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Although in-stream video advertising is common, its effects on advertisement (ad) information encoding remain unclear. We investigated the effects of in-stream video advertising by comparing two groups: those watching mid-roll (between the program) ads and those watching pre- and post-roll (before and after the program, respectively) ads. To elucidate how advertising content is encoded in the context of in-stream video advertising, we integrated two theoretical frameworks: the negative emotion–memory model (NEMM) and the limited capacity model of motivated–mediated message processing (LC4MP). We used electroencephalography (EEG) to assess negative emotions and bottom-up attention during advertisement viewing. The findings indicate that the first mid-roll ad induced negative emotions, but these feelings were attenuated during subsequent mid-rolls. In addition, negative emotions induced by mid-roll ads attenuated the role of bottom-up attention in the information encoding process. However, the pre- and post-roll ads were not accompanied by negative emotions; thus, bottom-up attention played a major role in the information encoding of these ads. The results also suggest that despite the negative emotions experienced during mid-rolls, such transient negative reactions did not affect purchase intention for the advertised products. [ABSTRACT FROM AUTHOR]

Oriented elements are part of geometry, and they come in two complementary types: intrinsic and extrinsic. Those different orientation types manifest themselves by behaving differently under reflection. Projective dualization in geometric algebras can encode them, or conversely, orientation types inform the interpretation of dualization. Oriented elements may be combined using the meet operation, and the dual‐join (which is here introduced for that purpose). We discuss algebra‐based implementation techniques on how to double the representational power of a geometric algebra, without explicitly doubling the algebra. [ABSTRACT FROM AUTHOR]

Data hiding is a technique that conceals a given mystery message within a cover medium in a secure way. It provides an additional security layer to protect valuable data embedded into the cover medium and transferred via the Internet. Steganography approaches based on transform domains are the most popular due to their high security in image steganography. Unfortunately, these approaches have two major existing disadvantages significant computing power and limited embedding capacity. To overcome these drawbacks, in this paper a hybrid scheme based on Lah transform is proposed. At first, the Lah transform is utilized to generate the Lah coefficients from the considered 4-pixel blocks of the considered cover image. Then, these Lah components are modified to embed the valuable data. For embedding rates equal to or less than 0.5 bpp, a modified Hamming code based method (MHCBM) is employed to conceal the given message bits in the generated Lah components. For payload larger than 0.5 bpp into the Lah transform coefficients, a modified LSB matching revisited approach (MLSBMR) is used. This principle allows us to minimize statistical distortion to the stego images, while at the same time enhancing the security of the hidden message against statistical steganalysis approaches. Experimental results indicated that the proposed scheme achieved better perceptual quality and higher security performance compared to the existing Lah transform based method and the relevant integer based transform approaches. [ABSTRACT FROM AUTHOR]

Despite the significant progress made in deep learning-based stereo matching, the accuracy of these methods significantly decreases when faced with challenges such as occlusions, reflections, textureless areas, and scale variations. In this paper, we propose MSCANet, a novel stereo matching network that integrates multi-scale inputs and context-aware aggregation ability. MSCANet effectively integrates rich multi-scale feature information and exhibits context-aware capability, thereby enabling it to achieve superior performance. Firstly, a multi-scale aware fusion module is designed to efficiently incorporate more comprehensive global context features at different scales, which allows the model to enhance its ability to generalize across images of varying scales. Secondly, a novel V-shaped encoder/decoder module is developed to effectively exploit the rich feature information. In the encoding stage, a 3D squeeze-and-excitation block is introduced to facilitate adaptively recalibration of learned feature maps. This block effectively suppresses irrelevant features while enhancing useful features, which improved efficiency and accuracy in disparity prediction. Additionally, a 3D context-aware decode block is designed to effectively utilize global context features to restore the original image structure during the decoding stage. Moreover, the high-level feature maps can be employed to augment low-level feature maps by incorporating more detailed information to avoid the side effects caused by the loss of information during the encoding process. Extensive ablation experiments and comparative experiments were conducted on Scene Flow dataset, KITTI2012 and KITTI2015 datasets to validate the effectiveness of each proposed module. The experimental results demonstrate MSCANet achieves competitive performance and offers a more straightforward and efficient model design, as well as faster inference speed. [ABSTRACT FROM AUTHOR]

Sequential Recommendation systems play a crucial role in predicting users' preferences based on their behavioral history. However, the existing methods ignore the extrapolation nature of sequences and do not make deep use of item provider information. This oversight limits the model's ability to fully utilize relevant external properties. To alleviate these limitations, we design a recommendation model that incorporates Position encoding and external reinforcement (Item -Provider), named DPSRec. Specifically, we design an Embed Encoding layer, in order to distinguish the Position Embedding of previous sequence models, we combine the time variability with the position encoding with extrapolation property, and encode the item and item provider sequences accordingly. Meanwhile, to avoid the noise that the initial item embeddings might cause with Position Encoding, we calculate the position Encoding separately from the item embedding. In addition, we design a Cross Propagation layer to capture implicit higher-order dependencies between item sequences. Extensive experiments on three real-world datasets demonstrate that the proposed model generally outperforms the baselines by about 1–12.5%. Our source code will be published after the paper is published. [ABSTRACT FROM AUTHOR]

This research presents a new approach to create two-color luminescent physically unclonable functions (PUFs) using an organic luminophore with tunable emission colors. These PUFs offer high security and stable performance, significantly enhancing anti-counterfeiting capabilities by exponentially increasing encoding capacity through dual-color integration and complex pattern formation. [ABSTRACT FROM AUTHOR]

Small object detection in aerial images is a challenge in remote sensing. Recently, convolutional neural networks (CNNs) have succeeded by learning localized filters that embed relative spatial information but fail to detect the small objects in the aerial images for the uneven padding. Even though the padding mechanism in CNNs allows for the capture of absolute position information and ensures consistent input–output resolution, it leads to a diminishing extraction of absolute position context from the edges to the center. This results in asymmetry bias, which negatively impacts position-dependent visual tasks like small object detection, causing blind spots and misdetection. In this study, we uncover that complex-valued CNNs, capable of explicitly encoding absolute position information, can significantly enhance conventional object detection techniques. To accomplish this, we introduce the position information encoding feature pyramid network (PieFPN), which consists of a complex-valued encoder and a real-valued decoder for explicit position information encoding. Additionally, we present the general Gaussian normalization and Gaussian error linear unit for multi-variables, incorporating them into end-to-end training schemes. To utilize the ImageNet pre-trained weights, we merge PieF with traditional feature pyramid networks, allowing for seamless integration into existing object detection pipelines. Our complex-valued designs outperform their real-valued counterparts, achieving state-of-the-art results on the DOTA-v2.0 oriented object detection in aerial images dataset. [ABSTRACT FROM AUTHOR]

Dye-encoded bead-based assays are widely used for diagnostics. Multiple bead populations are required for multiplexing and can be produced using different dye colors, labeling levels, or combinations of dye ratios. Ready-to-use multiplex bead populations restrict users to specific targets, are costly, or require specialized instrumentation. In-house methods produce few bead plexes or require many fine-tuning steps. To expand bead encoding strategies, we present a simple, safe, and cost-effective bench-top system for generating bead populations using photobleaching. By photobleaching commercially available dye-encoded magnetic beads for different durations, we produce three times as many differentiable bead populations on flow cytometry from a single dye color. Our photobleaching system uses a high-power LED module connected to a light concentrator and a heat sink. The beads are photobleached in solution homogeneously by constant mixing. We demonstrate this photobleaching method can be utilized for cross-testing antibodies, which is the first step in developing immunoassays. The assay uses multiple photobleached encoded beads conjugated with capture antibodies to test many binding pairs simultaneously. To further expand the number of antibodies that can be tested at once, several antibodies were conjugated to the same bead, forming a pooled assay. Our assay predicts the performance of antibody pairs used in ultrasensitive Simoa assays, narrowing the number of cross-tested pairs that need to be tested by at least two-thirds and, therefore, providing a rapid alternative for an initial antibody pair screening. The photobleaching system can be utilized for other applications, such as multiplexing, and for photobleaching other particles in solution. [ABSTRACT FROM AUTHOR]

Data storage in DNA has recently emerged as a promising archival solution, offering space-efficient and long-lasting digital storage solutions. Recent studies suggest leveraging the inherent redundancy of synthesis and sequencing technologies by using composite DNA alphabets. A major challenge of this approach involves the noisy inference process, obstructing large composite alphabets. This paper introduces a novel approach for DNA-based data storage, offering, in some implementations, a 6.5-fold increase in logical density over standard DNA-based storage systems, with near-zero reconstruction error. Combinatorial DNA encoding uses a set of clearly distinguishable DNA shortmers to construct large combinatorial alphabets, where each letter consists of a subset of shortmers. We formally define various combinatorial encoding schemes and investigate their theoretical properties. These include information density and reconstruction probabilities, as well as required synthesis and sequencing multiplicities. We then propose an end-to-end design for a combinatorial DNA-based data storage system, including encoding schemes, two-dimensional (2D) error correction codes, and reconstruction algorithms, under different error regimes. We performed simulations and show, for example, that the use of 2D Reed-Solomon error correction has significantly improved reconstruction rates. We validated our approach by constructing two combinatorial sequences using Gibson assembly, imitating a 4-cycle combinatorial synthesis process. We confirmed the successful reconstruction, and established the robustness of our approach for different error types. Subsampling experiments supported the important role of sampling rate and its effect on the overall performance. Our work demonstrates the potential of combinatorial shortmer encoding for DNA-based data storage and describes some theoretical research questions and technical challenges. Combining combinatorial principles with error-correcting strategies, and investing in the development of DNA synthesis technologies that efficiently support combinatorial synthesis, can pave the way to efficient, error-resilient DNA-based storage solutions. [ABSTRACT FROM AUTHOR]

The off-target activities within the CRISPR-Cas9 system remains a formidable barrier to its broader application and development. Recent advancements have highlighted the potential of deep learning models in predicting these off-target effects, yet they encounter significant hurdles including imbalances within datasets and the intricacies associated with encoding schemes and model architectures. To surmount these challenges, our study innovatively introduces an Efficiency and Specificity-Based (ESB) class rebalancing strategy, specifically devised for datasets featuring mismatches-only off-target instances, marking a pioneering approach in this realm. Furthermore, through a meticulous evaluation of various One-hot encoding schemes alongside numerous hybrid neural network models, we discern that encoding and models of moderate complexity ideally balance performance and efficiency. On this foundation, we advance a novel hybrid model, the CRISPR-MCA, which capitalizes on multi-feature extraction to enhance predictive accuracy. The empirical results affirm that the ESB class rebalancing strategy surpasses five conventional methods in addressing extreme dataset imbalances, demonstrating superior efficacy and broader applicability across diverse models. Notably, the CRISPR-MCA model excels in off-target effect prediction across four distinct mismatches-only datasets and significantly outperforms contemporary state-of-the-art models in datasets comprising both mismatches and indels. In summation, the CRISPR-MCA model, coupled with the ESB rebalancing strategy, offers profound insights and a robust framework for future explorations in this field. Author summary: In the field of gene editing, the application of deep learning technologies holds significant promise for predicting off-target effects in the CRISPR-Cas9 system. Nevertheless, one of the primary challenges encountered is the extreme imbalance among classes within the off-target datasets, which severely hampers the predictive accuracy for certain classes. Furthermore, as an array of sequence encoding methods continue to evolve, there has been a corresponding increase in model complexity. Addressing these issues, we introduce a novel Efficiency and Specificity-Based (ESB) class rebalancing strategy designed to mitigate the impact of class imbalance. Additionally, we assess the influence of six encoding schemes and four distinct architectural approaches on the prediction performance, employing four benchmark datasets for validation. Building upon these insights, we have developed a new hybrid model, termed CRISPR-MCA. Our experimental results demonstrate that the ESB strategy significantly surpasses the performance of existing baseline methods across multiple models. Moreover, the CRISPR-MCA model exhibits robust performance on two distinct types of datasets, affirming its effectiveness in enhancing the accuracy of deep learning predictions for off-target activities. [ABSTRACT FROM AUTHOR]

The existing signed graph neural networks mainly focus on the design process of neighbor aggregation function, but ignore the correlation between nodes, which leads to the decline of the representation ability of neural networks. In order to solve the above problems, a SDEGNN (Signed Distance Encoding based on Graph Neural Network) model based on enhanced signed distance encoding is proposed in this paper. Firstly, the problem of limited representation ability in signed graph neural networks is discussed. Secondly, in order to capture the correlation between nodes, signed distance encoding is proposed as the node feature representation to enhance the representation ability of the model. Thirdly, the signed distance encoding is injected into the information aggregation process of the signed graph convolutional network, and the objective function is proposed to optimize the SDEGNN model. The SDEGNN model is verified performance by three real signed network datasets Bitcoin-OTC, Bitcoin-Alpha, and Wiki-RfA. The experimental results show that the SDEGNN model can effectively improve the accuracy of link sign prediction tasks. [ABSTRACT FROM AUTHOR]

