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

To better analyze human posture in ice sports, a method based on deep learning has been established. In this method, the input gate, forget gate and output gate are configured to make them more suitable for human pose analysis relying on the LSTM network framework. The input data of LSTM are encoded using the sequence of human skeleton and joint angles in ice skating. During the experiment, abstract models of human bones and joints were provided, with continuous jumping movements in figure skating as the analysis object. LSTM provided detailed analysis results. The experimental results show that the accuracy of deep learning continues to improve in analyzing human posture during ice sports with the number of iterations increasing. [ABSTRACT FROM AUTHOR]

To fulfill the explosion of multi-modal data, multi-modal sentiment analysis (MSA) emerged and attracted widespread attention. Unfortunately, conventional multi-modal research relies on large-scale datasets. On the one hand, collecting and annotating large-scale datasets is challenging and resource-intensive. On the other hand, the training on large-scale datasets also increases the research cost. However, the few-shot MSA (FMSA), which is proposed recently, requires only few samples for training. Therefore, in comparison, it is more practical and realistic. There have been approaches to investigating the prompt-based method in the field of FMSA, but they have not sufficiently considered or leveraged the information specificity of visual modality. Thus, we propose a vision-enhanced prompt-based model based on graph structure to better utilize vision information for fusion and collaboration in encoding and optimizing prompt representations. Specifically, we first design an aggregation-based multi-modal attention module. Then, based on this module and the biaffine attention, we construct a syntax–semantic dual-channel graph convolutional network to optimize the encoding of learnable prompts by understanding the vision-enhanced information in semantic and syntactic knowledge. Finally, we propose a collaboration-based optimization module based on the collaborative attention mechanism, which employs visual information to collaboratively optimize prompt representations. Extensive experiments conducted on both coarse-grained and fine-grained MSA datasets have demonstrated that our model significantly outperforms the baseline models. [ABSTRACT FROM AUTHOR]

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds. [ABSTRACT FROM AUTHOR]

Somatosensory coding in rodents has been mostly studied in the whisker system and hairy skin, whereas the function of low-threshold mechanoreceptors (LTMRs) in the rodent glabrous skin has received scant attention, unlike in primates where the glabrous skin has been the focus. The relative activation of different LTMR subtypes carries information about vibrotactile stimuli, as does the rate and temporal patterning of LTMR spikes. Rate coding depends on the probability of a spike occurring on each stimulus cycle (reliability), whereas temporal coding depends on the timing of spikes relative to the stimulus cycle (precision). Using in vivo extracellular recordings in male rats and mice of either sex, we measured the reliability and precision of LTMR responses to tactile stimuli including sustained pressure and vibration. Similar to other species, rodent LTMRs were separated into rapid-adapting (RA) or slow-adapting based on their response to sustained pressure. However, unlike the dichotomous frequency preference characteristic of RA1 and RA2/Pacinian afferents in other species, rodent RAs fell along a continuum. Fitting generalized linear models to experimental data reproduced the reliability and precision of rodent RAs. The resulting model parameters highlight key mechanistic differences across the RA spectrum; specifically, the integration window of different RAs transitions from wide to narrow as tuning preferences across the population move from low to high frequencies. Our results show that rodent RAs can support both rate and temporal coding, but their heterogeneity suggests that coactivation patterns play a greater role in population coding than for dichotomously tuned primate RAs. [ABSTRACT FROM AUTHOR]

Basolateral amygdala (BLA) neurons are engaged by emotionally salient stimuli. An area of increasing interest is how BLA dynamics relate to evolving reward-seeking behavior, especially under situations of uncertainty or ambiguity. Here, we recorded the activity of individual BLA neurons in male rats across the acquisition and extinction of conditioned reward seeking. We assessed ongoing neural dynamics in a task where long reward cue presentations preceded an unpredictable, variably time reward delivery. We found that, with training, BLA neurons discriminated the CS+ and CS- cues with sustained cue-evoked activity that correlated with behavior and terminated only after reward receipt. BLA neurons were bidirectionally modulated, with a majority showing prolonged inhibition during cued reward seeking. Strikingly, population-level analyses revealed that neurons showing cue-evoked inhibitions and those showing excitations similarly represented the CS+ and behavioral state. This sustained population code rapidly extinguished in parallel with conditioned behavior. We next assessed the contribution of the orbitofrontal cortex (OFC), amajor reciprocal partner to the BLA. Inactivation of the OFC while simultaneously recording in the BLA revealed a blunting of sustained cue-evoked activity in the BLA that accompanied reduced reward seeking. Optogenetic disruption of BLA activity and OFC terminals in the BLA also reduced reward seeking. Our data indicate that the BLA represents reward-seeking states via sustained, bidirectional cue-driven neural encoding. This code is regulated by cortical input and is important for the maintenance of vigilant reward-seeking behavior. [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]

