Function Space Bayesian Pseudocoreset for Bayesian Neural Networks

A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is constructed by minimizing a divergence measure between the posterior conditioning on the pseudocoreset and the posterior conditioning on the full dataset. However, evaluating the divergence can be challenging, particularly for the models like deep neural networks having high-dimensional parameters. In this paper, we propose a novel Bayesian pseudocoreset construction method that operates on a function space. Unlike previous methods, which construct and match the coreset and full data posteriors in the space of model parameters (weights), our method constructs variational approximations to the coreset posterior on a function space and matches it to the full data posterior in the function space. By working directly on the function space, our method could bypass several challenges that may arise when working on a weight space, including limited scalability and multi-modality issue. Through various experiments, we demonstrate that the Bayesian pseudocoresets constructed from our method enjoys enhanced uncertainty quantification and better robustness across various model architectures

Regularizing Towards Soft Equivariance Under Mixed Symmetries

Datasets often have their intrinsic symmetries, and particular deep-learning models called equivariant or invariant models have been developed to exploit these symmetries. However, if some or all of these symmetries are only approximate, which frequently happens in practice, these models may be suboptimal due to the architectural restrictions imposed on them. We tackle this issue of approximate symmetries in a setup where symmetries are mixed, i.e., they are symmetries of not single but multiple different types and the degree of approximation varies across these types. Instead of proposing a new architectural restriction as in most of the previous approaches, we present a regularizer-based method for building a model for a dataset with mixed approximate symmetries. The key component of our method is what we call equivariance regularizer for a given type of symmetries, which measures how much a model is equivariant with respect to the symmetries of the type. Our method is trained with these regularizers, one per each symmetry type, and the strength of the regularizers is automatically tuned during training, leading to the discovery of the approximation levels of some candidate symmetry types without explicit supervision. Using synthetic function approximation and motion forecasting tasks, we demonstrate that our method achieves better accuracy than prior approaches while discovering the approximate symmetry levels correctly.Comment: Proceedings of the International Conference on Machine Learning (ICML), 202

Portable Amperometric Perchlorate Selective Sensors with Microhole Array-water/organic Gel Interfaces

A novel stick-shaped portable sensing device featuring a microhole array interface between the polyvinylchloride- 2-nitrophenyloctylether (PVC-NPOE) gel and water phase was developed for in-situ sensing of perchlorate ions in real water samples. Perchlorate sensitive sensing responses were obtained based on measuring the current changes with respect to the assisted transfer reaction of perchlorate ions by a perchlorate selective ligand namely, bis(dibenzoylmethanato)Ni(II) (Ni(DBM)2) across the polarized microhole array interface. Cyclic voltammetry was used to characterize the assisted transfer reaction of perchlorate ions by the Ni(DBM)2 ligand when using the portable sensing device. The current response for the transfer of perchlorate anions by Ni(DBM)2 across the micro-water/gel interface linearly increased as a function of the perchlorate ion concentration. The technique of differential pulse stripping voltammetry was also utilized to improve the sensitivity of the perchlorate anion detection down to 10 ppb. This was acquired by preconcentrating perchlorate anions in the gel layer by means of holding the ion transfer potential at 0 mV (vs. Ag/AgCl) for 30 s followed by stripping the complexed perchlorate ion with the ligand. The effect of various potential interfering anions on the perchlorate sensor was also investigated and showed an excellent selectivity over Br−, NO2 −, NO3 −, CO3 2−, CH3COO− and SO4 2− ions. As a final demonstration, some regional water samples from the Sincheon river in Daegu city were analyzed and the data was verified with that of ion chromatography (IC) analysis from one of the Korean-certified water quality evaluation centers

Solitary Extrahepatic Intraabdominal Metastasis from Hepatocellular Carcinoma after Liver Transplantation

A liver transplantation is a treatment option in selected patients with hepatocellular carcinoma (HCC). Despite the adequate selection of candidates, recurrences of HCC may still develop. Solitary extrahepatic metastasis from HCC after a liver transplantation is rare. Here we report two cases of HCC demonstrated extrahepatic recurrence to the adrenal gland and spleen, respectively, within one year after a liver transplantation. Since the treatment of solitary extrahepatic metastasis from HCC after a liver transplantation is not standardized, surgical resection was performed. In the case of HCC adrenal metastasis, innumerable intrahepatic metastases were found two months after the adrenalectomy. And 16 months after adrenalectomy, the patient expired due to tumor progression and hepatic failure. In the case of HCC splenic metastasis, postoperative radiation therapy was performed. However, two recurrent HCC nodules were found 15 months after the splenectomy and received transarterial chemoembolization (TACE). And 29 month after the splenectomy, the patient also expired as same causes of former patient

3D Cascaded U-Net with a Squeeze-and-Exicitation Block for Semantic Segmentation on Kidney and Renal Cell Carcinoma in Abdonimal Volumetric CT

Segmentation is a fundamental process in medical image analysis. Recently, convolutional neural networks (CNNs) has allowed for automatic segmentation; however, segmentaiton of complex organs and diseases including the kidney or renal cell carcinoma (RCC) remains a different task due to limited data and labor-intensive labeling work. The purpose of this study is to segment kideny and RCC in CT using cascaded 3D U-Net with a squeeze-and-excitation (SE) block using a cascaded method. 210 kidneys and their RCC in abdominal CT images were used as training and validation sets. The Dice similarity coefficients (DSCs) of kidney and RCC in test set were 0.963 and 0.734 respectively. The cascaded semantic segmentation can potentially reduce segmentation efforts and increase the efficiency in clinical workflow

A Study on the Android Based Livestock Vehicle Management System

In domestic livestock industry, economic damages of livestock farmhouses have been increased because the livestock mortality rate grows due to the spread of infectious animal diseases. The main cause of animal disease spread is a lack of systems to manage livestock vehicles transporting livestock or feed etc. This paper proposes a livestock vehicle management system based on Android for solving such a problem. The proposed system could prevent the spread of animal diseases in advance by collecting and analyzing the moving routes and access information of livestock-related vehicles. It could monitor moving routes of the contamination-suspected vehicles that visited a farm where the animal disease broke out. It is expected to prevent the livestock disease spread in advance through prompt initial prevention such as controlling the movement of vehicles by systematically collecting and managing information on vehicles accessing to livestock farmhouses through this system

Design and Implementation of ICT-Based System for Information Management of Livestock Farm

This paper proposes ICT based system for information management of livestock farm to provide efficiency operation in livestock farm by managing information of livestock farm such as livestock information and environment information and fire information. Proposed system provides optimal breeding environment by monitoring real-time information of livestock farm and manage overall information of livestock such as disease forecasting and estrus detection and delivery time. It is expected to increase productivity and earnings rate in livestock farm by systematically managing livestock information and economically operating livestock farm