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The realm of classical phase retrieval concerns itself with the arduous task of recovering a signal from its Fourier magnitude measurements, which are fraught with inherent ambiguities. A single-exposure intensity measurement is commonly deemed insufficient for the reconstruction of the primal signal, given that the absent phase component is imperative for the inverse transformation. In this work, we present a novel single-shot phase retrieval paradigm from a fractional Fourier transform (FrFT) perspective, which involves integrating the FrFT-based physical measurement model within a self-supervised reconstruction scheme. Specifically, the proposed FrFT-based measurement model addresses the aliasing artifacts problem in the numerical calculation of Fresnel diffraction, featuring adaptability to both short-distance and long-distance propagation scenarios. Moreover, the intensity measurement in the FrFT domain proves highly effective in alleviating the ambiguities of phase retrieval and relaxing the previous conditions on oversampled or multiple measurements in the Fourier domain. Furthermore, the proposed self-supervised reconstruction approach harnesses the fast discrete algorithm of FrFT alongside untrained neural network priors, thereby attaining preeminent results. Through numerical simulations, we demonstrate that both amplitude and phase objects can be effectively retrieved from a single-shot intensity measurement using the proposed approach and provide a promising technique for support-free coherent diffraction imaging.

The control logic models built by Simulink or Ptolemy have been widely used in industry scenes. It is an urgent need to ensure the safety and security of the control logic models. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art model testing tools employ model checking techniques or search-based methods to generate test cases. Traditional search based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. In this manner, our work combines the advantages of the model checking methods and fuzzing techniques in a novel way. Since the control logic models always receive signal inputs, we specifically design novel mutation operators for signals to improve the existing fuzzing method in model testing. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works., Comment: 10 pages

Compressive imaging aims to recover a latent image from under-sampled measurements, suffering from a serious ill-posed inverse problem. Recently, deep neural networks have been applied to this problem with superior results, owing to the learned advanced image priors. These approaches, however, require training separate models for different imaging modalities and sampling ratios, leading to overfitting to specific settings. In this paper, a dynamic proximal unrolling network (dubbed DPUNet) was proposed, which can handle a variety of measurement matrices via one single model without retraining. Specifically, DPUNet can exploit both the embedded observation model via gradient descent and imposed image priors by learned dynamic proximal operators, achieving joint reconstruction. A key component of DPUNet is a dynamic proximal mapping module, whose parameters can be dynamically adjusted at the inference stage and make it adapt to different imaging settings. Experimental results demonstrate that the proposed DPUNet can effectively handle multiple compressive imaging modalities under varying sampling ratios and noise levels via only one trained model, and outperform the state-of-the-art approaches.

The square kernel is a standard unit for contemporary CNNs, as it fits well on the tensor computation for convolution operation. However, the retinal ganglion cells in the biological visual system have approximately concentric receptive fields. Motivated by this observation, we propose to use circular kernel with a concentric and isotropic receptive field as an option for the convolution operation. We first propose a simple yet efficient implementation of the convolution using circular kernels, and empirically show the significant advantages of large circular kernels over the counterpart square kernels. We then expand the operation space of several typical Neural Architecture Search (NAS) methods with the convolutions of large circular kernels. The searched new neural architectures do contain large circular kernels and outperform the original searched models considerably. Our additional analysis also reveals that large circular kernels could help the model to be more robust to the rotated or sheared images due to their better rotation invariance. Our work shows the potential of designing new convolutional kernels for CNNs, bringing up the prospect of expanding the search space of NAS with new variants of convolutions., Comment: 20 pages, 10 figures, submitted to a conference