Modern language models and vector-symbolic architectures show that vector-based models are capable of handling the compositional, structured, and symbolic properties required for human concepts. Vectors are also able to handle key phenomena from the psychology, including computation of features and similarities, reasoning about relations and analogies, and representation of theories. Language models show how vector representation of word semantics and sentences can interface between concepts and language, as seen in definitional theories of concepts or ad hoc concepts. The idea of Church encoding, from logic, allows us to understand how meaning can arise in vector-based or symbolic systems. By combining these recent computational results with classic findings in psychology, vector-based models provide a compelling account of human conceptual representation. For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations. It must also support the development of theories, ad hoc categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures. This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and symbolic computational processes. [ABSTRACT FROM AUTHOR]

In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. Theoretically, an implication of this fact is that a filter may know what it is looking at, but not where it is positioned in the image. In this paper, we first test this hypothesis and reveal that a surprising degree of absolute position information is encoded in commonly used CNNs. We show that zero padding drives CNNs to encode position information in their internal representations, while a lack of padding precludes position encoding. This observation gives rise to deeper questions about the role of position information in CNNs: (i) What boundary heuristics enable optimal position encoding for downstream tasks? (ii) Does position encoding affect the learning of semantic representations? (iii) Does position encoding always improve performance? To provide answers, we perform the largest case study to date on the role that padding and border heuristics play in CNNs. We design novel tasks that allow us to quantify boundary effects as a function of the distance to the border. Numerous semantic objectives reveal the effect of the border on semantic representations. Finally, we demonstrate the implications of these findings on multiple real-world tasks to show that position information can both help or hurt performance. [ABSTRACT FROM AUTHOR]

Urban regeneration is an important strategy for land redevelopment, to address the urban decay in cities. Among many tasks, urban layout is the foundation for urban regeneration. In this paper, we target a new task called function-aware urban layout regeneration, and propose UrbanEvolver, a function-aware deep generative model for the task. Given a target region to be regenerated, our model outputs a regenerated urban layout (i.e., roads and buildings) for the target region by considering the function (i.e., land use type) of the target region and its surrounding context (i.e., the functions and urban layouts of the surrounding regions). UrbanEvolver first extracts implicit regeneration rules from the target function and the surrounding context by encoding them separately in different scales through the function-layout adaptive (FA) blocks, and then constrains the regenerated urban layout based on the learned regeneration rules. To enforce the regenerated layout to be valid and to follow the road structure, we design a set of losses covering both pixel-level and geometry-level constraints. To train our model, we collect a large-scale urban layout dataset covering more than 147 K regions under 1300 km 2 with rich annotations, including functions, region shapes, urban road layouts, and urban building layouts. We conduct extensive experiments to show that our model outperforms the baseline methods in generating practical and function-aware urban layouts based on the given target function and surrounding context. [ABSTRACT FROM AUTHOR]

This paper introduces a new network model - the Image Guidance Encoder-Decoder Model (IG-ED), designed to enhance the efficiency of image captioning and improve predictive accuracy. IG-ED, a fusion of the convolutional network VGGNet-16 and the long short-term memory network (LSTM), is designed based on the encoder-decoder structure. The image captioning performance sees significant enhancements when leveraging the IG-ED network model. The network training process unfolds in a series of steps. Initially, the input image undergoes convolution via the VGGNet-16 network, producing a 512-dimensional vector. Concurrently, each word in the image's caption is encoded to generate a corresponding 512-dimensional vector consistent with the image feature dimension. These two vectors form the input for the decoding process. Subsequently, the vectors are fed into the redesigned fusion LSTM (F-LSTM) network at different time steps to gradually train the parameters of the IG-ED framework. The training process is completed by utilizing a loss function for determining convergence. Evaluation of the IG-ED model's performance is conducted using CIDEr and seven other evaluation metrics on the MSCOCO 2014 dataset. The results exhibit substantial improvements over the "Adaptive Attention Mode" network and "Neural Talk" network. Additionally, the parameter count of the IG-ED architecture is significantly reduced compared to the "Adaptive Attention Mode" network, leading to decreased computational resource requirements and enabling edge computing on the neural network. [ABSTRACT FROM AUTHOR]

Partial Fourier encoding is popular in single‐shot (ss) diffusion‐weighted (DW) echo planar imaging (EPI) because it enables a shorter echo time (TE) and, hence, improves the signal‐to‐noise‐ratio. Motion during diffusion encoding causes k‐space shifting and dispersion, which compromises the quality of the homodyne reconstruction. This work provides a comprehensive understanding of the artifacts in homodyne reconstruction of partial Fourier ss‐DW‐EPI data in the presence of motion‐induced phase and proposes the motion‐induced phase‐corrected homodyne (mpc‐hdyne) reconstruction method to ameliorate these artifacts. Simulations with different types of motion‐induced phase were performed to provide an understanding of the potential artifacts that occur in the homodyne reconstruction of partial Fourier ss‐DW‐EPI data. To correct for the artifacts, the mpc‐hdyne reconstruction is proposed. The algorithm recenters k‐space, updates the partial Fourier factor according to detected global k‐space shifts, and removes low‐resolution nonlinear phase before the conventional homodyne reconstruction. The mpc‐hdyne reconstruction is tested on both simulation and in vivo data. Motion‐induced phase can cause signal overestimation, worm artifacts, and signal loss in partial Fourier ss‐DW‐EPI data with the conventional homodyne reconstruction. Simulation and in vivo data showed that the proposed mpc‐hdyne reconstruction ameliorated artifacts, yielding higher quality DW images compared with conventional homodyne reconstruction. Based on the understanding of the artifacts in homodyne reconstruction of partial Fourier ss‐DW‐EPI data, the mpc‐hdyne reconstruction was proposed and showed superior performance compared with the conventional homodyne reconstruction on both simulation and in vivo data. [ABSTRACT FROM AUTHOR]

The Intrusion Detection System (IDS) has gained significant attention due to enhanced network utilization. However, various types of IDS approaches have been established in conventional research which focus on recognizing intrusions from datasets with the assistance of classification problems. However, the conventional techniques are unable to recognize malicious attacks due to the class imbalance issue. To overcome this issue, the Rat Swarm Optimization with Improved Self-Attention based Gated Recurrent Unit (RSO-ISAGRU) is proposed in this research for IDS classification. The RSO selects a set of best features by updating their positions based on their chasing and attack behavior. The weights are assigned by a self-attention mechanism which enables the ISAGRU to adopt attack patterns and enhance classification accuracy. The dataset is preprocessed by hash encoding and min-max normalization which convert the categorical feature into an integer format and normalizes the features. The accuracy, precision, recall and f1-score are taken as parameters for estimate RSO-ISAGRU performance. The RSO-ISAGRU achieves accuracy of 99.86%, 98.64%, 99.72%, and 99.83% for NSL-KDD, UNSW-NB15, CICIDS-2017, and CICIDS-2018 datasets when compared to ImmuneNet and Deep Neural Network (DNN). [ABSTRACT FROM AUTHOR]

Emerging communication standards prioritize both the assurance of secure and reliable communications as well as the reduction of transmission delay and latency. Nevertheless, the task of achieving these objectives presents a complex and demanding challenge. Ensuring secure transmissions while simultaneously minimizing error rates requires the implementation of multistage information processing techniques that integrate coding and encryption methods, which may unfortunately lead to an undesired increase in transmission delay and latency. This letter presents an innovative crypto-coding framework capable of concurrently achieving essential encryption and coding within a single, streamlined process. By leveraging this approach, we aim to address the tradeoff between security, efficiency, and transmission delay, thus contributing to the advancement of secure and efficient communication standards. In this letter, we present a comprehensive evaluation of the proposed scheme, assessing its impact on final system error probability, encryption efficacy, and the complexity of hardware implementation. Our findings shed light on the potential benefits of this approach for future communication standards. [ABSTRACT FROM AUTHOR]

Multi-label Short Text Classification (MSTC) is a challenging subtask of Multi-Label Text Classification (MLTC) for tagging a short text with the most relevant subset of labels from a given set of labels. Recent studies have attempted to address MSTC task using MLTC methods and Pre-trained Language Models (PLM) based fine-tuning approaches, but suffering the low performance from the following three reasons, 1) failure to address the issue of data sparsity of short texts, 2) lack of adaptation to the long-tail distribution of labels in multi-label scenarios and 3) an implicit weakness in the encoding length for PLM, which limits the ability of the prompt learning paradigm. Therefore, in this paper, we propose KSSVPT, a Knowledge and Separating Soft Verbalizer based Prompt Tuning method for MSTC to address the above challenges. Firstly, to mitigate the sparsity issue in short texts, we propose a novel approach that enhances the semantic information of short texts by integrating external knowledge into the soft prompt template. Secondly, we construct a new soft prompt verbalizer for MSTC, called separating soft prompt verbalizer, to adapt to the long-tail distribution issue aggravated by multiple labels. Thirdly, we propose a mechanism of label cluster grouping in building a prompt template to directly alleviate limited encoding length and capture the label correlation. Extensive experiments conducted on six benchmark datasets demonstrate the superiority of our model compared to all competing models for MLTC and MSTC in the tackling of MSTC task. [ABSTRACT FROM AUTHOR]

Purpose: To study the additional value of FRONSAC encoding in 2D and 3D wave sequences, implementing a simple strategy to trajectory mapping for FRONSAC encoding gradients. Theory and Methods: The nonlinear gradient trajectory for each voxel was estimated by exploiting the sparsity of the point spread function in the frequency domain. Simulations and in‐vivo experiments were used to analyze the performance of combinations of wave and FRONSAC encoding. Results: Field mapping using the simplified approach produced similar image quality with much shorter calibration time than the comprehensive mapping schemes utilized in previous work. In‐vivo human brain images showed that the addition of FRONSAC encoding could improve wave image quality, particularly at very high undersampling factors and in the context of limited wave amplitudes. These results were further supported by g‐factor maps. Conclusion: Results show that FRONSAC can be used to improve image quality of wave at very high undersampling rates or in slew‐limited acquisitions. Our study illustrates the potential of the proposed fast field mapping approach. [ABSTRACT FROM AUTHOR]

Purpose: Demonstrate the potential of spatiotemporal encoding (SPEN) MRI to deliver largely undistorted 2D, 3D, and diffusion weighted images on a 110 mT portable system. Methods: SPEN's quadratic phase modulation was used to subsample the low‐bandwidth dimension of echo planar acquisitions, delivering alias‐free images with an enhanced immunity to image distortions in a laboratory‐built, low‐field, portable MRI system lacking multiple receivers. Results: Healthy brain images with different SPEN time‐bandwidth products and subsampling factors were collected. These compared favorably to EPI acquisitions including topup corrections. Robust 3D and diffusion weighted SPEN images of diagnostic value were demonstrated, with 2.5 mm isotropic resolutions achieved in 3 min scans. This performance took advantage of the low specific absorption rate and relative long TEs associated with low‐field MRI. Conclusion: SPEN MRI provides a robust and advantageous fast acquisition approach to obtain faithful 3D images and DWI data in low‐cost, portable, low‐field systems without parallel acceleration. [ABSTRACT FROM AUTHOR]

Purpose: To develop a method that achieves simultaneous brain and neck time‐of‐flight (ToF) magnetic resonance angiography (MRA) within feasible scan timeframes. Methods: Localized quadratic (LQ) encoding is efficient for both signal‐to‐noise ratio (SNR) and in‐flow enhancement. We proposed a spiral multiband LQ method to enable simultaneous intracranial and carotid ToF‐MRA within a single scan. To address the venous signal contamination that becomes a challenge with multiband (MB) ToF, tilt‐optimized non‐saturated excitation (TONE) and partial‐Fourier slice selection (PFSS) were further introduced in the LQ framework to mitigate the venous signal and improve artery contrast. A sequential spiral MB and LQ reconstruction pipeline was employed to obtain the brain‐and‐neck image volumes. Results: The proposed MB method was able to achieve simultaneous brain and neck ToF‐MRA within a 2:50‐min scan. The complementarily boosted SNR‐efficiency by MB and LQ acquisitions allows for the increased spatial coverage without increase in scan time or noticeable compromise in SNR. The incorporation of both TONE and PFSS effectively alleviated the venous contamination with improved small vessel sensitivity. Selection of scan parameters such as the LQ factor and flip angle reflected the trade‐off among SNR, blood contrast, and venous suppression. Conclusions: A novel MB spiral LQ approach was proposed to enable fast intracranial and carotid ToF‐MRA with minimized venous corruption. The method has shown promise in MRA applications where large spatial coverage is necessary. [ABSTRACT FROM AUTHOR]

