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Purpose: Spatiotemporal encoding (SPEN) MRI offers a unique alternative to address image distortion problems in echo planar acquisition‐based techniques, at portable low‐field systems that lack multiple receiver coils. However, existing 2‐π multislice SPEN schemes fail to keep consistent SNRs and contrasts with different numbers of slice settings. This work proposes a new multislice SPEN scheme (SPENms) to achieve stable quality imaging in portable low‐field MRI systems. Methods: The proposed SPENms includes the insertion of one selective π pulse and one non‐selective π pulse, closely arranged together, before the frequency‐swept π pulse in the original 2D SPEN sequence. Theoretical simulations and experiments on phantoms and human brains were conducted to validate its SNR and contrast performances under different parameters compared to the existing 2‐π multislice SPEN scheme. Results: Both simulations and experiments demonstrate the consistent image quality of SPENms with different scanning parameters and targets, as well as good distortion resistance and scan efficiency. Robust diffusion weighted multislice SPEN images of diagnostic value were also highlighted. Conclusion: SPENms provides a robust fast echo planar acquisition approach to obtain multislice 2D images with less distortions, consistent SNRs and contrasts at portable low‐field MRI systems. [ABSTRACT FROM AUTHOR]

Background: Accurate musculoseletal ultrasound (MSKUS) image segmentation is crucial for diagnosis and treatment planning. Compared with traditional segmentation methods, deploying deep learning segmentation methods that balance segmentation efficiency, accuracy, and model size on edge devices has greater advantages. Purpose: This paper aims to design a MSKUS image segmentation method that has fewer parameters, lower computation complexity and higher segmentation accuracy. Methods: In this study, an attention mechanism‐based lightweight UNet (AML‐UNet) is designed to segment target muscle regions in MSKUS images. To suppress the transmission of redundant feature, Channel Reconstruction and Spatial Attention Module is designed in the encoding path. In addition, considering the inherent characteristic of MSKUS image, Multiscale Aggregation Module is developed to replace the skip connection architecture of U‐Net. Deep supervision is also introduced to the decoding path to refine predicted masks gradually. Our method is evaluated on two MSKUS 2D‐image segmentation datasets, including 3917 MSKUS and 1534 images respectively. In the experiments, a five‐fold cross‐validation method is adopted in ablation experiments and comparison experiments. In addition, Wilcoxon Signed‐Rank Test and Bonferroni correction are employed to validate the significance level. 0.01 was used as the statistical significance level in our paper. Results: AML‐UNet yielded a mIoU of 84.17% and 90.14% on two datasets, representing a 3.38% (p<0.001$p<0.001$) and 3.48% (p<0.001$p<0.001$) over the Unext model. The number of parameters and FLOPs are only 0.21M and 0.96G, which are 1/34 and 1/29 of those in comparison with UNet. Conclusions: Our proposed model achieved superior results with fewer parameters while maintaining segmentation efficiency and accuracy compared to other methods. [ABSTRACT FROM AUTHOR]

ABSTRACT This article deals with the design and defense issues of false data injection attack in distributed state estimation. At each time step, each sensor sends its estimate to neighboring sensors. To verify the security of the received data, a column vector composed of state estimate differences is used as the detection variable. Based on the data directly obtained by the attacker in the wireless communication network, an attack strategy is established to degrade the system estimation performance while not being detected by the detector. Under the attack strategy, the update of the estimation error covariance is analyzed and the optimal attack parameters are obtained. To enhance the security of the system, a periodic dynamic encoding mechanism is proposed to prevent attackers from eavesdropping on original data, which consists of two stages: preparation stage and periodic encoding stage. It is proved that the proposed encoding mechanism can destroy the stealthiness of the attack, thus ensuring the detection ability of the detector. Numerical simulations are given to verify the results of the theoretical analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT This study adopted a cognitive‐processing approach to explore the role of planning modes in L2 writing production and the allocation of attentional resources by L2 learners during planning and writing. A total of 120 L2 learners in an ESL writing course at a university in the United States were assigned to one of four conditions: (1) written planning (10 min note‐taking + 40 min writing), (2) visual planning (10 min using visual tools + 40 min writing), (3) collaborative planning (10 min discussing with peers + 40 min writing), or (4) no planning (40 min writing without pre‐task planning). After completing the writing task, participants took part in a stimulated recall interview. The findings showed that different planning modes had significant effects on the syntactic and lexical complexity and accuracy of L2 writing compared to no planning. However, planning modes did not affect L2 writing fluency. The stimulated recalls revealed that attentional resources primarily focused on content and organization during pre‐task planning, regardless of the planning mode used. Attention was also directed toward lexical and grammatical encodings during pre‐task planning and writing. Additionally, regardless of the planning mode, attentional resources were predominantly dedicated to monitoring during the writing stage. This mixed‐methods study provided deeper insights into the cognitive processes involved in planning and writing. [ABSTRACT FROM AUTHOR]

