Self-Calibrating, Fully Differentiable NLOS Inverse Rendering

Existing time-resolved non-line-of-sight (NLOS) imaging methods reconstruct hidden scenes by inverting the optical paths of indirect illumination measured at visible relay surfaces. These methods are prone to reconstruction artifacts due to inversion ambiguities and capture noise, which are typically mitigated through the manual selection of filtering functions and parameters. We introduce a fully-differentiable end-to-end NLOS inverse rendering pipeline that self-calibrates the imaging parameters during the reconstruction of hidden scenes, using as input only the measured illumination while working both in the time and frequency domains. Our pipeline extracts a geometric representation of the hidden scene from NLOS volumetric intensities and estimates the time-resolved illumination at the relay wall produced by such geometric information using differentiable transient rendering. We then use gradient descent to optimize imaging parameters by minimizing the error between our simulated time-resolved illumination and the measured illumination. Our end-to-end differentiable pipeline couples diffraction-based volumetric NLOS reconstruction with path-space light transport and a simple ray marching technique to extract detailed, dense sets of surface points and normals of hidden scenes. We demonstrate the robustness of our method to consistently reconstruct geometry and albedo, even under significant noise levels

Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards

Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text—without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue histories of ≥10 sentences

Ensemble-Based Deep Reinforcement Learning for Chatbots

Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing this challenge, but its successful application remains an open question. This article describes a novel ensemble-based approach applied to value-based DRL chatbots, which use finite action sets as a form of meaning representation. In our approach, while dialogue actions are derived from sentence clustering, the training datasets in our ensemble are derived from dialogue clustering. The latter aim to induce specialised agents that learn to interact in a particular style. In order to facilitate neural chatbot training using our proposed approach, we assume dialogue data in raw text only – without any manually-labelled data. Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent. In addition to evaluations using held-out data, our results are further supported by a human evaluation that rated dialogues in terms of fluency, engagingness and consistency – which revealed that our proposed dialogue rewards strongly correlate with human judgements

Effects of heat stress on conception in Holstein and Jersey cattle and oocyte maturation in vitro

Korea, located in East Asia in the northern hemisphere, is experiencing severe climate changes. Specifically, the heat stress caused by global warming is negatively affecting the dairy sector, including milk production and reproductive performance, as the major dairy cattle Holstein-Friesian is particularly susceptible to heat stress. Here, we collected artificial insemination and pregnancy data of the Holstein and the Jersey cows from a dairy farm from 2014 to 2021 and analyzed the association between the conception rate and the temperature-humidity index, calculated using the data from the closest official weather station. As the temperature-humidity index threshold increased, the conception rate gradually decreased. However, this decrease was steeper in the Holstein breed than in the Jersey one at a temperature-humidity index threshold of 75. To evaluate the effects of heat stress on the oocyte quality, we examined the nuclear and cytoplasmic maturation of Holstein (n = 158, obtained from six animals) and Jersey oocytes (n = 123, obtained from six animals), obtained by ovum pick-up. There were no differences in the nuclear maturation between the different conditions (heat stress: 40.5°C, non- heat stress: 37.5°C) or breeds, although the Holstein oocytes seemed to have a lower metaphase II development (p = 0.0521) after in vitro maturation under heat stress conditions. However, we found that the Holstein metaphase II oocytes exposed to heat stress presented more reactive oxygen species and a peripheral distribution of the mitochondria, compared to those of the Jersey cattle. Here, we show that weather information from local meteorological stations can be used to calculate the temperature-humidity index threshold at which heat stress influences the conception rate, and that the Jersey cows are more tolerant to heat stress in terms of their conception rate at a temperature-humidity index over 75. The lower fertility of the Holstein cows is likely attributed to impaired cytoplasmic maturation induced by heat stress. Thus, the Jersey cows can be a good breed for the sustainability of dairy farms for addressing climate changes in South Korea, as they are more resistant to hyperthermia

Differences of Clinical Characteristics and Phenotypes between Prepubertal- and Adolescent-Onset Bipolar Disorders

The aim of this study is to describe the clinical characteristics of prepubertal- and adolescent-onset bipolar disorder (BD) and to identify any clinical differences between patients with prepubertal- and adolescent-onset BD. We analyzed the clinical records of 53 inpatients with BD. These patients were divided into prepubertal-onset and adolescent-onset groups. We also divided the subjects into narrow, intermediate, and broad phenotypes according to the definitions proposed by Leibenluft and colleagues. Of the total sample, 16 patients (30.2%) were in the prepubertal-onset group and 37 (69.8%) were in the adolescent-onset group. Patients with prepubertal-onset BD were more likely to display an insidious clinical presentation, atypical features, and comorbid psychopathology. And the majority of the subjects, especially in the prepubertal-onset group, were classified under the intermediate and broad phenotypes. These results suggest that the clinical presentation of BD with prepubertal-onset is different from that of adolescent-onset BD. It is inferred that a significant number of patients with prepubertal- and adolescent-onset BD do not meet DSM-IV criteria for mania or hypomania from the results of this study

Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation

Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented