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We give a geometric condition for two finite subgroups G ≃ G ′ of the plane Cremona group Cr 2 (C) to be conjugate. The argument is based on the properties of a "Burnside-type" ring Ω (P 2 , G) encoding rational G -surfaces. [ABSTRACT FROM AUTHOR]

• Bayesian brain theory (BBT) mathematically formalizes the dynamic of information processing through belief encoding and perceptual inference. • Beliefs are represented as probability densities about the latent causes of sensory data encoded in neural networks. • Organization of beliefs into hierarchies allows for the representation of a multitude of spatiotemporal scales and causal relationships within a unified probability space. • Belief-updating occurs through synaptic plasticity, relying on the timing of neuronal spikes and the balance of glutamatergic excitation and inhibition. • Precision (inverse of the variance) reflect the signal-to-noise ratio of sensory signals and is encoded by monoaminergic neuromodulators. Bayesian brain theory, a computational framework grounded in the principles of Predictive Processing (PP), proposes a mechanistic account of how beliefs are formed and updated. This theory assumes that the brain encodes a generative model of its environment, made up of probabilistic beliefs organized in networks, from which it generates predictions about future sensory inputs. The difference between predictions and sensory signals produces prediction errors, which are used to update belief networks. In this article, we introduce the fundamental principles of Bayesian brain theory, and show how the brain dynamics of prediction are associated with the generation and evolution of beliefs. [ABSTRACT FROM AUTHOR]

Judgments of Learning (JOLs) have been repeatedly shown to be reactive on memory. However, the specific processes underlying JOL reactivity differ based on the type of stimuli participants study and the method by which their memory is assessed. Recently, item-specific encoding has been proposed as a mechanism explaining JOL reactivity on word list learning. To test this account, participants studied categorised and uncategorised word lists (Experiments 1A/1B) or DRM lists (Experiment 2) while providing item-level JOLs, global JOLs, or silently reading each word. Across experiments, item-level JOLs improved correct memory for all list types but only when recognition testing was used (Experiments 1B and 2). Separately, global JOLs improved free-recall of categorised but not uncategorised lists (Experiment 1A) but were non-reactive on correct recognition (Experiments 1B and 2). Finally, Experiment 2 found that global but not item-level JOLs increased false recognition in the DRM false memory illusion. Taken together, when JOLs are elicited separately for each word, they improve memory via item-specific processes. However, when JOLs emphasise list-wise relations (e.g., global JOLs), reactivity may instead reflect a relational encoding process. [ABSTRACT FROM AUTHOR]

The effects of speech-based variables on the immediate serial recall (ISR) task constitute fundamental evidence underpinning the concept of the Phonological Loop component of Working Memory. Somewhat surprisingly, the Phonological Loop has yet to be applied to the immediate free recall (IFR) task although both tasks share similar memoranda and presentation methods. We believe that the separation of theories of ISR and IFR has contributed to the historical divergence between the Working Memory and Episodic Memory literature. We review more recent evidence showing that the two tasks are approached by participants in similar ways, with similar encoding and rehearsal strategies, and are similarly affected by manipulations of word length, phonological similarity, articulatory suppression/concurrent articulation, and irrelevant speech/sound. We present new analyses showing that the outputs of the two tasks share similar runs of successive items that include the first and last items– which we term start- and end-sequences, respectively—that the remaining residual items exhibit strong recency effects, and that start- and end-sequences impose constraints on output order that help account for error transposition gradients in ISR. Such analyses suggest that similar mechanisms might convey serial order information in the two tasks. We believe that recency effects are often under-appreciated in theories of ISR, and IFR mechanisms could generate error transpositions. We hope that our review and new analyses encourage greater theoretical integration between ISR and IFR and between the Working Memory and Episodic Memory literature. [ABSTRACT FROM AUTHOR]

This study investigates the growth of an avascular tumour described through the interchange of mass among its constituents and the production of inelastic distortions induced by growth itself. A key contribution of this research examines the role of non-local diffusion arising from the complex and heterogeneous tumour micro-environment. In our context, the non-local diffusion is enhanced by a variable-order fractional operator that incorporates crucial information about regions of limited nutrient availability within the tissue. Our research also focuses on the identification of an evolution law for the growth-induced inelastic distortions recast through the identification of non-conventional forces dual to suitable kinematic descriptors associated with the growth tensor. The establishment of such evolution law stems from examining the dissipation inequality and subsequently determining a posteriori connections between the inelastic distortions and the source/sink terms in the mass balance laws. To gain insights into the dynamics of tumour growth and its response to the proposed modelling framework, we first study how the variables governing the tissue evolution are affected by the introduction of the new growth law. Second, we investigate how regions of limited diffusion within the tumour, encoded into a fractional operator of variable-order, influence its growth. [ABSTRACT FROM AUTHOR]

