Long Covid Syndrome and its impact on Psychological and Social Health, an Indian Perspective

Background Long Covid Syndrome, also referred to as Post Covid conditions, Long haul covid etc  encompasses a wide range of new returning or ongoing symptoms afflicting patients in the aftermath of a Covid 19 infection. While numerous studies have been published on its clinical and epidemiological characteristics, very few research has gone on the psychological and social bearings of this entity on the survivors’ day-to-day lives Aims To garner knowledge on the neuropsychiatric manifestations of this condition and their impact on the patients’ socio-economic productivity.To determine appropriate management strategies to enable the afflicted to cope and function adequately Methods A systematic search for scholarly articles was made on the websites Google scholar and Pubmed. Results Detailed knowledge has been obtained on the nature and prevalence of neuropsychiatric manifestations, and the resultant disability burden. The study has exposed the acute dearth of existing research on the ever-neglected arena of mental health in Indian society. Conclusion The study has revealed the far-reaching debilitating consequences of neuropsychiatric manifestations on the survivors’ lives, and helped to identify suitable management approaches. It has also exposed the dire necessity of more research on this arena

XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars

Compute In-Memory platforms such as memristive crossbars are gaining focus as they facilitate acceleration of Deep Neural Networks (DNNs) with high area and compute-efficiencies. However, the intrinsic non-idealities associated with the analog nature of computing in crossbars limits the performance of the deployed DNNs. Furthermore, DNNs are shown to be vulnerable to adversarial attacks leading to severe security threats in their large-scale deployment. Thus, finding adversarially robust DNN architectures for non-ideal crossbars is critical to the safe and secure deployment of DNNs on the edge. This work proposes a two-phase algorithm-hardware co-optimization approach called XploreNAS that searches for hardware-efficient & adversarially robust neural architectures for non-ideal crossbar platforms. We use the one-shot Neural Architecture Search (NAS) approach to train a large Supernet with crossbar-awareness and sample adversarially robust Subnets therefrom, maintaining competitive hardware-efficiency. Our experiments on crossbars with benchmark datasets (SVHN, CIFAR10 & CIFAR100) show upto ~8-16% improvement in the adversarial robustness of the searched Subnets against a baseline ResNet-18 model subjected to crossbar-aware adversarial training. We benchmark our robust Subnets for Energy-Delay-Area-Products (EDAPs) using the Neurosim tool and find that with additional hardware-efficiency driven optimizations, the Subnets attain ~1.5-1.6x lower EDAPs than ResNet-18 baseline.Comment: 16 pages, 8 figures, 2 table

HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms

Today, there are a plethora of In-Memory Computing (IMC) devices- SRAMs, PCMs & FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC device offers its own pros & cons during inference of Deep Neural Networks (DNNs) on crossbars in terms of area overhead, programming energy and non-idealities. A design-space exploration is, therefore, imperative to derive a hybrid-device architecture optimized for accurate DNN inference under the impact of non-idealities from multiple devices, while maintaining competitive area & energy-efficiencies. We propose a two-phase search framework (HyDe) that exploits the best of all worlds offered by multiple devices to determine an optimal hybrid-device architecture for a given DNN topology. Our hybrid models achieve upto 2.30-2.74x higher TOPS/mm^2 at 22-26% higher energy-efficiencies than baseline homogeneous models for a VGG16 DNN topology. We further propose a feasible implementation of the HyDe-derived hybrid-device architectures in the 2.5D design space using chiplets to reduce design effort and cost in the hardware fabrication involving multiple technology processes.Comment: Accepted to IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS

COMPARATIVE STUDY OF ANTICONVULSANT EFFECT OF THE LEAVES OF SAPINDUS EMARGINATUS AND ACORUS CALAMUS IN EXPERIMENTALLY INDUCED ANIMAL MODELS OF EPILEPSY

Objective: To compare anticonvulsant activity of methanol extracts of Sapindus emarginatus (MESE) and Acorus calamus (MEAC) in experimental seizure models in mice. Methods: Hind limb tonic extension (HLTE) in Maximal electroshock (MES) seizure and clonic seizure in Pentylenetetrazol (PTZ) seizure models were assessed. Group I (control) mice received 1% gum acacia in distilled water (1 ml/100 g). Topiramate (50 mg/kg) was administered in group II (standard) animals. Group III and IV mice were treated with 200 and 400 mg/kg of MESE, respectively. Mice in group V and VI were given MEAC at the dose of 200 and 400 mg/kg, respectively. Drugs were given orally suspended in 1% gum acacia suspension (1 ml/100 g) for 7 d. Next day after 1 h of drug administration, the seizure was induced for evaluation. Results: Anticonvulsant property of both extracts was confirmed by reduction (p<0.001) in HLTE phase in MES model; delayed onset of the clonic seizure (p<0.001) and its shortened phase (p<0.001) in PTZ model when compared with the control. MESE-200 mg/kg produced significantly longer (p<0.001) HLTE phase with lower protection (40.34%) among the different doses of the extracts. Clonic seizure onsets and durations in PTZ model were comparable among the different extract-treated groups; however, mortality was higher (66.6%) with MESE-200 mg/kg. Conclusion: Anticonvulsant activity of MESE and MEAC was evident; however, MESE at the dose of 200 mg/kg was less effective

COMPARATIVE STUDY OF ANTIDEPRESSANT-LIKE EFFECT OF THE LEAVES OF SAPINDUS EMARGINATUS AND ACORUS CALAMUS IN EXPERIMENTAL ANIMAL MODELS

Objective: Depression is an affective disorder characterized by a change in mood, lack of confidence, lack of interest in surroundings and many natural products that have been tried to treat the disease. The study was aimed to evaluate and compare the antidepressant activity of methanol leave extract of SapindusemarginatusVahl. (MESE) and Acoruscalamus Linn. (MEAC) in experimental models in albino mice. Methods: Methanol Extracts of the plants were prepared by soxhlet extraction method. Forced swimming test (FST) and Tail suspension test (TST) models were chosen to evaluate antidepressant activity.Albino mice were selected and divided into six groups of six animals for each experimental model. Group I received 1% gum acacia in distilled water (DW) at a dose of 1 ml/100 g orally. Group II received sertraline-10 mg/kg orally. Group III and IV were administered 200 and 400 mg/kg of MESE respectively. Group V and VI were treated with 200 and 400 mg/kg of MEAC, respectively. Results: Methanol extracts of Sapindusemarginatus and Acoruscalamus at the two different doses of 200 and 400 mg/kg demonstrated a significant decrease in immobility time when compared with the control in both animal models. The extracts at the higher dose of 400 mg/kg revealed a significant reduction in immobility time compared to 200 mg/kg of the same extract. Conclusion: The results suggest that the methanol extracts of SapindusemarginatusVahl. andAcoruscalamus Linn. possessthe anticonvulsant activityand justify their use in folk medicine

XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing

The hardware-efficiency and accuracy of Deep Neural Networks (DNNs) implemented on In-memory Computing (IMC) architectures primarily depend on the DNN architecture and the peripheral circuit parameters. It is therefore essential to holistically co-search the network and peripheral parameters to achieve optimal performance. To this end, we propose XPert, which co-searches network architecture in tandem with peripheral parameters such as the type and precision of analog-to-digital converters, crossbar column sharing and the layer-specific input precision using an optimization-based design space exploration. Compared to VGG16 baselines, XPert achieves 10.24x (4.7x) lower EDAP, 1.72x (1.62x) higher TOPS/W,1.93x (3x) higher TOPS/mm2 at 92.46% (56.7%) accuracy for CIFAR10 (TinyImagenet) datasets. The code for this paper is available at https://github.com/Intelligent-Computing-Lab-Yale/XPert.Comment: Accepted to Design and Automation Conference (DAC

Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing

In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & compute-efficiencies. However, the intrinsic non-idealities in crossbars, which are often non-deterministic and non-linear, degrade the performance of the deployed DNNs. In addition to quantization errors, most frequently encountered non-idealities during inference include crossbar circuit-level parasitic resistances and device-level non-idealities such as stochastic read noise and temporal drift. In this work, our goal is to closely examine the distortions caused by these non-idealities on the dot-product operations in analog crossbars and explore the feasibility of a nearly training-less solution via crossbar-aware fine-tuning of batchnorm parameters in real-time to mitigate the impact of the non-idealities. This enables reduction in hardware costs in terms of memory and training energy for IMC noise-aware retraining of the DNN weights on crossbars.Comment: Accepted in Great Lakes Symposium on VLSI 2023 (GLSVLSI 2023) conferenc

Barriers to access to holistic care for women victims of sexual violence in the eastern region of the Democratic Republic of Congo: A brief report

Synopsis: Sexual violence against women is a human rights violation and a major and serious problem for women's health