We propose a new Real-coded GA(RCGA) using the combination of two crossovers, UNDX-m and EDX. The search region of UNDX-m is biased to the inside area that the population of the RCGA covers. Because of this search bias, the GA using UNDX-m causes stagnation of its search if the cost function has a kind of structure, so called, a ridge structure or a multiple-peak structure. In order to overcome this stagnation, we propose (...) a new crossover EDX, whose search is biased toward extrapolative one. Experimental results show that RCGA with EDX can deal with both ridge-structure function whose dimension reaches more than hundreds and multiple-peak function whose optimum resides at the corner of the search area. (shrink)

This paper presents a new method that improves robustness of real-coded Genetic Algorithm (GA) for function optimization. It is reported that most of crossover operators for real-coded GA have sampling bias, which prevents to find the optimum when it is near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the boundary of (...) the search space. Although several methods have been proposed to relax this sampling bias, they could not cancel whole bias. In this paper, we propose a new method, Toroidal Search Space Conversion (TSC), to remove this sampling bias. TSC converts bounded search space into toroidal one without any parameter. Experimental results show that a GA with TSC has higher performance to find the optimum near the boundary of search space and the GA has more robustness concerning the relative position of the optimum. (shrink)

This paper presents the Real-coded Genetic Algorithms(RCGA) which can treat with high-dimensional ill-scaled structures, what is called, k -tablet structure. The k -tablet structure is the landscape that the scale of the fitness function is different between the k -dimensional subspace and the orthogonal (n-k) -dimensional subspace. The search speed of traditional RCGAs degrades when high-dimensional k -tablet structures are included in the landscape of fitness function. In this structure, offspring generated by crossovers is (...) likely to spread wider region than the region where the parental population covers. This phenomenon causes the stagnation of the search. To resolve this problem, we propose a new crossover LUNDX- m, which uses only m -dimensional latent variables. The effectiveness of the proposal method is tested with several benchmark functions including k -tablet structures and we show that our proposal method performs better than traditional crossovers especially when the dimensionality n is higher than 100. (shrink)

Biologically inspired Evolution Algorithms, that use individuals as searching points and progress search by evolutions or adaptations of the individuals, are widely applied to many optimization problems. Many real world problems, which could be transformed to optimization problems, are very often difficult because the problems have complex landscapes that are multimodal, epistatic and having strong local minima. Current real-coded genetic algorithms could solve high-dimensional multimodal functions, but could not solve strong deceptive functions. Niching GAs are applied (...) to low-dimensional multimodal functions by maintaining diversity of searching population, but could not be applicable to highdimensional functions. In order to optimize high dimensional deceptive multimodal functions, we propose a new EA called Adaptive Neighboring Search, that is structured with a selection for reproduction by restricting mating individuals to neighbors, a crossover-like mutation using the mating individuals and an elitist selection for survival within one centered parent and its offsprings. By individualized generation alternation and complementary crossover-like mutation, the ANS realizes self-distributive and locally adaptive search, and individuals in the search divide into plural promising valleys and converge within same valley. The ANS is applicable to high dimensional deceptive multimodal function optimization, because the feature is independent of number of problem’s dimensions. By applying to high dimensional Fletcher and Powell function as a deceptive multimodal one, we show the ANS can obtain various solutions and several optimal solutions in high probability. (shrink)

This paper presents a kernel density estimation method by means of real-coded crossovers. Functions of real-coded crossover operators are composed of probabilistic density estimation from parental populations and sampling from estimated models. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate probabilistic distributions, however, probabilistic model estimation is implicitly included in algorithms of real-coded crossovers. Based on this understanding, we exploit the implicit estimation of probabilistic distribution of crossovers as a kernel (...) density estimator. We also propose an application of crossover kernels to Expectation-Maximization estimation (EM) of Gaussian mixtures. (shrink)

This article presents the design and development of a genetic algorithm to generate long-range transmit codes with low autocorrelation side lobes for radar to minimize target profile estimation error. The GA described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a radar target profile.

The structural-parametric synthesis of neural networks of deep learning, in particular convolutional neural networks used in image processing, is considered. The classification of modern architectures of convolutional neural networks is given. It is shown that almost every convolutional neural network, depending on its topology, has unique blocks that determine its essential features, Residual block, Inception module, ResNeXt block. It is stated the problem of structural-parametric synthesis of convolutional neural networks, for the solution of which it is proposed to use a (...) genetic algorithm. The genetic algorithm is used to effectively overcome a large search space: on the one hand, to generate possible topologies of the convolutional neural network, namely the choice of specific blocks and their locations in the structure of the convolutional neural network, and on the other hand to solve the problem of structural-parametric synthesis of convolutional neural network of selected topology. The most significant parameters of the convolutional neural network are determined. An encoding method is proposed that allows to repre- sent each network structure in the form of a string of fixed length in binary format. After that, several standard genetic operations were identified, i.e. selection, mutation and crossover, which eliminate weak individuals of the previous generation and use them to generate competitive ones. An example of solving this problem is given, a database of patients with thyroid disease was used as a training sample. (shrink)

