Scarred Resonances and Steady Probability Distribution in a Chaotic Microcavity

We investigate scarred resonances of a stadium-shaped chaotic microcavity. It is shown that two components with different chirality of the scarring pattern are slightly rotated in opposite ways from the underlying unstable periodic orbit, when the incident angles of the scarring pattern are close to the critical angle for total internal reflection. In addition, the correspondence of emission pattern with the scarring pattern disappears when the incident angles are much larger than the critical angle. The steady probability distribution gives a consistent explanation about these interesting phenomena and makes it possible to expect the emission pattern in the latter case.Comment: 4 pages, 5 figure

Abducens Nerve Palsy Complicated by Inferior Petrosal Sinus Septic Thrombosis Due to Mastoiditis

We present a very rare case of a 29-month-old boy with acute onset right abducens nerve palsy complicated by inferior petrosal sinus septic thrombosis due to mastoiditis without petrous apicitis. Four months after mastoidectomy, the patient fully recovered from an esotropia of 30 prism diopters and an abduction limitation (-4) in his right eye

Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization

Large language models (LLMs) face the challenges in fine-tuning and deployment due to their high memory demands and computational costs. While parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage of the optimizer state during fine-tuning, the inherent size of pre-trained LLM weights continues to be a pressing concern. Even though quantization techniques are widely proposed to ease memory demands and accelerate LLM inference, most of these techniques are geared towards the deployment phase. To bridge this gap, this paper presents Parameter-Efficient and Quantization-aware Adaptation (PEQA) - a simple yet effective method that combines the advantages of PEFT with quantized LLMs. By updating solely the quantization scales, PEQA can be directly applied to quantized LLMs, ensuring seamless task transitions. Parallel to existing PEFT methods, PEQA significantly reduces the memory overhead associated with the optimizer state. Furthermore, it leverages the advantages of quantization to substantially reduce model sizes. Even after fine-tuning, the quantization structure of a PEQA-tuned LLM remains intact, allowing for accelerated inference on the deployment stage. We employ PEQA-tuning for task-specific adaptation on LLMs with up to 65 billion parameters. To assess the logical reasoning and language comprehension of PEQA-tuned LLMs, we fine-tune low-bit quantized LLMs using a instruction dataset. Our results show that even when LLMs are quantized to below 4-bit precision, their capabilities in language modeling, few-shot in-context learning, and comprehension can be resiliently restored to (or even improved over) their full-precision original performances with PEQA.Comment: Published at NeurIPS 2023. Camera-ready versio

Onion peel extracts ameliorate hyperglycemia and insulin resistance in high fat diet/streptozotocin-induced diabetic rats

<p>Abstract</p> <p>Background</p> <p>Quercetin derivatives in onions have been regarded as the most important flavonoids to improve diabetic status in cells and animal models. The present study was aimed to examine the hypoglycemic and insulin-sensitizing capacity of onion peel extract (OPE) containing high quercetin in high fat diet/streptozotocin-induced diabetic rats and to elucidate the mechanism of its insulin-sensitizing effect.</p> <p>Methods</p> <p>Male Sprague-Dawley rats were fed the AIN-93G diet modified to contain 41.2% fat and intraperitoneally injected with a single dose of streptozotocin (40 mg/kg body weight). One week after injection, the rats with fasting blood glucose levels above 126 mg/dL were randomly divided into 4 groups to treat with high fat diet containing 0 (diabetic control), 0.5, or 1% of OPE or 0.1% quercetin (quercetin equivalent to 1% of OPE) for 8 weeks. To investigate the mechanism for the effects of OPE, we examined biochemical parameters (insulin sensitivity and oxidative stresses) and protein and gene expressions (pro-inflammatory cytokines and receptors).</p> <p>Results</p> <p>Compared to the diabetic control, hypoglycemic and insulin-sensitizing capability of 1% OPE were demonstrated by significant improvement of glucose tolerance as expressed in incremental area under the curve (<it>P </it>= 0.0148). The insulin-sensitizing effect of OPE was further supported by increased glycogen levels in liver and skeletal muscle (<it>P </it>< 0.0001 and <it>P </it>= 0.0089, respectively). Quantitative RT-PCR analysis showed increased expression of insulin receptor (<it>P </it>= 0.0408) and GLUT4 (<it>P </it>= 0.0346) in muscle tissues. The oxidative stress, as assessed by superoxide dismutase activity and malondialdehyde formation, plasma free fatty acids, and hepatic protein expressions of IL-6 were significantly reduced by 1% OPE administration (<it>P </it>= 0.0393, 0.0237, 0.0148 and 0.0025, respectively).</p> <p>Conclusion</p> <p>OPE might improve glucose response and insulin resistance associated with type 2 diabetes by alleviating metabolic dysregulation of free fatty acids, suppressing oxidative stress, up-regulating glucose uptake at peripheral tissues, and/or down-regulating inflammatory gene expression in liver. Moreover, in most cases, OPE showed greater potency than pure quercetin equivalent. These findings provide a basis for the use of onion peel to improve insulin insensitivity in type 2 diabetes.</p

