A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data

Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not easy to deal with incomplete data. They are usually involved with complicated parameter selection operations, also sensitive to noise and time-consuming. In this paper, we reported a new deep learning reconstruction framework for incomplete data DPC-CT. It is the tight coupling of the deep learning neural network and DPC-CT reconstruction algorithm in the phase-contrast projection sinogram domain. The estimated result is the complete phase-contrast projection sinogram not the artifacts caused by the incomplete data. After training, this framework is determined and can reconstruct the final DPC-CT images for a given incomplete phase-contrast projection sinogram. Taking the sparse-view DPC-CT as an example, this framework has been validated and demonstrated with synthetic and experimental data sets. Embedded with DPC-CT reconstruction, this framework naturally encapsulates the physical imaging model of DPC-CT systems and is easy to be extended to deal with other challengs. This work is helpful to push the application of the state-of-the-art deep learning theory in the field of DPC-CT

Privacy-Preserving Model Aggregation for Asynchronous Federated Learning

We present a novel privacy-preserving model aggregation for asynchronous federated learning, named PPA-AFL that removes the restriction of synchronous aggregation of local model updates in federated learning, while enabling the protection of the local model updates against the server. In PPA-AFL, clients can proactive decide when to engage in the training process, and sends local model updates to the server when the updates are available. Thus, it is not necessary to keep synchronicity with other clients. To safeguard client updates and facilitate local model aggregation, we employ Paillier encryption for local update encryption and support homomorphic aggregation. Furthermore, secret sharing is utilized to enable the sharing of decryption keys and facilitate privacy-preserving asynchronous aggregation. As a result, the server remains unable to gain any information about the local updates while asynchronously aggregating to produce the global model. We demonstrate the efficacy of our proposed PPA-AFL framework through comprehensive complexity analysis and extensive experiments on a prototype implementation, highlighting its potential for practical adoption in privacy-sensitive asynchronous federated learning scenarios

Experimental Study on Energy Dissipation of Electrolytes in Nanopores

When a nonwetting fluid is forced to infiltrate a hydrophobic nanoporous solid, the external mechanical work is partially dissipated into thermal energy and partially converted to the liquid-solid interface energy to increase its enthalpy, resulting in a system with a superior energy absorption performance. To clarify the energy dissipation and conversion mechanisms, experimental infiltration and defiltration tests of liquid/ion solutions into nanopores of a hydrophobic ZSM-5 zeolite were conducted. The characteristics of energy dissipation were quantified by measuring the temperature variation of the immersed liquid environment and compared against that estimated from pressure-infiltration volume isotherms during infiltration and defiltration stages of the test. Both stages were observed to be endothermic, with the temperature of the liquid phase showing a steady increase with changes in liquid saturation. The confinement of the molecular-sized pore space causes the liquid molecules/ions to transit between statuses of orderly and disorderly motions, resulting in dissipation behaviors that vary with liquid infiltration/defiltration rates and the types and concentrations of additive electrolytes in the liquid—both factors of which alter the characteristics of the nanofluidic transport behavior

Thermally Responsive Fluid Behaviors in Hydrophobic Nanopores

A fundamental understanding of the thermal effects on nanofluid behaviors is critical for developing and designing innovative thermally responsive nanodevices. Using molecular dynamics (MD) simulation and experiment, we investigate the temperature-dependent intrusion/adsorption of water molecules into hydrophobic nanopores (carbon nanotubes and nanoporous carbon) and the underlying mechanisms. The critical infiltration pressure is reduced for elevated temperature or increased pore size. The variation of wettability is related to the thermally responsive fluid characteristics, such as the surface tension and contact angle, which arise from the variations of multiple atomic variables including the confined water density, hydrogen bond, and dipole orientation. With thermal perturbation, most of these physical quantities are found to be more significantly influenced in the confined nanoenvironment than in the bulk. By utilizing the prominent thermal effect at the nanoscale, the feasibility and prospective efficiency of employing nanofluidics for energy storage, actuation, and thermal monitoring are discussed

M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis

Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained Sentiment Analysis task, which has attracted growing research interests recently. Existing work mainly utilizes image information to improve the performance of MABSA task. However, most of the studies overestimate the importance of images since there are many noise images unrelated to the text in the dataset, which will have a negative impact on model learning. Although some work attempts to filter low-quality noise images by setting thresholds, relying on thresholds will inevitably filter out a lot of useful image information. Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data. To achieve this goal, we borrow the idea of Curriculum Learning and propose a Multi-grained Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by adjusting the order of training data. Extensive experimental results show that our framework consistently outperforms state-of-the-art work on three sub-tasks of MABSA.Comment: Accepted by EMNLP 202

Two-dimensional simulation of large-scale wind field via a joint wavenumber-frequency power spectrum

The demand of large span spatial structures is booming in developing countries as their urbanization processes keep steady pace. These structures are characterized with long-span roofs, which probably will suffer wind-induced damages in their lifecycles because of their flexible natures and harmful aerodynamic actions caused by complicated forms of roof surface and stochastic wind field around them. Relevant studies are yet relatively rare in history. The recently proposed wavenumberfrequency joint PSD based spectral representation method (WN-SRM) has greatly reduced the computational burden of traditional wind field simulation methods, which makes possible for performing stochastic wind field simulation for large size structures. This paper presents a two-dimensional, homogeneous wind field simulation for a long-span, unevenly curved roof structure. The results show that the improved spectral representation method, i.e. WN-SRM works well in wind field simulation for flexible, long-span roof structure in terms of efficiency and effectiveness

Mechanisms of water infiltration into conical hydrophobic nanopores

Fluid channels with inclined solid walls (e.g. cone- and slit-shaped pores) have wide and promising applications in micro- and nano-engineering and science. In this paper, we use molecular dynamics (MD) simulations to investigate the mechanisms of water infiltration (adsorption) into cone-shaped nanopores made of a hydrophobic graphene sheet. When the apex angle is relatively small, an external pressure is required to initiate infiltration and the pressure should keep increasing in order to further advance the water front inside the nanopore. By enlarging the apex angle, the pressure required for sustaining infiltration can be effectively lowered. When the apex angle is sufficiently large, under ambient condition water can spontaneously infiltrate to a certain depth of the nanopore, after which an external pressure is still required to infiltrate more water molecules. The unusual involvement of both spontaneous and pressure-assisted infiltration mechanisms in the case of blunt nanocones, as well as other unique nanofluid characteristics, is explained by the Young’s relation enriched with the size effects of surface tension and contact angle in the nanoscale confinement

TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision

End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on Region-of-Interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an Adaptive Global aGgregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the sub-optimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.Comment: MIR 2023, 15 page