Cryptocurrencies have become a prominent investment tool recently with increasing interest in them and their relationships with stock and foreign exchange markets. We analyze here the cross-correlations of price changes of different cryptocurrencies using Random Matrix Theory and extract community structures by constructing minimum spanning trees, finding their eigenvalues contrast sharply with universal

Since Shahshahani and Landgrebe published their seminal paper (Shahshahani and Landgrebe, 1994) [1] in 1994, the study on semi-supervised learning (SSL) developed fast and has already become one of the main streams of machine learning (ML) research. However, there are still some areas or problems where the capability of SSL remains seriously limited. Firstly, according to our observation, almost all

One of the key ideas in realizing human-like intelligence is to understand enough information-processing mechanisms in the human brain. Brain Informatics is a rapidly expanding interdisciplinary field to systematically utilize brain-related data, information, and knowledge coming from the entire research process for deeply brain investigation. In the past few years, a data-centric conceptual brain

We address multiple aspects of the numerical modeling of turbulence and mixing. Fundamental issues of thermodynamics related to nonuniqueness of solutions of the fluid Euler are addressed here. Recent developments of Front Tracking, a part of this analysis, is reviewed as well. The work presented is the result of multiple collaboration teams, and includes in some cases announcements of results yet

We address the link between the controllability or observability of a stochastic complex system and concepts of information theory. We show that the most influential degrees of freedom can be detected without acting on the system, by measuring the time-delayed multi-information. Numerical and analytical results support this claim, which is developed in the case of a simple stochastic model on a graph

We develop a spatio-temporal model to forecast sensor output at five locations in North East England. The signal is described using coupled dynamic linear models, with spatial effects specified by a Gaussian process. Data streams are analysed using a stochastic algorithm, known as iterated batch importance sampling (IBIS), which sequentially propagates a discrete approximation of the parameter posterior

Portfolio allocation and risk management make use of correlation matrices and heavily rely on the choice of a proper correlation matrix to be used. In this regard, one important question is related to the choice of the proper sample period to be used to estimate a stable correlation matrix. This paper addresses this question and proposes a new methodology to estimate the correlation matrix which doesn’t

Deterministic equivalent models reformulate optimization problems from a computational perspective. Nonetheless, these models become computationally intractable quickly when the number of stages increase. In this context, a framework to reduce the size of scenario tree and multistage stochastic optimization problems is proposed. Scenario trees are generated using the Knuth transformation for a more

For a considerable time, researchers have focused on defining different measures capable to characterizing the importance of vertices in networks. One type of these networks, the cities, are complex systems that generate large quantity of information. These data are an important part of the characteristics of the urban network itself. Because of this, it is crucial to have a classification system,

Cardiovascular disease (CVD) affects more than 90 million adults in the United States. In recent years, computational hemodynamic models have helped improve our understanding of CVD progression; however, such research workflows can be challenging and unintuitive to operate. We thus developed Harvis, a software platform with a flexible GUI for performing vascular simulations and a VR-capable interface

We study the diffusivity of a small particle immersed in a square box filled with a non-ideal multicomponent fluid in the presence of thermal fluctuations. Our approach is based on the numerical integration of fluctuating lattice Boltzmann models (LBM) for multicomponent mixtures. At changing the wettability on the particle's surface, we measure the mean square displacement (MSD) and compare with the

The gravitational force has been considered as one of the most important factors leading to composition variation of multicomponent chemical species mixture in many industrial processes and natural phenomena. This has been largely studied through experimental and numerical modeling, especially in chemical processes and petroleum reservoir engineering. The modeling and simulation of dynamical process

Network dismantling aims to find the minimal set of nodes that, if removed, will break the network into small components with their largest one limited to a certain threshold. This problem underlies many practical applications in various areas, such as bioinformatics, transportation and the Internet. There are two major kinds of network dismantling methods, i.e. centrality measure based methods and

In this paper, a new traffic model is presented which characterizes spatial changes in traffic density to smoothly align the traffic flow with forward conditions. This alignment is a function of the difference in velocities so there is no change in flow when the velocities are the same. The commonly employed Payne-Witham (PW) model aligns traffic with a constant speed (velocity) regardless of the transitions

This paper investigates the behavior and synchronization of a network of reaction–diffusion neural dynamics models using a highly efficient numerical method. In fact, the dynamical modeling, behavior analysis and controlled synchronization of a network of FitzHugh–Nagumo (FHN) neurons which promising the understanding of cognitive processing are studied by considering the unidirectional gap junctions

