Miracle-dz/UniFAR-FTUs huggingface dataset:
UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World

Synthetic data has been a critical tool for training scene text detection and recognition models. On the one hand, synthetic word images have proven to be a successful substitute for real images in training scene text recognizers. On the other hand, however, scene text detectors still heavily rely on a large amount of manually annotated real-world images, which are expensive. In this paper, we introduce UnrealText, an efficient image synthesis method that renders realistic images via a 3D graphics engine. 3D synthetic engine provides realistic appearance by rendering scene and text as a whole, and allows for better text region proposals with access to precise scene information, e.g. normal and even object meshes. The comprehensive experiments verify its effectiveness on both scene text detection and recognition. We also generate a multilingual version for future research into multilingual scene text detection and recognition. Additionally, we re-annotate scene text recognition datasets in a case-sensitive way and include punctuation marks for more comprehensive evaluations. The code and the generated datasets are released at: this https URL .
A simplified model for expected development of the SARS-CoV-2 (Corona) spread in Germany and US after social distancing

Widespread opinions and discussion exist regarding the efficiency of social distancing after crucial spread of the SARS-CoV-2 virus during the actual Covid-19 pandemic. While Germany has released a federal law that prohibits any type of direct contact for more than 2 people other countries including the US released curfews. People are now wondering whether these measures are helpful to stop or hamper the Covid-19 pandemic and to limit the spread of the new corona virus. A quantitative statement on this question depends on many parameters that are difficult to grasp mathematically and cannot therefore be made conclusively (they include consistent adherence to the measures decided, the estimated number of unreported cases, the possible limitation by test capacities, possible mutations of the virus, etc .). However, it turns out that a reduction in the actual daily new infection rate (actual daily growth rate of reported cases, in short: infection rate) from the current value of 30-35% in the US to 10% would be extremely effective in stopping the spread of the virus. The severe restrictions in Germany which closed any public events, schools and universities a week ago might already have contributed to a reduction of the growth rate of reported cases below 30%.
An Incentive Mechanism for Building a Secure Blockchain-based Industrial Internet of Things

The world-changing blockchain technique provides a novel method to establish a secure, trusted and decentralized system for solving the security and personal privacy problems in Industrial Internet of Things (IIoT) applications. The mining process in blockchain requires miners to solve a proof-of-work puzzle, which requires high computational power. However, the lightweight IIoT devices cannot directly participate in the mining process due to the limitation of power and computational resources. The edge computing service makes it possible for IIoT applications to build a blockchain network, in which IIoT devices purchase computational resources from edge servers and thus can offload their computational tasks. The amount of computational resource purchased by IIoT devices depends on how many profits they can get in the mining process, and will directly affect the security of the blockchain network. In this paper, we investigate the incentive mechanism for the blockchain platform to attract IIoT devices to purchase more computational power from edge servers to participate in the mining process, thereby building a more secure blockchain network. We model the interaction between the blockchain platform and IIoT devices as a two-stage Stackelberg game, where the blockchain platform act as the leader, and IIoT devices act as followers. We analyze the existence and uniqueness of the Stackelberg equilibrium, and propose an efficient algorithm to compute the Stackelberg equilibrium point. Furthermore, we evaluate the performance of our algorithm through extensive simulations, and analyze the strategies of blockchain platform and IIoT devices under different situations.
FELIX: Flexible Text Editing Through Tagging and Insertion

We present FELIX – a flexible text-editing approach for generation, designed to derive maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pretraining. In contrast to conventional sequenceto-sequence (seq2seq) models, FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input. The tagging model employs a novel Pointer mechanism, while the insertion model is based on a Masked Language Model (MLM). Both of these models are chosen to be non-autoregressive to guarantee faster inference. FELIX performs favourably when compared to recent text-editing methods and strong seq2seq baselines when evaluated on four NLG tasks: Sentence Fusion, Machine Translation Automatic Post-Editing, Summarization, and Text Simplification
An Inverse-free Truncated Rayleigh-Ritz Method for Sparse Generalized Eigenvalue Problem

