SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks

Deep neural networks have achieved state-of-the-art accuracies in a wide range of computer vision, speech recognition, and machine translation tasks. However the limits of memory bandwidth and computational power constrain the range of devices capable of deploying these modern networks. To address this problem, we propose SQuantizer, a new training method that jointly optimizes for both sparse and low-precision neural networks while maintaining high accuracy and providing a high compression rate. This approach brings sparsification and low-bit quantization into a single training pass, employing these techniques in an order demonstrated to be optimal. Our method achieves state-of-the-art accuracies using 4-bit and 2-bit precision for ResNet18, MobileNet-v2 and ResNet50, even with high degree of sparsity. The compression rates of 18x for ResNet18 and 17x for ResNet50, and 9x for MobileNet-v2 are obtained when SQuantizing both weights and activations within 1% and 2% loss in accuracy for ResNets and MobileNet-v2 respectively. An extension of these techniques to object detection also demonstrates high accuracy on YOLO-v3. Additionally, our method allows for fast single pass training, which is important for rapid prototyping and neural architecture search techniques. Finally extensive results from this simultaneous training approach allows us to draw some useful insights into the relative merits of sparsity and quantization

Tactile feedback display with spatial and temporal resolutions.

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications

Gender-specific association of decreased estimated glomerular filtration rate and left vertical geometry in the general population from rural Northeast China

Abstract Background Left ventricular hypertrophy (LVH) is common and associated with cardiovascular outcomes among patients with known chronic kidney disease (CKD). However, the link between decreased estimated glomerular filtration rate (eGFR) and left ventricular (LV) geometry remains poorly explored in general population. In this study, we examined the gender-specific association between eGFR and LVH in the general population from rural Northeast China. Methods This survey was conducted from July 2012 to August 2013. A total of 10907 participants (5,013 men and 5,894 women) from the rural Northeast China were randomly selected and examined. LV mass index (LVMI) was used to define LVH (LVMI\u2009>\u200946.7\ua0g/m 2.7 in women; > 49.2\ua0g/m 2.7 in men). LV geometry was defined as normal, or with concentric remodeling, eccentric or concentric hypertrophy, according to relative wall thickness (RWT) and LVMI. Mildly decreased eGFR was defined as eGFR\u2009\u2265\u200960 and\u2009<\u200990\ua0ml/min/1.73\ua0m 2 , and moderate-severely decreased eGFR was defined as eGFR\u2009<\u200960\ua0ml/min/1.73\ua0m 2 . Results As eGFR decreased, LVH showed a gradual increase in the entire study population. Multivariate analysis revealed a gender-specific relationship between eGFR and LV geometry. Only in men, mildly decreased eGFR was associated with concentric remodeling [odds ratio (OR): =1.58; 95% CI: 1.14\u20132.20; P \u2009<\u20090.01] and concentric LVH OR \u2009=\u20091.63; 95% CI: 1.15\u20132.31; P \u2009<\u20090.01). And only in men, moderate-severely decreased eGFR was a risk factor for concentric LVH ( OR \u2009=\u20094.56; 95% CI: 2.14\u20139.73; P \u2009<\u20090.001) after adjusting for confounding factors. Conclusions These findings suggested that decreased eGFR was a risk factor for LV geometry in men, and a gender-specific difference should be taken into account in clinical practice