Molecular dynamics (MD) simulations accelerated by high-performance computing (HPC) methods are powerful tools to investigate and extract the microscopic mechanisms characterizing the properties of soft materials such as self-assembled nanoparticles, virus capsids, confined electrolytes, and polymeric fluids. In this paper, we extend the idea developed in our earlier work of integrating machine learning

We present a Weak Constraint Gaussian Process (WCGP) model to integrate noisy inputs into the classical Gaussian Process (GP) predictive distribution. This model follows a Data Assimilation approach (i.e. by considering information provided by observed values of a noisy input in a time window). Due to the increased number of states processed from real applications and the time complexity of GP algorithms

Chip multicore processors (CMPs) constitute the architecture of choice for a wide spectrum of computing systems, ranging from power-efficient mobile devices to high-performance server platforms. Despite their benefits, the contention that appears when multiple applications compete for the use of shared resources among cores, such as the last-level cache (LLC), may lead to substantial performance degradation

The instance of the epistemic uncertainty quantification concerning the estimation of the approximation error norm is analyzed using the ensemble of numerical solutions obtained via independent numerical algorithms. The analysis is based on the geometry considerations: the triangle inequality and the diameter of the ensemble related with the measure concentration phenomenon in spaces of great dimension

Optimisation problems usually take the form of having a single or multiple objectives with a set of constraints. The model itself concerns a single problem for which the best possible solution is sought. Problems are usually static in the sense that they do not consider changes over time in a cumulative manner. Dynamic optimisation problems to incorporate changes. However, these are memoryless in that

Many believe that the future of innovation lies in simulation. However, as computers are becoming ever more powerful, so does the hyperbole used to discuss their potential in modelling across a vast range of domains, from subatomic physics to chemistry, climate science, epidemiology, economics and cosmology. As we are about to enter the era of quantum and exascale computing, machine learning and artificial

Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there

We discuss how computational data science and agent-based modeling, are shedding new light on the age-old issue of human conflict. While social science approaches focus on individual cases, the recent proliferation of empirical data and complex systems thinking has opened up a computational approach based on identifying common statistical patterns and building generative but minimal agent-based models

This paper presents tackling of a hard optimization problem of computational linguistics, specifically automatic multi-document text summarization, using grid computing. The main challenge of multi-document summarization is to extract the most relevant and unique information effectively and efficiently from a set of topic-related documents, constrained to a specified length. In the Big Data/Text era

This paper tackles the slowness issue of the well-known expectation-maximization (EM) algorithm in the context of Gaussian mixture models. To cope with this slowness problem, an Exact Line Search scheme is proposed. It is based on exact computation of the step size required to jump, for a given search direction, towards the final solution. Computing this exact step size is easily done by only rooting

Electrophoreticdeposition (EPD) coating is one of the key processes in automotive manufacturing. Here, car bodies are moved through a tank of paint in which electrically charged paint particles lead to a successive increase of the paint film thickness on the car body. This paint film is used to protect the car body from corrosion. However, in order to properly protect the car body from corrosion, it

Particle Swarm Optimization (PSO) is a powerful nature-inspired metaheuristic optimization method that may determine optimal solutions of engineering problems in fewer evaluations compared to other optimization methods. However, the literature shows that PSO may suffer from converging prematurely to a local solution, and this occurs due to poor tuning of the control parameters in PSO. In this paper

Vehicle traffic congestion is an increasing concern in metropolitan areas, with negative implications for health, environment, and economy. Researchers, city managers, and entrepreneurs have shown great interest in Smart Mobility, and several approaches have been proposed to reduce these non-desired effects. In this work, we focus on using the existing infrastructure (traffic lights) to tackle these

This paper proposes a novel competitive information dissemination macro model CISIR (Competitive Information Susceptible Infected Recovered) in online social network (OSN). Firstly, the Markov chain theory is used to analyze the intrinsic relationship between the competition mechanism among different types of information on the network and the rules of node state transition and information propagation

In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However

This paper addresses the problem of predicting the tertiary structure of a protein given its amino acid sequence, which has been reported to belong to the NP-Complete class of problems. We design an ad-hoc distributed memetic algorithm (DMA) and evaluate several algorithm configurations in terms of different distributed population structures, ad-hoc local search strategies and the combination of two

