HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method for the classical SVM in the second phase. Both SVM formulations are adapted to knowledge incorporation. Our proposed algorithm addresses the challenges of automatic feature selection, high optimization accuracy, and algorithmic flexibility for taking advantage of prior knowledge. We demonstrate the effectiveness and efficiency of our algorithm and compare it with existing methods on a collection of synthetic and real-world data.Comment: Proceedings of 8th Learning and Intelligent OptimizatioN (LION8) Conference, 201

Tensor Regression with Applications in Neuroimaging Data Analysis

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.Comment: 27 pages, 4 figure

The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference

Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference. Keywords: Granger causality, vector autoregressive modelling, time series analysi

NETWORK CONTROLLER DIRECTED BOOT-UP SEQUENCING TO PREVENT TRAFFIC LOSS ON ENTERPRISE MODULAR SWITCHES

The ability to monitor/change the boot order of line-cards on a modular chassis based on topology and user configuration presents a significant value-addition to the functionality of a network controller. Such functionality not only prevents problems like traffic loss and its undesirable consequences, but it also gives a user the ability to observe the order in which line-cards and their ports are booted. This metering may provide valuable insights to help improve the boot-up and link-up time of modular chassis solutions

A New Accountable Data Transfer Protocol In Malicious Environments

We show a nonspecific information genealogy structure LIME for information stream over numerous elements that take two trademark, essential parts (i.e., proprietor and customer). We characterize the correct security ensures required by such an information heredity instrument toward recognizable proof of a guilty entity, and distinguish the improving non-denial and genuineness presumptions. We at that point create and break down a novel responsible information exchange protocal between two elements inside a noxious situation by expanding upon unaware exchange, robust watermarking, and signature primitives

A wearable anti-gravity supplement to therapy does not improve arm function in chronic stroke: a randomized trial

Background: Gravity confounds arm movement ability in post-stroke hemiparesis. Reducing its influence allows effective practice leading to recovery. Yet, there is a scarcity of wearable devices suitable for personalized use across diverse therapeutic activities in the clinic. Objective: In this study, we investigated the safety, feasibility, and efficacy of anti-gravity therapy using the ExoNET device in post-stroke participants. Methods: Twenty chronic stroke survivors underwent six, 45-minute occupational therapy sessions while wearing the ExoNET, randomized into either the treatment (ExoNET tuned to gravity-support) or control group (ExoNET tuned to slack condition). Clinical outcomes were evaluated by a blinded-rater at baseline, post, and six-week follow-up sessions. Kinetic, kinematic, and patient experience outcomes were also assessed. Results: Mixed-effect models showed a significant improvement in Box and Blocks scores in the post-intervention session for the treatment group (effect size: 2.1, p = .04). No significant effects were found between the treatment and control groups for ARAT scores and other clinical metrics. Direct kinetic effects revealed a significant reduction in muscle activity during free exploration with an effect size of (-7.12%, p< 005). There were no significant longitudinal kinetic or kinematic trends. Subject feedback suggested a generally positive perception of the anti-gravity therapy. Conclusions: Anti-gravity therapy with the ExoNET is a safe and feasible treatment for post-stroke rehabilitation. The device provided anti-gravity forces, did not encumber range of motion, and clinical metrics of anti-gravity therapy demonstrated improvements in gross manual dexterity. Further research is required to explore potential benefits in broader clinical metrics

Analysis of maternal and foetal outcome of post-dated pregnancy in a tertiary care centre

Background: Managing pregnancy with post-dates is becoming a challenging issue due to increasing fetal morbidity and mortality. The study aimed to analyse the maternal and fetal outcomes of post-term pregnancies among Indian women, considering their earlier fetal maturation. Conducted over 18 months with 100 cases, the prospective observational study focused on pregnant mothers at or beyond 40 weeks gestational age, excluding those with certain medical complications. Methods: After obtaining approval from the ethics committee and informed consent from eligible participants, detailed histories and examinations were conducted, with close monitoring until delivery and postnatal care. Inclusive criteria encompassed singleton pregnancies with cephalic presentation, while exclusions included non-cephalic presentation, congenital anomalies, and various medical complications. Results: Revealed a predominance of primigravida women aged 20 to 35 years at 40 to 40 weeks and 6 days gestation. Spontaneous delivery occurred in 58%, with 90% delivering vaginally, while all multigravida births were vaginal post-induction. Cesarean sections were performed in 14%, primarily due to failed induction followed by fetal distress. Meconium-stained liquor was most prevalent at 42 weeks or later, correlating with higher perinatal mortality and NICU admissions in infants born beyond 42 weeks. Conclusions: Vigilant monitoring proved crucial in averting fetal jeopardy, emphasizing the importance of timely interventions to mitigate complications associated with post-term pregnancies. This study sheds light on the unique considerations and outcomes of post-dated pregnancies in the Indian population, contributing valuable insights for maternal and neonatal care in similar settings.

Increasing the Quality Answers and Decreasing the Time for SocialQ&A System

We deliberate and executed SocialQ&amp;A, an online interpersonal organization based Q&amp;A framework. Social Q&amp;A use the informal organization properties of normal intrigue and common trust companion relationship to distinguish an asker through fellowship who are well on the way to answer the inquiry, and improve the client security. Enhance SocialQ&amp;A with security and effectiveness upgrades by ensuring client protection and recognizes, and recovering answers naturally for repetitive questions