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Generating multivariate normal distributions is widely used in various fields, including engineering, statistics, finance and machine learning. In this paper, simulating large multivariate normal distributions truncated on the intersection of a set of hyperplanes is investigated. Specifically, the proposed methodology focuses on cases where the prior multivariate normal is extracted from a stationary Gaussian process (GP). It is based on combining both Karhunen-Loève expansions (KLE) and Matheron's update rules (MUR). The KLE requires the computation of the decomposition of the covariance matrix of the random variables, which can become expensive when the random vector is too large. To address this issue, the input domain is split in smallest subdomains where the eigendecomposition can be computed. Due to the stationary property, only the eigendecomposition of the first subdomain is required. Through this strategy, the computational complexity is drastically reduced. The mean-square truncation and block errors have been calculated. The efficiency of the proposed approach has been demonstrated through both synthetic and real data studies. [ABSTRACT FROM AUTHOR]

Underwater visual detection technology is crucial for marine exploration and monitoring. Given the growing demand for accurate underwater target recognition, this study introduces an innovative architecture, YOLOv8-MU, which significantly enhances the detection accuracy. This model incorporates the large kernel block (LarK block) from UniRepLKNet to optimize the backbone network, achieving a broader receptive field without increasing the model's depth. Additionally, the integration of C2fSTR, which combines the Swin transformer with the C2f module, and the SPPFCSPC_EMA module, which blends Cross-Stage Partial Fast Spatial Pyramid Pooling (SPPFCSPC) with attention mechanisms, notably improves the detection accuracy and robustness for various biological targets. A fusion block from DAMO-YOLO further enhances the multi-scale feature extraction capabilities in the model's neck. Moreover, the adoption of the MPDIoU loss function, designed around the vertex distance, effectively addresses the challenges of localization accuracy and boundary clarity in underwater organism detection. The experimental results on the URPC2019 dataset indicate that YOLOv8-MU achieves an mAP@0.5 of 78.4%, showing an improvement of 4.0% over the original YOLOv8 model. Additionally, on the URPC2020 dataset, it achieves 80.9%, and, on the Aquarium dataset, it reaches 75.5%, surpassing other models, including YOLOv5 and YOLOv8n, thus confirming the wide applicability and generalization capabilities of our proposed improved model architecture. Furthermore, an evaluation on the improved URPC2019 dataset demonstrates leading performance (SOTA), with an mAP@0.5 of 88.1%, further verifying its superiority on this dataset. These results highlight the model's broad applicability and generalization capabilities across various underwater datasets. [ABSTRACT FROM AUTHOR]

Determining the maximum number of unit vectors in Rr$\mathbb {R}^r$ with no pairwise inner product exceeding α$\alpha$ is a fundamental problem in geometry and coding theory. In 1955, Rankin resolved this problem for all α⩽0$\alpha \leqslant 0$, and in this paper, we show that the maximum is (2+o(1))r$(2+o(1))r$ for all 0⩽α≪r−2/3$0 \leqslant \alpha \ll r^{-2/3}$, answering a question of Bukh and Cox. Moreover, the exponent −2/3$-2/3$ is best possible. As a consequence, we obtain an upper bound on the size of a q$q$‐ary code with block length r$r$ and distance (1−1/q)r−o(r1/3)$(1-1/q)r - o(r^{1/3})$, which is tight up to the multiplicative factor 2(1−1/q)+o(1)$2(1 - 1/q) + o(1)$ for any prime power q$q$ and infinitely many r$r$. When q=2$q = 2$, this resolves a conjecture of Tietäväinen from 1980 in a strong form and the exponent 1/3$1/3$ is best possible. Finally, using a recently discovered connection to q$q$‐ary codes, we obtain analogous results for set‐coloring Ramsey numbers. [ABSTRACT FROM AUTHOR]

The HIV-1 provirus mainly consists of internal coding region flanked by 1 long terminal repeats (LTRs) at each terminus. The LTRs play important roles in HIV-1 reverse transcription, integration, and transcription. However, despite of the significant study advances of the internal coding regions of HIV-1 by using definite reference classification, there are no systematic and phylogenetic classifications for HIV-1 5' LTRs, which hinders our elaboration on 5' LTR and a better understanding of the viral origin, spread and therapy. Here, by analyzing all available resources of 5' LTR sequences in public databases following 4 recognized principles for the reference classification, 83 representatives and 14 consensus sequences were identified as representatives of 2 groups, 6 subtypes, 6 sub-subtypes, and 9 CRFs. To test the reliability of the supplemented classification system, the constructed references were applied to identify the 5' LTR assignment of the 22 clinical isolates in China. The results revealed that 16 out of 22 tested strains showed a consistent subtype classification with the previous LTR-independent classification system. However, 6 strains, for which recombination events within 5' LTR were demonstrated, unexpectedly showed a different subtype classification, leading a significant change of binding sites for important transcription factors including SP1, p53, and NF-κB. The binding change of these transcriptional factors would probably affect the transcriptional activity of 5' LTR. This study supplemented a unified classification system for HIV-1 5' LTRs, which will facilitate HIV-1 characterization and be helpful for both basic and clinical research fields. [ABSTRACT FROM AUTHOR]

The latent block model is used to simultaneously rank the rows and columns of a matrix to reveal a block structure. The algorithms used for estimation are often time consuming. However, recent work shows that the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models and the groups posterior distribution to converge as the size of the data increases to a Dirac mass located at the actual groups configuration. Based on these observations, the algorithm Largest Gaps is proposed in this paper to perform clustering using only the marginals of the matrix, when the number of blocks is very small with respect to the size of the whole matrix in the case of binary data. In addition, a model selection method is incorporated with a proof of its consistency. Thus, this paper shows that studying simplistic configurations (few blocks compared to the size of the matrix or very contrasting blocks) with complex algorithms is useless since the marginals already give very good parameter and classification estimates. [ABSTRACT FROM AUTHOR]

