Crossover and coexistence of quasiparticle excitations in the fractional quantum Hall regime at nu <= 1/3

New low-lying excitations are observed by inelastic light scattering at filling factors nu=p/(phip+/-1) of the fractional quantum Hall regime with phi=4. Coexisting with these modes throughout the range nuless than or equal to1/3 are phi=2 excitations seen at 1/3. Both phi=2 and phi=4 excitations have distinct behaviors with temperature and filling factor. The abrupt first appearance of the new modes in the low-energy excitation spectrum at nuless than or similar to1/3 suggests a marked change in the quantum ground state on crossing the phi=2-->phi=4 boundary at nu=1/3

Particle production in the outflow of a midlatitude storm

The concentrations of atmospheric gases and condensation nuclei (CN) or aerosol in the outflow of a storm were measured aboard a NASA DC-8 aircraft, as described in a companion paper [Twohy et al., 2002]. The data are used here to study the production of the aerosol. Major fluctuations in CN concentration are observed, in correlation with gas-phase species, but these are shown to arise as the result of the mixing of two distinct air masses. It is deduced that the CN originated in a storm outflow air mass and that its concentration before mixing was approximately uniform over a flight distance of about 200 km. The formation of the aerosol by nucleation followed by growth and coagulation is analyzed assuming that it consists of water and sulphuric acid produced locally by the oxidation of SO2. The analysis uses analytic models, and it is concluded that a 5 min burst of nucleation was followed by growth and coagulation over a period of about 5 hours. Both the mass and number concentrations of the observed aerosol can be reproduced by this analysis within a timescale consistent with that of the storm. The final number concentration is very insensitive to the initial SO2 concentration

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Viral delivery of antioxidant genes as a therapeutic strategy in experimental models of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with no effective treatment to date. Despite its multi-factorial aetiology, oxidative stress is hypothesized to be one of the key pathogenic mechanisms. It is thus proposed that manipulation of the expression of antioxidant genes that are downregulated in the presence of mutant SOD1 may serve as a therapeutic strategy for motor neuronal protection. Lentiviral vectors expressing either PRDX3 or NRF2 genes were tested in the motor neuronal-like NSC34 cell line, and in the ALS tissue culture model, NSC34 cells expressing the human SOD1(G93A) mutation. The NSC34 SOD1(G93A) cells overexpressing either PRDX3 or NRF2 showed a significant decrease in endogenous oxidation stress levels by 40 and 50% respectively compared with controls, whereas cell survival was increased by 30% in both cases. The neuroprotective potential of those two genes was further investigated in vivo in the SOD1(G93A) ALS mouse model, by administering intramuscular injections of adenoassociated virus serotype 6 (AAV6) expressing either of the target genes at a presymptomatic stage. Despite the absence of a significant effect in survival, disease onset or progression, which can be explained by the inefficient viral delivery, the promising in vitro data suggest that a more widespread CNS delivery is needed

MEANINGFUL LOCAL SIGNALING IN SINOATRIAL NODE IDENTIFIED BY RANDOM MATRIX THEORY AND PCA

The sinoatrial node (SAN) is the pacemaker of the heart. Recently calcium signals, believed to be crucially important in rhythm generation, have been imaged in intact SAN and shown to be heterogeneous in various regions of the SAN with a lot of analysis relying on visual inspection rather than mathematical tools. Here we apply methods of random matrix theory (RMT) developed for financial data and various biological data sets including β -cell collectives and electroencephalograms (EEG) to analyse correlations in SAN calcium signals using eigenvalues and eigenvectors of the correlation matrix. We use principal component analysis to locate signalling modules corresponding to localization properties the eigenvectors corresponding to high eigenvalues. We find that the top eigenvector captures the global behaviour of the SAN i.e. action potential (AP) induced calcium transient. In some cases, the eigenvector corresponding to the second highest eigenvalue yields a pacemaker region whose calcium signals predict the AP. Furthermore, using new analytic methods, we study the relationship between covariance coefficients and distance, and find that even inside the central zone, there are non-trivial long range correlations, indicating intercellular interactions in most cases. Lastly, we perform an analysis of nearest-neighbour eigenvalue distances and find that it coincides with universal Wigner surmise under all available experimental conditions, while the number variance, which captures eigenvalue correlations, is sensitive to experimental conditions. Thus RMT application to SAN allows to remove noise and the global effects of the AP-induced calcium transient and thereby isolate the local and meaningful correlations in calcium signalling

