Learning Discriminative Shrinkage Deep Networks for Image Deconvolution

Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. In this paper, we propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. In contrast to most existing methods that use deep convolutional neural networks (CNNs) or radial basis functions to simply learn the regularization term, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with a Maxout layer to learn discriminative shrinkage functions to directly approximate the solutions of these two sub-problems. Moreover, given the fast-Fourier-transform-based image restoration usually leads to ringing artifacts while conjugate-gradient-based approach is time-consuming, we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy

Interpretations of Domain Adaptations via Layer Variational Analysis

Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method.Comment: Published at ICLR 202

Time-Domain Multi-modal Bone/air Conducted Speech Enhancement

Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE). However, video clips usually contain large amounts of data and pose a high cost in terms of computational resources and thus may complicate the SE system. As an alternative source, a bone-conducted speech signal has a moderate data size while manifesting speech-phoneme structures, and thus complements its air-conducted counterpart. In this study, we propose a novel multi-modal SE structure in the time domain that leverages bone- and air-conducted signals. In addition, we examine two ensemble-learning-based strategies, early fusion (EF) and late fusion (LF), to integrate the two types of speech signals, and adopt a deep learning-based fully convolutional network to conduct the enhancement. The experiment results on the Mandarin corpus indicate that this newly presented multi-modal (integrating bone- and air-conducted signals) SE structure significantly outperforms the single-source SE counterparts (with a bone- or air-conducted signal only) in various speech evaluation metrics. In addition, the adoption of an LF strategy other than an EF in this novel SE multi-modal structure achieves better results.Comment: multi-modal, bone/air-conducted signals, speech enhancement, fully convolutional networ

Study on the Correlation between Objective Evaluations and Subjective Speech Quality and Intelligibility

Subjective tests are the gold standard for evaluating speech quality and intelligibility, but they are time-consuming and expensive. Thus, objective measures that align with human perceptions are crucial. This study evaluates the correlation between commonly used objective measures and subjective speech quality and intelligibility using a Chinese speech dataset. Moreover, new objective measures are proposed combining current objective measures using deep learning techniques to predict subjective quality and intelligibility. The proposed deep learning model reduces the amount of training data without significantly impacting prediction performance. We interpret the deep learning model to understand how objective measures reflect subjective quality and intelligibility. We also explore the impact of including subjective speech quality ratings on speech intelligibility prediction. Our findings offer valuable insights into the relationship between objective measures and human perceptions

High Serum Estradiol Levels are not Detrimental to In Vitro Fertilization Outcome

SummaryObjectiveTo evaluate the impact of high estradiol (E2) levels and a high number of retrieved oocytes on the outcome of in vitro fertilization (IVF) cycles.Materials and MethodsWe retrospectively reviewed 274 IVF cycles. These patients were divided into five groups according to their peak E2 levels on the human chorionic gonadotropin day: ≤ 2,000 pg/mL (130 cycles); 2,001–3,000 pg/mL (53 cycles); 3,001–4,000 pg/mL (46 cycles); 4,001–5,000 pg/mL (29 cycles); > 5,000 pg/mL (16 cycles). Fertilization, pregnancy, and implantation rates were analyzed between these groups. We also compared the outcome of IVF for high responders (> 15 retrieved oocytes) and normal responders (≤ 15 retrieved oocytes).ResultsThe oocyte fertilization and embryo cleavage rates were not significantly different among these five groups. Although decrease in pregnancy and implantation rates was observed when E2 levels were > 5,000 pg/mL compared with those having lower E2 levels, there were no statistically significant differences between these five groups. In addition, similar IVF outcome was detected for those cycles with > 15 oocytes and ≤ 15 oocytes obtained.ConclusionHigh serum E2 levels and high oocyte yield are not detrimental to IVF outcome. More studies are needed to characterize the threshold E2 levels above which implantation rates are reduced

Case report: Heterogenous SMARCA4-deficient thoracic non-small cell lung carcinoma with various responses to nivolumab

SMARCA4-deficient non-small cell carcinoma is an aggressive neoplasm with poor outcome. Several studies have highlighted its immunochemistry, pathophysiology, and underlying mechanisms, but studies of its definite treatment are few. Here, we report on a 69-year-old male with heterogenous pathological presentations of SMARCA4-deficient non-small cell carcinoma. He initially presented with neck lymphadenopathies. Immunohistochemistry staining and genomic profiling confirmed the diagnosis of SMARCA4-deficient non-small cell carcinoma. The patient responded well to immune checkpoint inhibitors with nivolumab. However, new lesions with various pathological presentations and various responses to nivolumab appeared during the treatment course. The patient survived more than 3 years from the initial diagnosis. This case shows the efficacy of nivolumab to treat SMARCA4-deficient non-small cell lung carcinoma