We extend a result due to Kawai on block varieties for blocks with abelian defect groups to blocks with arbitrary defect groups. Kawai's result is a tool to calculate the cohomology variety of a module in a block B of a finite group algebra kG restricted to subgroups of a defect group P, provided that P is abelian. Kawai's result coincides with a Theorem of Avrunin and Scott specialized to modules in the principal block and their restrictions to p-subgroups. J. Rickard raised the question whether Kawai's result can be extended to modules in blocks with arbitrary defect groups. We show that this is indeed the case for modules whose corresponding module over some almost source algebra is fusion stable. We show that this fusion stability hypothesis is automatically satisfied for principal blocks and blocks with abelian defect groups. [ABSTRACT FROM AUTHOR]

Grasping detection, which involves identifying and assessing the grasp ability of objects by robotic systems, has garnered significant attention in recent years due to its pivotal role in the development of robotic systems and automated assembly processes. Despite notable advancements in this field, current methods often grapple with both practical and theoretical challenges that hinder their real‐world applicability. These challenges encompass low detection accuracy, the burden of oversized model parameters, and the inherent complexity of real‐world scenarios. In response to these multifaceted challenges, a novel lightweight grasping detection model that not only addresses the technical aspects but also delves into the underlying theoretical complexities is introduced. The proposed model incorporates attention mechanisms and residual modules to tackle the theoretical challenges posed by varying object shapes, sizes, materials, and environmental conditions. To enhance its performance in the face of these theoretical complexities, the proposed model employs a Convolutional Block Attention Module (CBAM) to extract features from RGB and depth channels, recognising the multifaceted nature of object properties. Subsequently, a feature fusion module effectively combines these diverse features, providing a solution to the theoretical challenge of information integration. The model then processes the fused features through five residual blocks, followed by another CBAM attention module, culminating in the generation of three distinct images representing capture quality, grasping angle, and grasping width. These images collectively yield the final grasp detection results, addressing the theoretical complexities inherent in this task. The proposed model's rigorous training and evaluation on the Cornell Grasp dataset demonstrate remarkable detection accuracy rates of 98.44% on the Image‐wise split and 96.88% on the Object‐wise split. The experimental results strongly corroborate the exceptional performance of the proposed model, underscoring its ability to overcome the theoretical challenges associated with grasping detection. The integration of the residual module ensures rapid training, while the attention module facilitates precise feature extraction, ultimately striking an effective balance between detection time and accuracy. [ABSTRACT FROM AUTHOR]

This study designs a two-stage algorithm to address the bid generation problem of carriers when adding new vehicle routes in the presence of the existing vehicle routes to provide transportation service. To obtain the best auction combination and bid price of the carrier, a hybrid integer nonlinear programming model is introduced. According to the characteristics of the problem, a set of two-stage hybrid algorithms is proposed, innovatively integrating block coding within a genetic algorithm framework with a depth-first search approach. This integration effectively manages routing constraints, enhancing the algorithm's efficiency. The block coding and each route serve as decision variables in the set partition formula, enabling a comprehensive exploration of potential solutions. After a simulation-based analysis, the algorithm has been comprehensively validated analytically and empirically. The improvement of this research lies in the effectiveness of the proposed algorithm, i.e., the ability to handle a broader range of problem scales with less time in addressing complex operator bid generation in combinatorial auctions. [ABSTRACT FROM AUTHOR]

In recent years, deep learning algorithms have achieved remarkable results in medical image segmentation. These networks with an enormous number of parameters often encounter challenges in handling image boundaries and details, which may result in suboptimal segmentation results. To solve the problem, we develop atrous spatial pyramid pooling (ASPP) and combine it with the squeeze‐and‐excitation block (SE block), as well as present the PS module, which employs a broader and multiscale receptive field at the network's bottom to obtain more detailed semantic information. We also propose the local guided block (LG block) and also its combination with the SE block to form the LS block, which can obtain more abundant local features in the feature map, so that more edge information can be retained in each down sampling process, thereby improving the performance of boundary segmentation. We propose PLU‐Net and integrate our PS module and LS block into U‐Net. We put our PLU‐Net to the test on three benchmark datasets, and the results show that by fewer parameters and FLOPs, it outperforms on medical semantic segmentation tasks. [ABSTRACT FROM AUTHOR]

• Grandmothers (vs. mothers) in urban China taught toddlers more advanced concepts. • Grandmothers (vs. mothers) in urban China were more dominant during block play. • Grandmothers (vs. mothers) in urban China showed more negative affect and feedback. • Chinese caregivers' interaction styles were related to toddlers' effortful control. The goal of this study was to compare two generations of Chinese caregivers (N = 129) in terms of their interactions with toddlers during block play. We additionally explored the associations between caregivers' interaction styles and toddlers' effortful control. Caregivers were mothers (n = 68, mean age = 33.56 years) and grandmothers (n = 61, mean age = 59.17 years) from 70 families in urban China. Concepts (block construction, body parts and color, literacy, and math) taught by caregivers at basic and advanced levels as well as their interaction styles (dominance, positive and negative feedback, positive and negative affect) were coded from semi-structured home observations. Caregivers also reported on children's effortful control. Compared to mothers, grandmothers taught at more advanced levels, dominated the interaction more, displayed less positive affect as well as more negative affect and negative feedback toward children. Moreover, mothers' dominance and negativity during block play were uniquely associated with lower levels of child effortful control. Findings advance knowledge of intergenerational differences in caregivers' interactions with young children in a rapidly changing society. [ABSTRACT FROM AUTHOR]

