DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization

With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rises substantially, leading to lost work and resource wastage. Such failures are typically offset by a checkpointing mechanism, which comes at the cost of storage and network bandwidth overhead. State-of-the-art approaches involve lossy model compression mechanisms, which induce a tradeoff between the resulting model quality (accuracy) and compression ratio. Delta compression is then used to further reduce the overhead by only storing the difference between consecutive checkpoints. We make a key enabling observation that the sensitivity of model weights to compression varies during training, and different weights benefit from different quantization levels (ranging from retaining full precision to pruning). We propose (1) a non-uniform quantization scheme that leverages this variation, (2) an efficient search mechanism that dynamically finds the best quantization configurations, and (3) a quantization-aware delta compression mechanism that rearranges weights to minimize checkpoint differences, thereby maximizing compression. We instantiate these contributions in DynaQuant - a framework for DL workload checkpoint compression. Our experiments show that DynaQuant consistently achieves a better tradeoff between accuracy and compression ratios compared to prior works, enabling a compression ratio up to 39x and withstanding up to 10 restores with negligible accuracy impact for fault-tolerant training. DynaQuant achieves at least an order of magnitude reduction in checkpoint storage overhead for training failure recovery as well as transfer learning use cases without any loss of accuracy

Subcutaneous Fat Obesity in a High Body Mass Index Donor Is Not a Contraindication to Living Donor Hepatectomy

Background. Living donor liver transplantation (LDLT) has revolutionized the field of transplantation without compromising donor safety. Donor safety is of paramount concern to the transplant team. BMI >35 kg/m2 is mostly considered a contraindication to liver donation. Here, we present a successful right donor hepatectomy from a donor with a BMI of 36.5 kg/m2. Case Summary. A 39-year-old wife donated her right lobe of liver to her 43-year-old husband with nonalcoholic steatohepatitis-related chronic liver disease (CLD). His indications were refractory ascites, hepatic encephalopathy, acute kidney injury, recurrent elbow and urine infections leading to cachexia. She was initially rejected due to a high BMI but failed to lose weight over the next 2 months, and the need for a transplant in her husband was imminent. With no other potential living donors, we decided to proceed with donor evaluation as she had no other comorbidity. We were surprised to find normal liver function tests and a good liver attenuation index (LAI) of +16 on a computed tomography (CT) scan. Magnetic resonance (MR) imaging revealed a fat fraction of 3%. Volumetry confirmed a remnant of 37.9% and a potential graft-to-recipient weight ratio of 1.23. V/S ratio on CT scan (visceral fat area/subcutaneous fat area at L4-level) was 35 kg/m2. A small percentage of healthy individuals will not have visceral fat obesity and may not have steatotic livers. The CT scan and MR fat fraction estimation can confirm the findings. Biopsy may be avoided if MR fat estimation is 35 kg/m2) with pure subcutaneous fat obesity in the absence of other suitable living donors

Pattern of reporting and practices for the management of traumatic brain injury: An overview of published literature from India

Background: Published literature regarding the demographics and mechanism of injury for traumatic brain injury (TBI) in India has not been analyzed in an organized sample. Objectives: The objective of this systematic review was to organize the published literature from India related to TBI and analyze it in a very specific sample to identify the specific patterns of injury and associated mortality. Materials and Methods: A search strategy with specific inclusion criteria was performed in PubMed, Cochrane, Web of Science, and the World Health Organisation (WHO) Global Health Library. The process included an additional search within the indexed literature and the website-based population survey reports. Results: Our review identified 72 studies from 300 potentially relevant articles based on the broad criteria that defined the demographics of the patients suffering from TBI and the details of trauma sustained, including the mechanism of injury as well as its diagnosis, management, and outcome. Changes in demographic patterns, the patterns of the body regions involved, the associated injuries, the clinical presentation, the follow-up status of patients suffering from TBI, who may or may not have shown clinical improvement, the overall outcome, as well as the mortality and disability status reported in the literature were analyzed. A high incidence of TBI in the productive population is of serious concern. Extremes of ages are more vulnerable to severe injury and a poor outcome. Conclusion: Quantitative analysis of injuries and outcomes of TBI victims shows a bigger health impact in the economically active population and in patients in the extremes of age groups

A decade of plant proteomics and mass spectrometry: translation of technical advancements to food security and safety issues

Tremendous progress in plant proteomics driven by mass spectrometry (MS) techniques has been made since 2000 when few proteomics reports were published and plant proteomics was in its infancy. These achievements include the refinement of existing techniques and the search for new techniques to address food security, safety, and health issues. It is projected that in 2050, the world’s population will reach 9–12 billion people demanding a food production increase of 34–70% (FAO, 2009) from today’s food production. Provision of food in a sustainable and environmentally committed manner for such a demand without threatening natural resources, requires that agricultural production increases significantly and that postharvest handling and food manufacturing systems become more efficient requiring lower energy expenditure, a decrease in postharvest losses, less waste generation and food with longer shelf life. There is also a need to look for alternative protein sources to animal based (i.e., plant based) to be able to fulfill the increase in protein demands by 2050. Thus, plant biology has a critical role to play as a science capable of addressing such challenges. In this review, we discuss proteomics especially MS, as a platform, being utilized in plant biology research for the past 10 years having the potential to expedite the process of understanding plant biology for human benefits. The increasing application of proteomics technologies in food security, analysis, and safety is emphasized in this review. But, we are aware that no unique approach/technology is capable to address the global food issues. Proteomics-generated information/resources must be integrated and correlated with other omics-based approaches, information, and conventional programs to ensure sufficient food and resources for human development now and in the future