I-SplitEE: Image classification in Split Computing DNNs with Early Exits

The recent advances in Deep Neural Networks (DNNs) stem from their exceptional performance across various domains. However, their inherent large size hinders deploying these networks on resource-constrained devices like edge, mobile, and IoT platforms. Strategies have emerged, from partial cloud computation offloading (split computing) to integrating early exits within DNN layers. Our work presents an innovative unified approach merging early exits and split computing. We determine the 'splitting layer', the optimal depth in the DNN for edge device computations, and whether to infer on edge device or be offloaded to the cloud for inference considering accuracy, computational efficiency, and communication costs. Also, Image classification faces diverse environmental distortions, influenced by factors like time of day, lighting, and weather. To adapt to these distortions, we introduce I-SplitEE, an online unsupervised algorithm ideal for scenarios lacking ground truths and with sequential data. Experimental validation using Caltech-256 and Cifar-10 datasets subjected to varied distortions showcases I-SplitEE's ability to reduce costs by a minimum of 55% with marginal performance degradation of at most 5%.Comment: To appear in proceedings of IEEE International Conference on Communications 202

SplitEE: Early Exit in Deep Neural Networks with Split Computing

Deep Neural Networks (DNNs) have drawn attention because of their outstanding performance on various tasks. However, deploying full-fledged DNNs in resource-constrained devices (edge, mobile, IoT) is difficult due to their large size. To overcome the issue, various approaches are considered, like offloading part of the computation to the cloud for final inference (split computing) or performing the inference at an intermediary layer without passing through all layers (early exits). In this work, we propose combining both approaches by using early exits in split computing. In our approach, we decide up to what depth of DNNs computation to perform on the device (splitting layer) and whether a sample can exit from this layer or need to be offloaded. The decisions are based on a weighted combination of accuracy, computational, and communication costs. We develop an algorithm named SplitEE to learn an optimal policy. Since pre-trained DNNs are often deployed in new domains where the ground truths may be unavailable and samples arrive in a streaming fashion, SplitEE works in an online and unsupervised setup. We extensively perform experiments on five different datasets. SplitEE achieves a significant cost reduction ($>50\%$) with a slight drop in accuracy ($<2\%$) as compared to the case when all samples are inferred at the final layer. The anonymized source code is available at \url{https://anonymous.4open.science/r/SplitEE_M-B989/README.md}.Comment: 10 pages, to appear in the proceeding AIMLSystems 202

Gemini Imidazolinium Surfactants: A Versatile Class of Molecules

Gemini imidazolinium surfactants fascinated the researchers and many industries towards it due to their distinct molecular structure. It belongs to the cationic surfactant group. The variation in the physicochemical properties of the gemini surfactant can be achieved by changing the characteristics in the structure. There are several applications of imidazolinium such as antistatic agents, fabric softener that makes it a demanding surfactant in detergent industries as well as in the laundry industries due to the immense number of properties like dispersibility, viscosity, desirable storage stability, emulsification, critical micelle concentration and fabric conditioning etc. This book chapter discussed about the Gemini imidazolinium surfactants and its various properties, synthesis methods and applications in various fields

Capacity for the management of kidney failure in the International Society of Nephrology South Asia region:Report from the 2023 ISN Global Kidney Health Atlas (ISN-GKHA)

The South Asia region is facing a high burden of chronic kidney disease (CKD) with limited health resources and low expenditure on health care. In addition to the burden of CKD and kidney failure from traditional risk factors, CKD of unknown etiologies from India and Sri Lanka compounds the challenges of optimal management of CKD in the region. From the third edition of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA), we present the status of CKD burden, infrastructure, funding, resources, and healthcare personnel, using the World Health Organization’s building blocks for health systems in the ISN South Asia region. The poor status of the public healthcare system and low healthcare expenditure resulted in high out-of-pocket expenditures for people with kidney disease which further compounded the situation. There is insufficient country capacity across the region to provide kidney replacement therapies to cover the burden. The infrastructure was also not uniformly distributed amongst the countries in the region. There were no chronic hemodialysis centers in Afghanistan, and peritoneal dialysis services were only available in Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Kidney transplantation was not available in Afghanistan, Bhutan, and the Maldives. Conservative kidney management was reported as available in 63% (n=5) of the countries, yet no country reported availability of the core CKM care components. There was a high hospitalization rate and early mortality due to inadequate kidney care. The lack of national registries and actual disease burden estimates reported in the region prevent policymakers' attention to CKD as an important cause of morbidity and mortality. Data from the 2023 ISN-GKHA, although with some limitations, may be used for advocacy and improving CKD care in the region

