High Quality Consistent Digital Curved Rays via Vector Field Rounding

We consider the consistent digital rays (CDR) of curved rays, which approximates a set of curved rays emanating from the origin by the set of rooted paths (called digital rays) of a spanning tree of a grid graph. Previously, a construction algorithm of CDR for diffused families of curved rays to attain an O(?{n log n}) bound for the distance between digital ray and the corresponding ray is known [Chun et al., 2019]. In this paper, we give a description of the problem as a rounding problem of the vector field generated from the ray family, and investigate the relation of the quality of CDR and the discrepancy of the range space generated from gradient curves of rays. Consequently, we show the existence of a CDR with an O(log ^{1.5} n) distance bound for any diffused family of curved rays

Efficacy of Abdominal Ultrasonography for Differentiation of Gastrointestinal Diseases in Calves

Gastrointestinal diseases represent one of the common causes of bovine acute abdomen, such as abdominal distention, abdominal pain, and cessation of defecation. In addition to the observable signs when performing routine auscultation, rectal palpation, and biochemical examinations of ruminal fluid and blood, these clinical observations can provide evidence suggestive of these diseases, but they generally result in an inconclusive diagnosis. Therefore, exploratory laparotomy is often used because it facilitates both diagnosis and therapeutic decisions. For bovines, abdominal ultrasonography is frequently utilized as a convenient imaging modality to assist accurate diagnosis and contribute to subsequent appropriate therapeutic choices for bovine gastrointestinal diseases. According to recent trends in human medicine and small animal practice, technical improvements have led to developments in the diagnostic value of abdominal ultrasonography, including scanning methods and the establishment of valuable diagnostic signs specific to a particular disease, e.g., a target sign for intussusception.This study investigated the clinical efficacy of abdominal ultrasonography for abomasal dilation in three calves, intestinal volvulus in five calves, intussusception in one calf, and internal hernia in one calf. In the abdominal ultrasonograms of the abomasal dilation cases, this disease was commonly characterized by severely extended lumens, including heterogeneously hyperechoic ingesta without intraluminal accumulations of gas. In the animals with intestinal volvulus and intussusception, a to-and-fro flow was observed to be a common ultrasonographic characteristic that led to suspicion of an intestinal obstruction. The use of abdominal ultrasonography for five cases with intestinal volvulus gave no reason to suspect this disease, despite its efficacy in one case, based on an acutely angled narrowing. Although three of five animals with intestinal volvulus had intestinal ruptures, no ultrasonographic evidence could be obtained. When abdominal ultrasonography was used for one case with intussusception, this pathological condition could be strongly suspected, as a “target” sign was observed. This finding supported surgical intervention for this case, followed by treatment with manual reduction, resulting in a favorable outcome. In terms of the differential and definitive diagnosis for various intestinal diseases, abdominal ultrasonography may be poor at providing indicative evidence, but very helpful for confirming intestinal obstruction

DynamicISP: Dynamically Controlled Image Signal Processor for Image Recognition

Image signal processor (ISP) plays an important role not only for human perceptual quality but also for computer vision. In most cases, experts resort to manual tuning of many parameters in the ISPs for perceptual quality. It failed in sub-optimal, especially for computer vision. Aiming to improve ISPs, two approaches have been actively proposed; tuning the parameters with machine learning, or constructing an ISP with DNN. The former is lightweight but lacks expressive powers. The latter has expressive powers but it was too heavy to calculate on edge devices. To this end, we propose DynamicISP, which consists of traditional simple ISP functions but their parameters are controlled dynamically per image according to what the downstream image recognition model felt to the previous frame. Our proposed method successfully controlled parameters of multiple ISP functions and got state-of-the-art accuracy with a small computational cost

Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments

Image recognition models that work in challenging environments (e.g., extremely dark, blurry, or high dynamic range conditions) must be useful. However, creating training datasets for such environments is expensive and hard due to the difficulties of data collection and annotation. It is desirable if we could get a robust model without the need for hard-to-obtain datasets. One simple approach is to apply data augmentation such as color jitter and blur to standard RGB (sRGB) images in simple scenes. Unfortunately, this approach struggles to yield realistic images in terms of pixel intensity and noise distribution due to not considering the non-linearity of Image Signal Processors (ISPs) and noise characteristics of image sensors. Instead, we propose a noise-accounted RAW image augmentation method. In essence, color jitter and blur augmentation are applied to a RAW image before applying non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a noise amount alignment method that calibrates the domain gap in the noise property caused by the augmentation. We show that our proposed noise-accounted RAW augmentation method doubles the image recognition accuracy in challenging environments only with simple training data.Comment: Accepted to CVPR202

Preliminary Report of the Waseda University Excavations at Dahshur North:Tenth Season,2004-2005

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Objcache: An Elastic Filesystem over External Persistent Storage for Container Clusters

Container virtualization enables emerging AI workloads such as model serving, highly parallelized training, machine learning pipelines, and so on, to be easily scaled on demand on the elastic cloud infrastructure. Particularly, AI workloads require persistent storage to store data such as training inputs, models, and checkpoints. An external storage system like cloud object storage is a common choice because of its elasticity and scalability. To mitigate access latency to external storage, caching at a local filesystem is an essential technique. However, building local caches on scaling clusters must cope with explosive disk usage, redundant networking, and unexpected failures. We propose objcache, an elastic filesystem over external storage. Objcache introduces an internal transaction protocol over Raft logging to enable atomic updates of distributed persistent states with consistent hashing. The proposed transaction protocol can also manage inode dirtiness by maintaining the consistency between the local cache and external storage. Objcache supports scaling down to zero by automatically evicting dirty files to external storage. Our evaluation reports that objcache speeded up model serving startup by 98.9% compared to direct copies via S3 interfaces. Scaling up with dirty files completed from 2 to 14 seconds with 1024 dirty files.Comment: 13 page