Information conveyed through feedback enables individuals to learn new routines and better adapt to their environment. However, the neural mechanisms of rule‐related information of feedback have not been fully elucidated. Herein, we quantified the effect of informative value on feedback via a rule induction task (RIT), in which participants were required to find the correct sorting rule based on feedback. To disengage the effects of informative value and valence on feedback in the RIT, a control task was developed in which feedback only involved the valence aspect and no reference for subsequent selections. We measured power and intertrial phase clustering (ITPC) values via EEG to determine the neural mechanisms of rule‐related feedback. The results revealed that (1) differences in oscillatory activities between positive and negative feedback were only observed during the control task, and no such effect was found in the RIT task. This finding suggests that the participants paid more attention to rule‐related information than to the correctness of feedback during rule learning. (2) The task differences under positive or negative feedback were associated with the delta‐theta and alpha‐beta bands, and this pattern was similar within the frontal and parietal regions. These findings suggest that the processing of rule‐related information of feedback relies on broad frequency bands within the frontoparietal cortex to facilitate rule information integration. In summary, these findings indicate that multiple frequency bands are involved in encoding the informative value aspect of feedback, and individuals rely on this aspect of feedback rather than valence during rule learning. [ABSTRACT FROM AUTHOR]

Achieving high‐performance spectral imaging over large fields of view (FOV) has remained challenging, particularly with conventional bulky optical systems. Here, a compact transversely dispersive metasurface array is introduced, designed for wide FOV spectral imaging. The system achieves efficient aperture encoding via distinct phase gradients tailored to different incident angles. A genetic algorithm optimizes the spectral reconstruction process, allowing for high‐accuracy imaging with a single snapshot. The system demonstrates 120° FOV spectral imaging across 11 channels in the 400–700 nm visible range, using a 91 × 91 metasurface array with 81.5% efficiency in light focusing and first‐order diffraction. This approach represents an advancement over traditional methods, offering broad potential in material classification, chemical analysis, remote sensing, and defect detection. [ABSTRACT FROM AUTHOR]

To accelerate the fractal decoding process, a minimum domain block set (MDBS)‐based fast fractal decoding method is proposed here. In fractal encoding process, it is found that there exists a MDBS which can provide the best‐matched domain blocks for all range blocks. In the decoding process, MDBS is first identified before the first iteration. Then, only the range blocks inside MDBS are reconstructed in each of the first to penultimate iterations, and the computations of reconstructing the remaining range blocks outside MDBS can be saved to speedup the decoding process. Finally, all range blocks are reconstructed to obtain the decoded image in the last iteration. Experimental results show that about 5%–17% of total computations in decoding process can be saved. [ABSTRACT FROM AUTHOR]

The original online fountain codes discard a large number of symbols that do not meet the requirements at the decoder. To improve channel utilization, this article proposes a new online fountain code. In the completion phase, the proposed code improves the receiving rules of encoded symbols, that is, the encoded symbols discarded in the original online fountain codes are selectively cached. Moreover, an optimal degree selection strategy of encoded symbols is obtained in the proposed scheme. The valid degree range of the proposed strategy is also analyzed, leading to an upper bound of cached events which eventually limits the number of feedbacks. The theoretical analysis and simulation results reveal that the proposed scheme outperforms two state‐of‐the‐art online fountain codes in terms of overhead factors, number of feedback transmissions, and encoding/decoding efficiency. [ABSTRACT FROM AUTHOR]

Summary: Although a period of sleep seems to benefit the retention of declarative memories, recent studies have challenged both the size of this effect and its active influence on memory consolidation. This study aimed to further investigate the effect of sleep and its time dependency on the consolidation of factual information. In a within‐subjects design, 48 participants (Mage = 24.37 ± 4.18 years, 31F) were asked to learn several facts in a multi‐sensory "flashcard‐like" memory task at 21:00 hours (sleep first condition) or at 09:00 hours (wake first condition). Then, in each condition, participants performed an immediate recall test (T0), and two delayed tests 12 hr (T1) and 24 hr (T2) later. Participants' sleep was recorded at their homes with a portable device. Results revealed that memory retention was better after a night of sleep compared with wakefulness, regardless of the delay from encoding (a few hr versus 12+ hr), but the sleep effect was modest. The decline in memory during the wake period following sleep was smaller compared with the decline observed during the 12 hr of wakefulness after encoding. However, after 24 hr from the encoding, when all participants experienced a period of both sleep and wakefulness, memory performance in the two conditions was similar. Overall, our data suggest that sleep exerts a small, yet beneficial, influence on memory retention by likely reducing interference and actively stabilizing memory traces. [ABSTRACT FROM AUTHOR]

Given a finitely generated group G$G$, the possible actions of G$G$ on the real line (without global fixed points), considered up to semi‐conjugacy, can be encoded by the space of orbits of a flow on a compact space (Y,Φ)$(Y, \Phi)$ naturally associated with G$G$ and uniquely defined up to flow equivalence, that we call the Deroin space of G$G$. We show a realisation result: every expansive flow (Y,Φ)$(Y, \Phi)$ on a compact metrisable space of topological dimension 1, satisfying some mild additional assumptions, arises as the Deroin space of a finitely generated group. This is proven by identifying the Deroin space of an explicit family of groups acting on suspension flows of subshifts, which is a variant of a construction introduced by the second and fourth authors. This result provides a source of examples of finitely generated groups satisfying various new phenomena for actions on the line, related to their rigidity/flexibility properties and to the structure of (path‐)connected components of the space of actions. [ABSTRACT FROM AUTHOR]

