Fully Deep Blind Image Quality Predictor Kim, Jongyoo; Lee, Sanghoon
IEEE journal of selected topics in signal processing,
02/2017, Letnik:
11, Številka:
1
Journal Article
Recenzirano
In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality ...assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.
Technological innovation and profit Lee, Sanghoon
Journal of open innovation,
September 2024, 2024-09-00, 2024-09-01, Letnik:
10, Številka:
3
Journal Article
Recenzirano
Odprti dostop
This study examines the effect of technological innovation on economic performance. Solow’s growth accounting measures the contribution of productivity to economic growth, but the active role of ...technological change in attaining economic growth is ignored. Kaldor emphasizes capital accumulation in technological process, but does not cover the dynamic process of technological innovation. Schumpeter explains the dynamic process by focusing on capital accumulation and efficiency. Following Schumpeter’s view of innovation, we empirically analyze the dynamics of technological innovation measured in terms of capital turnover and return on sales. Capital turnover represents process and organizational innovation, and return on sales reflects product innovation. For the empirical analysis, a large panel data set of 445 Korean firms during the period 2000–2015 is used. The empirical study performs panel regression analysis by using fixed effects model, FGLS model, dynamic GMM model, and a split sample method. The empirical results support the positive relationship between technological innovation and profitability.
Ovarian cancer is the most malignant gynecological tumor. Previous studies have reported that metabolic alterations resulting from deregulated lipid metabolism promote ovarian cancer aggressiveness. ...Lipid metabolism involves the oxidation of fatty acids, which leads to energy generation or new lipid metabolite synthesis. The upregulation of fatty acid synthesis and related signaling promote tumor cell proliferation and migration, and, consequently, lead to poor prognosis. Fatty acid-mediated lipid metabolism in the tumor microenvironment (TME) modulates tumor cell immunity by regulating immune cells, including T cells, B cells, macrophages, and natural killer cells, which play essential roles in ovarian cancer cell survival. Here, the types and sources of fatty acids and their interactions with the TME of ovarian cancer have been reviewed. Additionally, this review focuses on the role of fatty acid metabolism in tumor immunity and suggests that fatty acid and related lipid metabolic pathways are potential therapeutic targets for ovarian cancer.
This paper addresses the problems of feature representation of skeleton joints and the modeling of temporal dynamics to recognize human actions. Traditional methods generally use relative coordinate ...systems dependent on some joints, and model only the long-term dependency, while excluding short-term and medium term dependencies. Instead of taking raw skeletons as the input, we transform the skeletons into another coordinate system to obtain the robustness to scale, rotation and translation, and then extract salient motion features from them. Considering that Long Shortterm Memory (LSTM) networks with various time-step sizes can model various attributes well, we propose novel ensemble Temporal Sliding LSTM (TS-LSTM) networks for skeleton-based action recognition. The proposed network is composed of multiple parts containing short-term, mediumterm and long-term TS-LSTM networks, respectively. In our network, we utilize an average ensemble among multiple parts as a final feature to capture various temporal dependencies. We evaluate the proposed networks and the additional other architectures to verify the effectiveness of the proposed networks, and also compare them with several other methods on five challenging datasets. The experimental results demonstrate that our network models achieve the state-of-the-art performance through various temporal features. Additionally, we analyze a relation between the recognized actions and the multi-term TS-LSTM features by visualizing the softmax features of multiple parts.
Since human observers are the ultimate receivers of digital images, image quality metrics should be designed from a human-oriented perspective. Conventionally, a number of full-reference image ...quality assessment (FR-IQA) methods adopted various computational models of the human visual system (HVS) from psychological vision science research. In this paper, we propose a novel convolutional neural networks (CNN) based FR-IQA model, named Deep Image Quality Assessment (DeepQA), where the behavior of the HVS is learned from the underlying data distribution of IQA databases. Different from previous studies, our model seeks the optimal visual weight based on understanding of database information itself without any prior knowledge of the HVS. Through the experiments, we show that the predicted visual sensitivity maps agree with the human subjective opinions. In addition, DeepQA achieves the state-of-the-art prediction accuracy among FR-IQA models.
