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We study the problem of sub-trajectory nearest-neighbor queries on polygonal curves under the continuous Fréchet distance. Given an n vertex trajectory P and an m vertex query trajectory Q , we seek to report a vertex-aligned sub-trajectory P ′ of P that is closest to Q , i.e., P′ must start and end on contiguous vertices of P . Since in real data P typically contains a very large number of vertices, we focus on answering queries, without restrictions on P or Q , using only precomputed structures of 𝒪(n) size. We use three baseline algorithms from straightforward extensions of known work; however, they have impractical performance on realistic inputs. Therefore, we propose a new Hierarchical Simplification Tree (HST) data structure and an adaptive clustering-based query algorithm that efficiently explores relevant parts of P . The core of our query methods is a novel greedy-backtracking algorithm that solves the Fréchet decision problem using 𝒪(n+m) space and 𝒪O(nm) time in the worst case. Experiments on real and synthetic data show that our heuristic effectively prunes the search space and greatly reduces computations compared to baseline approaches.

This work deals with the clustering of information sources for epipole estimation in a multi-camera system. For this problem, each pair of matched visual features in the images can be considered as an elementary information source. The epipole is then estimated by combining these elementary sources taking into account their inadequacy, in particular large imprecision and presence of outliers, as well as the very large number of sources. We address the challenges introduced by a large number of sources with a strategy based on clustering and intra-cluster fusion using the Belief Functions framework. When evaluated on real data, the proposed algorithm exhibits more robustness in terms of accuracy and precision than the standard approaches which provide singular solutions.

A large multiple-input multiple-output satellite communication system is proposed, where satellite and Earth stations (ESs) contain a very large number of antennas. ESs want to exchange their data via a satellite. Data of transmitting ES is transmitted by using a beamforming vector. At the receiving ES, the data of unwanted ESs is canceled by utilizing null space based approach. Then a combining vector is used at the receiving ES in order to retrieve the data. It is observed that sufficiently large number of antennas at the ESs are needed by the proposed scheme. By following the standard moment generating function based approach, analytical results of symbol error rate of the proposed system are formulated.

There is a very large number in the health sector and special methods are also used systematically. Data exchange is one of the most commonly used methods. Heart disease is one of the leading causes of death in the world. This system predicts the possibility of heart disease. The results of this system provide a 100% risk of heart disease. The data used are categorized according to medical criteria. The system evaluates these parameters using data extraction methods used in Python using two basic machine learning algorithms, the Solution Tree Algorithm, and the algorithm that demonstrates the best accuracy in heart disease.

Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state-of-the-art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, on one hand to avoid an excessive number of iterations and waste computational time, on the other to avoid getting a sub-optimal registration. With this work, we compare different termination criteria on several datasets, and prove that the chosen one produces very good results that are comparable to those obtained using a very large number of iterations, while saving computational time.

In the field of supervised learning, the problem of class imbalance is one of the most difficult problems, and has attracted a great deal of research attention in recent years. In an imbalanced dataset, minority classes are those that contain very small numbers of data samples, while the remaining classes have a very large number of data samples. This type of imbalance reduces the predictive performance of machine learning models. There are currently three approaches for dealing with the class imbalance problem: algorithm-level, data-level, and ensemble-based approaches. Of these, data-level approaches are the most widely used, and consist of three sub-categories: under-sampling, oversampling, and hybrid techniques. Oversampling techniques generate synthetic samples for the minority class to balance an imbalanced dataset. However, existing oversampling approaches do not have a strategy for handling noise samples in imbalanced and noisy datasets, which leads to a reduction in the predictive performance of machine learning models. This study therefore proposes a noise-adaptive synthetic oversampling technique (NASOTECH) to deal with the class imbalance problem in imbalanced and noisy datasets. The noise-adaptive synthetic oversampling (NASO) strategy is first introduced, which is used to identify the number of samples generated for each sample in the minority class, based on the concept of the noise ratio. Next, the NASOTECH algorithm is proposed, based on the NASO strategy, to handle the class imbalance problem in imbalanced and noisy datasets. Finally, empirical experiments are conducted on several synthetic and real datasets to verify the effectiveness of the proposed approach. The experimental results confirm that NASOTECH outperforms three state-of-the-art oversampling techniques in terms of accuracy and geometric mean (G-mean) on imbalanced and noisy datasets.

