In underwater acoustic target recognition, wave-based methods and time–frequency (T-F) representation-based methods are typically used to identify the target from different perspectives. Both methods have advantages and disadvantages. In this study, a complementary space between the wave-based model and the T-F representation-based model was proven to exist. Advanced lightweight technologies make the fusion of the two types of models technically possible. First, a lightweight multiscale residual deep neural network (MSRDN) is designed using lightweight network design techniques, in which 64.18% of parameters and 79.45% of floating point operations (FLOPs) are reduced from the original MSRDN with a small loss of accuracy. Then, a joint model combining wave and T-F representation-based models was presented. An effective synchronous deep mutual learning method that saves approximately 11.54%–16.27% training time is proposed to train the joint model. Two datasets acquired from real-world scenarios were used to verify the effectiveness of the proposed method. Compared with state-of-the-art methods, the joint model with synchronous deep mutual learning achieved the best recognition accuracies of 85.20% and 79.50% in the two datasets, respectively. The results of ablation explorations prove that the performance improvements of the proposed methods benefit from the deep mutual learning of the two branches and are not unique to certain models. Finally, a discussion reveals the essential mechanism of the proposed method.

Explainable AI (XAI) promotes understandability and credibility in the complex decision-making process of machine learning models. Post-hoc explanations for deep neural networks are crucial because they focus on faithful explanation of the learned representations, decision mechanisms, and uncertainty of the model. Explaining deep convolutional neural networks (DCNN) is particularly challenging because of the high dimensionality of deep features and the complexity of model inference. Most post-hoc explanation methods follow one single paradigm of explanation, confining the diversity and consistency of the explanation. This study proposes a joint Gaussian mixture model (JGMM), which is a probabilistic model that jointly models deep inter-layer features and produces faithful and consistent post-hoc explanations. The JGMM explains deep features by Gaussian mixture model and model inference by the posterior distribution of the latent component variables. JGMM enables a versatile explanation framework that unifies the interpretable proxy model and mining global and local explanatory examples. Experiments are performed on various DCNN image classifiers in comparison with other explaining methods. The results show that the JGMM can efficiently produce versatile, consistent, faithful, and understandable explanations.

Gastric cancer (GC) is the third leading cause of cancer-associated deaths globally. Accurate risk prediction of the overall survival (OS) for GC patients shows significant prognostic value, which helps identify and classify patients into different risk groups to benefit from personalized treatment. Many methods based on machine learning algorithms have been widely explored to predict the risk of OS. However, the accuracy of risk prediction has been limited and remains a challenge with existing methods. Few studies have proposed a framework and pay attention to the low-level and high-level features separately for the risk prediction of OS based on computed tomography images of GC patients. To achieve high accuracy, we propose a multi-focus fusion convolutional neural network. The network focuses on low-level and high-level features, where a subnet to focus on lower-level features and the other enhanced subnet with lateral connection to focus on higher-level semantic features. Three independent datasets of 640 GC patients are used to assess our method. Our proposed network is evaluated by metrics of the concordance index and hazard ratio. Our network outperforms state-of-the-art methods with the highest concordance index and hazard ratio in independent validation and test sets. Our results prove that our architecture can unify the separate low-level and high-level features into a single framework, and can be a powerful method for accurate risk prediction of OS.

Morphological analysis, which includes analysis of part-of-speech (POS) tagging, stemming, and morpheme segmentation, is one of the key components in natural language processing (NLP), particularly for agglutinative languages. In this article, we investigate the morphological analysis of the Uyghur language, which is the native language of the people in the Xinjiang Uyghur autonomous region of western China. Morphological analysis of Uyghur is challenging primarily because of factors such as (1) ambiguities arising due to the likelihood of association of a multiple number of POS tags with a word stem or a multiple number of functional tags with a word suffix, (2) ambiguous morpheme boundaries, and (3) complex morphopholonogy of the language. Further, the unavailability of a manually annotated training set in the Uyghur language for the purpose of word segmentation makes Uyghur morphological analysis more difficult. In our proposed work, we address these challenges by undertaking a semisupervised approach of learning a Markov model with the help of a manually constructed dictionary of “suffix to tag” mappings in order to predict the most likely tag transitions in the Uyghur morpheme sequence. Due to the linguistic characteristics of Uyghur, we incorporate a prior belief in our model for favoring word segmentations with a lower number of morpheme units. Empirical evaluation of our proposed model shows an accuracy of about 82%. We further improve the effectiveness of the tag transition model with an active learning paradigm. In particular, we manually investigated a subset of words for which the model prediction ambiguity was within the top 20%. Manually incorporating rules to handle these erroneous cases resulted in an overall accuracy of 93.81%.

The number fluctuation theory based on the McWhorter's charge-trapping model and the bulk mobility fluctuation theory based on Hooge's hypothesis are the two major existing theories to explain the origins of the flicker noise, which is the dominant low-frequency noise source in silicon metal-oxide-semiconductor field-effect transistors (MOSFETs). We have done the flicker noise measurements and SPICE simulations for both long-channel (5 /spl mu/m) and short-channel (1.2 /spl mu/m and 0.6 /spl mu/m) p-type channel metal-oxide-semiconductor (PMOS) transistors. HSPICE [device model: level 3, level 46 (BSIM 3v2) and level 47 (BSIM 3v3); noise model: NLEV=0 and NLEV=2 and 3] and PSPICE [device model: level 3, level 6 (BSIM 3v2) and level 7 (BSIM 3v3); noise model: NLEV=0 and NLEV=2 and 3] were used for the simulations. Our measurement results suggest that in the saturation region, for long-channel PMOS transistors, the flicker noise is due to the bulk effect and it follows the mobility fluctuation theory, while for short-channel ones, it is due to the surface state effect and the number fluctuation theory applies. Our simulation results showed that for both HSPICE and PSPICE, level 3 and NLEV=0 are the appropriate models for the simulations of long-channel PMOS transistor flicker noise; HSPICE with level 47 or 49 and NLEV=2 and 3 and PSPICE with level 6 and NLEV=2 and 3 are applied for the short-channel PMOS devices. The simulation results are consistent with the measurements.