Miracle-dz/UniFAR-FTUs huggingface dataset:
PP-OCR: A Practical Ultra Lightweight OCR System

The Optical Character Recognition (OCR) systems have been widely used in various of application scenarios, such as office automation (OA) systems, factory automations, online educations, map productions etc. However, OCR is still a challenging task due to the various of text appearances and the demand of computational efficiency. In this paper, we propose a practical ultra lightweight OCR system, i.e., PP-OCR. The overall model size of the PP-OCR is only 3.5M for recognizing 6622 Chinese characters and 2.8M for recognizing 63 alphanumeric symbols, respectively. We introduce a bag of strategies to either enhance the model ability or reduce the model size. The corresponding ablation experiments with the real data are also provided. Meanwhile, several pre-trained models for the Chinese and English recognition are released, including a text detector (97K images are used), a direction classifier (600K images are used) as well as a text recognizer (17.9M images are used). Besides, the proposed PP-OCR are also verified in several other language recognition tasks, including French, Korean, Japanese and German. All of the above mentioned models are open-sourced and the codes are available in the GitHub repository, i.e., this https URL.
Empathetic Dialogue Generation via Knowledge Enhancing and Emotion Dependency Modeling

Enabling the machines with empathetic abilities to provide context-consistent responses is crucial on both semantic and emotional levels. The task of empathetic dialogue generation is proposed to address this problem. However, two challenges still exist in this task: perceiving nuanced emotions implied in the dialogue context and modelling emotional dependencies. Lacking useful external knowledge makes it challenging to perceive implicit fine-grained emotions. Missing the emotional interactions among interlocutors also restricts the performance of empathetic dialogue generation. To address above challenges, we propose a knowledge-enhanced framework, named Know-EDG. We first enrich dialogue context by bunches of emotion-related concepts and construct a knowledge-enhanced context graph. Then we introduce a graph-aware Transformer encoder to learn graph's semantic and emotional representations, which are the prerequisites of the emotion identifier to predicate the target emotion signal. Finally, we propose an emotion-focused attention mechanism to exploit the emotional dependencies between dialogue context and target empathetic response. Conducted on a benchmark dataset, extensive experimental results show that our proposed framework outperforms state-of-the-art baselines in terms of automatic metrics and human evaluations.
Sum Secret Key Rate Maximization for TDD Multi-User Massive MIMO Wireless Networks

Physical-layer key generation (PKG) based on channel reciprocity has recently emerged as a new technique to establish secret keys between devices. Most works focus on pairwise communication scenarios with single or small-scale antennas. However, the fifth generation (5G) wireless communications employ massive multiple-input multiple-output (MIMO) to support multiple users simultaneously, bringing serious overhead of reciprocal channel acquisition. This paper presents a multi-user secret key generation in massive MIMO wireless networks. We provide a beam domain channel model, in which different elements represent the channel gains from different transmit directions to different receive directions. Based on this channel model, we analyze the secret key rate and derive a closed-form expression under independent channel conditions. To maximize the sum secret key rate, we provide the optimal conditions for the Kronecker product of the precoding and receiving matrices and propose an algorithm to generate these matrices with pilot reuse. The proposed optimization design can significantly reduce the pilot overhead of the reciprocal channel state information acquisition. Furthermore, we analyze the security under the channel correlation between user terminals (UTs), and propose a low overhead multi-user secret key generation with non-overlapping beams between UTs. Simulation results demonstrate the near-optimal performance of the proposed precoding and receiving matrices design and the advantages of the non-overlapping beam allocation.
Structure-Guided Processing Path Optimization with Deep Reinforcement Learning

A major goal of material design is the inverse optimization of processing-structure-property relationships. In this paper, we propose and investigate a deep reinforcement learning approach for the optimization of processing paths. The goal is to find optimal processing paths in the material structure space that lead to target structures, which have been identified beforehand to yield desired material properties. The contribution completes the desired inversion of the processing-structure-property chain in a flexible and generic way. As the relation between properties and structures is generally nonunique, typically a whole set of goal structures can be identified, that lead to desired properties. Our proposed method optimizes processing paths from a start structure to one of the equivalent goal-structures. The algorithm learns to find near-optimal paths by interacting with the structure-generating process. It is guided by structure descriptors as process state features and a reward signal, which is formulated based on a distance function in the structure space. The model-free reinforcement learning algorithm learns through trial and error while interacting with the process and does not rely on a priori sampled processing data. We instantiate and evaluate the proposed method by optimizing paths of a generic metal forming process to reach near-optimal structures, which are represented by one-point statistics of crystallographic textures.
Clumpy Wind Accretion in Cygnus X-1