Distinguishing the “Truly National” From the “Truly Local”: Customary Allocation, Commercial Activity, and Collective Action

This Essay makes two claims about different methods of defining the expanse and limits of the Commerce Clause. My first claim is that approaches that privilege traditional subjects of state regulation are unworkable and undesirable. These approaches are unworkable in light of the frequency with which the federal government and the states regulate the same subject matter in our world of largely overlapping federal and state legislative jurisdiction. The approaches are undesirable because the question of customary allocation is unrelated to the principal reason why Congress possesses the power to regulate interstate commerce: solving collective action problems involving multiple states. These problems are evident in the way that some federal judges invoked regulatory custom in litigation over the constitutionality of the minimum coverage provision in the Patient Protection and Affordable Care Act. The areas of health insurance and health care are not of exclusive state concern, and it is impossible to lose—or to win—a competition requiring skillful lawyers or judges to describe them as more state than federal, or more federal than state. Nor is it most important what the answer is. More promising are the approaches that view congressional authority as turning on either commercial activity or collective action problems facing the states. My second claim is that these two approaches have advantages and disadvantages, and that the choice between them exemplifies the more general tension between applying rules and applying their background justifications. I have previously defended a collective action approach to Article I, Section 8. My primary purpose in this Essay is to clarify the jurisprudential stakes in adopting one method or the other and to identify the problems that advocates of each approach must address

Sleep promotes offline enhancement of an explicitly learned discrete but not an explicitly learned continuous task

Catherine F Siengsukon, Alham Al-SharmanDepartment of Physical Therapy and Rehabilitation Science, University of Kansas Medical Center, Kansas City, KS, USABackground: Healthy young individuals benefit from sleep to promote offline enhancement of a variety of explicitly learned discrete motor tasks. It remains unknown if sleep will promote learning of other types of explicit tasks. The purpose of this study is to verify the role of sleep in learning an explicitly instructed discrete motor task and to determine if participants who practice an explicitly instructed continuous tracking task demonstrate sleep-dependent offline learning of this task.Methods: In experiment 1, 28 healthy young adults (mean age 25.6 &amp;plusmn; 3.8 years) practiced a serial reaction time (SRT) task at either 8 am (SRT no-sleep group) or 8 pm (SRT sleep group) and underwent retention testing 12 &amp;plusmn; 1 hours later. In experiment 2, 20 healthy young individuals (mean age 25.6 &amp;plusmn; 3.3 years) practiced a continuous tracking task and were similarly divided into a no-sleep (continuous tracking no-sleep group) or sleep group (continuous tracking sleep group). Individuals in both experiments were provided with explicit instruction on the presence of a sequence in their respective task prior to practice.Results: Individuals in the SRT sleep group demonstrated a significant offline reduction in reaction time whereas the SRT no-sleep group did not. Results for experiment 1 provide concurrent evidence that explicitly learned discrete tasks undergo sleep-dependent offline enhancement. Individuals in the continuous tracking sleep group failed to demonstrate a significant offline reduction in tracking error. However, the continuous tracking no-sleep group did demonstrate a significant offline improvement in performance. Results for experiment 2 indicate that sleep is not critical for offline enhancement of an explicit learned continuous task.Conclusion: The findings that individuals who practiced an explicitly instructed discrete task experienced sleep-dependent offline learning while those individuals who practiced an explicitly instructed continuous task did not may be due to the difference in motor control or level of complexity between discrete and continuous tasks.Keywords: sleep, motor learning, discrete task, continuous tas

A Rapid Dynamical Monte Carlo Algorithm for Glassy Systems

In this paper we present a dynamical Monte Carlo algorithm which is applicable to systems satisfying a clustering condition: during the dynamical evolution the system is mostly trapped in deep local minima (as happens in glasses, pinning problems etc.). We compare the algorithm to the usual Monte Carlo algorithm, using as an example the Bernasconi model. In this model, a straightforward implementation of the algorithm gives an improvement of several orders of magnitude in computational speed with respect to a recent, already very efficient, implementation of the algorithm of Bortz, Kalos and Lebowitz.Comment: RevTex 7 pages + 4 figures (uuencoded) appended; LPS preprin