Calibration of Built-in Accelerometer Using a Commercially Available Smartphone

Wearable trackers that detect sleep offer users a way to track their sleep quality and patterns without the use of expensive equipment. Few studies have tested the validity of these trackers on sleep measure. PURPOSE: To examine the validity of the Actigraph GT9X (AG), SenseWear Mini Armband (SW), Basis Peak (BP), Fitbit Charge HR (FB), Jawbone UP3 (JU), and Garmin Vivosmart (GV) for estimating sleep variables as compared with a sleep diary. METHODS: 78 healthy individuals participated in the study. Group 1 (n= 38) and wore the AG, SW, BP, and FB or Group 2 (n = 40) and wore the AG, JU, and GV. Monitors were worn on the non-dominant arm for 3 nights and a sleep log was completed. Sleep variables were total sleep time (TST), time in bed (TIB), sleep efficiency (SE), and wake after sleep onset (WASO). Pearson correlation, mean absolute percentage errors (MAPE), equivalence testing, Bland-Altman plots, and ANOVA were used to assess validity compared with the diary. RESULTS: Overall, monitors that showed the greatest correlation with the sleep diary for TST were the JU and FB (effect size= 0.09 and 0.23, respectively). The greatest correlation with the sleep diary for TIB was seen with the SW, GV, and JU (effect size= 0.09, 0.16, and 0.07, respectively). SE and WASO showed very poor correlation with the log. Measures for equivalence testing confirmed the success of the JU, SW, FB, and GV for measureing TIB and TST. CONCLUSION: The FB, SW, JU, and GV could be valid measure of TST and TIB. The monitors are not valid regarding wake times during sleep. Further research is needed to validate these monitors with polysomnography

Co-occurrence matrix analysis-based semi-supervised training for object detection

One of the most important factors in training object recognition networks using convolutional neural networks (CNNs) is the provision of annotated data accompanying human judgment. Particularly, in object detection or semantic segmentation, the annotation process requires considerable human effort. In this paper, we propose a semi-supervised learning (SSL)-based training methodology for object detection, which makes use of automatic labeling of un-annotated data by applying a network previously trained from an annotated dataset. Because an inferred label by the trained network is dependent on the learned parameters, it is often meaningless for re-training the network. To transfer a valuable inferred label to the unlabeled data, we propose a re-alignment method based on co-occurrence matrix analysis that takes into account one-hot-vector encoding of the estimated label and the correlation between the objects in the image. We used an MS-COCO detection dataset to verify the performance of the proposed SSL method and deformable neural networks (D-ConvNets) as an object detector for basic training. The performance of the existing state-of-the-art detectors (DConvNets, YOLO v2, and single shot multi-box detector (SSD)) can be improved by the proposed SSL method without using the additional model parameter or modifying the network architecture.Comment: Submitted to International Conference on Image Processing (ICIP) 201