MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images

We address the problem of photorealistic 3D face avatar synthesis from sparse images. Existing Parametric models for face avatar reconstruction struggle to generate details that originate from inputs. Meanwhile, although current NeRF-based avatar methods provide promising results for novel view synthesis, they fail to generalize well for unseen expressions. We improve from NeRF and propose a novel framework that, by leveraging the parametric 3DMM models, can reconstruct a high-fidelity drivable face avatar and successfully handle the unseen expressions. At the core of our implementation are structured displacement feature and semantic-aware learning module. Our structured displacement feature will introduce the motion prior as an additional constraints and help perform better for unseen expressions, by constructing displacement volume. Besides, the semantic-aware learning incorporates multi-level prior, e.g., semantic embedding, learnable latent code, to lift the performance to a higher level. Thorough experiments have been doen both quantitatively and qualitatively to demonstrate the design of our framework, and our method achieves much better results than the current state-of-the-arts

Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters

Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generative tasks. Yet, the large size of LLMs often leads to the high computational cost of model training and online deployment. In our work, we present ALTER, a system that effectively builds the multi-tAsk Learners with mixTure-of-task-adaptERs upon small language models (with <1B parameters) to address multiple NLP tasks simultaneously, capturing the commonalities and differences between tasks, in order to support domain-specific applications. Specifically, in ALTER, we propose the Mixture-of-Task-Adapters (MTA) module as an extension to the transformer architecture for the underlying model to capture the intra-task and inter-task knowledge. A two-stage training method is further proposed to optimize the collaboration between adapters at a small computational cost. Experimental results over a mixture of NLP tasks show that our proposed MTA architecture and the two-stage training method achieve good performance. Based on ALTER, we have also produced MTA-equipped language models for various domains

Weighted energy efficiency maximization for a UAV-assisted multi-platoon mobile edge computing system

With the rapid development of mobile computing, mobile edge computing has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this paper, we studied a UAV-assisted mobile edge computing system with multi-platoon vehicles. Our paper aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a two-dimensional path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the non-convexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming method. The simulation results show that the proposed method significantly surpasses conventional schemes

Facilitating Self-monitored Physical Rehabilitation with Virtual Reality and Haptic feedback

Physical rehabilitation is essential to recovery from joint replacement operations. As a representation, total knee arthroplasty (TKA) requires patients to conduct intensive physical exercises to regain the knee's range of motion and muscle strength. However, current joint replacement physical rehabilitation methods rely highly on therapists for supervision, and existing computer-assisted systems lack consideration for enabling self-monitoring, making at-home physical rehabilitation difficult. In this paper, we investigated design recommendations that would enable self-monitored rehabilitation through clinical observations and focus group interviews with doctors and therapists. With this knowledge, we further explored Virtual Reality(VR)-based visual presentation and supplemental haptic motion guidance features in our implementation VReHab, a self-monitored and multimodal physical rehabilitation system with VR and vibrotactile and pneumatic feedback in a TKA rehabilitation context. We found that the third point of view real-time reconstructed motion on a virtual avatar overlaid with the target pose effectively provides motion awareness and guidance while haptic feedback helps enhance users' motion accuracy and stability. Finally, we implemented \systemname to facilitate self-monitored post-operative exercises and validated its effectiveness through a clinical study with 10 patients

Progress of regulatory RNA in small extracellular vesicles in colorectal cancer

Colorectal cancer (CRC) is the second most common malignant tumor of the gastrointestinal tract with the second highest mortality rate and the third highest incidence rate. Early diagnosis and treatment are important measures to reduce CRC mortality. Small extracellular vesicles (sEVs) have emerged as key mediators that facilitate communication between tumor cells and various other cells, playing a significant role in the growth, invasion, and metastasis of cancer cells. Regulatory RNAs have been identified as potential biomarkers for early diagnosis and prognosis of CRC, serving as crucial factors in promoting CRC cell proliferation, invasion and metastasis, angiogenesis, drug resistance, and immune cell differentiation. This review provides a comprehensive summary of the vital role of sEVs as biomarkers in CRC diagnosis and their potential application in CRC treatment, highlighting their importance as a promising avenue for further research and clinical translation

A secretory hexokinase plays an active role in the proliferation of Nosema bombycis

The microsporidian Nosema bombycis is an obligate intracellular parasite of Bombyx mori, that lost its intact tricarboxylic acid cycle and mitochondria during evolution but retained its intact glycolysis pathway. N. bombycis hexokinase (NbHK) is not only a rate-limiting enzyme of glycolysis but also a secretory protein. Indirect immunofluorescence assays and recombinant HK overexpressed in BmN cells showed that NbHK localized in the nucleus and cytoplasm of host cell during the meront stage. When N. bombycis matured, NbHK tended to concentrate at the nuclei of host cells. Furthermore, the transcriptional profile of NbHK implied it functioned during N. bombycis’ proliferation stages. A knock-down of NbHK effectively suppressed the proliferation of N. bombycis indicating that NbHK is an important protein for parasite to control its host

Fractional-Order PD Attitude Control for a Type of Spacecraft with Flexible Appendages

As large-sized spacecraft have been developed, they have been equipped with flexible appendages, such as solar cell plates and mechanical flexible arms. The attitude control of spacecraft with flexible appendages has become more complex, with higher requirements. In this paper, a fractional-order PD attitude control method for a type of spacecraft with flexible appendages is presented. Firstly, a lumped parameter model of a spacecraft with flexible appendages is constructed, which provides the transfer function of the attitude angle and external moment. Then, a design method for the fractional-order PD controller for the attitude control of a spacecraft with flexible appendages is provided. Based on the designed steps, a numerical example is provided to compare the control performances between the fractional-order and integer-order PD controllers. Finally, the obtained numerical results are presented to verify the effectiveness of the proposed control method

Automatic Generation of 3D Indoor Navigation Networks from Building Information Modeling Data Using Image Thinning

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM