Miracle-dz/UniFAR-FTUs huggingface dataset:
A System for Efficient 3D Printed Stop-motion Face Animation

Computer animation in conjunction with 3D printing has the potential to positively impact traditional stop-motion animation. As 3D printing every frame of a computer animation is prohibitively slow and expensive, 3D printed stop-motion can only be viable if animations can be faithfully reproduced using a compact library of 3D printed and efficiently assemblable parts. We thus present the first system for processing computer animation sequences (typically faces) to produce an optimal set of replacement parts for use in 3D printed stop-motion animation. Given an input animation sequence of topology invariant deforming meshes, our problem is to output a library of replacement parts and per-animation-frame assignment of the parts, such that we maximally approximate the input animation, while minimizing the amount of 3D printing and assembly. Inspired by current stop-motion workflows, a user manually indicates which parts of the model are preferred for segmentation; then, we find curves with minimal deformation along which to segment the mesh. We then present a novel algorithm to zero out deformations along the segment boundaries, so that replacement sets for each part can be interchangeably and seamlessly assembled together. The part boundaries are designed to ease 3D printing and instrumentation for assembly. Each part is then independently optimized using a graph-cut technique to find a set of replacements, whose size can be user defined, or automatically computed to adhere to a printing budget or allowed deviation from the original animation. Our evaluation is threefold: we show results on a variety of facial animations, both digital and 3D printed, critiqued by a professional animator; we show the impact of various algorithmic parameters; and we compare our results to naive solutions. Our approach can reduce the printing time and cost significantly for stop-motion animated films.
Universality of the Langevin diffusion as scaling limit of a family of Metropolis-Hastings processes

Given a target distribution $\mu$ on a general state space $\mathcal{X}$ and a proposal Markov jump process with generator $Q$, in this paper we study the scaling limit of two types of Metropolis-Hastings (MH) processes with generators $M_1(Q,\mu)$ and $M_2(Q,\mu)$ respectively. First, we motivate our study of $M_2$ by offering a geometric interpretation of $M_1$, $M_2$ and their convex combinations as $L^1$ minimizers between $Q$ and the set of $\mu$-reversible generators of Markov jump processes, extending the results by Billera and Diaconis '01. Specializing into the case of $\mathcal{X} = \mathbb{R}^d$ along with a Gaussian proposal with vanishing variance and Gibbs target distribution, we prove that, upon appropriate scaling in time, the family of Markov jump processes corresponding to $M_1$, $M_2$ or their convex combinations all converge weakly to an universal Langevin diffusion.
Double domination and total $2$-domination in digraphs and their dual problems

A subset $S$ of vertices of a digraph $D$ is a double dominating set (total $2$-dominating set) if every vertex not in $S$ is adjacent from at least two vertices in $S$, and every vertex in $S$ is adjacent from at least one vertex in $S$ (the subdigraph induced by $S$ has no isolated vertices). The double domination number (total $2$-domination number) of a digraph $D$ is the minimum cardinality of a double dominating set (total $2$-dominating set) in $D$. In this work, we investigate these concepts which can be considered as two extensions of double domination in graphs to digraphs, along with the concepts $2$-limited packing and total $2$-limited packing which have close relationships with the above-mentioned concepts.
Quantum Compressed Sensing with Unsupervised Tensor Network Machine Learning

We propose tensor-network compressed sensing (TNCS) for compressing and communicating classical information via the quantum states trained by the unsupervised tensor network (TN) machine learning. The main task of TNCS is to reconstruct as accurately as possible the full classical information from a generative TN state, by knowing as small part of the classical information as possible. In the applications to the datasets of hand-written digits and fashion images, we train the generative TN (matrix product state) by the training set, and show that the images in the testing set can be reconstructed from a small number of pixels. Related issues including the applications of TNCS to quantum encrypted communication are discussed.
Arrow's Theorem Through a Fixpoint Argument

We present a proof of Arrow's theorem from social choice theory that uses a fixpoint argument. Specifically, we use Banach's result on the existence of a fixpoint of a contractive map defined on a complete metric space. Conceptually, our approach shows that dictatorships can be seen as fixpoints of a certain process.
Learning Spatial Transform for Video Frame Interpolation

