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The article introduces a small bit of data for Romanticists' consideration, a collection of seventeen commonplace books kept from 1814 to 1817. It explores how Mary and Sarah Leigh and their cousin Maria Leigh used their commonplace books as archives of shared intimacy. The strategies the Leigh sisters used to encode embodied data include linking immaterial ideas with the materiality of the notebook and paratext that teaches how to read the verse in the context of the sisters' lived experience.

We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\mathrm{ Geometric}}(p_{n})$ , we characterize the capacity as $C = e^{-\alpha }$ , where $\alpha = \lim _{n\to \infty } p_{n} \log n$. Our results show that the case of ${\mathrm{ Geometric}}(p_{n})$ -length fragments and the case of deterministic length- $(1/p_{n})$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity. [ABSTRACT FROM AUTHOR]

This study investigates multi-layer coding over a dirty-paper channel. First, it is demonstrated that superposition coding in such channel still achieves the capacity of interference-free additive white Gaussian noise channel when the transmitter is non-causally aware of interference signal. Then, the problem is extended to the dirty-paper block fading channel, where it is shown that in the lack of channel information at the transmitter, the so-called broadcast approach maximises the average achievable rate of such channel. [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]

The “carbon copy onto dirty paper” (CCDP) channel is the compound “writing on dirty paper” channel in which the channel output is obtained as the sum of the channel input, white Gaussian noise and a Gaussian state sequence randomly selected among a set possible realizations. The transmitter has non-causal knowledge of the set of possible state sequences but does not know which sequence is selected to produce the channel output. We study the capacity of the CCDP channel for two scenarios: 1) the state sequences are independent and identically distributed; and 2) the state sequences are scaled versions of the same sequence. In the first scenario, we show that a combination of superposition coding, time-sharing, and Gel’fand-Pinsker binning is sufficient to approach the capacity to within 3 bits per channel use for any number of possible state realizations. In the second scenario, we derive capacity to within 4 bits per channel use for the case of two possible state sequences. This result is extended to the CCDP channel with any number of possible state sequences under certain conditions on the scaling parameters, which we denote as “strong fading” regime. We conclude by providing some remarks on the capacity of the CCDP channel in which the state sequences have any jointly Gaussian distribution. [ABSTRACT FROM AUTHOR]

In the framework of givenness hierarchy, this paper analyzes the asymmetries between the definite article and demonstratives in Spanish and makes a comparison between Spanish and Chinese. The paper concludes that despite some similarities, the asymmetries in Chinese are more moderate because the instruction encoded in Chinese demonstratives is more general. In addition, the asymmetries in the two languages should be understood in different ways. In Spanish, the main factor that causes the asymmetries is that the article does not impose restrictions on the context construction. However, with the demonstratives, the selection of encyclopedic context is impossible. In Chinese, the demonstratives do not impose restrictions on the context choices. However, being an element not fully developed as grammaticalized representation of definiteness (Cao, 2014), the demonstratives remain inadequate in the use of the first mention and associative anaphora. In cases of direct anaphora, due to the lack of economy and the fact that most anaphoric expressions are incomplete descriptions, the percentage of use of demonstratives in Chinese is also low. [ABSTRACT FROM AUTHOR]

Reaction systems are a formal model for computational processing in which reactions operate on sets of entities (molecules) providing a framework for dealing with qualitative aspects of biochemical systems. This paper is concerned with reaction systems in which entities can have discrete concentrations, and so reactions operate on multisets rather than sets of entities. The resulting framework allows one to deal with quantitative aspects of reaction systems, and a bespoke linear-time temporal logic allows one to express and verify a wide range of key behavioural system properties. In practical applications, a reaction system with discrete concentrations may only be partially specified, and the possibility of an effective automated calculation of the missing details provides an attractive design approach. With this idea in mind, the current paper discusses parametric reaction systems with parameters representing unknown parts of hypothetical reactions. The main result is a method aimed at replacing the parameters in such a way that the resulting reaction system operating in a specified external environment satisfies a given temporal logic formula.This paper provides an encoding of parametric reaction systems in smt, and outlines a synthesis procedure based on bounded model checking for solving the synthesis problem. It also reports on the initial experimental results demonstrating the feasibility of the novel synthesis method. [ABSTRACT FROM AUTHOR]