Cayley graph is a graph that encodes the abstract structure of a group. It gives a way of encoding information about a group in a graph. On the other hand, hypergroup is a generalization of group in which the composition of any two elements is a non-empty set. The purpose of this paper is to find a suitable generalization of Cayley graphs to cover hypergroups. More precisely, we introduce generalized Cayley graphs over hypergroups, study their properties and find a simple tool to construct large connected GCH-graphs from smaller GCH -graphs by using graph product. [ABSTRACT FROM AUTHOR]

In 1976, Deligne and Lusztig realized the representation theory of finite groups of Lie type inside étale cohomology of certain algebraic varieties. Recently, a p -adic version of this theory started to emerge: there are p -adic Deligne–Lusztig spaces, whose cohomology encodes representation theoretic information for p -adic groups – for instance, it partially realizes the local Langlands correspondence with characteristic zero coefficients. However, the parallel case of coefficients of positive characteristic ℓ ≠ p has not been inspected so far. The purpose of this article is to initiate such an inspection. In particular, we relate cohomology of certain p -adic Deligne–Lusztig spaces to Vignéras's modular local Langlands correspondence for GL n. [ABSTRACT FROM AUTHOR]

Quantum secret sharing (QSS) is a fundamental primitive in quantum cryptography. The existing QSS protocols are either (n , n) -threshold or (t , n) -threshold access structure, where n denotes the number of players and t denotes the threshold number of players. Here, we introduce a novel verifiable QSS scheme that executes a restricted hyperstar access structure by using entangled states. Our method involves encoding selected quantum states and their corresponding measurements to yield outcomes of either + 1 or − 1. Furthermore, we give the validation of the scheme through the system of equations derived from the measurements, which then guarantees the honesty of the participants. At last, we also analyze the security of our scheme in two primary quantum attacks, such as external attacks and internal attacks. [ABSTRACT FROM AUTHOR]

Interictal epileptiform spikes (spikes) and epileptogenic focus are strongly correlated. However, partial spikes are insensitive to epileptogenic focus, which restricts epilepsy neurosurgery. Therefore, identifying spike subtypes that are strongly associated with epileptogenic focus (traceable spikes) could facilitate their use as reliable signal sources for accurately tracing epileptogenic focus. However, the sparse firing phenomenon in the transmission of intracranial neuronal discharges leads to differences within spikes that cannot be observed visually. Therefore, neuro-electro-physiologists are unable to identify traceable spikes that could accurately locate epileptogenic focus. Herein, we propose a novel sparse spike feature learning method to recognize traceable spikes and extract discrimination information related to epileptogenic focus. First, a multilevel eigensystem feature representation was determined based on a multilevel feature representation module to express the intrinsic properties of a spike. Second, the sparse feature learning module expressed the sparse spike multi-domain context feature representation to extract sparse spike feature representations. Among them, a sparse spike encoding strategy was implemented to effectively simulate the sparse firing phenomenon for the accurate encoding of the activity of intracranial neurosources. The sensitivity of the proposed method was 97.1%, demonstrating its effectiveness and significant efficiency relative to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Few-shot object detection (FSOD) studies how to detect novel objects with few annotated examples effectively. Recently, it has been demonstrated that decent feature embeddings, including the general feature embeddings that are more invariant to visual changes and the specific feature embeddings that are more discriminative for different object classes, are both important for FSOD. However, current methods lack appropriate mechanisms to sensibly cooperate both types of feature embeddings based on their importance to detecting objects of novel classes, which may result in sub-optimal performance. In this paper, to achieve more effective FSOD, we attempt to explicitly encode both general and specific feature embeddings using learnable tensors and apply a Transformer to help better incorporate them in FSOD according to their relations to the input object features. We thus propose a Transformer-based general and specific embedding learning (T-GSEL) method for FSOD. In T-GSEL, learnable tensors are employed in a three-stage pipeline, encoding feature embeddings in general level, intermediate level, and specific level, respectively. In each stage, we apply a Transformer to first model the relations of the corresponding embedding to input object features and then apply the estimated relations to refine the input features. Meanwhile, we further introduce cross-stage connections between embeddings of different stages to make them complement and cooperate with each other, delivering general, intermediate, and specific feature embeddings stage by stage and utilizing them together for feature refinement in FSOD. In practice, a T-GSEL module is easy to inject. Extensive empirical results further show that our proposed T-GSEL method achieves compelling FSOD performance on both PASCAL VOC and MS COCO datasets compared with other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Learned video compression has developed rapidly and shown competitive rate-distortion performance compared with the latest traditional video coding standard H.266 (VVC). However, existing works were restricted to fixed prediction direction and GoP size. The inflexibility on prediction structure hinders learned video compression towards optimal compression efficiency in diverse motion scenarios. In this article, we propose to advance learned video compression with adaptive prediction structure decision. Specifically, we propose a unified compression framework that supports both forward prediction and bi-directional prediction. The framework can flexibly switch to different prediction direction to achieve better prediction performance. Meanwhile, we propose a low-complexity prediction structure decision algorithm, where prediction direction and GoP size are adaptively determined based on motion complexity to achieve optimal compression efficiency. Experimental results demonstrate that the proposed unified framework with adaptive decision algorithm improves compression efficiency of pure forward prediction-based or bi-directional prediction-based framework with neglectable (\(0.9\%\)) encoding time increment. Meanwhile, it achieves comparable compression performance with VVC and recent learned video coding methods. [ABSTRACT FROM AUTHOR]