Purpose: An analytical approach to Bloch simulations for MRI sequences is introduced that enables time efficient calculations of signals free of Monte‐Carlo noise, while providing full flexibility and differentiability in RF flip angles, RF phases, magnetic field gradients and time, as well as insights into image formation. Theory and Methods: We present an implementation of the extended phase graph (EPG) concept implemented in PyTorch, including an efficient state selection algorithm. This simulation is compared with an isochromat‐based Bloch simulation with random isochromat distribution as well as with in vivo measurements using the Pulseq standard. Additionally, different sequences are tested and analyzed using this novel simulation approach. Results: Our simulation outperforms isochromat‐based simulations in terms of computation time as well as signal quality, without exhibiting any kind of Monte‐Carlo noise. The novel approach allows extracting useful information about the magnetization evolution not available otherwise. Our approach extends the common state‐tensor‐based EPG simulation approach for the contribution of dephased states including spatial encoding and T2′$$ {T}_2^{\prime } $$ effects, and arbitrary timing. This allows calculation of echo shapes in addition to echo amplitudes only. Our implementation provides full differentiability in all input parameters allowing gradient descent optimization. Simulation of non‐instantaneous pulses via hard‐pulse approximation is left for future work, as the performance and accuracy characteristics are not yet analyzed. Conclusions: Phase distribution graphs provide fast, differentiable, and spatially encoded Bloch simulations for most MRI sequences. It allows efficient simulation and optimization of arbitrary MRI sequences, which was previously only possible via high isochromat counts. [ABSTRACT FROM AUTHOR]

Approximate computing is a technique for optimising algorithms while taking into account both application quality of service and computational complexity. Video encoding is a computationally difficult operation, and the complexity is growing as new compression standards, such as Versatile Video Coding (VVC), emerge. Because it uses several new coding methods, the VVC standard delivers superior video compression. However, the human perceptual constraint presents a good chance for a new alternative form of computation, such as approximation computing, which allows for some acceptable level of inaccuracy in the output. In this paper, first, the VVC coding tools are analysed to determine the most computationally complex functions of various coding tools as the best candidates to perform approximate computing. Because addition is a frequent operation employed in all reletaed functions of various coding tools and no one would be feasible without an efficient adder circuit, correct approximate computing is done to the adder circuits of these chosen candidate functions. First, the LOA approximation adder is employed, which achieves 9.67x and 5x power and area reductions, respectively, as compared to the original VVC as the baseline, with no discernible influence on subjective quality. The AxMAP framework is then examined to build more effective approximate adders, which lowered the area and power consumption by 14.88% and 9.11%, respectively, over the LOA technique. [ABSTRACT FROM AUTHOR]

Golden jackal optimization (GJO) is inspired by mundane characteristics and collaborative hunting behaviour, which mimics foraging, trespassing and encompassing, and capturing prey to refresh a jackal's position. However, the GJO has several limitations, such as a slow convergence rate, low computational accuracy, premature convergence, poor solution efficiency, and weak exploration and exploitation. To enhance the global detection ability and solution accuracy, this paper proposes a novel complex-valued encoding golden jackal optimization (CGJO) to achieve function optimization and engineering design. The complex-valued encoding strategy deploys a dual-diploid organization to encode the real and imaginary portions of the golden jackal and converts the dual-dimensional encoding region to the single-dimensional manifestation region, which increases population diversity, restricts search stagnation, expands the exploration area, promotes information exchange, fosters collaboration efficiency and improves convergence accuracy. CGJO not only exhibits strong adaptability and robustness to achieve supplementary advantages and enhance optimization efficiency but also balances global exploration and local exploitation to promote computational precision and determine the best solution. The CEC 2022 test suite and six real-world engineering designs are utilized to evaluate the effectiveness and feasibility of CGJO. CGJO is compared with three categories of existing optimization algorithms: (1) WO, HO, NRBO and BKA are recently published algorithms; (2) SCSO, GJO, RGJO and SGJO are highly cited algorithms; and (3) L-SHADE, LSHADE-EpsSin and CMA-ES are highly performing algorithms. The experimental results reveal that the effectiveness and feasibility of CGJO are superior to those of other algorithms. The CGJO has strong superiority and reliability to achieve a quicker convergence rate, greater computation precision, and greater stability and robustness. [ABSTRACT FROM AUTHOR]

According to the attentional boost effect (ABE), detecting a target in a dual-task paradigm can facilitate memory encoding of concurrently presented stimuli, but the detection stimuli always appears transiently (only for 100 ms). In order to gain deeper insights into the mechanisms behind ABE, it is crucial to investigate whether the brief presentation of the detection stimulus is a necessary prerequisite for generating ABE. To address this issue, the present study manipulated the presentation time of the detection stimulus (500-ms vs. 100 ms) and controlled the study-testing time interval (immediate test vs. 24 h test). It turned out that, the short-duration (100 ms) detection condition produced an ABE similar to that of the long-duration (500 ms) condition at the immediate test, but only the ABE produced by the short-duration condition could be continued until the 24 h test (Experiments 1), and the ABE produced by the long-duration condition disappeared in the 24 h test. Nonetheless, when a retrieval practice session was introduced before the 24 h test, the ABE generated by the long-duration condition also extended to the 24 h test (Experiment 2). Regarding ERP components, the short-duration detection condition elicited a more negative mean amplitude in the 500–700 ms time window compared to the long detection-duration condition (Experiment 3). This implies that the duration of the detection stimulus likely plays a central role in influencing ABE through its effect on engram stability, with the short-duration condition potentially leading to a more stable engram compared to the long-duration condition. [ABSTRACT FROM AUTHOR]

Luminescent materials are emerging promising tools for digital data encoding due to their reduced cost, facile reading, robustness, and durability. For this, the use of fluorescent systems that combine multicolor emission with sensitivity to external stimuli will be highly desirable, as they can offer rewritable data encoding together with enhanced encryption security and storage density. Herein a novel strategy is pioneered to reach this goal, which exploits the temperature‐responsive emission properties of the mixtures of regular fluorophores with simple organic phase change materials (PCMs) such as paraffins. By preparing a diversity of microcapsules of these mixtures comprising different dyes and PCMs, thermosensitive fluorescent pixels can be prepared in a low‐cost, straightforward, and scalable manner that exhibits multicolor dynamic emission behavior. These features are capitalized to fabricate pixel arrays that perform two advanced digital encoding operations: high‐security 3D information encryption, and 4D data storage. [ABSTRACT FROM AUTHOR]

Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace. Here we show how these node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We consider the problem of community detection in hypergraphs and develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions and to detect communities more accurately than using either of these types of information alone. The method learns automatically from the input data the extent to which structure and attributes contribute to explain the data, down weighing or discarding attributes if not informative. Our algorithmic implementation is efficient and scales to large hypergraphs and interactions of large numbers of units. We apply our method to a variety of systems, showing strong performance in hyperedge prediction tasks and in selecting community divisions that correlate with attributes when these are informative, but discarding them otherwise. Our approach illustrates the advantage of using informative node attributes when available with higher-order data. Many networked datasets encoded with hypergraphs include additional node attributes, such as roles in a workplace, that can enhance the understanding of higher-order interactions. This study presents a model that integrates higher-order interactions and node attributes for improved community detection in hypergraphs, demonstrating superior accuracy and efficiency in hyperedge prediction and community division tasks. [ABSTRACT FROM AUTHOR]

The surge in image data has significantly increased the pressure on storage and transmission, posing new challenges for image compression technology. The structural texture of an image implies its statistical characteristics, which is effective for image encoding and decoding. Consequently, content-adaptive compression methods based on learning can better capture the content attributes of images, thereby enhancing encoding performance. However, learned image compression methods do not comprehensively account for both the global and local correlations among the pixels within an image. Moreover, they are constrained by rate-distortion optimization, which prevents the attainment of a compact representation of image attributes. To address these issues, we propose a syntax-guided content-adaptive transform framework that efficiently captures image attributes and enhances encoding efficiency. Firstly, we propose a syntax-refined side information module that fully leverages syntax and side information to guide the adaptive transformation of image attributes. Moreover, to more thoroughly exploit the global and local correlations in image space, we designed global–local modules, local–global modules, and upsampling/downsampling modules in codecs, further eliminating local and global redundancies. The experimental findings indicate that our proposed syntax-guided content-adaptive image compression model successfully adapts to the diverse complexities of different images, which enhances the efficiency of image compression. Concurrently, the method proposed has demonstrated outstanding performance across three benchmark datasets. [ABSTRACT FROM AUTHOR]

Extensive research has been conducted in image forensics on the analysis of double-compressed images, particularly in the widely adopted JPEG format. However, there is a lack of methods to detect double compression in the HEIF format, which has recently gained popularity since it allows for reduced file size while maintaining image quality. Traditional JPEG-based techniques do not apply to HEIF due to its distinct encoding algorithms. We previously proposed a method to detect double compression in HEIF images based on Farid's work on coding ghosts in JPEG images. However, this method was limited to scenarios where the quality parameter used for the first encoding was larger than for the second encoding. In this study, we propose a lightweight image classifier to extend the existing model, enabling the identification of double-compressed images without heavily depending on the input image's quantization history. This extended model outperforms the previous approach and, despite its lightness, demonstrates excellent detection accuracy. [ABSTRACT FROM AUTHOR]

Verbal working memory (vWM) is an essential limited‐capacity cognitive system that spans the fronto‐parietal network and utilizes the subprocesses of encoding, maintenance, and retrieval. With the recent widespread use of noninvasive brain stimulation techniques, multiple recent studies have examined whether such stimulation may enhance cognitive abilities such as vWM, but the findings to date remain unclear in terms of both behavior and critical brain regions. In the current study, we applied high‐definition direct current stimulation to the left and right parietal cortices of 39 healthy adults in three separate sessions (left anodal, right anodal, and sham). Following stimulation, participants completed a vWM task during high‐density magnetoencephalography (MEG). Significant neural responses at the sensor‐level were imaged using a beamformer and whole‐brain ANOVAs were used to identify the specific neuromodulatory effects of the stimulation conditions on neural responses serving distinct phases of vWM. We found that right stimulation had a faciliatory effect relative to left stimulation and sham on theta oscillations during encoding in the right inferior frontal, while the opposite pattern was observed for left supramarginal regions. Stimulation also had a faciliatory effect on theta in occipital regions and alpha in temporal regions regardless of the laterality of stimulation. In summary, our data suggest that parietal HD‐tDCS both facilitates and interferes with neural responses underlying both the encoding and maintenance phases of vWM. Future studies are warranted to determine whether specific tDCS parameters can be tuned to accentuate the facilitation responses and attenuate the interfering aspects. [ABSTRACT FROM AUTHOR]

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain‐general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain‐general predictive network that encompasses all well‐established cognitive domains exists remains unanswered. The present meta‐analysis aims to address this gap by testing the hypothesis that PP relies on a large‐scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta‐analytic approach was employed, along with Meta‐Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena‐independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross‐domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large‐scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross‐domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta‐Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain‐general, large‐scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context‐ and stimulus‐dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world. [ABSTRACT FROM AUTHOR]

When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once--such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally and theoretical explanations have been proposed, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms by analyzing neural population recordings from monkeys performing two multistimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location or will be shown at the corresponding location. Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results provide evidence that swap errors emerge during selection of correctly remembered information from working memory, and highlight this selection as a crucial--yet surprisingly brittle--neural process. [ABSTRACT FROM AUTHOR]

We extend the qubit-efficient encoding presented in (Tan et al. in Quantum 5:454, 2021) and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed methods consists of a simplification in varying the number of qubits used to encode correlations as well as a new class of variational circuits which incorporate symmetries thereby reducing sampling overhead, improving numerical stability and recovering the expression of the cost objective as a Hermitian observable. We also propose optimality-preserving methods to reduce variance in real-world data and substitute continuous slack variables. We benchmark our methods against standard QAOA for problems consisting of 16 transactions and obtain competitive results. Our newly proposed variational ansatz performs best overall. We demonstrate tackling problems with 128 transactions on real quantum hardware, exceeding previous results bounded by NISQ hardware by almost two orders of magnitude. [ABSTRACT FROM AUTHOR]

The visual system can compute summary statistics of several visual elements at a glance. Numerous studies have shown that an ensemble of different visual features can be perceived over 50-200 ms; however, the time point at which the visual system forms an accurate ensemble representation associated with an individual's perception remains unclear. This is mainly because most previous studies have not fully addressed time-resolved neural representations that occur during ensemble perception, particularly lacking quantification of the representational strength of ensembles and their correlation with behavior. Here, we conducted orientation ensemble discrimination tasks and electroencephalogram (EEG) recordings to decode orientation representations over time while human observers discriminated an average of multiple orientations. We modeled EEG signals as a linear sum of hypothetical orientation channel responses and inverted this model to quantify the representational strength of orientation ensemble. Our analysis using this inverted encoding model revealed stronger representations of the average orientation over 400-700 ms. We also correlated the orientation representation estimated from EEG signals with the perceived average orientation reported in the ensemble discrimination task with adjustment methods. We found that the estimated orientation at approximately 600-700 ms significantly correlated with the individual differences in perceived average orientation. These results suggest that although ensembles can be quickly and roughly computed, the visual system may gradually compute an orientation ensemble over several hundred milliseconds to achieve a more accurate ensemble representation. [ABSTRACT FROM AUTHOR]

