On the Cauchy problem of defocusing mKdV equation with finite density initial data: long time asymptotics in soliton-less regions

We investigate the long-time asymptotics for the solutions to the Cauchy problem of defocusing modified Kortweg-de Vries (mKdV) equation with finite density initial data. The present paper is the subsequent work of our previous paper [arXiv:2108.03650], which gives the soliton resolution for the defocusing mKdV equation in the central asymptotic sector $\{(x,t): \vert \xi \vert<6\}$ with $\xi:=x/t$. In the present paper, via the Riemann-Hilbert (RH) problem associated to the Cauchy problem, the long-time asymptotics in the soliton-less regions $\{(x,t): \vert \xi \vert>6, |\xi|=\mathcal{O}(1)\}$ for the defocusing mKdV equation are further obtained. It is shown that the leading term of the asymptotics are in compatible with the ``background solution'' and the error terms are derived via rigorous analysis.Comment: 51 page

Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction

Reconstructing 3D clothed human avatars from single images is a challenging task, especially when encountering complex poses and loose clothing. Current methods exhibit limitations in performance, largely attributable to their dependence on insufficient 2D image features and inconsistent query methods. Owing to this, we present the Global-correlated 3D-decoupling Transformer for clothed Avatar reconstruction (GTA), a novel transformer-based architecture that reconstructs clothed human avatars from monocular images. Our approach leverages transformer architectures by utilizing a Vision Transformer model as an encoder for capturing global-correlated image features. Subsequently, our innovative 3D-decoupling decoder employs cross-attention to decouple tri-plane features, using learnable embeddings as queries for cross-plane generation. To effectively enhance feature fusion with the tri-plane 3D feature and human body prior, we propose a hybrid prior fusion strategy combining spatial and prior-enhanced queries, leveraging the benefits of spatial localization and human body prior knowledge. Comprehensive experiments on CAPE and THuman2.0 datasets illustrate that our method outperforms state-of-the-art approaches in both geometry and texture reconstruction, exhibiting high robustness to challenging poses and loose clothing, and producing higher-resolution textures. Codes will be available at https://github.com/River-Zhang/GTA.Comment: Accepted by NeurIPS 2023. Project page: https://river-zhang.github.io/GTA-projectpage

Co-infusion of haplo-identical CD19-chimeric antigen receptor T cells and stem cells achieved full donor engraftment in refractory acute lymphoblastic leukemia

Abstract Background Elderly patients with relapsed and refractory acute lymphoblastic leukemia (ALL) have poor prognosis. Autologous CD19 chimeric antigen receptor-modified T (CAR-T) cells have potentials to cure patients with B cell ALL; however, safety and efficacy of allogeneic CD19 CAR-T cells are still undetermined. Case presentation We treated a 71-year-old female with relapsed and refractory ALL who received co-infusion of haplo-identical donor-derived CD19-directed CAR-T cells and mobilized peripheral blood stem cells (PBSC) following induction chemotherapy. Undetectable minimal residual disease by flow cytometry was achieved, and full donor cell engraftment was established. The transient release of cytokines and mild fever were detected. Significantly elevated serum lactate dehydrogenase, alanine transaminase, bilirubin and glutamic-oxalacetic transaminase were observed from days 14 to 18, all of which were reversible after immunosuppressive therapy. Conclusions Our preliminary results suggest that co-infusion of haplo-identical donor-derived CAR-T cells and mobilized PBSCs may induce full donor engraftment in relapsed and refractory ALL including elderly patients, but complications related to donor cell infusions should still be cautioned. Trial registration Allogeneic CART-19 for Elderly Relapsed/Refractory CD19+ ALL. NCT0279955

A Non-Destructive Method for Hardware Trojan Detection Based on Radio Frequency Fingerprinting

Hardware Trojans (HTs) pose a security threat to the Internet of Things (IoT). Attackers can take control of devices in IoT through HTs, which seriously jeopardize the security of many systems in transportation, finance, healthcare, etc. Since subtle differences in the circuit are reflected in far-field signals emitted by the system, the detection of HT status can be performed by monitoring the radio frequency fingerprinting (RFF) of the transmitting signals. For the detection of HTs, a non-destructive detection method based on RFF is proposed in this paper. Based on the proposed method, the detection of HTs can be achieved without integrating additional devices in the receiver, which reduces associated costs and energy consumption. QPSK and triangular-wave signals are measured and identified via experimentation, and the results validate the proposed method. For identifying the presence and operating state of Trojan, the average accuracy achieved measures as high as 98.7%. Notably, with regard to capturing the moment of Trojan activation in the AES encryption circuit, the accuracy of the proposed method is 100% and can provide warning of the threat in a timely manner

A Non-Destructive Method for Hardware Trojan Detection Based on Radio Frequency Fingerprinting

Hardware Trojans (HTs) pose a security threat to the Internet of Things (IoT). Attackers can take control of devices in IoT through HTs, which seriously jeopardize the security of many systems in transportation, finance, healthcare, etc. Since subtle differences in the circuit are reflected in far-field signals emitted by the system, the detection of HT status can be performed by monitoring the radio frequency fingerprinting (RFF) of the transmitting signals. For the detection of HTs, a non-destructive detection method based on RFF is proposed in this paper. Based on the proposed method, the detection of HTs can be achieved without integrating additional devices in the receiver, which reduces associated costs and energy consumption. QPSK and triangular-wave signals are measured and identified via experimentation, and the results validate the proposed method. For identifying the presence and operating state of Trojan, the average accuracy achieved measures as high as 98.7%. Notably, with regard to capturing the moment of Trojan activation in the AES encryption circuit, the accuracy of the proposed method is 100% and can provide warning of the threat in a timely manner

Thermal Conductivity of PNIPAm Hydrogels and Heat Management as Smart Windows

Abstract Though thermoresponsive lower critical solution temperature (LCST) hydrogels have attracted intense attention to be applied in smart windows, less efforts are paid on the LCST effect on the heat transfer process. Herein, the research mainly focuses on heat transfer process of thermoresponsive PNIPAm hydrogels. It is the first time reported that LCST behaviors can decrease thermal conductivity upon heating process. To be utilized as a smart window, thermal conduction flux is investigated yet the thermal conduction energy occupies little to manage the thermal transfer process in a model house. It is found that radiation thermal transfer predominates the heat transfer process for PNIPAm‐based smart windows. These results are meaningful to provide basic data for energy transfer in using thermoresponsive hydrogels

Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images

With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses

A smart conflict resolution model using multi-layer knowledge graph for conceptual design

Reducing the impact of conflicts on requirement-function-structure mapping in the early stage of product design is an important measure to achieve conceptual innovation, which relies on accurate reasoning of multi-domain knowledge. As product requirements become more personalized and diverse, traditional discrete knowledge organization and reasoning methods are difficult to adapt to the challenges of continuity and precision in conceptual solution. Knowledge graphs with complex networks have obvious advantages in association detection, knowledge visualization, and explainable reasoning of implicit knowledge, which offer innovative opportunities for conflict resolution in conceptual design. Therefore, a smart conflict resolution model using a multi-layer Knowledge Graph for Conceptual Design(mKGCD) is proposed in this study. A knowledge expression form of FBS-oriented design patent vocabulary is proposed, which is used for knowledge entity recognition and relation extraction based on natural language processing. A label mapping method based on inventive principles is used for patent classification and a four-layer semantic network for conflict resolution is constructed. Through semantic distance calculation, the designer's requirements for function/behavior/structure are smart deployed to obtain appropriate knowledge. A case study of the conceptual design of a collapsible installation and handling equipment demonstrates the feasibility of the proposed approach. The proposed method can not only meet the functional solution and innovation in the context of different design requirements, but also effectively improve the design efficiency in the iterative design process by means of multiple meanings of one graph