How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective

Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data

Relative Etiologic Role of Hepatitis B Virus and Hepatitis C Virus in Chronic Liver Diseases and Hepatocellular Carc inoma among Age-Spec i f ic Groups in Ko rea: the Poss ib Ie Presence of Non-B, Non-C Agents

Korea is one of the endemic areas of chronic hepatitis B virus (HBV) infection. To investigate the relative etiologic role of HBV and hepatitis C virus (HCV) in chronic liver diseases (CLD) including hepatocellular carcinoma (HCC) among age-specific groups in Korea, we enrolled consecutively 673 patients with chronic active hepatitis (CAH), 677 patients with liver cirrhosis (LC) and patients with HCC who had been diagnosed in the liver unit at Seoul National University Hospital. HBsAg and anti-HCV were tested using commercially available radioimmunoassay and enzyme immunoassay kits, respectively. From this study, we were reached at suggestion for the possible presence of non-B, non-C type CLD agent(s) by exclusion method. The prevalence rates of HBsAg were 45.3%, 62.5% and 69.3% in patients with CAH, LC and HCC, respectively. The general prevalence rates of anti-HCV in patients with CAH, LC and HCC were 27.3%, 19. 6% and 17%, respectively, and, however, in HBsAg-negative patients with CAH, LC and HCC those were 48.1%, 46.1% and 42.7%, respectively. The coinfection rates of HBV and HCV in patients with CAH, LC and Hec were 1%, 2.4% and 3. 9%, respectively. The rates of CAH, LC and HCC patients who were negative for both HBsAg and anti-HCV and therefore, serologically classified as non-B, non-C type were 28.4%, 20.2% and 17.6%, respectively. There was a significant differeence in mean age between B- and C-type, and Band non-B, non-C type patients with CAH (41.7 vs 54.5 and 50.4 years), LC (48.5 vs 60.1 and 54.9 years) and HCC (51.6 vs 60.4 and 56.1 years) (p < 0.001, respectively). Before the age of 50, the etiology of CAH and LC was almost exclusively HBV, while over the age of 50, the etiologic role of HCV and non-B, non-C was more predominant than that of HBV. In elderly (older than 60 years of age) patients even with HCC, HCV played an etiologic role as important as HBV. In conclusion, H8V is the most common etiologic agent of CLD in Korea. However, HCV and non-B, non-C infection is a more important etiology in elderly patients with CLD older than 50 years of age

Active motions of Brownian particles in a generalized energy-depot model

We present a generalized energy-depot model in which the conversion rate of the internal energy into motion can be dependent on the position and the velocity of a particle. When the conversion rate is a general function of the velocity, the active particle exhibits diverse patterns of motion including a braking mechanism and a stepping motion. The phase trajectories of the motion are investigated in a systematic way. With a particular form of the conversion rate dependent on the position and velocity, the particle shows a spontaneous oscillation characterizing a negative stiffness. These types of active behaviors are compared with the similar phenomena observed in biology such as the stepping motion of molecular motors and the amplification in hearing mechanism. Hence, our model can provide a generic understanding of the active motion related to the energy conversion and also a new control mechanism for nano-robots. We also investigate the noise effect, especially on the stepping motion and observe the random walk-like behavior as expected.Comment: to appear in New J. Phy

Development of an Evaluation Methodology for Loss of Large Area induced from Extreme Events with malicious origin

Event of loss of large area (LOLA) induced from extreme external event at multi-units nuclear installation has been emerged a new challenges in the realm of nuclear safety and regulation after Fukushima Dai-Ichi accident. The relevant information and experience on evaluation methodology and regulatory requirements are rarely available and negative to share due to the security sensitivity. Most of countries has been prepared their own regulatory requirements and methodologies to evaluate impact of LOLA at nuclear power plant. In Korea, newly amended the Nuclear Safety Acts requires to assess LOLA in terms of EDMG (Extended Damage Mitigation Guideline). Korea Institute of Nuclear Safety (KINS) has performed a pilot research project to develop the methodology and regulatory review guidance on LOLA at multi-units nuclear power plant since 2014. Through this research, we proposed a methodology to identify the strategies for preventive and mitigation of the consequences of LOLA utilizing PSA techniques or its results. The proposed methodology is comprised of 8 steps including policy consideration, threat evaluation, identification of damage path sets, SSCs capacity evaluation and identification of mitigation measures and strategies. The consequence of LOLA due to malevolent aircraft crash may significantly susceptible with analysis assumptions including type of aircraft, amount of residual fuel, and hittable angle and so on, which cannot be shared overtly. This paper introduces a evaluation methodology for LOLA using PSA technique and its results. Also we provide a case study to evaluate hittable access angle using flight simulator for two types of aircrafts and to identify potential path sets leading to core damage by affected SSCs within damaged area

Polyelectrolyte complex micelles by self-assembly of polypeptide-based triblock copolymer for doxorubicin delivery

AbstractPolyelectrolyte complex micelles were prepared by self-assembly of polypeptide-based triblock copolymer as a new drug carrier for cancer chemotherapy. The triblock copolymer, poly(l-aspartic acid)-b-poly(ethylene glycol)-b-poly(l-aspartic acid) (PLD-b-PEG-b-PLD), spontaneously self-assembled with doxorubicin (DOX) via electrostatic interactions to form spherical micelles with a particle size of 60–80 nm (triblock ionomer complexes micelles, TBIC micelles). These micelles exhibited a high loading capacity of 70% (w/w) at a drug/polymer ratio of 0.5 at pH 7.0. They showed pH-responsive release patterns, with higher release at acidic pH than at physiological pH. Furthermore, DOX-loaded TBIC micelles exerted less cytotoxicity than free DOX in the A-549 human lung cancer cell line. Confocal microscopy in A-549 cells indicated that DOX-loaded TBIC micelles were transported into lysosomes via endocytosis. These micelles possessed favorable pharmacokinetic characteristics and showed sustained DOX release in rats. Overall, these findings indicate that PLD-b-PEG-b-PLD polypeptide micelles are a promising approach for anti-cancer drug delivery

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described