we show how future-making is often unequally distributed and governed. We show that future-making and the governance of socio-technical innovation can be democratized if the means to make and explore futures are themselves democratized. Future-making depends upon assemblages in becoming and if we want to change future-making and innovation processes we need to focus on how we can create conditions that make desirable changes of assemblages more likely.
Nonlinear regression via incremental decision trees

We study sequential nonlinear regression and introduce an online algorithm that elegantly mitigates, via an adaptively incremental hierarchical structure, convergence and undertraining issues of conventional nonlinear regression methods. Particularly, we present a piecewise linear (or nonlinear) regression algorithm that partitions the regressor space and learns a linear model at each region to combine. Unlike the conventional approaches, our algorithm effectively learns the optimal regressor space partition with the desired complexity in a completely sequential and data driven manner. Our algorithm sequentially and asymptotically achieves the performance of the optimal twice differentiable regression function for any data sequence without any statistical assumptions. The introduced algorithm can be efficiently implemented with a computational complexity that is only logarithmic in the length of data. In our experiments, we demonstrate significant gains for the well-known benchmark real data sets when compared to the state-of-the-art techniques.
Monotonicity of Step Sizes of MSE-Optimal Symmetric Uniform Scalar Quantizers

For generalized gamma probability densities, this paper studies the monotonicity of step sizes of optimal symmetric uniform scalar quantizers with respect to mean squared-error distortion. The principal results are that for the special cases of Gaussian, Laplacian, two-sided Rayleigh, and gamma densities, optimal step size monotonically decreases when the number of levels N increases by two, and that for any generalized gamma density and all sufficiently large N, optimal step size again decreases when N increases by two. Also, it is shown that for a Laplacian density and sufficiently large N, optimal step size decreases when N increases by just one.
Learning structured and non-redundant representations with deep neural networks

This paper proposes a novel regularizer named Structured Decorrelation Constraint, to address both the generalization and optimization of deep neural networks, including multiple-layer perceptrons and convolutional neural networks. Our proposed regularizer reduces overfitting by breaking the co-adaptions between the neurons with an explicit penalty. As a result, the network is capable of learning non-redundant representations. Meanwhile, the proposed regularizer encourages the networks to learn structured high-level features to aid the networks’ optimization during training. To this end, neurons are constrained to behave obeying a group prior. Our regularizer applies to various types of layers, including fully connected layers, convolutional layers and normalization layers. The loss of our regularizer can be directly minimized along with the network’s classification loss by stochastic gradient descent. Experiments show that the proposed regularizer obviously relieves the overfitting problem of the existing deep networks. It yields much better performance on extensive datasets than the conventional regularizers like Dropout.
Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning

High-spatial hyperspectral (HH) image reconstruction using both high-spatial multispectral (HM) image and low-spatial hyperspectral (LH) image over the same scene is widely used in many real applications. Nevertheless, the pair of HM image and LH image over the same scene is hard to obtain. To solve this problem, a new HH image reconstruction method using spectral library-based dictionary learning (named as HIRSL) is proposed in this paper, only from one HM image. The above reconstruction problem is formulated in the framework of sparse representation, as an estimation of the band matching matrix, the spectral dictionary, and the sparse coefficients. More specifically, a band matching method is proposed for mapping the common spectral library to a specific spectral library corresponding to the reconstructed HH image in spectral domain. Then, an efficient spectral dictionary learning method is proposed for the construction of spectral dictionary using the matched specific spectral library, which avoids the dependence of the LH image over the same scene. Finally, the sparse coefficients of the HM image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers without nonnegative constraint. Comparison results on simulated and real data sets with the relative state-of-the-art methods demonstrate that even only using one HM image, our proposed method achieves a comparable reconstruction quality of high-spatial hyperspectral image both in spatial and spectral domains.
A Box Particle Filter Method for Tracking Multiple Extended Objects

Extended objects generate a variable number of multiple measurements.In contrast with point targets, extended objects are characterized with their size or volume, and orientation.Multiple object tracking is a notoriously challenging problem due to complexities caused by data association.This paper develops a box particle filter method for multiple extended object tracking, and for the first time it is shown how interval based approaches can deal efficiently with data association problems and reduce the computational complexity of the data association.The box particle filter relies on the concept of a box particle.A box particle represents a random sample and occupies a controllable rectangular region of non-zero volume in the object state space.A theoretical proof of the generalized likelihood of the box particle filter for multiple extended objects is given based on a binomial expansion.Next the performance of the box particle filter is evaluated using a challenging experiment with the appearance and disappearance of objects within the area of interest, with real laser rangefinder data.The box particle filter is compared with a state-of-the-art particle filter with point particles.Accurate and robust estimates are obtained with the box particle filter, both for the kinematic states and extent parameters, with significant reductions in computational complexity.The box particle filter reduction of computational time is at least 32% compared with the particle filter working with point particles for the experiment presented.Another advantage of the box particle filter is its robustness to initialization uncertainty.