According to social-cognitive accounts, the other-race effect in face recognition is driven by our tendency to attend to identity cues when encoding own-race faces (“individuation”) and racial cues when encoding other-race faces (“categorization”). This study employed novel one-back tasks to ensure that participants processed identity (individuation) or racial cues (categorization) when learning faces in an old/new recognition task. Categorization encoding eliminated the other-race effect in recognition sensitivity when list length was short (Experiment 2) but not when list length was long (Experiment 1B). Importantly, this elimination was driven by weaker performance for own-race faces, rather than stronger performance for other-race faces. Individuation encoding failed to reduce the other-race effect, regardless of list length (Experiment 1A and Experiment 2). We compare these findings with previous studies and propose several methodological features (e.g., presentation duration, task difficulty) that might influence the observed impacts of individuation and categorization encoding on the other-race effect. [ABSTRACT FROM AUTHOR]

I outline the perspective that searching the contents of memory is a form of mental time travel (MTT) in non-humans that is relatively tractable because it focuses on the contents of memory. I propose that an animal model of MTT requires three elements: (i) the animal remembers multiple events using episodic memory, (ii) the order of events in time is included in the representation, and (iii) the sequence of events can be searched to find a target that occurred at a particular time. I review experiments suggesting that rats represent multiple items in episodic memory (element 1) in order of occurrence (element 2) and engage in memory replay to search representations in episodic memory in sequential order to find information at particular points in the sequence (element 3). The cognitive building blocks needed for MTT may be quite old in the evolutionary timescale. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'. [ABSTRACT FROM AUTHOR]

The predictive processing framework posits that one of the main functions of the brain is to anticipate the incoming information. Internal models facilitate interactions with the world by predicting future states against which actual evidence is compared. The difference between predicted and actual states, the prediction error (PE), signals novel information. However, how PE affects cognitive processing downstream is not fully understood: one such aspect pertains to how PE influences episodic memories, and whether those effect on memory differ across the lifespan. We examine the relationship between PE and episodic memory in children, young and older adults. We use a novel paradigm whereby rich visual narratives are used to build action schemas that enable probing different mnemonic aspects. To create different levels of PE, we manipulate the story endings to be either expected, neutral or unexpected with respect to the unfolded action. We show that (i) expected endings are better encoded than neutral endings and (ii) unexpected endings improve the encoding of mismatching events and other aspects of the narrative. These effects are differentially modulated across the lifespan with PE-driven encoding being more prominent in children and young adults and with schema integration playing a larger role on memory encoding in older adults. These results highlight the role of predictions by enriching past experiences and informing future anticipations. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'. [ABSTRACT FROM AUTHOR]

Antonymy is the lexical relation of opposition. The nature of the oppositeness may differ – e.g., contradictory ('true'–'false') or gradable ('tall'–'short') – and there may be variation as to the relationship in their formal encoding, whether the antonyms are expressed as distinct lexical forms (e.g., true vs. false) or if one form is derived from the other (e.g., true vs. untrue). We investigate the relationship between the two members of 37 antonym pairs across 55 spoken languages in order to see whether there are patterns in how antonymy is expressed and which of the two antonym members is more likely to be derived from the other. We find great variation in the extent to which languages use derivation (labeled "neg-constructed forms") as an antonym-formation strategy. However, when we do find a derived form, this tends to target the member of the pair that is either lower in valence (positive vs. negative) or magnitude (more vs. less), in line with our hypotheses. We also find that antonyms that belong to a core set of property concepts are more likely to encode antonyms as distinct lexical forms, whereas peripheral property concepts are relatively more likely to encode the antonyms with derived forms. [ABSTRACT FROM AUTHOR]

The local binary pattern (LBP) has been widely used for extracting texture features. However, the LBP and most of its variants tend to focus on pixel units within small neighborhoods, neglecting differences in direction and relationships among different directions. To alleviate this issue, in this paper, we propose a novel local directional difference and relational descriptor (LDDRD) for texture classification. Our proposed LDDRD utilizes information from multiple pixels along the radial direction. Specifically, a directional difference pattern (DDP) is first extracted by performing binary encoding on the differences between the central pixel and multiple neighboring pixels along the radial direction. Furthermore, by taking the central pixel as a reference, we extract the directional relation pattern (DRP) by comparing binary encodings representing different directions. Finally, we fuse the above DDP and DRP to form the LDDRD feature vector. Experimental results on six texture datasets reveal that our proposed LDDRD is effective and outperforms eight representative methods. [ABSTRACT FROM AUTHOR]

Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques in Quantum Machine Learning (QML) while highlighting their significance in transforming classical data into quantum systems. We analyze basis, amplitude, angle, and other high-level encodings in depth to demonstrate how various strategies affect encoding improvements in quantum algorithms. However, they identify major problems with encoding in the framework of QML, including scalability, computational burden, and noise. Future directions for research outline these challenges, aiming to enhance the excellence of encoding techniques in the constantly evolving quantum technology setting. This review shall enable the researcher to gain an enhanced understanding of data encoding in QML, and it also suggests solutions to the current limitations in this area. [ABSTRACT FROM AUTHOR]

This study examines the interplay of attraction and differentiation through the diachronic encoding of causative alternations in Chinese. A corpus-based analysis is conducted to profile the use of two Change of State verbs (COS verbs), pò 'break' and kāi 'open', focusing on their argument structure constructions. The analysis yields two main insights: (i) In Chinese, there are four pairs of causative alternations. The first pair, CA1, involving the alternation between NP1+COS+NP2 and NP2+COS, serves as the source for two diachronic trajectories. In the first trajectory, NP1+COS+NP2 is replaced by NP1+V+COS+NP2, and forms CA2 with NP2+COS. Subsequently, NP2+COS transitions into NP2+V+COS, and constitutes CA3 with NP1+V+COS+NP2. In the second trajectory, NP1+COS+V+NP2 and NP2+COS+V emerge and form CA4. (ii) Evidence of attraction is found in the mutual influence between the causative variants NP1+V+COS+NP2 and NP1+COS+V+NP2, and in the modeling effect of the causative variant NP1+V+COS+NP2 on the inchoative variant NP2+V+COS. Differentiation is supported by the anchoring force of a larger constructional network, including the transitive/intransitive opposition and compound constructions. This study advances the theoretical understanding of the intertwinement of attraction and differentiation by elucidating the diachronic evolution of causative alternations in Chinese. [ABSTRACT FROM AUTHOR]

The Chinese sequence yǒu rén [exist person] is both the minimal existential-presentational construction and the functional equivalent of indefinite pronouns in encoding indefinite human reference. This dual characteristic prompts the question of whether yǒu rén functions as a presentational construction comparable to similar forms in other languages. Building on a literary corpus of Chinese contemporary novels, this study aims to determine if yǒu rén displays the behavioral properties of presentational constructions. First, presentational yǒu rén constructions are distinguished from both locative-existential and generic-existential ones based on a set of features including predicate selection in the coda, presence and function of the locative expression, and the co-dependent interpretation of the nominal rén. Next, the discourse function of yǒu rén is examined by assessing its contrast with regular nonpresentational sentences and the anaphoric potential of the entity it introduces. While establishing the boundaries between the three construction types, the study also analyzes the co-expression pattern observed within a gradient and compositional approach. [ABSTRACT FROM AUTHOR]

This paper is a study of the visual characteristics of the medieval manuscript diagrams found in Theodosios' Spherics. It provides a new taxonomy of eight types of diagrams found in the manuscript sources, and examines the way in which geometric information is encoded in the manuscript diagrams using a number of visual encodings. It then argues that these diagrams can be used to address ongoing research debates about whether the striking visual characteristics of medieval diagrams in mathematical texts are the result of deliberate choices by the premodern authors, the result of accidents of transmission, or both. Along the way, the paper identifies a further type of transmission error that is prevalent in the medieval transmission of mathematical diagrams. [ABSTRACT FROM AUTHOR]

Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P system performs computations through the encoding, processing, and transmission of spiking information and can be regarded as a third-generation neural network. As a variant of the SN P system, the global asynchronous numerical spiking neural P system (ANSN P system) is adaptable to a broader range of application scenarios. However, in biological neuroscience, some neurons work synchronously within a community to perform specific functions in the brain. Inspired by this, our work investigates a global asynchronous spiking neural P system (ANSN P system) that incorporates certain local synchronous neuron sets. Within these local synchronous sets, neurons must execute their production functions simultaneously, thereby reducing dependence on thresholds and enhancing control uncertainty in ANSN P systems. By analyzing the ADD, SUB, and FIN modules in the generating mode, as well as the INPUT and ADD modules in the accepting mode, this paper demonstrates the novel system's computational capacity as both a generator and an acceptor. Additionally, this paper compares each module to those in other SN P systems, considering the maximum number of neurons and rules per neuron. The results show that this new ANSN P system is at least as effective as the existing SN P systems. [ABSTRACT FROM AUTHOR]

Image captioning aims to describe the content in an image, which plays a critical role in image understanding. Existing methods tend to generate the text for more distinct natural images. These models can not be well for paintings containing more abstract meaning due to the limitation of objective parsing without related knowledge. To alleviate, we propose a novel cross-modality decouple model to generate the objective and subjective parsing separately. Concretely, we propose to encode both subjective semantic and implied knowledge contained in the paintings. The key point of our framework is decoupled CLIP-based branches (DecoupleCLIP). For the objective caption branch, we utilize the CLIP model as the global feature extractor and construct a feature fusion module for global clues. Based on the objective caption branch structure, we add a multimodal fusion module called the artistic conception branch. In this way, the objective captions can constrain artistic conception content. We conduct extensive experiments to demonstrate our DecoupleCLIP's superior ability over our new dataset. Our model achieves nearly 2% improvement over other comparison models on CIDEr. [ABSTRACT FROM AUTHOR]

