Adaptive state construction for reinforcement learning and its application to robot navigation problems

This paper applies our state construction method by ART neural network to robot navigation problems. Agents in this paper consist of ART neural network and contradiction resolution mechanism. The ART neural network serves as a mean of state recognition which maps stimulus inputs to a certain state and state construction which creates a new state when a current stimulus input cannot be categorized into any known states. On the other hand, the contradiction resolution mechanism (CRM) uses agents' state transition table to detect inconsistency among constructed states. In the proposed method, two kinds of inconsistency for the CRM are introduced: &#34;Different results caused by the same states and the same actions&#34; and &#34;Contradiction due to ambiguous states.&#34; The simulation results on the robot navigation problems confirm the effectiveness of the proposed method</p

An incremental state-segmentation method for reinforcement learning using ART neural network

In this paper, we propose a new incremental state segmentation method by utilizing information of the agents' state transition table which consists of a tuple of (state; action, state) in order to reduce the effort of designers and which is generated using the ART neural network. In the proposed method, if an inconsistent situation in the state transition table is observed, agents refine their map from perceptual inputs to states such that inconsistency is resolved. We introduce two kinds of inconsistency, i.e., different results caused by the same states and the same actions, and contradiction due to ambiguous states. Several computational simulations on cart-pole problems confirm the effectiveness of the proposed method</p

Coevolutionary GA with schema extraction by machine learning techniques and its application to knapsack problems

The authors introduce a novel coevolutionary genetic algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in a natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm the effectiveness of the proposed method</p

Accelerating revised RBF neural network

This study aimed to accelerate the segmentation of organs in medical imaging with the revised radial basis function (RBF) network, using a graphics processing unit (GPU). We segmented the lung and liver regions from 250 chest x-ray computed tomography (CT) images and 160 abdominal CT images, respectively, using the revised RBF network. We compared the time taken to segment images and their accuracy between serial processing by a single-core central processing unit (CPU), parallel processing using four CPU cores, and GPU processing. Segmentation times for lung and liver organ regions shortened to 57.80 and 35.35 seconds for CPU parallel processing and 20.16 and 11.02 seconds for GPU processing, compared to 211.03 and 124.21 seconds for CPU serial processing, respectively. The concordance rate of the segmented region to the normal region in slices excluding the upper and lower ends (173 lung and 111 liver slices) was 98% for lung and 96% for liver. The use of CPU parallel processing and GPU shortened the organ segmentation time in the revised RBF network without compromising segmentation accuracy. In particular, segmentation time was shortened to less than 10% with GPU. This processing method will contribute to workload reduction in imaging analysis

Impact of postoperative complications after primary tumor resection on survival in patients with incurable stage IV colorectal cancer: A multicenter retrospective cohort study

[Aims] Primary tumor resection for patients with incurable stage IV colorectal cancer can prevent tumor-related complications but may cause postoperative complications. Postoperative complications delay the administration of chemotherapy and can lead to the spread of malignancy. However, the impact of postoperative complications after primary tumor resection on survival in patients with incurable stage IV colorectal cancer remains unclear. Therefore, this study aimed to investigate how postoperative complications after primary tumor resection affect survival in this patient group. [Methods] We reviewed data on 966 patients with stage IV colorectal cancer who underwent palliative primary tumor resection between January 2006 and December 2007. We examined the association between major complications (National Cancer Institute Common Terminology Criteria for Adverse Events v3.0 grade 3 or more) and overall survival using Cox proportional hazard model and explored risk factors associated with major complications using multivariable logistic regression analysis. [Results] Ninety-three patients (9.6%) had major complications. The 2-year overall survival rate was 32.7% in the group with major complications and 50.3% in the group with no major complications. Patients with major complications had a significantly poorer prognosis than those without major complications (hazard ratio: 1.62; 95% confidence interval: 1.21-2.18; P < .01). Male, rectal tumor, and open surgery were identified to be risk factors for major complications. [Conclusions] Postoperative complications after primary tumor resection was associated with decreased long-term survival in patients with incurable stage IV colorectal cancer

Effect of Asian dust on pulmonary function in adult asthma patients in western Japan: A panel study

AbstractBackgroundAsian dust (AD) has become a major health concern. The concentration of AD is typically expressed in particulate matter less than 10 μm (PM10) and 2.5 μm (PM2.5). However, PM10 and PM2.5 consist of various substances besides AD. Light detection and ranging (LIDAR) systems can selectively measure the quantity of AD particles to distinguish non-spherical airborne particles from spherical airborne particles. The objective of this study was to investigate the relationship between pulmonary function in adult asthma patients and AD using LIDAR data.MethodsSubjects were 231 adult asthma patients who had their morning peak expiratory flow (PEF) measured from March to May 2012. A linear mixed model was used to estimate the association of PEF with sand dust particles detected by LIDAR.ResultsIncreases in the interquartile range of AD particles (0.018 km−1) led to changes in PEF of −0.42 L/min (95% confidence interval [CI], −0.85 to 0.01). An increase of 11.8 μg/m3 in suspended particulate matter and 6.9 μg/m3 in PM2.5 led to decreases of −0.17 L/min (−0.53 to 0.21) and 0.03 L/min (−0.35 to 0.42), respectively. A heavy AD day was defined as a day with a level of AD particles >0.032 km−1, which was the average plus one standard deviation during the study period, and six heavy AD days were identified. Change in PEF after a heavy AD day was −0.97 L/min (−1.90 to −0.04).ConclusionsHeavy exposure to AD particles was significantly associated with decreased pulmonary function in adult asthma patients

Development and validation of a new scoring system for prognostic prediction of community-acquired pneumonia in older adults

The discriminative power of CURB-65 for mortality in community-acquired pneumonia (CAP) is suspected to decrease with age. However, a useful prognostic prediction model for older patients with CAP has not been established. This study aimed to develop and validate a new scoring system for predicting mortality in older patients with CAP. We recruited two prospective cohorts including patients aged ≥ 65 years and hospitalized with CAP. In the derivation (n = 872) and validation cohorts (n = 1, 158), the average age was 82.0 and 80.6 years and the 30-day mortality rate was 7.6% (n = 66) and 7.4% (n = 86), respectively. A new scoring system was developed based on factors associated with 30-day mortality, identified by multivariate analysis in the derivation cohort. This scoring system named CHUBA comprised five variables: confusion, hypoxemia (SpO2 ≤ 90% or PaO2 ≤ 60 mmHg), blood urea nitrogen ≥ 30 mg/dL, bedridden state, and serum albumin level ≤ 3.0 g/dL. With regard to 30-day mortality, the area under the receiver operating characteristic curve for CURB-65 and CHUBA was 0.672 (95% confidence interval, 0.607–0.732) and 0.809 (95% confidence interval, 0.751–0.856; P < 0.001), respectively. The effectiveness of CHUBA was statistically confirmed in the external validation cohort. In conclusion, a simpler novel scoring system, CHUBA, was established for predicting mortality in older patients with CAP