The Frobenius–Schur indicators of characters in a real $2$ -block with dihedral defect groups have been determined by Murray ['Real subpairs and Frobenius–Schur indicators of characters in 2-blocks', J. Algebra 322 (2009), 489–513]. We show that two infinite families described in his work do not exist and we construct examples for the remaining families. We further present some partial results on Frobenius–Schur indicators of characters in other tame blocks. [ABSTRACT FROM AUTHOR]

In this paper we present a concrete algebraic construction of a novel class of convolutional codes. These codes are built upon generalized Vandermonde matrices and therefore can be seen as a natural extension of Reed–Solomon block codes to the context of convolutional codes. For this reason we call them weighted Reed–Solomon (WRS) convolutional codes. We show that under some constraints on the defining parameters these codes are Maximum Distance Profile (MDP), which means that they have the maximal possible growth in their column distance profile. We study the size of the field needed to obtain WRS convolutional codes which are MDP and compare it with the existing general constructions of MDP convolutional codes in the literature, showing that in many cases WRS convolutional codes require significantly smaller fields. [ABSTRACT FROM AUTHOR]

Most previous advanced backbones usually ignore the requirements on real-time and speed, which are directly affected by model size. In our study, we present a light-weight model, IDPNet. We design a dense layer and identity block parallel block as the basic block of the backbone. And we introduce an intra-level block fusion representation head to fuse high-resolution. As a result, our IDPNet decreases the number of parameters by 85.3% on both two datasets, and the GFLOPs by 80.4% and 60.7%, respectively. To extend the usability, we propose two extra variant networks IDPNet-Balance and IDPNet-Precision. We train and test our IDPNet over the COCO keypoint detection dataset and the MPII human pose dataset without pretrain. The best accuracy in both datasets is prior than previous networks. During testing process, all models can predict per image at the speed of 13 ms, 20 ms and 21 ms, respectively, and they also achieve real-time fundamentally. [ABSTRACT FROM AUTHOR]

The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder's lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct short code blocks containing mistakes. This makes code composition easier and more time-efficient, thus improving the efficacy of large-scale development environments. Because Python is becoming more popular, the goal of our study is to enhance the efficiency of writing Python code by offering an automatic code-correcting approach. Furthermore, automatic program evaluation has been performed to assist in the debugging of small code blocks, which will ultimately be employed in the creation of real-time computer applications. The proposed technique would be useful for new learners who wish to create small Python code blocks for ease of writing and debugging on online platforms (like edX, Coursera, and Udacity). One of the major contributions of the project is to create an erroneous dataset of Python coding that contains all potential forms of probable syntax errors. The dataset induces variety through the use of multiple coding templates and is used to train deep learning models. We used the state-of-the-art text-to-text T5 transformer network model to automatically repair and evaluate the incorrect code. The outcomes of auto-correction are examined using the ROUGE and BLEU scores, as well as accuracy. The model corrects Python code with single, double, and the multiple number of errors with greater than 80% accuracy. Similarly, the performance of the basic T5 transformer network for program auto-evaluation with and without mistakes has been examined, and the model achieves greater than 65% accuracy in both cases. The proposed T5 base transformer outperforms the SOTA auto-correction models in terms of accuracy, according to a comparison study of the proposed method with the earlier techniques for auto-correcting codes. [ABSTRACT FROM AUTHOR]

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709. [ABSTRACT FROM AUTHOR]

In general, the problem of building optimal convolutional codes under a certain criteria is hard, especially when size field restrictions are applied. In this paper, we confront the challenge of constructing an optimal 2D convolutional code when communicating over an erasure channel. We propose a general construction method for these codes. Specifically, we provide an optimal construction where the decoding method presented in the bibliography is considered. [ABSTRACT FROM AUTHOR]

We investigate the block basis for modular coinvariants of finite pseudo-reflection groups. We are particularly interested in the case of a subgroup G of the parabolic subgroups of GL n (q) which generalizes the Weyl groups of restricted Cartan type Lie algebra. [ABSTRACT FROM AUTHOR]

The need for efficient video coding technology is more important than ever in the current scenario where video applications are increasing worldwide, and Internet of Things (IoT) devices are becoming widespread. In this context, it is necessary to carefully review the recently completed MPEG-5 Essential Video Coding (EVC) standard because the EVC Baseline profile is customized to meet the specific requirements needed to process IoT video data in terms of low complexity. Nevertheless, the EVC Baseline profile has a notable disadvantage. Since it is a codec composed only of simple tools developed over 20 years, it tends to represent numerous coding artifacts. In particular, the presence of blocking artifacts at the block boundary is regarded as a critical issue that must be addressed. To address this, this paper proposes a post-filter using a block partitioning information-based Convolutional Neural Network (CNN). The proposed method in the experimental results objectively shows an approximately 0.57 dB for All-Intra (AI) and 0.37 dB for Low-Delay (LD) improvements in each configuration by the proposed method when compared to the pre-post-filter video, and the enhanced PSNR results in an overall bitrate reduction of 11.62% for AI and 10.91% for LD in the Luma and Chroma components, respectively. Due to the huge improvement in the PSNR, the proposed method significantly improved the visual quality subjectively, particularly in blocking artifacts at the coding block boundary. [ABSTRACT FROM AUTHOR]

