Search

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces. [ABSTRACT FROM AUTHOR]

In this paper, we define a window code to be the portion of a Spatially-coupled low-density parity check (SC-LDPC) code seen by a single iteration of a windowed decoder. We consider the design of SC-LDPC codes for windowed decoding via optimization of the window code. In particular, because iterative decoding is optimal on codes with cycle-free graph representations, we ask fundamental questions about the construction and parameters of cycle-free window codes. We show that it is possible to have an SC-LDPC code with cycles and with cycle-free window codes. We consider the relationship between the distance of the window code and the distance of the SC-LDPC code. Further, we show that SC-LDPC codes with MDS window codes exist, and all such codes are asymptotically bad. This work gives insight into the tradeoffs between window code parameters and performance of the SC-LDPC code. [ABSTRACT FROM AUTHOR]

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces. [ABSTRACT FROM AUTHOR]

An adaptively hybrid method was proposed to improve the performance of fractal coding methods. First, it is found that the range blocks with large variances (RBLVs) play a crucial role in degrading decoded images, and the effect of the remaining range blocks with small variances (RBSVs) can be ignored. Then, an adaptive method was proposed to divide the range blocks into the above two categories: RBLVs and RBSVs. Second, RBLVs were designed to be encoded in an extended domain block pool (EDBP). Then, better block‐matching effect can be obtained, which will result in better decoded image quality. Further, the no‐search fractal encoding method is adopted for RBSVs to achieve faster encoding speed and fewer bits per pixel (bpp). Finally, four fractal coding methods were adopted to assess the performance of the proposed method. Experimental results show that compared with the previous methods, the PSNR quality of decoded images in the proposed method can be improved by about 0.15–0.4 dB, about 20%–35% of the total computations in encoding process can be saved, and about 0.2 bpp can be saved. Moreover, under the same decoding time, the proposed method can achieve comparable or smaller deviations regarding the decoded image. [ABSTRACT FROM AUTHOR]

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels. [ABSTRACT FROM AUTHOR]

Genetically engineered mouse models play a pivotal role in the modeling of diseases, exploration of gene functions, and the development of novel therapies. In recent years, clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9)-mediated genome editing technology has revolutionized the process of developing such models by enabling precise genome modifications of the multiple interested genes simultaneously. Following genome editing, an efficient genotyping methodology is crucial for subsequent characterization. However, current genotyping methods are laborious, timeconsuming, and costly. Here, using targeting the mouse trypsinogen genes as an example, we introduced common applications of CRISPR-Cas9 editing and a streamlined cost-effective genotyping workflow for CRISPR-edited mouse models, in which Sanger sequencing is required only at the initial steps. In the F0 mice, we focused on identifying the presence of positive editing by PCR followed by Sanger sequencing without the need to know the exact sequences, simplifying the initial screening. In the F1 mice, Sanger sequencing and algorithms decoding were used to identify the precise editing. Once the edited sequence was established, a simple and effective genotyping strategy was established to distinguish homozygous and heterozygous status by PCR from tail DNA. The genotyping workflow applies to deletions as small as one nucleotide, multiple-gene knockout, and knockin studies. This simplified, efficient, and cost-effective genotyping shall be instructive to new investigators who are unfamiliar with characterizing CRISPR-Cas9-edited mouse strains. [ABSTRACT FROM AUTHOR]

The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining. [ABSTRACT FROM AUTHOR]

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug–target interactions (DTIs) and drug–drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve (AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32 |$\%$| in AUROC and 3.56 |$\%$| in AUPR for DTIs and 6.56 |$\%$| in AUROC and 8.17 |$\%$| in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein–protein interactions prediction, ablation studies and case studies further validate its effectiveness. [ABSTRACT FROM AUTHOR]

Among numerous dynamic scheduling strategies for low‐density parity‐check codes, many of them are of high complexity due to repetitive computation and ordering of belief residuals. To address these challenges, two novel approaches are proposed: the derived informed variable‐to‐check residual belief propagation and the derived variable‐node and variable‐to‐check‐edge residual belief propagation for low‐density parity‐check codes. Simulation results show that the derived dynamic scheduling exhibits a substantial gain of approximately 0.15 dB in signal‐to‐noise ratio over the non‐derived one with considerably lower decoding complexity when targeting a bit‐error rate of 10−4. [ABSTRACT FROM AUTHOR]