This paper studies the secure transmission in a multiuser broadcast channel where only the statistical channel state information of the eavesdropper is available. We propose to apply secret dirty-paper coding (S-DPC) in this scenario to support the secure transmission of one user and the normal (unclassified) transmission of the other users. By adopting the S-DPC and encoding the secret message in the first place, all the information-bearing signals of the normal transmission are treated as noise by potential eavesdroppers and thus provide secrecy for the secure transmission. In this way, the proposed approach exploits the intrinsic secrecy of multiuser broadcasting and can serve as an energy-efficient alternative to the traditional artificial noise (AN) scheme. To evaluate the secrecy performance of this approach and compare it with the AN scheme, we propose two S-DPC-based secure transmission schemes for maximizing the secrecy rate under constraints on the secrecy outage probability (SOP) and the normal transmission rates. The first scheme directly optimizes the covariance matrices of the transmit signals, and a novel approximation of the intractable SOP constraint is derived to facilitate the optimization. The second scheme combines zero-forcing dirty-paper coding and AN, and the optimization involves only power allocation. We establish efficient numerical algorithms to solve the optimization problems for both schemes. Theoretical and simulation results confirm that, in addition to supporting the normal transmission, the achievable secrecy rates of the proposed schemes can be close to that of the traditional AN scheme, which supports only the secure transmission of one user. [ABSTRACT FROM PUBLISHER]

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]

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]

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

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]

Many Boolean functions that need to be encoded as CNF in practice, have only exponential size CNF representations. To avoid this effect, one usually introduces nondeterministic variables. For example, whereas the minimum number of clauses in a CNF computing the parity function x 1 ⊕ x 2 ⊕ ⋯ ⊕ x n is 2 n - 1 , one can use n - 1 nondeterministic variables to get a CNF encoding with 4n clauses. In this paper, we prove tradeoffs between various parameters (the number of clauses, the width of clauses, and the number of nondeterministic variables) of CNF encodings of various symmetric functions. In particular, we show that a folklore way of encoding parity as CNF is provably optimal. We do this by using a tight connection between CNF encodings and depth-3 circuits. This connection shows that CNF encodings is an interesting computational model for Boolean functions: on the one hand, it is routinely used in practice when translating a computational problem to a format acceptable by a SAT solver, on the other hand, lower bounds on the size of CNF encodings imply depth-3 circuit lower bounds. [ABSTRACT FROM AUTHOR]

This paper presents an efficient decoding method (namely SORTED decoding) for the de facto encoding in size optimization of trusses with automatic member grouping. The efficiency of the proposed method is evaluated through numerical experiments driven by two metaheuristic algorithms of different search mechanisms: Colliding Bodies Optimization and Jaya Algorithm. The first group of experiments show that the SORTED decoding method significantly outperforms the de facto and the two other decoding methods introduced in this study (ACCUMULATED and SORTED-ACCUMULATED) in terms of solution quality; moreover, the authors proved that the superiority of the SORTED decoding is not algorithm-dependent. Considerably better member-grouping configurations that provide up to 15% material economy are discovered for some of the well-known pre-grouped benchmark problems in the second group of experiments; surprisingly, the worst member-grouping configuration discovered by the SORTED decoding for one of the problems is better than that of the pre-grouped version. [ABSTRACT FROM AUTHOR]

Parallel Concatenated Block (PCB) codes are conventionally represented as high-rate codes with low error correcting capability. To form a reliable and outstanding code, this paper presents a modification on the structure of PCB codes, which is accomplished by encoding some parity bits of one of their component codes. For the newly proposed code, named as the braided code, non-stuff bit-based convolutional interleavers are applied, aiming to minimize the design complexity while ensuring the proper permutations of the original message and selected parity bits. To precisely determine the error correcting capability, a tight bound for the minimum weight of braided code is presented. Additionally, further analyses are provided, which verify iterative decoding performance and the complexity of the constructed code. It is concluded that an outstanding braided code is formed by utilizing a reasonable number of iterations applied at its decoding processes, while maintaining its design complexity at a level similar to other well-known codes. The significant performance of short and long-length-based braided codes is evident in both waterfall and error floor regions. [ABSTRACT FROM AUTHOR]

Coded blockchain leverages error correction codes to create multiple coded fragments that are then stored in a distributed manner by nodes. Its primary objective is to reduce the storage requirements of participants or nodes, achieving efficient storage and fault tolerance. However, nodes randomly store arbitrary coded fragments, which leads to high communication cost when a node attempts to decode a block. To this end, this paper proposed a novel reinforcement learning inspired distributed protocol to efficiently assign coded fragments to nodes. Specifically, nodes learn to store coded fragments based on a feedback signal that related to the storage cost of coded fragments and the communication cost incurred during block decoding attempts. After convergence, it compared the proposed protocol with existing centralized and distributed blockchain encoding storage methods. The results show that nodes have 7% higher reward, and critically, their communication cost is lower by 55%. In summary, the proposed reinforcement learning-based distributed protocol provides an effective solution for enhancing the storage and transmission performance of encoded blockchains. [ABSTRACT FROM AUTHOR]