In this paper, we perform theoretical analysis and experiments on the Simplex Crossover (SPX), which we have proposed. Real-coded GAs are expected to be a powerful function optimization technique for real-world applications where it is often hard to formulate the objective function. However, we believe there are two problems which will make such applications difficult; 1) performance of real-coded GAs depends on the coordinate system used to express the objective function, and 2) it costs much (...) labor to adjust parameters so that the GAs always find an optimum point efficiently. The result of our theoretical analysis and experiments shows that a performance of SPX is independent of linear coordinate transformation and that SPX always optimizes various test function efficiently when theoretical value for expansion rate, which is a parameter of SPX, is applied. We also show that BLX-α is equivalent to degenerate form of SPX. Experiments show that we have something misunderstood effect of epistasis on performance degradation of real-coded GAs. (shrink)

In multiple robot systems, the problem of allocation of complex tasks to heterogeneous teams of robots, also known as the multiple robot coalition formation problem, has begun to receive considerable attention. Efforts to address the problem range from heuristics based approaches that search the subspaces of the coalition structure to evolutionary learning approaches. Conventional approaches typically strive to optimize a single objective function such as the number of tasks executed or the time required to execute all tasks, (...) or a weighted function of such objectives. In real world applications, objectives such as minimizing distance traveled and maximizing the number of tasks completed are often conflicting in nature. The coalition formation problem thus naturally lends itself to a multi-objective optimization approach based on evolutionary learning. In this paper, we formulate the problem of mapping coalitions of robots to a set of tasks as a multi-objective optimization problem and propose a variant of non-dominated sorting genetic algorithm to arrive at trade-off solutions. Additionally, we extend the solution to domains where robot resources are non-additive. Simulations demonstrate the efficacy of the proposed approach in generating the set of Pareto-optimal solutions. (shrink)

For genetic algorithms, it is important to maintain the population diversity. Some genetic algorithms have been proposed, which have an ability to control the diversity. But these algorithms use the distance between two individuals to control the diversity. Therefore, these performances become worse on ill-scaled functions. In this paper, we propose a new genetic algorithm, DIDC(a genetic algorithm with Distance Independent Diversity Control), that does not use a distance to control the population diversity. For controlling the (...) diversity, DIDC uses two GAs that have different natures. For realizing different natures, one GA uses a crossover operator as a search operator, and the other GA uses a mutation operator in DIDC. By applying DIDC to several benchmark problems, we show that DIDC has a good performance on high dimensional, multimodal, non-separable and ill-scaled problems. Finally, we show that the control parameter of DIDC has the same effect on the search with the number of generating children n c. (shrink)

Community detection is one of the key research directions in complex network studies. We propose a community detection algorithm based on a density peak clustering model and multiple attribute decision-making strategy, TOPSIS. First, the two-dimensional dataset, which is transformed from the network by taking the density and distance as the attributes of nodes, is clustered by using the DBSCAN algorithm, and outliers are determined and taken as the key nodes. Then, the initial community frameworks are formed and expanded (...) by adding the most similar node of the community as its new member. In this process, we use TOPSIS to cohesively integrate four kinds of similarities to calculate an index, and use it as a criterion to select the most similar node. Then, we allocate the nonkey nodes that are not covered in the expanded communities. Finally, some communities are merged to obtain a stable partition in two ways. This paper designs some experiments for the algorithm on some real networks and some synthetic networks, and the proposed method is compared with some popular algorithms. The experimental results testify for the effectiveness and show the accuracy of our algorithm. (shrink)

Many real-world problems entail multiple conflicting objectives, which makes multiobjective optimization an important subject. Much attention has been paid to Genetic Algorithm as a potent multiobjective optimization method, and the effectiveness of its hybridization with local search has recently been reported in the literature. However, there have been a relatively small number of studies on LS methods for multiobjective function optimization. Although each of the existing LS methods has some strong points, they have respective drawbacks such as (...) high computational cost and inefficiency of improving objective functions. Hence, a more effective and efficient LS method is being sought, which can be used to enhance the performance of the hybridization. Pareto descent directions are defined in this paper as descent directions to which no other descent directions are superior in improving all objective functions. Moving solutions in such directions is expected to maximally improve all objective functions simultaneously. This paper proposes a new LS method, Pareto Descent Method, which finds Pareto descent directions and moves solutions in such directions. In the case part or all of them are infeasible, it finds feasible Pareto descent directions or descent directions as necessary and moves solutions in these directions. PDM finds these directions by solving linear programming problems. Thus, it is computationally inexpensive. Experiments have shown that PDM is superior to existing methods. (shrink)

Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring -based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper (...) explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence -based algorithms, i.e., Artificial Neural Network, hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN. (shrink)