Cygnus X-1 is one of the brightest X-ray sources observed and shows the X-ray intensity variations on timescales from milliseconds to months in both the soft and hard X-rays. The accretion onto the black hole is believed to be wind fed due to focused stellar wind from the binary companion HDE-226868. We aim to understand the physical mechanism responsible for the short timescale X-ray variability (<100 s) of the source in its hard/low state. We compute the 2D relativistic hydrodynamic simulation of the low angular momentum accretion flow with a time-dependent outer boundary condition that reflects the focused, clumpy wind from the supergiant in this X-ray binary system. We follow the dynamical evolution of our model for about 100 s and present the results showing an oscillatory shock, being a potential explanation of variability observed in hard X-rays. The simulated model with shock solutions is in good agreement with the observed power density spectra of the source.
A quantitative demonstration that stellar feedback locally regulates galaxy growth

We have applied stellar population synthesis to 500 pc sized regions in a sample of 102 galaxy discs observed with the MUSE spectrograph. We derived the star formation history and analyse specifically the "recent" ($20\rm{Myr}$) and "past" ($570\rm{Myr}$) age bins. Using a star formation self-regulator model we can derive local mass-loading factors, $\eta$ for specific regions, and find that this factor depends on the local stellar mass surface density, $\Sigma_*$, in agreement with the predictions form hydrodynamical simulations including supernova feedback. We integrate the local $\eta$-$\Sigma_*$ relation using the stellar mass surface density profiles from the Spitzer Survey of Stellar Structure in Galaxies (S4G) to derive global mass-loading factors, $\eta_{\rm{G}}$, as a function of stellar mass, $M_*$. The $\eta_{\rm{G}}$-$M_*$ relation found is in very good agreement with hydrodynamical cosmological zoom-in galaxy simulations. The method developed here offers a powerful way of testing different implementations of stellar feedback, to check on how realistic are their predictions.
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods.
Addressing reward bias in Adversarial Imitation Learning with neutral reward functions

Generative Adversarial Imitation Learning suffers from the fundamental problem of reward bias stemming from the choice of reward functions used in the algorithm. Different types of biases also affect different types of environments - which are broadly divided into survival and task-based environments. We provide a theoretical sketch of why existing reward functions would fail in imitation learning scenarios in task based environments with multiple terminal states. We also propose a new reward function for GAIL which outperforms existing GAIL methods on task based environments with single and multiple terminal states and effectively overcomes both survival and termination bias.
Mid-Air Drawing of Curves on 3D Surfaces in AR/VR

Complex 3D curves can be created by directly drawing mid-air in immersive environments (AR/VR). Drawing mid-air strokes precisely on the surface of a 3D virtual object however, is difficult; necessitating a projection of the mid-air stroke onto the user "intended" surface curve. We present the first detailed investigation of the fundamental problem of 3D stroke projection in AR/VR. An assessment of the design requirements of real-time drawing of curves on 3D objects in AR/VR is followed by the definition and classification of multiple techniques for 3D stroke projection. We analyze the advantages and shortcomings of these approaches both theoretically and via practical pilot testing. We then formally evaluate the two most promising techniques spraycan and mimicry with 20 users in VR. The study shows a strong qualitative and quantitative user preference for our novel stroke mimicry projection algorithm. We further illustrate the effectiveness and utility of stroke mimicry, to draw complex 3D curves on surfaces for various artistic and functional design applications.
PPTP: Price-based Path-specified Transport Protocol for Named Data Network using Blockchain

Serving as a potential future Internet architecture, Named Data Network (NDN) offers superior information-centric architectural support for mobile ad-hoc networking. Using NDN as an underlying protocol, end-user devices (e.g. IoT device and smart phone) formulate a multi-hop (mesh) network, in which certain devices play a role of forwarding packets for others and/or act as gateways to access the Internet. Nevertheless, an autonomous (selfish) node in an ad-hoc network has two disincentives for forwarding packets for others: energy expenditure and possible delays for its own data. This paper introduces Price-based Path-specified Transport Protocol (PPT) for NDN, using blockchain as a payment platform to support money transfers between autonomous nodes thus to incentivise packet forwarding. In PPTP, routers advertise their expected prices for packet forwarding and consumers estimate the costs and select the appropriate paths for content downloading. PPTP is still an on-going project therefore this paper will present the design principle and planed functions, and show how PPTP are related to other existing blockchain-based networking solutions.
Perturbation and Stability of Continuous Frames for $End_{\mathcal{A}}^{\ast}(\mathcal{H})$

Frame Theory has a great revolution in recent years. This Theory have been extended from Hilbert spaces to Hilbert $C^{\ast}$-modules. In this paper we consider the stability of continuous operator frame for $End^{\ast}_{\mathcal{A}}(\mathcal{H})$ and continuous $K$-operator frames for $End^{\ast}_{\mathcal{A}}(\mathcal{H})$ under perturbation and we establish some properties
Data Augmentation for Graph Classification

Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc. However, the limitation of scale of benchmark datasets makes it easy for graph classification models to fall into over-fitting and undergeneralization. Towards this, we introduce data augmentation on graphs and present two heuristic algorithms: \emrandom mapping and \emmotif-similarity mapping, to generate more weakly labeled data for small-scale benchmark datasets via heuristic modification of graph structures. Furthermore, we propose a generic model evolution framework, named \emM-Evolve, which combines graph augmentation, data filtration and model retraining to optimize pre-trained graph classifiers. Experiments conducted on six benchmark datasets demonstrate that \emM-Evolve helps existing graph classification models alleviate over-fitting when training on small-scale benchmark datasets and %achieve significant improvement of classification performance. yields an average improvement of 3-12% accuracy on graph classification tasks.
Engineering photonic environments for two-dimensional materials

Abstract A fascinating photonic platform with a small device scale, fast operating speed, as well as low energy consumption is two-dimensional (2D) materials, thanks to their in-plane crystalline structures and out-of-plane quantum confinement. The key to further advancement in this research field is the ability to modify the optical properties of the 2D materials. The modifications typically come from the materials themselves, for example, altering their chemical compositions. This article reviews a comparably less explored but promising means, through engineering the photonic surroundings. Rather than modifying materials themselves, this means manipulates the dielectric and metallic environments, both uniform and nanostructured, that directly interact with the materials. For 2D materials that are only one or a few atoms thick, the interaction with the environment can be remarkably efficient. This review summarizes the three degrees of freedom of this interaction: weak coupling, strong coupling, and multifunctionality. In addition, it reviews a relatively timing concept of engineering that directly applied to the 2D materials by patterning. Benefiting from the burgeoning development of nanophotonics, the engineering of photonic environments provides a versatile and creative methodology of reshaping light–matter interaction in 2D materials.
Variational simulation of Schwinger's Hamiltonian with polarization qubits

The numerical emulation of quantum physics and quantum chemistry often involves an intractable number of degrees of freedom and admit no known approximations in a general form. In practice, representing quantum-mechanical states using available numerical methods become exponentially more challenging with increasing system size. Recently quantum algorithms implemented as variational models, have been proposed to accelerate such simulations. Here we study the effect of noise on the quantum phase transition in the Schwinger model, within a variational framework. The experiments are built using a free space optical scheme to realize a pair of polarization qubits and enable any two-qubit state to be experimentally prepared up to machine tolerance. We specifically exploit the possibility to engineer noise and decoherence for polarization qubits to explore the limits of variational algorithms for NISQ architectures in identifying and quantifying quantum phase transitions with noisy qubits. We find that noise does not impede the detection of the phase transition point in a large range of noise levels.
Standard $\lambda$-lattices, rigid ${\rm C}^*$ tensor categories, and (bi)modules.

In this article, we construct a 2-shaded rigid ${\rm C}^*$ multitensor category with canonical unitary dual functor directly from a standard $\lambda$-lattice. We use the notions of traceless Markov towers and lattices to define the notion of module and bimodule over standard $\lambda$-lattice(s), and we explicitly construct the associated module category and bimodule category over the corresponding 2-shaded rigid ${\rm C}^*$ multitensor category. As an example, we compute the modules and bimodules for Temperley-Lieb-Jones standard $\lambda$-lattices in terms of traceless Markov towers and lattices. Translating into the unitary 2-category of bigraded Hilbert spaces, we recover DeCommer-Yamshita's classification of $\mathcal{TLJ}$ modules in terms of edge weighted graphs, and a classification of $\mathcal{TLJ}$ bimodules in terms of biunitary connections on square-partite weighted graphs. As an application, we show that every (infinite depth) subfactor planar algebra embeds into the bipartite graph planar algebra of its principal graph.
A study of the morphoanatomical characters of the leaves of Chamaecrista (L.) Moench sect. Apoucouita (Leguminosae-Caesalpinioideae)

