Encoding Time Research Articles

Holiday or hell? Emotion regulation and memory of depressive symptoms during lockdown.

The ongoing repercussions of the COVID-19 pandemic provide an unparalleled context to examine how distressing events are remembered. Prior theory and research suggest that (a) distress during lockdowns may fade and be remembered as less distressing, or remain salient and be remembered as more distressing, than initially experienced and (b) emotional suppression and cognitive reappraisal may predict these memory biases. We test these possibilities by assessing depressive symptoms and emotion regulation during two lockdowns: at the start of the pandemic (Lockdown 2020) and 17 months later (Lockdown 2021) in a sample of parents with young children (N = 272). We assessed tracking accuracy, directional bias, and projection bias in memory of depressive symptoms in Lockdown 2020, and the moderating role of emotion regulation at the time of encoding (Lockdown 2020) and recall (Lockdown 2021). People experiencing more depressive symptoms later in the pandemic (2021) remembered the start of the pandemic (2020) to involve more depressive symptoms than initially experienced (projection bias). People engaging in greater emotional suppression at recall (2021) were less likely to project their current depressive symptoms onto memory of the prior lockdown (lower projection bias) and remembered feeling lower depressive symptoms than initially experienced (underestimation directional bias). By contrast, people engaging in cognitive reappraisal at recall (2021) remembered feeling greater depressive symptoms than they initially experienced (overestimation directional bias). These unexpected results indicate that emotion regulation may shape memory of emotion during real-life challenges differently than patterns observed in lab-based research and may reflect helpful coping with life events. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Learning-based CU partition prediction for fast panoramic video intra coding

In recent years, Virtual Reality (VR) technology has been applied in various aspects of life. As an indispensable component of VR, panoramic video offers viewers immersive experiences, thereby attracting widespread attention. The high resolution of panoramic video poses significant challenges for its storage and transmission, requiring efficient video compression. The Versatile Video Coding (VVC) exhibits superior coding efficiency, albeit at the expense of a several dozen-fold increase in encoding complexity. In this paper, we propose a fast Coding Unit (CU) partition algorithm for intra-coding based on Support Vector Machine (SVM), which selects appropriate features for online training of multiple classifiers based on frame texture characteristics. Additionally, in response to distortion issues in panoramic video coding, we divide video frames into different regions and adjust the Quantization Parameter (QP) values of CUs within each region to improve the encoding efficiency. Experimental results show that the proposed method achieves an average encoding time reduction of 46.21%, with an average BDBR increase of only 1.01%, achieving a good trade-off between time-saving and encoding efficiency.

AS-APF: Encoding time series as images for human activity recognition with SK-based convolutional networks

AS-APF: Encoding time series as images for human activity recognition with SK-based convolutional networks

Complexity analysis of the VVenC versus VVC encoder

The joint video experts team (JVET) has recently finalized a next-generation open-source video codec, called versatile video coding (VVC). The new standard presents a higher gain in the run time and rate compressions. Based on the reference software (VTM, VVC Test Model) of VVC, an optimized encoder was developed these last years resulting in a fast and efficient encoder, called Fraunhofer versatile video encoder (VVenC). Based on the Bjontegaard methodology and the GPROF profiling tool, this paper presents a technical complexity analysis and comparison of both VVC and VVenC. The appropriate comparisons cover the percentage taken by each block in terms of processing time and the resulting whole encoding time. The peak signal-to-noise ratio (PSNR) and bit rate between VVC and VVenC encoders based on the common test conditions manual are also analyzed and compared. The profiling results show that the VVenC encoder presents a maximum gain of runtime and bit rate of 90% and 20% respectively in classes A and D test sequences, compared to the VVC encoder.

Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks.

Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six functional magnetic resonance imaging studies, resulting in a sample of $155$ ($77$ women, $25 \pm 5$ years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared with categorical representations.

Diagnostic Performance of Pointwise Encoding Time Reduction with Radial Acquisition Subtraction-based MR Angiography in the Follow-up of Intracranial Aneurysms after Clipping.

