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The stimuli-responsive self-folding structure is ubiquitous in nature, for instance, the mimosa folds its leaves in response to external touch or heat, and the Venus flytrap snaps shut to trap the insect inside. Thus, modeling self-folding structures has been of great interest to predict the final configuration and understand the folding mechanism. Here, we apply a simple yet effective method to predict the folding angle of the temperature-responsive nanocomposite hydrogel/elastomer bilayer structure manufactured by 3D printing, which facilitates the study of the effect of the inevitable variations in manufacturing and material properties on folding angles by comparing the simulation results with the experimentally measured folding angles. The defining feature of our method is to use thermal expansion to model the temperature-responsive nanocomposite hydrogel rather than the nonlinear field theory of diffusion model that was previously applied. The resulted difference between the simulation and experimentally measured folding angle ( i.e. , error) is around 5%. We anticipate that our method could provide insight into the design, control, and prediction of 3D printing of stimuli-responsive shape morphing ( i.e. , 4D printing) that have potential applications in soft actuators, robots, and biomedical devices.

Observations of the young solar wind by the Parker Solar Probe (PSP) mission reveal the existence of intense plasma wave bursts with frequencies between 0.05 and 0.20f_ce(tens of hertz up to ∼300 Hz) in the spacecraft frame. The wave bursts are often collocated with inhomogeneities in the solar wind magnetic field, such as local dips in magnitude or sudden directional changes. The observed waves are identified as electromagnetic whistler waves that propagate either sunward, anti-sunward, or in counter-propagating configurations during different burst events. Being generated in the solar wind flow, the waves experience significant Doppler downshift and upshift of wave frequency in the spacecraft frame for sunward and anti-sunward waves, respectively. Their peak amplitudes can be larger than 2 nT, where such values represent up to 10% of the background magnetic field during the interval of study. The amplitude is maximum for propagation parallel to the background magnetic field. We (i) evaluate the properties of these waves by reconstructing their parameters in the plasma frame, (ii) estimate the effective length of the PSP electric field antennas at whistler frequencies, and (iii) discuss the generation mechanism of these waves.

We demonstrate the realization of a very low energy, on-the-eye vergence-type distance ranger based on sensing of a locally-uniform vector field, specifically the earth’s magnetic field. This ranging method is passive, only requiring measurement of the magnetic field vector at both eyeballs utilizing magnetometer chips placed on the eye scleral regions. The eye vergence angle and range distance are calculated from these two vector quantities. The method can obtain a range reading with as little as 118 nJ of energy consumed per eye for 3.3V and 50 nJ when operated at 1.9V. This method is thus suitable for applications where energy storage is very limited such as in smart contacts vision correcting microsystems.

Supervised training of optical flow predictors generally yields better accuracy than unsupervised training. However, the improved performance comes at an often high annotation cost. Semi-supervised training trades off accuracy against annotation cost. We use a simple yet effective semi-supervised training method to show that even a small fraction of labels can improve flow accuracy by a significant margin over unsupervised training. In addition, we propose active learning methods based on simple heuristics to further reduce the number of labels required to achieve the same target accuracy. Our experiments on both synthetic and real optical flow datasets show that our semi-supervised networks generally need around 50% of the labels to achieve close to full-label accuracy, and only around 20% with active learning on Sintel. We also analyze and show insights on the factors that may influence active learning performance. Code is available at https://github.com/duke-vision/ optical-flow-active-learning-release.

Emoji are commonly used in social media to convey attitudes and emotions. While popular, their use in educational contexts has been sparsely studied. This paper reports on the students’ use of emoji in an online course forum in which students annotate and discuss course material in the margins of the online textbook. For this study, instructors created 11 custom emoji-hashtag pairs that enabled students to quickly communicate affects and reactions in the forum that they experienced while interacting with the course material. Example reporting includes, inviting discussion about a topic, declaring a topic as interesting, or requesting assistance about a topic. We analyze emoji usage by over 1,800 students enrolled in multiple offerings of the same course across multiple academic terms. The data show that some emoji frequently appear together in posts associated with the same paragraphs, suggesting that students use the emoji in this way to communicating complex affective states. We explore the use of computational models for predicting emoji at the post level, even when posts are lacking emoji. This capability can allow instructors to infer information about students’ affective states during their ”at home” interactions with course readings. Finally, we show that partitioning the emoji into distinctmore »groups, rather than trying to predict individual emoji, can be both of pedagogical value to instructors and improve the predictive performance of our approach using the BERT language model. Our procedure can be generalized to other courses and for the benefit of other instructors.« less

We propose MONet, a convolutional neural network that jointly detects motion boundaries and occlusion regions in video both forward and backward in time. Detection is difficult because optical flow is discontinuous along motion boundaries and undefined in occlusion regions, while many flow estimators assume smoothness and a flow defined everywhere. To reason in the two time directions simultaneously, we direct-warp the estimated maps between the two frames. Since appearance mismatches between frames often signal vicinity to motion boundaries or occlusion regions, we construct a cost block that for each feature in one frame records the lowest discrepancy with matching features in a search range. This cost block is two-dimensional, and much less expensive than the four-dimensional cost volumes used in flow analysis. Cost-block features are computed by an encoder, and motion boundary and occlusion region estimates are computed by a decoder. We found that arranging decoder layers fine-to- coarse, rather than coarse-to-fine, improves performance. MONet outperforms the prior state of the art for both tasks on the Sintel and FlyingChairsOcc benchmarks without any fine-tuning on them.