Developing a high-quality, real-time, dense visual SLAM system poses a significant challenge in the field of computer vision. NeRF introduces neural implicit representation, marking a notable advancement in visual SLAM research. However, existing neural implicit SLAM methods suffer from long runtimes and face challenges when modeling complex structures in scenes. In this paper, we propose a neural implicit dense visual SLAM method that enables high-quality real-time reconstruction even on a desktop PC. Firstly, we propose a novel neural scene representation, encoding the geometry and appearance information of the scene as a combination of the basis and coefficient factors. This representation allows for efficient memory usage and the accurate modeling of high-frequency detail regions. Secondly, we introduce feature integration rendering to significantly improve rendering speed while maintaining the quality of color rendering. Extensive experiments on synthetic and real-world datasets demonstrate that our method achieves an average improvement of more than 60% for Depth L1 and ATE RMSE compared to existing state-of-the-art methods when running at 9.8 Hz on a desktop PC with a 3.20 GHz Intel Core i9-12900K CPU and a single NVIDIA RTX 3090 GPU. This remarkable advancement highlights the crucial importance of our approach in the field of dense visual SLAM. [ABSTRACT FROM AUTHOR]

Point cloud completion aims to restore full shapes of objects from partial scans, and a typical network pipeline is AutoEncoder, which has coarse-to-fine refinement modules. Although existing approaches using this kind of architecture achieve promising results, they usually neglect the usage of shallow geometry features in partial inputs and the fusion of multi-stage features in the upsampling process, which prevents network performances from further improving. Therefore, in this paper, we propose a new method with dense interactions between different encoding and decoding steps. First, we introduce the Decoupled Multi-head Transformer (DMT), which implements and integrates semantic prediction and resolution upsampling in a unified network module, which serves as a primary ingredient in our pipeline. Second, we propose an Encoding-aware Coarse Decoder (ECD) that compactly makes the top–down shape-decoding process interact with the bottom–up feature-encoding process to utilize both shallow and deep features of partial inputs for coarse point cloud generation. Third, we design a Stage-aware Refinement Group (SRG), which comprehensively understands local semantics from densely connected features across different decoding stages and gradually upsamples point clouds based on them. In general, the key contributions of our method are the DMT for joint semantic-resolution generation, the ECD for multi-scale feature fusion-based shape decoding, and the SRG for stage-aware shape refinement. Evaluations on two synthetic and three real-world datasets illustrate that our method achieves competitive performances compared with existing approaches. [ABSTRACT FROM AUTHOR]

This paper studies an LSTM-based adaptive transmission method for a 360-degree video and proposes a non-uniform encoding transmission strategy based on LSTM. Our goal is to maximize the user's video experience by dynamically dividing the 360-degree video into tiles of different numbers and sizes, and selecting different bitrates for each tile. This aims to reduce buffering events and video jitter. To determine the optimal number and size of tiles at the current moment, we constructed a dual-layer stacked LSTM network model. This model predicts, in real-time, the number, size, and bitrate of the tiles needed for the next moment of the 360-degree video based on the distance between the user's eyes and the screen. In our experiments, we used an exhaustive algorithm to calculate the optimal tile division and bitrate selection scheme for a 360-degree video under different network conditions, and used this dataset to train our prediction model. Finally, by comparing with other advanced algorithms, we demonstrated the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

Knowledge selection plays a crucial role in knowledge-driven dialogue generation methods, directly influencing the accuracy, relevance, and coherence of generated responses. Existing research often overlooks the handling of disparities between dialogue statements and external knowledge, leading to inappropriate knowledge representation in dialogue generation. To overcome this limitation, this paper proposes an innovative Multi-hop Reasoning Knowledge Selection Module (KMRKSM). Initially, multi-relational graphs containing rich composite operations are encoded to capture graph-aware representations of concepts and relationships. Subsequently, the multi-hop reasoning module dynamically infers along multiple relational paths, aggregating triple evidence to generate knowledge subgraphs closely related to dialogue history. Finally, these generated knowledge subgraphs are combined with dialogue history features and synthesized into comprehensive knowledge features by a decoder. Through automated and manual evaluations, the exceptional performance of KMRKSM in selecting appropriate knowledge is validated. This module efficiently selects knowledge matching the dialogue context through multi-hop reasoning, significantly enhancing the appropriateness of knowledge representation and providing technical support for achieving more natural and human-like dialogue systems. [ABSTRACT FROM AUTHOR]

Spectral encoding is an important way of describing spectral features and patterns. Traditional methods focused on encoding the spectral amplitude information (SAI). Abundant spectral shape information (SSI) was wasted. In addition, traditional statistical encoding methods might only gain local adaptability since different objects should have their own best encoding scales. In order to obtain differential signals from hyperspectral images (HSI) for detecting ground objects correctly, a multi-scale encoding (MSE) method with SSI and two optimization strategies were proposed in this research. The proposed method concentrated on describing the SAI and SSI of the spectral reflectance signals. Four widely used open data sets were adopted to validate the performance of the proposed method. Experimental results indicated that the MSE method with SSI could describe the details of spectral signals accurately. It could obtain excellent performance for detecting similar objects with a small number of samples. In addition, the optimization strategies contributed to obtaining the best result from dynamic encoding scales. [ABSTRACT FROM AUTHOR]

In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms. [ABSTRACT FROM AUTHOR]

Selectively remembering more valuable information can improve memory efficiency. Such value effects have been observed on long-term memory for item-colour binding, but the possible contributory factors are unclear. The current study explored contributions from attention (Experiment 1) and verbal rehearsal (Experiment 2). Across two experiments, memory was superior for item-colour bindings that were associated with high (relative to low) point values at encoding, both in an immediate test and a delayed re-test. When availability of attentional resources was reduced during encoding, value only influenced immediate and not delayed memory (Experiment 1). This indicates that a transient value effect can be obtained with little attentional resources, but attentional resources are involved in creating a longer lasting effect. When articulatory suppression was implemented during encoding (Experiment 2), value effects were somewhat reduced in the immediate test and abolished in the delayed re-test, suggesting a role for verbal rehearsal in value effects on item-colour binding memory. These patterns of value effects did not interact with encoding presentation format (i.e., sequential vs. simultaneous presentation of objects). Together, these results suggest that attentional resources and verbal rehearsal both contribute to value effects on item-colour binding memory, with varying impacts on the durability of these effects. [ABSTRACT FROM AUTHOR]