In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images. [Display omitted] • Structural reparameterization dehazenet: outstanding performance, faster speed. • Replacing Tanh with ReLU leads to better results. • Transfer learning addresses the problem of insufficient samples. • Dual subnets method proves highly effective in datasets of different scales. [ABSTRACT FROM AUTHOR]

Error-correcting codes (ECCs) employed in the state-of-the-art DNA digital storage (DDS) systems suffer from a trade-off between error-correcting capability and the proportion of redundancy. To address this issue, in this study, we introduce soft-decision decoding approach into DDS by proposing a DNA-specific error prediction model and a series of novel strategies. We demonstrate the effectiveness of our approach through a proof-of-concept DDS system based on Reed-Solomon (RS) code, named as Derrick. Derrick shows significant improvement in error-correcting capability without involving additional redundancy in both in vitro and in silico experiments, using various sequencing technologies such as Illumina, PacBio and Oxford Nanopore Technology (ONT). Notably, in vitro experiments using ONT sequencing at a depth of 7× reveal that Derrick, compared with the traditional hard-decision decoding strategy, doubles the error-correcting capability of RS code, decreases the proportion of matrices with decoding-failure by 229-fold, and amplifies the potential maximum storage volume by impressive 32 388-fold. Also, Derrick surpasses 'state-of-the-art' DDS systems by comprehensively considering the information density and the minimum sequencing depth required for complete information recovery. Crucially, the soft-decision decoding strategy and key steps of Derrick are generalizable to other ECCs' decoding algorithms. [ABSTRACT FROM AUTHOR]

Many families of binary nonlinear codes (e.g., Kerdock, Goethals, Delsarte–Goethals, Preparata) can be very simply constructed from linear codes over the Z 4 ring (ring of integers modulo 4), by applying the Gray map to the quaternary symbols. Generalized Kerdock codes represent an extension of classical Kerdock codes to the Z 2 S ring. In this paper, we develop two novel soft-input decoders, designed to exploit the unique structure of these codes. We introduce a novel soft-input ML decoding algorithm and a soft-input soft-output MAP decoding algorithm of generalized Kerdock codes, with a complexity of O (N S log 2 N) , where N is the length of the Z 2 S code, that is, the number of Z 2 S symbols in a codeword. Simulations show that our novel decoders outperform the classical lifting decoder in terms of error rate by some 5 dB. [ABSTRACT FROM AUTHOR]

Remote sensing is one of the major domains witnessing the increasingly significant interest in Hyperspectral image (HSI) classification. One recent approach achieving great success in HSI classification is deep learning. However, most current HSI classification methods are performed on small datasets, using overlapped sub-regions both for training and testing, causing information leakage. Data leaks lead to improper ideal classification results. Thus, more research work is still needed to boost classification performance for non-leaking methods. This paper proposes an Encoder-Decoder deep learning-based method that can further improve the accuracy of HSI classification. The method contains two main parts: an encoder and a decoder. The encoder consists of depthwise separable convolution, squeeze, and excitation blocks based on the popular MobileNetV3 deep learning network, and the decoder is inherited from the U-Net deep learning model. This encoder-decoder model aims to take into account the relationships between neighboring pixels when performing HSI pixel classification. Since the pixel distribution in an HSI dataset is usually imbalanced, the focal loss function is employed during the model's training process to counterbalance the weight difference between the pixel classes and avoid overfitting the classes with few pixels. For performance assessment, we carried out experiments on the AeroRIT dataset, consisting of non-overlapping training, validation, and test sets. We achieved an overall accuracy of 95.54% and a mean intersection over union score of 83.53%, setting state-of-the-art results on this dataset. [ABSTRACT FROM AUTHOR]

Motivation While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this article, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Results Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: First, API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; second, GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; finally, different types of errors are enriched in different tasks, providing valuable insights for future improvements. Availability and implementation The GeneGPT code and data are publicly available at https://github.com/ncbi/GeneGPT. [ABSTRACT FROM AUTHOR]