Little attention has been paid to species of Chamaecrista sect. Apoucouita (Leguminosae-Caesalpinioideae), especially regarding anatomical studies. When only vegetative material is available, the identification of such species may be difficult. Additionally, vegetative material of some species of C. sect. Apoucouita may be even harder to identify because they can resemble species of Inga Mill. (Leguminosae-Mimosoideae). The present study focused on recognizing morphoanatomical characters of leaves that are taxonomically useful for the species of C. sect. Apoucouita by employing standard anatomical techniques. The arrangement of the vascular system in the petiole/rachis, dorsiventral mesophyll, mucilage idioblasts in the epidermis of leaflets and hypostomatic leaves were some of the characters shared by all species studied. Length of the petiole, position and type of extrafloral nectaries, leaflet venation, presence and type of papillae on the epidermis of the leaflet blades and sclereids in the mesophyll were some of the characters useful in the distinction of taxa. The vascular arrangement of the petiole/rachis is a promising character in the distinction of species of C. sect. Apoucouita and Inga. Based on morphoanatomical data, the taxonomic revision of some species and varieties ascribed to C. sect. Apoucouita is suggested.
AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis

Analysis of mobile app reviews has shown its important role in requirement engineering, software maintenance and evolution of mobile apps. Mobile app developers check their users’ reviews frequently to clarify the issues experienced by users or capture the new issues that are introduced due to a recent app update. App reviews have a dynamic nature and their discussed topics change over time. The changes in the topics among collected reviews for different versions of an app can reveal important issues about the app update. A main technique in this analysis is using topic modeling algorithms. However, app reviews are short texts and it is challenging to unveil their latent topics over time. Conventional topic models such as Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) suffer from the sparsity of word co-occurrence patterns while inferring topics for short texts. Furthermore, these algorithms cannot capture topics over numerous consecutive time-slices (or versions). Online topic modeling algorithms such as Online LDA (OLDA) and Online Biterm Topic Model (OBTM) speed up the inference of topic models for the texts collected in the latest time-slice by saving a fraction of data from the previous time-slice. But these algorithms do not analyze the statistical-data of all the previous time-slices, which can confer contributions to the topic distribution of the current time-slice.In this paper, we propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in short texts adaptively. AOBTM alleviates the sparsity problem in short-texts and considers the statistical-data for an optimal number of previous time-slices. We also propose parallel algorithms to automatically determine the optimal number of topics and the best number of previous versions that should be considered in topic inference phase. Automatic evaluation on collections of app reviews and real-world short text datasets confirm that AOBTM can find more coherent topics and outperforms the state-of-the-art baselines. For reproducibility of the results, we open source all scripts.
Privacy-Preserving Machine Learning Training in Aggregation Scenarios

To develop Smart City, the growing popularity of Machine Learning (ML) that appreciates high-quality training datasets generated from diverse IoT devices raises natural questions about the privacy guarantees that can be provided in such settings. Privacy-preserving ML training in an aggregation scenario enables a model demander to securely train ML models with the sensitive IoT data gathered from personal IoT devices. Existing solutions are generally server-aided, cannot deal with the collusion threat between the servers or between the servers and data owners, and do not match the delicate environments of IoT. We propose a privacy-preserving ML training framework named Heda that consists of a library of building blocks based on partial homomorphic encryption (PHE) enabling constructing multiple privacy-preserving ML training protocols for the aggregation scenario without the assistance of untrusted servers and defending the security under collusion situations. Rigorous security analysis demonstrates the proposed protocols can protect the privacy of each participant in the honest-but-curious model and defend the security under most collusion situations. Extensive experiments validate the efficiency of Heda which achieves the privacy-preserving ML training without losing the model accuracy.
Multiuser MIMO Systems with Discrete Precoding and Adjusted Soft Receiver Design with PSK Modulation

Low-resolution precoding techniques gained considerable attention in the wireless communications area recently. Unlike prior studies, the proposed work considers a coded transmission with a soft detection process. Based on the assumption that the distortion brought by the discrete precoding is Gaussian distributed, different approaches about the computation of effective log-likelihood-ratios are presented in the study. Numerical results based on PSK modulation and an LDPC block code indicate a superior performance as compared to state-of-the-art system designs in terms of bit-error-rate.