Video frame interpolation is one of the most challenging tasks in the video processing area. Recently, many related studies based on deep learning have been suggested, which can be categorized into kernel estimation and flow map estimation approaches. Most of the methods focus on finding the locations with useful information to estimate each output pixel since the information needed to estimate an intermediate frame is fully contained in the two adjacent frames. However, we redefine the task as finding the spatial transform between adjacent frames and propose a new neural network architecture that combines the two abovementioned approaches, namely Adaptive Deformable Convolution. Our method is able to estimate both kernel weights and offset vectors for each output pixel, and the output frame is synthesized by the deformable convolution operation. The experimental results show that our method outperforms the state-of-the-art methods on several datasets and that our proposed approach contributes to performance enhancement.
Efficient GAN-based method for cyber-intrusion detection

Ubiquitous anomalies endanger the security of our system constantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.
Rossby-number effects on columnar eddy formation and the energy dissipation law in homogeneous rotating turbulence

Two aspects of homogeneous rotating turbulence are quantified through forced direct numerical simulations in an elongated domain, which, in the direction of rotation, is approximately 340 times larger than the typical initial eddy size. First, by following the time evolution of the integral length scale along the axis of rotation $\ell _{\Vert }$, the growth rate of the columnar eddies and its dependence on the Rossby number $Ro_{\unicode[STIX]{x1D700}}$ is determined as $\unicode[STIX]{x1D6FE}=3.90\exp (-16.72\,Ro_{\unicode[STIX]{x1D700}})$ for $0.06\leqslant Ro_{\unicode[STIX]{x1D700}}\leqslant 0.31$, where $\unicode[STIX]{x1D6FE}$ is the non-dimensional growth rate. Second, a scaling law for the energy dissipation rate $\unicode[STIX]{x1D700}_{\unicode[STIX]{x1D708}}$ is sought. Comparison with current available scaling laws shows that the relation proposed by Baqui & Davidson (Phys. Fluids, vol. 27(2), 2015, 025107), i.e. $\unicode[STIX]{x1D700}_{\unicode[STIX]{x1D708}}\sim {u^{\prime }}^{3}/\ell _{\Vert }$, where $u^{\prime }$ is the root-mean-square velocity, approximates well part of our data, more specifically the range $0.39\leqslant Ro_{\unicode[STIX]{x1D700}}\leqslant 1.54$. However, relations proposed in the literature fail to model the data for the second and most interesting range, i.e. $0.06\leqslant Ro_{\unicode[STIX]{x1D700}}\leqslant 0.31$, which is marked by the formation of columnar eddies. To find a similarity relation for the latter, we exploit the concept of a spectral transfer time introduced by Kraichnan (Phys. Fluids, vol. 8(7), 1965, p. 1385). Within this framework, the energy dissipation rate is considered to depend on both the nonlinear time scale and the relaxation time scale. Thus, by analysing our data, expressions for these different time scales are obtained that result in $\unicode[STIX]{x1D700}_{\unicode[STIX]{x1D708}}\sim (u^{\prime 4}Ro_{\unicode[STIX]{x1D700}}^{0.62}\unicode[STIX]{x1D70F}_{nl}^{iso})/\ell _{\bot }^{2}$, where $\ell _{\bot }$ is the integral length scale in the direction normal to the axis of rotation and $\unicode[STIX]{x1D70F}_{nl}^{iso}$ is the nonlinear time scale of the initial homogeneous isotropic field.
Receding Horizon Optimization for Energy-Efficient UAV Communication

In this letter, we study a wireless communication system with a fixed-wing unmanned aerial vehicle (UAV) employed to collect information from a group of ground nodes (GNs). Our objective is to maximize the UAV’s energy efficiency (EE), which is defined as the achievable throughput among all GNs per unit propulsion energy consumption of the UAV. To efficiently solve this problem with continuous-time functions, we propose a new method based on receding horizon optimization (RHO), which significantly reduces the computational complexity compared to the conventional time discretization method. Specifically, we sequentially solve the EE maximization problem over a moving time-window of finite duration, for each of which the number of optimization variables is greatly reduced. Simulation results are provided to show the effectiveness of the proposed method.
Dense Feature Aggregation and Pruning for RGBT Tracking

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.
Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks

Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The dataset used in this study was taken from National Institute of Health named NIH Malaria Dataset. The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture. To maximize the performance, existing standard pre-processing techniques from the literature has also been experimented. In addition, several other complex architectures have been implemented and tested to pick the best performing model. A holdout test has also been conducted to verify how well the proposed model generalizes on unseen data. Our best model achieves an accuracy of almost 97.77%.
Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays

A resistive memory device-based computing architecture is one of the promising platforms for energy-efficient Deep Neural Network (DNN) training accelerators. The key technical challenge in realizing such accelerators is to accumulate the gradient information without a bias. Unlike the digital numbers in software which can be assigned and accessed with desired accuracy, numbers stored in resistive memory devices can only be manipulated following the physics of the device, which can significantly limit the training performance. Therefore, additional techniques and algorithm-level remedies are required to achieve the best possible performance in resistive memory device-based accelerators. In this paper, we analyze asymmetric conductance modulation characteristics in RRAM by Soft-bound synapse model and present an in-depth analysis on the relationship between device characteristics and DNN model accuracy using a 3-layer DNN trained on the MNIST dataset. We show that the imbalance between up and down update leads to a poor network performance. We introduce a concept of symmetry point and propose a zero-shifting technique which can compensate imbalance by programming the reference device and changing the zero value point of the weight. By using this zero-shifting method, we show that network performance dramatically improves for imbalanced synapse devices.
Delving Deep into Liver Focal Lesion Detection: A Preliminary Study

Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work uses the existing large-scale annotation medical image data to delve deep into liver lesion detection from multiple directions. To solve technical difficulties, such as the image-recognition task, traditional deep learning with convolution neural networks (CNNs) has been widely applied in recent years. However, this kind of neural network, such as Faster Regions with CNN features (R-CNN), cannot leverage the spatial information because it is applied in natural images (2D) rather than medical images (3D), such as computed tomography (CT) images. To address this issue, we propose a novel algorithm that is appropriate for liver CT imaging. Furthermore, according to radiologists' experience in clinical diagnosis and the characteristics of CT images of liver cancer, a liver cancer-detection framework with CNN, including image processing, feature extraction, region proposal, image registration, and classification recognition, was proposed to facilitate the effective detection of liver lesions.
Kinect Sensor on Mobile Robot Autonomous Indoor Mobile Robot Navigation

Vehicle had been founded thousands years ago. It was evolved from human powered to engine propelled and its development showing no sign of stopping. The criteria of recent automobile development are focusing on safety and environment friendly. Autonomous vehicle is the current trend of research which emphasizing on improves traffic efficiency and reduces accident caused by human error. Autonomous mobile robot even developed into indoor applications to carry out numerous logistic tasks. Autonomous robotics in indoor environments is traditionally done using a combination of laser range finders, time of flight cameras, and/or stereo vision. This work proposes the use of the Microsoft® Kinect sensor as an alternative lower-cost modality for low-cost mobile robotics. The indoor environment is scanned by Kinect sensor and processed to produce a global map which contains all robots’ navigable region within the indoor environment. By using the global map, robot path planner is able to search through the environment and generates path to reach its destination from a starting point. For better motion performance, path is being smoothened afterward to reduce the number of turning point in path. During its navigations, it might face new obstacle that never recorded in global map. Obstacle detection and avoidance module are implemented on the robot to detect new static and dynamic obstacle during robot’s forward motion. A modification on global map will carry-out after a static obstacle is found to facilitate subsequence path planning routine. As the result, the mobile robot is shown to be capable of navigating a previously captured global map autonomously with obstacle detection and avoidance functions.
Pose-variant 3D Facial Attribute Generation

We address the challenging problem of generating facial attributes using a single image in an unconstrained pose. In contrast to prior works that largely consider generation on 2D near-frontal images, we propose a GAN-based framework to generate attributes directly on a dense 3D representation given by UV texture and position maps, resulting in photorealistic, geometrically-consistent and identity-preserving outputs. Starting from a self-occluded UV texture map obtained by applying an off-the-shelf 3D reconstruction method, we propose two novel components. First, a texture completion generative adversarial network (TC-GAN) completes the partial UV texture map. Second, a 3D attribute generation GAN (3DA-GAN) synthesizes the target attribute while obtaining an appearance consistent with 3D face geometry and preserving identity. Extensive experiments on CelebA, LFW and IJB-A show that our method achieves consistently better attribute generation accuracy than prior methods, a higher degree of qualitative photorealism and preserves face identity information.
STD: Sparse-to-Dense 3D Object Detector for Point Cloud