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]

In this paper, we propose a new quantum signature protocol with identity-based threshold multiple verifiers. In this protocol, first, the signature process combines the HMAC hash function with the participant identity to realize the compression encryption of the signature message, at the same time, the signature particles are encoded and generated by selecting the 3-particle GHZ state of the C2 ⊗ C2 ⊗ C2 spatially localizable distinguished (LOCC), which avoids the complex entanglement operation and effectively improves the signature efficiency; second, it takes at least t verifying members of the set of valid verifications specified by the signer to verify the validity of the final signature, which increases the utility of the signature protocol in multi-verification scenarios; finally, the security analysis shows that the proposed threshold multiparty verifiable quantum signature protocol scheme can resist entanglement measurement attacks, intercept retransmission attacks, and at the same time be unforgeable, nonrepudiable, and traceable. Most importantly, the protocol does not require Quantum One-Way Function (QOWF), Quantum State Swap Test (SWAP), and has no complex entanglement operations. Therefore, the proposed scheme is more efficient than similar multiparty signature protocols. [ABSTRACT FROM AUTHOR]

Interfered by external factors, the receptive field limits the traditional CNN multispectral remote sensing building change detection method. It is difficult to obtain detailed building changes entirely, and redundant information is reused in the encoding stage, which reduces the feature representation and detection performance. To address these limitations, we design a Siamese network of shared attention aggregation to learn the detailed semantics of buildings in multispectral remote sensing images. On the one hand, a special attention embedding module is introduced into each subspace of the feature extractor to promote the interaction between multi-scale local features and enhance the representation of global features. On the other hand, a highly efficient channel and position multi-head attention module is added to the Siamese features to encode position details while sharing channel information. In addition, adopting a feature aggregation module with a residual strategy to fuse the features of different stages of the Siamese network is beneficial for detecting different scales and irregular object buildings. Finally, experimental results on LEVIR-CD and CDD datasets show that designed SAASNets have better accuracy and robustness. [ABSTRACT FROM AUTHOR]

Synergistic drug combination screening is a promising strategy in drug discovery, but it involves navigating a costly and complex search space. While AI, particularly deep learning, has advanced synergy predictions, its effectiveness is limited by the low occurrence of synergistic drug pairs. Active learning, which integrates experimental testing into the learning process, has been proposed to address this challenge. In this work, we explore the key components of active learning to provide recommendations for its implementation. We find that molecular encoding has a limited impact on performance, while the cellular environment features significantly enhance predictions. Additionally, active learning can discover 60% of synergistic drug pairs with only exploring 10% of combinatorial space. The synergy yield ratio is observed to be even higher with smaller batch sizes, where dynamic tuning of the exploration-exploitation strategy can further enhance performance. The code can be found at https://github.com/LBiophyEvo/DrugSynergy [ABSTRACT FROM AUTHOR]

In this paper, we show that the dynamics encoded in the nonlinear realization of the semi-direct product of the very extended algebra K 2 7 with its vector representation contains the low-energy effective action of the closed bosonic string. [ABSTRACT FROM AUTHOR]

Background: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion. Results: We introduce the novel Joint Embedding Learning-classifier for improved Interpretability (JELI). By combining the training of a structured collaborative-filtering classifier and an embedding learning task, JELI predicts new user-item associations based on jointly learned item and user embeddings while providing feature-wise importance scores. Therefore, JELI flexibly allows the introduction of priors on the connections between users, items, and features. In particular, JELI simultaneously (a) learns feature, item, and user embeddings; (b) predicts new item-user associations; (c) provides importance scores for each feature. Moreover, JELI instantiates a generic approach to training recommender systems by encoding generic graph-regularization constraints. Conclusions: First, we show that the joint training approach yields a gain in the predictive power of the downstream classifier. Second, JELI can recover feature-association dependencies. Finally, JELI induces a restriction in the number of parameters compared to baselines in synthetic and drug-repurposing data sets. [ABSTRACT FROM AUTHOR]

