3,923 research outputs found
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal
We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse, and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price’s covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise
Inference of the Russian drug community from one of the largest social networks in the Russian Federation
The criminal nature of narcotics complicates the direct assessment of a drug
community, while having a good understanding of the type of people drawn or
currently using drugs is vital for finding effective intervening strategies.
Especially for the Russian Federation this is of immediate concern given the
dramatic increase it has seen in drug abuse since the fall of the Soviet Union
in the early nineties. Using unique data from the Russian social network
'LiveJournal' with over 39 million registered users worldwide, we were able for
the first time to identify the on-line drug community by context sensitive text
mining of the users' blogs using a dictionary of known drug-related official
and 'slang' terminology. By comparing the interests of the users that most
actively spread information on narcotics over the network with the interests of
the individuals outside the on-line drug community, we found that the 'average'
drug user in the Russian Federation is generally mostly interested in topics
such as Russian rock, non-traditional medicine, UFOs, Buddhism, yoga and the
occult. We identify three distinct scale-free sub-networks of users which can
be uniquely classified as being either 'infectious', 'susceptible' or 'immune'.Comment: 12 pages, 11 figure
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach
Maximizing influences in complex networks is a practically important but
computationally challenging task for social network analysis, due to its NP-
hard nature. Most current approximation or heuristic methods either require
tremendous human design efforts or achieve unsatisfying balances between
effectiveness and efficiency. Recent machine learning attempts only focus on
speed but lack performance enhancement. In this paper, different from previous
attempts, we propose an effective deep reinforcement learning model that
achieves superior performances over traditional best influence maximization
algorithms. Specifically, we design an end-to-end learning framework that
combines graph neural network as the encoder and reinforcement learning as the
decoder, named DREIM. Trough extensive training on small synthetic graphs,
DREIM outperforms the state-of-the-art baseline methods on very large synthetic
and real-world networks on solution quality, and we also empirically show its
linear scalability with regard to the network size, which demonstrates its
superiority in solving this problem
Strengthening China's technological capability
China is increasing its outlay on research and development and seeking to build an innovation system that will deliver quick results not just in absorbing technology but also in pushing the technological envelope. China's spending on R&D rose from 1.1 percent of GDP in 2000 to 1.3 percent of GDP in 2005. On a purchasing power parity basis, China's research outlay was among the world's highest, far greater than that of Brazil, India, or Mexico. Chinese firms are active in the fields of biotechnology, pharmaceuticals, alternative energy sources, and nanotechnology. This surge in spending has been parallel by a sharp increase in patent applications in China, with the bulk of the patents registered in the areas of electronics, information technology, and telecoms. However, of the almost 50,000 patents granted in China, nearly two-thirds were to nonresidents. This paper considers two questions that are especially important for China. First, how might China go about accelerating technology development? Second, what measures could most cost-effectively deliver the desired outcomes? It concludes that although the level of financing for R&D is certainly important, technological advance is closely keyed to absorptive capacity which is a function of the volume and quality of talent and the depth as well as the heterogeneity of research experience. It is also a function of how companies maximize the commercial benefits of research and development, and the coordination of research with production and marketing.Technology Industry,Tertiary Education,E-Business,ICT Policy and Strategies,Agricultural Knowledge&Information Systems
Conditional Reliability in Uncertain Graphs
Network reliability is a well-studied problem that requires to measure the
probability that a target node is reachable from a source node in a
probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned
a probability of existence. Many approaches and problem variants have been
considered in the literature, all assuming that edge-existence probabilities
are fixed. Nevertheless, in real-world graphs, edge probabilities typically
depend on external conditions. In metabolic networks a protein can be converted
into another protein with some probability depending on the presence of certain
enzymes. In social influence networks the probability that a tweet of some user
will be re-tweeted by her followers depends on whether the tweet contains
specific hashtags. In transportation networks the probability that a network
segment will work properly or not might depend on external conditions such as
weather or time of the day. In this paper we overcome this limitation and focus
on conditional reliability, that is assessing reliability when edge-existence
probabilities depend on a set of conditions. In particular, we study the
problem of determining the k conditions that maximize the reliability between
two nodes. We deeply characterize our problem and show that, even employing
polynomial-time reliability-estimation methods, it is NP-hard, does not admit
any PTAS, and the underlying objective function is non-submodular. We then
devise a practical method that targets both accuracy and efficiency. We also
study natural generalizations of the problem with multiple source and target
nodes. An extensive empirical evaluation on several large, real-life graphs
demonstrates effectiveness and scalability of the proposed methods.Comment: 14 pages, 13 figure
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