This paper considers the sparse generalized eigenvalue problem (SGEP), which aims to find the leading eigenvector with at most $k$ nonzero entries. SGEP naturally arises in many applications in machine learning, statistics, and scientific computing, for example, the sparse principal component analysis (SPCA), the sparse discriminant analysis (SDA), and the sparse canonical correlation analysis (SCCA). In this paper, we focus on the development of a three-stage algorithm named {\em inverse-free truncated Rayleigh-Ritz method} ({\em IFTRR}) to efficiently solve SGEP. In each iteration of IFTRR, only a small number of matrix-vector products is required. This makes IFTRR well-suited for large scale problems. Particularly, a new truncation strategy is proposed, which is able to find the support set of the leading eigenvector effectively. Theoretical results are developed to explain why IFTRR works well. Numerical simulations demonstrate the merits of IFTRR.
First Investigation into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain

This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain. It specifically investigates the use of Bilinear Convolutional Neural Network (B-CNN) to extract facial features during different levels of postoperative pain followed by modeling the temporal pattern using Recurrent Neural Network (RNN). Although acute and postoperative pain have some common characteristics (e.g., visual action units), postoperative pain has a different dynamic, and it evolves in a unique pattern over time. Our experimental results indicate a clear difference between the pattern of acute and postoperative pain. They also suggest the efficiency of using a combination of bilinear CNN with RNN model for the continuous assessment of postoperative pain intensity.
The Quantum Zeno effect appears in stages

In the quantum Zeno effect, quantum measurements can block the coherent oscillation of a two-level system by freezing its state to one of the measurement eigenstates. The effect is conventionally controlled by the measurement frequency. Here we study the development of the Zeno regime as a function of the measurement strength for continuous partial measurement. We show that the onset of the Zeno regime is marked by a cascade of transitions in the system dynamics as the measurement strength is increased. They include the appearance of a region of dynamically inaccessible states and of singularities in the steady-state probability distribution of states. These newly predicted dynamical features, which can be readily observed in current experiments, show the coexistence of fundamentally unpredictable quantum jumps with those continuously monitored and reverted in recent experiments.
Multi-Scale Progressive Fusion Network for Single Image Deraining

Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rain streak representation. In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal. For the similar rain streaks at different positions, we employ recurrent calculation to capture the global texture, thus allowing to explore the complementary and redundant information at the spatial dimension to characterize target rain streaks. Besides, we construct multi-scale pyramid structure, and further introduce the attention mechanism to guide the fine fusion of these correlated information from different scales. This multi-scale progressive fusion strategy not only promotes the cooperative representation, but also boosts the end-to-end training. Our proposed method is extensively evaluated on several benchmark datasets and achieves the state-of-the-art results. Moreover, we conduct experiments on joint deraining, detection, and segmentation tasks, and inspire a new research direction of vision task driven image deraining. The source code is available at https://github.com/kuihua/MSPFN.
Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data

In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN. The U-Net was trained to accomplish the task in a fully supervised setting. To create the training image pairs, we employed simulated X-Ray or Digitally Reconstructed Radiographs (DRR), derived from 664 CT scans belonging to the LIDC-IDRI dataset. Using HU based segmentation of bone structures in the CT domain, a synthetic 2D “Bone x-ray” DRR is produced and used for training the network. For the reconstruction loss, we utilize two loss functions- L1 Loss and perceptual loss. Once the bone structures are extracted, the original image can be enhanced by fusing the original input x-ray and the synthesized “Bone X-ray”. We show that our enhancement technique is applicable to real x-ray data, and display our results on the NIH Chest X-Ray-14 dataset.
Towards Physically Adversarial Intelligent Networks (PAINs) for Safer Self-Driving

Neural networks in autonomous vehicles suffer from overfitting, poor generalizability, and untrained edge cases due to limited data availability. Researchers often synthesize randomized edge-case scenarios to assist in the training process, though simulation introduces the potential for overfitting to latent rules and features. Automating worst-case scenario generation could yield informative data for improving self-driving. To this end, we present a “Physically Adversarial Intelligent Network”, wherein self-driving vehicles interact aggressively in the CARLA simulation. We train two agents, a protagonist, and an adversary, using dueling double deep Q networks with prioritized experience replay. The coupled networks alternately seek to collide and avoid collisions such that the “defensive” avoidance algorithm increases the mean time to failure and distance traveled under non-hostile operating conditions. The trained protagonist becomes more resilient to environmental uncertainty and less prone to corner case failures resulting in collisions than the agent trained without an adversary.
ParasNet: Fast Parasites Detection with Neural Networks