Event sequence data widely exists in real life, where each event is typically represented as a tuple, event type and occurrence time. Recently, neural point process (NPP), a probabilistic model that learns the next event distribution with events history given, has gained a lot of attention for event sequence modelling. Existing NPP models use one single vector to encode the whole events history. However, each type of event has its own historical events of concern, which should have led to a different encoding for events history. To this end, we propose Type-wise Neural Point Process (TNPP), with each type of event having a history vector to encode the historical events of its own interest. Type-wise encoding further leads to the realization of type-wise decoding, which together makes a more effective neural point process. Experimental results on six datasets show that TNPP outperforms existing models on the event type prediction task under both extrapolation and interpolation setting. Moreover, the results in terms of scalability and interpretability show that TNPP scales well to datasets with many event types and can provide high-quality event dependencies for interpretation. The code and data can be found at https://github.com/lbq8942/TNPP. [ABSTRACT FROM AUTHOR]

Delta encoding is a data reduction technique capable of calculating the differences (i.e., delta) among very similar files and chunks. It is widely used for various applications, such as synchronization replication, backup/archival storage, cache compression, and so on. However, delta encoding is computationally costly due to its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches either run at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this article, we propose Gdelta, a fast delta encoding approach with a high compression ratio. The key idea behind Gdelta is the combined use of five techniques: (1) employing an improved Gear-based rolling hash to replace Adler32 hash for fast scanning overlapping words of similar chunks, (2) adopting a quick array-based indexing for word-matching, (3) applying a sampling indexing scheme to reduce the cost of traditional building full indexes for base chunks' words, (4) skipping unmatched words to accelerate delta encoding through non-redundant areas, and (5) last but not least, after word-matching, further batch compressing the remainder to improve the compression ratio. Our evaluation results driven by seven real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 3.5X∼25X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10%∼240%. [ABSTRACT FROM AUTHOR]

Multi-view subspace clustering has attracted extensive attention due to its ability to efficiently handle data from diverse sources. In recent years, plentiful multi-view subspace clustering methods have emerged and achieved satisfactory clustering performance. However, these methods rarely consider simultaneously handling data with a nonlinear structure and exploiting the structural and multi-level information inherent in the data. To remedy these shortcomings, we propose the novel multi-view deep subspace clustering via level-by-level guided multi-level features learning (MDSC-LGMFL). Specifically, an autoencoder is used for each view to extract the view-specific multi-level features, and multiple self-representation layers are introduced into the autoencoder to learn the subspace representations corresponding to the multi-level features. These self-representation layers not only provide multiple information flow paths through the autoencoder but also enforce multiple encoder layers to produce the multi-level features that satisfy the linear subspace assumption. With the novel level-by-level guidance strategy, the last-level feature is guaranteed to encode the structural information from the view and the previous-level features. Naturally, the subspace representation of the last-level feature can more reliably reflect the data affinity relationship and thus can be viewed as the new, better representation of the view. Furthermore, to guarantee the structural consistency among different views, instead of simply learning the common subspace structure by enforcing it to be close to different view-specific new, better representations, we conduct self-representation on these new, better representations to learn the common subspace structure, which can be applied to the spectral clustering algorithm to achieve the final clustering results. Numerous experiments on six widely used benchmark datasets show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

We present an architecture for encoding two qubits within the optical "clock" transition and nuclear spin-1/2 degree of freedom of neutral ytterbium-171 atoms. Inspired by recent high-fidelity control of all pairs of states within this four-dimensional quotes space, we present a toolbox for intra-ququart (single-atom) one- and two-qubit gates, inter-ququart (two-atom) Rydberg-based two- and four-qubit gates, and quantum nondemolition (QND) readout. We then use this toolbox to demonstrate the advantages of the ququart encoding for entanglement distillation and quantum error correction which exhibit superior hardware efficiency and better performance in some cases since fewer two-atom operations are required. Finally, leveraging single-state QND readout in our ququart encoding, we present a unique approach to studying interactive circuits and to realizing a symmetry protected topological phase of a spin-1 chain with a shallow, constant-depth circuit. [ABSTRACT FROM AUTHOR]

Although age is typically associated with significant impairments in memory performance, several domains exist in which these impairments are reduced or even eliminated. These "pockets of preservation" in older adults' memory can be seen in tasks involving socioemotional processing and may be supported by distinct encoding or retrieval modes relative to neutral content. The current study examines whether engaging in socioemotional tasks prior to encoding or retrieval allows older adults to enter an encoding or retrieval mode that better supports memory performance. In two online studies, adults across the lifespan were asked to complete a memory task where they incidentally encoded and retrieved neutral (Experiment 1, N = 1621) or emotional (Experiment 2, N = 409) word-image pairs. Participants were randomly assigned to a control (i.e., no manipulation), pre-encoding, or pre-retrieval socioemotional manipulation condition. There were no main effects of manipulation condition, suggesting that such manipulations may not reliably enhance memory. However, future research is needed to follow up on exploratory analyses that highlighted particular conditions under which these manipulations may convey benefits. There were also no age-by-manipulation interactions. While these null effects may suggest that these manipulations are not better suited to older adults, this may also be a result of the unexpected age-related increases (Experiment 1) and age invariance (Experiment 2) in overall memory accuracy in the current study. Socioemotional manipulations should also be examined in older adults who underperform younger adults. [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]