While follow-up assessment of clipped aneurysms (CAs) using magnetic resonance angiography (MRA) can be challenging due to susceptibility artifacts, anovel MRA sequence pointwise encoding time reduction with radial acquisition (PETRA) subtraction-based MRA, has been developed to reduce these artifacts. The aim of the study was to validate the diagnostic performance of PETRA-MRA by comparing it with digital subtraction angiography (DSA) as areference for follow-up of CAs using a 3T MR scanner. Patients with clipping who underwent both PETRA-MRA and DSA between September 2019 and December 2021 were retrospectively included. Two neuroradiologists independently reviewed with the reconstructed images of PETRA-MRA to assess the visibility of the arteries around the clips and aneurysm recurrence or remnants of CA using a3-point scale. The diagnostic accuracy of PETRA-MRA was evaluated in comparison to DSA. The study included 34patients (28females, mean age 59 ± 9.6years) with 48CAs. The PETRA-MRA allowed visualization of the parent vessels around the clips in 98% of cases, compared to 39% with time-of-flight (TOF) MRA (p < 0.0001). The DSA confirmed 14 (29.2%) residual or recurrent aneurysms. The PETRA-MRA demonstrated a high accuracy, specificity, positive predictive value, and negative predictive value of 99.2%, 100%, 100%, and 97.8%, respectively, while the sensitivity was 66.7%. This retrospective study demonstrates that PETRA-MRA provides excellent visibility of adjacent vessels near clips and has ahigh diagnostic accuracy in detecting aneurysm remnants or recurrences in CAs. Further prospective studies are warranted to establish its utility as areliable alternative for follow-up after clipping.

Abnormal state prediction of flotation process based on dual attention mechanism and multivariate information fusion

AbstractPredicting abnormal conditions in flotation processes is vital for safety, efficiency, and product quality. However, existing studies lack predictions of abnormal working conditions in flotation processes and neglect temporal information in data. To address this, this paper proposes a novel approach for predicting abnormal work conditions in flotation processes. It utilizes a dual attention mechanism and multivariate information fusion. Features are extracted from froth images using the Xception model, a pre‐trained convolutional neural network. These features are combined with flotation process monitoring variables, creating fused data. An encoder and decoder time feature seq2seq (EDTF‐seq2seq) model with time and feature attention modules enables end‐to‐end information fusion and work condition prediction. The attention modules assign weights to each feature point, capturing the time–feature relationship and improving prediction accuracy. Four sets of experiments using real flotation process data validate the effectiveness of the proposed method, achieving favourable prediction accuracy.

Latitude-Redundancy-Aware All-Zero Block Detection for Fast 360-Degree Video Coding.

The sphere-to-plane projection of 360-degree video introduces substantial stretched redundant data, which is discarded when reprojected to the 3D sphere for display. Consequently, encoding and transmitting such redundant data is unnecessary. Highly redundant blocks can be referred to as all-zero blocks (AZBs). Detecting these AZBs in advance can reduce computational and transmission resource consumption. However, this cannot be achieved by existing AZB detection techniques due to the unawareness of the stretching redundancy. In this paper, we first derive a latitude-adaptive redundancy detection (LARD) approach to adaptively detect coefficients carrying redundancy in transformed blocks by modeling the dependency between valid frequency range and the stretching degree based on spectrum analysis. Utilizing LARD, a latitude-redundancy-aware AZB detection scheme tailored for fast 360-degree video coding (LRAS) is proposed to accelerate the encoding process. LRAS consists of three sequential stages: latitude-adaptive AZB (L-AZB) detection, latitude-adaptive genuine-AZB (LG-AZB) detection and latitude-adaptive pseudo-AZB (LP-AZB) detection. Specifically, L-AZB refers to the AZB introduced by projection. LARD is used to detect L-AZB directly. LG-AZB refers to the AZB after hard-decision quantization and zeroing redundant coefficients. A novel latitude-adaptive sum of absolute difference estimation model is built to derive the threshold for LG-AZB detection. LP-AZB refers to the AZB in terms of rate-distortion optimization considering redundancy. A latitude-adaptive rate-distortion model is established for LP-AZB detection. Experimental results show that LRAS can achieve an average total encoding time reduction of 25.85% and 20.38% under low-delay and random access configurations compared to the original HEVC encoder, with only 0.16% and 0.13% BDBR increases and 0.01dB BDPSNR loss, respectively. The transform and quantization time savings are 60.13% and 59.94% on average.

