Introducing Inventiveness into the Patent System: Submission to the Review of the National Innovation System

Because of the potential impact of the patent system on innovation diffusion, particularly on continuous and/or incremental innovation, patent policy should be of central importance to the review of the national innovation system. Substantial empirical evidence shows that most industrial innovations are not induced by the patent system. Even in very large markets, such as the USA, only a minority of patents are likely to be induced by the patent system. To the extent that patents do induce innovations, it is the inventiveness of the innovation which gives rise to possible social benefits (externalities, mainly in the form of knowledge spillovers) which may offset the costs of a patent system and thus give rise to a net economic benefit. On the basis of this evidence about the inducement effect of the patent system, and evidence on the current very low inventiveness standard for patent grant, policy proposals are put forward to re-introduce inventiveness into the patent system, thus making it potentially welfare-enhancing. These proposed changes would also have a major impact in ameliorating the negative impact of the patent system on continuous/incremental innovation

Cryo-EM of full-length α-synuclein reveals fibril polymorphs with a common structural kernel.

α-Synuclein (aSyn) fibrillar polymorphs have distinct in vitro and in vivo seeding activities, contributing differently to synucleinopathies. Despite numerous prior attempts, how polymorphic aSyn fibrils differ in atomic structure remains elusive. Here, we present fibril polymorphs from the full-length recombinant human aSyn and their seeding capacity and cytotoxicity in vitro. By cryo-electron microscopy helical reconstruction, we determine the structures of the two predominant species, a rod and a twister, both at 3.7 Å resolution. Our atomic models reveal that both polymorphs share a kernel structure of a bent β-arch, but differ in their inter-protofilament interfaces. Thus, different packing of the same kernel structure gives rise to distinct fibril polymorphs. Analyses of disease-related familial mutations suggest their potential contribution to the pathogenesis of synucleinopathies by altering population distribution of the fibril polymorphs. Drug design targeting amyloid fibrils in neurodegenerative diseases should consider the formation and distribution of concurrent fibril polymorphs

Preventing extinction and outbreaks in chaotic populations

Interactions in ecological communities are inherently nonlinear and can lead to complex population dynamics including irregular fluctuations induced by chaos. Chaotic population dynamics can exhibit violent oscillations with extremely small or large population abundances that might cause extinction and recurrent outbreaks, respectively. We present a simple method that can guide management efforts to prevent crashes, peaks, or any other undesirable state. At the same time, the irregularity of the dynamics can be preserved when chaos is desirable for the population. The control scheme is easy to implement because it relies on time series information only. The method is illustrated by two examples: control of crashes in the Ricker map and control of outbreaks in a stage-structured model of the flour beetle Tribolium. It turns out to be effective even with few available data and in the presence of noise, as is typical for ecological settings.Comment: 10 pages, 6 figure

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting

Does financial activity cause economic growth?

To clarify the causal links between financial activity and economic growth, three theoretical models are analyzed and a structural equation path models is estimated. In the modeling part, poverty traps result from large fixed costs or high proportions of real investment to run a financial sector. Human capital allocated to financial activities will improve long-run levels but may reduce growth rates in the short run. Empirically, based on data for 93 countries during the 198090 period, it is shown that during the 1980s finance was predominantly a supply-leading determinant of economic growth. Our analysis suggests, however, that this general finding cannot be confirmed for the less developed countries, thereby giving some support to the conclusions derived from the theoretical modeling. --financial development,economic growth,financial sector,causality

Investigating Genotype-Phenotype relationship extraction from biomedical text

During the last decade biomedicine has developed at a tremendous pace. Every day a lot of biomedical papers are published and a large amount of new information is produced. To help enable automated and human interaction in the multitude of applications of this biomedical data, the need for Natural Language Processing systems to process the vast amount of new information is increasing. Our main purpose in this research project is to extract the relationships between genotypes and phenotypes mentioned in the biomedical publications. Such a system provides important and up-to-date data for database construction and updating, and even text summarization. To achieve this goal we had to solve three main problems: finding genotype names, finding phenotype names, and finally extracting phenotype--genotype interactions. We consider all these required modules in a comprehensive system and propose a promising solution for each of them taking into account available tools and resources. BANNER, an open source biomedical named entity recognition system, which has achieved good results in detecting genotypes, has been used for the genotype name recognition task. We were the first group to start working on phenotype name recognition. We have developed two different systems (rule-based and machine-learning based) for extracting phenotype names from text. These systems incorporated the available knowledge from the Unified Medical Language System metathesaurus and the Human Phenotype Onotolgy (HPO). As there was no available annotated corpus for phenotype names, we created a valuable corpus with annotated phenotype names using information available in HPO and a self-training method which can be used for future research. To solve the final problem of this project i.e. , phenotype--genotype relationship extraction, a machine learning method has been proposed. As there was no corpus available for this task and it was not possible for us to annotate a sufficiently large corpus manually, a semi-automatic approach has been used to annotate a small corpus and a self-training method has been proposed to annotate more sentences and enlarge this corpus. A test set was manually annotated by an expert. In addition to having phenotype-genotype relationships annotated, the test set contains important comments about the nature of these relationships. The evaluation results related to each system demonstrate the significantly good performance of all the proposed methods

Graph-based Regularization in Machine Learning: Discovering Driver Modules in Biological Networks

Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel\u27s law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today\u27s scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, and train classification models that serve two goals: 1) class prediction for previously unseen samples; 2) knowledge discovery of the underlying causes of class differences. Application of our methods in genetic studies can help scientist take advantage of existing biological networks, generate diagnosis with higher accuracy, and discover the driver networks behind the differences. We proposed three new graph-based regularization algorithms. Graph Connectivity Constrained AdaBoost algorithm combines a connectivity module, a deletion function, and a model retraining procedure with the AdaBoost classifier. Graph-regularized Linear Programming Support Vector Machine integrates penalty term based on submodular graph cut function into linear classifier\u27s objective function. Proximal Graph LogisticBoost adds lasso and graph-based penalties into logistic risk function of an ensemble classifier. Results of tests of our models on simulated biological datasets show that the proposed methods are able to produce accurate, sparse classifiers, and can help discover true genetic differences between phenotypes

How Not To Drown in Data:A Guide for Biomaterial Engineers

High-throughput assays that produce hundreds of measurements per sample are powerful tools for quantifying cell–material interactions. With advances in automation and miniaturization in material fabrication, hundreds of biomaterial samples can be rapidly produced, which can then be characterized using these assays. However, the resulting deluge of data can be overwhelming. To the rescue are computational methods that are well suited to these problems. Machine learning techniques provide a vast array of tools to make predictions about cell–material interactions and to find patterns in cellular responses. Computational simulations allow researchers to pose and test hypotheses and perform experiments in silico. This review describes approaches from these two domains that can be brought to bear on the problem of analyzing biomaterial screening data

Archives of Data Science, Series A. Vol. 1,1: Special Issue: Selected Papers of the 3rd German-Polish Symposium on Data Analysis and Applications

The first volume of Archives of Data Science, Series A is a special issue of a selection of contributions which have been originally presented at the {\em 3rd Bilateral German-Polish Symposium on Data Analysis and Its Applications} (GPSDAA 2013). All selected papers fit into the emerging field of data science consisting of the mathematical sciences (computer science, mathematics, operations research, and statistics) and an application domain (e.g. marketing, biology, economics, engineering)