81 research outputs found
Perturbated Gradients Updating within Unit Space for Deep Learning
In deep learning, optimization plays a vital role. By focusing on image
classification, this work investigates the pros and cons of the widely used
optimizers, and proposes a new optimizer: Perturbated Unit Gradient Descent
(PUGD) algorithm with extending normalized gradient operation in tensor within
perturbation to update in unit space. Via a set of experiments and analyses, we
show that PUGD is locally bounded updating, which means the updating from time
to time is controlled. On the other hand, PUGD can push models to a flat
minimum, where the error remains approximately constant, not only because of
the nature of avoiding stationary points in gradient normalization but also by
scanning sharpness in the unit ball. From a series of rigorous experiments,
PUGD helps models to gain a state-of-the-art Top-1 accuracy in Tiny ImageNet
and competitive performances in CIFAR- {10, 100}. We open-source our code at
link: https://github.com/hanktseng131415go/PUGD
An Evaluation of the Center of Pressure by the Multi-Layers Rubber Mats Using Image-base Rapid Pressure Measuring System
The Multi-Layers Rubber Mats (MLRM) ink footprint has been used to analyze plantar pressure in qualitative data and foot arch index for the customized insole which reflected pressure beneath barefoot. In this study, the MLRM was evaluated the center of pressure (COP) in quantify data by image-base measuring system. The footprint in static and dynamics states were performed to investigate significant difference of the COP. Subsequently, the COP results were compared with a pressure platform. The results showed that, there were some estimate disparity in XCOP (17.4%, p<0.001) and showed good relationship on the YCOP (4.23%, p=0.278) coordinate where compared with the pressure platform. Then, the COP from dynamic state which affected of accumulated pressure during performed dynamic action and the results showed good relation in disparity to static state. Therefore, the evaluation of the COP by image-base measuring system is helpful for clinician or insole maker which able to check the COP from MLRM ink footprint
EDU-level Extractive Summarization with Varying Summary Lengths
Extractive models usually formulate text summarization as extracting top-k
important sentences from document as summary. Few work exploited extracting
finer-grained Elementary Discourse Unit (EDU) and there is little analysis and
justification for the extractive unit selection. To fill such a gap, this paper
firstly conducts oracle analysis to compare the upper bound of performance for
models based on EDUs and sentences. The analysis provides evidences from both
theoretical and experimental perspectives to justify that EDUs make more
concise and precise summary than sentences without losing salient information.
Then, considering this merit of EDUs, this paper further proposes EDU-level
extractive model with Varying summary Lengths (EDU-VL) and develops the
corresponding learning algorithm. EDU-VL learns to encode and predict
probabilities of EDUs in document, and encode EDU-level candidate summaries
with different lengths based on various values and select the best
candidate summary in an end-to-end training manner. Finally, the proposed and
developed approach is experimented on single and multi-document benchmark
datasets and shows the improved performances in comparison with the
state-of-the-art models
Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning
An intelligent robot agent based on domain ontology, machine learning
mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning
is presented in this paper. The machine-human co-learning model is established
to help various students learn the mathematical concepts based on their
learning ability and performance. Meanwhile, the robot acts as a teacher's
assistant to co-learn with children in the class. The FML-based knowledge base
and rule base are embedded in the robot so that the teachers can get feedback
from the robot on whether students make progress or not. Next, we inferred
students' learning performance based on learning content's difficulty and
students' ability, concentration level, as well as teamwork sprit in the class.
Experimental results show that learning with the robot is helpful for
disadvantaged and below-basic children. Moreover, the accuracy of the
intelligent FML-based agent for student learning is increased after machine
learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie
UPANets: Learning from the Universal Pixel Attention Networks
Among image classification, skip and densely-connection-based networks have
dominated most leaderboards. Recently, from the successful development of
multi-head attention in natural language processing, it is sure that now is a
time of either using a Transformer-like model or hybrid CNNs with attention.
However, the former need a tremendous resource to train, and the latter is in
the perfect balance in this direction. In this work, to make CNNs handle global
and local information, we proposed UPANets, which equips channel-wise attention
with a hybrid skip-densely-connection structure. Also, the extreme-connection
structure makes UPANets robust with a smoother loss landscape. In experiments,
UPANets surpassed most well-known and widely-used SOTAs with an accuracy of
96.47% in Cifar-10, 80.29% in Cifar-100, and 67.67% in Tiny Imagenet. Most
importantly, these performances have high parameters efficiency and only
trained in one customer-based GPU. We share implementing code of UPANets in
https://github.com/hanktseng131415go/UPANets
Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels
Motion mode (M-mode) recording is an essential part of echocardiography to
measure cardiac dimension and function. However, the current diagnosis cannot
build an automatic scheme, as there are three fundamental obstructs: Firstly,
there is no open dataset available to build the automation for ensuring
constant results and bridging M-mode echocardiography with real-time instance
segmentation (RIS); Secondly, the examination is involving the time-consuming
manual labelling upon M-mode echocardiograms; Thirdly, as objects in
echocardiograms occupy a significant portion of pixels, the limited receptive
field in existing backbones (e.g., ResNet) composed from multiple convolution
layers are inefficient to cover the period of a valve movement. Existing
non-local attentions (NL) compromise being unable real-time with a high
computation overhead or losing information from a simplified version of the
non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode
echocardiography measurement scheme, contributes three aspects to answer the
problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance
segmentation, to enable consistent results and support the development of an
automatic scheme; 2) propose panel attention, local-to-global efficient
attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS
scheme toward big object detection with global receptive field; 3) develop and
implement AMEM, an efficient algorithm of automatic M-mode echocardiography
measurement enabling fast and accurate automatic labelling among diagnosis. The
experimental results show that RAMEM surpasses existing RIS backbones (with
non-local attention) in PASCAL 2012 SBD and human performances in real-time
MEIS tested. The code of MEIS and dataset are available at
https://github.com/hanktseng131415go/RAME
Reliability of flexible low temperature poly-silicon thin film transistor
This work reports the effect of mechanical stress-induced degradation in flexible low-temperature polycrystalline-silicon thin-film transistors. After 100,000 iterations of channel-width-direction mechanical compression at R=2mm, a significant shift of extracted threshold voltage and an abnormal hump at the subthreshold region were found. Simulation reveals that both the strongest mechanical stress and electrical field takes place at both sides of the channel edge, between the polycrystalline silicon and gate insulator. The gate insulator suffered from a serious mechanical stress and result in a defect generation in the gate insulator. The degradation of the threshold voltage shift and the abnormal hump can be ascribed to the electron trapping in these defects. In addition, this work introduced three methods to reduce the degradation cause by the mechanical stress, including the quality improvement of the gate insulator, organic trench structure and active layer with a wing structure.
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Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial
Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie
Color multiplexing method to capture front and side images with a capsule endoscope
This paper proposes a capsule endoscope (CE), based on color multiplexing, to simultaneously record front and side images. Only one lens associated with an X-cube prism is employed to catch the front and side view profiles in the CE. Three color filters and polarizers are placed on three sides of an X-cube prism. When objects locate at one of the X-cube's three sides, front and side view profiles of different colors will be caught through the proposed lens and recorded at the color image sensor. The proposed color multiplexing CE (CMCE) is designed with a field of view of up to 210 deg and a 180 lp/mm resolution under f-number 2.8 and overall length 13.323 mm. A ray-tracing simulation in the CMCE with the color multiplexing mechanism verifies that the CMCE not only records the front and side view profiles at the same time, but also has great image quality at a small size
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