On designing light-weight object trackers through network pruning: Use CNNs or transformers?

Object trackers deployed on low-power devices need to be light-weight, however, most of the current state-of-the-art (SOTA) methods rely on using compute-heavy backbones built using CNNs or transformers. Large sizes of such models do not allow their deployment in low-power conditions and designing compressed variants of large tracking models is of great importance. This paper demonstrates how highly compressed light-weight object trackers can be designed using neural architectural pruning of large CNN and transformer based trackers. Further, a comparative study on architectural choices best suited to design light-weight trackers is provided. A comparison between SOTA trackers using CNNs, transformers as well as the combination of the two is presented to study their stability at various compression ratios. Finally results for extreme pruning scenarios going as low as 1% in some cases are shown to study the limits of network pruning in object tracking. This work provides deeper insights into designing highly efficient trackers from existing SOTA methods.Comment: Submitted at IEEE ICASSP 202

Process intensification for the small footprint compact heat transfer device

Process intensification for the development of compact heat exchanger with small footprint is greatest challenge of the heat exchanger technology today. In the present study, a heat transfer device, coiled flow inverter (CFI) is revamped for the better heat transfer efficiency with a smaller footprint. The proposed small foot-print coiled flow inverter (SFCFI) is fabricated by bending of helical coil at 90° with equal arm lengths before and after the bend with variable curvature radius. In integration to the improved centrifugal force due to variable curvature, the SFCFI additionally offers a complete 90 flow inversion caused by each 90 bend, which results in higher radial mixing and heat transfer. The velocity and temperature flow fields depict the improved radial mixing under the laminar flow regime for the Dean number ranges from 8 to 1581. The performance of existing CFI of same heat transfer area (0.17 m2) was studied and compared with the novel SFCFI device. The results suggest, the proposed SFCFI device provides three-fold heat transfer enhancement as compared to the straight tube of same heat transfer area at Dean number 400. Additionally, heat transfer coefficient in SFCFI enhanced by 48 % as compared to helical coil. Furthermore, SFCFI provides 18 % higher value of Nusselt number as compared to the CFI. The reason for improved heat transfer may be the enhanced centrifugal force due to additional curvature effect provided in each arm of SFCFI in the plane of vortex formation. It was interesting to note that the proposed device provides 11 % lower pressure drop as compared to the CFI. The present study may aids to the development of a novel design of compact coiled and small footprint heat transfer device.Papers presented at the 13th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Portoroz, Slovenia on 17-19 July 2017 .International centre for heat and mass transfer.American society of thermal and fluids engineers

The International Natural Product Sciences Taskforce (INPST) and the power of Twitter networking exemplified through #INPST hashtag analysis

Background: The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled "International Natural Product Sciences Taskforce" (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools. Methods: In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week "2021 INPST Twitter Networking Event" (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST. Results and Conclusion: The use of this hashtag during the networking event period was analyzed with Symplur Signals (https://www.symplur.com/), revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

An engagement-sensitive interactive neuromuscular electrical therapy system for post-stroke balance rehabilitation - concept study

by Deepesh Kumar et. a

Comparison of nebulized dexmedetomidine and ketamine for premedication in pediatric patients undergoing hernia repair surgery: a randomized comparative trial

Background Allaying anxiety and providing calm children in the operating room is a challenging task for anesthesiologists. This study was designed to compare the use of nebulized dexmedetomidine and ketamine for premedication in pediatric patients under general anesthesia. Methods Seventy patients, aged 2 to 8 years of both sexes, with American Society of Anesthesiologists physical status I/II scheduled for hernia repair surgery under general anesthesia, were randomized to two equal groups using a computer-generated random number table. Patients in group D received dexmedetomidine (2 µg/kg), and patients in group K received ketamine (2 mg/kg) by a jet nebulizer before the induction of anesthesia. The study's primary objective was comparing the level of sedation, which was achieved at 30 min after a study drug administration using the Ramsay sedation scale, between the two groups. The secondary objectives were the two-group comparison of parental separation anxiety scale, acceptance of the mask, hemodynamic variables, recovery time, incidence of emergence agitation, and adverse events. Results The median Ramsay sedation scale at 30 min was 3 (1–4) in group D and 3 (1–3) in group K (P = 0.002). Patients in group D had a more acceptable parental separation anxiety scale (P = 0.001) and a satisfactory mask acceptance scale (P = 0.042). Conclusions Nebulized dexmedetomidine (2 µg/kg) provided better sedation along with smooth parental separation and satisfactory mask acceptance during induction of anesthesia with a similar emergence agitation profile and adverse reactions compared to nebulized ketamine in pediatric patients

INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models

A salient characteristic of large pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing model capacity and pre-training dataset size. Consequently, we are witnessing the development of enormous models pushing the state-of-the-art. It is, however, imperative to realize that this inevitably leads to prohibitively long training times, extortionate computing costs, and a detrimental environmental impact. Significant efforts are underway to make PTLM training more efficient through innovations in model architectures, training pipelines, and loss function design, with scant attention being paid to optimizing the utility of training data. The key question that we ask is whether it is possible to train PTLMs by employing only highly informative subsets of the training data while maintaining downstream performance? Building upon the recent progress in informative data subset selection, we show how we can employ submodular optimization to select highly representative subsets of the training corpora. Our results demonstrate that the proposed framework can be applied to efficiently train multiple PTLMs (BERT, BioBERT, GPT-2) using only a fraction of data while retaining up to $\sim99\%$ of the performance of the fully-trained models

Factors associated with unexplained sudden deaths among adults aged 18-45 years in India – A multicentric matched case–control study

Background & objectives: In view of anecdotal reports of sudden unexplained deaths in India's apparently healthy young adults, linking to coronavirus disease 2019 (COVID-19) infection or vaccination, we determined the factors associated with such deaths in individuals aged 18-45 years through a multicentric matched case–control study. Methods: This study was conducted through participation of 47 tertiary care hospitals across India. Cases were apparently healthy individuals aged 18-45 years without any known co-morbidity, who suddenly (<24 h of hospitalization or seen apparently healthy 24 h before death) died of unexplained causes during 1st October 2021-31st March 2023. Four controls were included per case matched for age, gender and neighborhood. We interviewed/perused records to collect data on COVID-19 vaccination/infection and post-COVID-19 conditions, family history of sudden death, smoking, recreational drug use, alcohol frequency and binge drinking and vigorous-intensity physical activity two days before death/interviews. We developed regression models considering COVID-19 vaccination ≤42 days before outcome, any vaccine received anytime and vaccine doses to compute an adjusted matched odds ratio (aOR) with 95 per cent confidence interval (CI). Results: Seven hundred twenty nine cases and 2916 controls were included in the analysis. Receipt of at least one dose of COVID-19 vaccine lowered the odds [aOR (95% CI)] for unexplained sudden death [0.58 (0.37, 0.92)], whereas past COVID-19 hospitalization [3.8 (1.36, 10.61)], family history of sudden death [2.53 (1.52, 4.21)], binge drinking 48 h before death/interview [5.29 (2.57, 10.89)], use of recreational drug/substance [2.92 (1.1, 7.71)] and performing vigorous-intensity physical activity 48 h before death/interview [3.7 (1.36, 10.05)] were positively associated. Two doses lowered the odds of unexplained sudden death [0.51 (0.28, 0.91)], whereas single dose did not. Interpretation & conclusions: COVID-19 vaccination did not increase the risk of unexplained sudden death among young adults in India. Past COVID-19 hospitalization, family history of sudden death and certain lifestyle behaviors increased the likelihood of unexplained sudden death