Most of the existing RGB‐T salient object detection methods are usually based on dual‐stream encoding single‐stream decoding network architecture. These models always rely on the quality of fusion features, which often focus on modality‐shared features and overlook modality‐specific features, thus failing to fully utilise the rich information contained in multi‐modality data. To this end, a modality separate tri‐stream net (MSTNet), which consists of a tri‐stream encoding (TSE) structure and a tri‐stream decoding (TSD) structure is proposed. The TSE explicitly separates and extracts the modality‐shared and modality‐specific features to improve the utilisation of multi‐modality data. In addition, based on the hybrid‐attention and cross‐attention mechanism, we design an enhanced complementary fusion module (ECF), which fully considers the complementarity between the features to be fused and realises high‐quality feature fusion. Furthermore, in TSD, the quality of uni‐modality features is ensured under the constraint of supervision. Finally, to make full use of the rich multi‐level and multi‐scale decoding features contained in TSD, the authors design the adaptive multi‐scale decoding module and the multi‐stream feature aggregation module to improve the decoding capability. Extensive experiments on three public datasets show that the MSTNet outperforms 14 state‐of‐the‐art methods, demonstrating that this method can extract and utilise the multi‐modality information more adequately and extract more complete and rich features, thus improving the model's performance. The code will be released at https://github.com/JOOOOKII/MSTNet. [ABSTRACT FROM AUTHOR]

This article investigates the morpho‐syntactic marking of main polar questions, which is achieved across Italo‐Romance by crosslinguistically different strategies. I argue that the interpretation of main polar questions is related to the activation of a dedicated functional head that encodes the relevant formal feature within the left‐periphery. In particular, I explore the possibility that the process of clause typing in unembedded yes/no questions is linked in Italo‐Romance to the activation of a functional projection located in the right periphery of the CP‐layer, which I call Polarity Phrase. This hypothesis relies on the intuition that negation and affirmation can be reduced to a more abstract category encoding the open polarity of the sentence, which can be underspecified for either negative or positive value. [ABSTRACT FROM AUTHOR]

Recent progress in physics‐based character control has made it possible to learn policies from unstructured motion data. However, it remains challenging to train a single control policy that works with diverse and unseen motions, and can be deployed to real‐world physical robots. In this paper, we propose a two‐stage technique that enables the control of a character with a full‐body kinematic motion reference, with a focus on imitation accuracy. In a first stage, we extract a latent space encoding by training a variational autoencoder, taking short windows of motion from unstructured data as input. We then use the embedding from the time‐varying latent code to train a conditional policy in a second stage, providing a mapping from kinematic input to dynamics‐aware output. By keeping the two stages separate, we benefit from self‐supervised methods to get better latent codes and explicit imitation rewards to avoid mode collapse. We demonstrate the efficiency and robustness of our method in simulation, with unseen user‐specified motions, and on a bipedal robot, where we bring dynamic motions to the real world. [ABSTRACT FROM AUTHOR]

Intra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single‐neuron spike detection require high processing rates and power, hindering the up‐scaling for neurons‐population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold‐based neural activity detection. MIS leverages analogue multi‐state switching properties of metal‐oxide RRAM to compress neural inputs by encoding above‐threshold events in resistance displacement, facilitating efficient data down‐sampling in the post‐processing, enabling low‐power, high‐channel systems. Initially tested on spikes and local field potentials, here MIS is adapted to process multi‐unit activity envelope (eMUA)—the envelope of entire spiking activity—which has recently been proposed as crucial input for real‐time neuro‐prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal‐oxide devices: Pt/TiOx/Pt and TiN/HfOx/TiN, proving its platform‐agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, it is shown that it can reduce total system power consumption to below 1 µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional complementary metal‐oxide‐semiconductor (CMOS) front‐ends. This eMUA‐MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use. [ABSTRACT FROM AUTHOR]

Purpose: Balanced steady‐state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice‐encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice‐encoded calibration scan in addition to the 2D data, without being nearly as time‐demanding as our previous method. This coil localization method is titled UNfolding Coil Localized Errors from an imperfect slice profile using a Structured Autocalibration Matrix (UNCLE SAM). Methods: Retrospective and prospective evaluations were carried out. Both featured a 2D acquisition and a separate slice‐encoded calibration of the center in‐plane k‐space lines across all desired slice‐encoding steps. Results: Retrospective results featured a slice‐by‐slice comparison of the slice‐encoded images with UNCLE SAM. UNCLE SAM's subtraction from the slice‐encoded image was compared with a subtraction from the flow‐corrupted 2D image, to demonstrate UNCLE SAM's capability to unfold outflowing spins. UNCLE SAM's comparison with slice encoding showed that UNCLE SAM was able to unfold up to 74% of what slice encoding achieved. Prospective results showed significant reduction in outflow effects with only a marginal increase in scan time from the 2D acquisition. Conclusions: We developed a method that effectively unfolds most outflowing spins from corrupting the target slice and does not require the explicit use of slice‐encoding gradients. This development offers a method to reduce most outflow effects from the target slice within a clinically feasible scan duration compared with the fully sampled slice‐encoding technique. [ABSTRACT FROM AUTHOR]

