Miracle-dz/UniFAR-FTUs huggingface dataset:
Attentional Wavelet Network for Traditional Chinese Painting Transfer

Traditional Chinese paintings pay more attention to ‘Gongbi’ and ‘Xieyi’ in artworks, which raises a challenging task to generate Chinese paintings from photos. ‘Xieyi’ creates high-level conception for paintings, while ‘Gongbi’ refers to portraying local details in paintings. This paper proposes an attentional wavelet network for photo to Chinese painting transferring. We first introduce wavelets to obtain high-level conception and local details in Chinese paintings via 2-D haar wavelet transform. Moreover, we design high-level transform stream and local enhancement stream to dispose high frequencies and low frequency respectively. Furthermore, we exploit self-attention mechanism to compatibly pick up high-level information which is used to remedy the missing details when reconstructing the Chinese painting. To advance our experiment, we set up a new dataset named P2ADataset, with diverse photos and Chinese paintings on famous mountains around China. Experimental results comparing with the state-of-the-art style transferring algorithms verify the effectiveness of the proposed method. We will release the codes and data to the public.
Global and Local Feature Extraction Using a Convolutional Autoencoder and Neural Networks for Diagnosing Centrifugal Pump Mechanical Faults

Centrifugal pumps are important types of electro-mechanical machines used for fluid and energy conveyance. Mechanical faults in centrifugal pumps lead to abnormal impacts in the vibration signal of the system. Those impacts induce nonstationarity in vibration signals and hence complex time-frequency domain signal analysis techniques are required to investigate the mechanical fault features of centrifugal pumps. In this paper, an end-to-end pipeline for diagnosing faults in centrifugal pumps is proposed. To create a two-dimensional representation of the transients that appear in the vibration signals due to centrifugal pump operating conditions, first, a 1/3-binary tree fast kurtogram is computed. Next, a convolutional autoencoder and convolutional neural network are trained to autonomously extract global and local features from the kurtograms. Then, global, and local features are merged to form a joined feature vector that contains different visual features that are extracted using convolutional deep architectures using their specific loss functions during the training. Finally, this feature vector is propagated to a shallow-structured artificial neural network to accomplish fault identification. The proposed framework has been validated by the dataset collected from a real industrial centrifugal pump test rig. The results obtained during the series of experimental trials demonstrated that the introduced method achieved high classification accuracies when diagnosing faults based on signals collected under 3.0 and 4.0 bars of pressure.
Feature Extraction by Joint Robust Discriminant Analysis and Inter-class Sparsity

Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used for different classification tasks, these methods have some shortcomings. The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity. Furthermore, the targeted projection transformation focuses on the most discriminant original features, The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA _FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the $\ell_{2,1}$ constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.
Modeling the Power Sector of Wonderland: an integrated system dynamics model to assess a sustainable regional development

In a modern world, energy poverty limits opportunities and widens the gap between the rich and the poor. Thus, a broad understanding of the underlying mechanisms that relate to the lack of energy access to the regional economy, demography, and environment is crucial for the deployment of interventions aimed to eradicate extreme poverty. Some mathematical models have been proposed to analyze the power sector’s response to the growing energy demand and the increase in the number of distributed renewable energy generation units. However, they fail in assessing the impacts of such changes in a wider field of view, including the interactions between (a reliable) energy access and population growth, economic output, income, and the quality level of the environment. This work proposes a system dynamics model combining both perspectives that, by extending the Wonderland model and based on its equations and dynamics, permits the visual simulation of multiple regions and public policies or interventions. The proposed model is tested in a multi-country case study. In simulations, large time-scales were used to portray the effects of changes upon future generations.
Interstate Crossing‐Induced Chemiexcitation Mechanism as the Basis for Imidazopyrazinone Bioluminescence

Imidazopyrazinone dioxetanone is a central molecule in bioluminescence, as it is the scaffold responsible for light emission in many marine species. Herein, we have theoretically studied the thermolysis of its neutral and anionic forms. Our results allowed us to explain imidazopyrazinone bioluminescence with the Interstate Crossing-Induced Chemiexcitation (ICIC) mechanism, and explain why neutral forms emit light efficiently to the contrary of anionic ones. Both forms decompose via a stepwise-biradical pathway, but the biradical is formed either via an electron transfer (anion) or due to homolytic bond cleavage (neutral species). Absence of electron transfer leads to an entropic trap, a flat region where infinite possibilities for chemiexcitation exist. However, the resulting products can be in either triplet or singlet excited states. The triplet to singlet ratio is determined by the electron spin density distribution, which controls the rates of intersystem crossing.
Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial CT Scans

In this paper we define a deep multi-stage architecture for automated landmarking of craniomaxillofacial (CMF) CT images. Our model is composed of three subnetworks that first localize, on reduced-resolution images, areas where landmarks may be found and then refine the search, at full-resolution scale, through a hierarchical structure aiming at increasing the granularity of the investigated region. The multi-stage pipeline is designed to deal with full resolution data and does not require any additional pre-processing step to reduce search space, as opposed to existing methods that can be only adopted for searching landmarks located in well-defined anatomical structures (e.g., mandibles). The automated landmarking system is tested on identifying landmarks located in several CMF regions, achieving an average error of 0.8 mm, significantly lower than expert readings. The proposed model also outperforms baselines and is on par with existing models that employ additional upstream segmentation, on state-of-the-art benchmarks.
An Enhanced Method for Reverse Engineering CAN Data Payload

Recently, numerous electronic components are installed in vehicles, providing drivers and passengers with increased safety and convenience. The electronic components construct an in-vehicle network that internally shares relevant status information about the vehicle. As modern vehicles become more computerized, the potential for automotive cyber-security threats also increases a fact that has been illustrated clearly by various car-hacking demonstrations. Using the controller area network (CAN), the de facto standard protocol in the automotive industry that facilitates in-vehicle network communication, car-hacking demonstrations inject critical CAN messages to control vehicular functions. In efforts to address this security issue, car manufacturers, in turn, have made confidential the CAN database (i.e., DBC format file), where signal information assigned in the CAN data payload is specified. However, it has since become known that this policy does not hermetically seal a vehicular network against cyber attacks. On the contrary, in-depth automotive security research has been hindered significantly because of the limited information accessible by researchers. For example, automotive intrusion detection systems (IDS) identify and alert when there is a vehicular break-in, and this technology is a major area of study in automotive cyber security research. For the automotive IDS that analyzes CAN traffic, information in the DBC format file greatly improves detection veracity. However, most IDS technologies to date have been independently developed without the confidential CAN DB information and, as a result, do not mitigate threats to a satisfactory standard. In this paper, we propose an enhanced method that identifies signal boundaries in a CAN data payload, which is specified in the DBC format file. Unlike an existing method that is designed based on total bit-flip rates, our method analyzes bit-flip time series not total bit-flip rates so that signal boundaries can be more clearly identified. In this paper, we use a publicly available DBC format file called OpenDBC as a reference, and show that our method outperforms the existing method.
Fog-Enabled Joint Computation, Communication and Caching Resource Sharing for Energy-Efficient IoT Data Stream Processing

Fog/edge computing has been recently regarded as a promising approach for supporting emerging mission-critical Internet of Things (IoT) applications on capacity and battery constrained devices. By harvesting and collaborating a massive crowd of devices in close proximity for computation, communication and caching resource sharing (i.e., 3C resources), it enables great potentials in low-latency and energy-efficient IoT task execution. To efficiently exploit 3C resources of fog devices in proximity, we propose F3C, a fog-enabled 3C resource sharing framework for energy-efficient IoT data stream processing by solving an energy cost minimization problem under 3C constraints. Nevertheless, the minimization problem proves to be NP-hard via reduction from a Generalized Assignment Problem (GAP). To cope with such challenge, we propose an efficient F3C algorithm based on an iterative task team formation mechanism which regards each task's 3C resource sharing as a subproblem solved by the elaborated min cost flow transformation. Via utility improving iterations, the proposed F3C algorithm is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our F3C algorithm can achieve superior performance in energy saving compared to various benchmarks.
Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks

Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs). Recognizing that the kernels in a convolutional neural network (CNN) are 4-way tensors, we further exploit a new elasticity dimension along the input-output channels. Specifically, a novel nuclear-norm rank minimization factorization (NRMF) approach is proposed to dynamically and globally search for the reduced tensor ranks during training. Correlation between tensor ranks across multiple layers is revealed, and a graceful tradeoff between model size and accuracy is obtained. Experiments then show the superiority of NRMF over the previous non-elastic variational Bayesian matrix factorization (VBMF) scheme.
Generation of Hypergraphs from the N-Best Parsing of 2D-Probabilistic Context-Free Grammars for Mathematical Expression Recognition

We consider hypergraphs as a tool obtained with bidimensional Probabilistic Context-Free Grammars to compactly represent the result of the n-best parse trees for an input image that represents a mathematical expression. More specifically, in this paper we propose: i) an algorithm to compute the N-best parse trees from a 2D-PCFGs, ii) an algorithm to represent the n-best parse trees using a compact representation in the form of hypergraphs, and iii) a formal framework for the development of inference algorithms (inside and outside) and normalization strategies of hypergraphs.
Naturally Constrained Online Expectation Maximization

