Component Decoders Research Articles

Two Calcium Sensor-Activated Kinases Function in Root Hair Growth.

Plant pollen tubes and root hairs typical polarized tip growth. It is well established that calcium ions (Ca2+) play essential roles in maintaining cell polarity and guiding cell growth orientation. Ca2+ signals are encoded by Ca2+ channels and transporters and are decoded by a variety of Ca2+-binding proteins often called Ca2+ sensors, in which calcineurin B-like protein (CBL) proteins function by interacting with and activating a group of kinases, activate CBL-interacting protein kinases (CIPKs). Some CBL-CIPK complexes, such as CBL2/3-CIPK12/19, act as crucial regulators of pollen tube growth. Whether these calcium decoding components regulate the growth of root hairs, another type of plant cell featuring Ca2+-regulated polarized growth, remains unknown. In this study, we identified CIPK13 and CIPK18 as genes specifically expressed in Arabidopsis (Arabidopsis thaliana) root hairs. The cipk13 cipk18 double mutants showed reduced root hair length and lower growth rates. The calcium oscillations at the root hair tip were attenuated in the cipk13 cipk18 mutants as compared to the wild-type plants. Through yeast two-hybrid screens, CBL2 and CBL3 were identified as interacting with CIPK13 and CIPK18. cbl2 cbl3 displayed a shortened root hair phenotype similar to cipk13 cipk18. This genetic analysis, together with biochemical assays showing activation of CIPK13/18 by CBL2/3, supported the conclusion that CBL2/3 and CIPK13/18 may work as Ca2+-decoding modules in controlling root hair growth. Thus, the findings that CIPK12/19 and CIPK13/18 function in pollen tube and root hair growth, respectively, illustrate a molecular mechanism in which the same CBLs recruit distinct CIPKs in regulating polarized tip growth in different types of plant cells.

A Divide-and-Rule Combined Learning Method for Truly Multivariate Time Series Prediction

Multivariate time series prediction is a significant research area that aims to forecast future values based on past observations. Deep learning models with attention mechanisms have shown good predictive performance by emphasizing optimal-related sequences in the target series. However, these models ignore mutation information of nontarget sequences and the long short-term dependencies. To this end, a divide-and-rule combined learning method is proposed to address these limitations, which uses differentiated feature extractors to process different implicit features. First, we design a spatial and temporal information extractor to extract the time-dimensional feature information in the separation stage. Then, a multivariate mutation information extractor is constructed by convolution and maximum pooling layer to capture mutation information of nontarget sequences. Subsequently, the decoder component of the encoder-decoder model extracts long short-term dependencies while preserving the information of the target sequence to be predicted. Finally, in the cooperation stage, a feature fusion method based on a point attention mechanism is proposed, which can assign individual weights to each feature point and enhance the ability to focus on local areas. Experimental results on five real datasets in different domains show that the proposed method has better predictive performance compared to other baseline models.

ConvMedSegNet: A multi-receptive field depthwise convolutional neural network for medical image segmentation

In order to achieve highly precise medical image segmentation, this paper presents ConvMedSegNet, a novel convolutional neural network designed with a U-shaped architecture that seamlessly integrates two crucial modules: the multi-receptive field depthwise convolution module (MRDC) and the guided fusion module (GF). The MRDC module’s primary function is to capture texture information of varying sizes through multi-scale convolutional layers. This information is subsequently utilized to enhance the correlation of global feature data by expanding the network’s width. This strategy adeptly preserves the inherent inductive biases of convolution while concurrently amplifying the network’s ability to establish dependencies on global information. Conversely, the GF module assumes responsibility for implementing multi-scale feature fusion by connecting the encoder and decoder components. It facilitates the transfer of information between features that are separated over substantial distances through guided fusion, effectively minimizing the loss of critical data. In experiments conducted on public medical image datasets such as BUSI and ISIC2018, ConvMedSegNet outperforms several advanced competing methods, yielding superior results. Additionally, the code can be accessed at https://github.com/csust-yixin/ConvMedSegNet.