Background: Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities enables proactive measures to mitigate the risk of severe back pain and disability. Methods: We explore the use of shape auto-encoders for vertebrae, advancing the state of the art through robust automatic segmentation models trained without fracture labels and recent geometric deep learning techniques. Our shape auto-encoders are pre-trained on a large set of vertebrae surface patches. This pre-training step addresses the label scarcity problem faced when learning the shape information of vertebrae for fracture detection from image intensities directly. We further propose a novel shape decoder architecture: the point-based shape decoder. Results: Employing segmentation masks that were generated using the TotalSegmentator, our proposed method achieves an AUC of 0.901 on the VerSe19 testset. This outperforms image-based and surface-based end-to-end trained models. Our results demonstrate that pre-training the models in an unsupervised manner enhances geometric methods like PointNet and DGCNN. Conclusion: Our findings emphasize the advantages of explicitly learning shape features for diagnosing osteoporotic vertebrae fractures. This approach improves the reliability of classification results and reduces the need for annotated labels. [ABSTRACT FROM AUTHOR]

Given an observed low frame rate video, video frame interpolation (VFI) aims to generate a high frame rate video, which has smooth video frames with higher frames per second (FPS). Most existing VFI methods often focus on generating one frame at a specific timestep, e.g., 0.5, between every two frames, thus lacking the flexibility to increase the video's FPS by an arbitrary scale, e.g., 3. To better address this issue, in this paper, we propose an arbitrary timestep video frame interpolation (ATVFI) network with time-dependent decoding. Generally, the proposed ATVFI is an encoder–decoder architecture, where the interpolation timestep is an extra input added to the decoder network; this enables ATVFI to interpolate frames at arbitrary timesteps between input frames and to increase the video's FPS at any given scale. Moreover, we propose a data augmentation method, i.e., multi-width window sampling, where video frames can be split into training samples with multiple window widths, to better leverage training frames for arbitrary timestep interpolation. Extensive experiments were conducted to demonstrate the superiority of our model over existing baseline models on several testing datasets. Specifically, our model trained on the GoPro training set achieved 32.50 on the PSNR metric on the commonly used Vimeo90k testing set. [ABSTRACT FROM AUTHOR]

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding—and, therefore, likely reflect stable mechanisms of stimulus representation—have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations. [ABSTRACT FROM AUTHOR]

Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount of work has focused on the arguably more successful bio-mimetic approach. However, as chronic recordings have become more accessible and utilization of novel appendages such as computer cursors have become more universal, users can more easily spend time learning in a bio-feedback control paradigm. We believe a simple approach which leverages user learning and few assumptions will provide users with good control ability. To test the feasibility of this idea, we implemented a simple firing-rate-to-motion correspondence rule, assigned groups of neurons to virtual "directional keys" for control of a 2D cursor. Though not strictly required, to facilitate initial control, we selected neurons with similar preferred directions for each group. The groups of neurons were kept the same across multiple recording sessions to allow learning. Two Rhesus monkeys used this BMI to perform a center-out cursor movement task. After about a week of training, monkeys performed the task better and neuronal signal patterns changed on a group basis, indicating learning. While our experiments did not compare this bio-feedback BMI to bio-mimetic BMIs, the results demonstrate the feasibility of our control paradigm and paves the way for further research in multi-dimensional bio-feedback BMIs. [ABSTRACT FROM AUTHOR]

Marine oil spills are currently among the most challenging problems in marine environments. Synthetic Aperture Radar (SAR), with all-day and all-weather observation, has demonstrated great potential in oil disaster monitoring. A simple encoder-decoder network model was proposed by using SAR images for effective oil spill detection. The proposed model incorporates an optimized U-Net architecture that reduces computational requirements while maintaining detection performance. This is achieved through shortened encoder and decoder stages, depthwise separable convolutions, group normalization, and bilinear interpolation-based upsampling. To improve the model's generalization, the auto-learning rate and focal loss function are also included. Two public SAR datasets acquired by different sensors have been used to illustrate the efficiency of the proposed model. In addition, the polarimetric information has also been assessed for the detection of oil spill monitoring. The results show that the model can achieve high efficiency at a small model size. The sub-dataset with polarimetric features also achieves a high F1-score of 91.65% and an IoU of 84.59%. [ABSTRACT FROM AUTHOR]

A power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values. [ABSTRACT FROM AUTHOR]

Objective: Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs. Approach: To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations. Main results: Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve's design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort. Significance: The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use. [ABSTRACT FROM AUTHOR]