With the rise of big data sets, learning algorithms must be adapted to piece-wise mechanisms to tackle large-scale calculations' time and memory costs. Furthermore, for most learning embedded systems, the input data are fed sequentially and contingently: one by one, and possibly class by class. Thus, learning algorithms should not only run online but cope with time-varying, non-independent, and non-balanced training data for the system's entire life. Online Expectation-Maximization is a well-known algorithm for learning probabilistic models in realtime, due to its simplicity and convergence properties. However, these properties are only valid in the case of large, independent and identically distributed samples. In this paper, we propose to constrain the online Expectation-Maximization on the Fisher distance between the parameters. After presenting the algorithm, we make a thorough study of its use in Probabilistic Principal Components Analysis. First, we derive the update rules, and then we analyze the effect of the constraint on major problems of online and sequential learning: convergence, forgetting and interference. Furthermore, we use several algorithmic protocols: iid vs sequential data, and constraint parameters updated stepwise vs class-wise. Our results show that this constraint increases the convergence rate of online Expectation-Maximization, decreases forgetting and slightly introduces positive transfer learning
Sustainable Production of Carbon Fiber: Effect of Cross-Linking in Wool Fiber on Carbon Yields and Morphologies of Derived Carbon Fiber

Currently, most of the carbon fibers are made from unsustainable fossil fuel-based precursors including high purity polyacrylonitrile (PAN) and pitch. High purity PAN is not only more expensive than wool fiber but also a limited quantity is produced because of global shortage of its monomer. In this work, various cross-linking pathways are explored as a means of altering the yield and tensile properties of carbon fiber derived from the carbonization of cross-linked wool fiber at 800 °C under nitrogen. A range of ionic and covalent-bond-forming cross-linking agents including bifunctional carboxylic acids (succinic acid and sebacic acid), a disulfonic acid (naphthalene disulfonic acid), a dialdehyde (glyoxal), and dianhydrides (succinic anhydride and itaconic anhydride) was investigated. The resulting carbon fibers were characterized in terms of chemical composition, carbon yield, surface topology, crystal structure, hydrophilicity, and tensile properties. It was found that the carbon yield can be increased.
FashionGraph: Understanding fashion data using scene graph generation

Fashion analysis is an attractive domain for vision research due to its direct applications in e-commerce contexts. However, fashion datasets are commonly rather demanding, as both objects and attributes tend to be fine-grained and thus result in very long-tailed datasets. Furthermore, relationships between objects and attributes are often dense, but are crucial for the performance of fashion applications. In this paper, we propose to generate scene graphs for existing fashion datasets. By detecting relationships between fashion objects, their parts, and their attributes we gain a better understanding of the scenes. As no current fashion dataset provides scene graphs, we generate relationships between fashion objects from existing annotations. The output is post-processed and filtered to generate a meaningful scene graph for each image. In the experiments, we can show existing applications like image retrieval benefiting from the scene graph understanding. We first evaluate the accuracy of the generated scene graphs. Then, we employ scene graphs to fashion image retrieval in order to showcase their performance in real applications. The results show various benefits for fashion applications by exploiting scene graph knowledge. The resources and model for the proposed method are available on GitHub11https://github.com/shabnamsadegh/FashionGraph.
A Realizable Overlay Virtual Metrology System in Semiconductor Manufacturing: Proposal, Challenges and Future Perspective

Integrated circuits (IC) are fabricated on a wafer through stacked layers of circuit patterns. To ensure proper functionality, the overlay of each pattern layer must be within the tolerance. Inspecting each wafer’s overlay is unrealistic and impractical. Hence, wafers are selectively inspected at metrology stations through sampling strategies. With virtual metrology (VM), the metrology quality of the uninspected wafers can be estimated. Motivated by a real-world production environment of a 200mm semiconductor manufacturing plant (fab), a VM to estimate the overlay of the photolithography process is envisioned. Past researches on overlay VM leveraged fault detection and classification (FDC) data to estimate the overlay errors. As such, for fabs in the progress of completing their FDC development for photolithography equipment, a different modeling approach is required to realize an overlay VM that sustains the production line until FDC data can be leveraged for VM. With practical gaps that must be addressed in real fabs, this paper focuses on realizing an overlay VM for the photolithography process without leveraging FDC data. Therefore, the objectives of this paper are two folds: First, to identify the research challenges towards realizing the overlay VM. Second, to propose the future research perspectives of the envisioned overlay VM. Based on the future research perspectives, a two-steps overlay VM modeling approach utilizing data mining techniques is proposed toward realizing the envisioned overlay VM system. The proposed approach first classifies the process stability at the wafer lot level, and subsequently, performs overlay error estimations for wafers in the wafer lots classified with stable process. Linear regression models are proposed to perform overlay error estimations in this work to augment the interpretability of the overlay VM.
Bandwidth-Enhanced Low-Profile Magneto-Electric Dipole Antenna With Shorting Parasitic Elements