FGeo-TP: A Language Model-Enhanced Solver for Euclidean Geometry Problems

The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proofs has always been a grand challenge to the interdisciplinary field of mathematics and artificial intelligence. This is the fourth article in a series of our works, in our previous work, we established a geometric formalized system known as FormalGeo. Moreover, we annotated approximately 7000 geometric problems, forming the FormalGeo7k dataset. Despite the fact that FGPS (Formal Geometry Problem Solver) can achieve interpretable algebraic equation solving and human-like deductive reasoning, it often experiences timeouts due to the complexity of the search strategy. In this paper, we introduced FGeo-TP (theorem predictor), which utilizes the language model to predict the theorem sequences for solving geometry problems. The encoder and decoder components in the transformer architecture naturally establish a mapping between the sequences and embedding vectors, exhibiting inherent symmetry. We compare the effectiveness of various transformer architectures, such as BART or T5, in theorem prediction, and implement pruning in the search process of FGPS, thereby improving its performance when solving geometry problems. Our results demonstrate a significant increase in the problem-solving rate of the language model-enhanced FGeo-TP on the FormalGeo7k dataset, rising from 39.7% to 80.86%. Furthermore, FGeo-TP exhibits notable reductions in solution times and search steps across problems of varying difficulty levels.

Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset.

MAUNext: a lightweight segmentation network for medical images

Objective. The primary objective of this study is to enhance medical image segmentation techniques for clinical research by prioritizing accuracy and the number of parameters. Approach. To achieve this objective, a novel codec-based MAUNext approach is devised, focusing on lightweight backbone design and the integration of skip connections utilizing multiscale, attention mechanism, and other strategic components. The approach is composed of three core modules: a multi-scale attentional convolution module for improved accuracy and parameter reduction, a collaborative neighbourhood-attention MLP encoding module to enhance segmentation performance, and a tiny skip-connected cross-layer semantic fusion module to bridge the semantic gap between encoder and decoder. Main results. The study extensively evaluates the MAUNext approach alongside eight state-of-the-art methods on three renowned datasets: Kagglelung, ISIC, and Brain. The experimental outcomes robustly demonstrate that the proposed approach surpasses other methods in terms of both parameter numbers and accuracy. This achievement holds promise for effectively addressing medical image segmentation tasks. Significance. Automated medical image segmentation, particularly in organ and lesion identification, plays a pivotal role in clinical diagnosis and treatment. Manual segmentation is resource-intensive, thus automated methods are highly valuable. The study underscores the clinical significance of automated segmentation by providing an advanced solution through the innovative MAUNext approach. This approach offers substantial improvements in accuracy and efficiency, which can significantly aid clinical decision-making and patient treatment.

Blind single-image-based thin cloud removal using a cloud perception integrated fast Fourier convolutional network

Remote sensing images are frequently contaminated by clouds that often degrade the performance of subsequent applications. Cloud removal, therefore, is a standard step in remote sensing image preprocessing, and single-image-based thin cloud removal is a well-established area of research. Existing single-image-based thin cloud removal methods however, lack the capacity for simultaneous executions of efficient long-range modeling and physical attribute consideration. To extend this work and fill this gap, a novel blind single-image-based thin cloud removal method, called cloud perception integrated fast Fourier convolutional network (CP-FFCN), was designed and implemented. The CP-FFCN consists of two modules: the cloud perception module (CPM) and a fast Fourier convolution (FFC)-conducted reconstruction module (FFCN). The CPM uses a frequency spatial attention mechanism to realize long-range modeling of clouds, globally detect them in the cloudy image. It helps the CP-FFCN remove the clouds without external prior knowledge of the cloud distribution. The reconstruction module was designed with an FFC-conducted U-Net architecture to recover the clean images from cloudy scenarios, guided by the locations of clouds as detected by the CPM. In addition, the FFC blocks deployed in the encoder and decoder components in the U-Net architecture selectively learn the attributes of clouds and fogs from the frequency spectrograms to remove the clouds and reconstruct the underlying ground objects. The CP-FFCN selectively learns the frequency features for adequate cloud separation and at the same time efficiently models the long-range information for comprehensive scenario reconstruction with the help of these two modules. We adopted the Google Earth data and Landsat-8 imagery to train the CP-FFCN model and evaluate it on simulated and naturally occurring cloudy scenarios. The visual outcomes illustrate that the proposed CP-FFCN successfully removes thin and small-scale thick clouds with complex ground object scenarios, without external cloud masks and additional reference data. The quantitative analyses further demonstrate the higher effectiveness of the CP-FFCN when compared with several other state-of-the-art thin cloud removal methods, yielding a PSNR value over 39.24 and a SSIM value over 0.98 on the Landsat 8 images.