This paper provides the results of some computer searches for disjoint maximal (52, 4)-arcs in the known planes of order 16. Thirty-seven new such sets are discovered: four in Johnson plane and thirty-three in Mathon plane, eighteen of which give examples of 104-sets of type (4,8) coming from non-isomorphic pairs of maximal (52, 4)-arcs, providing first examples for such sets. A new lower bound on the number of 104-sets of type (4,8) coming from disjoint maximal (52, 4)-arcs in the known planes of order 16 is obtained. The 104-length binary and ternary linear codes generated by the blocks of 1-designs associated with the known 104-sets of type (4,8) are classified. [ABSTRACT FROM AUTHOR]

The primary application of codes with locality and availability in distributed storage is for data recovery in case that data are lost on some damaged servers. Locality enables the recovery of lost data by contacting only a restricted number of remaining servers. Availability means that more than one subset of servers providing locality is available for data recovery for each server. The secondary application of these codes in distributed storage is to allow access to hot data in times of high demand. In this paper it is shown that the binary linear [14, 4, 7] code has locality 2 and availability 6 and it can be interpreted as a three-dimensional graph obtained from a [7, 3, 4] Simplex code. It is achieving upper bounds on basic parameters for codes with all-symbols locality and availability. This code can be a building element of more complex codes with scalability inspired by three-dimensional structures. The availability spectrum is introduced as a tool for analyzing codes with locality and availability. [ABSTRACT FROM AUTHOR]

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value. [ABSTRACT FROM AUTHOR]

Data hiding encompasses a wide range of applications related to hiding messages in digital images. The visual redundancy of images makes it possible to embed data in the images without attracting attention. Increasing the hiding capacity and decreasing the hiding distortion are prime objectives that data hiding intends to achieve. In this paper, we propose a high-payload data hiding scheme based on absolute moment block truncation coding (AMBTC) to minimize the impact of hiding. A two-level minimum mean square error (MMSE) quantizer generated by AMBTC is used to decrease the distortion associated with hiding. Also, we present a lookup table based on the symmetric property for adaptively hiding secrets in pixels to achieve high hiding capacity. We can embed almost 1.9 bits per pixel (bpp) with a high image quality of an average of 31 dB. Only 5.3% of pixels are changed during the data-hiding process. Compared with other schemes, we can use 1 bpp more relative payload for embedding with the same stego image quality. The experimental results show that the proposed scheme has better hiding performance because it allows a huge amount of secret data to be hidden while maintaining the high visual quality of the stego image. [ABSTRACT FROM AUTHOR]

Incomplete block designs bring out useful information from agricultural experimentation with high precision when there is a crunch in resources in terms of homogeneous blocks that can accommodate all the treatments. Balanced incomplete block designs or 2-designs are the most popular class of incomplete block designs. t-designs represent a generalized class of balanced incomplete block designs in which the number of blocks in which any t treatments (t≥2) occur together is a constant. In order to obtain tbalance, we may require large number of blocks and replications. Partially balanced t-designs are introduced for experimental situations where it is not possible to get balanced t-designs. Two series of such designs are obtained using cyclic development of initial block(s). Association of the proposed designs with t-packing designs are also demonstrated. [ABSTRACT FROM AUTHOR]

Limitations on high latency and low scalability of classical blockchain systems retard their adoptions and applications. Reconstructed blockchain systems have been proposed to avoid the consumption of competitive transactions caused by linear sequenced blocks. These systems, instead, structure transactions/blocks in the form of Directed Acyclic Graph (DAG) and consequently rebuild upper layer components. The promise of DAG-based blockchain systems is to enable fast confirmation (complete transactions within million seconds) and high scalability (attach transactions in parallel) without significantly compromising security. However, this field still lacks systematic work that summarises DAG techniques. To bridge the gap, this Systematization of Knowledge (SoK) provides a comprehensive analysis of ever-existing and ongoing DAG-based blockchain systems. We abstract a general model to capture the main features and identify six types of design patterns. Then, we evaluate these systems from the perspectives of structure, consensus, property, security, and performance. We further discuss the trade-off between different factors, open challenges, and the potentiality of DAG-based solutions, indicating their promising directions for future research. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108-gene panel from 453,843 cells from four mouse brain coronal slices in less than 2 d. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimer's disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms. [ABSTRACT FROM AUTHOR]

Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in neural dynamics. A prime example are studies on predictive coding. These often ask whether confirmed predictions about inputs or predictions errors between internal predictions and inputs are passed on in a hierarchical neural system—while at the same time looking for the neural correlates of coding for errors and predictions. If we do not know exactly what a neural system predicts at any given moment, this results in a circular analysis—as has been criticized correctly. To circumvent such circular analysis, we propose to express information processing strategies (such as predictive coding) by local information-theoretic quantities, such that they can be estimated directly from neural data. We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy. We define testable hypotheses on the relationship of both quantities, allowing us to identify which of the assumed strategies was used. We demonstrate our approach on spiking data collected from the retinogeniculate synapse of the cat (N = 16). Applying our local information dynamics framework, we are able to show that the synapse codes for predictable rather than surprising input. To support our findings, we estimate quantities applied in the partial information decomposition framework, which allow to differentiate whether the transferred information is primarily bottom-up sensory input or information transferred conditionally on the current state of the synapse. Supporting our local information-theoretic results, we find that the synapse preferentially transfers bottom-up information. Author summary: Many neuroscience studies investigate how neural systems, e.g., neural circuits or cortical areas, process information. Popular theories propose that such systems operate by constantly predicting future sensory inputs from internal models built from previous inputs. Here, opposing accounts exist on how these predictions and actual inputs are reconciled to improve future predictions. One popular theory proposes that neural systems make use of prediction errors to update the internal model, while other theories propose that predominantly correctly predicted information is used. Testing which of these two strategies is actually employed by neural systems is conceptually difficult because it requires to define beforehand when a prediction error should occur and by what changes in the data such an occurrence should be indicated. This knowledge is typically not available, making it difficult to test both strategies against each other. Instead, we propose to use information-theoretic quantities to make central information processing concepts such as predictions and prediction errors measurable from data, which in turn allows to formulate testable hypotheses on information processing carried out by neural systems. We demonstrate our approach on data recorded at retinal synapses of the cat and successfully describe which of the two processing strategies is used. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have demonstrated impressive results in additive white Gaussian noise removal due to their strong fitting ability. However, their performance in mixed noise removal remains unsatisfactory, primarily due to their limited receptive field that focuses only on the local features of images and disregards global information. To ameliorate this issue, recent state‐of‐the‐art approaches employ attention mechanism (AM) to capture the global information. However, most AM based methods still suffer from low computational efficiency. In this paper, a novel model named simple dual attention mechanism UNet (SDAUNet) for mixed noise removal is proposed. In SDAUNet, the UNet architecture is used to gradually acquire multi‐scale image features and provide a more comprehensive and accurate representation of the image features than other CNNs. A simple dual attention convolutional block is presented to acquire the global image features that can successfully capture image details with a low burden. The experimental results demonstrate that the SDAUNet model can achieve better measurement metrics and visual performance than other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Capricious digital filter (CDF) plays a significant role of signal processing application field to eradicate noise. Any prototype filter desired frequency response is attained by developing all pass makeover-based capricious digital filter (APM-CDF) that sustains full control on cutoff frequency. The benefits of APM-CDF are limited through its speed, area, and power consume. In this manuscript, Baugh–Wooley multiplier (BWM) with error reduced carry prediction approximate adder (ERCPAA) is proposed to accelerate the filter design, decreasing the area and power consume. ERCPAA is a rapid binary adder that takes low power and area. ERCPAA adder is separated as 3 blocks: approximate full adder cells, carry prediction logic, constant truncation including error diminishing logic, these reduce power with area. BWM is utilized to decrease the hardware complex including high speed, lesser area, and lesser power consume. The proposed filter is applicable in ECG signal noise removing applications to offer filtered higher-quality signals. The proposed filter is implemented in Verilog and simulation is activated in Xilinx ISE 14.5 tool. The simulation outcomes shows lesser delay 32.87%, 26.88%, and 32.88%, and lower area 20%, 80%, and 65% comparing to the existing filters, like partial product adding in Vedic design-ripple carry adder model FIR filter for electrocardiogram signal denoising algorithm (DF-4VM-CSA), Vedic design-carry look ahead (VMD-CLA), respectively. The proposed filter is activated in MATLAB/Simulink for reading input ECG signal. Finally, the proposed filter attains 34.86%, 26.98% higher SNR analyzed to the existing filters, like DF-4VM-CSA, DF-VMD-CLA, respectively. [ABSTRACT FROM AUTHOR]

Cabamiquine is a novel antimalarial that inhibits Plasmodium falciparum translation elongation factor 2. We investigated the causal chemoprophylactic activity and dose–exposure–response relationship of single oral doses of cabamiquine following the direct venous inoculation (DVI) of P falciparum sporozoites in malaria-naive, healthy volunteers. This was a phase 1b, randomised, double-blind, placebo-controlled, adaptive, dose-finding, single-centre study performed in Leiden, Netherlands. Malaria-naive, healthy adults aged 18–45 years were divided into five cohorts and randomly assigned (3:1) to receive cabamiquine or placebo. Randomisation was done by an independent statistician using codes in a permuted block schedule with a block size of four. Participants, investigators, and study personnel were masked to treatment allocation. A single, oral dose regimen of cabamiquine (200, 100, 80, 60, or 30 mg) or matching placebo was administered either at 2 h (early liver-stage) or 96 h (late liver-stage) after DVI. The primary endpoints based on a per-protocol analysis set were the number of participants who developed parasitaemia within 28 days of DVI, time to parasitaemia, number of participants with documented parasite blood-stage growth, clinical symptoms of malaria, and exposure–efficacy modelling. The impact of cabamiquine on liver stages was evaluated indirectly by the appearance of parasitaemia in the blood. The Clopper–Pearson CI (nominal 95%) was used to express the protection rate. The secondary outcomes were safety and tolerability, assessed in those who had received DVI and were administered one dose of the study intervention. The trial was prospectively registered on ClinicalTrials.gov (NCT04250363). Between Feb 17, 2020 and April 29, 2021, 39 healthy participants were enrolled (early liver-stage: 30 mg [n=3], 60 mg [n=6], 80 mg [n=6], 100 mg [n=3], 200 mg [n=3], pooled placebo [n=6]; late liver-stage: 60 mg [n=3], 100 mg [n=3], 200 mg [n=3], pooled placebo [n=3]). A dose-dependent causal chemoprophylactic effect was observed, with four (67%) of six participants in the 60 mg, five (83%) of six participants in the 80 mg, and all three participants in the 100 and 200 mg cabamiquine dose groups protected from parasitaemia up to study day 28, whereas all participants in the pooled placebo and 30 mg cabamiquine dose group developed parasitaemia. A single, oral dose of 100 mg cabamiquine or higher provided 100% protection against parasitaemia when administered during early or late liver-stage malaria. The median time to parasitaemia in those with early liver-stage malaria was prolonged to 15, 22, and 24 days for the 30, 60, and 80 mg dose of cabamiquine, respectively, compared with 10 days for the pooled placebo. All participants with positive parasitaemia showed documented blood-stage parasite growth, apart from one participant in the pooled placebo group and one participant in the 30 mg cabamiquine group. Most participants did not exhibit any malaria symptoms in both the early and late liver-stage groups, and those reported were mild in severity. A positive dose–exposure–efficacy relationship was established across exposure metrics. The median maximum concentration time was 1–6 h, with a secondary peak observed between 6 h and 12 h in all cabamiquine dose groups (early liver-stage). All cabamiquine doses were safe and well tolerated. Overall, 26 (96%) of 27 participants in the early liver-stage group and ten (83·3%) of 12 participants in the late liver-stage group reported at least one treatment-emergent adverse event (TEAE) with cabamiquine or placebo. Most TEAEs were of mild severity, transient, and resolved without sequelae. The most frequently reported cabamiquine-related TEAE was headache. No dose-related trends were observed in the incidence, severity, or causality of TEAEs. The results from this study show that cabamiquine has a dose-dependent causal chemoprophylactic activity. Together with previously demonstrated activity against the blood stages combined with a half-life of more than 150 h, these results indicate that cabamiquine could be developed as a single-dose monthly regimen for malaria prevention. The healthcare business of Merck KGaA, Darmstadt, Germany. [ABSTRACT FROM AUTHOR]

In this paper, a novel selective mapping (SLM) method is proposed for reducing the peak‐to‐average power ratio (PAPR) of orthogonal frequency division multiplexing with index modulation (OFDM‐IM) systems. In the proposed method, polar codes are used to construct the SLM phase generation mechanism. This systematic structural design is superior to conventional methods in which phases are randomly generated. Moreover, polar codes and Reed–Muller codes are selected to protect the input data of the OFDM‐IM system. The weight distribution of the Reed–Muller codes is then used to determine the location data of the active subcarriers in the OFDM‐IM system, and these data are used to determine the error correction capacity. Simulation results indicated that the PAPR improvement achieved using the proposed method is similar to that achieved with conventional SLM methods. Moreover, linear block codes are used to construct the active subcarrier selection and SLM phase generation mechanisms to ensure that both mechanisms have systematic structural designs. [ABSTRACT FROM AUTHOR]

In this paper, the goal is to obtain constacyclic codes over Lipschitz integers in terms of Lipschitz metric. A decoding procedure is proposed for these codes, some of which have been shown to be perfect codes. Performance of constacyclic codes over Lipschitz integers is investigated over Additive White Gaussian Channel (AWGN) by means of symbol error rates and coding gain. According to the achieved results, these codes can be used in coded modulation schemes based on Quadrature Amplitude Modulation (QAM)-type constellations. Furthermore, it is shown that the Lipschitz metric is more suitable than Hamming metric and Lee metric for QAM type two dimensional constellations. [ABSTRACT FROM AUTHOR]

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [321, 326] is an influential HDC/VSA model that is well known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey [222] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain; however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners. [ABSTRACT FROM AUTHOR]

In the current stereo matching field, the accuracy of the derived disparity map is highly dependent on the processing capability of ill regions. Fortunately, we find that the use of local information will eliminate the negative effects associated with ill regions. As a result of the above discovery, we propose the Concatenated Dilated Convolution (CDC) block and the Multi‐scale Feature Fusion module (MFF), which are capable of effectively extracting regional context by increasing the receptive field in parallel and channel‐wise ways. The CDC block can expand the receptive field by applying multiple dilated convolutions at different dilation rates in parallel to enhance the smoothness of the feature map. By constructing parallel CDC blocks in a multiple dilated manner, the MFF module can improve the smoothness of the feature map. In addition, to control the number of parameters in the MFF network, a high‐performance channel‐distribution algorithm is proposed, capable of adjusting the weights of each module and convolution in an adaptive manner while reducing the number of parameters. Extensive experiments have demonstrated that MFF and CDC can effectively improve the performance of ill areas and networks with a minimal number of parameters. [ABSTRACT FROM AUTHOR]

Most works on system identification of block‐oriented nonlinear systems were devoted to Wiener and Hammerstein systems. This article is focused on a more general, and so more complex, nonlinear model structure. Specifically, the system under study is a series connection of two subsystems each one being constituted of linear dynamic block and a nonlinear static block coupled in parallel. Clearly, this system structure generalizes those of Wiener and Hammerstein systems. Interestingly, the linear blocks can be parametric or not and are allowed to be of unknown structure. We develop a two‐stage frequency‐type identification method that provides accurate estimates of the all system parts. The proposed identification method enjoys the simplicity and the consistency of developed estimators. Furthermore, the parameters of linear dynamic blocks can be easily decoupled without any further experiments. The performances of the proposed identification method are highlighted by several simulation results. As perspectives, one or the two nonlinearities can be considered non‐static, for example, of backlash type. [ABSTRACT FROM AUTHOR]

Software Fault Forecasting (SFF) pertains to timely identifying sections in software projects that are prone to faults and may result in significant development expenses. Deep learning models have become widespread in software fault monitoring and management, and these models rely on the design metrics and the code pattern features for classifying the code as erroneous or safe. The proposed model works based on the collective formulation of the fault localization model, which acquires the model-specific metadata for building a global model that would perform software fault forecasting globally. The proposed model works by ranking the suspicious code blocks based on the symmetry of the semantic features of the erroneous code and the implementation code. The feature selection and scaling process is initially performed to precisely identify the features contributing to fault forecasting. The data extraction that is portrayed as the intermediate phase would assist in focusing on the code statements and ranking them based on the impact of the fault. A fine-tuned spectrum-based fault localization technique is used in ranking the statements. The FEDRak model facilitates ongoing adaptation in instances where there are adjustments in the feature contribution of data over time. The federated learning model would update the feature weights of the global model based on the weights synchronized by locally built fault forecasting approaches. FEDRak is statistically analyzed in relation to other contemporary techniques in fault localization in terms of metrics like sensitivity, specificity, accuracy, F1-score, and ROC curves. The proposed model's performances are analyzed in terms of local and global models. [ABSTRACT FROM AUTHOR]

