Bayesian Inference of Self-intention Attributed by Observer

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor's mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person's own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people's judgment. The results showed that the agent's intention that people attributed to the agent's movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent's behavior

Modeling Reliance on XAI Indicating Its Purpose and Attention

This study used XAI, which shows its purposes and attention as explanations of its process, and investigated how these explanations affect human trust in and use of AI. In this study, we generated heat maps indicating AI attention, conducted Experiment 1 to confirm the validity of the interpretability of the heat maps, and conducted Experiment 2 to investigate the effects of the purpose and heat maps in terms of reliance (depending on AI) and compliance (accepting answers of AI). The results of structural equation modeling (SEM) analyses showed that (1) displaying the purpose of AI positively and negatively influenced trust depending on the types of AI usage, reliance or compliance, and task difficulty, (2) just displaying the heat maps negatively influenced trust in a more difficult task, and (3) the heat maps positively influenced trust according to their interpretability in a more difficult task

Phosphorylated Smad2 in Advanced Stage Gastric Carcinoma

<p>Abstract</p> <p>Background</p> <p>Transforming growth factor β (TGFβ) receptor signaling is closely associated with the invasion ability of gastric cancer cells. Although Smad signal is a critical integrator of TGFβ receptor signaling transduction systems, not much is known about the role of Smad2 expression in gastric carcinoma. The aim of the current study is to clarify the role of phosphorylated Smad2 (p-Smad2) in gastric adenocarcinomas at advanced stages.</p> <p>Methods</p> <p>Immunohistochemical staining with anti-p-Smad2 was performed on paraffin-embedded specimens from 135 patients with advanced gastric adenocarcinomas. We also evaluated the relationship between the expression levels of p-Smad2 and clinicopathologic characteristics of patients with gastric adenocarcinomas.</p> <p>Results</p> <p>The p-Smad2 expression level was high in 63 (47%) of 135 gastric carcinomas. The p-Smad2 expression level was significantly higher in diffuse type carcinoma (p = 0.007), tumours with peritoneal metastasis (p = 0.017), and tumours with lymph node metastasis (p = 0.047). The prognosis for p-Smad2-high patients was significantly (p = 0.035, log-rank) poorer than that of p-Smad2-low patients, while a multivariate analysis revealed that p-Smad2 expression was not an independence prognostic factor.</p> <p>Conclusion</p> <p>The expression of p-Smad2 is associated with malignant phenotype and poor prognosis in patients with advanced gastric carcinoma.</p

Microstructural transitions in resistive random access memory composed of molybdenum oxide with copper during switching cycles

The switching operation of a Cu/MoOx/TiN resistive random access memory (ReRAM) device was investigated using in situ transmission electron microscopy (TEM), where the TiN surface was slightly oxidized (ox-TiN). The relationship between the switching properties and the dynamics of the ReRAM microstructure was confirmed experimentally. The growth and/or shrinkage of the conductive filament (CF) can be classified into two set modes and two reset modes. These switching modes depend on the device's switching history, factors such as the amount of Cu inclusions in the MoOx layer and the CF geometry. High currents are needed to produce an observable change in the CF. However, sharp and stable switching behaviour can be achieved without requiring such a major change. The local region around the CF is thought to contribute to the ReRAM switching process

The Status of Stress Myocardial Perfusion Imaging Using 99mTc Pharmaceuticals in Japan: Results from a Nationwide Survey

Objective(s): To appropriately use one-day myocardial perfusion imaging(MPI) with 99mTc radiopharmaceuticals (i.e. to avoid shine-throughartifacts), injection doses need to be optimized and dose ratios betweenthe 1st and 2nd scans should be maintained at ≥ 3. However, the current stateof practice in Japan is unclear. Thus, the aim of this study was to clarify thedetails of MPI protocols using 99mTc radiopharmaceuticals in Japan.Methods: A nationwide survey was conducted in June and July 2016.Questionnaires about stress MPI protocols using 99mTc radiopharmaceuticalswere sent to 641 nuclear medicine facilities.Results: Responses were received from 246 facilities. One-day protocolswere used in 97.1% of the facilities. The most common injection dose ratiowas 2.5. Only 18.2% of facilities achieved the recommended injection doseratio. Stress-only protocols were performed in only 1.7% of facilities; theprimary reasons for not performing stress-only protocols were as follows:1) “The reading-physician cannot interpret the image just after the firstscan,” and 2) “Preparation of radiopharmaceuticals and scan arrangementsturn out to be complicated.”Conclusion: Approximately 80% of nuclear medicine facilities do notfollow the recommended injection dose ratio. Stress-only protocols areideal, but are performed at very few facilities. Both optimization andstandardization of stress MPI protocols using 99mTc radiopharmaceuticalsare needed in Japan

Mask and Cloze: Automatic Open Cloze Question Generation Using a Masked Language Model

This paper conducts the first trial to apply a masked language AI model and the &#x201C;Gini coefficient&#x201D; to the field of English study. We propose an algorithm named CLOZER that generates open cloze questions that inquiry knowledge of English learners. Open cloze questions (OCQ) have been attracting attention for both measuring the ability and facilitating the learning of English learners. However, since OCQ is in free form, teachers have to ensure that only a ground truth answer and no additional words will be accepted in the blank. A remarkable benefit of CLOZER is to relieve teachers of the burden of producing OCQ. Moreover, CLOZER provides a self-study environment for English learners by automatically generating OCQ. We evaluated CLOZER through quantitative experiments on 1,600 answers and show its effectiveness statistically. Compared with human-generated questions, we also revealed that CLOZER can generate OCQs better than the average non-native English teacher. Additionally, we conducted a field study at a high school to clarify the benefits and hurdles when introducing CLOZER. Then, on the basis of our findings, we proposed several design improvements