Purposes: To enhance the functional capability of MRI, this study aims to develop a novel MR elastography (MRE) sequence that achieves rapid acquisition without distortion artifacts. Methods: A displacement‐encoded stimulated echo (DENSE) with multiphase acquisition scheme was used to capture wave images. A center‐out golden‐angle stack‐of‐stars sampling pattern was introduced for improved SNR and data incoherence. A combination of Hadamard encoding and interleaved multislab acquisition schemes was used to increase the acquisition efficiency of MRE data with multiple directions and phase offsets. A generalized parallel‐imaging and compressed‐sensing method was further applied to accelerate the acquisition process. The imaging results of the proposed sequence were compared with those from six gradient echo (GRE)/EPI/DENSE–based MRE sequences via phantom and brain acquisitions. Results: The proposed sequence achieved a 6‐fold acceleration compared with GRE MRE. With the application of a conventional parallel‐imaging and compressed‐sensing algorithm, the scanning speed was further accelerated by 8‐fold, matching the speed of EPI‐based MRE. Phantom tests revealed small variances in stiffness measurements across the seven sequences (< 9.23%). The proposed sequence exhibited a higher contrast‐to‐noise ratio (1.38) than the two EPI‐based sequences (0.61/0.76) and similar to GRE‐based sequences (1.34/1.22/1.58). Brain imaging validated the effectiveness of the proposed sequence in accurate stiffness estimation and distortion artifact avoidance. Conclusion: A rapid DENSE‐based MRE sequence with interleaved multislab acquisition and Hadamard encoding was developed at a speed matching EPI‐based sequences, without compromising SNR or introducing distortion artifacts. [ABSTRACT FROM AUTHOR]

Purpose: To develop an iterative deep learning (DL) reconstruction with spatio‐coil regularization and multichannel k‐space data consistency for accelerated cine imaging. Methods: This study proposes a Spatio‐Coil Regularized DL (SCR‐DL) approach for iterative deep learning reconstruction incorporating multicoil information in data consistency and regularizer. SCR‐DL uses shift‐invariant convolutional kernels to interpolate missing k‐space lines and reconstruct individual coil images, followed by a regularizer that operates simultaneously across spatial and coil dimensions using learned image priors. At 8‐fold acceleration, SCR‐DL was compared with Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA), sensitivity encoding (SENSE)‐based DL and spatio‐temporal regularized (STR)–DL reconstruction. In the retrospective undersampled cine, images were quantitatively evaluated using normalized mean square error (NMSE) and structural similarity index measure (SSIM). Additionally, agreement for left‐ventricular ejection fraction and left‐ventricular mass were assessed using prospectively accelerated cine images at 2‐fold and 8‐fold accelerations. Results: The SCR‐DL algorithm successfully reconstructed highly accelerated cine images. SCR‐DL had significant improvements in NMSE (0.03 ± 0.02) and SSIM (91.4% ± 2.7%) compared with GRAPPA (NMSE: 0.09 ± 0.04, SSIM: 69.9% ± 11.1%; p < 0.001), SENSE‐DL (NMSE: 0.07 ± 0.04, SSIM: 86.9% ± 3.2%; p < 0.001), and STR‐DL (NMSE: 0.04 ± 0.03, SSIM: 90.0% ± 2.5%; p < 0.001) with retrospective undersampled cine. Despite the 3‐fold reduction in scan time, there was no difference between left‐ventricular ejection fraction (59.8 ± 4.5 vs. 60.8 ± 4.8, p = 0.46) or left‐ventricular mass (73.6 ± 19.4 g vs. 73.2 ± 19.7 g, p = 0.95) between R = 2 and R = 8 prospectively accelerated cine images. Conclusions: SCR‐DL enabled highly accelerated cardiac cine imaging, significantly reducing breath‐hold time. Compared with GRAPPA or SENSE‐DL, images reconstructed with SCR‐DL showed superior NMSE and SSIM. [ABSTRACT FROM AUTHOR]

Research on low‐light image enhancement focuses on improving image quality in dim conditions. Recently, deep learning has driven significant advancements, with many studies using neural networks to enhance low‐light images. However, most focus on complex network designs to increase nonlinearity, often neglecting the implicit information from local image transformations. This paper introduces an improved U‐net‐based method for low‐light enhancement, retaining the original encoding network and adding branch links in the decoding network. The method uses attention feature fusion to handle image noise and gradients separately, adjusting brightness through a gradient‐adaptive transform. This approach optimizes performance using loss functions like peak signal‐to‐noise ratio and colour consistency. Unlike previous methods, the approach emphasizes extracting implicit information from image gradients, achieving enhancement that aligns with the original brightness distribution. The result is enhanced images with high detail similarity to the original, achieved through end‐to‐end inference in experiments. [ABSTRACT FROM AUTHOR]

In this paper, we view the equivariant orientation theory of equivariant vector bundles from the lenses of equivariant Picard spectra. This viewpoint allows us to identify, for a finite group G$\mathrm{G}$, a precise condition under which an R$\mathrm{R}$‐orientation of a G$\mathrm{G}$‐equivariant vector bundle is encoded by a Thom class. Consequently, we are able to construct a generalization of the first Stiefel–Whitney class of a "homogeneous" G$\mathrm{G}$‐equivariant bundle with respect to an E∞G$\mathbb {E}_\infty ^{\mathrm{G}}$‐ring spectrum R$\mathrm{R}$. As an application, we show that the 2‐fold direct sum of any homogeneous bundle is HA̲G$\mathrm{H}\underline{\mathcal {A}}_{\mathrm{G}}$‐orientable, where A̲G$\underline{\mathcal {A}}_{\mathrm{G}}$ is the Burnside Mackey functor. We notice that HA̲G$\mathrm{H}\underline{\mathcal {A}}_{\mathrm{G}}$‐orientability is equivalent to HZ̲$\mathrm{H}\underline{\mathbb {Z}}$‐orientability when the order of G$\mathrm{G}$ is odd. When the order of G$\mathrm{G}$ is even, we show that a G$\mathrm{G}$‐equivariant analog of the tautological line bundle over RP∞$\mathbb {RP}^\infty$ is HZ̲$\mathrm{H}\underline{\mathbb {Z}}$‐orientable but not HA̲G$\mathrm{H}\underline{\mathcal {A}}_{\mathrm{G}}$‐orientable. [ABSTRACT FROM AUTHOR]

