4D Facial Expression Diffusion Model

Facial expression generation is one of the most challenging and long-sought aspects of character animation, with many interesting applications. The challenging task, traditionally having relied heavily on digital craftspersons, remains yet to be explored. In this paper, we introduce a generative framework for generating 3D facial expression sequences (i.e. 4D faces) that can be conditioned on different inputs to animate an arbitrary 3D face mesh. It is composed of two tasks: (1) Learning the generative model that is trained over a set of 3D landmark sequences, and (2) Generating 3D mesh sequences of an input facial mesh driven by the generated landmark sequences. The generative model is based on a Denoising Diffusion Probabilistic Model (DDPM), which has achieved remarkable success in generative tasks of other domains. While it can be trained unconditionally, its reverse process can still be conditioned by various condition signals. This allows us to efficiently develop several downstream tasks involving various conditional generation, by using expression labels, text, partial sequences, or simply a facial geometry. To obtain the full mesh deformation, we then develop a landmark-guided encoder-decoder to apply the geometrical deformation embedded in landmarks on a given facial mesh. Experiments show that our model has learned to generate realistic, quality expressions solely from the dataset of relatively small size, improving over the state-of-the-art methods. Videos and qualitative comparisons with other methods can be found at https://github.com/ZOUKaifeng/4DFM. Code and models will be made available upon acceptance

New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound

Saliency methods compute heat maps that highlight portions of an input that were most {\em important} for the label assigned to it by a deep net. Evaluations of saliency methods convert this heat map into a new {\em masked input} by retaining the $k$ highest-ranked pixels of the original input and replacing the rest with \textquotedblleft uninformative\textquotedblright\ pixels, and checking if the net's output is mostly unchanged. This is usually seen as an {\em explanation} of the output, but the current paper highlights reasons why this inference of causality may be suspect. Inspired by logic concepts of {\em completeness \& soundness}, it observes that the above type of evaluation focuses on completeness of the explanation, but ignores soundness. New evaluation metrics are introduced to capture both notions, while staying in an {\em intrinsic} framework -- i.e., using the dataset and the net, but no separately trained nets, human evaluations, etc. A simple saliency method is described that matches or outperforms prior methods in the evaluations. Experiments also suggest new intrinsic justifications, based on soundness, for popular heuristic tricks such as TV regularization and upsampling.Comment: NeurIPS 2022 (Oral

PartDiff: Image Super-resolution with Partial Diffusion Models

Denoising diffusion probabilistic models (DDPMs) have achieved impressive performance on various image generation tasks, including image super-resolution. By learning to reverse the process of gradually diffusing the data distribution into Gaussian noise, DDPMs generate new data by iteratively denoising from random noise. Despite their impressive performance, diffusion-based generative models suffer from high computational costs due to the large number of denoising steps.In this paper, we first observed that the intermediate latent states gradually converge and become indistinguishable when diffusing a pair of low- and high-resolution images. This observation inspired us to propose the Partial Diffusion Model (PartDiff), which diffuses the image to an intermediate latent state instead of pure random noise, where the intermediate latent state is approximated by the latent of diffusing the low-resolution image. During generation, Partial Diffusion Models start denoising from the intermediate distribution and perform only a part of the denoising steps. Additionally, to mitigate the error caused by the approximation, we introduce "latent alignment", which aligns the latent between low- and high-resolution images during training. Experiments on both magnetic resonance imaging (MRI) and natural images show that, compared to plain diffusion-based super-resolution methods, Partial Diffusion Models significantly reduce the number of denoising steps without sacrificing the quality of generation

A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries.

The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its energy management and safety protection. A new equivalent modeling method is proposed for the mathematical expression of different structural characteristics, and an improved reduce particle-adaptive Kalman filtering model is designed and built, in which the incorporate multiple featured information is absorbed to explore the optimal representation by abandoning the redundant and abnormal information. And then, the multiple parameter identification is investigated that has the ability of adapting the current varying conditions, according to which the hybrid pulse power characterization test is accommodated. As can be known from the experimental results, the polynomial fitting treatment is carried out by conducting the curve fitting treatment and the maximum estimation error of the closed-circuit-voltage is 0.48% and its state of charge estimation error is lower than 0.30% in the hybrid pulse power characterization test, which is also within 2.00% under complex current varying working conditions. The iterate calculation process is conducted for the unmanned aerial vehicle lithium ion batteries together with the compound equivalent modeling, realizing its adaptive power state estimation and safety protection effectively

SERVE 4U: evaluación de competencias de servicio al cliente a través de cuestionario virtual

Curso de Especial InterésSe diseña un plan de mercadeo para crear el cuestionario SERVE4U, este evalúa las competencias con relación al servicio al cliente. El cuestionario contó con la validación de jueces, coeficiente RGW y el estadístico Kolmogorov Smirnov y finalmente se compone por 28 ítems. Concluyendo que el instrumento es válido y confiable para ser aplicado en aspirantes a cargos comerciales.1. RESUMEN. 2. JUSTIFICACIÓN. 3. MARCO REFERENCIAL. 4. OBJETIVOS. 5. MÉTODO. 6. ESTUDIO DE MERCADO. 7. RESULTADOS. 8. CONCLUSIONES. 9. REFERENCIAS.PregradoPsicólog