Facial recognition has attracted the attention of researchers and has been one of the most prominent topics in the fields of image processing and pattern recognition since 1990. This resulted in a very large number of recognition methods and techniques with the aim of increasing the accuracy and robustness of existing systems. Many techniques have been developed to address the challenges and reliable recognition systems have been reached but require considerable processing time, suffer from high memory consumption and are relatively complex. The focus of this paper is on extracting subset of descriptors (less correlated and less calculations) from the co-occurrence matrix with the goal of enhancing the performance of Haralick’s descriptors. Improvements are achieved by adding the image pre-processing and selecting the proper method according to the database problem and by extracting features from image local regions.

Major airstreams in tropical cyclones (TCs) are rarely described from a Lagrangian perspective. Such a perspective, however, is required to account for asymmetries and time dependence of the TC circulation. We present a procedure that identifies main airstreams in TCs based on trajectory clustering. The procedure takes into account the TC’s large degree of inherent symmetry and is suitable for a very large number of trajectories . A large number of trajectories may be needed to resolve both the TC’s inner-core convection as well as the larger-scale environment. We define similarity of trajectories based on their shape in a storm-relative reference frame, rather than on proximity in physical space, and use Fréchet distance, which emphasizes differences in trajectory shape, as a similarity metric. To make feasible the use of this elaborate metric, data compression is introduced that approximates the shape of trajectories in an optimal sense. To make clustering of large numbers of trajectories computationally feasible, we reduce dimensionality in distance space by so-called landmark multidimensional scaling. Finally, k-means clustering is performed in this low-dimensional space. We investigate the extratropical transition of Tropical Storm Karl (2016) to demonstrate the applicability of our clustering procedure. All identified clusters prove to be physically meaningful and describe distinct flavors of inflow, ascent, outflow, and quasi-horizontal motion in Karl’s vicinity. Importantly, the clusters exhibit gradual temporal evolution, which is most notable because the clustering procedure itself does not impose temporal consistency on the clusters. Finally, TC problems are discussed for which the application of the clustering procedures seems to be most fruitful.

Wireless Sensor Networks (WSNs) have a very large number of applications in different domains of the industry. One of those applications is the industrial use of WSN. Industrial Wireless Sensor Net...

Mine planning optimization aims at maximizing the profit obtained from extracting valuable ore. Beyond its theoretical complexity—the open-pit mining problem with capacity constraints reduces to a knapsack problem with precedence constraints, which is NP-hard—practical instances of the problem usually involve a large to very large number of decision variables, typically of the order of millions for large mines. Additionally, any comprehensive approach to mine planning ought to consider the underlying geostatistical uncertainty as only limited information obtained from drill hole samples of the mineral is initially available. In this regard, as blocks are extracted sequentially, information about the ore grades of blocks yet to be extracted changes based on the blocks that have already been mined. Thus, the problem lies in the class of multi-period large scale stochastic optimization problems with decision-dependent information uncertainty. Such problems are exceedingly hard to solve, so approximations are required. This paper presents an adaptive optimization scheme for multi-period production scheduling in open-pit mining under geological uncertainty that allows us to solve practical instances of the problem. Our approach is based on a rolling-horizon adaptive optimization framework that learns from new information that becomes available as blocks are mined. By considering the evolution of geostatistical uncertainty, the proposed optimization framework produces an operational policy that reduces the risk of the production schedule. Our numerical tests with mines of moderate sizes show that our rolling horizon adaptive policy gives consistently better results than a non-adaptive stochastic optimization formulation, for a range of realistic problem instances.