Merging information across sensory modalities is key to forming robust percepts, yet how the brain achieves this feat remains unclear. Recent studies report cross-modal influences in the primary sensory cortex, suggesting possible multisensory integration in the early stages of cortical processing. We test several hypotheses about the function of auditory influences on mouse primary somatosensory cortex (S1) using in vivo two-photon calcium imaging. We found sound-evoked spiking activity in an extremely small fraction of cells, and this sparse activity did not encode auditory stimulus identity. Moreover, S1 did not encode information about specific audio-tactile feature conjunctions. Auditory and audio-tactile stimulus encoding remained unchanged after both passive experience and reinforcement. These results suggest that while primary sensory cortex is plastic within its own modality, the influence of other modalities is remarkably stable and stimulus nonspecific. [Display omitted] • Primary somatosensory cortex (S1) does not encode auditory stimulus identity • S1 does not encode the identity of audio-tactile stimulus combinations • Passive pairing of auditory and tactile stimuli cannot alter their encoding in S1 • Reinforcement learning cannot alter auditory and audio-tactile stimulus encoding in S1 Kato and Bruno show that, despite responding to sounds, rodent primary somatosensory cortex (S1) encodes neither the identity of pure auditory stimuli nor simultaneous auditory-tactile stimulus pairs. Moreover, this insensitivity to auditory and audio-tactile stimulus identity remains unchanged even following prolonged passive exposure to audio-tactile correlations and reward-reinforced behavioral training with audio-tactile stimuli. [ABSTRACT FROM AUTHOR]

Blind millionaire (BM) problem is an extended version of the initial millionaire problem required to compare the sum of the participants' secrets between different groups. As a new topic of quantum secure multiparty computing, existing protocols with some special entangled states may not be easily achieved in practice. This study proposes a non-entangled method of solving the quantum blind millionaire (QBM) problem with special d-level single-particle states for the first time. To protect the confidentiality of transmission secrets, this protocol exploits the property of randomly generated d-level single-particle states. Furthermore, simple shift operations are used to encode the respective secrets. Detailed security analysis demonstrates that this protocol is impervious to internal and external threats. The presented methods can not only be used to solve the blind millionaire problem but also be used as a basic module to solve other secure multiparty computing problems. [ABSTRACT FROM AUTHOR]

In recent years, large-scale point cloud semantic segmentation has been widely applied in various fields, such as remote sensing and autonomous driving. Most existing point cloud networks use local aggregation to abstract unordered point clouds layer by layer. Among these, position embedding serves as a crucial step. However, current methods of position embedding have limitations in modeling spatial relationships, especially in deeper encoders where richer spatial positional relationships are needed. To address these issues, this paper summarizes the advantages and disadvantages of mainstream position embedding methods and proposes a novel Hybrid Offset Position Encoding (HOPE) module. This module comprises two branches that compute relative positional encoding (RPE) and offset positional encoding (OPE). RPE combines explicit encoding to enhance position features through attention, learning position bias implicitly, while OPE calculates absolute position offset encoding by considering differences with grouping embeddings. These two encodings are adaptively mixed in the final output. The experiment conducted on multiple datasets demonstrates that our module helps the deep encoders of the network capture more robust features, thereby improving model performance on various baseline models. For instance, PointNet++ and PointMetaBase enhanced with HOPE achieved mIoU gains of 2.1% and 1.3% on the large-scale indoor dataset S3DIS area-5, 2.5% and 1.1% on S3DIS 6-fold, and 1.5% and 0.6% on ScanNet, respectively. RandLA-Net with HOPE achieved a 1.4% improvement on the large-scale outdoor dataset Toronto3D, all with minimal additional computational cost. PointNet++ and PointMetaBase had approximately only a 0.1 M parameter increase. This module can serve as an alternative for position embedding, and is suitable for point-based networks requiring local aggregation. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for successfully evading antivirus detection by encoding malicious shellcode with fountain codes. The Meterpreter framework for Microsoft Windows 32-bit and 64-bit architectures was used to produce the shellcode used in this investigation. The experimental results proved that detection rates were substantially decreased. Specifically, the number of detected instances using antivirus vendors for 32-bit shellcode decreased from 18 to 3, while for 64-bit shellcode, it decreased from 16 to 1. This method breaks up a malicious payload into many packets, each with their own distinct structure, and then encodes them. This obfuscation approach maintains the shellcode's integrity, ensuring correct code execution. However, in the persistence phase of the penetration testing process, this method offers an additional means of evading antivirus techniques. [ABSTRACT FROM AUTHOR]