Availability and prioritisation of COVID-19 vaccines among patients with advanced chronic kidney disease and kidney failure during the height of the pandemic: a global survey by the International Society of Nephrology

OBJECTIVE Patients with advanced chronic kidney disease (CKD) or kidney failure receiving replacement therapy (KFRT) are highly vulnerable to COVID-19 infection, morbidity and mortality. Vaccination is effective, but access differs around the world. We aimed to ascertain the availability, readiness and prioritisation of COVID-19 vaccines for this group of patients globally. SETTING AND PARTICIPANTS Collaborators from the International Society of Nephrology (ISN), Dialysis Outcomes and Practice Patterns Study and ISN-Global Kidney Health Atlas developed an online survey that was administered electronically to key nephrology leaders in 174 countries between 2 July and 4 August 2021. RESULTS Survey responses were received from 99 of 174 countries from all 10 ISN regions, among which 88/174 (50%) were complete. At least one vaccine was available in 96/99 (97%) countries. In 71% of the countries surveyed, patients on dialysis were prioritised for vaccination, followed by patients living with a kidney transplant (KT) (62%) and stage 4/5 CKD (51%). Healthcare workers were the most common high priority group for vaccination. At least 50% of patients receiving in-centre haemodialysis, peritoneal dialysis or KT were estimated to have completed vaccination at the time of the survey in 55%, 64% and 51% of countries, respectively. At least 50% of patients in all three patient groups had been vaccinated in >70% of high-income countries and in 100% of respondent countries in Western Europe.The most common barriers to vaccination of patients were vaccine hesitancy (74%), vaccine shortages (61%) and mass vaccine distribution challenges (48%). These were reported more in low-income and lower middle-income countries compared with high-income countries. CONCLUSION Patients with advanced CKD or KFRT were prioritised in COVID-19 vaccination in most countries. Multiple barriers led to substantial variability in the successful achievement of COVID-19 vaccination across the world, with high-income countries achieving the most access and success

Genome-wide association study identifies loci and candidate genes for grain micronutrients and quality traits in wheat (Triticum aestivum L.)

Malnutrition due to micronutrients and protein deficiency is recognized among the major global health issues. Genetic biofortification of wheat is a cost-effective and sustainable strategy to mitigate the global micronutrient and protein malnutrition. Genomic regions governing grain zinc concentration (GZnC), grain iron concentration (GFeC), grain protein content (GPC), test weight (TW), and thousand kernel weight (TKW) were investigated in a set of 184 diverse bread wheat genotypes through genome-wide association study (GWAS). The GWAS panel was genotyped using Breeders' 35 K Axiom Array and phenotyped in three different environments during 2019–2020. A total of 55 marker-trait associations (MTAs) were identified representing all three sub-genomes of wheat. The highest number of MTAs were identified for GPC (23), followed by TKW (15), TW (11), GFeC (4), and GZnC (2). Further, a stable SNP was identified for TKW, and also pleiotropic regions were identified for GPC and TKW. In silico analysis revealed important putative candidate genes underlying the identified genomic regions such as F-box-like domain superfamily, Zinc finger CCCH-type proteins, Serine-threonine/tyrosine-protein kinase, Histone deacetylase domain superfamily, and SANT/Myb domain superfamily proteins, etc. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M&gt;70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0&lt;e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM