Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

A 9 bp cis-element in the promoters of class I small heat shock protein genes on chromosome 3 in rice mediates L-azetidine-2-carboxylic acid and heat shock responses

In rice, the class I small heat shock protein (sHSP-CI) genes were found to be selectively induced by L-azetidine-2-carboxylic acid (AZC) on chromosome 3 but not chromosome 1. Here it is shown that a novel cis-responsive element contributed to the differential regulation. By serial deletion and computational analysis, a 9 bp putative AZC-responsive element (AZRE), GTCCTGGAC, located between nucleotides –186 and –178 relative to the transcription initiation site of Oshsp17.3 was revealed. Deletion of this putative AZRE from the promoter abolished its ability to be induced by AZC. Moreover, electrophoretic mobility shift assay (EMSA) revealed that the AZRE interacted specifically with nuclear proteins from AZC-treated rice seedlings. Two AZRE–protein complexes were detected by EMSA, one of which could be competed out by a canonical heat shock element (HSE). Deletion of the AZRE also affected the HS response. Furthermore, transient co-expression of the heat shock factor OsHsfA4b with the AZRE in the promoter of Oshsp17.3 was effective. The requirement for the putative AZRE for AZC and HS responses in transgenic Arabidopsis was also shown. Thus, AZRE represents an alternative form of heat HSE, and its interaction with canonical HSEs through heat shock factors may be required to respond to HS and AZC

Recent progress and debates in molecular physiology of Na+ uptake in teleosts

How teleosts take up Na+ from the surrounding freshwater (FW) as well as the underlying mechanisms associated with this process have received considerable attention over the past 85 years. Owing to an enormous ion gradient between hypotonic FW and fish body fluids, teleosts gills have to actively absorb Na+ (via ionocytes) to compensate for the passive loss of Na+. To date, three models have been proposed for Na+ uptake in teleost ionocytes, including Na+/H+ exchanger (NHE)-mediated, acid-sensing ion channel (ASIC)-mediated, Na+-Cl- co-transporter (NCC)-mediated pathways. However, some debates regarding these models and unclear mechanisms still remain. To better understand how teleosts take up Na+ from FW, this mini-review summarizes the main progress and related regulatory mechanisms of Na+ uptake, and discusses some of the challenges to the current models

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%

An Evaluation of the Additive Effect of Natural Herbal Medicine on SARS or SARS-like Infectious Diseases in 2003: A Randomized, Double-blind, and Controlled Pilot Study

Natural herbal medicine (NHM) has been used to control infectious diseases for thousands of years. In view of the possible beneficial effect of NHM on SARS, we conducted this study to examine whether NHM is of any benefit as a supplementary treatment of SARS or SARS-like infectious disease. This was a randomized, double-blind, placebo-controlled trial. Twenty-eight patients fulfilled the WHO inclusion criteria and our exclusion criteria. All enrolled patients received routine western-medicine treatment. Patients were randomly allocated to one of the three supplementary treatment groups: NHM A (Group A, n = 9) NHM B (Group B, n = 9) or placebo (Group C, n = 10). Chest X-ray was done every 1 or 2 days for every patient. Reading radiologists use a standard 0–3 scoring system (0: no infiltration; 1: focal haziness or even small patchy lesion; 2: ground glass picture; 3: lobar consolidation) according to the severity of infiltration in each lung field (three lung fields in both right and left lungs). The main outcome measurements were the improving chest radiographic scores (IRS) and the duration (days) till improvement (DI). One patient from the placebo group passed away. Patients from NHM A took less days before showing improvement (6.7 ± 1.8) compared with placebo group (11.2 ± 4.9), which showed statistical significance (P = 0.04). The cases were too few to be conclusive, the initial observations seem to indicate NHM appears to be safe in non-criticallly ill patients and clinical trials are feasible in the setting of pandemic outbreaks

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports