26 research outputs found
Making Scalable Meta Learning Practical
Despite its flexibility to learn diverse inductive biases in machine learning
programs, meta learning (i.e., learning to learn) has long been recognized to
suffer from poor scalability due to its tremendous compute/memory costs,
training instability, and a lack of efficient distributed training support. In
this work, we focus on making scalable meta learning practical by introducing
SAMA, which combines advances in both implicit differentiation algorithms and
systems. Specifically, SAMA is designed to flexibly support a broad range of
adaptive optimizers in the base level of meta learning programs, while reducing
computational burden by avoiding explicit computation of second-order gradient
information, and exploiting efficient distributed training techniques
implemented for first-order gradients. Evaluated on multiple large-scale meta
learning benchmarks, SAMA showcases up to 1.7/4.8x increase in throughput and
2.0/3.8x decrease in memory consumption respectively on single-/multi-GPU
setups compared to other baseline meta learning algorithms. Furthermore, we
show that SAMA-based data optimization leads to consistent improvements in text
classification accuracy with BERT and RoBERTa large language models, and
achieves state-of-the-art results in both small- and large-scale data pruning
on image classification tasks, demonstrating the practical applicability of
scalable meta learning across language and vision domains
Recurrent Syncope Triggered by Temporal Lobe Epilepsy: Ictal Bradycardia Syndrome
Ictal asystole is potentially lethal, and known to originate from the involvement of limbic autonomic regions. Appropriate treatment must include an antiepileptic drug and the implantation of a pacemaker. We report the case of a 54-year-old male with recurrent syncope secondary to ictal asystole triggered by temporal lobe epilepsy. This was confirmed by combined Holter and video-electroencephalogram monitoring
Traumatic Entrapment of the Vertebrobasilar Junction Due to a Longitudinal Clival Fracture: A Case Report
Vertebrobasilar junction entrapment due to a clivus fracture is a rare clinical observation. The present case report describes a 54-yr-old man who sustained a major craniofacial injury. The patient displayed a stuporous mental state (Glasgow Coma Scale [GCS]=8) and left hemiparesis (Grade 3). The initial computed tomography (CT) scan revealed a right subdural hemorrhage in the frontotemporal region, with a midline shift and longitudinal clival fracture. A decompressive craniectomy with removal of the hematoma was performed. Two days after surgery, a follow-up CT scan showed cerebellar and brain stem infarction, and a CT angiogram revealed occlusion of the left vertebral artery and entrapment of vertebrobasilar junction by the clival fracture. A decompressive suboccipital craniectomy was performed and the patient gradually recovered. This appears to be a rare case of traumatic vertebrobasilar junction entrapment due to a longitudinal clival fracture, including a cerebellar infarction caused by a left vertebral artery occlusion. A literature review is provided
Betty: An Automatic Differentiation Library for Multilevel Optimization
Multilevel optimization has been widely adopted as a mathematical foundation
for a myriad of machine learning problems, such as hyperparameter optimization,
meta-learning, and reinforcement learning, to name a few. Nonetheless,
implementing multilevel optimization programs oftentimes requires expertise in
both mathematics and programming, stunting research in this field. We take an
initial step towards closing this gap by introducing Betty, a high-level
software library for gradient-based multilevel optimization. To this end, we
develop an automatic differentiation procedure based on a novel interpretation
of multilevel optimization as a dataflow graph. We further abstract the main
components of multilevel optimization as Python classes, to enable easy,
modular, and maintainable programming. We empirically demonstrate that Betty
can be used as a high-level programming interface for an array of multilevel
optimization programs, while also observing up to 11\% increase in test
accuracy, 14\% decrease in GPU memory usage, and 20\% decrease in wall time
over existing implementations on multiple benchmarks. The code is available at
http://github.com/leopard-ai/betty
Effect of Histone Acetylation Modification with Sodium Butyrate, a Histone Deacetylase Inhibitor, on Cell Cycle, Apoptosis, Ploidy and Gene Expression in Porcine Fetal Fibroblasts
Glutamate의 락트산유도 근육수축 저하 방지효과
The accumulation of lactate in exercising muscle causes a fatigue phenomenon
with deterioration in muscle performance. In a relative anaerobic condition, it is a normal
physiological response to generate lactate for the compensation of NAD/NADH balance of
the tissues in order to continue ATP production. The alternative route of lactate metabolism
in such a state would be opened via transamination reaction to alanine through pyruvate.
Since LDH and GPT are near-equilibrium enzymes, whose kinetics are operated under the
simple mass-action law of substrates, it is possible to modulate the metabolism of lactate
through the supply of glutamate. In the present experiment, we analyzed the effect of lactate
perfusion on muscular contractility and the preventive effect of glutamate on the lactate-induced
decrease of muscle performance. The maximal twitch tension was decreased by lactate
infusion in contrast to its increase by glutamate and its rapid decrease by alanine
infusion. From these results, it can be concluded that the administration of glutamate may
improve exercise efficiency through prevention of lactate accumulation in exercising muscle
tissues