Covert fault detection with imbalanced data using an improved autoencoder for optical networks

Covert faults are characterized by the performance parameters falling within the normal range, without any observable abnormalities. These types of faults pose a significant risk as they present no apparent warning signs of potential danger. Therefore, it is crucial to establish an efficient covert fault detection method to ensure the reliable and stable operation of optical networks. Data-driven technology, which reveals the internal relations and data patterns between the historical data by mining and analyzing the historical data, offers a new perspective for covert fault detection. However, equipment failures are extremely rare in real optical network systems, and the data imbalance of covert fault samples poses a challenge for standard machine learning classifiers in learning precise decision boundaries. To address this challenge, we propose a fault detection scheme based on an improved autoencoder for covert fault detection under data imbalance. The designed covert fault detection model exclusively utilizes normal samples during training and remains unaffected by data imbalance. Specifically, the model is specifically designed according to a number of encoder and decoder components to learn the normal sample data patterns in the latent space and detect covert faults based on the reconstruction errors in that space. To validate the proposed scheme, we conducted experiments using actual backbone data. According to the results, the detection accuracy and F1 score of the designed model on the test set were 0.9811 and 0.9527, and the false negative and false positive rates were 0.0026 and 0.0227, respectively. Furthermore, the visualization of the latent space reconstruction error principle for detecting covert faults was implemented using the principal component analysis dimension reduction and scatter plots.

Multimodal fusion for sensorimotor control in steering angle prediction

Efficient reasoning about the spatial and temporal structure of the environment is crucial for perception in autonomous driving, particularly in an end-to-end approach. Although different sensor modalities are employed to capture the complex nature of the environment, they each have their limitations. For example, frame-based RGB cameras are susceptible to variations in illumination conditions. However, these limitations at the sensor level can be addressed by complementing them with sensor fusion techniques, enabling the learning of efficient feature representations for end-to-end autonomous perception. In this study, we address the end-to-end perception problem by fusing a frame-based RGB camera with an event camera to improve the learned representation for predicting lateral control. To achieve this, we propose a convolutional encoder–decoder architecture called DRFuser. DRFuser encodes the features from both sensor modalities and leverages self-attention to fuse the frame-based RGB and event camera features in the encoder part. The decoder component unrolls the learned features to predict lateral control, specifically in the form of a steering angle. We extensively evaluate the proposed method on three datasets: our collected Dataset, Davis Driving dataset, and the EventScape dataset for simulation. The results demonstrate the generalization capability of our method on both real-world and simulated datasets. We observe qualitative and quantitative improvements in the performance of the proposed method for predicting lateral control by incorporating the event camera in fusion with the frame-based RGB camera. Notably, our method outperforms state-of-the-art techniques on the Davis Driving Dataset, achieving a 5.6% improvement in the root mean square error (RMSE) score.

Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systems

AbstractWith the rapid development of artificial intelligence technology, commercial robots have gradually entered our daily lives. In order to promote product dissemination, shopping guide robots are a new service options of commerce platforms that use tag recommendation systems to identify users' intentions. A large number of applications combine user historical tagging information with the multi‐round dialogue ability of shopping guide robots to help users efficiently search for and retrieve products of interest. Recently, tensor decomposition methods have become a common approach for modelling entity interaction relationships in tag recommendation systems. However, due to the sparsity of data, these methods only consider low‐order information of entities, making it difficult to capture the higher‐order collaborative signals among entities. Recommendation methods by autoencoders can effectively extract abstract feature representations while they only focus on the two‐dimensional relationship between users and items, ignoring the interaction relationship among users, items and tags in real complex recommendation scenarios. The authors focus on modelling the similarity relationship among entities and propose a method called deep feature fusion tag (DFFT) based on the deep feature fusion of stacked denoising autoencoders. This method can extract high‐order information with different embedding dimensions and fuse them in a unified framework. To extract robust feature representations, the authors inject random noise (mask‐out/drop‐out noise) into the tag information corresponding to users and items to generate corrupted input data, and then utilise autoencoders to encode the interaction relationship among entities. To further obtain the interaction relationship with different dimensions, different encoding layers are stacked and combined to produce a better expanded model which can reinforce each other. Finally, a decoding component is used to reconstruct the original input data. According to the experimental results on two common datasets, the proposed DFFT method outperforms other baselines in terms of the F1@N, NDCG@N and Recall@N evaluation metrics.

A novel method for driven stability of NFSRs via STP

The nonlinear feedback shift register (NFSR) is the main component of the convolutional decoder. This paper provides a novel method for investigating the driven stability of NFSRs with input via semi-tensor product (STP). By using the STP method, a backward state transition matrix, as well as a backward NFSR, is constructed. Backward NFSRs can help to find the predecessors of any states so that an algorithm for the global stability of autonomous NFSRs is designed. On this basis, two algorithms are proposed for the driven stability of NFSRs with input, and they have lower computational complexity than the existing methods. Meanwhile, some numerical examples are presented to support the results of this paper. Finally, the relationship between stability and connectedness is discussed fully, which derives some interesting results.

How phonological and orthographic decoding complicates the simple view of reading in Chinese: examining mediation through listening comprehension.

Advocates of the science of reading have invoked the simple view of reading (SVR) to justify an approach that foregrounds decoding in early reading instruction. SVR describes reading comprehension as the product of decoding and listening comprehension. The current study examined the complexity of the SVR, focusing on the phonological and orthographic decoding in third-grade readers of Chinese. One hundred and forty-three students participated in this study. The measures included phonological decoding (pinyin invented spelling), orthographic decoding, listening comprehension and reading comprehension. Drawing upon regression analyses and multivariate path models, the study found that phonological decoding at both segmental and suprasegmental processing significantly predicted Chinese reading comprehension, yet a more significant effect of orthographic decoding surfaced. And the best-fitting model suggested listening comprehension was better characterized as a mediator rather than a covariance with decoding component in the decoding-reading relation when orthographic decoding was incorporated into decoding component to evaluate the validity of the SVR in Chinese. The results imply that orthographic decoding is a legitimate decoding component and that the two decoding constructs alone are insufficient for higher-level reading ability (reading comprehension) since the contribution appears to be mediated through the path route of oral language capacity (indexed by listening comprehension). This enriches the current understanding of the SVR in non-alphabetic languages and indicates that decoding training on both phonological and orthographic dimension should be highlighted in early Chinese reading instruction.

Nonparametric Representation of Neutron Star Equation of State Using Variational Autoencoder

We introduce a new nonparametric representation of the neutron star (NS) equation of state (EOS) by using the variational autoencoder (VAE). As a deep neural network, the VAE is frequently used for dimensionality reduction since it can compress input data to a low-dimensional latent space using the encoder component and then reconstruct the data using the decoder component. Once a VAE is trained, one can take the decoder of the VAE as a generator. We employ 100,000 EOSs that are generated using the nonparametric representation method based on Han et al. as the training set and try different settings of the neural network, then we get an EOS generator (the trained VAE’s decoder) with four parameters. We use the mass–tidal-deformability data of binary NS merger event GW170817, the mass–radius data of PSR J0030+0451, PSR J0740+6620, PSR J0437-4715, and 4U 1702-429, and the nuclear constraints to perform the Bayesian inference. The overall results of the analysis that includes all the observations are , , and (90% credible levels), where R 1.4/Λ1.4 are the radius/tidal deformability of a canonical 1.4 M ⊙ NS, and is the maximum mass of a nonrotating NS. The results indicate that the implementation of these VAE techniques can obtain reasonable results, while accelerating calculation by a factor of ∼3–10 or more, compared with the original method.

TransRVNet: LiDAR Semantic Segmentation With Transformer

Effective and efficient 3D semantic segmentation from large-scale LiDAR point cloud is a fundamental problem in the field of autonomous driving. In this paper, we present Transformer-Range-View Network (TransRVNet), a novel and powerful projection-based CNN-Transformer architecture to infer point-wise semantics. First, a Multi Residual Channel Interaction Attention Module (MRCIAM) is introduced to capture channel-level multi-scale feature and model intra-channel, inter-channel correlations based on attention mechanism. Then, in the encoder stage, we use a well-designed Residual Context Aggregation Module (RCAM), including a residual dilated convolution structure and a context aggregation module, to fuse information from different receptive fields while reducing the impact of missing points. Finally, a Balanced Non-square-Transformer Module (BNTM) is employed as fundamental component of decoder to achieve locally feature dependencies for more discriminative feature learning by introducing the non-square shifted window strategy. Extensive qualitative and quantitative experiments conducted on challenging SemanticKITTI and SemanticPOSS benchmarks have verified the effectiveness of our proposed technique. Our TransRVNet presents superior performance over most existing state-of-the-art approaches. The source code and trained model are available at https://github.com/huixiancheng/TransRVNet.

ANALYSIS OF TRANSFORMER-DEEP NEURAL NETWORK USING DEEP LEARNING

Transformers were first used for natural language processing (NLP) tasks, but they quickly spread to other deep learning fields, including computer vision. They assess the interdependence of pairs. Attention is a part that enables to dynamically highlight relevant features of the input data (words in the case of text strings, parts of images in the case of visual Transformers). The cost grows continually with the number of tokens. The most common Trans- former Architecture for image classification uses only the Transformer Encoder to transform the various input tokens. However, the decoder component of the traditional Transformer Architecture is also used in a variety of other applications. In this section, we first introduce the Attention Mechanism (Section 1), followed by the Basic Transformer Block, which includes the Vision Transformer (Section 2).

A Low-Complexity RS-SPC Product Coding Scheme for Optical Systems

This paper presents a product coding scheme based on binary images of Reed-Solomon (RS) codes and single-parity-check (SPC) codes for high-speed communications. Utilizing the special selection of components, we propose hybrid soft- and hard-decision iterative decoding (ID) algorithms for these codes, during which many RS component decoders can be early terminated. The proposed hybrid ID only exchanges ternary messages between component decoders and early stops the RS component decoders satisfying a proposed stopping criterion, thus low hardware and computational complexities are guaranteed. The hybrid ID of RS-SPC product codes not only performs comparable to the soft-decision decoding of low-density parity-check (LDPC) codes and block turbo codes (BTCs), but also takes much lower computational complexity and smaller decoder data flow. We also propose semi-analytical methods to estimate the waterfall and error-floor performances of the proposed scheme, which yield relatively accurate estimated results. The simulation results and error-floor estimations indicate that some good RS-SPC product codes have very low error floors that satisfy the requirements of optical systems.