Summary: Power lines have complex transmission characteristics and noise interference when used as information transmission channel, thus limiting their data‐carrying rate and reliability. Impulsive noise is the most dominant factor degrading the performance of communication systems over power lines. To combat the impulsive noise, polar codes are introduced into the orthogonal frequency division multiplexing (OFDM)‐based power line communication (PLC) system. Because the successive cancelation list bit‐flip (SCLF) decoding algorithm of polar codes has high decoding complexity, a low complexity segmented cyclic redundancy check (CRC)‐aided SCLF decoding is proposed to improve the decoding performance. The decoding process can be terminated early with segmented CRC, which can reduce the bit‐flip decoding complexity significantly with short and medium block lengths. Experimental results demonstrate that for various configurations, the proposed decoder outperforms the traditional CRC‐aided SCL decoder in terms of decoding performance and complexity, and the suggested polar codes‐based OFDM‐based PLC system can reduce the bit error rate of the system in the presence of impulsive noise. [ABSTRACT FROM AUTHOR]

Context and Background: This paper addresses the challenge of encoding and decoding numerical data by introducing innovative algorithms utilizing Neutrosophic ASCII codes and ASCII Neutrosophic codes. These codes serve to represent uncertain or imprecise character values through the incorporation of neutrosophic numbers, encompassing degrees of truth, falsity, and indeterminacy. Motivation: The study stems from the necessity to effectively represent uncertain or imprecise character values in numerical data. This is crucial in diverse applications where handling uncertain or ambiguous data is a prevalent concern. Hypothesis: We hypothesize that employing Neutrosophic ASCII codes and ASCII Neutrosophic codes in encoding and decoding numerical data can provide a robust solution for representing uncertain or imprecise character values. Methods: The encoding algorithm in this study transforms each character in the ASCII string into its corresponding ASCII code, utilizing either 7 or 8 bits based on the code type. This algorithm calculates the degree of truth, falsity, and indeterminacy for each bit, considering the uncertainty or ambiguity associated with the character. The resulting neutrosophic numbers are appended to create the Neutrosophic ASCII code or ASCII Neutrosophic code. The decoding algorithm partitions the code into groups of neutrosophic numbers, calculates the associated degrees of truth, falsity, and indeterminacy for each ASCII bit, and converts the neutrosophic numbers back to ASCII codes, reconstructing the original ASCII character string. Results: Our study yields a novel and effective approach for encoding and decoding numerical data, demonstrating the potential of Neutrosophic ASCII codes and ASCII Neutrosophic codes in representing uncertain or imprecise character values. Conclusions: The proposed algorithms offer a promising solution for handling uncertain or ambiguous data in numerical encoding and decoding. The specific and quantitative results highlight the efficacy of Neutrosophic ASCII codes and ASCII Neutrosophic codes, showcasing their potential applicability in various domains requiring robust solutions for uncertain or imprecise character representation in numerical data. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) plays a critical role in many scientific and engineering applications, with aerodynamic design optimization being a primary area of interest. Recently, there has been much interest in using artificial intelligence approaches to accelerate this process. One promising method is the graph convolutional neural network (GCN), a deep learning method based on artificial neural networks (ANNs). In this paper, we propose a novel GCN-based aerodynamic design optimization acceleration framework, GCN-based aerodynamic design optimization acceleration framework. The framework significantly improves processing efficiency by optimizing data flow and data representation. We also introduce a network model called GCN4CFD that uses the GCF framework to create a compact data representation of the flow field and an encoder–decoder structure to extract features. This approach enables the model to learn underlying physical laws in a space-time efficient manner. We then evaluate the proposed method on an airfoil aerodynamic design optimization task and show that GCN4CFD provides a significant speedup compared to traditional CFD solvers while maintaining accuracy. Our experimental results demonstrate the robustness of the proposed framework and network model, achieving a speedup average of 3. 0 ×. [ABSTRACT FROM AUTHOR]

Encoding a dictionary into another representation means that all the words can be stored in the dictionary in a more efficient way. In this way, we can complete common operations in dictionaries, such as (1) searching for a word in the dictionary, (2) adding some words to the dictionary, and (3) removing some words from the dictionary, in a shorter time. Binary decision diagrams (BDDs) are one of the most famous representations of such encoding and are widely popular due to their excellent properties. Recently, some people have proposed encoding dictionaries into BDDs and some variants of BDDs and showed that it is feasible. Hence, we further investigate the topic of encoding dictionaries into decision diagrams. Tagged sentential decision diagrams (TSDDs), as one of these variants based on structured decomposition, exploit both the standard and zero-suppressed trimming rules. In this paper, we first introduce how to use Boolean functions to represent dictionary files and then design an algorithm that encodes dictionaries into TSDDs with the help of tries and a decoding algorithm that restores TSDDs to dictionaries. We utilize the help of tries in the encoding algorithm, which greatly accelerates the encoding process. Considering that TSDDs integrate two trimming rules, we believe that using TSDDs to represent dictionaries would be more effective, and the experiments also show this. [ABSTRACT FROM AUTHOR]

This paper studies binary reverse multi-delimiter (RMD) data compression codes. RMD codes have a range of useful properties, such as unique decodability, completeness, universality, synchronizability, recognition using a finite automaton, and the ability for rapid data retrieval within an encoded file. The authors have constructed a simple monotonic mapping from the set of non-negative integers to the codeword set. Based on this mapping, they have developed a fast byte-aligned decoding algorithm. Computer experiments demonstrate that we can decode RMD codes nearly as quickly as the SCDC code and several times faster than the Fibonacci code. Compared to known codes of a similar type, RMD codes exhibit a better compression ratio for natural language texts (more than four times closer to the entropy bound than SCDC). Additionally, the paper describes a technology for preprocessing natural language texts, which, combined with encoding using RMD codes, enhances the efficiency of powerful modern archivers. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of quantum computing technology, in order to meet the security requirements of cryptographic applications, this paper proposed an ElGamal public key cryptosystem based on Polar codes. The paper adopted Polar codes as the public code in the ElGamal public key cryptosystem based on error-correcting codes, used SC decoding algorithm to decode, and analyzed the decoding failure probability and security of the scheme. The results show that the algorithm has a high transmission rate, the selected parameters meet the requirements of information set decoding complexity and decoding failure probability, and the algorithm meets IND-CPA security. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree." The correction addresses errors in Figure 1, where an operator symbol on the Long Short-Term Memory (LSTM) neural network was incorrect and should have been a multiplication instead of addition. Additionally, Equation 3 in the LSTM subsection of the article was incorrect, with a missing right bracket. The corrected figure and equation are provided in the notice. The authors of the article are Yanning Lu, Yanzheng Xiang, Bo Chen, Haiyang Zhu, Junfeng Yue, Yawei Jin, Pengfei He, Yibo Zhao, Yingjie Zhu, Jiasheng Si, and Deyu Zhou. [Extracted from the article]

Node classification is a popular and challenging task in graph neural networks, and existing approaches are mainly developed for a single network. With the advances in domain adaptation, researchers tend to leverage knowledge extracted from a fully-labeled source network to further improve the node classification performance in an unlabeled target network. This learning paradigm refers to cross-network node classification, which is the topic we studied in this paper. Specifically, we propose a novel model named Transferable Graph Auto-Encoders (TGAE), which first encodes the initial network data into latent representations and then decodes the learned features to preserve graph information. In the encoding phase, TGAE adopts the attentional mechanism to fuse the local and global information of nodes to discover latent node representations. To obtain transferable features between the source and target networks, TGAE aligns their distributions based on the learned representations by reducing marginal and conditional distribution differences. In the decoding phase, the latent representations are subjected to pairwise and reconstruction constraints, thus preserving structural proximity and graph topology information to learn discriminative features. Besides, a node classifier is trained to enhance the discriminant of the node representations further. Experimental results on several real-world datasets demonstrate that the proposed model achieves state-of-the-art performance in cross-network node classification tasks compared with existing methods. • TGAE is proposed to handle cross-network node classification problem. • TGAE encodes local and global information and preserves structural graph information. • Experimental results demonstrate the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

List-decoding and list-recovery are important generalizations of unique decoding and receive considerable attention over the years. We study the optimal trade-off among the list-decoding (resp. list-recovery) radius, the list size, and the code rate, when the list size is constant and the alphabet size is large (both compared with the code length). We prove a new Singleton-type bound for list-decoding, which, for a wide range of parameters, is asymptotically tight up to a 1 + o (1) factor. We also prove a Singleton-type bound for list-recovery, which is the first such bound in the literature. We apply these results to obtain near optimal lower bounds on the list size for list-decodable and list-recoverable codes with rates approaching capacity. Moreover, we show that under some indivisibility condition of the parameters and over a sufficiently large alphabet, the largest list-decodable nonlinear codes can have much more codewords than the largest list-decodable linear codes. Such a large gap is not known to exist in unique decoding. We prove this by a novel connection between list-decoding and the notion of sparse hypergraphs in extremal combinatorics. Lastly, we show that list-decodability or recoverability implies in some sense good unique decodability. [ABSTRACT FROM AUTHOR]