A Microalbuminuria Threshold to Predict the Risk for the Development of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients

OBJECTIVE: To test the hypothesis that a microalbuminuria (MA) threshold can help predict the risk for the development of diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM)_ patients. DESIGN: We conducted a cross-sectional study of 4739 subjects with T2DM and a prospective study of 297 subjects with T2DM in China respectively. METHODS: Clinical and laboratory data were collected and biologic risk factors associated with any DR were analysed. RESULTS: In the cross-sectional study, we found that MA was an independent risk factor for DR development; further, when the patients were divided into MA deciles, odds ratio (ORs) of DR for the patients in the sixth MA decile (10.7 mg/24 h) was 1.579-fold (1.161-2.147) compared to that for patients in the first MA decile. Furthermore, the OR of DR increased with a gradual increase in MA levels. Similarly, in the prospective study, during a mean follow-up of 4.5 years, we found that 51 patients (29.0%) of the 176 subjects with high MA level (10.7-30 mg/24 h) developed DR, while 17 patients (14.1%) of the 121 subjects with lower MA (<10.7 mg/24 h) developed DR, and the relative risk ratio of the development of DR is 2.13(95% CI, 1.58-3.62, P<0.001). CONCLUSION: These data suggest that an MA threshold can predict the risk for the development of DR in type 2 diabetes mellitus, although it is still within the traditionally established normal range

CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation

A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information. Insufficient work has been done on 2.5D methods, in which 2D convolution is mainly used in concert with volumetric information. These models focus on learning the relationship across slices, but typically have many parameters to train. We offer a Cross-Slice Attention Module (CSAM) with minimal trainable parameters, which captures information across all the slices in the volume by applying semantic, positional, and slice attention on deep feature maps at different scales. Our extensive experiments using different network architectures and tasks demonstrate the usefulness and generalizability of CSAM. Associated code is available at https://github.com/aL3x-O-o-Hung/CSAM

Long-Term Safety Evaluation of Ubrogepant for the Acute Treatment of Migraine: Phase 3, Randomized, 52-Week Extension Trial.

OBJECTIVE: To evaluate the long-term safety and tolerability of ubrogepant for the acute treatment of migraine. BACKGROUND: Ubrogepant is an oral, calcitonin gene-related receptor antagonist in development for the acute treatment of migraine. The efficacy of ubrogepant was demonstrated in 2 phase 3 trials in which a significant improvement was observed in migraine headache pain, migraine-associated symptoms, and ability to function. METHODS: This was a phase 3, multicenter, randomized, open-label, 52-week extension trial. Adults with migraine with or without aura entered the trial after completing one of 2 phase 3 lead-in trials and were re-randomized 1:1:1 to usual care, ubrogepant 50 mg, or ubrogepant 100 mg. Randomization to ubrogepant dose was blinded. Those randomized to usual care continued to treat migraine attacks with their own medication. The usual care arm was included in this trial to capture background rates of hepatic laboratory parameters and contextualize hepatic safety assessments. Safety and tolerability were the primary outcome measures. The safety population for the ubrogepant arms included all randomized participants who received at least 1 dose of treatment. All cases of alanine aminotransferase (ALT)/aspartate aminotransferase (AST) elevations of ≥3 times the upper limit of normal were adjudicated by an independent panel of liver experts who were blinded to dose. RESULTS: The safety population included 1230 participants (404 in the ubrogepant 50-mg group, 409 in the ubrogepant 100-mg group, and 417 in the usual care group). Participants were on average 42 years of age, 90% (1106/1230) female and 85% (1043/1230) white, with an average BMI of 30 kg/m CONCLUSIONS: Long-term intermittent use of ubrogepant 50 and 100 mg given as 1 or 2 doses per attack for the acute treatment of migraine was safe and well tolerated, as indicated by a low incidence of treatment-related TEAEs and SAEs and discontinuations due to adverse events in this 1-year trial

An Unstructured Phylogeographic Pattern with Extensive Gene Flow in an Endemic Bird of South China: Collared Finchbill (Spizixos semitorques)

Recent phylogeographical studies indicated that glacial oscillations played a key role on the phylogeographic pattern of extant species. As most studies have previously been carried out on heavily ice-covered regions, such as in European and North American regions, potential effects of climatic oscillations on species that are distributed on ice-free regions are less known. To address this, we investigated the phylogeographic pattern of an avian species endemic to South China, which was not glaciated during the Pleistocene glaciations. By using 2142 bp mitochondrial DNA, we identified 89 haplotypes defined by 39 polymorphic sites. A combination of high haplotype diversity (0.786–1.00) and low nucleotide diversity (0.00132–0.00252) was detected among geographic populations. Explicit genetic divergence was observed between S. s. semitorques and S. s. cinereicapillus but not detected among geographic populations of S. s. semitorques. Divergence time of the two subspecies was dated back to 87 Kyr which is congruent with the interglacial MIS 5. A weak phylogeographic structure due to strong gene flow among geographic populations was identified in this species, suggesting complex topography of South China has not formed barriers for this species