Affective Behaviour Analysis Using Pretrained Model with Facial Priori

Affective behaviour analysis has aroused researchers' attention due to its broad applications. However, it is labor exhaustive to obtain accurate annotations for massive face images. Thus, we propose to utilize the prior facial information via Masked Auto-Encoder (MAE) pretrained on unlabeled face images. Furthermore, we combine MAE pretrained Vision Transformer (ViT) and AffectNet pretrained CNN to perform multi-task emotion recognition. We notice that expression and action unit (AU) scores are pure and intact features for valence-arousal (VA) regression. As a result, we utilize AffectNet pretrained CNN to extract expression scores concatenating with expression and AU scores from ViT to obtain the final VA features. Moreover, we also propose a co-training framework with two parallel MAE pretrained ViT for expression recognition tasks. In order to make the two views independent, we random mask most patches during the training process. Then, JS divergence is performed to make the predictions of the two views as consistent as possible. The results on ABAW4 show that our methods are effective

EFFECT OF HARVESTING QUOTA AND PROTECTION ZONE IN A REACTION-DIFFUSION MODEL ARISING FROM FISHERY MANAGEMENT

A reaction-diffusion logistic population model with spatially nonhomogeneous harvesting is considered. It is shown that when the intrinsic growth rate is larger than the principal eigenvalue of the protection zone, then the population is always sustainable; while in the opposite case, there exists a maximum allowable catch to avoid the population extinction. The existence of steady state solutions is also studied for both cases. The existence of an optimal harvesting pattern is also shown, and theoretical results are complemented by some numerical simulations for one-dimensional domains

Huge myxoid chondrosarcoma expanded into the thoracic cavity with spinal involvement

En bloc resection is the treatment of choice of myxoid chondrosarcoma. These tumors can produce huge masses. Anatomical constraints limit the possibility to perform en bloc resection in the spine. A very huge myxoid chondrosarcoma (14.2 × 10.8 × 11.4 cm) arising from T2 to T5 and invading the whole higher left pleural cavity was observed. Surgical planning according to WBB staging system was performed. The tumor was successfully submitted to en bloc resection achieving a tumor-free margin as demonstrated by the pathologist's report. A careful planning and a multidisciplinary collaboration make possible to perform en bloc resection even in apparently impossible cases

A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks

The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in language comprehension and generation. However, the extremely high data and computational resource requirements for the performance of LLMs compel developers to resort to outsourcing training or utilizing third-party data and computing resources. These strategies may expose the model within the network to maliciously manipulated training data and processing, providing an opportunity for attackers to embed a hidden backdoor into the model, termed a backdoor attack. Backdoor attack in LLMs refers to embedding a hidden backdoor in LLMs that causes the model to perform normally on benign samples but exhibit degraded performance on poisoned ones. This issue is particularly concerning within communication networks where reliability and security are paramount. Despite the extensive research on backdoor attacks, there remains a lack of in-depth exploration specifically within the context of LLMs employed in communication networks, and a systematic review of such attacks is currently absent. In this survey, we systematically propose a taxonomy of backdoor attacks in LLMs as used in communication networks, dividing them into four major categories: input-triggered, prompt-triggered, instruction-triggered, and demonstration-triggered attacks. Furthermore, we conduct a comprehensive analysis of the benchmark datasets. Finally, we identify potential problems and open challenges, offering valuable insights into future research directions for enhancing the security and integrity of LLMs in communication networks

Unmet healthcare needs predict frailty onset in the middle-aged and older population in China: A prospective cohort analysis

