The self-focusing of relativistically intense laser light pulses is analyzed, where the pulse length is short enough that ion inertia prevents any significant motion of ions. Self-focusing occurs as a result of an increase of the wave refractive index arising from two effects: the mass increase of electrons caused by their relativistic quiver velocity in the light wave, and the reduction of the electron density as a result of ponderomotive force expulsion of the electrons. The latter effect is significant even for rather small values of (P−PL)/PL, where P is the laser beam power and PL is the critical power above which self-focusing occurs. In fact, for (P−PL)/PL≳0.1 the effect is so strong that all electrons are expelled within a core radial region of the self-focused laser light channel (this new phenomenon is called electron cavitation).

We show that a recurrent, second-order neural network using a real-time, forward training algorithm readily learns to infer small regular grammars from positive and negative string training samples. We present simulations that show the effect of initial conditions, training set size and order, and neural network architecture. All simulations were performed with random initial weight strengths and usually converge after approximately a hundred epochs of training. We discuss a quantization algorithm for dynamically extracting finite state automata during and after training. For a well-trained neural net, the extracted automata constitute an equivalence class of state machines that are reducible to the minimal machine of the inferred grammar. We then show through simulations that many of the neural net state machines are dynamically stable, that is, they correctly classify many long unseen strings. In addition, some of these extracted automata actually outperform the trained neural network for classification of unseen strings.

L O W that a recurrent, second-order neural network using a red-time, feed-forward training algorithm readily learns to infer regular grammars from positive and negative string training samples. We present numerous simulations which show the effect of initial conditions, training set size and order, and neuron architecture. All simulations were performed with random initial weight strengths and usually converge after approximately a hundred epochs of training. We discuss a quantization algorithm for dynamically extracting a finite state automata during and after training. For a well-trained neural net, the extracted automata constitute an equivalence class of state machines that are reducible to the minimal _ _ _ --I . : --

Effective ranking functions are an essential part of commercial search engines. We focus on developing a regression framework for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish two types of relevance judgments used as the training data: 1) absolute relevance judgments arising from explicit labeling of search results; and 2) relative relevance judgments extracted from user clickthroughs of search results or converted from the absolute relevance judgments. We propose a novel optimization framework emphasizing the use of relative relevance judgments. The main contribution is the development of an algorithm based on regression that can be applied to objective functions involving preference data, i.e., data indicating that a document is more relevant than another with respect to a query. Experimental results are carried out using data sets obtained from a commercial search engine. Our results show significant improvements of our proposed methods over some existing methods.

Domestication and breeding have reshaped the genomic architecture of chicken, but the retention and loss of genomic elements during these evolutionary processes remain unclear. We present the first chicken pan-genome constructed using 664 individuals, which identified an additional ∼66.5 Mb sequences that are absent from the reference genome (GRCg6a). The constructed pan-genome encoded 20,491 predicated protein-coding genes, of which higher expression level are observed in conserved genes relative to dispensable genes. Presence/absence variation (PAV) analyses demonstrated that gene PAV in chicken was shaped by selection, genetic drift, and hybridization. PAV-based GWAS identified numerous candidate mutations related to growth, carcass composition, meat quality, or physiological traits. Among them, a deletion in the promoter region of IGF2BP1 affecting chicken body size is reported, which is supported by functional studies and extra samples. This is the first time to report the causal variant of chicken body size QTL located at chromosome 27 which was repeatedly reported. Therefore, the chicken pan-genome is a useful resource for biological discovery and breeding. It improves our understanding of chicken genome diversity and provides materials to unveil the evolution history of chicken domestication.