On Dynamic Optimality for Binary Search Trees

Does there exist O(1)-competitive (self-adjusting) binary search tree (BST) algorithms? This is a well-studied problem. A simple offline BST algorithm GreedyFuture was proposed independently by Lucas and Munro, and they conjectured it to be O(1)-competitive. Recently, Demaine et al. gave a geometric view of the BST problem. This view allowed them to give an online algorithm GreedyArb with the same cost as GreedyFuture. However, no o(n)-competitive ratio was known for GreedyArb. In this paper we make progress towards proving O(1)-competitive ratio for GreedyArb by showing that it is O(\log n)-competitive

Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study

This study addresses two important problem statements, namely, selection of training datasets for online Brain-Computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate electroencephalograms (EEGs) and Near Infra Red Spectroscopy (NIRS) for possible applications such as the BCI and for measuring cognitive levels. Two experiments are presented, the first being a mathematical task interleaved with rest states using NIRS only. In the next, integration of the EEG-NIRS with reference to P300-based BCI systems as well as the experimental conditions designed to elicit the concentration levels (denoted as ON and OFF states here) during the paradigm, are presented. The first experiment indicates that NIRS can be used to differentiate a concentrated (i.e., mental activity) level from the rest. However, the second experiment reveals statistically significant results using the EEG only. We present details about the equipment used, the participants as well as the signal processing and machine learning techniques implemented to analyse the EEG and NIRS data. After discussing the results, we conclude by describing the research scope as well as the possible pitfalls in this work from a NIRS viewpoint, which presents an opportunity for future research exploration for BCI and cognitive performance measures

Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.Comment: 10 Pages, 4 figure

Comptonization by Reconnection Plasmoids in Black Hole Coronae III: Dependence on the Guide Field in Pair Plasma

We perform two-dimensional particle-in-cell simulations of magnetic reconnection for various strengths of the guide field (perpendicular to the reversing field), in magnetically-dominated electron-positron plasmas. Magnetic reconnection under such conditions could operate in accretion disk coronae around black holes. There, it has been suggested that the trans-relativistic bulk motions of reconnection plasmoids containing inverse-Compton-cooled electrons could Compton-upscatter soft photons to produce the observed non-thermal hard X-rays. Our simulations are performed for magnetizations $3 \leq \sigma \leq 40$ (defined as the ratio of enthalpy density of the reversing field to plasma enthalpy density) and guide field strengths $0 \leq B_{\rm g}/B_0 \leq 1$ (normalized to the reversing field strength $B_0$). We find that the mean bulk energy of the reconnected plasma depends only weakly on the flow magnetization but strongly on the guide field strength -- with $B_{\rm g}/B_0 = 1$ yielding a mean bulk energy twice smaller than $B_{\rm g}/B_0 = 0$. Similarly, the dispersion of bulk motions around the mean -- a signature of stochasticity in the plasmoid chain's motions -- is weakly dependent on magnetization (for $\sigma \gtrsim 10$) but strongly dependent on the guide field strength -- dropping by more than a factor of two from $B_{\rm g}/B_0 = 0$ to $B_{\rm g}/B_0 = 1$. In short, reconnection in strong guide fields ($B_{\rm g}/B_0 \sim 1$) leads to slower and more ordered plasmoid bulk motions than its weak guide field ($B_{\rm g}/B_0 \sim 0$) counterpart

Unique Brain Network Identification Number for Parkinson's Individuals Using Structural MRI