Deep learning has dramatically improved the performance in many application areas such as image classification, object detection, speech recognition, drug discovery and etc since 2012. Where deep learning algorithms promise to discover the intricate hidden information inside the data by leveraging the large dataset, advanced model and computing power. Although deep learning techniques show medical expert level performance in a lot of medical applications, but some of the applications are still not explored or under explored due to the variation of the species. In this work, we studied the bright field based cell level Cryptosporidium and Giardia detection in the drink water with deep learning. Our experimental demonstrates that the new developed deep learning-based algorithm surpassed the handcrafted SVM based algorithm with above 97 percentage in accuracy and 700+fps in speed on embedded Jetson TX2 platform. Our research will lead to real-time and high accuracy label-free cell level Cryptosporidium and Giardia detection system in the future.
Nebulosities of the Symbiotic Binary R Aquarii - A Short Review

In this proceeding, we present a short review of the fascinating nebulosities of symbiotic binary R Aquarii. The R Aquarii system, comprising the central binary and surrounding nebular material, has been the subject of near-continuous study since its discovery, with a few hundred papers listed in ADS. As such, it is impossible to provide here the comprehensive review that R Aquarii deserves, instead we chose to focus on the nebulosities -- covering both our own research and other relevant results from the literature.
A jamming plane of sphere packings

Significance Jamming is a ubiquitous phenomenon that occurs when the viscosity diverges or the rigidity emerges in many soft matter systems, such as granular materials, glasses, foams, colloidal suspensions, emulsions, and polymers. Jamming of frictionless spheres is also closely related to the sphere packing and optimization problems. In practice, jammed packings can be obtained by various ways, among which, compression and shear are two widely employed protocols. Here, we demonstrate that compression and shear-jammed frictionless packings can be described under a unified framework called jamming-plane. Using computer simulations, we show that compression and shear jamming of frictionless spheres can occur at different densities in the limit of large systems, contrary to conventional wisdom, but the jamming criticality is protocol-independent. The concept of jamming has attracted great research interest due to its broad relevance in soft-matter, such as liquids, glasses, colloids, foams, and granular materials, and its deep connection to sphere packing and optimization problems. Here, we show that the domain of amorphous jammed states of frictionless spheres can be significantly extended, from the well-known jamming-point at a fixed density, to a jamming-plane that spans the density and shear strain axes. We explore the jamming-plane, via athermal and thermal simulations of compression and shear jamming, with initial equilibrium configurations prepared by an efficient swap algorithm. The jamming-plane can be divided into reversible-jamming and irreversible-jamming regimes, based on the reversibility of the route from the initial configuration to jamming. Our results suggest that the irreversible-jamming behavior reflects an escape from the metastable glass basin to which the initial configuration belongs to or the absence of such basins. All jammed states, either compression- or shear-jammed, are isostatic and exhibit jamming criticality of the same universality class. However, the anisotropy of contact networks nontrivially depends on the jamming density and strain. Among all state points on the jamming-plane, the jamming-point is a unique one with the minimum jamming density and the maximum randomness. For crystalline packings, the jamming-plane shrinks into a single shear jamming-line that is independent of initial configurations. Our study paves the way for solving the long-standing random close-packing problem and provides a more complete framework to understand jamming.
Predicting play calls in the National Football League using hidden Markov models

In recent years, data-driven approaches have become a popular tool in a variety of sports to gain an advantage by, for example, analysing potential strategies of opponents. Whereas the availability of play-by-play or player tracking data in sports such as basketball and baseball has led to an increase of sports analytics studies, equivalent data sets for the National Football League (NFL) were not freely available for a long time. In this contribution, we consider a comprehensive play-by-play NFL dataset provided by www.kaggle. com, comprising 289,191 observations in total, to predict play calls in the NFL using hidden Markov models. The resulting out-of-sample prediction accuracy for the 2018 NFL season is 71.6%, which is similar compared to existing studies on play call predictions in the NFL. In practice, such predictions are helpful for NFL teams, especially for defense coordinators, to make adjustments in real time on the field.
Neural Game Engine: Accurate learning of generalizable forward models from pixels.

Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms. Learning forward models is an interesting and important challenge in order to address problems where a model is not available. Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels. The learned models are able to generalize to different size game levels to the ones they were trained on without loss of accuracy. Results on 10 deterministic General Video Game AI games demonstrate competitive performance, with many of the games’ models being learned perfectly both in terms of pixel predictions and reward predictions. The pre-trained models are available through the OpenAI Gym interface and are available publicly for future research here: https://github.com/Bam4d/Neural-Game-Engine
Organ segmentation from full-size CT images using memory-efficient FCN

In this work, we present a memory-efficient fully convolutional network (FCN) incorporated with several memory-optimized techniques to reduce the run-time GPU memory demand during training phase. In medical image segmentation tasks, subvolume cropping has become a common preprocessing. Subvolumes (or small patch volumes) were cropped to reduce GPU memory demand. However, small patch volumes capture less spatial context that leads to lower accuracy. As a pilot study, the purpose of this work is to propose a memory-efficient FCN which enables us to train the model on full size CT image directly without subvolume cropping, while maintaining the segmentation accuracy. We optimize our network from both architecture and implementation. With the development of computing hardware, such as graphics processing unit (GPU) and tensor processing unit (TPU), now deep learning applications is able to train networks with large datasets within acceptable time. Among these applications, semantic segmentation using fully convolutional network (FCN) also has gained a significant improvement against traditional image processing approaches in both computer vision and medical image processing fields. However, unlike general color images used in computer vision tasks, medical images have larger scales than color images such as 3D computed tomography (CT) images, micro CT images, and histopathological images. For training these medical images, the large demand of computing resource become a severe problem. In this paper, we present a memory-efficient FCN to tackle the high GPU memory demand challenge in organ segmentation problem from clinical CT images. The experimental results demonstrated that our GPU memory demand is about 40% of baseline architecture, parameter amount is about 30% of the baseline.
Diaphragmatic plication for eventration or paralysis

Diaphragmatic eventration are congenital developmental defects affecting the muscular portion of the diaphragm, while the normal attachment to sternum ribs and dorsolumbar spine are maintained. Clinically diaphragm eventration is impossible to differentiate from acquired paralysis. In contrast to true diaphragmatic eventration, diaphragm paralysis is a more common acquired condition that can result from a number of abnormalities that affect the neuromuscular axis between the cervical spinal cord and the diaphragm. In most cases, patients with eventration or paralysis are asymptomatic and diagnosis is made as an incidental finding on a chest radiograph or as a diagnosis of exclusion in patient with dyspnoea. It is important to determine the start of the symptoms: generally patients with paralysis can recall the moment of the onset of symptoms or when the dyspnoea started or worsened while patients with eventration may not be able to determine a specific starting point. The only goal of diaphragmatic plication is to manage dyspnea. Therefore surgery is warranted exclusively for symptomatic patients. There are two main techniques to manage diaphragmatic elevation due to eventration or paralysis in open surgery through a thoracotomy access: central imbrication technique and radial plication technique. Diaphragmatic plication should be only performed on symptomatic patients with evidence of elevated diaphragm not caused by any other pathologic process other besides paralysis and eventration. The choice of plication approach is up to the surgeon’s expertise, training and preference. Improvement in dyspnoea is the most important measure of clinical success.
Perceptions and adoption of male contraceptives among men in Indonesia

Introduction : The Family Planning program implemented in Indonesia in the 1970s successfully increases the prevalence of contraception, and on the other hand, lowers fertility. Nevertheless, this success is marred by gender inequality. The majority of family planning acceptors are women. Method : The study (1) explores male narratives on male contraceptives and male perceptions of their risks and benefits; (2) identifies the factors affecting male decisions about contraception; and (3) shows how men negotiate and discuss contraception with women if at all before making decisions. The study was conducted in Bandar Lampung (Lampung) and Kulon Progo (Special Region of Yogyakarta). A total of 30 participants, consisting of married men who received a vasectomy, married men who used condoms, sexually active unmarried men who used condoms, and married men who did not use contraceptives were interviewed in depth. In addition, focus group discussions (FGD) were conducted involving stakeholders in each region. Results: That men who used contraceptives experienced positive changes after receiving information and support from their peer group. Men who use contraceptives start off with views (myths) similar to those of the surrounding communities. Gender equality perspective was a key factor affecting contraceptive decision-making among men. Discussion: that male perception of male contraceptives, especially vasectomy, is in flux. Negative prior to use, it gradually transforms into positive after men experience the benefits. Male decisions to use contraceptives are determined by their perceptions .