In this paper, on the design process of a multiparty quantum key agreement protocol within a d$d$‐dimensional Hilbert space is elaborated upon. A circular‐type multiparty quantum key agreement protocol based on the generalized Bell state is introduced. To enhance security against external attacks, n$n$ decoy states into the transmission process is incorporated. Transmission sequences of the generalized Bell state and decoy states are passed between participants. The participants then encode their secret information into the corresponding particles. Ultimately, all participants are able to derive the same key. In addition, a combination of d$d$‐dimensional Pauli operators is utilized, making the proposed protocol feasible with current technology. Analysis and protection against intercept‐resend attack, entangle‐measure attack and dishonest participants attacks, demonstrating the feasibility of the protocol in a d$d$‐dimensional Hilbert space. The protocol has certain advantages over other protocols in terms of a comprehensive consideration of security and efficiency. [ABSTRACT FROM AUTHOR]

The article argues that, linguistically speaking, there is no uniform class of personal taste predicate. There is an F(un)‐type PPT that takes infinitive complements expressing events. In effect, these PPTs are predicates of events involving participants. There is also a T(asty)‐type that cannot take an infinitive complement and does not enter into the alternation pattern of the F‐type predicates. These predicates express dispositions of objects to generate experiences or responses. Some experiencer/judge is involved in the truth of the respective kinds of claims, but for different reasons, and in neither case is such a role encoded linguistically as an index for the predicate. [ABSTRACT FROM AUTHOR]

Federated analytics (FA) over spatial data with local differential privacy (LDP) has attracted considerable research attention recently. Existing solutions for this problem mostly employ a uniform grid (UG) structure, which recursively decomposes the whole spatial domain into fine‐grained regions in the distributed setting. In each round, the sampled clients perturb their locations using a random response mechanism with a fixed probability. This approach, however, cannot encode the client's location effectively and will lead to ill‐suited query results. To address the deficiency of existing solutions, we propose LDP‐FSRQ, a spatial range query algorithm that relies on a hybrid spatial structure composed of the UG and quad‐tree with nonuniform perturbation (NUP) probability to encode and perturb clients' locations. In each iteration of LDP‐FSRQ, each client adopts the quad‐tree to encode his/her location into a binary string and uses four local perturbation mechanisms to protect the encoded string. Then, the collector prunes the quad‐tree of the current round according to the clients' reports and shares the pruned tree with the clients of the next round. We demonstrate the application of LDP‐FSRQ on Beijing, Landmark, Check‐in, and NYC datasets, and the experimental results show that our approach outperforms its competitors in terms of queries' utility. [ABSTRACT FROM AUTHOR]

ABSTRACT Recently, significant progress has been made in completing static knowledge graphs. However, knowledge tends to evolve with time, and static knowledge graph completion (KGC) methods struggle to capture the changes. Therefore, temporal knowledge graph (TKG) reasoning has become a focus of research. Most existing TKG methods incorporate temporal information into triplets and transform them into KGC tasks, ignoring the important influence of time information and implicit relationships between entities. In this paper, we propose a new method called TD‐RKG, which addresses the challenges of temporal variability and implicit entity correlations based on a dynamic fusion representation learning approach. The method consists of four modules: dynamic local recurrent encoding layer, dynamic implicit encoding layer, dynamic global information attention layer and decoding layer. Experimental results on three benchmark datasets demonstrate substantial improvements in TD‐RKG across multiple evaluation metrics. [ABSTRACT FROM AUTHOR]

A new image encryption algorithm based on the triple chaotic maps is proposed to deal with the issues of inadequate security and low encryption efficiency. Coloured images consist of three linked channels used in the scheme. This method uses different keys to break the correlations between adjacent pixels in each channel. The triple chaotic maps are Lorenz, 2D‐Logistic, and Henon. First, the plain image is split into RGB channels to encrypt each channel separately. Second, the triple chaotic maps generate two groups of keys. The first group of keys performs a pixel permutation, resulting in scrambled channels used as input for the following step. Finally, the second group of keys is used to diffuse the scrambled channels independently, resulting in diffused channels, which are then merged to obtain a cipher image. The triple chaotic maps of different orders generate the cipher image with great unpredictability and security. The security is evaluated using various measures. The results demonstrated a high level of security attained by successfully encrypting coloured images. Recent encryption algorithms are compared in terms of entropy, correlation coefficients, and attack robustness. The proposed method provided outstanding security and outperformed existing image encryption algorithms. [ABSTRACT FROM AUTHOR]