Efficient systematic turbo polar decoding based on optimized scaling factor and early termination mechanism

In this paper, an efficient early termination (ET) mechanism for systematic turbo-polar code (STPC) based on optimal estimation of scaling factor (SF) is proposed. The gradient of the regression line which best fits the distance between a priori and extrinsic information is used to estimate the SF. The multiplication of the extrinsic information by the proposed SF presents effectiveness in resolving the correlation issue between intrinsic and extrinsic reliability information traded between the two typical parallel concatenated soft-cancellation (SCAN) decoders. It is shown that the SF has improved the conventional STPC by about 0.3 dB with an interleaver length of 64 bits, and about 1 dB over the systematic polar code (SPC) at a bit error rate (BER) of . A new scheme is proposed as a stopping criterion, which is mainly based on the estimated value of SF at the second component decoder and the decoded frozen bits for each decoding iteration. It is shown that the proposed ET results in halving the average number of iterations (ANI) without adding considerable complexity. Moreover, the modified codes present comparable results in terms of BER to the codes that utilize fix number of iterations.

Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequential use of two components: homeomorphic data space transformation and synthetic data generation. Special attention is given to the privacy of image data. Specifics of image representation require special approaches towards encryption and synthetic image generation. We suggest use of (convolutional, variational) autoencoders and pre-trained feature extractors to enable applying privacy protection algorithms on top of the latent feature vectors captured from the images, and we updated the hybrid algorithms composed of homeomorphic transformation-as-encryption plus synthetic image generation accordingly. We show that an encrypted image can be reconstructed (by the pre-trained Decoder component of the convolutional variational autoencoder) into a secured representation from the extracted (by either the Encoder or a feature extractor) and encrypted (homeomorphic transformation of the latent space) feature vector.

Clustering-based Sequence to Sequence Model for Generative Question Answering in a Low-resource Language

Despite the impressive success of sequence to sequence models for generative question answering, they need a vast amount of question-answer pairs during training, which is hard and expensive to obtain, especially for low-resource languages. In this article, we present a framework that exploits the semantic clusters among the question-answer pairs to compensate for the lack of enough training data. In the training phase, the question-answer pairs are clustered, and a cluster predictor is trained to identify the cluster each question belongs to. Then, a sequence to sequence model is trained, where there is a different generator for each cluster in the decoder component. During the test phase, the cluster of the input question is first identified using the trained cluster predictor, and the appropriate decoder is exploited. Our experiments on a Persian religious dataset show that the proposed method outperforms the standard sequence to sequence model by a large margin in terms of ROUGE and BLEU scores. This is traced back to the lower number of words in each cluster, leading to a reduction in the number of effective parameters each generator needs to learn, which help the model learn from fewer training data with less overfitting.

Clinical Application of the Persian Version of Buffalo Model Questionnaire by examining Its correlation with the Buffalo Model Test Battery

Background and Aim: The Buffalo Model Questionnaire (BMQ) has been proposed for the screening, helping to diagnose and also monitoring the effect of rehabilitation on the improvement of Central Auditory Processing Disorder ((C)APD). In this regard, the applicability and accuracy of Persian-BMQ (P-BMQ) are evaluated by examining the correlation between the results of this questionnaire and the Buffalo model test battery. Methods: Overall, 254 children, normal and with Specific Learning Disorder (SLD) aged between 7–12 years old participated in this cross-sectional study. The questionnaire was completed by the parents of children who were subjected to the Buffalo model test battery evaluations. Results: In the normal group, the highest correlation (0.648) was shown between the Decoding (D) component of the P-BMQ with the Row Staggered Spondaic Word (RSSW) variable. In the SLD group, the highest correlation (0.318) was shown between the Variance-tolerance fading memory (V) component of the P-BMQ and the qualitative Persian version of the Phonemic Sentence Test (P-PST) variable. The highest correlation was considered to be between the D component of the P-BMQ with the Right Competitive word, started in the Left ear (RC-LEF) variable in the SSW test (0.498), and qualitative P-PST variable (0.471); and 0.360 between the V component of the P-BMQ and Persian version of the Speech in Noise with S/N=4 in the Left ear (P-SIN4L) of variable in the P-SIN test. Conclusion: P-BMQ is a useful screening tool complementing the Persian Buffalo model test battery for evaluating auditory processing evaluation in children. Keywords: Central auditory processing disorder; specific learning disorder; Buffalo model questionnaire