The Vehicle Routing Problem (VRP) has been extensively studied by different researchers from all over the world in recent years. Multiple solutions have been proposed for different variations of the problem, such as Capacitive Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Time Windows (VRP-TW), Vehicle Routing Problem with Pickup and Delivery (VRPPD), among others, all of them with deterministic times. In the last years, researchers have been interested in including in their different models the variations that travel times (...) may experience when exposed to all kind of phenomena, mainly vehicle traffic. This article addresses the VRP from this perspective, proposing the design and implementation of a genetic algorithm based on neutrosophic theory for calculating the fitness function of each route, considering the variability and uncertainty present in travel times. A deterministic genetic algorithm is also implemented with the average travel times to compare it with the neutrosophic algorithm using simulation. As conclusion, a deterministic algorithm does not necessarily generate the best solution in the real world, full of uncertainty. Also, the quantification of uncertainty using neutrosophic theory can be used in route planning, opening a broad and interesting field of research for future investigations. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:breatheJohn Cayley (bio)To view the current version of John Cayley's digital work "breathe" as a standalone website, please visit https://work.programmatology.com/breathe/. Use of a Chrome browser is advised, and, for mobiles, the site has only been tested for iOS devices. On the desktop, switching to full screen will avoid having to manually resize the browser window to a more or less 16:9 aspect. To view Cayley's notebook with digital code, (...) visit https://observablehq.com/@shadoof/breathe.breathe is a brief, prose-poetic essay in explicitly paragrammatic language art. The 'supply text' that underlies a fundamentally dynamic, temporal making of meaning is what in other contexts might be read as an original composition by myself, its subject the interior oceanscape of breath that we experienced during the pandemic year of 2020, our respiratory rhythms threatened, invisibly, by disease. The piece is also representative of a category of still largely potential poetic artifacts to which computation lends a dynamic and here, a paragrammatic concretism.Despite Julia Kristeva having made a convincing formalist-polemical case for the paragram,1 it seems to be more or less absent from most contemporary milieux of poetic praxis as an explicit, generative principle.2 For 'poetic praxis' I might have said 'writing' or 'literary practice' but the common usage of the former is overly constrained, and its sense after Derrida is, perforce, radically ambiguous. 'Literary' (with its root and other derivations) I now tend to reserve for language art practices within the domain of which the letter is properly and compositionally constitutive. Is breathe an instance of 'writing' or 'literary practice'? Well, literary practice: yes, but not as usually conceived. 'Letteral,' definitely.In Kristeva's abstracted models of poetic language, the paragram is everywhere in both prose and poetry as such: the (chiefly) literal inscription of alternate, multiplied, yet readable 'grams,' at once within and beyond those of the text as received, generative of new meanings, and of various modes for the aestheticization of poetic objects. In subsequent or adjacent criticism–regardless of whether or not it is psychoanalytically inclined–paragram may be further associated with Lacan's "lalangue" (surfacing in breathe as 'lalanguage'), that part of Language (an anglophone term, distinctly capitalized) which clearly exceeds and eludes most of the dubious, fundamentally orthographic, or designative 'sciences' of linguistics.3 But even lalanguage is only ever at best commensurate with [End Page 97] the 'not all,' despite (or perhaps due to) the ineluctable fact that, as T. S. Eliot's Sweeney puts it, "I gotta use words when I talk to you." What should be clear, however, even from this most cursory of outlines, is that paragrams should be read as poetic and that–particularly if allowed to embrace non-literal, metaphoric instances–a case may be (has been) made that paragrams are (Kristeva would have written =) poetic language.In actually existing 'literature' we might suggest that paragrams are hypostasized or stunned (historically, by literal inscription) in latency or in hiding but, clearly, there are potential movements and/or transformative processes, realized through reading, from the 'grams' of the received text to their paragrams. While the usual sense of the prefix 'para-' is "parallel to, separate from or going beyond" (OED), Kristeva and others call attention to this real if usually implicit movement. (I quote extensively from her essay without further comment except to point out that these words are highly suggestive of other aspects of language art in digital media which are not directly addressed in the present, more focused remarks.)[...] the literary text presents itself as a system of multiple connections that could be described as a structure of paragrammatic networks. [...] The term network replaces univocity (linearity) by encompassing it, and suggests that each set (sequence) is the outcome and the beginning of a plurivalent relation. In this network, the elements will be presented as the peaks of a graph ( [...] ), enabling us to formalize the symbolic operation of language as a dynamic mark, as a moving 'gram' (hence as a paragram) which makes rather than expresses a meaning.4The movements-as-transformative(or deformative)-process in breathe are various modes of letteral replacement, particularly of letters... (shrink)

This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem (...) will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results. (shrink)

The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and (...) psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the nonlinear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique’s application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a “Programming Basics” module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data. (shrink)

To realize the coordinated and sustainable development of hydropower projects and regional society, comprehensively evaluating hydropower projects’ influence is critical. Usually, hydropower project development has an impact on environmental geology and social and regional cultural development. Based on comprehensive consideration of complicated geological conditions, fragile ecological environment, resettlement of reservoir area, and other factors of future hydropower development in each country, we have constructed a comprehensive evaluation index system of hydropower projects, including 4 first-level indicators of social economy, environment, safety, (...) and fairness, which contain 26 second-level indicators. To solve the problem that existing models cannot evaluate dynamic nonlinear optimization, a projection pursuit model is constructed by using rough set reduction theory to simplify the index. Then, an accelerated genetic algorithm based on real number coding is used to solve the model and empirical study is carried out with the Y hydropower station as a sample. The evaluation results show that the evaluation index system and assessment model constructed in our paper effectively reduce the subjectivity of index weight. Applying our model to the social impact assessment of related international hydropower projects can not only comprehensively analyze the social impact of hydropower projects but also identify important social influencing factors and effectively analyze the social impact level of each dimension. Furthermore, SIA assessment can be conducive to project decision-making, avoiding social risks and social stability. (shrink)

Bayesian learning theory and evolutionary theory both formalize adaptive competition dynamics in possibly high‐dimensional, varying, and noisy environments. What do they have in common and how do they differ? In this paper, we discuss structural and dynamical analogies and their limits, both at a computational and an algorithmic‐mechanical level. We point out mathematical equivalences between their basic dynamical equations, generalizing the isomorphism between Bayesian update and replicator dynamics. We discuss how these mechanisms provide analogous answers to the challenge of adapting (...) to stochastically changing environments at multiple timescales. We elucidate an algorithmic equivalence between a sampling approximation, particle filters, and the Wright‐Fisher model of population genetics. These equivalences suggest that the frequency distribution of types in replicator populations optimally encodes regularities of a stochastic environment to predict future environments, without invoking the known mechanisms of multilevel selection and evolvability. A unified view of the theories of learning and evolution comes in sight. (shrink)