This paper presents a low-profile magneto-electric (ME) dipole antenna with an enhanced impedance bandwidth realized by the loading of shorting parasitic elements. The antenna contains a horizontal planar electric dipole antenna, a <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>-shaped shorted patch antenna, a pair of parasitic L-shaped shorted patches, and a direct feed. By loading of the parasitic L-shaped patches such that they are shorted to the ground, the proposed antenna attains a significant bandwidth enhancement while maintaining excellent broadside radiation characteristics. An antenna prototype with a low profile of only <inline-formula> <tex-math notation="LaTeX">$0.11\lambda _{0}$ </tex-math></inline-formula> was fabricated and tested (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the free-space wavelength referring to the center frequency). The antenna achieves a measured impedance bandwidth of 75.64% (1.34–2.97 GHz) for a voltage standing wave ratio (VSWR) of less than 2. Within the operating band, the measured gain ranges from 6.23 to 8.49 dBi. Additionally, stable radiation patterns on both E- and H-planes with a high front-to-back ratio of more than 20.5 dB are achieved.
Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions

Unsupervised domain adaptation (UDA)-based methods have made great progress in mechanical fault diagnosis under variable working conditions. In UDA, three types of information, including class label, domain label, and data structure, are essential to bridging the labeled source domain and unlabeled target domain. However, most existing UDA-based methods use only the former two information and ignore the modeling of data structure, which make the information contained in the features extracted by the deep network incomplete. To tackle this issue, a domain adversarial graph convolutional network (DAGCN) is proposed to model the three types of information in a unified deep network and achieving UDA. The first two types of information are modeled by the classifier and the domain discriminator, respectively. In data structure modeling, a convolutional neural network (CNN) is first employed to exact features from input signals. After that, the CNN features are input to the proposed graph generation layer to construct instance graphs by mining the relationship of structural characteristics of samples. Then, the instance graphs are modeled by a graph convolutional network, and the maximum mean discrepancy metric is leveraged to estimate the structure discrepancy of instance graphs from different domains. Experimental results conducted on two case studies demonstrate that the proposed DAGCN can not only obtain the best performance among the comparison methods, but also can extract transferable features for domain adaptation. The code library is available at: https://github.com/HazeDT/DAGCN.
Multi-Scanning Based Recurrent Neural Network for Hyperspectral Image Classification

As the specialty of hyperspectral image (HSI), it consists of 2D spatial and 1D spectral information. In the field of deep learning, HSI classification is an appealing research topic. Many existing methods process the HSI in spatial or spectral domain separately, which cannot fully extract the representative features, and the most used 3D convolutional neural network (3D-CNN) will suffer from mixing up complex spectral information. In this paper, we propose a spatial-spectral unified method by using recurrent neural networks (RNN) and multi-scanning direction strategy to construct spatial-spectral information sequences for learning the spatial dependencies among the central pixel and neighboring pixels. Meanwhile, residual connections and dense connections are introduced into multi-scanning direction sequences to overcome the memory problem in the RNN. The proposed method got 99.58% and 99.81% accuracy respectively on two benchmark datasets: the Pavia University dataset and the Pavia Center dataset. It demonstrates the proposed method can achieve state-of-the-art results.
One-stage Multi-task Detector for 3D Cardiac MR Imaging

Fast and accurate landmark location and bounding box detection are important steps in 3D medical imaging. In this paper, we propose a novel multi-task learning framework, for real-time, simultaneous landmark location and bounding box detection in 3D space. Our method extends the famous single-shot multibox detector (SSD) from single-task learning to multitask learning and from 2D to 3D. Furthermore, we propose a post-processing approach to refine the network landmark output, by averaging the candidate landmarks. Owing to these settings, the proposed framework is fast and accurate. For 3D cardiac magnetic resonance (MR) images with size 224×224×64, our framework runs ~128 volumes per second (VPS) on GPU and achieves 6.75mm average point-to-point distance error for landmark location, which outperforms both state-of-the-art and baseline methods. We also show that segmenting the 3D image cropped with the bounding box results in both improved performance and efficiency.