A Web video retrieval method using hierarchical structure of Web video groups

In this paper, we propose a Web video retrieval method that uses hierarchical structure of Web video groups. Existing retrieval systems require users to input suitable queries that identify the desired contents in order to accurately retrieve Web videos; however, the proposed method enables retrieval of the desired Web videos even if users cannot input the suitable queries. Specifically, we first select representative Web videos from a target video dataset by using link relationships between Web videos obtained via metadata “related videos” and heterogeneous video features. Furthermore, by using the representative Web videos, we construct a network whose nodes and edges respectively correspond to Web videos and links between these Web videos. Then Web video groups, i.e., Web video sets with similar topics are hierarchically extracted based on strongly connected components, edge betweenness and modularity. By exhibiting the obtained hierarchical structure of Web video groups, users can easily grasp the overview of many Web videos. Consequently, even if users cannot write suitable queries that identify the desired contents, it becomes feasible to accurately retrieve the desired Web videos by selecting Web video groups according to the hierarchical structure. Experimental results on actual Web videos verify the effectiveness of our method

Role of Signaling Transduction Pathways in Development of Castration-Resistant Prostate Cancer

Almost all patients who succumb to prostate cancer die of metastatic castration-resistant disease. Although docetaxel is the standard treatment for this disease and is associated with modest prolongation of survival, there is an urgent need for novel treatments for castration-resistant prostate cancer (CRPC). Great advances in our understanding of the biological and molecular mechanisms of prostate cancer progression have resulted in many clinical trials of numerous targeted therapies. In this paper, we review mechanisms of CRPC development, with particular focus on recent advances in the understanding of specific intracellular signaling pathways participating in the proliferation of CRPC cells

Sensitivity to Rocuronium-Induced Neuromuscular Block and Reversibility with Sugammadex in a Patient with Myotonic Dystrophy

We report a patient with myotonic dystrophy who showed prolonged rocuronium-induced neuromuscular blockade, although with a fast recovery with sugammadex. During general anesthesia with propofol and remifentanil, the times to spontaneous recovery of the first twitch (T1) of train of four to 10% of control values after an intubating dose of rocuronium 1 mg/kg and an additional dose of 0.2 mg/kg were 112 min and 62 min, respectively. Despite the high sensitivity to rocuronium, sugammadex 2 mg/kg administered at a T1 of 10% safely and effectively antagonized rocuronium-induced neuromuscular block in 90 s

モデル生物線虫 Caenorhabditis elegans を用いた熟成ニンニク抽出液（aged garlic extract: AGE）の抗酸化作用解明研究

広島大学(Hiroshima University)博士(理学)Doctor of Sciencedoctora

Wedge holography in flat space and celestial holography

In this paper, we study codimension two holography in flat spacetimes, based on the idea of the wedge holography. We propose that a region in a d+1 dimensional flat spacetime surrounded by two end of the world branes, which are given by d dimensional hyperbolic spaces, is dual to a conformal field theory (CFT) on a d-1 dimensional sphere. Similarly, we also propose that a d+1 dimensional region in the flat spacetime bounded by two d dimensional de Sitter spaces is holographically dual to a CFT on a d-1 dimensional sphere. Our calculations of the partition function, holographic entanglement entropy and two point functions, support these duality relations and imply that such CFTs are nonunitary. Finally, we glue these two dualities along null surfaces to realize a codimension two holography for a full Minkowski spacetime and discuss a possible connection to the celestial holography

Self-Supervised Learning for Gastritis Detection with Gastric X-Ray Images

We propose a novel self-supervised learning method for medical image analysis. Great progress has been made in medical image analysis because of the development of supervised learning based on deep convolutional neural networks. However, annotating complex medical images usually requires expert knowledge, making it difficult for a wide range of real-world applications ($e.g.$, computer-aided diagnosis systems). Our self-supervised learning method introduces a cross-view loss and a cross-model loss to solve the insufficient available annotations in medical image analysis. Experimental results show that our method can achieve high detection performance for gastritis detection with only a small number of annotations

Development of a liquid scintillator containing a zirconium β-keto ester complex for the ZICOS experiment

AbstractA liquid scintillator containing a zirconium β-keto ester complex has been developed for the ZIrconium Complex in Organic Scintillator (ZICOS) neutrinoless double beta decay experiment. We are aiming to develop a detector which has a good energy resolution (4% at 2.5 MeV), a large light yield (60% that of BC505) and a low background rate (0.1 counts/tonne⋅year) with several tonnes of 96Zr isotope, so we have investigated the zirconium β-keto ester complexes tetrakis(isopropyl acetoacetato)zirconium and tetrakis(ethyl acetoacetato)zirconium, which have high solubility (over 10 wt.%) in anisole. We measured the performance of liquid scintillators containing these zirconium β-keto ester complexes and obtained 40% of the light yield of BC505 and energy resolution of 4.1% at 2.5 MeV assuming 40% photo coverage of the photomultiplier in the ZICOS detector. Thus we almost achieved our initial goal. Preliminary investigations indicate that tetrakis(diethyl malonato)zirconium will give us no quenching of the light yield and an energy resolution of 2.9% at 2.5 MeV. This will be a suitable complex for the ZICOS experiment, if it has a large solubility

Few-shot Personalized Saliency Prediction Based on Inter-personnel Gaze Patterns

This paper presents few-shot personalized saliency prediction based on inter-personnel gaze patterns. In contrast to a general saliency map, a personalized saliecny map (PSM) has been great potential since its map indicates the person-specific visual attention that is useful for obtaining individual visual preferences from heterogeneity of gazed areas. The PSM prediction is needed for acquiring the PSM for the unseen image, but its prediction is still a challenging task due to the complexity of individual gaze patterns. For modeling individual gaze patterns for various images, although the eye-tracking data obtained from each person is necessary to construct PSMs, it is difficult to acquire the massive amounts of such data. Here, one solution for efficient PSM prediction from the limited amount of data can be the effective use of eye-tracking data obtained from other persons. In this paper, to effectively treat the PSMs of other persons, we focus on the effective selection of images to acquire eye-tracking data and the preservation of structural information of PSMs of other persons. In the experimental results, we confirm that the above two focuses are effective for the PSM prediction with the limited amount of eye-tracking data.Comment: 5pages, 3 figure

Soft-Label Anonymous Gastric X-ray Image Distillation

This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. The sharing of medical data is demanded to construct high-accuracy computer-aided diagnosis (CAD) systems. However, the large size of the medical dataset and privacy protection are remaining problems in medical data sharing, which hindered the research of CAD systems. The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution. Different from model distillation, our method aims to find the optimal distilled images, distilled labels and the optimized learning rate. Experimental results show that the proposed method can not only effectively compress the medical dataset but also anonymize medical images to protect the patient's private information. The proposed approach can improve the efficiency and security of medical data sharing.Comment: Published as a conference paper at ICIP 202