ObjectivesOlder populations have a relatively high prevalence of unmet healthcare needs, which can result in poor health status. Moreover, in the coming century, frailty is expected to become one of the most serious global public health challenges. However, there is a lack of clear evidence proving an association between unmet healthcare needs and frailty. This study aimed to assess whether unmet healthcare needs predict the onset of frailty in China.MethodsThe association between frailty and unmet healthcare needs was explored by analyzing data from the China Health and Retirement Longitudinal Study (CHARLS) using random-effects logistic regression and Cox regression with time-varying exposure.ResultsAt baseline, 7,719 respondents were included in the analysis. Random-effects logistic regression shows that unmet outpatient healthcare needs were associated with increased risk of both contemporaneous (adjusted OR [aOR], 1.17; 95% CI, 1.02–1.35) and lagged (aOR, 1.24; 95% CI, 1.05–1.45) frailty, as were unmet inpatient needs (contemporaneous: aOR, 1.28; 95% CI, 1.00–1.64; lagged: aOR, 1.55; 95% CI, 1.17–2.06). For respondents not classified as frail at baseline (n = 5,392), Cox regression with time-varying exposure shows significant associations of both unmet outpatient needs (adjusted HR, 1.23; 95% CI, 1.05–1.44) and unmet inpatient needs (adjusted HR, 1.48; 95% CI, 1.11–1.99) with increased risk of developing frailty.ConclusionsReducing unmet healthcare needs would be a valuable intervention to decrease frailty risk and promote healthy aging in middle-aged and older populations. It is urgent and essential that the equity and accessibility of the medical insurance and health delivery systems be strengthened

Conditional Image-to-Video Generation with Latent Flow Diffusion Models

Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given image and condition. In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image. Compared to previous direct-synthesis-based works, our proposed LFDM can better synthesize spatial details and temporal motion by fully utilizing the spatial content of the given image and warping it in the latent space according to the generated temporally-coherent flow. The training of LFDM consists of two separate stages: (1) an unsupervised learning stage to train a latent flow auto-encoder for spatial content generation, including a flow predictor to estimate latent flow between pairs of video frames, and (2) a conditional learning stage to train a 3D-UNet-based diffusion model (DM) for temporal latent flow generation. Unlike previous DMs operating in pixel space or latent feature space that couples spatial and temporal information, the DM in our LFDM only needs to learn a low-dimensional latent flow space for motion generation, thus being more computationally efficient. We conduct comprehensive experiments on multiple datasets, where LFDM consistently outperforms prior arts. Furthermore, we show that LFDM can be easily adapted to new domains by simply finetuning the image decoder. Our code is available at https://github.com/nihaomiao/CVPR23_LFDM.Comment: CVPR 202

Insulin-like peptide 8 (Ilp8) regulates female fecundity in flies

Introduction: Insulin-like peptides (Ilps) play crucial roles in nearly all life stages of insects. Ilp8 is involved in developmental stability, stress resistance and female fecundity in several insect species, but the underlying mechanisms are not fully understood. Here we report the functional characterization of Ilp8s in three fly species, including Bactrocera dorsalis, Drosophila mercatorum and Drosophila melanogaster.Methods: Phylogenetic analyses were performed to identify and characterize insect Ilp8s. The amino acid sequences of fly Ilp8s were aligned and the three-dimensional structures of fly Ilp8s were constructed and compared. The tissue specific expression pattern of fly Ilp8s were examined by qRT-PCR. In Bactrocera dorsalis and Drosophila mercatorum, dsRNAs were injected into virgin females to inhibit the expression of Ilp8 and the impacts on female fecundity were examined. In Drosophila melanogaster, the female fecundity of Ilp8 loss-of-function mutant was compared with wild type control flies. The mutant fruit fly strain was also used for sexual behavioral analysis and transcriptomic analysis.Results: Orthologs of Ilp8s are found in major groups of insects except for the lepidopterans and coleopterans, and Ilp8s are found to be well separated from other Ilps in three fly species. The key motif and the predicted three-dimensional structure of fly Ilp8s are well conserved. Ilp8 are specifically expressed in the ovary and are essential for female fecundity in three fly species. Behavior analysis demonstrates that Ilp8 mutation impairs female sexual attractiveness in fruit fly, which results in decreased mating success and is likely the cause of fecundity reduction. Further transcriptomic analysis indicates that Ilp8 might influence metabolism, immune activity, oocyte development as well as hormone homeostasis to collectively regulate female fecundity in the fruit fly.Discussion: Our findings support a universal role of insect Ilp8 in female fecundity, and also provide novel clues for understanding the modes of action of Ilp8