The deterministic economic system will also produce chaotic dynamic behaviour, so economic chaos is getting more and more attention, and the research of economic chaos forecasting methods has become an important topic at present. The traditional economic chaos forecasting models are mostly based on large samples, but in actual production activities, there are a large number of small-sample economic chaos problems, and there is still no effective solution. This paper proposes a combined forecasting model based on the traditional economic chaos (...) forecasting method. First of all, through the decision tree classification, priority selection of features, rough prediction is achieved. Secondly, we use BP neural network to make secondary prediction. Because the initial weight is randomly selected, it is easy to fall into the defect of local minimum. This paper optimizes the BP neural network. Finally, the decision tree model and the BP neural network model optimized by the improved genetic algorithm are combined, and the combined model is optimized by the improved GA. This method can take advantage of many prediction models and combine the prediction information of multiple different prediction models to effectively improve the fitting ability of the model and improve the prediction accuracy. (shrink)

On the basis of analyzing the complexity of railway flatcar loading optimization problem, according to the characteristics of railway flatcar loading, based on the situation of railway transport loading unit of multiple railway flatcars, this study puts forward the optimal loading optimization method of multimodel railway flatcars based on improved genetic algorithm, constructs the linear programming model of railway flatcar loading optimization problem, and combines with the improved genetic algorithm to solve the problem. The study also analyzes (...) the structural characteristics of the optimal loading materials of multimodel railway flatcars, selects the optimal materials and inputs the relevant data into the computer, and uses MATLAB to program the optimal loading algorithm of multimodel railway flatcars. Through the analysis of the calculation example, the study discusses its scope of application. The experimental results show that the average general utilization rate of the proposed method is 73%, which has higher universality, more effective application, and fully meets the research requirements. It is verified that the proposed method has a statistically significant impact on the optimal loading of multi-type railway flatcars. (shrink)

Like Kant, Foucault challenges us to rethink the way we relate freedom and truth by stressing the idea of "maturity" understood as a release from the "self-incurred tutelage" (the expression is from Kant) that otherwise characterizes so much of our lives. Though, rather than linking freedom and truth via the concept of autonomy (or lawfulness), as Kant does, Foucault outlines a possible experience of ethics as an individualizing ideal that contrasts with the model of establishing codes within a conception of (...) moral order. Foucault's project can be read as attempting to decouple ethics from the epistemological concern with the objectivity of a moral order in order to reconnect it to the formation of a political sensibility that sees truth(fulness) in a certain practice of freedom, as opposed to seeing our freedom confirmed or granted by a certain kind of (mode of relating to the) truth. I call this reading of Foucault's investigations into ethics the "methodological/prescriptive" approach (asking and responding to what we can do, ethically-speaking) and contrast it with the sketch of an alternative reading which I call "ontological/historical" (asking and responding to what we are, ethically-speaking). This reading, instead of pushing beyond Kant in the direction of a Nietzschean sensibility, rather challenges Kant's moral law from the space of a Hegelian insistence on the (historical) realization or actualization of freedom in and through the normative constraints structuring existing power-relations. (shrink)

In this paper, we propose an extension of the Minimal Generation Gap (MGG) to reduce the number of fitness evaluation for the real-coded GAs (RCGA). When MGG is applied to actual engineering problems, for example applied to optimization of design parameters, the fitness calculating time is usually huge because MGG generates many children from one pair of parents and the fitness is calculated by repetitive simulation or analysis. The proposed method called Saving MGG reduces the number of fitness (...) evaluation by estimating the promising degrees of children using individual distribution and fitness information of population, and selecting children based on the promising degree before evaluating the fitness. Experimental results show that RCGA with Saving MGG can provide large reducing effects on 20 or 30 dimensional Sphere functions, Rosenbrock functions, ill-scaled Rosenbrock functions, and Rastrigin function. (shrink)

This article compares three models for conceptualizing the political and ethical challenges of contemporary genetics, genomics, and postgenomics. The three analytical approaches are referred to as the state-politics model, the biopolitical model, and the infopolitical model. Each of these models is valuable for different purposes. But comparing these models in terms of their influence in contemporary discussions, the first is by far the dominant approach, the second is gaining in importance, and the third is almost entirely neglected. The widespread neglect (...) of the infopolitical dimensions of genetic sciences focused by the third model is puzzling in light of the fact that genetics, genomics, and postgenomics are all pre-eminent information sciences. The infopolitical model thus aims to bring into clearer view the specific political and ethical problems engendered by this informational nature of the genetic sciences. This model offers a way of understanding how ethically-salient and politically-fraught conceptual assumptions can be embedded in informational architectures such as algorithms and the formats (or data structures) upon which they rely. (shrink)