This paper is written because I receive several inquiry emails saying it is hard to achieve good results when applying token repetition learning techniques. If REP (proposed by me) or Pointer-Mixture (proposed by Jian Li) is directly applied to source code to decide all token repetitions, the model performance will decrease sharply. As we use pre-order traversal to traverse the Abstract Syntax Tree (AST) to generate token sequence, tokens corresponding to AST grammar are ignored when learning token repetition. For non-grammar tokens, there are many kinds: strings, chars, numbers and identifiers. For each kind of tokens, we try to learn its repetition pattern and find that only identifiers have the property of token repetition. For identifiers, there are also many kinds such as variables, package names, method names, simple types, qualified types or qualified names. Actually, some kinds of identifiers such as package names, method names, qualified names or qualified types are unlikely to be repeated. Thus, we ignore these kinds of identifiers that are unlikely to be repeated when learning token repetition. This step is crucial and this important implementation trick is not clearly presented in the paper because we think it is trivial and too many details may bother readers. We offer the GitHub address of our model in our conference paper and readers can check the description and implementation in that repository. Thus, in this paper, we supplement the important implementation optimization details for the already published papers., Comment: This paper contains the supplement to the implementation details of the origin paper

In the field of software engineering, applying language models to the token sequence of source code is the state-of-art approach to build a code recommendation system. The syntax tree of source code has hierarchical structures. Ignoring the characteristics of tree structures decreases the model performance. Current LSTM model handles sequential data. The performance of LSTM model will decrease sharply if the noise unseen data is distributed everywhere in the test suite. As code has free naming conventions, it is common for a model trained on one project to encounter many unknown words on another project. If we set many unseen words as UNK just like the solution in natural language processing, the number of UNK will be much greater than the sum of the most frequently appeared words. In an extreme case, just predicting UNK at everywhere may achieve very high prediction accuracy. Thus, such solution cannot reflect the true performance of a model when encountering noise unseen data. In this paper, we only mark a small number of rare words as UNK and show the prediction performance of models under in-project and cross-project evaluation. We propose a novel Hierarchical Language Model (HLM) to improve the robustness of LSTM model to gain the capacity about dealing with the inconsistency of data distribution between training and testing. The newly proposed HLM takes the hierarchical structure of code tree into consideration to predict code. HLM uses BiLSTM to generate embedding for sub-trees according to hierarchies and collects the embedding of sub-trees in context to predict next code. The experiments on inner-project and cross-project data sets indicate that the newly proposed Hierarchical Language Model (HLM) performs better than the state-of-art LSTM model in dealing with the data inconsistency between training and testing and achieves averagely 11.2\% improvement in prediction accuracy., Comment: 7 pages

We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important problem for photo enhancement. Inspired by iterative structure reduction for hidden community detection in social networks, we propose an Iterative Boost Convolutional LSTM Network (IBCLN) that enables cascaded prediction for reflection removal. IBCLN is a cascaded network that iteratively refines the estimates of transmission and reflection layers in a manner that they can boost the prediction quality to each other, and information across steps of the cascade is transferred using an LSTM. The intuition is that the transmission is the strong, dominant structure while the reflection is the weak, hidden structure. They are complementary to each other in a single image and thus a better estimate and reduction on one side from the original image leads to a more accurate estimate on the other side. To facilitate training over multiple cascade steps, we employ LSTM to address the vanishing gradient problem, and propose residual reconstruction loss as further training guidance. Besides, we create a dataset of real-world images with reflection and ground-truth transmission layers to mitigate the problem of insufficient data. Comprehensive experiments demonstrate that the proposed method can effectively remove reflections in real and synthetic images compared with state-of-the-art reflection removal methods., Comment: 10 pages, 8 figures

Language models such as RNN, LSTM or other variants have been widely used as generative models in natural language processing. In last few years, taking source code as natural languages, parsing source code into a token sequence and using a language model such as LSTM to train that sequence are state-of-art methods to get a generative model for solving the problem of code completion. However, for source code with hundreds of statements, traditional LSTM model or attention-based LSTM model failed to capture the long term dependency of source code. In this paper, we propose a novel statement-level language model (SLM) which uses BiLSTM to generate the embedding for each statement. The standard LSTM is adopted in SLM to iterate and accumulate the embedding of each statement in context to help predict next code. The statement level attention mechanism is also adopted in the model. The proposed model SLM is aimed at token level code completion. The experiments on inner-project and cross-project data sets indicate that the newly proposed statement-level language model with attention mechanism (SLM) outperforms all other state-of-art models in token level code completion task., Comment: The experimental data is not complete and has some error!