Comet 9P/Tempel 1: Interpretation with the Deep Impact Results

According to our common understandings, the original surface of a short-period comet nucleus has been lost by sublimation processes during its close approaches to the Sun. Sublimation results in the formation of a dust mantle on the retreated surface and in chemical differentiation of ices over tens or hundreds of meters below the mantle. In the course of NASA's Deep Impact mission, optical and infrared imaging observations of the ejecta plume were conducted by several researchers, but their interpretations of the data came as a big surprise: (1) The nucleus of comet 9P/Tempel 1 is free of a dust mantle, but maintains its pristine crust of submicron-sized carbonaceous grains; (2) Primordial materials are accessible already at a depth of several tens of cm with abundant silicate grains of submicrometer sizes. In this study, we demonstrate that a standard model of cometary nuclei explains well available observational data: (1) A dust mantle with a thickness of ~1-2 m builds up on the surface, where compact aggregates larger than tens of micrometers dominate; (2) Large fluffy aggregates are embedded in chemically differentiated layers as well as in the deepest part of the nucleus with primordial materials. We conclude that the Deep Impact results do not need any peculiar view of a comet nucleus.Comment: 11 pages, 1 figure, 1 table. ApJ letters, 673, L199-20

Where did the super-small sized large bowel advanced cancer come from?

Our study suggested that the super-small sized (less than 15 mm in maximum diameter) large bowel advanced cancers, which were sometimes found, were derived from the superficial depressed-type or flat elevation-type of the colorectal early cancers, not polyp-type of those

Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis

Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive learning, the investigation of how to characterize the clusters of the learned representations is still limited. In this paper, we aim to elucidate the characterization from theoretical perspectives. To this end, we consider a kernel-based contrastive learning framework termed Kernel Contrastive Learning (KCL), where kernel functions play an important role when applying our theoretical results to other frameworks. We introduce a formulation of the similarity structure of learned representations by utilizing a statistical dependency viewpoint. We investigate the theoretical properties of the kernel-based contrastive loss via this formulation. We first prove that the formulation characterizes the structure of representations learned with the kernel-based contrastive learning framework. We show a new upper bound of the classification error of a downstream task, which explains that our theory is consistent with the empirical success of contrastive learning. We also establish a generalization error bound of KCL. Finally, we show a guarantee for the generalization ability of KCL to the downstream classification task via a surrogate bound

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

Domain shifts in the training data are common in practical applications of machine learning, they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. Moreover, privacy concerns regarding the source also require a domain-invariant representation. In this work, we provide theoretical results that link domain invariant representations -- measured by the Wasserstein distance on the joint distributions -- to a practical semi-supervised learning objective based on a cross-entropy classifier and a novel domain critic. Quantitative experiments demonstrate that the proposed approach is indeed able to practically learn such an invariant representation (between two domains), and the latter also supports models with higher predictive accuracy on both domains, comparing favorably to existing techniques.Comment: 20 pages including appendix. Under Revie

Major liver resection reduces nonprotein respiratory quotient and increases nonesterified fatty acid at postoperative day 14 in patients with hepatocellular carcinoma

Background & aims: We reported decreased nonprotein respiratory quotient (npRQ) after liver resection in patients with hepatocellular carcinoma (HCC); however, whether liver resection volume affects energy metabolism in these patients is unclear. We aimed to examine the relationship between liver resection and energy metabolism indices. Methods: NpRQ was measured in 53 patients with HCC and seven with at the pre- and postoperative days. Patients were classified into four groups: Minor-lowICG group (n = 17): minor (subsegment or less) resection and low indocyanine green retention rate at 15 min (ICGR15) (<15%); Minor-highICG group (n = 18): minor resection and high ICGR15 (≥15%) and Major-lowICG group (n = 18): major (lobe) resection and low ICGR15 (<15%). We investigated dietary intake and blood biochemistry at energy measurement. The difference in npRQ and nonesterified fatty acid (NEFA) pre- and post-hepatectomy was shown as ΔnpRQ and ΔNEFA, respectively. Results: Compared with the preoperative values, npRQ significantly decreased in the Minor-highICG and Major-lowICG groups and NEFA significantly increased in the Major-lowICG group at postoperative day 14. In single regression analysis, ΔnpRQ significantly correlated with HCV infection and ΔNEFA with resection volume, HCV infection, and ICGR15. In multiple regression analysis, ΔNEFA significantly correlated with resection volume after adjusting for age, etiology, and ICGR15. Conclusions: These results suggest that postoperative nutritional recovery is slower in major resection than in minor resection patients. Hence, nutritional care to prevent starvation is needed in major resection patients

Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning

Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstream tasks efficiently. However, recent works pay little attention to the fact that real-world datasets used during the stage of representation learning are commonly contaminated by noise, which can degrade the quality of learned representations. This paper tackles the problem to learn robust representations against noise in a raw dataset. To this end, inspired by recent works on denoising and the success of the cosine-similarity-based objective functions in representation learning, we propose the denoising Cosine-Similarity (dCS) loss. The dCS loss is a modified cosine-similarity loss and incorporates a denoising property, which is supported by both our theoretical and empirical findings. To make the dCS loss implementable, we also construct the estimators of the dCS loss with statistical guarantees. Finally, we empirically show the efficiency of the dCS loss over the baseline objective functions in vision and speech domains

Carnitine for Body Composition in Hemodialysis Patients

Background: Authors and colleagues have continued clinical research for hemodialysis patients. Currently, a pilot study presents intervention of carnitine for changes of the body composition. Subjects and Methods: Subjects were six patients on hemodialysis with intervention of carnitine (group 1). Average data were 74.3 years, 65.4 kg, 22.6 in BMI. As levocarnitine, L-Cartin FF injection 1000 mg was administered three times a week for six months. Group 2 has six control patients for age-, sex-, body weight, BMI-matched (group 2). Body composition of muscle and fat tissues were measured by InBody 770 on 0 and 6 months. Results: In group 1, muscle volume and skeletal muscle showed increasing tendency without statistical significance. In contrast, there were significant decreases of body fat volume (22.3 kg vs 20.5 kg, 39.0% vs 35.8%) (p<0.05). No significant differences were found in hemoglobin, total protein, albumin and Cardio-Thoracic Ratio (CTR) of chest X-ray. Group 2 showed no significant changes. Discussion and Conclusion: Hemodialysis patients often have muscular reduction. Previous reports showed improved lean body mass by carnitine administration, which may support our result. These results from current pilot study would be expected to become useful reference data in the pathophysiological investigation in patients on hemodialysis