Flexible electronics technology dramatically changes the capability of sensors, which allows us to detect human biological signals not only on the skin but also inside the human body. Wearable ...sensors that stick to the skin surface can detect various biomechanical movements, biological signals, and byproducts such as useful elements from sweat and body temperature. On the other hand, implantable sensors directly or indirectly contact with biological components inside the body, such as tissue, organs, or muscles, to support or treat bodily functions or problems. With the development of these advanced sensors, we can live together with a huge number of sensors in the future. Toward body sensor networks that can be fully implanted in the future, sustainable energy sources that support the operation of sensors as well as the development of materials that enable long-term use inside the body remain challenges. In this review, we first summarize various state-of-the-art sensors in terms of flexible and wearable platforms. Additionally, we review the recent research trends of energy harvesters in mechanical and thermal energy conversion into useful electricity for the operation of the sensors. Furthermore, we cover recent studies in the aspect of materials for implantable sensors. Finally, we discuss future direction of the sensors that may enable implanted body sensor networks in the body.
This paper presents the theoretical and experimental results of a new approach for multitarget vital-signs monitoring using an electromagnetic-based Doppler radar. A phased-array radar is designed ...and implemented using a hybrid beamforming architecture to generate two simultaneous beams. The proposed system significantly mitigates the phase collision problem in the presence of multiple targets. Comprehensive discussions on the theory of multibeam systems alongside detailed simulations are provided. For the purpose of demonstration, a prototype dual-beam phased-array continuous-wave Doppler radar has been designed and implemented at 2.4 GHz. The system is fully characterized, and the measurement results confirm the feasibility of the proposed method. The experimental measurements show that for the first time, the breathing rates of two individuals can be monitored at the same time and using the same frequency. Several practical aspects of the system are examined, and a pilot study on the subject tracking is presented. The proposed dual-beam system prevents the phase collision of the signatures of the targets and hence provides multiperson detection capability to the system.
Previously, no-reference (NR) stereoscopic 3D (S3D) image quality assessment (IQA) algorithms have been limited to the extraction of reliable hand-crafted features based on an understanding of the ...insufficiently revealed human visual system or natural scene statistics. Furthermore, compared with full-reference (FR) S3D IQA metrics, it is difficult to achieve competitive quality score predictions using the extracted features, which are not optimized with respect to human opinion. To cope with this limitation of the conventional approach, we introduce a novel deep learning scheme for NR S3D IQA in terms of local to global feature aggregation. A deep convolutional neural network (CNN) model is trained in a supervised manner through two-step regression. First, to overcome the lack of training data, local patch-based CNNs are modeled, and the FR S3D IQA metric is used to approximate a reference ground-truth for training the CNNs. The automatically extracted local abstractions are aggregated into global features by inserting an aggregation layer in the deep structure. The locally trained model parameters are then updated iteratively using supervised global labeling, i.e., subjective mean opinion score (MOS). In particular, the proposed deep NR S3D image quality evaluator does not estimate the depth from a pair of S3D images. The S3D image quality scores predicted by the proposed method represent a significant improvement over those of previous NR S3D IQA algorithms. Indeed, the accuracy of the proposed method is competitive with FR S3D IQA metrics, having ~ 91% correlation in terms of MOS.
This paper focuses on the influence of injection pressures up to 30 MPa on single liquid jet break-up and atomization processes. For this purpose, a single jet from a multi-hole GDI injector has been ...characterized performing visualization and PDPA (phase Doppler particle analyzer) experiments. Using a thin sheet of light generated by a Nd:Yag laser and capturing a sequence of jet development images with a CCD camera, the internal structure was visualized. In order to quantify the droplet diameter and velocity, a 2-D PDPA system were carried out in addition to the spray visualization. Analyzing the images of the internal structure of jet and the result of PDPA, including droplet diameter and velocity distribution with increasing injection pressure up to 30 MPa, the elevated injection pressure on a jet break-up and atomization was characterized.
Abstract
Background
Invasive lobular breast carcinoma (ILC), the second most prevalent histological subtype of breast cancer, exhibits unique molecular features compared with the more common invasive ...ductal carcinoma (IDC). While genomic and transcriptomic features of ILC and IDC have been characterized, genome-wide chromatin accessibility pattern differences between ILC and IDC remain largely unexplored.
Methods
Here, we characterized tumor-intrinsic chromatin accessibility differences between ILC and IDC using primary tumors from The Cancer Genome Atlas (TCGA) breast cancer assay for transposase-accessible chromatin with sequencing (ATAC-seq) dataset.
Results
We identified distinct patterns of genome-wide chromatin accessibility in ILC and IDC. Inferred patient-specific transcription factor (TF) motif activities revealed regulatory differences between and within ILC and IDC tumors. EGR1, RUNX3, TP63, STAT6, SOX family, and TEAD family TFs were higher in ILC, while ATF4, PBX3, SPDEF, PITX family, and FOX family TFs were higher in IDC.
Conclusions
This study reveals the distinct epigenomic features of ILC and IDC and the active TFs driving cancer progression that may provide valuable information on patient prognosis.
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