Increasing attentional resources for a task can enhance the performance of a concurrent memory task; this phenomenon is referred to as the Attentional Boost Effect (ABE). This effect is consistently observed in item memory. However, the results are mixed regarding its presence in the two primary types of associative memory with vocabulary as the material: source memory and context memory. Previous research has yet to examine item memory and these two types of associative memory ABE within the same experimental context. Therefore, this study investigates the impact of encoding awareness, encoding frequency, and encoding duration on the ABE in vocabulary memory through three experiments. The results indicate that the ABE for vocabulary item memory and source memory is relatively stable and unaffected by encoding awareness, encoding frequency, and encoding duration. However, the presence of ABE in vocabulary context memory is influenced by encoding frequency. These findings suggest that the ABE in vocabulary memory is affected by the specific type of memory. This result also supports the notion that ABE is not a broad, undifferentiated enhancement of background information but rather involves a specific allocation mechanism. [ABSTRACT FROM AUTHOR]

The quantum key distribution (QKD) technique provided a promising resolution to the current security threats in Quantum Communication. However, the conventional QKD approach is vulnerable to hacker attacks and the Quantum particles used in QKD lose their energy during long-distance communication. To increase the distance coverage of quantum communication, our system proposed a Multi-layer Proxy Encryption Scheme (MPES) using entangled quantum particles. The main advantage of MPES over other conventional communication techniques is that each quantum repeater in the network acts as a different source of encryption with both sender and receiver nodes. For effective long-distance communication, this system adopted a trust-based short-distance protocol to find the path of photon transfer. The entangled photon that is used in communication is done through normal fibre optic cable. The presence of an eavesdropper can be measured with the help of an error correction protocol and a public key is sent through the normal communication channel. This pattern is checked throughout the communication path and malicious nodes that change its pattern will be eliminated from the network. This multiple encoding enhances the security level and reduces the end decryption time effectiveness. Quantum repeaters are used in the QKD protocol to extend the transmission distance by implementing quantum correlations. Unlike the conventional QR, our proposed structure performs one-way communication by encoding and decoding the data within a single node. The reader can decode the information from the actual sender and the writer transforms this decoded data to another node of quantum repeaters. The qubits that are decoded will be in a bell state, and qubits transfer the data in the form of polarization.Moreover, the shortest path algorithm-based photon transfer is done in this approach which increased the execution period of the proposed approach and also turned into the enhanced cost-effective technique. The obtained key error rate of the proposed system is compared with the conventional BB84 protocol and the comparison result proved an increase of 30% in error reduction and reduced energy consumption. [ABSTRACT FROM AUTHOR]

3D-HEVC, which is a HEVC-compatible 3D video coding standard, was mainly developed to efficiently compress both a texture image and a depth map. Since the characteristics of the depth map are drastically different from those of the texture image, many advanced tools were adopted for depth intra coding in 3D-HEVC. In particular, a depth modelling mode (DMM) is evaluated to accurately predict sharp edges between objects. As a result, encoding complexity becomes very high. In order to reduce the high complexity, a fast depth intra mode decision method employing intra prediction cost and probability is proposed in this paper. Based on the cost and probability, the proposed method adaptively skips HEVC prediction modes and DMM in the mode decision. Experimental results demonstrate that it significantly reduces the encoding complexity, compared to conventional methods. [ABSTRACT FROM AUTHOR]

In recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user. [ABSTRACT FROM AUTHOR]

In this paper, we study supersymmetric Berry connections of 2d 풩=(2,2) gauged linear sigma models (GLSMs) quantized on a circle, which are periodic monopoles, with the aim to provide a fruitful physical arena for recent mathematical constructions related to the latter. These are difference modules encoding monopole solutions via a Hitchin–Kobayashi correspondence established by Mochizuki. We demonstrate how the difference modules arise naturally by studying the ground states as the cohomology of a one-parameter family of supercharges. In particular, we show how they are related to one kind of monopole spectral data, a quantization of the Cherkis–Kapustin spectral curve, and relate them to the physics of the GLSM. By considering states generated by D-branes and leveraging the difference modules, we derive novel difference equations for brane amplitudes. We then show that in the conformal limit, these degenerate into novel difference equations for hemisphere partition functions, which are exactly calculable. When the GLSM flows to a nonlinear sigma model with Kähler target X, we show that the difference modules are related to the equivariant quantum cohomology of X. [ABSTRACT FROM AUTHOR]