More than 80% of video coding times are consumed by motion estimation calculations, which are the most complex aspect of the process. This method eliminates temporal redundancies in a video sequence to achieve maximum compression. Numerous efforts have been made to bring calculations closer to real time, yielding fruitful results. This study proposes a fast subpixel motion estimation algorithm for video encoding with fewer search points. This method employs the capabilities of human visual systems (HVSs), physical motion characteristics of real‐world objects, and special image information from successive frames. The number of search points (NSP) using the statistical data of the movement of the blocks in the frames of video sequences is reduced to apply fewer calculations to the system while maintaining the quality of images. Therefore, it is possible to approach fast and real‐time calculations instead of time‐consuming algorithms by accurately modeling this algorithm. [ABSTRACT FROM AUTHOR]

ABSTRACT The cross‐race recognition deficit (CRD) involves poorer recognition of faces perceived as cross‐race compared to faces perceived as the same race. In this literature, research has examined social and perceptual encoding in the CRD separately. Recent research comparing these strategies shows that encoding faces based on social information (personality traits) enhances face recognition relative to encoding faces based on perceptual information (facial features), with a similar effect for both same‐race and cross‐race faces. Expanding on this research, we conducted three experiments (one pre‐registered), manipulating the perception of face race within participants and using different social and perceptual encoding questions to examine their impacts on the CRD. Results showed that social encoding equally facilitated the recognition of faces perceived as same‐race and cross‐race replicating previous research (Experiment 1). However, perceptual encoding divergently impacted face recognition, improving cross‐race but disrupting same‐race (Experiment 2) or having comparable effects to standard encoding (Experiment 3). We discuss these findings and their implications for existing CRD theories. [ABSTRACT FROM AUTHOR]

Summary: Sleep consolidates declarative memory after deep but not shallow incidental encoding, but little is known about this form of consolidation. One unexplored area is the extent to which the amount of exposure to incidentally encoded information affects consolidation processes. In two experiments, we manipulated the number of times information was presented. In Experiment 1, participants encoded words either one or three times in a deep or shallow incidental encoding task and completed a surprise recognition test after sleep or wake. Sleep consolidated information after deep encoding after one and three exposures, but not after shallow encoding. In Experiment 2, we explored the relationship between sleep architecture and memory after deep encoding. There was a trend for accuracy to be negatively related to N1 sleep, and reaction time to be negatively related to slow‐wave sleep for words encoded once; however, the correlations did not survive corrections for multiple comparisons. These results are discussed with respect to active and passive consolidation processes. [ABSTRACT FROM AUTHOR]

Information security holds paramount importance in the contemporary information society. Here, a novel cryptography method is proposed based on the multi‐wavelength complex‐amplitude metasurface, which can achieve a high security level by generating a frequency‐selective key as an additional layer of protection. Moreover, the quality of the decrypted holographic images can be significantly improved by employing the complex‐amplitude encoding method and the modified visual secret sharing scheme, which adopts a new superposition operator to prevent fidelity loss. As a proof‐of‐concept demonstration, a prototype metasurface sample is designed, fabricated, and characterized, where the measured results agree very well with the numerical ones and the design goals. The proposed method can reinforce security level and improve fidelity of the decrypted message, rendering it appealing for applications in communication and anti‐counterfeiting. [ABSTRACT FROM AUTHOR]

The emergence of learning‐based motion in‐betweening techniques offers animators a more efficient way to animate characters. However, existing non‐generative methods either struggle to support long transition generation or produce results that lack diversity. Meanwhile, diffusion models have shown promising results in synthesizing diverse and high‐quality motions driven by text and keyframes. However, in these methods, keyframes often serve as a guide rather than a strict constraint and can sometimes be ignored when keyframes are sparse. To address these issues, we propose a lightweight yet effective diffusion‐based motion in‐betweening framework that generates animations conforming to keyframe constraints. We incorporate keyframe constraints into the training phase to enhance robustness in handling various constraint densities. Moreover, we employ relative positional encoding to improve the model's generalization on long range in‐betweening tasks. This approach enables the model to learn from short animations while generating realistic in‐betweening motions spanning thousands of frames. We conduct extensive experiments to validate our framework using the newly proposed metrics K‐FID, K‐Diversity, and K‐Error, designed to evaluate generative in‐betweening methods. Results demonstrate that our method outperforms existing diffusion‐based methods across various lengths and keyframe densities. We also show that our method can be applied to text‐driven motion synthesis, offering fine‐grained control over the generated results. [ABSTRACT FROM AUTHOR]

Neural rendering bakes global illumination and other computationally costly effects into the weights of a neural network, allowing to efficiently synthesize photorealistic images without relying on path tracing. In neural rendering approaches, G‐buffers obtained from rasterization through direct rendering provide information regarding the scene such as position, normal, and textures to the neural network, achieving accurate and stable rendering quality in real‐time. However, due to the use of G‐buffers, existing methods struggle to accurately render transparency and refraction effects, as G‐buffers do not capture any ray information from multiple light ray bounces. This limitation results in blurriness, distortions, and loss of detail in rendered images that contain transparency and refraction, and is particularly notable in scenes with refracted objects that have high‐frequency textures. In this work, we propose a neural network architecture to encode critical rendering information, including texture coordinates from refracted rays, and enable reconstruction of high‐frequency textures in areas with refraction. Our approach is able to achieve accurate refraction rendering in challenging scenes with a diversity of overlapping transparent objects. Experimental results demonstrate that our method can interactively render high quality refraction effects with global illumination, unlike existing neural rendering approaches. Our code can be found at https://github.com/ziyangz5/CrystalNet [ABSTRACT FROM AUTHOR]

We propose a novel ray reordering technique designed to accelerate the ray tracing process by encoding and sorting rays prior to traversal. Our method, called "hierarchy cut code", involves encoding rays based on the cuts of the hierarchical acceleration structure, rather than relying solely on spatial coordinates. This approach allows for a more effective adaptation to the acceleration structure, resulting in a more reliable and efficient encoding outcome. Furthermore, our research identifies "bounding drift" as a major obstacle in achieving better acceleration effects using longer sorting keys in existing reordering methods. Fortunately, our hierarchy cut code successfully overcomes this issue, providing improved performance in ray tracing. Experimental results demonstrate the effectiveness of our approach, showing up to a 1.81 times faster secondary ray tracing compared to existing methods. These promising results highlight the potential for further enhancement in the acceleration effect of reordering techniques, warranting further exploration and research in this exciting field. [ABSTRACT FROM AUTHOR]

Indoor scene point clouds exhibit diverse distributions and varying levels of sparsity, characterized by more intricate geometry and occlusion compared to outdoor scenes or individual objects. Despite recent advancements in 3D point cloud analysis introducing various network architectures, there remains a lack of frameworks tailored to the unique attributes of indoor scenarios. To address this, we propose DSGI‐Net, a novel indoor scene point cloud learning network that can be integrated into existing models. The key innovation of this work is selectively grouping more informative neighbor points in sparse regions and promoting semantic consistency of the local area where different instances are in proximity but belong to distinct categories. Furthermore, our method encodes both semantic and spatial relationships between points in local regions to reduce the loss of local geometric details. Extensive experiments on the ScanNetv2, SUN RGB‐D, and S3DIS indoor scene benchmarks demonstrate that our method is straightforward yet effective. [ABSTRACT FROM AUTHOR]

Neural surface reconstruction methods have demonstrated their ability to recover 3D surfaces from multiple images. However, current approaches struggle to rapidly achieve high‐fidelity surface reconstructions. In this work, we propose TaNSR, which inherits the speed advantages of multi‐resolution hash encodings and extends its representation capabilities. To reduce training time, we propose an efficient numerical gradient computation method that significantly reduces additional memory access overhead. To further improve reconstruction quality and expedite training, we propose a feature aggregation strategy in volume rendering. Building on this, we introduce an adaptively weighted aggregation function to ensure the network can accurately reconstruct the surface of objects and recover more geometric details. Experiments on multiple datasets indicate that TaNSR significantly reduces training time while achieving better reconstruction accuracy compared to state‐of‐the‐art nerual implicit methods. [ABSTRACT FROM AUTHOR]

Fault‐tolerant quantum computation requires error‐correcting codes that enable reliable universal quantum operations. This study introduces a novel approach that executes the logical Hadamard with low‐space requirements while preserving the original definition of logical operators within the framework of the rotated surface codes. Our method leverages a boundary deformation method to rotate the logical qubit transformed by transversal Hadamard. Following this, the original encoding of the logical qubit is reinstated through logical flip‐and‐shift operations. The estimated space–time cost for a logical Hadamard operation with a code distance d is 5d2 + 3d2. The efficiency enhancement of the proposed method is approximately four times greater than those of previous approaches, regardless of the code distance. Unlike the traditional method, implementing a logical Hadamard requires only two patches instead of seven. Furthermore, the proposed method ensures the parallelism of quantum circuits by preventing interferences between adjacent logical data qubits. [ABSTRACT FROM AUTHOR]

In spiking neural networks (SNNs), artificial sensor neurons are crucial for converting real‐world analog information into encoded spikes. However, existing SNNs face challenges due to the inefficient implementation of input sensor neurons. Here, this study proposes an SNN‐compatible spike mode sensor, designed to directly convert analog current signals into real‐time encoded spikes, feeding the SNN concurrently. The input sensor neuron is realized using a stable neuron circuit employing a threshold switching (TS) memristor and secondary order RC block. This design enables time delay‐free spike firing, operates at low voltage, and offers a wide signal sensing range. Furthermore, this study presents an expression delineating the relationship between the pulse emission properties of the circuit and the parameters of its components, laying the basis for circuit components design and development. Analytical analysis confirms the sensor's efficacy in implementing rate‐based and time‐to‐first spike encoding schemes. Integrating the sensor into SNNs as the input layer for image training and recognition tasks yields an impressive accuracy of 87.58% on the MNIST dataset, showcasing its applicability as a crucial interface between the physical world and the SNN framework. [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]

Source monitoring (SM) refers to attributing sources of information. There are various methods for measuring SM in children. We searched the PubMed, PsycINFO, Embase, Web of Science and Cochrane Library databases from their inception to February 24, 2023, to summarize the methods and measures of SM in children, identifying 141 studies. The procedure for the SM tasks encompassed encoding, interval and testing. The encoding tasks were classified into ground‐based activities (n = 67), computer‐based experiments (n = 42) and mixed methods (n = 34). The testing approaches were categorized into old/new recognition and source discrimination, n‐alternative forced‐choice, yes/no questions and recall/direct questions. Among 10 commonly used indicators, source‐correct, source‐incorrect, discrimination score and source accuracy were recommended to present the SM function. We also discussed the advantages and disadvantages of different SM methods in children, providing a reference for researchers to design and select SM measurements that meet their research objectives. [ABSTRACT FROM AUTHOR]