While rapid advances in artificial intelligence hiring tools promise to transform the workplace, these algorithms risk exacerbating existing biases against marginalized groups. In light of these ethical issues, AI vendors have sought to translate normative concepts such as fairness into measurable, mathematical criteria that can be optimized for. However, questions of disability and access often are omitted from these ongoing discussions about algorithmic bias. In this paper, I argue that the multiplicity of different kinds and intensities of people’s disabilities and (...) the fluid, contextual ways in which they manifest point to the limits of algorithmic fairness initiatives. In particular, existing de-biasing measures tend to flatten variance within and among disabled people and abstract away information in ways that reinforce pathologization. While fair machine learning methods can help mitigate certain disparities, I argue that fairness alone is insufficient to secure accessible, inclusive AI. I then outline a disability justice approach, which provides a framework for centering disabled people’s experiences and attending to the structures and norms that underpin algorithmic bias. (shrink)

The genetic code has been regarded as arbitrary in the sense that the codon-amino acid assignments could be different than they actually are. This general idea has been spelled out differently by previous, often rather implicit accounts of arbitrariness. They have drawn on the frozen accident theory, on evolutionary contingency, on alternative causal pathways, and on the absence of direct stereochemical interactions between codons and amino acids. It has also been suggested that the arbitrariness of the genetic code (...) justifies attributing semantic information to macromolecules, notably to DNA. I argue that these accounts of arbitrariness are unsatisfactory. I propose that the code is arbitrary in the sense of Jacques Monod's concept of chemical arbitrariness: the genetic code is arbitrary in that any codon requires certain chemical and structural properties to specify a particular amino acid, but these properties are not required in virtue of a principle of chemistry. This notion of arbitrariness is compatible with several recent hypotheses about code evolution. I maintain that the code's chemical arbitrariness is neither sufficient nor necessary for attributing semantic information to nucleic acids. (shrink)

Recent investigations have revealed 1) that the isochores of the human genome group into two super‐families characterized by two different long‐range 3D structures, and 2) that these structures, essentially based on the distribution and topology of short sequences, mold primary chromatin domains (and define nucleosome binding). More specifically, GC‐poor, gene‐poor isochores are low‐heterogeneity sequences with oligo‐A spikes that mold the lamina‐associated domains (LADs), whereas GC‐rich, gene‐rich isochores are characterized by single or multiple GC peaks that mold the topologically associating (...) domains (TADs). The formation of these “primary TADs” may be followed by extrusion under the action of cohesin and CTCF. Finally, the genomic code, which is responsible for the pervasive encoding and molding of primary chromatin domains (LADs and primary TADs, namely the “gene spaces”/“spatial compartments”) resolves the longstanding problems of “non‐coding DNA,” “junk DNA,” and “selfish DNA” leading to a new vision of the genome as shaped by DNA sequences. (shrink)

if and only if for every W in V, W is independent of the set of all its non-descendants conditional on the set of its parents. One natural question that arises with respect to DAGs is when two DAGs are “statistically equivalent”. One interesting sense of “statistical equivalence” is “d-separation equivalence” (explained in more detail below.) In the case of DAGs, d-separation equivalence is also corresponds to a variety of other natural senses of statistical equivalence (such as representing the same (...) set of distributions). Theorems characterizing d-separation equivalence for directed acyclic graphs and that can be used as the basis for polynomial time algorithms for checking d-separation equivalence were provided by Verma and Pearl (1990), and Frydenberg (1990). The question we will examine is how to extend these results to cases where a DAG may have latent (unmeasured) variables or selection bias (i.e. some of the variables in the DAG have been conditioned on.) D-separation equivalence is of interest in part because there are algorithms for constructing DAGs with latent variables and selection bias that are based on observed conditional independence relations. For this class of algorithms, it is impossible to determine which of two d-separation equivalent causal structures generated a given probability distribution, given only the set of conditional independence and dependence relations true of the observed distribution. We will describe a polynomial (in the number of vertices) time algorithm for determining when two DAGs which may have latent variables or selection bias are d-separation equivalent. (shrink)

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying (...) domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries. (shrink)

Designing a data-responsive system requires accurate input to ensure efficient results. The growth of technology in sensing methods and the needs of various kinds of data greatly impact data fusion (DF)-related study. A coordinative DF framework entails the participation of many subsystems or modules to produce coordinative features. These features are utilized to facilitate and improve solving certain domain problems. Consequently, this paper proposes a general Multiple Coordinative Data Fusion Modules (MCDFM) framework for real-time and heterogeneous data sources. (...) We develop the MCDFM framework to adapt various DF application domains requiring macro and micro perspectives of the observed problems. This framework consists of preprocessing, filtering, and decision as key DF processing phases. These three phases integrate specific purpose algorithms or methods such as data cleaning and windowing methods for preprocessing, extended Kalman filter (EKF) for filtering, fuzzy logic for local decision, and software agents for coordinative decision. These methods perform tasks that assist in achieving local and coordinative decisions for each node in the network of the framework application domain. We illustrate and discuss the proposed framework in detail by taking a stretch of road intersections controlled by a traffic light controller (TLC) as a case study. The case study provides a clearer view of the way the proposed framework solves traffic congestion as a domain problem. We identify the traffic features that include the average vehicle count, average vehicle speed (km/h), average density (%), interval (s), and timestamp. The framework uses these features to identify three congestion periods, which are the nonpeak period with a congestion degree of 0.178 and a variance of 0.061, a medium peak period with a congestion degree of 0.588 and a variance of 0.0593, and a peak period with a congestion degree of 0.796 and a variance of 0.0296. The results of the TLC case study show that the framework provides various capabilities and flexibility features of both micro and macro views of the scenarios being observed and clearly presents viable solutions. (shrink)

