Temporal evolution of generalization during learning in linear networks

We study generalization in a simple framework of feedforward linear networks with n inputs and n outputs, trained from examples by gradient descent on the usual quadratic error function. We derive analytical results on the behavior of the validation function corresponding to the LMS error function calculated on a set of validation patterns. We show that the behavior of the validation function depends critically on the initial conditions and on the characteristics of the noise. Under certain simple assumptions, if the initial weights are sufficiently small, the validation function has a unique minimum corresponding to an optimal stopping time for training for which simple bounds can be calculated. There exists also situations where the validation function can have more complicated and somewhat unexpected behavior such as multiple local minima (at most n) of variable depth and long but finite plateau effects. Additional results and possible extensions are briefly discussed

Neural Networks for Fingerprint Recognition

After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed

Hybrid modeling, HMM/NN architectures, and protein applications

We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs

Ethynylogation approach in antitumor lipid pharmacochemistry: from dialkynyl-carbinols to trialkynyl-carbinols

A recently proposed "ethynylogation" pharmacochemical approach, first envisaged in the series of anticancer lipidic dialkynylcarbinols (DACs) H–C≡C–CH(OH)–C≡C–R at the levels of the H–C⋮ and ⋮C–R bonds for R = n-C12H25, is completed here at the level of the (HO)C–H bond. The so-devised mono-lipidic trialkynylcarbinol (TAC) target (HC≡C)2C(OH)–C≡CR and its bis-lipidic counterpart HC≡C–C(OH)(C≡CR)2 were synthesized in 4 steps and with 33 % and 23 % overall yield, respectively. Their antitumor cytotoxicity has been evaluated towards HCT116 cells: while the latter TAC is totally inactive, the former DAC-ethynylogous TAC still exhibits a significant toxicity with an IC50 of 10 µM

Fluorinated analogues of lipidic dialkynylcarbinol pharmacophores: synthesis and cytotoxicity in HCT116 cancer cells

Lipidic alkynylcarbinols (LACs) have been identified as potential antitumor compounds, and a thorough understanding of their pharmacophoric environment is now required to elucidate their biological mode of action. In the dialkynylcarbinol (DAC) series, a specific study of the pharmacophore potential has been undertaken by focusing on the synthesis of three fluorinated derivatives followed by their biological evaluation. This work highlights the requirement of an electron-rich secondary carbinol center as a key structure for cytotoxicity in HCT116 cells

On terminal alkynylcarbinols and derivatization thereof

The chemistry of three prototypes of secondary alkynylcarbinols (ACs), recently highlighted as challenging targets in anti-tumoral medicinal chemistry, is further documented by results on n-alkyl, alkynyl and alkenyl representatives. The N-naphthyl carbamate of an n-butyl-AC is thus characterized by X-ray crystallography. A novel dialkynylcarbinol (DAC) with synthetic potential is described, namely the highly dissymmetrical triisopropylsilyl-protected version of diethynylmethanol. The latter is shown to act as a dipolarophile in a selective Huisgen reaction with benzyl azide under CuAAC click conditions, giving an alkenyl-AC, where the alkene unsaturation is embedded in a 1,4-disubstituted 1,2,3-triazole ring, as confirmed by X-ray crystallography.Supplementary information (CIF file

Star formation triggered by non-head-on cloud-cloud collisions, and clouds with pre-collision sub-structure

In an earlier paper, we used smoothed particle hydrodynamics (SPH) simulations to explore star formation triggered by head-on collisions between uniform-density 500 M clouds, and showed that there is a critical collision velocity, vCRIT. At collision velocities below vCRIT, a hub-and-spoke mode operates and delivers a monolithic cluster with a broad mass function, including massive stars (M 10 M) formed by competitive accretion. At collision velocities above vCRIT, a spider’s-web mode operates and delivers a loose distribution of small sub-clusters with a relatively narrow mass function and no massive stars. Here we show that,if the head-on assumption is relaxed, vCRIT is reduced. However, if the uniform-density assumption is also relaxed, the collision velocity becomes somewhat less critical: a low collision velocity is still needed to produce a global hub-and-spoke system and a monolithic cluster, but, even at high velocities, large cores – capable of supporting competitive accretion and thereby producing massive stars – can be produced. We conclude that cloud–cloud collisions may be a viable mechanism for forming massive stars – and we show that this might even be the major channel for forming massive stars in the Galaxy