Most non-consequentialists “let the numbers count” when one can save either a lesser or greater number from equal or similar harm. But they are wary of doing so when one can save either a small number from grave harm or instead a very large number from minor harm. Limited aggregation is an approach that reconciles these two commitments. It is motivated by a powerful idea: our decision whom to save should respect each person who has a claim to our help, including those whom we fail to save. However, it has recently been argued that it is open to decisive objections. I develop a new limitedly aggregative view:Hybrid Balance Relevant Claims. This view is well grounded in the reasons we have to be skeptical of aggregation and resolves all recent challenges by paying careful attention to the rationale for limited aggregation.

The introduction in 2015 of Residual Neural Networks (RNN) and ResNET allowed for outstanding improvements of the performance of learning algorithms for evolution problems containing a “large” number of layers. Continuous-depth RNN-like models called Neural Ordinary Differential Equations (NODE) were then introduced in 2019. The latter have a constant memory cost, and avoid the a priori specification of the number of hidden layers. In this paper, we derive and analyze a parallel (-in-parameter and time) version of the NODE, which potentially allows for a more efficient implementation than a standard/naive parallelization of NODEs with respect to the parameters only. We expect this approach to be relevant whenever we have access to a very large number of processors, or when we are dealing with high dimensional ODE systems. Moreover, when using implicit ODE solvers, solutions to linear systems with up to cubic complexity are then required for solving nonlinear systems using for instance Newton’s algorithm; as the proposed approach allows to reduce the overall number of time-steps thanks to an iterative increase of the accuracy order of the ODE system solvers, it then reduces the number of linear systems to solve, hence benefiting from a scaling effect.

Private processing of database queries protects the confidentiality of sensitive data when queries are answered. It is important to design collusion-resistant protocols ensuring that privacy remains protected even when a certain number of honest-but-curious participants collude to share their knowledge in order to gain unauthorised access to sensitive information. A novel setting arises when aggregated queries need to be answered for a large distributed database, but legal requirements or commercial interests forbid making access to records in each subdatabase available to other counterparts. For example, a very large number of medical records may be stored in a distributed database, which is a union of several separate databases from different hospitals, or even from different countries. The present article introduces and investigates two protocols for collusion-resistant private processing of aggregated queries in this novel setting: Accelerated Multi-round Iterative Protocol (AMIP) and Restricted Multi-round Iterative Protocol (RMIP). We define a large collection of query functions and show that AMIP and RMIP protocols can answer all queries in this collection. Our experiments demonstrate that the AMIP protocol outperforms all other applicable algorithms, and this achievement is especially significant in terms of the communication complexity.

We introduce a general methodology to consistently estimate multidimensional ARCH models equation-by-equation, possibly with a very large number of parameters through penalization (Sparse Group Las...

Supervised Machine Learning (ML) requires that smart algorithms scrutinize a very large number of labeled samples before they can make right predictions. And this is not always true either. In our experience, in fact, a neural network trained with a huge database comprised of over fifteen million water meter readings had essentially failed to predict when a meter would malfunction/need disassembly based on a history of water consumption measurements. With a second step, we developed a methodology, based on the enforcement of a specialized data semantics, that allowed us to extract only those samples for training that were not noised by data impurities. With this methodology, we re-trained the neural network up to a prediction accuracy of over 80%. Yet, we simultaneously realized that the new training dataset was significantly different from the initial one in statistical terms, and much smaller, as well. We had reached a sort of paradox: We had alleviated the initial problem with a better interpretable model, but we had changed the replicated form of the initial data. To reconcile that paradox, we further enhanced our data semantics with the contribution of field experts. This has finally led to the extrapolation of a training dataset truly representative of regular/defective water meters and able to describe the underlying statistical phenomenon, while still providing an excellent prediction accuracy of the resulting classifier. At the end of this path, the lesson we have learnt is that a human-in-the-loop approach may significantly help to clean and re-organize noised datasets for an empowered ML design experience.

Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C(α) atom of a residue, which is repeated for each residue of a protein. We designed a nine-layer 3D deep convolutional neural network (CNN) that takes as input a gridded box with the atomic coordinates and types around a residue. Several CNN layers were designed to capture structure information at different scales, such as bond lengths, bond angles, torsion angles, and secondary structures. Trained on a very large number of protein structures, the method, called ProDCoNN (protein design with CNN), achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets.

Cell-based approximations of PDE traffic models are widely used for traffic prediction and control. However, in order to represent the traffic state with good resolution, cell-based models often require a short cell length, which results in a very large number of states. We propose a new transition system traffic model, based on the front tracking method for solving the LWR PDE model. Assuming piecewise-linear flux function and piecewise-constant initial conditions, this model gives an exact solution. Furthermore, it is easier to extend, has fewer states and, although its dynamics are intrinsically hybrid, is faster to simulate than an equivalent cell-based approximation. The model is extended to enable handling moving bottlenecks as well as probabilistic traffic breakdowns and capacity drops at static bottlenecks. A control strategy that utilizes controlled moving bottlenecks for bottleneck decongestion is described and tested in simulation. It is shown that we are able to keep the static bottleneck in free flow by creating controlled moving bottlenecks at specific instances along on the road, and using them to regulate the incoming traffic flow.

Very large numbers words such as “hundred,” “thousand,” “million,” “billion,” and “trillion” pose a learning problem for children because they are sparse in everyday speech and children's experience with extremely large quantities is scarce. In this study, we examine when children acquire the relative ordering of very large number words as a first step toward understanding their acquisition. In Study 1, a hundred and twenty-five 5–8-year-olds participated in a verbal number comparison task involving very large number words. We found that children can judge which of two very large numbers is more as early as age 6, prior to entering first grade. In Study 2, we provided a descriptive analysis on the usage of very large number words using the CHILDES database. We found that the relative frequency of large number words does not change across the years, with “hundred” uttered more frequently than others by an order of magnitude. We also found that adults were more likely to use large number words to reference units of quantification for money, weight, and time, than for discrete, physical entities. Together, these results show that children construct a numerical scale for large number words prior to learning their precise cardinal meanings, and highlight how frequency and context may support their acquisition. Our results have pedagogical implications and highlight a need to investigate how children acquire meanings for number words that reference quantities beyond our everyday experience.

Testing for equality of two high-dimensional distributions is a challenging problem, and this becomes even more challenging when the sample size is small. Over the last few decades, several graph-based two-sample tests have been proposed in the literature, which can be used for data of arbitrary dimensions. Most of these test statistics are computed using pairwise Euclidean distances among the observations. But, due to concentration of pairwise Euclidean distances, these tests have poor performance in many high-dimensional problems. Some of them can have powers even below the nominal level when the scale-difference between two distributions dominates the location-difference. To overcome these limitations, we introduce some new dissimilarity indices and use them to modify some popular graph-based tests. These modified tests use the distance concentration phenomenon to their advantage, and as a result, they outperform the corresponding tests based on the Euclidean distance in a wide variety of examples. We establish the high-dimensional consistency of these modified tests under fairly general conditions. Analyzing several simulated as well as real data sets, we demonstrate their usefulness in high dimension, low sample size situations.

International audience; Massive biological datasets are available in various sources. To answer a biological question (e.g., ''which are the genes involved in a given disease?''), life scientists query and mine such datasets using various techniques. Each technique provides a list of results usually ranked by importance (e.g., a list of ranked genes). Combining the results obtained by various techniques, that is, combining ranked lists of elements into one list of elements is of paramount importance to help life scientists make the most of various results and prioritize further investigations. Rank aggregation techniques are particularly well-fitted with this context as they take in a set of rankings and provide a consensus, that is, a single ranking which is the ''closest'' to the input rankings. However, (i) the problem of rank aggregation is NP-hard in most cases (using an exact algorithm is currently not possible for more than a few dozens of elements) and (ii) several (possibly very different) exact solutions can be obtained. As answer to (i), many heuristics and approximation algorithms have been proposed. However, heuristics cannot guarantee how far from an exact solution the consensus ranking will be, and the approximation ratio of approximation algorithms dedicated to the problem is fairly high (not less than 3/2). No solution has yet been proposed to help true-users dealing with the problem encountered in point (ii). In this paper we present a complete system able to perform rank aggregation of massive biological datasets. Our solution is efficient as it is based on an original partitioning method making it possible to compute a high-quality consensus using an exact algorithm in a large number of cases. Our solution is robust as it clearly identifies for the user groups of elements whose relative order is the same in any optimal solution. These features provide answers to points (i) and (ii) and lie in mathematical bases offering guarantees on the computed result. Also, our solution is effective as it has been implemented into a real tool, ConquR-BioV2 is used for the life science community, and evaluated at large-scale using a very large number of datasets.

The aim of this paper is to establish a theorem associated with the product of the Aleph-function, the multivariable Aleph-function, and the general class of polynomials. The results of this theorem are unified in nature and provide a very large number of analogous results (new or known) involving simpler special functions and polynomials (of one or several variables) as special cases. The derived results lead to significant applications in physics and engineering sciences.

The basic theory of the probability has been discussed in Chap. 1. That knowledge based on the set theory is essential to understand the notion of probability. However, it lacks applicability in many cases. For instance, let consider that very large number of events is under investigation. Calculations of probabilities of any arbitrary outcomes would require a great deal of effort. For this reason, the general concept of the probability theory is developed by means of random variables and corresponding probability functions, namely the probability distribution function and the probability density function. In general, a function maps elements of a well-defined set to another well-defined set. Let \(x \in A \) and \(y \in B\), a function f(x) defines the transformation from set A to set B, and it is denoted as; $$\begin{aligned} f : x \rightarrow y \end{aligned}$$ A random variable, X, is a function related with the outcomes of a certain random experiment.

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive to infer when the number of variables is large (N~100). Here, we address both these issues with Super-statistical Generative Model for binary Data (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same ‘bath’ whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows us to model collections of a very large number (N>1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using multiple datasets spanning several time- and length-scales., Author summary Collectively varying binary variables are ubiquitous in modern biology. Given that the number of possible configurations of these systems typically far exceeds the number of available samples, generative models have become an essential tool in quantitative descriptions of binary data. The state-of-the-art approaches to build generative models have several conceptual limitations. Specifically, they rely on the modeler choosing system-appropriate constraints, which can be challenging in systems with many complex interactions. Moreover, they are computationally expensive to infer when the number of variables is large (N~100). To address this issue, we propose a theoretical generalization of the maximum entropy approach that allows us to model very high dimensional data; at least an order of magnitude higher than what is currently possible. This framework will be a significant advancement in the computational analysis of covarying binary variables.

While the problem of Routing and Spectrum Allocation (RSA) has been widely studied, very few studies attempt to solve realistic sized instances. Indeed, the state of the art is always below the standard transport capacity of a fiber link with 384 frequency slots, regardless of what the authors consider, heuristics or exact methods with a few exceptions. In this paper, we are interested in reducing the gap between realistic data sets and testbed instances that are often considered, using exact methods. Even if exact methods may fail to solve in reasonable time very large instances, they can, however, output solutions with a very good and proven accuracy. The novelty of this paper is to exploit the observations that optimal solutions contain a very large number of lightpaths associated with shortest paths or k-shortest paths with a small k. We propose an original efficient large-scale optimization model and decomposition algorithm to solve the RSA problem for flexgrid optical networks. It allows the exact or near optimal solution of much larger instances than in the literature.

