Deep Saliency with Encoded Low level Distance Map and High Level Features

Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1X1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.Comment: Accepted by IEEE Conference on Computer Vision and Pattern Recognition(CVPR) 2016. Project page: https://github.com/gylee1103/SaliencyEL

Highly reliable and uniform carbon nanotubes transistors for biosensor application

School of Energy and Chemical Engineering (Energy Engineering)SWNTs have excellent mechanical, electrical, and thermal properties, which distinguishes them from other device materials. Therefore, they are being actively studied in various electrical device fields. In particular, the small sizes as nanostructure, large surface area-to-volume ratio and their chemical functionalization allow the high-sensitivity signal conversion, which is an attractive factor in biosensors. However, to use the superior properties of SWNTs for research, it is necessary to solve the problems of bundling by van der Waals force and mixed electrical properties. These problem can be overcome as sc-SWNTs could be simultaneously separated and dispersed by the interaction between ?? electrons of the carbon nanotubes and the conjugated polymers. SWNTs FETs fabricated by these sorted sc-SWNTs are expected to their potential because of their high carrier mobility and excellent electrostatic properties. But, SWNTs FETs with great performances exceeding that of conventional MOS-FETs haven???t yet been realized at technologies node due to several problems with SWNTs materials. For the ideal SWNTs FETs, the film stability and long-time of fabrication by most physical adsorption methods must be solved. It is also necessary to find an appropriate density of SWNTs layers on designated active region to achieve high and uniform performance. Similarly, in a biosensor having frequent washing processes, the intrinsic electrical properties of SWNTs films fluctuated due to the unstable anchoring of SWNTs on the platform surfaces after several washing steps of biosensors, so SWNTs film stability with substrates must be ensured. In order to solve these problems and utilize the SWNTs, a chemical immobilization method of SWNTs on a substrate with wrapping polymer that can form chemical bonds between sc-SWNTs and adhesive layer is proposed. Azide-functionalized polyfluorene derivatives were synthesized as wrapping polymers, and only sc-SWNTs were selectively enriched by this polymers. Then, the alkyne functionalized polymer was synthesized for Click reaction, and successfully patterned by UV light attachment. Sorted sc-SWNTs solution of low concentration was reacted with alkyne adhesive layer of the substrate by Click reaction to achieve a dense and firmly immobilized SWNTs pattern networks in a short time. Patterned SWNTs films, which form strong bonds with the substrate even after the sonication washing, can be obtained through a simple adhesive layer pre-patterning without a photoresist process. By minimizing the interference and leakage current between devices, the patterned SWNTs FETs have excellent performance and homogeneity compared to the un-patterned FETs because the device is manufactured by accurately dividing the channel region. Then, to apply this patterned SWNTs FETs as biosensors, a linker and an antibody were immobilized on the SWNTs films, and Alzheimer's antigen was detected by confirming the change of the electrical resistance response. The resistance value of SWNTs FETs showed almost the same value without change even after the sonication washing by PBS solution. This means that the SWNTs film is still bonded to the substrate by the Click reaction even after the harsh washing process, which solves the problem of film exfoliation of common SWNTs biosensor platform. In A??1-42 peptides detection, a significant increase in resistance was identified only when the antibodies were immobilized to the devices. This result shows that the patterned SWNTs devices fabricated by Click reaction can be successfully applied as biosensor platforms with specificity to target materials.ope

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

This paper proposes a new protocol that can be used to reduce transmission delay and energy consumption effectively. This will be done by adjusting the duty-cycle (DC) ratio of the receiver node and the contention window size of the sender node according to the traffic congestion for various devices in the Internet of Things (IoT). In the conventional duty-cycle MAC protocol, the data transmission delay latency and unnecessary energy consumption are caused by a high collision rate. This is because the receiver node cannot sufficiently process the data of the transmitting node during the traffic peak time when the transmission and reception have the same duty-cycle ratio. To solve this problem, this paper proposes an algorithm that changes the duty-cycle ratio of the receiver and broadcasts the contention window size of the senders through Early Acknowledgment (E-ACK) at peak time and off/peak time. The proposed algorithm, according to peak and off/peak time, can transmit data with fewer delays and minimizes energy consumption. Document type: Articl

Quetiapine Misuse and Abuse: Is It an Atypical Paradigm of Drug Seeking Behavior?

Recent case reports in medical literatures suggest that more and more second-generation atypical antipsychotics (AAs) have been prescribed for off-label use; quetiapine (Brand name: Seroquel®) showed increase in its trend for off-label use. Little is known about the reasons behind this trend, although historical sedative and hypnotic prescription patterns suggest that despite relatively superior safety profiles of quetiapine (especially for movement disorders), it may be used for treating substance abuse disorder. In addition, recent studies have shown a strong potential for misuse and abuse (MUA) of quetiapine beyond Food and Drug Administration-approved indications. This includes drug-seeking behaviors, such as feigning symptoms, motivated by quetiapine and use of quetiapine in conjunction with alcohol. Quetiapine appears to be the most documented AA with street values bartered illicitly on the street. A recent report from the Drug Abuse Warning Network has shown a high prevalence of quetiapine-related emergency department visits involving MUA. Several other case studies have found that quetiapine causes seeking behaviors observed in substance use disorder. In fact, the majority of quetiapine MUA involved patients diagnosed with substance use disorder. In the absence of a definitive mechanism of action of quetiapine\u27s reinforcing properties, it is imperative to gather robust evidence to support or refute increasing off-label use of AAs

Added value of high-resolution regional climate model in simulating precipitation based on the changes in kinetic energy

As the resolution of regional climate models has increased with the development of computing resources, Added Values (AVs) have always been a steady research topic. Most previous studies examined AVs qualitatively by comparing model results with different model resolutions qualitatively. This study tried to quantitatively investigate the AV of the high-resolution regional climate model for precipitation by analyzing the distribution of kinetic energy according to the different wavelengths at two different resolutions (36 km vs. 4 km), away from the traditional comparative analysis. In addition, the experiment that the low-resolution topography was forced to the high-resolution model was additionally conducted to separate the AVs associated with the topographic effect. Among the three experiments, two with the same topography and two with the exact horizontal resolution were compared separately. With identical topography, the high-resolution model simulated amplified precipitation intensity more than the low-resolution model in all quantiles, especially for extreme precipitation. The precipitation generated by mesoscale or smaller scale weather/climate events was also simulated with greater intensity in the high-resolution model. With the same grid spacing, the more detailed topography model showed AV for increasing spatial variability of precipitation, especially in mountainous regions. The AVs identified in this study were related to kinetic energy with wavelengths at the meso-beta or smaller scale. On the other hand, the kinetic energy above the meso-alpha or larger scale has no significant correlation with the AV of precipitation

3D-aware Blending with Generative NeRFs

Image blending aims to combine multiple images seamlessly. It remains challenging for existing 2D-based methods, especially when input images are misaligned due to differences in 3D camera poses and object shapes. To tackle these issues, we propose a 3D-aware blending method using generative Neural Radiance Fields (NeRF), including two key components: 3D-aware alignment and 3D-aware blending. For 3D-aware alignment, we first estimate the camera pose of the reference image with respect to generative NeRFs and then perform 3D local alignment for each part. To further leverage 3D information of the generative NeRF, we propose 3D-aware blending that directly blends images on the NeRF's latent representation space, rather than raw pixel space. Collectively, our method outperforms existing 2D baselines, as validated by extensive quantitative and qualitative evaluations with FFHQ and AFHQ-Cat.Comment: ICCV 2023, Project page: https://blandocs.github.io/blendner