Fast mode decision algorithm for AVS3 intra prediction

AVS3 is the latest generation of video coding standard developed independently by China. It obtains 50% bitrate savings under the same visual quality as compared with the previous generation coding standard AVS2, largely due to availability of numerous new coding tools. However, these newly introduced coding tools increase the computational complexity significantly and thus severely hinder the real applications of AVS3. In light of the above, this paper presents an innovative fast intra mode decision method that exploits texture information. In particular, for each Prediction Unit (PU), the Sobel operator is employed to obtain the gradient information, which helps to produce candidate intra prediction modes. In addition, to trade off coding efficiency against computational complexity, the PU content flatness is exploited to determine the number of candidate modes. The proposed method is implemented on HPM-4.0.1, and the experimental results demonstrate that it obtains 35.2% encoding time saving with moderate compression performance loss.

It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication.

Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity. The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2s), considerably increasing communication speed, and decreasing the cognitive load of BCI users. To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination. The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials. The study demonstrates that an encoding time as short as 2s can enable immediate, efficient, and convenient fNIRS-BCI communication.

Reduced encoder time complexity using enhanced adaptive multiple transform selection in versatile video coding

&lt;span&gt;When compressing a video, the architecture is provided with the width and height of the frame along with the video as input. The multiple transform selection (MTS) techniques are used to compress video. Traditional discrete cosine transforms (DCT) and other trigonometric (sine and cosine) transforms, such as DCT-8 and DST-7, are included in the pool of transforms. For the best outcomes, two additional transforms-DST-2 and DCT-5-were also utilized to compress any remaining video frames. To improve coding effectiveness, peak signal-to-noise ratio (PSNR), and delay, 2D Transforms are broken into 1D Directional Transforms for block sizes ranging from 4×4 to 64×64. The temporal complexity was greatly decreased by adding DST-2 to the existing video test model (VTM). The MTS’s adaptive multiple transform (AMT) scheme already included a pool of transforms, but DST-2 was introduced as a fourth transform. Encoder time complexity was reduced by 22% on average without compromising PSNR and bitrate.&lt;/span&gt;

Paired decision trees for fast intra decision in H.266/VVC

The Versatile Video Coding (H.266/VVC) has demonstrated significant improvements to its predecessor High-Efficiency Video Coding (H.265/HEVC) in terms of compression efficiency. One of its advancements is attributed to the introduction of the QuadTree with nested Multi-type Tree (QTMT). However, the accompanying computing complexity challenges practical applications. To this end, this paper proposes a fast encoding algorithm to accelerate the encoding process of H.266/VVC while maintaining its compression efficiency as much as possible. Essentially, the Coding Unit (CU) partition can be modeled as a classification problem, and thus conventional machine learning methods such as the decision tree can be used to solve it. Previous decision tree-based works generally use one decision tree to dyadically select one partition from a set of candidate partition modes, easily leading to a local optimum. In contrast, this work models the classification from a new viewpoint and devises Paired Decision Trees (PDT) to tackle it. Specifically, the first decision tree determines whether to divide the current CU or not, and the second decision tree further determines whether to divide the CU horizontally or vertically. However, inaccurate results in the first decision tree can be propagated to the second one, resulting in error accumulation. To address this issue, we introduce the Indeterminate Space to both decision trees and perform Rate-Distortion Optimization (RDO) on samples falling within this space. Experimental results show that compared with H.266/VVC reference software VTM-15.0, the proposed PDT method saves 52.86% encoding time on average while maintaining a small BDBR increase of only 1.36%, which significantly outperforms previous works.

Fraction Execution Resolver Using a Hybrid Multi-CPU/GPU Encoding Scheme

Modern video coding standards make use of sub-pixel motion estimation to improve the video quality and reduce the bitrate. It is known that the fraction motion estimation (FME) part follows the integer motion estimation (IME) and adds an extra computational overhead due to the interpolation and the additional motion searches. In this paper, we propose a fraction execution resolver (FER) algorithm that lets the encoder skip the fraction part when specific criteria are met by introducing a preliminary fast test decision point (pFTDP) function for the IME part. If the pFTDP returns zero motion vectors (MVs) and the displacement search area center is also zero, then the fraction part is skipped. The pFTDP decision maker is executed only once, when a 2N × 2N block is first met, while all subsequent blocks follow this initial decision either by receiving the necessary MVs and RD from the pFTDP function or by using the precalculated IME values from the GPU kernel. For our experiments, we use a multithreaded CPU environment that also makes use of GPUs only for the integer part. Our evaluations provide a greater than 1600% encoding time saving at its peak in comparison with the default HEVC sequential mode and ideally a saving of greater than 2286% for still video frame sequences. The total average speedup for both Class A and Class B video sequences is ×13.45. The gain of the FER itself is more than ×3.9 compared with the same multithreaded setup environment. The PSNR and bitrate overhead observed are proportional to the tiling scheme used and are more related to the way CABAC works internally. The FER’s negative effects on coding efficiency are proven to be negligible. A balance between speed and quality achieved by using a lower tiling pattern is shown to minimize the negative effects of the encoding scheme pattern. The experimental results confirm the validity of our motivation, namely, that we can benefit from a software fraction execution resolver without any extra hardware costs. The gain is further increased when video sequences have more static blocks than others.

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

Anti-Hebbian plasticity in the motor cortex promotes defensive freezing

Regional brain activity often decreases from baseline levels in response to external events, but how neurons develop such negative responses is unclear. To study this, we leveraged the negative response that develops in the primary motor cortex (M1) after classical fear learning. We trained mice with a fear conditioning paradigm while imaging their brains with standard two-photon microscopy. This enabled monitoring changes in neuronal responses to the tone with synaptic resolution through learning. We found that M1 layer 5 pyramidal neurons (L5 PNs) developed negative tone responses within an hour after conditioning, which depended on the weakening of their dendritic spines that were active during training. Blocking this form of anti-Hebbian plasticity using an optogenetic manipulation of CaMKII activity disrupted negative tone responses and freezing. Therefore, reducing the strength of spines active at the time of memory encoding leads to negative responses of L5 PNs. In turn, these negative responses curb M1's capacity for promoting movement, thereby aiding freezing. Collectively, this work provides a mechanistic understanding of how area-specific negative responses to behaviorally relevant cues can be achieved.

Protocol Efficiency Using Multiple Level Encoding in Quantum Secure Direct Communication Protocol

One of the objectives of information security is to maintain the confidentiality and integrity of the information by ensuring that information is transferred in a way that is secure from any listener or attacker. There was no comparison experiment conducted in earlier studies regarding different level encoding performance towards the multiphoton technique. In Quantum Secure Direct Communication (QSDC), when unpolarized light enters into the polarizer, the light value will be changed into a different value when it hit the Half Wave Plate (HWP) along the quantum communication channel. The multiphoton technique in the earlier study is particular to transmission time for data transfer encoding and extra time for polarizers to change polarization angles, both of which contribute to longer transmission times. With four different sizes of qubits, the three simulation experiments are carried out using Python coding with 2,4 and 8 levels of encoding. Experiment results demonstrate that the most efficient average photon transmission derived from 18 qubit size ranges from 98.71% to 98.73% depending on encoding level. With 18 qubit size, the four-level encoding result has the highest average efficiency, followed by the eight-level and two-level encodings, respectively. 4-level encoding exhibits the highest average photon efficiency between 2 and 8-level encoding.

Hippocampal ensemble dynamics and memory performance are modulated by respiration during encoding

During offline brain states, such as sleep and memory consolidation, respiration coordinates hippocampal activity. However, the role of breathing during online memory traces remains unclear. Here, we show that respiration can be recruited during online memory encoding. Optogenetic manipulation was used to control activation of the primary inspiratory rhythm generator PreBötzinger complex (PreBötC) in transgenic mice. When intermittent PreBötC-induced apnea covered the object exploration time during encoding, novel object detection was impaired. Moreover, the mice did not exhibit freezing behavior during presentation of fear-conditioned stimuli (CS+) when PreBötC-induced apnea occurred at the exact time of encoding. This apnea did not evoke changes in CA3 cell ensembles between presentations of CS+ and conditioned inhibition (CS−), whereas in normal breathing, CS+ presentations produced dynamic changes. Our findings demonstrate that components of central respiratory activity (e.g., frequency) during online encoding strongly contribute to shaping hippocampal ensemble dynamics and memory performance.