Proteins account for the catalytic and structural versatility displayed by all cells, yet they are assembled from a set of only 20 common amino acids. With few exceptions, only 61 nucleotide triplets also direct incorporation of these amino acids. Endeavors to expand the genetic code recently progressed to nucleus‐containing cells, after Chin et al.1 transferred Escherichia coli genes for a mutant tyrosine‐adaptor molecule and its synthetase into Saccharomyces cerevisiae. Transformed yeast cells were produced that exhibit efficient site‐specific incorporation of (...) non‐biotic amino acids into proteins. This makes it likely that code complexity can be elevated experimentally in mammals. BioEssays 26:111–115, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

Whole genome sequencing and SNP genotyping arrays can paint strikingly different pictures of demographic history and natural selection. This is because genotyping arrays contain biased sets of pre‐ascertained SNPs. In this short review, we use comparisons between high‐coverage whole genome sequences of African hunter‐gatherers and data from genotyping arrays to highlight how SNP ascertainment bias distorts population genetic inferences. Sample sizes and the populations in which SNPs are discovered affect the characteristics of observed variants. We find that SNPs on (...) genotyping arrays tend to be older and present in multiple populations. In addition, genotyping arrays cause allele frequency distributions to be shifted towards intermediate frequency alleles, and estimates of linkage disequilibrium are modified. Since population genetic analyses depend on allele frequencies, it is imperative that researchers are aware of the effects of SNP ascertainment bias. With this in mind, we describe multiple ways to correct for SNP ascertainment bias. (shrink)

Assuming 0 ♯ does not exist, we present a combinatorial approach to Jensen's method of coding by a real. The forcing uses combinatorial consequences of fine structure (including the Covering Lemma, in various guises), but makes no direct appeal to fine structure itself.

The efficient coding hypothesis posits that sensory systems are tuned to the regularities of their natural input. The statistics of natural image databases have been the topic of many studies, which have revealed biases in the distribution of orientations that are related to neural representations as well as behavior in psychophysical tasks. However, commonly used natural image databases contain images taken with a camera with a planar image sensor and limited field of view. Thus, these images do not incorporate the (...) physical properties of the visual system and its active use reflecting body and eye movements. Here, we investigate quantitatively, whether the active use of the visual system influences image statistics across the visual field by simulating visual behaviors in an avatar in a naturalistic virtual environment. Images with a field of view of 120° were generated during exploration of a virtual forest environment both for a human and cat avatar. The physical properties of the visual system were taken into account by projecting the images onto idealized retinas according to models of the eyes' geometrical optics. Crucially, different active gaze behaviors were simulated to obtain image ensembles that allow investigating the consequences of active visual behaviors on the statistics of the input to the visual system. In the central visual field, the statistics of the virtual images matched photographic images regarding their power spectra and a bias in edge orientations toward cardinal directions. At larger eccentricities, the cardinal bias was superimposed with a gradually increasing radial bias. The strength of this effect depends on the active visual behavior and the physical properties of the eye. There were also significant differences between the upper and lower visual field, which became stronger depending on how the environment was actively sampled. Taken together, the results show that quantitatively relating natural image statistics to neural representations and psychophysical behavior requires not only to take the structure of the environment into account, but also the physical properties of the visual system, and its active use in behavior. (shrink)

The present article reports several applied experiments in the generation of aesthetic forms from algorithms and data. In these experiments algorithms and data are the driving morphogenetic force to such an extent that the role of the human creator must be reexamined case-by-case. Artists that program the graphics or sound generating algorithms may in turn be said to be programmed perceptually by the resulting artworks, in the sense that they must adapt their perception in a conscious or involuntary effort to (...) detect or project meaning in their form. While forms generated by algorithms or data are to a large extent unpredictable, an intimate relationship between programmer, performer/listener/viewer and the piece is created through instinctive, empirical familiarity with the tools and the creative process, which is afforded by the immediate and highly interactive nature of the frameworks employed. A simple iterative algorithm that produces a novel kind of chaotic geometric shapes is presented. Examples of an application of Paul Klee’s principles of visual form in programming interactive audio-visual dance performance are given. These principles are directly derived from the language-based metaphor of active, medial and passive voice. Finally, ongoing experiments in letting language structures emerge from data derived from a sound database are presented. (shrink)

The literature on algorithmic fairness has examined exogenous sources of biases such as shortcomings in the data and structural injustices in society. It has also examined internal sources of bias as evidenced by a number of impossibility theorems showing that no algorithm can concurrently satisfy multiple criteria of fairness. This paper contributes to the literature stemming from the impossibility theorems by examining how informational richness affects the accuracy and fairness of predictive algorithms. With the aid of a computer simulation, (...) we show that informational richness is the engine that drives improvements in the performance of a predictive algorithm, in terms of both accuracy and fairness. The centrality of informational richness suggests that classification parity, a popular criterion of algorithmic fairness, should be given relatively little weight. But we caution that the centrality of informational richness should be taken with a grain of salt in light of practical limitations, in particular, the so-called bias-variance trade off. (shrink)