We propose a two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up proposal generation network that uses raw point clouds as input to generate accurate proposals by seeding each point with a new spherical anchor. It achieves a higher recall with less computation compared with prior works. Then, PointsPool is applied for proposal feature generation by transforming interior point features from sparse expression to compact representation, which saves even more computation. In box prediction, which is the second stage, we implement a parallel intersection-over-union (IoU) branch to increase awareness of localization accuracy, resulting in further improved performance. We conduct experiments on KITTI dataset, and evaluate our method on 3D object and Bird’s Eye View (BEV) detection. Our method outperforms other methods by a large margin, especially on the hard set, with 10+ FPS inference speed.
Time irreversibility of resting brain activity in the healthy brain and pathology

Characterising brain activity at rest is of paramount importance to our understanding both of general principles of brain functioning and of the way brain dynamics is affected in the presence of neurological or psychiatric pathologies. We measured the time-reversal symmetry of spontaneous electroencephalographic brain activity recorded from three groups of patients and their respective control group under two experimental conditions (eyes open and closed). We evaluated differences in time irreversibility in terms of possible underlying physical generating mechanisms. The results showed that resting brain activity is generically time-irreversible at sufficiently long time scales, and that brain pathology is generally associated with a reduction in time-asymmetry, albeit with pathology-specific patterns. The significance of these results and their possible dynamical aetiology are discussed. Some implications of the differential modulation of time asymmetry by pathology and experimental condition are examined.
Two classes of events in sheared particulate matter

Under shear, a system of particles changes its contact network and becomes unstable as it transitions between mechanically stable states. For hard spheres at zero pressure, contact breaking events necessarily generate an instability, but this is not the case at finite pressure, where we identify two types of contact changes: network events that do not correspond to instabilities and rearrangement events that do. The relative fraction of such events is constant as a function of system size, pressure and interaction potential, consistent with our observation that both nonlinearities obey the same finite-size scaling. Thus, the zero-pressure limit of the nonlinear response is highly singular.
Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts

This paper tackles the problem of learning a questioner in the goal-oriented visual dialog task. Several previous works adopt model-free reinforcement learning. Most pretrain the model from a finite set of human-generated data. We argue that using limited demonstrations to kick-start the questioner is insufficient due to the large policy search space. Inspired by a recently proposed information theoretic approach, we develop two analytic experts to serve as a source of high-quality demonstrations for imitation learning. We then take advantage of reinforcement learning to refine the model towards the goal-oriented objective. Experimental results on the GuessWhat? dataset show that our method has the combined merits of imitation and reinforcement learning, achieving the state-of-the-art performance.
GLOBAL ANALYSIS OF THE SHADOW GIERER-MEINHARDT SYSTEM WITH GENERAL LINEAR BOUNDARY CONDITIONS IN A RANDOM ENVIRONMENT

The global analysis of the shadow Gierer-Meinhardt system with multiplicative white noise and general linear boundary conditions is investigated in this paper. For this reaction-diffusion system, we employ a fixed point argument to prove local existence and uniqueness. Our results on global existence are based on a priori estimates of solutions.
Effects of retrieval formats on second language vocabulary learning

Abstract The present study set out to examine how we can optimize paired-associate learning of second language (L2) vocabulary. In paired-associate learning, retrieval, where learners are required to access information about an L2 word from memory, is found to increase vocabulary learning. Retrieval can be categorized according to dichotomies of (a) recognition versus recall and (b) receptive versus productive. In order to identify the optimal retrieval format, the present study compared the effects of the following four conditions: recognition, recall, hybrid (combination of recall and recognition), and productive recall only. In this study, 64 English-speaking college students studied 60 Swahili-English word pairs using computer-based flashcard software. Results suggested that for paired-associate learning of L2 vocabulary, (a) recall formats are more effective than recognition for the acquisition of productive knowledge of orthography and (b) recognition formats are more desirable than recall when knowledge of spelling is not required.