Quantum ratchets in dissipative chaotic systems

Using the method of quantum trajectories we study a quantum chaotic dissipative ratchet appearing for particles in a pulsed asymmetric potential in the presence of a dissipative environment. The system is characterized by directed transport emerging from a quantum strange attractor. This model exhibits, in the limit of small effective Planck constant, a transition from quantum to classical behavior, in agreement with the correspondence principle. We also discuss parameter values suitable for implementation of the quantum ratchet effect with cold atoms in optical lattices.Comment: Significant changes: Several text improvements and new results. Figure 2 modified. Figure 4 adde

Dynamical Localization: Hydrogen Atoms in Magnetic and Microwave fields

We show that dynamical localization for excited hydrogen atoms in magnetic and microwave fields takes place at quite low microwave frequency much lower than the Kepler frequency. The estimates of localization length are given for different parameter regimes, showing that the quantum delocalization border drops significantly as compared to the case of zero magnetic field. This opens up broad possibilities for laboratory investigations.Comment: revtex, 11 pages, 3 figures, to appear in Phys. Rev. A, Feb (1997

DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data

With recent advancements in machine learning, the research community has made tremendous advances towards the classification of neurological disorders from time-series functional MRI signals. However, existing classification techniques rely on hand-crafted features and classical machine learning models. In this paper, we propose an end-to-end model that utilizes the representation learning capability of deep learning to classify a neurological disorder from fMRI data. The proposed DeepFMRI model is comprised of three networks, namely (1) a feature extractor, (2) a similarity network, and (3) a classification network. The model takes fMRI raw time-series signals as input and outputs the predicted labels; and is trained end-to-end using back-propagation. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative model outperforms previous state-of-the-art

Neural Circuitry of Novelty Salience Processing in Psychosis Risk: Association With Clinical Outcome

Psychosis has been proposed to develop from dysfunction in a hippocampal-striatal-midbrain circuit, leading to aberrant salience processing. Here, we used functional magnetic resonance imaging (fMRI) during novelty salience processing to investigate this model in people at clinical high risk (CHR) for psychosis according to their subsequent clinical outcomes. Seventy-six CHR participants as defined using the Comprehensive Assessment of At-Risk Mental States (CAARMS) and 31 healthy controls (HC) were studied while performing a novelty salience fMRI task that engaged an a priori hippocampal-striatal-midbrain circuit of interest. The CHR sample was then followed clinically for a mean of 59.7 months (~5 y), when clinical outcomes were assessed in terms of transition (CHR-T) or non-transition (CHR-NT) to psychosis (CAARMS criteria): during this period, 13 individuals (17%) developed a psychotic disorder (CHR-T) and 63 did not. Functional activation and effective connectivity within a hippocampal-striatal-midbrain circuit were compared between groups. In CHR individuals compared to HC, hippocampal response to novel stimuli was significantly attenuated (P = .041 family-wise error corrected). Dynamic Causal Modelling revealed that stimulus novelty modulated effective connectivity from the hippocampus to the striatum, and from the midbrain to the hippocampus, significantly more in CHR participants than in HC. Conversely, stimulus novelty modulated connectivity from the midbrain to the striatum significantly less in CHR participants than in HC, and less in CHR participants who subsequently developed psychosis than in CHR individuals who did not become psychotic. Our findings are consistent with preclinical evidence implicating hippocampal-striatal-midbrain circuit dysfunction in altered salience processing and the onset of psychosis

FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI

Investigation of functional brain connectivity patterns using functional MRI has received significant interest in the neuroimaging domain. Brain functional connectivity alterations have widely been exploited for diagnosis and prediction of various brain disorders. Over the last several years, the research community has made tremendous advancements in constructing brain functional connectivity from timeseries functional MRI signals using computational methods. However, even modern machine learning techniques rely on conventional correlation and distance measures as a basic step towards the calculation of the functional connectivity. Such measures might not be able to capture the latent characteristics of raw time-series signals. To overcome this shortcoming, we propose a novel convolutional neural network based model, FCNet, that extracts functional connectivity directly from raw fMRI time-series signals. The FCNet consists of a convolutional neural network that extracts features from time-series signals and a fully connected network that computes the similarity between the extracted features in a Siamese architecture. The functional connectivity computed using FCNet is combined with phenotypic information and used to classify individuals as healthy controls or neurological disorder subjects. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative framework can improve classification accuracy, which indicates that the features learnt from FCNet have superior discriminative power

Delocalizing effect of the Hubbard repulsion for electrons on a two-dimensional disordered lattice

We study numerically the ground-state properties of the repulsive Hubbard model for spin-1/2 electrons on two-dimensional lattices with disordered on-site energies. The projector quantum Monte Carlo method is used to obtain very accurate values of the ground-state charge density distributions with $N_p$ and $N_p+1$ particles. The difference in these charge densities allows us to study the localization properties of an added particle. The results obtained at quarter-filling on finite clusters show that the Hubbard repulsion has a strong delocalizing effect on the electrons in disordered 2D lattices. However, numerical restrictions do not allow us to reach a definite conclusion about the existence of a metal-insulator transition in the thermodynamic limit in two-dimensions.Comment: revtex, 7 pages, 7 figure

Quantum Poincare Recurrences for Hydrogen Atom in a Microwave Field

We study the time dependence of the ionization probability of Rydberg atoms driven by a microwave field, both in classical and in quantum mechanics. The quantum survival probability follows the classical one up to the Heisenberg time and then decays algebraically as P(t) ~ 1/t. This decay law derives from the exponentially long times required to escape from some region of the phase space, due to tunneling and localization effects. We also provide parameter values which should allow to observe such decay in laboratory experiments.Comment: revtex, 4 pages, 4 figure

Run Scenarios for the Linear Collider

Scenarios are developed for runs at a Linear Collider, in the case that there is a rich program of new physics.Comment: 12 pages, 10 tables, Latex; Snowmass 2001 plenary repor

Hundred photon microwave ionization of Rydberg atoms in a static electric field

We present analytical and numerical results for the microwave excitation of nonhydrogenic atoms in a static electric field when up to 1000 photons are required to ionize an atom. For small microwave fields, dynamical localization in photon number leads to exponentially small ionization while above quantum delocalization border ionization goes in a diffusive way. For alkali atoms in a static field the ionization border is much lower than in hydrogen due to internal chaos.Comment: revtex, 4 pages, 5 figure

Thermodynamics of aggregation of two proteins

We investigate aggregation mechanism of two proteins in a thermodynamically unambiguous manner by considering the finite size effect of free energy landscape of HP lattice protein model. Multi-Self-Overlap-Ensemble Monte Carlo method is used for numerical calculations. We find that a dimer can be formed spontaneously as a thermodynamically stable state when the system is small enough. It implies the possibility that the aggregation of proteins in a cell is triggered when they are confined in a small region by, for example, being surrounded by other macromolecules.We also find that the dimer exhibits a transition between unstable state and metastable state in the infinite system.Comment: jpsj2.cls, 7 pages, 14 figures; misconfigurations of Fig.Nos. correcte