• A novel full-scale hierarchical encoder-decoder network is proposed to unify the full-scale long-range semantic information and single-scale shallow details of multi-modal images. • To comprehensively utilize the multilayer effective information and inject the edge prior, a triple fusion mechanism is proposed based on dual-attention fusion (DAF) strategy. • A cascading edge-prior branch is designed to jointly guide the decoder focusing on abundant details layer-by-layer. • A novel loss function consisting of SSIM loss, intensity loss and edge loss is constructed to further maintain the network with better edge representation and reconstruction capability. Most existing deep learning-based infrared and visible image fusion methods always fail to consider the full-scale long-range correlation and the prior knowledge, resulting in the fused images with low-contrast salient objects and blurred edge details. To overcome these drawbacks, a full-scale hierarchical encoder-decoder network with cascading edge-prior for infrared and visible image fusion is proposed. First, a top-down encoder extracts the hierarchical representations from source image. Then, to inject edge priors into the network and capture the progressive semantic correlations, a triple fusion mechanism is proposed including edge image fusion based on maximum fusion strategy, single-scale shallow layer fusion and full-scale semantic layer fusion based on dual-attention fusion (DAF) strategy. The fused full-scale semantic features (F2SF) are obtained by capturing the long-range affinities of the full-scale. At the same time, a cascading edge-prior branch (CEPB) is designed to embed the fused edge knowledge into fused single-scale shallow features, jointly guiding the decoder to focus on abundant details layer-by-layer on the basis of F2SF, thus recovering the edge and texture details of the fused image well. Finally, a novel loss function consisting of SSIM, intensity and edge loss is constructed to further maintain the network with better edge representation and reconstruction capability. Compared with existing state-of-the-art fusion methods, the proposed method has better performance in terms of both visual evaluation and objective evaluation on public datasets. The source code is available at https://github.com/lxq-jnu/FSFusion. [ABSTRACT FROM AUTHOR]

Due to the influence of volume conduction, information redundancy exists between electrodes in electroencephalogram (EEG) signals, resulting in low spatial resolution. Furthermore, the current method of extracting EEG spatial features in Euclidean space cannot accurately depict the complex relationships among multiple electrodes, leading to poor decoding performance of EEG signals. A novel decoding framework for motor imagery EEG signals, known as the Spatial Filter Temporal Graph Convolutional Network (SF-TGCN), is proposed in this study. The spatial resolution of motor imagery EEG signals is enhanced by constructing the spatial filtering module using the Laplacian graph operator. Deep temporal features of the EEG signals are extracted using multiple layers of temporal convolutional modules, and the graph convolutional network module is employed to learn the topology of electrode connections, facilitating the decoding of motor imagery EEG signals. Superior performance is demonstrated by the proposed decoding model, achieving an average accuracy of 80.82% on the BCI IV 2a public dataset and 87.98% on a private dataset, as compared to other state-of-the-art decoding approaches. • We proposed a novel decoding framework for motor imagery EEG signals. • Our spatial filtering module enhances the spatial resolution of EEG signals. • We utilize graph convolutional modules to better learn the spatial features of EEG signals. • Based on the decoding model proposed in this paper, we design an online BCI intelligent wheelchair control system. [ABSTRACT FROM AUTHOR]

Real-time semantic segmentation is critical in industries, such as autonomous driving and robotics, requiring both accuracy and speed. However, existing real-time segmentation algorithms often sacrifice low-level details to improve inference speed, leading to decreased segmentation accuracy. Therefore, we propose a new real-time semantic segmentation model dual interaction fusion network (DIFNet) to alleviate this problem. First, we propose a lightweight dual decoding fusion structure, which increases the focus on the low-level feature information and can extract richer edge details, while the structure reduces the computational overhead by decreasing the number of channels of the feature map during fusion. In addition, we construct a cross attention module to cross-weight fusion of high-level and low-level features through attention mechanism, which increases the interaction between features and effectively extracts features at different levels. Finally, we design a comprehensive perception module that introduces dilated convolution to expand the model's receptive field, enabling it to better capture global features. Our network was validated on the Cityscapes and CamVid datasets. Specifically, on a single Nvidia GTX 2080 Ti, DIFNet achieves 77.6% mIoU at 83.9 frames per second (FPS) for 1536×768 inputs on Cityscapes test set and 77.0% mIoU at 135.8 FPS for 960×720 inputs on CamVid. [ABSTRACT FROM AUTHOR]