BackgroundAimsMethods & proceduresOutcomes & resultsConclusionsPersons with aphasia (PWAs) are often impaired in time reference/tense production. It has been suggested that this impairment is due to encoding or/and retrieval deficits. However, to the best of our knowledge, no experimental design that enables teasing apart selective encoding and retrieval deficits has been proposed thus far.This study aims at disentangling time reference-related encoding deficits from time reference-related retrieval deficits in PWAs.Two sentence completion tasks tapping production of time reference and subject-verb agreement (control condition) were administered to eight Greek-speaking PWAs, eight Russian-speaking PWAs, six Italian-speaking PWAs, seven English-speaking PWAs and four groups of language-, age- and education-matched healthy controls. Task 1 tapped encoding and retrieval processes to a similar extent. Task 2 predominantly tapped retrieval processes. Comparisons between each PWA and the corresponding control group, as well as within-participant comparisons were performed.All four control groups performed at ceiling. Twenty-eight out of 29 PWAs were impaired in time reference in at least one of the two completion tasks, and all but three PWAs were impaired in production of subject-verb agreement in at least one of the two tasks. In all language groups, there were PWAs exhibiting between-task dissociations. A double dissociation emerged in the time reference condition, as some Greek-, Russian- and English-speaking PWAs performed better on Task 1 than on Task 2, whereas other Greek- and Italian-speaking PWAs performed worse on Task 1 than on Task 2. In the agreement condition, in each language group, there were PWAs performing better on Task 1 than on Task 2. However, none PWA exhibited the opposite pattern. Based on the results, we identified both PWAs with selective time reference-related encoding deficits and PWAs with selective time reference-related retrieval deficits.The present experimental design provides a sound basis for teasing apart selective time reference-related encoding deficits and time reference-related retrieval deficits. [ABSTRACT FROM AUTHOR]

One of the challenges in planning a digital edition is the selection of the encoding system and representation format that the edition will use to encode musical information. This decision affects the form of the edition not only through the descriptive capabilities of the encoding format, but also through the format's adherence to certain data structures such as tables, trees, and graphs. Far from being straightforward containers, each of these structures possess unique qualities that constrain the encoding process into considering the music within the parameters of that data model. Data that does not fit must be cajoled into the chosen model, with varying degrees of success. Recent work has developed arguments for the use of certain formats in specific cases based on features and interoperability, but a thorough review of the suitability and sustainability of the underlying data structures has yet to be conducted.This paper explores the problem of music representation in data structures from the perspective of musical domain. By encoding the same passages of music in multiple hypothetical formats it demonstrates the musical aspects and, particularly, the relationships that the data structures commonly used in music encoding variously excel at, privilege, or struggle with representing. I argue that encoding projects should consider the constraints that the data structures of a format impose on their encoding; whether there are instances that would be unsatisfactorily modelled, the data biases this creates, and therefore whether it would be advisable to extend the representation format to accommodate another data structure. [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]

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory. Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here, the authors use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. [ABSTRACT FROM AUTHOR]

For animals to locate resources and stay safe, navigation is an essential cognitive skill. Blind people use different navigational strategies to encode the environment. Path integration significantly influences spatial navigation, which is the ongoing update of position and orientation during self-motion. This study examines two separate things: (i) how guided and non-guided strategies affect blind individuals in encoding and mentally representing a trajectory and (ii) the sensory preferences for potential navigational aids through questionnaire-based research. This study first highlights the significant role that the absence of vision plays in understanding body centered and proprioceptive cues. Furthermore, it also underscores the urgent need to develop navigation-assistive technologies customized to meet the specific needs of users. [ABSTRACT FROM AUTHOR]

In this work we introduce GrapheNet, a deep learning framework based on an Inception-Resnet architecture using image-like encoding of structural features for the prediction of the properties of nanographenes. The model is validated on datasets of computed structure/property data on graphene oxide and defected graphene nanoflakes. By exploiting the planarity of quasi-bidimensional systems and through encoding structures into images, and leveraging the flexibility and power of deep learning in image processing, Graphenet achieves significant accuracy in predicting the physicochemical properties of nanographenes. This approach is able to efficiently encode structures composed of hundreds of atoms, scaling efficiently with the size of the model and enabling the prediction of the properties of large systems, which contrasts with the limitations of current atomistic-level representations for deep learning applications. The approach proposed based on image encoding exhibit a significant numerical accuracy and outperforms the computational efficiency of current representations of materials at the atomistic level, with significant advantages especially in the representation of nanostructures and large planar systems. [ABSTRACT FROM AUTHOR]