A new parallel generic approximate sparse pseudoinverse matrix technique using a decoupled column-wise approach, based on modified row-threshold incomplete QR factorization techniques, is proposed. The explicit preconditioned conjugate gradient least squares method in conjunction with the new parallel generic approximate sparse pseudoinverse matrix technique is used for solving linear least square

Colorectal cancer (CRC) is a major cause of cancer mortality. It is known that loss of APC gene function through mutation is followed by the expansion of a field of mutated tissue, but the mechanisms behind this expansion are poorly understood. This study aimed to examine the processes involved in field expansion using two agent-based computational models: a cell-scale model allowing mapping of Apc-mutated

The CACOC (Chaotic Ant Colony optimisation for Coverage) algorithm has been developed to manage the mobility of a swarm of Unmanned Aerial Vehicles (UAVs). Using a specific chaotic dynamic obtained from the Rössler system, CACOC provides waypoints for UAVs that aim to optimise the coverage of an unknown area while having unpredictable trajectories. Since the chaotic dynamics are obtained from a three

Several computational models of Vein Graft Bypass (VGB) adaptation have been developed in order to improve the surgical outcome and they all share a common property: their accuracy relies on a winning choice of their driving coefficients which are best to be retrieved from experimental data. Since experiments are time-consuming and resources-demanding, the golden standard is to know in advance which

The study of viscous fluid flow coupled with rigid or deformable solids has many applications in biological and engineering problems, e.g., blood cell transport, drug delivery, and particulate flow. We developed a partitioned approach to solve this coupled Multiphysics problem. The fluid motion was solved by Palabos (Parallel Lattice Boltzmann Solver), while the solid displacement and deformation was

The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC is an e-learning platform that empowers the biomedical community to develop, launch and share open training materials. It deploys hands-on software

Structurally cohesive subgroups are a powerful and mathematically rigorous way to characterize network robustness. Their strength lies in the ability to detect strong connections among vertices that not only have no neighbors in common, but that may be distantly separated in the graph. Unfortunately, identifying cohesive subgroups is a computationally intensive problem, which has limited empirical

We present a computational model of three-dimensional and unsteady hemodynamics within the primary large arteries in the human on 1,572,864 cores of the IBM Blue Gene/Q. Models of large regions of the circulatory system are needed to study the impact of local factors on global hemodynamics and to inform next generation drug delivery mechanisms. The HARVEY code successfully addresses key challenges

The failure rate for vascular interventions (vein bypass grafting, arterial angioplasty/stenting) remains unacceptably high. Over the past two decades, researchers have applied a wide variety of approaches to investigate the primary failure mechanisms, neointimal hyperplasia and aberrant remodeling of the wall, in an effort to identify novel therapeutic strategies. Despite incremental progress, specific

The construction of distributed algorithms for matrix computations built on top of distributed data aggregation algorithms with randomized communication schedules is investigated. For this purpose, a new aggregation algorithm for summing or averaging distributed values, the push-flow algorithm, is developed, which achieves superior resilience properties with respect to failures compared to existing

Virus infection is a complex biological phenomenon for which in vitro experiments provide a uniquely concise view where data is often obtained from a single population of cells, under controlled environmental conditions. Nonetheless, data interpretation and real understanding of viral dynamics is still hampered by the sheer complexity of the various intertwined spatio-temporal processes. In this paper

Follicular Lymphoma (FL) is one of the most common non-Hodgkin Lymphoma in the United States. Diagnosis and grading of FL is based on the review of histopathological tissue sections under a microscope and is influenced by human factors such as fatigue and reader bias. Computer-aided image analysis tools can help improve the accuracy of diagnosis and grading and act as another tool at the pathologist's

A variant of the Thomson problem, which is about placing a set of points uniformly on the surface of a sphere, is that of generating uniformly distributed points on the sphere that are endowed with antipodal symmetry, i.e., if x is an element of the point set then -x is also an element of that point set. Point sets with antipodal symmetry are of special importance to many scientific and engineering

The problem of constructing a set of uniformly-distributed points on the surface of a sphere, also known as the Thomson problem, has a long and interesting history, which dates back to J.J. Thomson in 1904. A particular variant of the Thomson problem that is of great importance to biomedical imaging is that of generating a nearly uniform distribution of points on the sphere via a deterministic scheme

Here we present the Global Epidemic and Mobility (GLEaM) model that integrates sociodemographic and population mobility data in a spatially structured stochastic disease approach to simulate the spread of epidemics at the worldwide scale. We discuss the flexible structure of the model that is open to the inclusion of different disease structures and local intervention policies. This makes GLEaM suitable