Sample Variance of the Higher-Order Cumulants of Cosmic Density and Velocity Fields

If primordial fluctuation is Gaussian distributed, higher-order cumulants of the cosmic fields reflect nonlinear mode coupling and provide useful information of gravitational instability picture of structure formation. We show that their expected deviation (sample variance) from the universal values is nonvanishing even in linear theory in the case where observed volume is finite. As a result, we find that the relative sample variance of the skewness of the smoothed velocity divergence field remains as large as $\sim 30$ even if the survey depth is as deep as $\sim 150h^{1}Mpc$.Comment: 8 pages including 1 figure, aas.te

Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation

We present Success weighted by Completion Time (SCT), a new metric for evaluating navigation performance for mobile robots. Several related works on navigation have used Success weighted by Path Length (SPL) as the primary method of evaluating the path an agent makes to a goal location, but SPL is limited in its ability to properly evaluate agents with complex dynamics. In contrast, SCT explicitly takes the agent's dynamics model into consideration, and aims to accurately capture how well the agent has approximated the fastest navigation behavior afforded by its dynamics. While several embodied navigation works use point-turn dynamics, we focus on unicycle-cart dynamics for our agent, which better exemplifies the dynamics model of popular mobile robotics platforms (e.g., LoCoBot, TurtleBot, Fetch, etc.). We also present RRT*-Unicycle, an algorithm for unicycle dynamics that estimates the fastest collision-free path and completion time from a starting pose to a goal location in an environment containing obstacles. We experiment with deep reinforcement learning and reward shaping to train and compare the navigation performance of agents with different dynamics models. In evaluating these agents, we show that in contrast to SPL, SCT is able to capture the advantages in navigation speed a unicycle model has over a simpler point-turn model of dynamics. Lastly, we show that we can successfully deploy our trained models and algorithms outside of simulation in the real world. We embody our agents in an real robot to navigate an apartment, and show that they can generalize in a zero-shot manner

Liquid-Liquid Direct Contact Heat Exchange Using a Perfluorocarbon Liquid for Waste Heat Recovery : Heat Transfer Characteristics obtained with Perfluorocarbon Droplets Descending in a Hot Water Medium

This paper deals with the heat transfer characteristics of a liquid-liquid direct contact operation in which a Perfluorocarbon (PFC) liquid is released in a hot water stream, a low-grade heat source such as urban sewage, for the purpose of heat recovery from it. The paper reports on a set of experiments in which a PFC liquid (1800 kg/m^3 at 20℃) was continuously injected from a single, downward-facing nozzle into a slow, upward flow of hot water to be disintegrated into droplets descending in, and thereby heated from the water flow. The results of the experiments show how the size distribution and the translational motions of the droplets affect the overall coefficient for the water-flow-to-droplets heat transfer and also the temperature effectiveness for the droplets.近年、未利用エネルギー活用の観点から、工場や家庭温排水などを熱源として利用する廃熱回収用熱交換器の開発が急務となっている。従来のシェルアンドチューブなどの隔壁型交換器では、伝熱面に排水中のごみやスケールが堆積し、その伝熱効率が著しく低下する等の問題点があった。この問題解決として、このような汚濁排水中に、非水溶性熱媒体を噴射・注入し、直接接触熱交換により、熱抽出が可能となる。このような直接接触熱交換法は、個体壁伝熱面の汚れによる伝熱効率低下の問題がなくなり、廃熱回収用熱交換方式として極めて有効である。さらに、この熱交換法は、個体壁を介さないために高い熱通過率が得られ、小温度差での熱交換に有効である。また、液－液の直接接触する界面が、そのまま伝熱面に相当するため、単位体積当たりの伝熱面積が増加する利点を有する。本研究は、下水等の汚れた熱水源より効率的な熱回収をする手段として液液直接熱交換法に注目したものであり、熱回収媒体としてフッ素系不活性液体を熱源水へ噴射し、形成したフッ素系不活性液滴群と熱源水の直接接触による、流動及び熱伝達特性を検討するものである。すなわち、円形単孔ノズルから高密度のフッ素系不活性液体を熱源である温水槽へ上部より噴射し、その液滴群形成過程の観察及び液滴群の流れの特性の解明を通じて、熱水源からの直接熱交換法による熱抽出に関する基礎特性を明らかにすることを目的とする。最終的に、この種の熱源水よりの熱回収媒体としてフッ素系不活性液体を用いた場合における実用に寄与する無次元熱伝達率等に関する実験整理式の検討をも行うものである

Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge

Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation. However, while impressive progress has been made for teaching embodied agents to navigate static environments, much less progress has been made on more dynamic environments that may include moving pedestrians or movable obstacles. In this study, we aim to benchmark different augmentation techniques for improving the agent's performance in these challenging environments. We show that adding several dynamic obstacles into the scene during training confers significant improvements in test-time generalization, achieving much higher success rates than baseline agents. We find that this approach can also be combined with image augmentation methods to achieve even higher success rates. Additionally, we show that this approach is also more robust to sim-to-sim transfer than image augmentation methods. Finally, we demonstrate the effectiveness of this dynamic obstacle augmentation approach by using it to train an agent for the 2021 iGibson Challenge at CVPR, where it achieved 1st place for Interactive Navigation. Video link: https://www.youtube.com/watch?v=HxUX2HeOSE

Japanese university students' behavior when reading english: a questionnaire survey and factor analysis

This paper reports on a specific cognitive behavior often found when trying to understand a text not written in readers’ native language. Our research group conducted a questionnaire survey to examine Japanese readers’ cognitive behavior and awareness when reading English texts. We also conducted a factor analysis on this questionnaire to identify the behaviors often found when reading English. Participants were 56 Japanese students studying engineering at Chuo University. After reading the texts, a questionnaire consisting of 43 items was applied to the participants. We used exploratory factor analysis to identify the primary factors related to readers’ cognitive behavior and awareness when reading a non-native language. As a result of the analysis, mainly based on the highest contributing factors, it was suggested that readers may have made substitutions into Japanese, their own words, when reading the English texts. In other words, when reading a non-native language, the reader may read the texts by replacing them with their native language rather than comprehending it in that language. Based on the results of our experiment, it is expected that the research on the cognitive supporting systems may help readers to understand non-native languages quickly and smoothly

ViNL: Visual Navigation and Locomotion Over Obstacles

We present Visual Navigation and Locomotion over obstacles (ViNL), which enables a quadrupedal robot to navigate unseen apartments while stepping over small obstacles that lie in its path (e.g., shoes, toys, cables), similar to how humans and pets lift their feet over objects as they walk. ViNL consists of: (1) a visual navigation policy that outputs linear and angular velocity commands that guides the robot to a goal coordinate in unfamiliar indoor environments; and (2) a visual locomotion policy that controls the robot's joints to avoid stepping on obstacles while following provided velocity commands. Both the policies are entirely "model-free", i.e. sensors-to-actions neural networks trained end-to-end. The two are trained independently in two entirely different simulators and then seamlessly co-deployed by feeding the velocity commands from the navigator to the locomotor, entirely "zero-shot" (without any co-training). While prior works have developed learning methods for visual navigation or visual locomotion, to the best of our knowledge, this is the first fully learned approach that leverages vision to accomplish both (1) intelligent navigation in new environments, and (2) intelligent visual locomotion that aims to traverse cluttered environments without disrupting obstacles. On the task of navigation to distant goals in unknown environments, ViNL using just egocentric vision significantly outperforms prior work on robust locomotion using privileged terrain maps (+32.8% success and -4.42 collisions per meter). Additionally, we ablate our locomotion policy to show that each aspect of our approach helps reduce obstacle collisions. Videos and code at http://www.joannetruong.com/projects/vinl.htm

Haemorheology of dense suspension of red blood cells under oscillatory shear flow

We present a numerical analysis of the rheology of a suspension of red blood cells (RBCs) for different volume fractions in a wall-bounded, effectively inertialess, oscillatory shear flow. The RBCs are modeled as biconcave capsules, whose membrane is an isotropic and hyperelastic material following the Skalak constitutive law, and the suspension examined for a wide range of applied frequencies. The frequency-dependent viscoelasticity in the bulk suspension is quantified by the complex viscosity, defined by the amplitude of the particle shear stress and the phase difference between the stress and shear. Our numerical results show that deformations of RBCs wekaly depend on the shear frequency, and the normal stress differences, membrane tension and amplitude of the shear stress are reduced by the oscillations. The frequency-dependent complex viscosity is nevertheless consistent with the classical behavior of non-Newtonian fluids, where the real part of the complex viscosity $\eta^\prime$ decreases as the frequency increases, and the imaginary part $\eta^{\prime\prime}$ exhibit a maximum value at an intermediate frequency. Such local maximum frequency is the same in both dense and dilute conditions. The effect of the viscosity ratios between the cytoplasm and plasma, volume fractions of RBCs, and oscillatory amplitudes represented by a capillary number on the complex viscosity are also assessed