The network intrusion detection system (NIDS) plays a critical role in maintaining network security. However, traditional NIDS relies on a large volume of samples for training, which exhibits insufficient adaptability in rapidly changing network environments and complex attack methods, especially when facing novel and rare attacks. As attack strategies evolve, there is often a lack of sufficient samples to train models, making it difficult for traditional methods to respond quickly and effectively to new threats. Although existing few-shot network intrusion detection systems have begun to address sample scarcity, these systems often fail to effectively capture long-range dependencies within the network environment due to limited observational scope. To overcome these challenges, this paper proposes a novel elevated few-shot network intrusion detection method based on self-attention mechanisms and iterative refinement. This approach leverages the advantages of self-attention to effectively extract key features from network traffic and capture long-range dependencies. Additionally, the introduction of positional encoding ensures the temporal sequence of traffic is preserved during processing, enhancing the model's ability to capture temporal dynamics. By combining multiple update strategies in meta-learning, the model is initially trained on a general foundation during the training phase, followed by fine-tuning with few-shot data during the testing phase, significantly reducing sample dependency while improving the model's adaptability and prediction accuracy. Experimental results indicate that this method achieved detection rates of 99.90% and 98.23% on the CICIDS2017 and CICIDS2018 datasets, respectively, using only 10 samples. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for improving semantic segmentation performance in computer vision tasks. Our approach utilizes an enhanced UNet architecture that leverages an improved ResNet50 backbone. We replace the last layer of ResNet50 with deformable convolution to enhance feature representation. Additionally, we incorporate an attention mechanism, specifically ECA-ASPP (Attention Spatial Pyramid Pooling), in the encoding path of UNet to capture multi-scale contextual information effectively. In the decoding path of UNet, we explore the use of attention mechanisms after concatenating low-level features with high-level features. Specifically, we investigate two types of attention mechanisms: ECA (Efficient Channel Attention) and LKA (Large Kernel Attention). Our experiments demonstrate that incorporating attention after concatenation improves segmentation accuracy. Furthermore, we compare the performance of ECA and LKA modules in the decoder path. The results indicate that the LKA module outperforms the ECA module. This finding highlights the importance of exploring different attention mechanisms and their impact on segmentation performance. To evaluate the effectiveness of the proposed method, we conduct experiments on benchmark datasets, including Stanford and Cityscapes, as well as the newly introduced WildPASS and DensPASS datasets. Based on our experiments, the proposed method achieved state-of-the-art results including mIoU 85.79 and 82.25 for the Stanford dataset, and the Cityscapes dataset, respectively. The results demonstrate that our proposed method performs well on these datasets, achieving state-of-the-art results with high segmentation accuracy. [ABSTRACT FROM AUTHOR]

Previous findings have raised doubt in whether comparable conformity effects can be obtained for information from humans and computers or other systems of little or no social importance. In the present study, we compared the impact of “other choices” (i.e. choices of another agent that did or did not match the participant’s initial choices) of humans and computers on preferences of participants for one of two pictures. In Experiment 1, we found conformity effects only when the other choices came from humans. In Experiment 2, we manipulated the attention allocated to encoding picture-choice combinations by means of a secondary go/nogo task. Conformity effects were found for humans and computers if the secondary task did not require a response. In Experiment 3, we manipulated the attention allocated to retrieving picture-choice combinations, which resulted in conformity effects for all conditions. Taken altogether, our findings suggest that conformity effects can be obtained for “computerized” informational sources under attentional conditions that reduce the specificity of encoding or the selectivity of retrieving event files. [ABSTRACT FROM AUTHOR]

The central amygdala (CeA) has emerged as an important brain region for regulating both negative (fear and anxiety) and positive (reward) affective behaviors. The CeA has been proposed to encode affective information in the form of valence (whether the stimulus is good or bad) or salience (how significant is the stimulus), but the extent to which these two types of stimulus representation occur in the CeA is not known. Here, we used single cell calcium imaging in mice during appetitive and aversive conditioning and found that majority of CeA neurons (~65%) encode the valence of the unconditioned stimulus (US) with a smaller subset of cells (~15%) encoding the salience of the US. Valence and salience encoding of the conditioned stimulus (CS) was also observed, albeit to a lesser extent. These findings show that the CeA is a site of convergence for encoding oppositely valenced US information. [ABSTRACT FROM AUTHOR]

The current understanding of primate natural action organization derives from laboratory experiments in restrained contexts (RCs) under the assumption that this knowledge generalizes to freely moving contexts (FMCs). In this work, we developed a neurobehavioral platform to enable wireless recording of the same premotor neurons in both RCs and FMCs. Neurons often encoded the same hand and mouth actions differently in RCs and FMCs. Furthermore, in FMCs, we identified cells that selectively encoded actions untestable during RCs and others that displayed mixed selectivity for multiple actions, which is compatible with an organization based on cortical motor synergies at different levels of complexity. Cross-context decoding demonstrated that neural activity in FMCs is richer and more generalizable than in RCs, which suggests that neuroethological approaches are better suited to unveil the neural bases of behavior. [ABSTRACT FROM AUTHOR]

Privacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study proposes a zero-relationship encoding scheme that minimizes the linkage between source and encoded records to enhance PPRL systems' resistance to re-identification attacks. Our method's efficacy was validated through simulations on the Titanic and North Carolina Voter Records (NCVR) datasets, demonstrating a substantial reduction in re-identification rates. Security analysis confirms that our zero-relationship encoding effectively preserves privacy against graph-based re-identification threats, improving PPRL technology's security. [ABSTRACT FROM AUTHOR]

The hippocampus plays a critical role in the formation of declarative memories, and hippocampal damage leads to significant impairments in new memory formation. Drawing can serve as a form of multi-modal encoding that improves declarative memory performance relative to other multimodal encoding strategies such as writing. We examined whether, and to what extent, patients with hippocampal damage could benefit from the mnemonic strategy of drawing. Three patients with focal hippocampal damage, and one patient with both hippocampal and cortical lesions, in addition to 22 age-, sex-, and education-matched controls, were shown a list of words one at a time during encoding and instructed to either draw a picture or repeatedly write each word for 40 s. Following a brief filled delay, free recall and recognition memory for words from both encoding trial types were assessed. Controls showed enhanced recall and recognition memory for words drawn versus those that were written, an effect that was even more pronounced in patients with focal hippocampal damage. By contrast, the patient with both hippocampal and cortical lesions showed no drawing-mediated boost in either recall or recognition memory. These findings demonstrate that drawing is an effective encoding strategy, likely accruing from the engagement of extra-hippocampal processes including the integration of cortical-based motor, visual, and semantic processing, enabling more elaborative encoding. [ABSTRACT FROM AUTHOR]

In this paper, we construct q-ary codes for correcting a burst of at most t deletions, where t , q ≥ 2 are arbitrarily fixed positive integers. We consider two scenarios of error correction: the classical error correcting codes, which recover each codeword from one read (channel output), and the reconstruction codes, which allow to recover each codeword from multiple channel reads. For the first scenario, our construction has redundancy log n + 8 log log n + o (log log n) bits, encoding complexity O (q 7 t n (log n) 3) and decoding complexity O (n log n) . For the reconstruction scenario, our construction can recover the codewords with two reads and has redundancy 8 log log n + o (log log n) bits. The encoding complexity of this construction is O q 7 t n (log n) 3 , and decoding complexity is O q 9 t (n log n) 3 . Both of our constructions have lower redundancy than the best known existing works. We also give explicit encoding functions for both constructions that are simpler than previous works. [ABSTRACT FROM AUTHOR]

In the identification (ID) scheme proposed by Ahlswede and Dueck, the receiver's goal is simply to verify whether a specific message of interest was sent. Unlike Shannon's transmission codes, which aim for message decoding, ID codes for a discrete memoryless channel (DMC) are far more efficient; their size grows doubly exponentially with the blocklength when randomized encoding is used. This indicates that when the receiver's objective does not require decoding, the ID paradigm is significantly more efficient than traditional Shannon transmission in terms of both energy consumption and hardware complexity. Further benefits of ID schemes can be realized by leveraging additional resources such as feedback. In this work, we address the problem of joint ID and channel state estimation over a DMC with independent and identically distributed (i.i.d.) state sequences. State estimation functions as the sensing mechanism of the model. Specifically, the sender transmits an ID message over the DMC while simultaneously estimating the channel state through strictly causal observations of the channel output. Importantly, the random channel state is unknown to both the sender and the receiver. For this system model, we present a complete characterization of the ID capacity–distortion function. [ABSTRACT FROM AUTHOR]

Differential Computation Analysis (DCA) leverages memory traces to extract secret keys, bypassing countermeasures employed in white-box designs, such as encodings. Although researchers have made great efforts to enhance security against DCA, most solutions considerably decrease algorithmic efficiency. In our approach, the Feistel cipher SM4 is implemented by a series of table-lookup operations, and the input and output of each table are protected by affine transformations and nonlinear encodings generated randomly. We employ fourth-order non-linear encoding to reduce the loss of efficiency while utilizing a random sequence to shuffle lookup table access, thereby severing the potential link between memory data and the intermediate values of SM4. Experimental results indicate that the DCA procedure fails to retrieve the correct key. Furthermore, theoretical analysis shows that the techniques employed in our scheme effectively prevent existing algebraic attacks. Finally, our design requires only 1.44 MB of memory, significantly less than that of the known DCA-resistant schemes—Zhang et al.'s scheme (24.3 MB), Yuan et al.'s scheme (34.5 MB) and Zhao et al.'s scheme (7.8 MB). Thus, our SM4 white-box design effectively ensures security while maintaining a low memory cost. [ABSTRACT FROM AUTHOR]

Event boundaries help structure the content of episodic memories by segmenting continuous experiences into discrete events. Event boundaries may also serve to preserve meaningful information within an event, thereby actively separating important memories from interfering representations imposed by past and future events. Here, we tested the hypothesis that event boundaries organize emotional memory based on changing dynamics as events unfold. We developed a novel threat-reversal learning task whereby participants encoded trial-unique exemplars from two semantic categories across three phases: preconditioning, fear acquisition, and reversal. Shock contingencies were established for one category during acquisition (CS+) and then switched to the other during reversal (CS−). Importantly, reversal was either separated by a perceptible event boundary (Experiment 1) or occurred immediately after acquisition, with no perceptible context shift (Experiment 2). In a surprise recognition memory test the next day, memory performance tracked the learning contingencies from encoding in Experiment 1, such that participants selectively recognized more threat-associated CS+ exemplars from before (retroactive) and during acquisition, but this pattern reversed toward CS− exemplars encoded during reversal. By contrast, participants with continuous encoding—without a boundary between conditioning and reversal—exhibited undifferentiated memory for exemplars from both categories encoded before acquisition and after reversal. Further analyses highlight nuanced effects of event boundaries on reversing conditioned fear, updating mnemonic generalization, and emotional biasing of temporal source memory. These findings suggest that event boundaries provide anchor points to organize memory for distinctly meaningful information, thereby adaptively structuring memory based on the content of our experiences. [ABSTRACT FROM AUTHOR]

Although living and nonliving stimuli are known to rely on distinct brain regions during perception, it is largely unknown if their episodic memory encoding mechanisms differ as well. To investigate this issue, we asked participants to encode object pictures (e.g., a picture of a tiger) and to retrieve them later in response to their names (e.g., word "tiger"). For each of four semantic classes (living-animate, living-inanimate, nonliving-large, and nonliving-small), we examined differences in the similarity in activation patterns (neural pattern similarity [NPS]) for subsequently remembered versus forgotten items. Higher NPS for remembered items suggests an advantage of within-class item similarity, whereas lower NPS for remembered items indicates an advantage for item distinctiveness. We expect NPS within class-specific regions to be higher for remembered than for forgotten items. For example, the parahippocampal cortex has a well-known role in scene processing [Aminoff, E. M., Kveraga, K., & Bar, M. The role of the parahippocampal cortex in cognition. Trends in Cognitive Sciences, 17, 379–390, 2013], and the anterior temporal and inferior frontal gyrus have well-known roles in object processing [Clarke, A., & Tyler, L. K. Object-specific semantic coding in human perirhinal cortex. Journal of Neuroscience, 34, 4766–4775, 2014]. As such, we expect to see higher NPS for remembered items in these regions pertaining to scenes and objects, respectively. Consistent with this hypothesis, in fusiform, parahippocampal, and retrosplenial regions, higher NPS predicted memory for subclasses of nonliving objects, whereas in the left inferior frontal and left retrosplenial regions, lower NPS predicted memory for subclasses of living objects. Taken together, the results support the idea that subsequent memory depends on a balance of similarity and distinctiveness and demonstrate that the neural mechanisms of episodic encoding differ across semantic categories. [ABSTRACT FROM AUTHOR]

We investigate the Cauchy–Szegő projection for quaternionic Siegel upper half space to obtain the pointwise (higher order) regularity estimates for Cauchy–Szegő kernel and prove that the Cauchy–Szegő kernel is nonzero everywhere, which further yields a non-degenerated pointwise lower bound. As applications, we prove the uniform boundedness of Cauchy–Szegő projection on every atom on the quaternionic Heisenberg group, which is used to give an atomic decomposition of regular Hardy space H p on quaternionic Siegel upper half space for 2 3 < p ≤ 1 . Moreover, we establish the characterisation of singular values of the commutator of Cauchy–Szegő projection based on the kernel estimates. The quaternionic structure (lack of commutativity) is encoded in the symmetry groups of regular functions and the associated partial differential equations. [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]

People often engage in the selective remembering of valuable or important information, whether strategic and/or automatic. We examined potential age-related differences in the automatic processing of value during encoding on later remembering by presenting participants with words paired with point values (range: 1–10 twice or 1–20) to remember for a later test. On the first three lists, participants were told that they would receive the points associated with each word if they recalled it on the test (their goal was to maximize their score). On the last three lists, we told participants that all words were worth the same number of points if recalled on the tests, thus making the point value paired with each word meaningless. Results revealed that selective memory may be impaired in older adults using procedures with larger value ranges. Additionally, we demonstrated that the automatic effects of value may have a greater effect on younger adults relative to older adults, but there may be instances where older adults also exhibit these automatic effects. Finally, strategic and automatic processes may not be related within each learner, suggesting that these processes may rely on different cognitive mechanisms. This indicates that these processes could be underpinned by distinct cognitive mechanisms: strategic processes might engage higher-level cognitive operations like imagery, while automatic processes appear to be more perceptually driven. [ABSTRACT FROM AUTHOR]

In this paper, we review PUG, a cognitive architecture for embodied agents, and report extensions that let it represent and reason about spatial relations. The framework posits graded concepts that are grounded in perception and integrates symbolic reasoning with continuous control. After describing the architecture, we discuss how an extended version supports places, encoded as virtual objects defined by distances to reference entities, and reasons about them as if they were visible. We demonstrate PUG's control of a simulated robot that approaches targets and avoids obstacles in a simple two-dimensional environment. In closing, we discuss related research on agent architectures, robotic control, and spatial cognition, along with our plans to extend the framework's capabilities for spatial representation and reasoning. [ABSTRACT FROM AUTHOR]

An action with an object can be accomplished only if we encode the position of the object with respect to our body (i.e., egocentrically) and/or to another element in the environment (i.e., allocentrically). However, some actions with the objects are directed towards our body, such as brushing our teeth, and others away from the body, such as writing. Objects can be near the body, that is within arm reaching, or far from the body, that is outside arm reaching. The aim of this study was to verify if the direction of use of the objects influences the way we represent their position in both near and far space. Objects typically used towards (TB) or away from the body (AB) were presented in near or far space and participants had to judge whether an object was closer to them (i.e., egocentric judgement) or closer to another object (i.e., allocentric judgement). Results showed that egocentric judgements on TB objects were more accurate in near than in far space. Moreover, allocentric judgements on AB objects were less accurate than egocentric judgements in near space but not in far space. These results are discussed with respect to the different roles that visuo-motor and visuo-spatial mechanisms play in near space and far space, respectively. [ABSTRACT FROM AUTHOR]