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their function relies on distributed circuits to continuously encode relevant environmental and body-state features, enabling other areas to decode (interpret) these representations for computing meaningful decisions and executing precise movements. Thus, the distributed brain can be thought of as a series of computations that act to encode and decode information. In this perspective, we detail important concepts of neural encoding and decoding and highlight the mathematical tools used to measure them, including deep learning methods. We provide case studies where decoding concepts enable foundational and translational science in motor, visual, and language processing. Understanding how the brain works has been a formidable challenge in neuroscience, but advances at the computational level are now driving us closer. This perspective discusses core concepts of neural encoding and decoding and how moving toward causal modeling can enable translational and foundational insights into the neural code. [ABSTRACT FROM AUTHOR]

The encoding and evolution of specificity and affinity in protein-protein interactions is poorly understood. Here, we address this question by quantifying how all mutations in one protein, JUN, alter binding to all other members of a protein family, the 54 human basic leucine zipper transcription factors. We fit a global thermodynamic model to the data to reveal that most affinity changing mutations equally affect JUN's affinity to all its interaction partners. Mutations that alter binding specificity are relatively rare but distributed throughout the interaction interface. Specificity is determined both by features that promote on-target interactions and by those that prevent off-target interactions. Approximately half of the specificity-defining residues in JUN contribute both to promoting on-target binding and preventing off-target binding. Nearly all specificity-altering mutations in the interaction interface are pleiotropic, also altering affinity to all partners. In contrast, mutations outside the interface can tune global affinity without affecting specificity. Our results reveal the distributed encoding of specificity and affinity in an interaction interface and how coiled-coils provide an elegant solution to the challenge of optimizing both specificity and affinity in a large protein family. Here, the authors build a map of how mutations alter the binding of JUN to all 54 human bZIP proteins, revealing how affinity and specificity can be tuned and how determinants of specificity distribute in a protein interaction interface. [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]

In the quest for computational efficiency, binary neural networks (BNNs) have emerged as a promising paradigm, offering significant reductions in memory footprint and computational latency. In traditional BNN implementation, the first and last layers are typically full-precision, which causes higher logic usage in field-programmable gate array (FPGA) implementation. To solve these issues, we introduce a novel approach named Ponte (Represent Totally Binary Neural Network Toward Efficiency) that extends the binarization process to the first and last layers of BNNs. We challenge the convention by proposing a fully binary layer replacement that mitigates the computational overhead without compromising accuracy. Our method leverages a unique encoding technique, Ponte::encoding, and a channel duplication strategy, Ponte::dispatch, and Ponte::sharing, to address the non-linearity and capacity constraints posed by binary layers. Surprisingly, all of them are back-propagation-supported, which allows our work to be implemented in the last layer through extensive experimentation on benchmark datasets, including CIFAR-10 and ImageNet. We demonstrate that Ponte not only preserves the integrity of input data but also enhances the representational capacity of BNNs. The proposed architecture achieves comparable, if not superior, performance metrics while significantly reducing the computational demands, thereby marking a step forward in the practical deployment of BNNs in resource-constrained environments. [ABSTRACT FROM AUTHOR]

Spike timing-dependent plasticity (STDP) is crucial for training neural networks (SNNs), offering a hardware-compatible and energy-efficient alternative to backpropagation. Current STDP hardware platforms encounter significant challenges, such as slowness, high energy consumption, and limited configurability. To overcome these issues, this paper presents a high-performance SNN training platform. A parallel multi-ring first-in-first-out structure with event-driven processing for spike handling is proposed, which enhances training efficiency, and the flexible pre- and post-synaptic parallelism enhances speed and flexibility. A dataflow strategy that considers spike sparsity unifies spike representations by updating weights only upon spike arrival, thereby promoting logical symmetry and enabling parallelization. Additionally, three encoding strategies, including a hybrid encoding, are implemented to address diverse scenarios. Leveraging Xilinx field-programmable gate array and Jetson Xavier NX, the proposed platform achieved remarkable performance gains. On the MNIST dataset, the platform demonstrated a 22.51 × speedup, 2.13% accuracy boost, and 14.79 × reduction in energy consumption. On the Fashion-MNIST dataset, it improved accuracy by 10.74% and the training speed by 1.89 × . Thus, this platform can significantly advance the SNN training efficiency and performance. [ABSTRACT FROM AUTHOR]

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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]

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in temporal and channel modules enclosing two long short-term memory recurrent layers, Gaussian range encoding, a multi-head self-attention mechanism, and a block-recurrent transformer layer. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving equal error rate values of 3.25% using only five enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. To highlight the design rationale, an analysis of the experimental results of the different modules implemented in the development of TypeFormer is carried out. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement. [ABSTRACT FROM AUTHOR]