We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take into account statistical repetitions but also the likelihood of other computable patterns. Our results imply that the assembly index does not offer substantial improvements over existing methods, including traditional statistical ones, and imply that the separation between living and non-living compounds following these methods has been reported before. [ABSTRACT FROM AUTHOR]

Our ability to identify an object is often impaired by the presence of preceding and/or succeeding task-irrelevant items. Understanding this temporal interference is critical for any theoretical account of interference across time and for minimizing its detrimental effects. Therefore, we used the same sequences of 3 orientation items, orientation estimation task, and computational models, to examine temporal interference over both short (<150  ms; visual masking) and long (175–475  ms; temporal crowding) intervals. We further examined how inter-item similarity modifies these different instances of temporal interference. Qualitatively different results emerged for interference of different scales. Interference over long intervals mainly degraded the precision of the target encoding while interference over short intervals mainly affected the signalto-noise ratio. Although both interference instances modulated substitution errors (reporting a wrong item) and were alleviated with dissimilar items, their characteristics were markedly disparate. These findings suggest that different mechanisms mediate temporal interference of different scales. [ABSTRACT FROM AUTHOR]

The significance of a cryptograph method lies in its ability to provide high fidelity, high security, and large capacity. The emergence of metasurface-empowered cryptography offers a promising alternative due to its unparalleled wavefront modulation capabilities and easy integration with traditional schemes. However, the majority of reported strategies suffer from limited capacity as a result of restricted independent information channels. In this study, we present a novel method of cryptography that utilizes a dual-band complex-amplitude meta-hologram. The method allows for the encoding of 225 different patterns by combining a modified visual secret-sharing scheme (VSS) and a one-time-pad private key. The use of complex-amplitude modulation and the modified VSS enhances the quality and fidelity of the decrypted results. Moreover, the transmission of the private key through a separate mechanism can greatly heighten the security, and different patterns can be generated simply by altering the private key. To demonstrate the feasibility of our approach, we design, fabricate, and characterize a meta-hologram prototype. The measured results are in good agreement with the numerical ones and the design objectives. Our proposed strategy offers high security, ultra-capacity, and high fidelity, making it highly promising for applications in information encryption and anti-counterfeiting. [ABSTRACT FROM AUTHOR]

Encryption techniques have been proposed to improve security using chaos theory, mathematical computations, DNA computations, substitution-masking operations, and neural networks. However, existing encryption techniques have focused on intra-block variations to improve security and largely ignored the impact of inter-block variations. Inter-block variations are critical because they ensure that the encryption of a block is affected by its internal variation and also by the variations of all its previous blocks. This can significantly increase the confusion and the technique's ability to resist security attacks. This paper proposes a hybrid technique that addresses the weaknesses of existing techniques by combining both intra- and inter-block variations to greatly improve security. The proposed technique uses chaotic systems to increase confusion and conceal the key. It also incorporates fuzzy encoding and distortion methods that use both inter- and intra-block variations to further strengthen security. The technique was rigorously evaluated using state-of-the-art testing tools. The evaluation showed that the technique is effective because it passed the randomness tests with a high rate that exceeds the threshold for effective techniques, and it has an execution time that is better than state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The introduction of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement, have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth‐intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With an increase in channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel compared to an ideal classical channel. This novel attempt at constructing a quantum communication‐based image transmission system indicates the potential of the approach to be applied to satisfy the ever‐increasing demands of high‐quality media transmission applications. [ABSTRACT FROM AUTHOR]

Dual-tasks at the memory encoding stage have been shown to decrease recall performance and impair concurrent task performance. In contrast, studies on the effect of dual-tasks at the memory retrieval stage observed mixed results. Which cognitive mechanisms are underlying this dual-task interference is still an unresolved question. In the present study, we investigated the influence of a concurrent reaction-time task on the performance in a long-term memory task in two experiments. In Experiment 1, participants performed an auditory-verbal free recall memory task and a visual-manual spatial Stroop task in a single or dual-task condition, either at the encoding or retrieval stage of the memory task. In Experiment 2, we examined the influence of processing conflicts in a concurrent RT task on memory encoding. Both experiments showed detrimental effects on recall accuracy and concurrent RT task performance in dual-task conditions at the encoding stage. Dual-task conditions at the retrieval stage led to a slowdown in recall latency and impaired concurrent RT task performance, but recall accuracy was maintained. In addition, we observed larger Stroop congruency effects in the dual-task conditions, indicating an increased processing conflict. However, in Experiment 2, we analyzed the effect of the processing conflict in a time-locked manner and could not find a significant influence on success of memory encoding. These findings suggest that processes in both tasks share the same limited capacity and are slowed down due to parallel processing, but we could not find evidence that this is further influenced by task-specific processing conflicts. [ABSTRACT FROM AUTHOR]

• Neonatal P1 latencies to speech sounds are associated with rapid serial naming speed at the age of 28 months. • Larger amplitudes and shorter latencies of P1 reflect stronger neural speech encoding and predict better pre-reading skills. • A reduced contribution of the left hemisphere in neural speech encoding is related to poorer pre-reading skills. We longitudinally investigated whether infant P1 and N2 ERPs recorded in newborns and at 28 months could predict pre-reading skills at 28 months and 4–5 years. We recorded ERPs to a pseudoword in newborns and at 28 months in a sample over-represented by infants with familial dyslexia risk. Using multiple linear regression models, we examined P1 and N2 associations with pre-reading skills at 28 months and 4–5 years. Shorter latencies of the newborn P1 predicted faster serial naming at 28 months. Larger amplitudes and shorter latencies of P1 at 28 months predicted better serial naming abilities and auditory working memory across the pre-reading stage. Right-lateralized P1 and N2 were related to poorer pre-reading skills. Infant ERPs, particularly P1, providing information about neural speech encoding abilities, are associated with pre-reading skill development. Infant and early childhood neural speech encoding abilities may work as early predictive markers of reading development and impairment. This study may help to plan early interventions targeting phonological processing to prevent or ameliorate learning deficits. [ABSTRACT FROM AUTHOR]