Communication: Linear-expansion shooting techniques for accelerating self-consistent field convergence

Based on the corrected Hohenberg-Kohn-Sham total energy density functional [Y. A. Zhang and Y. A. Wang, J. Chem. Phys. 130, 144116 (2009)]10.1063/1. 3104662, we have developed two linear-expansion shooting techniques (LIST)- direct LIST (LISTd) and indirect LIST (LISTi), to accelerate the convergence of self-consistent field (SCF) calculations. Case studies show that overall LISTi is the most robust and efficient algorithm for accelerating SCF convergence, whereas LISTd is advantageous in the early stage of an SCF process. More importantly, LISTi outperforms Pulays direct inversion in the iterative subspace (DIIS) [P. Pulay, J. Comput. Chem. 3, 556 (1982)]10.1002/jcc.540030413 and its two recent improvements, energy-DIIS [K. N. Kudin, G. E. Scuseria, and E. Cancs, J. Chem. Phys. © 2011 American Institute of Physics.published_or_final_versio

Filtering of shrew DDoS attacks in frequency domain

The shrew Distributed Denial of Service (DDoS) attacks are periodic, bursty, and stealthy in nature. They are also known as Reduction of Quality (RoQ) attacks. Such attacks could be even more detrimental than the widely known flooding DDoS attacks because they damage the victim servers for a long time without being noticed, thereby denying new visitors to the victim servers, which are mostly e-commerce sites. Thus, In order to minimize the huge monetary losses, there is a pressing need to effectively detect such attacks in real-time. Unfortunately, effective detection of shrew attacks remains an open problem. In this paper, we meet this challenge by proposing a new signal processing approach to identifying and detecting the attacks by examining the frequency-domain characteristics of incoming traffic flows to a server. A major strength of our proposed technique is that its detection time is less than a few seconds. Furthermore, the technique entails simple software or hardware implementations, making it easily deployable in a real-life network environment. © 2005 IEEE.published_or_final_versio

Finite element model with continuous transverse shear stress for composite laminates in cylindrical bending

In the double superposition hypothesis, the global inplane displacement, which applies to the whole laminate, is enriched by local displacements which are restricted to each individual ply. To avoid the number of d.o.f.s growing with the number of plies, the transverse shear stress continuity is enforced as usual whereas the inplane displacement continuity is "doubly" constrained for two different groups of the local displacement. Based on the hypothesis, a two-node beam element is attempted. The element has the deflection and its derivative as its nodal d.o.f.s. Despite the fact that interpolated deflection is a cubic function of the longitudinal coordinate, the element yields poor accuracy. The cause is sorted out to be an algebraic constraint in the transverse shear. To overcome the constraint, a heterosis node is added. Remarkable improvement of the element accuracy is noted. © 1998 Elsevier Science B.V. All rights reserved.postprin

An EEG-based brain-computer interface for dual task driving detection

The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components. © 2013

Collaborative internet worm containment

Large-scale worm outbrakes that leads to distributed denial-of-dervice attacks pose a major threat to internet infrastructure security. To prevent computers from such attacks deployment of fast, scalable security overlay networks based on distributed hash tables to facilitate high-speed intrusion detection and alert-information exchange are proposed. An effective system for worm detection and cyberspace defence must have robustness, cooperation among multiple sites, responsiveness to unexpected worms and efficiency and scalability. Deployment of collaborative WormShield monitors on just 1 percent of the vulnerable edge networks can detect worm signatures roughly 10 times faster than with independent monitors.published_or_final_versio

C-Reactive Protein Promotes Diabetic Kidney Disease in db/db Mice via the CD32b-Smad3-mTOR signaling Pathway

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Streptokinase is ineffective in restoring early myocardial reperfusion in Asian patients with acute myocardial infarction

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WIJAM: a mobile collaborative improvisation platform under Master-players Paradigm

Music jamming is an extremely difficult task for musical novices. Trying to extend this meaningful and highly enjoyable activity to a larger recipient group, we present WIJAM, a mobile application for an ad-hoc group of musical novices to perform improvisation along with a music master. In this master-players' paradigm, the master offers a music backing, orchestrates the musical flow, and gives feedbacks to the players; the players improvise by tapping and sketching on their smartphones. We argue that this paradigm can be a significant contribution to the possibility of music playing by a group of novices with no instrumental training leading to decent musical results.published_or_final_versio

Striatal dopamine D₂/₃ receptors in medication-naïve schizophrenia: an [¹²³I] IBZM SPECT study

BACKGROUND: The hyper-function of the striatal dopamine system has been suggested to underlie key pathophysiological mechanisms in schizophrenia. Moreover, patients have been observed to present a significant elevation of dopamine receptor availability compared to healthy controls. Although it is difficult to measure dopamine levels directly in humans, neurochemical imaging techniques such as single-photon emission computed tomography (SPECT) provide indirect indices of in vivo dopamine synthesis and release, and putative synaptic levels. METHODS: We focused on the role of dopamine postsynaptic regulation using [123I] iodobenzamide (IBZM) SPECT. We compared D2/3 receptor availability between 53 healthy controls and 21 medication-naive patients with recent-onset schizophrenia. RESULT: The mean specific striatal binding showed no significant difference between patients and controls (estimated difference = 0.001; 95% CI -0.11 to 0.11; F = 0.00, df = 1, 69; p = 0.99). There was a highly significant effect of age whereby IBZM binding declined with advancing age [estimated change per decade of age = -0.01(binding ratio); 95% CI -0.01 to -0.004; F = 11.5, df = 1, 69; p = 0.001]. No significant correlations were found between the mean specific striatal binding and psychopathological or cognitive rating scores. CONCLUSIONS: Medication-naïve patients with recent-onset schizophrenia have similar D2/3 receptor availability to healthy controls. We suggest that, rather than focusing exclusively on postsynaptic receptors, future treatments should target the presynaptic control of dopamine synthesis and release

An SSVEP Stimuli Design using Real-time Camera View with Object Recognition

© 2020 IEEE. Most SSVEP-based stimuli BCIs are pre-defined using the white blocks. This kind of scenario lead less flexibility in the real life. To represent the flickers with the location, types and configurations of the objects in real world, this paper proposes an SSVEP-based BCI using real-time camera view with object recognition algorithm to provide intuitive BCI for users. A deep learning-based object recognition algorithm is used to calculate the location of the objects on the online camera view from a depth camera. After the bounding box of the objects is estimated, the location of the SSVEP flickers are designed to overlap on the object locations. An overlapping FFT and SVM is used to recognize the EEG signals into corresponding classes. In experimental results, the classification rate for camera view scenario is more than 94.1%. The results show that proposed SSVEP stimuli design is available to create an intuitive and reliable human machine interaction. The proposed results can be used for the users who have motor disabilities to further used to interact with assistive devices, such as: robotic arm and wheelchairs