Fast CU Decision Algorithm Based on CNN and Decision Trees for VVC

Compared with the previous generation of High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) introduces a quadtree and multi-type tree (QTMT) partition structure with nested multi-class trees so that the coding unit (CU) partition can better match the video texture features. This partition structure makes the compression efficiency of VVC significantly improved, but the computational complexity is also significantly increased, resulting in an increase in encoding time. Therefore, we propose a fast CU partition decision algorithm based on DenseNet network and decision tree (DT) classifier to reduce the coding complexity of VVC and save more coding time. We extract spatial feature vectors based on the DenseNet network model. Spatial feature vectors are constructed by predicting the boundary probabilities of 4 × 4 blocks in 64 × 64 coding units. Then, using the spatial features as the input of the DT classifier, through the classification function of the DT classifier model, the top N division modes with higher prediction probability are selected, and other division modes are skipped to reduce the computational complexity. Finally, the optimal partition mode is selected by comparing the RD cost. Our proposed algorithm achieves 47.6% encoding time savings on VTM10.0, while BDBR only increases by 0.91%.

Pre-associative item encoding influences associative memory: Behavioral and ERP evidence.

It is unknown whether the manner with which an item is encoded in isolation, immediately before it is encoded into an inter-inter association, influences associative memory. We therefore presented the items of to-be-encoded associative pairings sequentially and manipulated how each first item of a pair was encoded (before associative encoding could begin). Furthermore, we recorded ERPs during memory encoding to investigate the neurocognitive processes that might relate pre-associative item encoding to subsequent associative memory performance. Behaviorally, we found that pre-associative item elaboration (vs. no elaboration) led to a memory tradeoff-enhanced item memory relative to impaired associative memory. This tradeoff likely reflected that item elaboration reduced cognitive resources for ensuing associative encoding, indexed by a reduced P300 and frontal slow wave at the time of associative encoding. However, frontal slow wave subsequent memory effects measured during pre-associative item encoding revealed that, for a given item, greater semantic elaboration was related to better item and associative memory while greater visual elaboration was related to better item and worse associative memory. Thus, there are likely two opposing ways in which pre-associative item encoding can influence associative memory: (1) by depleting encoding resources to impair associative memory and (2) by scaffolding inter-item associations to enhance associative memory. When item encoding occurs immediately before associative encoding, it appears that the temporary depletion of encoding resources is more important in determining later memory performance. Future research should compare the independent effects of resource depletion and encoding strategy during pre-associative item encoding.

IOT monitoring membrane computing based on quantum inspiration to enhance security in cloud network

Human computation is a technology inspired by nature. The majority of its computational sources are physiologically motivated computations. The existing calculation, on the other hand, uses some conventional techniques to complete the work. In the cloud, IaaS is one of the most basic services, offering a wide range of functions to a large number of customers. Modelling a number of species using P systems with different membrane structure types to predict the number of individuals is a major advantage of the proposed work. Even though it provides large number services (or) features to the user always it faces many numerous obstacles in Infrastructure service in cloud network, such as Authentication and Authorization, Data leakage and monitoring, End to End encryption, there is a risk of lack of security. Traditional encryption approaches also employ efficient methods to build cloud network security. It does, however, have some flaws in terms of IaaS. The work proposed membrane infrastructure based on the quantum inspiration. It will used to overcome such challenges. It is possible to minimize current constraints by employing this strategy. The paper proposes to employ basic distributed computational methods in an open and natural setting to provide membrane systems as a suitable framework for cloud environments for providing unique security among cloud users. They employ a framework for defending against intruder attacks. It is possible to address key cloud computing security difficulties by integrating Quantum Computing between the membrane environments with Cloud Computing technology. The proposed effectiveness and outcomes are monitored with the help of Internet of Things (IoT). This level introduces a revolutionary method to cloud security by incorporating quantum protocols into a membrane environment. The proposed SEDFA is the better method with all types of datasets and the Communication Cost by 5%, Encoding Time (ET) by 2 s, and Decoding Time (DT) by 0.5 s.