DNA-based storage has been a promising technique of data storage, due to its high density and long duration. During synthesizing and sequencing of DNA storage, edit errors including insertions, deletions and substitutions are introduced inevitably. An effective way to reduce the error probability is to limit the content of G and C in DNA sequences to around 50%, which is called GC-balanced. To deal with edit errors, DNA sequences are also expected to have error-correcting capabilities. In this paper, GC globally balanced and GC locally balanced error-correcting codes are explicitly constructed, respectively. Inspired by repetition codes, the proposed codes are able to correct multiple edit errors. Furthermore, an efficient decoding algorithm applied for both codes is derived when only one kind of edit error occur. [ABSTRACT FROM AUTHOR]

In this paper, let q be a prime power, we focus on a class of double-twisted generalized Reed-Solomon code C over F q. We give a sufficient and necessary condition for C to be MDS or AMDS, and prove that C is non-GRS by calculating the Schur square of its dual code. Furthermore, we present a sufficient and necessary condition for C to be self-dual, and then construct several classes of self-dual NMDS or non-GRS MDS codes. [ABSTRACT FROM AUTHOR]

• A binary step-response serial coded target (BSSCT) is proposed, the binary step period in the image has a strong robustness to external noise interference, and the capacity can be greatly expanded by periodically encoding corner elements of coded targets. • A robust decoding algorithm based on multi-radius neighborhood search is proposed, the robust decoding of the dot-dispersing coded target using cross-ratio invariance needs to ensure P2-invariant stability. • The BSSCT coding and decoding method based on step period has a high recognition rate and location accuracy with different illumination environments, noise environments, and measurement angles. In close-range photogrammetry, it is difficult to meet the measurement requirements of large scenes in actual engineering due to the limited capacity of coded targets. To expand the capacity of the coded target, we propose a binary step-response serial-coded target (BSSCT). The BSSCT introduces periodic binary wave information as an additional feature in the dot-dispersing coded target. Also, a robust recognition algorithm for the BSSCT is developed, the P2-Invariant, and the step period is used for decoding. The capacity of the coded target reaches 7 magnitudes without increasing the auxiliary points. Under different lighting conditions and viewing angles, the measurement experiments show that the BSSCT outperforms other state-of-the-art coded targets. Our BSSCT is a promising standard method for large-field system calibration and object measurement. [ABSTRACT FROM AUTHOR]

Variational recurrent autoencoder model (VRAE) is an appealing technique for capturing the variabilities underlying complex sequential data, which is realized by introducing high-level latent random variables as hidden states. Existing models suffer from the well-known 'posterior collapse' problem, meaning that a powerful autoregressive decoder equipped in the model itself could capture all the variabilities and hence leave the latent variables learning nothing from the data. From the perspective of model training, the posterior collapse problem can result in a very low Kullback–Leibler divergence (KL-divergence) value, which means the posteriors of the latent variables tend to be just the priors. In this paper, we address this problem by proposing a Bayesian variational recurrent neural network (BVRNN) model, in which two additional decoders are added into the original VRAE. These extra decoders can force the latent variables to learn meaningful knowledge during the training process. We conduct experiments on MNIST and Fashion-MNIST dataset. The experimental results show that the proposed model outperforms several baseline models. We further adapt the proposed model to a very challenging task in natural language processing, namely Named-Entity Recognition (NER). Experimental results show that our model is competitive to the state-of-the-art models on NER. [ABSTRACT FROM AUTHOR]

In this paper, we give a parity check matrix of the (+) -extended twisted generalized Reed Solomon code, and then not only prove that it is MDS or NMDS, but also determine the weight distribution. Especially, based on Schur method, we show that the (+) -extended twisted generalized Reed Solomon code is neither the generalized Reed Solomon code nor extended generalized Reed Solomon code. Furthermore, we present necessary and sufficient conditions for the (+) -twisted generalized Reed Solomon code and the (+) -extended twisted generalized Reed Solomon code to be self-orthogonal, respectively. Finally, several classes of self-dual (+) -twisted generalized Reed Solomon code codes and almost self-dual (+) -extended twisted generalized Reed Solomon codes are constructed. [ABSTRACT FROM AUTHOR]