The Euler characteristic transform (ECT) is a simple to define yet powerful representation of shape. The idea is to encode an embedded shape by tracking how the Euler characteristic, a simple integer-valued topological invariant, changes as the shape is built up in a particular direction. Because the ECT has been shown to be injective on the space of embedded simplicial complexes, it has been used for applications spanning a range of disciplines, including plant morphology and protein structural analysis. In this survey article, we present a comprehensive overview of the Euler characteristic transform, highlighting the main idea on a simple leaf example, and surveying its key concepts, theoretical foundations, and available applications. [ABSTRACT FROM AUTHOR]

Temporal knowledge graphs (TKGs) are critical tools for capturing the dynamic nature of facts that evolve over time, making them highly valuable in a broad spectrum of intelligent applications. In the domain of temporal knowledge graph extrapolation reasoning, the prediction of future occurrences is of great significance and presents considerable obstacles. While current models consider the fact changes over time and recognize that historical facts may recur, they often overlook the influence of past events on future predictions. Motivated by these considerations, this work introduces a novel temporal knowledge graph reasoning model, named Temporal Reasoning with Recurrent Encoding and Contrastive Learning (TRCL), which integrates recurrent encoding and contrastive learning techniques. The proposed model has the ability to capture the evolution of historical facts, generating representations of entities and relationships through recurrent encoding. Additionally, TRCL incorporates a global historical matrix to account for repeated historical occurrences and employs contrastive learning to alleviate the interference of historical facts in predicting future events. The TKG reasoning outcomes are subsequently derived through a time decoder. A quantity of experiments conducted on four benchmark datasets demonstrate the exceptional performance of the proposed TRCL model across a range of metrics, surpassing state-of-the-art TKG reasoning models. When compared to the strong baseline Time-Guided Recurrent Graph Network (TiRGN) model, the proposed TRCL achieves 1.03% improvements on ICEWS14 using mean reciprocal rank (MRR) evaluation metric. This innovative proposed method not only enhances the accuracy of TKG extrapolation, but also sets a new standard for robustness in dynamic knowledge graph applications, paving the way for future research and practical applications in predictive intelligence systems. [ABSTRACT FROM AUTHOR]

The segmentation of visible ship images is an important part of intelligent ship monitoring systems. However, this task is faced with many difficulties in practical applications, such as complex background environments, variations in illumination, and target scale changes. In view of these situations, we present a dual-branch decoder network with a multiscale cascaded residual module for ship segmentation. Specifically, in the encoding stage, we introduce a multiscale cascaded residual module as a replacement for traditional convolution layers. By leveraging its multiscale architecture, the module effectively captures both the global context and fine-grained details. In the decoding phase, our framework incorporates two parallel branches, both of which utilize the cascading residual module to enhance feature extraction and representation. Additionally, one of the branches is equipped with spatial attention and channel attention mechanisms. Finally, comprehensive tests were conducted on the publicly available ship datasets MariBoatsSubclass and SeaShipsSeg. Our proposed network achieved impressive results, with Dice, Recall, Mcc, and Jaccard scores of 0.9003, 0.9105, 0.8706, and 0.8197 on the MariBoatsSubclass dataset. Similarly, it demonstrated outstanding performance on the SeaShipsSeg dataset, attaining Dice, Recall, Mcc, and Jaccard scores of 0.9538, 0.9501, 0.9519, and 0.9129, respectively. These results highlight the superior accuracy and robustness of DBD-Net in segmenting and detecting ships across diverse scenarios and datasets. [ABSTRACT FROM AUTHOR]

Traditional image classification often misclassifies unknown samples as known classes during testing, degrading recognition accuracy. Open-set image recognition can simultaneously detect known classes (KCs) and unknown classes (UCs) but still struggles to improve recognition performance caused by open space risk. Therefore, we introduce a cosine distance loss function (CDLoss), which exploits the orthogonality of one-hot encoding vectors to align known samples with their corresponding one-hot encoder directions. This reduces the overlap between the feature spaces of KCs and UCs, mitigating open space risk. CDLoss was incorporated into both Softmax-based and prototype-learning-based frameworks to evaluate its effectiveness. Experimental results show that CDLoss improves AUROC, OSCR, and accuracy across both frameworks and different datasets. Furthermore, various weight combinations of the ARPL and CDLoss were explored, revealing optimal performance with a 1:2 ratio. T-SNE analysis confirms that CDLoss reduces the overlap between the feature spaces of KCs and UCs. These results demonstrate that CDLoss helps mitigate open space risk, enhancing recognition performance in open-set image classification tasks. [ABSTRACT FROM AUTHOR]