It is well known that some harmful objects in the Tanner graph of low-density parity-check (LDPC) codes have a negative impact on their error correction performance under iterative message-passing decoding. Depending on the channel and the decoding algorithm, these harmful objects are different in nature and can be stopping sets, trapping sets, absorbing sets, or pseudocodewords. Differently from LDPC block codes, the design of spatially coupled LDPC codes must take into account the semi-infinite nature of the code, while still reducing the number of harmful objects as much as possible. We propose a general procedure, based on edge spreading, enabling the design of good quasi-cyclic spatially coupled LDPC (QC-SC-LDPC) codes. These codes are derived from quasi-cyclic LDPC (QC-LDPC) block codes and contain a considerably reduced number of harmful objects with respect to the original QC-LDPC block codes. We use an efficient way of enumerating harmful objects in QC-SC-LDPCCs to obtain a fast algorithm that spans the search space of potential candidates to select those minimizing the multiplicity of the target harmful objects. We validate the effectiveness of our method via numerical simulations, showing that the newly designed codes achieve better error rate performance than codes presented in previous literature. [ABSTRACT FROM AUTHOR]

Visible-light-based transmission application plays an important role in various types of sensor services for the Internet of Things (IoTs). However, in big data scenarios, current visible-light-based systems cannot achieve concurrent high-speed communication, low-speed communication, and positioning. Therefore, in this article, we propose a smart visible-light-based fusion applications system, named Fasys, to solve the above problem for the big data traffic with heterogeneity. Specifically, for low-speed data services, we propose a novel linear block coding and bit interleaving mechanism, which enhances the LED positioning accuracy and recovers the lost data bits in the interframe gap (IFG). For high-speed data services with traffic possessing burstiness, an elegant statistical reliability analysis framework in regard to latency is proposed based on martingale theory. The backlog martingale process is constructed. Leveraging stopping time theory, a tight upper bound of unreliability is obtained. An arrival abstraction and traffic allocation scheme is designed, which contributes to decouple the reliability requirement as the maximum supportable arrival load. Finally, we implement our Fasys system, and extensive experimental results show that our system can achieve consistent high-precision positioning and low-BER data communication for low-speed data services. And the proposed martingale-based traffic allocation scheme can achieve the provisioning of reliability in regard to the latency for high-speed data services. [ABSTRACT FROM AUTHOR]

Combining tooth extraction and implant placement reduces the number of surgical procedures that a patient must undergo. Thus, the present study aimed to compare the stability of two types of conical implants (TAC and INTRALOCK) and another cylindrical one (CYROTH), inserted with a range of angulation of 15–20 degrees in low-density polyurethane blocks (10 and 20 pounds per cubic foot, PCF) with or without a cortical lamina (30 PCF), which potentially mimicked the post-extraction in vivo condition. For this purpose, a total of 120 polyurethane sites were prepared (10 for each implant and condition) and the Insertion Torque (IT), Removal Torque (RT), and Resonance Frequency Analysis (RFA) were measured, following a Three-Way analysis of variance followed by Tukey's post hoc test for the statistical analysis of data. The IT and RT values registered for all implant types were directly proportional to the polyurethane density. The highest IT was registered by INTRALOCK implants in the highest-density block (32.44 ± 3.28 Ncm). In contrast, the highest RFA, a well-known index of Implant Stability Quotient (ISQ), was shown by TAC implants in all clinical situations (up to 63 ISQ in the 20 PCF block without the cortical sheet), especially in lower-density blocks. Although more pre-clinical and clinical studies are required, these results show a better primary stability of TAC conical implants in all tested densities of this post-extraction model, with a higher ISQ, despite their IT. [ABSTRACT FROM AUTHOR]

Most of the deep-learning based dehazing models have fixed receptive filed after the parameter are fixed. As a result, the dehazing network cannot adopt the optimal mode for dehazing each specific scene, resulting in ambiguity and distortion in the results. In view of these problems, this study proposes a dynamic receptive field feature selection dehazing network. A feature-attention atrous block with atrous convolution is designed as the basic module of the network. Multiple feature attention atrous blocks with different atrous rates are used in parallel to extract multi-scale features. Dynamic feature fusion is performed on these features to form a dynamic receptive field block. Multiple dynamic receptive field blocks are combined with residual connections to form a deep network. The features from different levels are dynamically mixed and decoded to obtain a haze-free image. The experimental results show that the proposed algorithm has a good dehazing performance on indoor, outdoor, and real hazy images, and can generate clear and natural dehazing images. [ABSTRACT FROM AUTHOR]

We introduce a class of stochastic processes with reinforcement consisting of a sequence of random partitions { P t } t ≥ 1 , where P t is a partition of { 1 , 2 , ... , R t }. At each time t , R numbers are added to the set being partitioned; of these, a random subset (chosen according to a time-dependent probability distribution) joins existing blocks, and the others each start new blocks on their own. Those joining existing blocks each choose a block with probability proportional to that block's cardinality, independently. We prove results concerning the asymptotic cardinality of a given block and central limit theorems for associated fluctuations about this asymptotic cardinality: these are proved both for a fixed block and for the maximum among all blocks. We also prove that with probability one, a single block eventually takes and maintains the leadership in cardinality. Depending on the way one sees this partition process, one can translate our results to Balls and Bins processes, Generalized Chinese Restaurant Processes, Generalized Urn models and Preferential attachment random graphs. [ABSTRACT FROM AUTHOR]

OpenSim is an open-source biomechanical package with a variety of applications. It is available for many users with bindings in MATLAB, Python, and Java via its application programming interfaces (APIs). Although the developers described well the OpenSim installation on different operating systems (Windows, Mac, and Linux), it is time-consuming and complex since each operating system requires a different configuration. This project aims to demystify the development of neuro-musculoskeletal modeling in OpenSim with zero configuration on any operating system for installation (thus cross-platform), easy to share models while accessing free graphical processing units (GPUs) on a web-based platform of Google Colab. To achieve this, OpenColab was developed where OpenSim source code was used to build a Conda package that can be installed on the Google Colab with only one block of code in less than 7 min. To use OpenColab, one requires a connection to the internet and a Gmail account. Moreover, OpenColab accesses vast libraries of machine learning methods available within free Google products, e.g. TensorFlow. Next, we performed an inverse problem in biomechanics and compared OpenColab results with OpenSim graphical user interface (GUI) for validation. The outcomes of OpenColab and GUI matched well ( r ≥ 0.82). OpenColab takes advantage of the zero-configuration of cloud-based platforms, accesses GPUs, and enables users to share and reproduce modeling approaches for further validation, innovative online training, and research applications. Step-by-step installation processes and examples are available at: . [ABSTRACT FROM AUTHOR]

The large number of inexpensive and energy-efficient terminals in IoT systems is one of the emerging elements of the recent landscape of information and communication technologies. IoT nodes are usually embedded systems with limited processing power devices and strict requirements on energy consumption. In this paper, we consider the design and implementation of a part of the IoT communication uplink stack, namely the error correction coding scheme, for energy-efficient operation. We examine how an efficient rate-adaptive coding scheme, namely the Raptor-like (RL) quasi-cyclic (QC) subclass of low-density parity check (LDPC) codes, can be applied. We present an encoding algorithm designed for an embedded CPU; a respective QC-RL-LDPC decoding scheme, based on typical LDPC iterative decoding; and a combined design procedure for construction of the QC-RL-LDPC parity check matrices. Next, we conduct experiments to explore the time intervals for implemented encoding and the goodput of the coding system with incremental redundancy. We provide the statistical results by combining the measured energy consumption of the encoder and the simulated radio transmitter energy consumption. We demonstrate the comparison of normalized energy consumption statistics with the fixed-rate LDPC coding case. As conclusion, we note that under an unknown channel corruption level, the short block QC-RL-LDPC code implemented in the CPU can achieve higher energy efficiency per information bit compared to a fixed-rate LDPC code. Future work will include an extension of the work to nonbinary QC-RL-LDPC coding design. [ABSTRACT FROM AUTHOR]

A radio is adaptive if it can autonomously analyze the communications environment and instantly modify its settings to achieve the best possible efficiency. In orthogonal frequency division multiplexing (OFDM) transmissions, identifying the space frequency block coding (SFBC) category utilized is one of the most important tasks of an adaptive receiver. Previous approaches to this problem did not take into consideration the fact that real systems typically suffer from transmission defects. This study offers a novel maximum likelihood recognizer capable of distinguishing between SFBC OFDM waveforms in the context of inphase and quadrature phase differences (IQDs). The theoretical findings show that IQDs arising from the transmitter and recipient can be combined with channel paths to generate so-called effective channel paths. The conceptual examination demonstrates that the outlined maximum likelihood strategy of the SFBC recognition and effective channel estimation processes is implemented by an expectation maximization tool utilizing the error control decoders' soft outputs. The simulations results reveal that the suggested strategy delivers a much greater recognition accuracy than the typical approaches outlined in the comparable literature. At a signal-to-noise ratio (SNR) of 14 dB, for example, the proposed approach achieves a bit error rate (BER) of 0.00002, which is very close to the case of perfect estimation and compensation for IQDs, outperforming the previous reported works which achieved BERs of 0.01 and 0.02. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reversible data hiding scheme in an encrypted image based on bit-plane redundancy of prediction error. The scheme greatly improves the embedding capacity while maintaining lossless image recovery and error-free secret data extraction. Firstly, the original image is preprocessed to obtain the prediction error image. After the error matrix is divided into blocks, the corresponding block type is obtained. Secondly, the predicted error image is encrypted with stream cipher and the encryption matrix blocks are scrambled to ensure the security of the scheme. Finally, after embedding the block type value into the encrypted image, the spare room corresponding to each block was obtained, which was used to embed the secret data. The scheme makes full use of the spatial correlation of the pixels in the block, so it improves the embedding rate. By selecting 100 images in each dataset of BOSSbase and BOWS-2, when the block size is 3 × 3 , the average embedding rate of our scheme can reach 3.56 bpp and 3.81 bpp, respectively. The performance of the proposed method is better than the other schemes with similar properties. [ABSTRACT FROM AUTHOR]

This paper describes an adaptation of a polar code decoding technique in favor of the extended Golay code. Based on the bridge provided by a permutation matrix between the code words of these two classes of codes, the Golay code can be decoded by any polar code technique. Contrary to the successive-cancellation list technique which is characterized by a serial estimation of the bits, we propose in this work an adaptation of the simplified successive-cancellation list technique to polar codes equivalent to the Golay code. The simulations have achieved the performance of a maximum likelihood decoding, with the low decoding complexity of polar codes, compared to one of the universal decoders of linear codes most known in the literature. [ABSTRACT FROM AUTHOR]