Despite the increase in the research field of ethics in artificial intelligence, most efforts have focused on the debate about principles and guidelines for responsible AI, but not enough attention has been given to the “how” of applied ethics. This paper aims to advance the research exploring the gap between practice and principles in AI ethics by identifying how companies are applying those guidelines and principles in practice. Through a qualitative methodology based on 22 semi-structured interviews and two focus groups, (...) the goal of the current study is to understand how companies approach ethical issues related to AI systems. A structured analysis of the transcripts brought out many actual practices and findings, which are presented around the following main research topics: ethics and principles, privacy, explainability, and fairness. The interviewees also raised issues of accountability and governance. Finally, some recommendations are suggested such as developing specific sector regulations, fostering a data-driven organisational culture, considering the algorithm’s complete life cycle, developing and using a specific code of ethics, and providing specific training on ethical issues. Despite some obvious limitations, such as the type and number of companies interviewed, this work identifies real examples and direct priorities to advance the research exploring the gap between practice and principles in AI ethics, with a specific focus on Spanish companies. (shrink)

The central dogma of biology holds that genetic information normally flows from DNA to RNA to protein. As a consequence it has been generally assumed that genes generally code for proteins, and that proteins fulfil not only most structural and catalytic but also most regulatory functions, in all cells, from microbes to mammals. However, the latter may not be the case in complex organisms. A number of startling observations about the extent of non-protein-coding RNA (ncRNA) transcription in the higher (...) eukaryotes and the range of genetic and epigenetic phenomena that are RNA-directed suggests that the traditional view of the structure of genetic regulatory systems in animals and plants may be incorrect. ncRNA dominates the genomic output of the higher organisms and has been shown to control chromosome architecture, mRNA turnover and the developmental timing of protein expression, and may also regulate transcription and alternative splicing. This paper re-examines the available evidence and suggests a new framework for considering and understanding the genomic programming of biological complexity, autopoletic development and phenotypic variation. BioEssays 25:930-939,2003. (C) 2003 Wiley Periodicals, Inc. (shrink)

We characterize þ-independence in a variety of structures, focusing on the field of real numbers expanded by predicate defining a dense multiplicative subgroup, G, satisfying the Mann property and whose pth powers are of finite index in G. We also show such structures are super-rosy and eliminate imaginaries up to codes for small sets.

We routinely observe others’ choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals, such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic relationship is part of a more complex social structure involving multiple social groups that are not directly observable. Here we suggest that human learners go (...) beyond dyadic similarities in choice behaviors or explicit group memberships; they infer the structure of social influence by grouping individuals based on choices, and they use these groups to decide whose choices to follow. We propose a computational model that formalizes this idea, and we test the model predictions in a series of behavioral experiments. In Experiment 1, we reproduce a well-established finding that people's choices are more likely to be influenced by someone whose past choices are more similar to their own past choices, as predicted by our model as well as dyadic similarity models. In Experiments 2–5, we test a set of unique predictions of our model by looking at cases where the degree of choice overlap between individuals is equated, but their choices indicate a latent group structure. We then apply our model to prior empirical results on infants’ understanding of others’ preferences, presenting an alternative account of developmental changes. Finally, we discuss how our model relates to classical findings in the social influence literature and the theoretical implications of our model. Taken together, our findings demonstrate that structure learning is a powerful framework for explaining the influence of social information on decision making in a variety of contexts. (shrink)

We explored the frequency with which typical adults make Theory of Mind attributions, and under what circumstances these attributions occur. We used an experience sampling method to query 30 typical adults about their everyday thoughts. Participants carried a Personal Data Assistant that prompted them to categorize their thoughts as Action, Mental State, or Miscellaneous at approximately 30 pseudo-random times during a continuous 10-h period. Additionally, participants noted the direction of their thought and degree of socializing at the time of (...) inquiry. We were interested in the relative frequency of ToM and how prominent they were in immediate social exchanges. Analyses of multiple choice answers suggest that typical adults: spend more time thinking about actions than mental states and miscellaneous things, exhibit a higher degree of own- versus other-directed thought when alone, and make mental state attributions more frequently when not interacting than while interacting with others . A significant 3-way interaction between thought type, direction of thought, and socializing emerged because action but not mental state thoughts about others occurred more frequently when participants were interacting with people versus when alone; whereas there was an increase in the frequency of both action and mental state attributions about the self when participants were alone as opposed to socializing. A secondary analysis of coded free text responses supports findings 1–3. The results of this study help to create a more naturalistic picture of ToM use in everyday life and the method shows promise for future study of typical and atypical thought processes. (shrink)

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via (...) interactions with continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss--Markov (``\OU'') processes and show how such \textit{OU networks} can express complex dynamic phenomena such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world. (shrink)

Professor Robinson provides a new critique of the often neglected problem of classification within the criminal law. He presents a discussion of the present conceptual framework of the law, and offers explanations of how and why formal structures do not match the operation of law in practice. In this scholarly exposition of applied criminal theory, Robinson argues that the current operational structure of the criminal law fails to take account of its different functions. He goes on to suggest new (...) sample codes of criminal conduct and criminal adjudication which mark a real departure from the pragmatic approach which presently dominates code-making. This rounded exploration of the structure of systems of criminal law is an important work for law teachers and policy makers world-wide. (shrink)

Investigation into the sequence structure of the genetic code by means of an informatic approach is a real success story. The features of human language are also the object of investigation within the realm of formal language theories. They focus on the common rules of a universal grammar that lies behind all languages and determine generation of syntactic structures. This universal grammar is a depiction of material reality, i.e., the hidden logical order of things and its relations (...) determined by natural laws. Therefore mathematics is viewed not only as an appropriate tool to investigate human language and genetic code structures through computer sciencebased formal language theory but is itself a depiction of material reality. This confusion between language as a scientific tool to describe observations/experiences within cognitive constructed models and formal language as a direct depiction of material reality occurs not only in current approaches but was the central focus of the philosophy of science debate in the twentieth century, with rather unexpected results. This article recalls these results and their implications for more recent mathematical approaches that also attempt to explain the evolution of human language. (shrink)