The compressed code of Absolute Moment Block Truncation Coding (AMBTC) consists of quantized values (QVs) and bitmaps. The QVs exhibit greater predictability, and the bitmaps themselves carry more randomness. While existing joint coding and data hiding based methods have focused on compressing the QVs, none of them have addressed the coding of bitmaps. Furthermore, evidence also reveals that the sub-divided bitmaps exhibit a highly uneven pattern distribution. Therefore, we propose an efficient method to compress the bitmaps by representing sub-divided bitmaps as decimal digits. This exploits the varying frequency of certain digits, allowing Huffman encoding for shorter codewords to represent frequently occurring digits. Moreover, we have observed that less frequent digits, which require longer codewords, tend to appear among blocks with smaller difference of QVs. As a solution, we employ an adaptive approach to directly record the original bitmap when the difference is below a specific threshold. Otherwise, we employ Huffman encoding to reduce the code length. The experimental results demonstrate the effectiveness of our approach in reducing the storage space required for Lena's bitmaps by 7.36%. Moreover, the reduction in bitrate is more pronounced when the test image exhibits a smooth texture. [ABSTRACT FROM AUTHOR]

During ongoing narratives, event boundaries trigger processes relevant for subsequent memory. Previous work has shown that novel, unrelated input presented at an event boundary can retroactively interfere with short-term retention of the preceding event. This interference was attributed to a perturbation of offset-related processes taking place within seconds after encoding and supporting the binding of elements into a coherent event memory. However, the temporal specificity of this memory interference and whether its impact extends to longer retention delays has not been addressed. Here, participants viewed either individual or pairs of short narrative movie clips. Susceptibility to interference at event boundaries was probed by presenting the second clip either immediately after the first, or with a 2s encoding delay. In free and cued recall, after 20 min and 24 h, only memory for movie clips that were immediately followed by a second clip was reduced compared to clips shown in isolation. Intact offset-related processes (as indexed by successful recall of the first movie) did not negatively affect encoding of the subsequent clip. Together, these results indicate that the 2s time-window immediately after an event is relevant for successful consolidation and long-term retention of memory. [ABSTRACT FROM AUTHOR]

This paper presents a spatiotemporal encoding metasurface design to independently achieve the good radiation and the low scattering performances in the same frequency band. The proposed metasurface unit cell is composed of a square patch and two orthogonally placed I-shaped strips. By switching the operating states of the PIN diode inserted into each I-shaped strip between ON state and OFF state, a phase difference of 180° can be obtained in the band of 7.6–7.95 GHz for two orthogonally polarized incident waves. When a coaxial probe is introduced at the diagonal line of the square patch, the dual-polarized radiation capability is achieved within the frequency range of 7.6–7.95 GHz. With the proposed metasurface unit cell, the manipulation of the radiating wave and the scattering wave has no influence on each other. By controlling the PIN operating states by the field programmable gate array in real time, the power of the scattering wave is transferred to some higher order harmonics, thus realizing radar cross section (RCS) reduction. With optimized spatial and temporal encoding layouts, the proposed metasurface realizes the RCS reduction over 10 dB within the range of 7.3–8 GHz, with the maximum RCS reduction of 35.6 dB, while maintaining good radiation capability. [ABSTRACT FROM AUTHOR]

We investigate the inclusive production of fully charmed tetraquarks, T 4 c (0 + +) or T 4 c (2 + +) radial excitations, in high-energy proton collisions. We build our study upon the collinear fragmentation of a single parton in a variable-flavor number scheme, suited to describe the tetraquark formation mechanism from moderate to large transverse-momentum regimes. To this extent, we derive a novel set of DGLAP-evolving collinear fragmentation functions, named TQ4Q1.0 determinations. They encode initial-scale inputs corresponding to both gluon and heavy-quark fragmentation channels, defined within the context of quark-potential and spin-physics inspired models, respectively. We work within the NLL/ NLO + hybrid factorization and make use of the JETHAD numeric interface along with the symJETHAD symbolic calculation plugin. With these tools, we provide predictions for high-energy observables sensitive to T 4 c plus jet emissions at center-of-mass energies ranging from 14 TeV at the LHC to the 100 TeV nominal energy of the FCC. [ABSTRACT FROM AUTHOR]

The well-known twin-field quantum key distribution (TF-QKD) protocol is the first one to overcome the fundamental rate-distance limit without quantum repeaters. It encodes the key information into phases of the light, and has a secret key rate scaling with the square root of the transmission transmittance by taking advantage of single-photon interference. Inspired by the idea in TF-QKD, we proposed a polarization encoding protocol to break the rate-distance limit by the property of the two states with orthogonal polarizations, named no-interfering QKD (NI-QKD). Two effective events are defined in which no interference happens. Simulation results show that the proposed protocol also holds the capacity of surpassing the Pirandola–Laurenza–Ottaviani–Banchi (PLOB) bound. Moreover, it has a better performance than no-phase-postselection TF-QKD (NPP-TF-QKD), one of TF-QKD's variants, and its communication distance can reach at most 424 km. The relationships between the performance and the polarization misalignment, the phase mismatch are discussed. It turns out that one event is very robust against polarization misalignment while the other is not, and both events are sensitive to phase mismatch. The mutual information of the effective events under collective attack is also calculated, which is lower than that of NPP-TF-QKD. This new protocol provides a new angle of exploring QKD and improving the secret rate. [ABSTRACT FROM AUTHOR]