Assessing the Impact of Saharan Dust on Atlantic Regional Climate and Tropical Cyclogenesis

Recent studies show that Saharan dust can exert substantial radiative and microphysical effects on the weather and regional climate. Moreover, the potential impacts of Saharan dust on the genesis and intensification of tropical cyclones (TCs) remain unclear. In this project, the influences of Saharan dust on the Atlantic regional climate and the genesis of TCs are investigated in the hurricane seasons of 2005 and 2006, which represent hurricane active years and inactive years, respectively. The atmospheric stand-alone version of the Community Earth System Model version 1.0.4 (CESM1.0.4), CAM5.1, is used to simulate the climate condition in full (dust) and none dust (non-dust) emission from the continents. Two regions of interest, the Atlantic TC genesis region (GNR, 50W-20W, 5N-15N) and the TC intensification region (ITR, 70W-40W, 15N-30N), are analyzed. Model output proves the important roles of Saharan Dust on the radiative budget, hydrological cycle, and TC genesis. Dust perturbs the large-scale circulation that moves the Inter Tropical Convective Zone (ITCZ) northward, enhances the West African monsoon, changes the cloud fraction, and perturbs the regional longwave and shortwave radiations. Dust favors the genesis of TCs thermodynamically by increasing the mid-level moisture in the GNR. On the other hand, the TC formation is suppressed by dust through increasing wind shear and decreasing low-level vorticity in the GNR. It is likely that the TC genesis region shifts northward with the ITCZ

Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation

Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full head generation methods require a large number of 3D scans or multi-view images to train the model, resulting in expensive data acquisition cost. To address this issue, we propose Head3D, a method to generate full 3D heads with limited multi-view images. Specifically, our approach first extracts facial priors represented by tri-planes learned in EG3D, a 3D-aware generative model, and then proposes feature distillation to deliver the 3D frontal faces into complete heads without compromising head integrity. To mitigate the domain gap between the face and head models, we present dual-discriminators to guide the frontal and back head generation, respectively. Our model achieves cost-efficient and diverse complete head generation with photo-realistic renderings and high-quality geometry representations. Extensive experiments demonstrate the effectiveness of our proposed Head3D, both qualitatively and quantitatively

LPT: Long-tailed Prompt Tuning for Image Classification

For long-tailed classification, most works often pretrain a big model on a large-scale dataset, and then fine-tune the whole model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization ability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning method for long-tailed classification. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into target domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with discrimination ability. Then we design a two-phase training paradigm to learn these prompts. In phase 1, we train the shared prompt via supervised prompt tuning to adapt a pretrained model to the desired long-tailed domain. In phase 2, we use the learnt shared prompt as query to select a small best matched set for a group of similar samples from the group-specific prompt set to dig the common features of these similar samples, then optimize these prompts with dual sampling strategy and asymmetric GCL loss. By only fine-tuning a few prompts while fixing the pretrained model, LPT can reduce training and deployment cost by storing a few prompts, and enjoys a strong generalization ability of the pretrained model. Experiments show that on various long-tailed benchmarks, with only ~1.1% extra parameters, LPT achieves comparable performance than previous whole model fine-tuning methods, and is more robust to domain-shift.Comment: ICLR 2023 (poster

The preprophase band-associated kinesin-14 OsKCH2 is a processive minus-end-directed microtubule motor.

In animals and fungi, cytoplasmic dynein is a processive minus-end-directed motor that plays dominant roles in various intracellular processes. In contrast, land plants lack cytoplasmic dynein but contain many minus-end-directed kinesin-14s. No plant kinesin-14 is known to produce processive motility as a homodimer. OsKCH2 is a plant-specific kinesin-14 with an N-terminal actin-binding domain and a central motor domain flanked by two predicted coiled-coils (CC1 and CC2). Here, we show that OsKCH2 specifically decorates preprophase band microtubules in vivo and transports actin filaments along microtubules in vitro. Importantly, OsKCH2 exhibits processive minus-end-directed motility on single microtubules as individual homodimers. We find that CC1, but not CC2, forms the coiled-coil to enable OsKCH2 dimerization. Instead, our results reveal that removing CC2 renders OsKCH2 a nonprocessive motor. Collectively, these results show that land plants have evolved unconventional kinesin-14 homodimers with inherent minus-end-directed processivity that may function to compensate for the loss of cytoplasmic dynein

Deep Koopman Learning of Nonlinear Time-Varying Systems

A data-driven method is developed to approximate an nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), based on Koopman Operator and deep neural networks. Analysis on the approximation error in system states of the proposed method is investigated. It is further shown by simulation on a simple NTVS that the resulted LTVS approximate the NTVS very well with small approximation errors in states. Furthermore, simulations on a cartpole further show that optimal controller developed based on the achieved LTVS works very well to control the original NTVS

Challenges and Opportunities of Self-healing Polymers and Devices for Extreme and Hostile Environments

Engineering materials and devices can be damaged during their service life as a result of mechanical fatigue, punctures, electrical breakdown, and electrochemical corrosion. This damage can lead to unexpected failure during operation, which requires regular inspection, repair, and replacement of the products, resulting in additional energy consumption and cost. During operation in challenging, extreme, or harsh environments, such as those encountered in high or low temperature, nuclear, offshore, space, and deep mining environments, the robustness and stability of materials and devices are extremely important. Over recent decades, significant effort has been invested into improving the robustness and stability of materials through either structural design, the introduction of new chemistry, or improved manufacturing processes. Inspired by natural systems, the creation of self-healing materials has the potential to overcome these challenges and provide a route to achieve dynamic repair during service. Current research on self-healing polymers remains in its infancy, and self-healing behavior under harsh and extreme conditions is a particularly untapped area of research. Here, the self-healing mechanisms and performance of materials under a variety of harsh environments are discussed. An overview of polymer-based devices developed for a range of challenging environments is provided, along with areas for future research