Seeing the Unheard: dynamics of thin liquid film in holographic ultrasonic field revealed by time-resolved Schlieren imaging

In this study, we introduce a unique approach that employs time-resolved Schlieren imaging to capture and visualize the dynamic changes of a thin liquid (mixture of water, soap and glycerin) film in ultrasonic wave field with high spatial and temporal resolution. By placing a soap film spanning a wire frame vertically in the path of light, we harnessed the vibrations induced by the ultrasonic waves, resulting in remarkable Schlieren imaging patterns. The investigation not only uncovers an unexpected branch flow phenomenon within the film, challenging existing assumptions, but also reveals a fascinating interplay between vortex flow and branch flow. The experiments have revealed a captivating spectrum of dynamic phenomena within the thin liquid films. The observation of small-scale capillary waves, large-scale standing waves, traveling waves, and the intricate fusion of capillary-gravity wave patterns underscores the rich complexity inherent in the interaction between the films and the holographic ultrasonic wave field. These diverse states of film dynamics provide a comprehensive understanding of the intricate interplay between various wave modes and fluid behavior, further enhancing comprehension of this fascinating phenomenon. The ability to visualize the pressure field opens up new avenues for optimizing acoustic levitation techniques, investigating particle behavior, and exploring potential applications in materials science and bioengineering.Comment: 10 pages, 8 page

QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning

Daily images may convey abstract meanings that require us to memorize and infer profound information from them. To encourage such human-like reasoning, in this work, we teach machines to predict where and when it was taken rather than performing basic tasks like traditional segmentation or classification. Inspired by Horn's QR theory, we designed a novel QR-CLIP model consisting of two components: 1) the Quantity module first retrospects more open-world knowledge as the candidate language inputs; 2) the Relevance module carefully estimates vision and language cues and infers the location and time. Experiments show our QR-CLIP's effectiveness, and it outperforms the previous SOTA on each task by an average of about 10% and 130% relative lift in terms of location and time reasoning. This study lays a technical foundation for location and time reasoning and suggests that effectively introducing open-world knowledge is one of the panaceas for the tasks.Comment: Technical Report. Github: https://github.com/Shi-Wm/QR-CLI

Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans

Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation classes (along with new training datasets or not) are required to be added. In real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually-trained then frozen encoder coupled with incrementally-added decoders can extract and preserve sufficiently representative image features for new classes to be subsequently and validly segmented. To maintain a single network model complexity, we trim each decoder progressively using neural architecture search and teacher-student based knowledge distillation. To incorporate with both healthy and pathological organs appearing in different datasets, a novel anomaly-aware and confidence learning module is proposed to merge the overlapped organ predictions, originated from different decoders. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment total 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task)

Experimental and Numerical Investigation of Micro/Mini Channel Flow-Boiling Heat Transfer with Non-Uniform Circumferential Heat Fluxes at Different Rotational Orientations

Flow-boiling of Perfluorohexane (FC-72) in horizontal micro/mini channels was investigated experimen- tally and numerically at different rotational orientations in terms of gravity. One-sided uniform channel heating was considered experimentally for rotational angles ranging from 0 °(heating from below) to 180 °(heating from above) in increments of 30 °. The micro/mini channel had a high aspect ratio of 10 (5 mm x 0.5 mm) and a hydraulic diameter of 909 μm. In-channel flow visualisations were recorded and heat transfer coefficients were determined for mass fluxes of 10, 20 and 40 kg/m 2 s at a saturation temperature of 56 °C. Suitable heat fluxes were applied to span the onset of nucleate boiling to near dry-out conditions within the channel. It was found that the rotational angle had a significant influence on the heat transfer performance due to its influence on bubble detachment. Bottom-heated cases (0 °orientation) resulted in local heat transfer coefficients that were up to 201% higher than for any other rotational orientation. Channel orientations of 60 °(slanted heating surface) and 90 °(heating from the side) generally produced the lowest local heat transfer coefficients. Insight into the influence of the grav- itational orientation on single-bubble growth within the nucleation and detachment region was obtained via two- and three-dimensional numerical simulations. Bubble behaviour after detachment and its effect on heat transfer were also investigated transiently until detachment. The numerical simulations mirrored the experimental trends and it was found that the presence of growing bubbles interrupted the velocity streamlines and the thermal boundary layer downstream of the nucleation sit

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of the extent and nature of the diseases. Therefore it is important for developing automated pulmonary computer-aided detection (CAD) systems. Conventionally, this task relies on experts’ manual identification of regions of interest (ROIs) as a prerequisite to diagnose potential diseases. This protocol is time consuming and inhibits fully automatic assessment. In this paper, we present a new method to classify ILD imaging patterns on CT images. The main difference is that the proposed algorithm uses the entire image as a holistic input. By circumventing the prerequisite of manually input ROIs, our problem setup is significantly more difficult than previous work but can better address the clinical workflow. Qualitative and quantitative results using a publicly available ILD database demonstrates state-of-the-art classification accuracy under the patch based classification and shows the potential of predicting the ILD type using holistic image