Novel precision oncology trial designs, such as basket and umbrella trials, are designed to test new anticancer agents in more effective and affordable ways. However, they present some ethical concerns referred to scientific validity, risk-benefit balance and informed consent. Our aim is to discuss these issues in basket and umbrella trials, giving examples of two ongoing cancer trials: NCI-MATCH (National Cancer Institute – Molecular Analysis for Therapy Choice) and Lung-MAP (Lung Cancer Master Protocol) study. We discuss three ethical requirements for (...) clinical trials which may be challenged in basket and umbrella trial designs. Firstly, we consider scientific validity. Thanks to the new trial designs, patients with rare malignancies have the opportunity to be enrolled and benefit from the trial, but due to insufficient accrual, the trial may generate clinically insignificant findings. Inadequate sample size in study arms and the use of surrogate endpoints may result in a drug approval without confirmed efficacy. Moreover, complexity, limited quality and availability of tumor samples may not only introduce bias and result in unreliable and unrepresentative findings, but also can potentially harm patients and assign them to an inappropriate therapy arm. Secondly, we refer to benefits and risks. Novel clinical trials can gain important knowledge on the variety of tumors, which can be used in future trials to develop effective therapies. However, they offer limited direct benefits to patients. All potential participants must wait about 2 weeks for the results of the genetic screening, which may be stressful and produce anxiety. The enrollment of patients whose tumors harbor multiple mutations in treatments matching a single mutation may be controversial. As to informed consent – the third requirement we discuss, the excessive use of phrases like “personalized medicine”, “tailored therapy” or “precision oncology” might be misleading and cause personal convictions that the study protocol is designed to fulfill the individual health-related needs of participants. We suggest that further approaches should be implemented to enhance scientific validity, reduce misunderstandings and risks, thus maximizing the benefits to society and to trial participants. (shrink)

In the wake of corporate ethical scandals that have harmed millions of employees and investors, there has been an increase in the number of works written in the last decade, which aim to answer one apparently simple question: what causes unethical behavior, and what can we do, if anything, to prevent similar transgressions in the future? The extensive research around this question is the best proof of its real complexity as the challenge of disentangling the background of ethical behavior (...) has obvious academic and practical interest. This study aims to take a further step toward that goal. Much research has noted the impact of multiple aspects of organizational contexts on individuals’ ethical behavior. However, studies that analyze the impact of organizational learning capability (OLC) on employees’ ethical behavior are few and far between. This was the first aim of this study. The second centered on gaining a deeper understanding of the relationship between OLC and ethical behavior by analyzing the mediating role of employability and organizational commitment. We tested our hypotheses through a structural equation methodology applied to a sample of 641 workers from 166 Spanish consultancy firms and found a positive, direct relationship between OLC and employability, OLC and organizational commitment, employability and organizational commitment, and organizational commitment and ethical behavior. (shrink)

Judges in multiple US states, such as New York, Pennsylvania, Wisconsin, California, and Florida, receive a prediction of defendants’ recidivism risk, generated by the COMPAS algorithm. If judges act on these predictions, they implicitly delegate normative decisions to proprietary software, even beyond the previously documented race and age biases. Using the ProPublica dataset, we demonstrate that COMPAS predictions favor jailing over release. COMPAS is biased against defendants. We show that this bias can largely be removed. Our proposed correction increases (...) overall accuracy, and attenuates anti-black and anti-young bias. However, it also slightly increases the risk that defendants are released who commit a new crime before tried. We argue that this normative decision should not be buried in the code. The tradeoff between the interests of innocent defendants and of future victims should not only be made transparent. The algorithm should be changed such that the legislator and the courts do make this choice. (shrink)

Background Investigative genetic genealogy (IGG) is a technique that involves uploading genotypes developed from perpetrator DNA left at a crime scene, or DNA from unidentified remains, to public genetic genealogy databases to identify genetic relatives and, through the creation of a family tree, the individual who was the source of the DNA. As policymakers demonstrate interest in regulating IGG, it is important to understand public perspectives on IGG to determine whether proposed policies are aligned with public attitudes.Methods (...) We conducted eight focus groups with members of the public (N = 72), sampled from four geographically diverse US regions, to explore general attitudes and perspectives regarding aspects of IGG practices, applications, and policies. Five major topics were explored in each focus group: when IGG should be used; who should perform IGG; how to approach consent for genetic database users; what systems of oversight should govern IGG practitioners; and whether to notify database users if their data are involved in law enforcement (LE) matching.Results Participants were supportive of IGG in most scenarios, especially for cold and violent cases. The favorable attitudes toward IGG were, however, tempered by distrust of law enforcement among some participants. All participants agreed that databases must inform users if IGG is allowed, but they did not agree on how individual database users should be allowed to opt out or whether to notify them if their data are involved in specific investigations. All participants agreed that IGG should be subject to some prescriptive guidelines, regulations, or accountability mechanisms.Conclusions These findings suggest broad public support for IGG, and interest in developing systems of accountability for its practice. Our study provides useful insight for policy makers, genomic database stewards, law enforcement, and other stakeholders in IGG’s practice, and suggests multiple directions for future research. (shrink)