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding brain networks of individual subject. To realize this objective, we employed T1-weighted structural MRI of 180 Parkinson's disease (PD) patients from National Institute of Mental Health and Neurosciences, India. We parcellated each subject's brain volume and constructed individual adjacency matrix using correlation between grey matter (GM) volume of every pair of regions. The unique code is derived from values representing connections of every node (i), weighted by a factor of 2^-(i-1). The numerical representation UBNIN was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. This model may be implemented as neural signature of a person's unique brain connectivity, thereby useful for brainprinting applications. Additionally, we segregated the above dataset into five age-cohorts: A:22-32years, B:33-42years, C:43-52years, D:53-62years and E:63-72years to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox-based MATLAB functions. For each age-cohort, a decreasing trend was observed in mean clustering coefficient with increasing sparsity. Significantly different clustering coefficient was noted between age-cohort B and C (sparsity: 0.63,0.66), C and E (sparsity: 0.66,0.69). Our findings suggest network connectivity patterns change with age, indicating network disruption due to the underlying neuropathology. Varying clustering coefficient for different cohorts indicate that information transfer between neighboring nodes change with age. This provides evidence on age-related brain shrinkage and network degeneration.Comment: 9 pages, 5 figures,1 algorithm, 1 main table, 1 appendix tabl

Progress in fluorescence biosensing and food safety towards point-of-detection (PoD) system

The detection of pathogens in food substances is of crucial concern for public health and for the safety of the natural environment. Nanomaterials, with their high sensitivity and selectivity have an edge over conventional organic dyes in fluorescent-based detection methods. Advances in microfluidic technology in biosensors have taken place to meet the user criteria of sensitive, inexpensive, user-friendly, and quick detection. In this review, we have summarized the use of fluorescence-based nanomaterials and the latest research approaches towards integrated biosensors, including microsystems containing fluorescence-based detection, various model systems with nano materials, DNA probes, and antibodies. Paper-based lateral-flow test strips and microchips as well as the most-used trapping components are also reviewed, and the possibility of their performance in portable devices evaluated. We also present a current market-available portable system which was developed for food screening and highlight the future direction for the development of fluorescence-based systems for on-site detection and stratification of common foodborne pathogens

SILICO MODELING AND DOCKING OF Cch1 PROTEIN OF CANDIDA GLABRATA WITH FDA-APPROVED DRUGS: A DRUG REPURPOSING APPROACH

Objective: Candida-associated mortality rate is increased worldwide in past few years due to increased resistance to available antifungal agents,where Candida glabrata has emerged as one of the most upcoming pathogens. To combat the Candida infection, new drug molecule is required. Hence,we have studied the antifungal potential of some FDA-approved drug by in silico tools against Cch1, membrane Ca+2 channel protein of C. glabrata.Methods: The 3D structure of Cch1 was predicted by Swiss modeling tool. Secondary structure was predicted by Sopma software. The docking ofFDA-approved drugs with C. glabrata Cch1 was done by iGemdock and Hex software separately.Results: We have tested total nine drugs against Cch1. Amlodipin besylate exhibited best binding energy (–372.16 kcal/mol and –185 kcal/mol foriGemdock and Hex, respectively) followed by Artesunate (–266.97 kcal/mol and –164.6 kcal/mol), Etazolate –244.35 kcal/mol and –163.9 kcal/mol).Conclusion: Amlodipin besylate has the best antifungal properties and could be used as drug after further in vitro and in vivo studies. It can be directlycome in practice since its toxicological testing has already been done.Keywords: Candida glabrata, CCH1, Calcium channel, Docking, Drug repurposing

Enhanced target detection using P300 and gamma band analysis

We present a novel method which uses the P300amplitudes and Gamma band energy for enhanced targetdetection in P300 paradigms which are being researchedtowards a brain biometric system. The novelty of thiswork lies in the usage of both P300 amplitudes alongwith gamma band features which were calculated fromaveraged waveforms using thirty trials (Target and Non-target) for eight channels. P300 amplitudes wascalculated in the 300-600ms range and Gamma bandenergy was computed in the 30-50 Hz using Shannonenergy for target and non-target. A simple decisionmaker was used to classify the obtained feature vectors.Initial results confirm the possibility of using gamma band energy along-with P300 analysis in an oddball paradigm for better classification accuracies. This studyoffers motivation warranting further need to explore thelinks between P300 and Gamma band in P300 paradigms

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