For many problems in science and engineering, it is necessary to describe the collective behavior of a very large number of grains. Complexity inherent in granular materials, whether due the variability of grain interactions or grain-scale morphological factors, requires modeling approaches that are both representative and tractable. In these cases, continuum modeling remains the most feasible approach; however, for such models to be representative, they must properly account for the granular nature of the material. The granular micromechanics approach has been shown to offer a way forward for linking the grain-scale behavior to the collective behavior of millions and billions of grains while keeping within the continuum framework. In this paper, an extended granular micromechanics approach is developed that leads to a micromorphic theory of degree n. This extended form aims at capturing the detailed grain-scale kinematics in disordered (mechanically or morphologically) granular media. To this end, additional continuum kinematic measures are introduced and related to the grain-pair relative motions. The need for enriched descriptions is justified through experimental measurements as well as results from simulations using discrete models. Stresses conjugate to the kinematic measures are then defined and related, through equivalence of deformation energy density, to forces conjugate to the measures of grain-pair relative motions. The kinetic energy density description for a continuum material point is also correspondingly enriched, and a variational approach is used to derive the governing equations of motion. By specifying a particular choice for degree n, abridged models of degrees 2 and 1 are derived, which are shown to further simplify to micro-polar or Cosserat-type and second-gradient models of granular materials.

Coarsely quantized MIMO signalling methods have gained popularity in the recent developments of massive MIMO as they open up opportunities for massive MIMO implementation using cheap and power-efficient radio-frequency front-ends. This paper presents a new one-bit MIMO precoding approach using spatial Sigma-Delta ( $\Sigma \Delta$ ) modulation. In previous one-bit MIMO precoding research, one mainly focuses on using optimization to tackle the difficult binary signal optimization problem that arises from the precoding design. Our approach attempts a different route. Assuming angular MIMO channels, we apply $\Sigma \Delta$ modulation—a classical concept in analog-to-digital conversion of temporal signals—in space. The resulting $\Sigma \Delta$ precoding approach has two main advantages: First, we no longer need to deal with binary optimization in $\Sigma \Delta$ precoding design. Particularly, the binary signal restriction is replaced by peak signal amplitude constraints. Second, the impact of the quantization error can be well controlled via modulator design and under appropriate operating conditions. Through symbol error probability analysis, we reveal that the very large number of antennas in massive MIMO provides favorable operating conditions for $\Sigma \Delta$ precoding. In addition, we develop a new $\Sigma \Delta$ modulation architecture that is capable of adapting the channel to achieve nearly zero quantization error for a targeted user. Furthermore, we consider multi-user $\Sigma \Delta$ precoding using the zero-forcing and symbol-level precoding schemes. These two $\Sigma \Delta$ precoding schemes perform considerably better than their direct one-bit quantized counterparts, as simulation results show.

In the development of a new compound in chemistry and molecular biology, especially a new medicine in pharmaceutical industry, we often need to find candidate(s), a molecule or molecules, with the best desired property (e.g., binding affinity in medicine) from a large set of molecules with the same scaffold but m distinct functional substitutes at each of its n different sites. The total number $$N_{\mathrm{lib}}$$ of molecules in this library is $$m^n$$ . In some cases, $$N_{\mathrm{lib}}$$ can be a very large number (e.g., millions). This is a challenging task because it is costly and often infeasible to synthesize and test all of these molecules. A new algorithm referred to as optimal sequential search is developed to overcome this difficulty. Especially, this algorithm is chemically intuitive which only uses the information of molecule composition, and accessible to practical chemists. The algorithm can be applied to small, medium and large size molecule libraries. With syntheses and property measurements for a limited number of molecules, the top best candidate molecules can be effectively captured from the whole library. Three examples with library size 64, 160,000 and 1,048,576, respectively, are used for illustration. For the first small library, syntheses and property measurements of 17 molecules are sufficient to capture the top 7 best candidate molecules; for the two medium and large libraries, syntheses and property measurements of about one thousand molecules can capture most or a large part of the top 500, especially the top 100 best candidate molecules. However, the algorithm needs to perform multiple (e.g., hundreds of) iterative syntheses and property measurements. The time cost may not be acceptable if the algorithm is performed manually. To make the algorithm practical, automation of the sequential searching process is the following task.

In this article, we consider the problems (unsolved in the literature) of computing the nearest normal matrix X to a given non-normal matrix A, under certain constraints, that are (i) if A is real, we impose that also X is real; (ii) if A has known entries on a given sparsity pattern Ω and unknown/uncertain entries otherwise, we impose to X the constraint xij = aij for all entries (i,j) in the pattern Ω. As far as we know, there do not exist in the literature specific algorithms aiming to solve these problems. For the case in which all entries of A can be modified, there exists an algorithm by Ruhe, which is able to compute the closest normal matrix. However, if A is real, the closest computed matrix by Ruhe’s algorithm might be complex, which motivates the development of a different algorithm preserving reality. Normality is characterized in a very large number of ways; in this article, we consider the property that the square of the Frobenius norm of a normal matrix is equal to the sum of the squares of the moduli of its eigenvalues. This characterization allows us to formulate as equivalent problem the minimization of a functional of an unknown matrix, which should be normal, fulfill the required constraints, and have minimal distance from the given matrix A.

Today consumers are confronted with a very large number of products and services to choose from. This makes it difficult for users to find relevant products among a huge number of alternatives. Recommender systems help users to find products of interest by analyzing past user transactions such as product views and purchases. There is a similar problem in the real estate industry where hundreds of thousands of properties are available for rentals or sales. In this work we present the details of a real estate recommender system developed for Zingat.com. The system developed is a hybrid of collaborative and content filtering approaches. We will explain the challenges we face, the recommendation techniques we use to overcome these challenges, and the final product used for recommendation.

Dynamic combinatorial chemistry (DCC) is a powerful tool to identify bioactive compounds. This efficient technique allows the target to select its own binders and circumvents the need for synthesis and biochemical evaluation of all individual derivatives. An ever-increasing number of publications report the use of DCC on biologically relevant target proteins. This minireview complements previous reviews by focusing on the experimental protocol and giving detailed examples of essential steps and factors that need to be considered, such as protein stability, buffer composition and cosolvents.

Detailed radiation analysis of instruments flown in space is critical to ensure mission safety, often requiring the use of state-of-the-art particle transport simulation tools. Geant4 is one of the most powerful toolkits to simulate the interaction and the passage of particles through matter, but it is not prepared to receive Standard for The Exchange of Product data (STEP) files, the most versatile Computer-Aided Design (CAD) format, as input, requiring previous conversion to other CAD formats. This may lead to loss of detail and under or overestimation of the quantities under study, especially when the instruments have complex shapes, and/or a large number of volumes. Though several solutions have been proposed to import complex geometries from STEP files into Geant4, so far, only commercial options are available. In this paper we present a new tool, GUIMesh, that embeds FreeCAD libraries, an open-source CAD editor, to tessellate volumes, and convert them to Geometry Description Markup Language (GDML), a Geant4 readable format , in a straightforward way. Several degrees of freedom are given to the user regarding mesh precision and choice of material. Different geometries were tested for material definition, geometry and navigation errors, and the method used was successfully validated. Program Summary Program Title: GUIMesh Program Files doi: http://dx.doi.org/10.17632/c3c9xbspzp.1 Licensing provisions : GNU General Public License 3 (GPLv3) Programming language: Python Nature of problem: No open-source software allows to import STEP geometries intro Geant4, one of the most powerful toolkits to simulate radiation interaction with matter. Since CAD software is extensively used in the design of particle and radiation detection instruments, it is highly desirable for physicists that STEP geometries can be imported to Geant4 with little effort instead of having to code structures made of a very large number of solid volumes which are difficult to accurately reproduce with Geant4 C++ classes. Solution method : STEP geometries are converted to tessellated volumes (with some margin of error) using FreeCAD libraries. A Python script then writes GDML files based on the results allowing users to import these geometries with Geant4. A graphical user interface provides several options to the user, including material assignment and mesh precision setting for each volume.

As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm-Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as, in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm-Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This paper reexamines the order picking process in a warehouse facing the challenges that e-commerce brings about and which are characterised by a very large number of small sized orders and return...