Applying 2D Japanese Super-Deformed character to traditional American animation

This project explores how to apply 2D Super Deformed style character expressions from traditional Japanese 2D animation to 3D animated characters. After analyzing Japanese 2D animations including Naruto, Sailor Moon, Fairy Tail and Dragon Ball Z, specific characteristics for each of the six emotions were determined. These characteristics were used to design 3D SD versions of those emotions and then they were applied to a normal 3D character in six separate animations. Keywords: Super-Deformed cartoon character, Exaggerated Animation, baby schema, Emotion, Facial expressionM.S., Digital Media -- Drexel University, 201

Subnatural-Linewidth Polarization-Entangled Photon Pairs with Controllable Temporal Length

We demonstrate an efficient experimental scheme for producing polarization-entangled photon pairs from spontaneous four-wave mixing (SFWM) in a laser-cooled $^{85}$Rb atomic ensemble, with a bandwidth (as low as 0.8 MHz) much narrower than the rubidium atomic natural linewidth. By stabilizing the relative phase between the two SFWM paths in a Mach-Zehnder interferometer configuration, we are able to produce all four Bell states. These subnatural-linewidth photon pairs with polarization entanglement are ideal quantum information carriers for connecting remote atomic quantum nodes via efficient light-matter interaction in a photon-atom quantum network.Comment: Title changed, published version, 5 pages + 3 pages Supplemental Materia

The νR\nu_{R}-philic scalar dark matter

Right-handed neutrinos ($\nu_{R}$) offer an intriguing portal to new physics in hidden sectors where dark matter (DM) may reside. In this work, we delve into the simplest hidden sector involving only a real scalar exclusively coupled to $\nu_{R}$, referred to as the $\nu_{R}$-philic scalar. We investigate the viability of the $\nu_{R}$-philic scalar to serve as a DM candidate, under the constraint that the coupling of $\nu_{R}$ to the standard model is determined by the seesaw relation and is responsible for the observed DM abundance. By analyzing the DM decay channels and solving Boltzmann equations, we identify the viable parameter space. In particular, our study reveals a lower bound ($\sim10^{4}$ GeV) on the mass of $\nu_{R}$ for the $\nu_{R}$-philic scalar to be DM. The DM mass may vary from sub-keV to sub-GeV. Within the viable parameter space, monochromatic neutrino lines from DM decay can be an important signal for DM indirect detection.Comment: 21 pages, 5 figure

Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances. In order to fuse the two modalities, we make use of a linear feature-wise fusion module that carries out an affine transformation on visual and auditory features. Finally, we conduct extensive experiments using the proposed dataset and approach. Experimental results show that introducing auditory information can benefit crowd counting under different illumination, noise, and occlusion conditions. The dataset and code will be released. Code and data have been made availabl

Perinatal depression trajectories and child development at one year: a study in China

Background: The objective of the current study was to investigate the correlation between trajectories of maternal perinatal depression (PND) spanning from early pregnancy to one year postpartum and developmental delays observed in one-year-old children. Methods: The dataset under examination encompassed 880 women who took part in a mother-child birth study conducted in China. Latent class growth analysis (LCGA) was employed to identify patterns in Edinburgh Postnatal Depression Scale (EPDS) scores of women, spanning from early pregnancy to one year postpartum. To assess the neurodevelopment of one-year-old children, a Chinese version of the Bayley Scale of Infant Development (BSID-CR) was employed. Logistic regression was employed to explore the association between PND trajectories and developmental delays in children, with appropriate covariate adjustments. Results: The trajectories of maternal PND identified in this study included a minimal-stable symptom group (n = 155), low-stable symptom group (n = 411), mild-stable symptom group (n = 251), and moderate-stable symptom group (n = 63). Logistic regression analysis revealed that mothers falling into the moderate-stable symptom group exhibited a notably heightened risk of having a child with psychomotor developmental delays at the age of one year. Conclusions: The findings drawn from a representative sample in China provide compelling empirical evidence that bolsters the association between maternal PND and the probability of psychomotor developmental delays in children. It is imperative to develop tailored intervention strategies and meticulously design mother-infant interactive intervention programs for women with PND

On the Optimal Lower and Upper Complexity Bounds for a Class of Composite Optimization Problems

We study the optimal lower and upper complexity bounds for finding approximate solutions to the composite problem $\min_x\ f(x)+h(Ax-b)$, where $f$ is smooth and $h$ is convex. Given access to the proximal operator of $h$, for strongly convex, convex, and nonconvex $f$, we design efficient first order algorithms with complexities $\tilde{O}\left(\kappa_A\sqrt{\kappa_f}\log\left(1/{\epsilon}\right)\right)$, $\tilde{O}\left(\kappa_A\sqrt{L_f}D/\sqrt{\epsilon}\right)$, and $\tilde{O}\left(\kappa_A L_f\Delta/\epsilon^2\right)$, respectively. Here, $\kappa_A$ is the condition number of the matrix $A$ in the composition, $L_f$ is the smoothness constant of $f$, and $\kappa_f$ is the condition number of $f$ in the strongly convex case. $D$ is the initial point distance and $\Delta$ is the initial function value gap. Tight lower complexity bounds for the three cases are also derived and they match the upper bounds up to logarithmic factors, thereby demonstrating the optimality of both the upper and lower bounds proposed in this paper

EDDA: An Efficient Distributed Data Replication Algorithm in VANETs

Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the dynamic nature of the network. To improve the performance of data dissemination, we study distributed data replication algorithms in VANETs for exchanging information and computing in an arbitrarily-connected network of vehicle nodes. To achieve low dissemination delay and improve the network performance, we control the number of message copies that can be disseminated in the network and then propose an efficient distributed data replication algorithm (EDDA). The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We calculate the number of communication stages for the network to enter into a balanced status and show that the proposed distributed algorithm can converge to a consensus in a small number of communication stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. The lower bound and upper bound are also provided in the analysis of the algorithm. Simulation results show that the proposed EDDA can efficiently disseminate messages to vehicles in a specific area with low dissemination delay and system overhead

FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation

The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods. To further improve the performance, recent works mainly focus on designing more complex network structures and exploiting extra supervised information, e.g., semantic segmentation. These methods optimize the models by exploiting the reconstructed relationship between the target and reference images in varying degrees. However, previous methods prove that this image reconstruction optimization is prone to get trapped in local minima. In this paper, our core idea is to guide the optimization with prior knowledge from pretrained Flow-Net. And we show that the bottleneck of unsupervised monocular depth estimation can be broken with our simple but effective framework named FG-Depth. In particular, we propose (i) a flow distillation loss to replace the typical photometric loss that limits the capacity of the model and (ii) a prior flow based mask to remove invalid pixels that bring the noise in training loss. Extensive experiments demonstrate the effectiveness of each component, and our approach achieves state-of-the-art results on both KITTI and NYU-Depth-v2 datasets.Comment: Accepted by ICRA202