Graph embedding learning is a fundamental task when dealing with diverse datasets. While encoder–decoder architectures, such as U-Nets, have shown great success in image pixel-wise prediction tasks, applying similar methods to graph data poses challenges due to the lack of natural pooling and up-sampling operations for graphs. Recent methods leverage learnable parameters to extract structural information from neural networks and extend pooling and unpooling to graphs using node features and graph structural information. This paper proposes a novel model called GIUNet (Graph Isomorphism U-Net) for the graph classification task. The proposed Graph U-Net structure is based on graph isomorphism convolution while using a comprehensive pqPooling layer. The pqPooling layer in our approach effectively combines node features and graph structure information during the graph down-sampling stage. To incorporate graph structure information, we utilize both the spectral representation and node centrality measurements. Node centrality measurements capture various structural aspects of nodes in the graph, while the spectral representation helps us focus on the informative low-frequency components of the graph structure. Through ablation studies, we have demonstrated that leveraging the GIUNet model leads to significant improvements compared to state-of-the-art methods across multiple benchmark datasets. • A new model called GIUNet is presented for the representation learning on graphs. • GIUNet exploits structural information and node features during the pooling stage. • GIUNet employs graph isomorphism convolution in encoder–decoder structure. [ABSTRACT FROM AUTHOR]

This article proposed a hybrid search mode-based differential evolution (HSM-DE) algorithm to overcome the complex design of an interval type-2 fuzzy logic system (IT2FLS). First, transform complex design processes of the IT2FLS into optimization problems for our proposed evolutionary algorithm by decoding the whole system into design parameters regarded as search objectives. Then, the DE algorithm with a rapid convergence characteristic is adopted as a global search method for exploration according to a memetic algorithm (MA) structure. Considering the conventional DE algorithm is lack of escaping ability for the local optimal solution, a local search method combined with a simulated binary crossover (SBX) and polynomial mutation (PM) is utilized to enhance the exploitation behavior of our proposed method. In addition, simple constraints on the design parameters of the membership function are added to balance the searching freedom of the HSM-DE algorithm and the readability of the optimal IT2FLS. Finally, a simulation of a mobile two-wheel vehicle (MTWV) under a goal search task is performed to verify the effectiveness of the optimal IT2FLS auto designed by HSM-DE. All results demonstrate that the HSM-DE realizes auto design for the IT2FLS with high-performance control and outperforms the comparative methods. Moreover, four comparison methods are performed to validate the high-performance of the proposed HSM-DE on the search ability of the optimal solution. • A hybrid search mode-based differential evolution algorithm is proposed. • Memetic algorithm structure avoids the early convergence and local optimal solution. • Proposed algorithm with strong search ability and robustness outperforms the comparison methods. • The interval type-2 logic system auto designed by the proposed algorithm has high performance. [ABSTRACT FROM AUTHOR]

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. We demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error. [ABSTRACT FROM AUTHOR]

It is known that Z p s -linear codes, which are the Gray map image of Z p s -additive codes (linear codes over Z p s ), are systematic and a systematic encoding has been found. This makes Z p s -linear codes suitable to apply the permutation decoding method. This technique is also based on the existence of r -PD-sets, which are subsets of the permutation automorphism group of the code. In this paper, we study the permutation automorphism group of Z p s -linear generalized Hadamard codes of type (n ; t 1 , ... , t s) and show how to construct r -PD-sets of size r + 1 , for all r up to an upper bound, in order to be able to perform a partial permutation decoding for these codes. [ABSTRACT FROM AUTHOR]

In this paper, we provide two distinct list decoding algorithms tailored for specific classes of maximal order number field codes. The first algorithm focuses on the unit codes, while the second algorithm is dedicated to the Arakelov divisor-based number field codes. These codes were originally introduced by Maire and Oggier. To achieve our decoding goals, we utilize the list decoding algorithm for general number field codes proposed by Biasse and Quintin. Our approach involves carefully calculating the required parameter B from Biasse and Quintin's algorithm, and adjusting the returned list to satisfy the desired conditions. For unit codes, we consider two cases: when the underlying number field is totally real and when it has an arbitrary signature. We separate these cases because the parameter B can be improved in the totally real scenario. Additionally, we present our list decoding algorithm for Arakelov divisor-based number field codes, focusing on number fields with arbitrary signatures. [ABSTRACT FROM AUTHOR]