MobileNetV2: Inverted Residuals and Linear Bottlenecks

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameter

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.Comment: 14 pages, 12 figure

Therapeutic Effect of Targeting Branched-Chain Amino Acid Catabolic Flux in Pressure-Overload Induced Heart Failure.

Background Branched-chain amino acid (BCAA) catabolic defect is an emerging metabolic hallmark in failing hearts in human and animal models. The therapeutic impact of targeting BCAA catabolic flux under pathological conditions remains understudied. Methods and Results BT2 (3,6-dichlorobenzo[b]thiophene-2-carboxylic acid), a small-molecule inhibitor of branched-chain ketoacid dehydrogenase kinase, was used to enhance BCAA catabolism. After 2 weeks of transaortic constriction, mice with significant cardiac dysfunctions were treated with vehicle or BT2. Serial echocardiograms showed continuing pathological deterioration in left ventricle of the vehicle-treated mice, whereas the BT2-treated mice showed significantly preserved cardiac function and structure. Moreover, BT2 treatment improved systolic contractility and diastolic mechanics. These therapeutic benefits appeared to be independent of impacts on left ventricle hypertrophy but associated with increased gene expression involved in fatty acid utilization. The BT2 administration showed no signs of apparent toxicity. Conclusions Our data provide the first proof-of-concept evidence for the therapeutic efficacy of restoring BCAA catabolic flux in hearts with preexisting dysfunctions. The BCAA catabolic pathway represents a novel and potentially efficacious target for treatment of heart failure

Speed/accuracy trade-offs for modern convolutional object detectors

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-to-apples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [Ren et al., 2015], R-FCN [Dai et al., 2016] and SSD [Liu et al., 2015] systems, which we view as "meta-architectures" and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.Comment: Accepted to CVPR 201

Enhancing photoelectrochemical CO2 reduction with silicon photonic crystals

The effectiveness of silicon (Si) and silicon-based materials in catalyzing photoelectrochemistry (PEC) CO2 reduction is limited by poor visible light absorption. In this study, we prepared two-dimensional (2D) silicon-based photonic crystals (SiPCs) with circular dielectric pillars arranged in a square array to amplify the absorption of light within the wavelength of approximately 450 nm. By investigating five sets of n + p SiPCs with varying dielectric pillar sizes and periodicity while maintaining consistent filling ratios, our findings showed improved photocurrent densities and a notable shift in product selectivity towards CH4 (around 25% Faradaic Efficiency). Additionally, we integrated platinum nanoparticles, which further enhanced the photocurrent without impacting the enhanced light absorption effect of SiPCs. These results not only validate the crucial role of SiPCs in enhancing light absorption and improving PEC performance but also suggest a promising approach towards efficient and selective PEC CO2 reduction

Acupuncture treatment for post-stroke depression: Intestinal microbiota and its role

Stroke-induced depression is a common complication and an important risk factor for disability. Besides psychiatric symptoms, depressed patients may also exhibit a variety of gastrointestinal symptoms, and even take gastrointestinal symptoms as the primary reason for medical treatment. It is well documented that stress may disrupt the balance of the gut microbiome in patients suffering from post-stroke depression (PSD), and that disruption of the gut microbiome is closely related to the severity of the condition in depressed patients. Therefore, maintaining the balance of intestinal microbiota can be the focus of research on the mechanism of acupuncture in the treatment of PSD. Furthermore, stroke can be effectively treated with acupuncture at all stages and it may act as a special microecological regulator by regulating intestinal microbiota as well. In this article, we reviewed the studies on changing intestinal microbiota after acupuncture treatment and examined the existing problems and development prospects of acupuncture, microbiome, and poststroke depression, in order to provide new ideas for future acupuncture research

Performance investigation of a micro-channel flat separated loop heat pipe system for data centre cooling

This paper investigates a novel micro-channel flat separated loop heat pipe system for cooling the information technology equipment in the data centres through theoretical and experimental analysis and by assessing the impact of the inlet water temperature on system performance. A computer model is developed to simulate the steady-state performance of the micro-channel flat separated loop heat pipe system. After comparing the experimental and modelling results, the new and conventional system under the same working conditions, the model is validated yielding high accuracy in predicting the performance of the micro-channel flat separated loop heat pipe system with recorded error being limited to 2.16–8.97%. The new system has better performance than the conventional system. Under the operating conditions of heat load intensity of 1,000 W/m2, water flow rate of 0.28 m3/h, refrigerant filling rate of 30%, ambient air temperature of 26°C, and evaporator and condenser height difference of 0.8 m, the performance of the system has been explored at inlet temperature from 15 to 24°C with increments of 3°C. The system’s averaged heat transfer efficiency was found to decrease with the increase in inlet temperature. This research provides valuable insight into the data centre information technology equipment cooling, which is of great significance for energy saving and environmentally friendly operation of data centres

Aggregation‐induced emission luminogen: A new perspective in the photo‐degradation of organic pollutants

Both the variety and uniqueness of organic semiconductors has contributed to the rapid development of environmental engineering applications and renewable fuel production, typified by the photodegradation of organic pollutants or water splitting. This paper presents a rare example of an aggregation‐induced emission luminogen as a highly efficient photocatalyst for pollutant decomposition in an environmentally relevant application. Under irradiation, the tetraphenylethene‐based AIEgen (TPE‐Ca) exhibited high photo‐degradation efficiency of up to 98.7% of Rhodamine B (RhB) in aqueous solution. The possible photocatalytic mechanism was studied by electron paramagnetic resonance and X‐ray photoelectron spectroscopy spectra, electrochemistry, thermal imaging technology, ultra‐performance liquid chromatography and high‐definition mass spectrometry, as well as by density functional theory calculations. Among the many diverse AIEgens, this is the first AIEgen to be developed as a photocatalyst for the degradation of organic pollutants. This research will open up new avenues for AIEgens research, particularly for applications of environmental relevance