Recently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array. [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]

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]

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]

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]

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]

Purpose: To evaluate the use of pre‐excitation gradients for eddy current–nulled convex optimized diffusion encoding (Pre‐ENCODE) to mitigate eddy current–induced image distortions in diffusion‐weighted MRI (DWI). Methods: DWI sequences using monopolar (MONO), ENCODE, and Pre‐ENCODE were evaluated in terms of the minimum achievable echo time (TE min$$ {}_{\mathrm{min}} $$) and eddy current‐induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers (N=6$$ N=6 $$). Results: Pre‐ENCODE provided a shorter TE min$$ {}_{\mathrm{min}} $$ than MONO (71.0 ±$$ \pm $$ 17.7ms vs. 77.6 ±$$ \pm $$ 22.9ms) and ENCODE (71.0 ±$$ \pm $$ 17.7ms vs. 86.2 ±$$ \pm $$ 14.2ms) in 100%$$ \% $$ of the simulated cases for a commercial 3T MRI system with b‐values ranging from 500 to 3000 s/mm 2$$ {}^2 $$ and in‐plane spatial resolutions ranging from 1.0 to 3.0mm 2$$ {}^2 $$. Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre‐ENCODE than with MONO (10.1%$$ \% $$ vs. 22.7%$$ \% $$, p=6−5$$ p={6}^{-5} $$) and comparable to ENCODE (10.1%$$ \% $$ vs. 10.4%$$ \% $$, p=0.12$$ p=0.12 $$). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 [0.28,1.45]×10−3$$ \times 1{0}^{-3} $$ mm 2$$ {}^2 $$/s vs. 0.38 [0.28,1.45]×10−3$$ \times 1{0}^{-3} $$mm 2$$ {}^2 $$/s, p=0.25$$ p=0.25 $$) and increased in edge brain pixels (0.80 [0.17,1.49]×10−3$$ \times 1{0}^{-3} $$ mm 2$$ {}^2 $$/s vs. 0.70 [0.18,1.48]×10−3$$ \times 1{0}^{-3} $$mm 2$$ {}^2 $$/s, p=0.02$$ p=0.02 $$) for MONO compared to Pre‐ENCODE. Conclusion: Pre‐ENCODE mitigated eddy current–induced image distortions for diffusion imaging with a shorter TE min$$ {}_{\mathrm{min}} $$ than MONO and ENCODE. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the utility of up to second‐order motion‐compensated diffusion encoding in multi‐shot human brain acquisitions. Methods: Experiments were performed with high‐performance gradients using three forms of diffusion encoding motion‐compensated through different orders: conventional zeroth‐order–compensated pulsed gradients (PG), first‐order–compensated gradients (MC1), and second‐order–compensated gradients (MC2). Single‐shot acquisitions were conducted to correlate the order of motion compensation with resultant phase variability. Then, multi‐shot acquisitions were performed at varying interleaving factors. Multi‐shot images were reconstructed using three levels of shot‐to‐shot phase correction: no correction, channel‐wise phase correction based on FID navigation, and correction based on explicit phase mapping (MUSE). Results: In single‐shot acquisitions, MC2 diffusion encoding most effectively suppressed phase variability and sensitivity to brain pulsation, yielding residual variations of about 10° and of low spatial order. Consequently, multi‐shot MC2 images were largely satisfactory without phase correction and consistently improved with the navigator correction, which yielded repeatable high‐quality images; contrarily, PG and MC1 images were inadequately corrected using the navigator approach. With respect to MUSE reconstructions, the MC2 navigator‐corrected images were in close agreement for a standard interleaving factor and considerably more reliable for higher interleaving factors, for which MUSE images were corrupted. Finally, owing to the advanced gradient hardware, the relative SNR penalty of motion‐compensated diffusion sensitization was substantially more tolerable than that faced previously. Conclusion: Second‐order motion‐compensated diffusion encoding mitigates and simplifies shot‐to‐shot phase variability in the human brain, rendering the multi‐shot acquisition strategy an effective means to circumvent limitations of retrospective phase correction methods. [ABSTRACT FROM AUTHOR]

High‐field preclinical functional MRI (fMRI) is enabled the high spatial resolution mapping of vessel‐specific hemodynamic responses, that is single‐vessel fMRI. In contrast to investigating the neuronal sources of the fMRI signal, single‐vessel fMRI focuses on elucidating its vascular origin, which can be readily implemented to identify vascular changes relevant to vascular dementia or cognitive impairment. However, the limited spatial and temporal resolution of fMRI is hindered hemodynamic mapping of intracortical microvessels. Here, the radial encoding MRI scheme is implemented to measure BOLD signals of individual vessels penetrating the rat somatosensory cortex. Radial encoding MRI is employed to map cortical activation with a focal field of view (FOV), allowing vessel‐specific functional mapping with 50 × 50 µm2 in‐plane resolution at a 1 to 2 Hz sampling rate. Besides detecting refined hemodynamic responses of intracortical micro‐venules, the radial encoding‐based single‐vessel fMRI enables the distinction of fMRI signals from vessel and peri‐vessel voxels due to the different contribution of intravascular and extravascular effects. [ABSTRACT FROM AUTHOR]