Robust recognition of objects is crucial across various applications, spanning from industrial automation to healthcare and security. Nevertheless, real-world datasets often introduce label noise, encoding inaccurate correlation patterns that compromise the performance of visual recognition systems. Recent approaches utilize the predicted label distributions of individual samples for noise verification and correction, potentially exacerbating the accumulation of noise. To tackle this concern, in this paper, we introduce the context-driven reliability assessment strategy (CRA) for object robust recognition, where the reliability of a candidate sample is re-evaluated and rectified by comparing it with its neighbors in feature space. Specifically, CRA is divided into two steps: (1) consistency-aware pure sample selection (CPS) for estimating and choosing reliable samples based on consistent relations with the candidate sample and its k-reciprocal neighbors; and (2) similarity-guided label noise refurbishment (SLR) for re-labeling noisy samples by penalizing the divergence of each example's prediction from a weighted combination of its neighbors' predictions, with weights determined by their similarity in feature space. Thereafter, auxiliary techniques are used to assist further model optimization. Extensive experiments on three commonly used benchmark datasets, i.e., CIFAR-10, CIFAR-100 and Webvision - 1.0, demonstrate that our proposed method considerably outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Many fMRI papers in the mind-reading literature present decoders as a final product, arguing that decoders provide neurophysiological insight and have real-world utility. However, mental state decoders are commonly built in a way that precludes straightforward physiological interpretations. This undermines the claim that decoders are interpretable or capture 'representations' of mental states. In contrast to decoding models, encoding models of task fMRI are computationally straightforward and more interpretable. Mind-reading research would benefit from shifting its focus from successful decoding per se to understanding how decoding is affected by different experimental parameters, which would demonstrate the information that decoders are sensitive to (e.g., the color of a banana versus its orientation). Decoding mental and perceptual states using fMRI has become increasingly popular over the past two decades, with numerous highly-cited studies published in high-profile journals. Nevertheless, what have we learned from these decoders? In this opinion, we argue that fMRI-based decoders are not neurophysiologically informative and are not, and likely cannot be, applicable to real-world decision-making. The former point stems from the fact that decoding models cannot disentangle neural mechanisms from their epiphenomena. The latter point stems from both logical and ethical constraints. Constructing decoders requires precious time and resources that should instead be directed toward scientific endeavors more likely to yield meaningful scientific progress. [ABSTRACT FROM AUTHOR]

Motivated by the question of the impact of selective advantage in populations with skewed reproduction mechanisms, we study a Moran model with selection. We assume that there are two types of individuals, where the reproductive success of one type is larger than the other. The higher reproductive success may stem from either more frequent reproduction, or from larger numbers of offspring, and is encoded in a measure Λ for each of the two types. Λ -reproduction here means that a whole fraction of the population is replaced at a reproductive event. Our approach consists of constructing a Λ -asymmetric Moran model in which individuals of the two populations compete, rather than considering a Moran model for each population. Provided the measure are ordered stochastically, we can couple them. This allows us to construct the central object of this paper, the Λ − asymmetric ancestral selection graph, leading to a pathwise duality of the forward in time Λ -asymmetric Moran model with its ancestral process. We apply the ancestral selection graph in order to obtain scaling limits of the forward and backward processes, and note that the frequency process converges to the solution of an SDE with discontinuous paths. Finally, we derive a Griffiths representation for the generator of the SDE and use it to find a semi-explicit formula for the probability of fixation of the less beneficial of the two types. [ABSTRACT FROM AUTHOR]

In this paper, a flexural Mikaelian lens in thin plate is designed by using conformation transformation. The propagation characteristics of flexural waves in the lens are investigated through rays trajectory equation, simulation analyses, and experimental tests, confirming the self-focusing properties of the Mikaelian lens. Additionally, the study explores the Talbot effect for flexural waves, revealing through simulation studies that the Talbot effect within the Mikaelian lens exhibits nearly diffraction-free properties. Building on the non-diffractive nature of the Talbot effect within the Mikaelian lens, we explore the potential for encoding flexural waves using active interference sources. The simulation and experiment results demonstrate the good performance of the designed active encoding system. This work opens up new avenues for the encoding of flexural waves, presenting promising implications for applications in communication such as structural health monitoring, wireless communication in solid media and data transmission in robotics and other areas related to flexural wave technology. [ABSTRACT FROM AUTHOR]

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality. [ABSTRACT FROM AUTHOR]

Shape descriptors are extensively used in shape analysis tasks such as shape correspondence, segmentation and retrieval, just to name a few. Their performances significantly determine the efficiency and effectiveness of subsequent applications. For this problem, we propose a novel powerful descriptor called Anisotropic Fractional Spectral Manifold Wavelet Descriptor (AFSMWD), built upon an extended manifold signal processing tool named Anisotropic Fractional Spectral Manifold Wavelet (AFSMW), which is also presented for the first time in this paper. The novelty of AFSMW is integrating the fractional theory into the common anisotropic spectral manifold wavelet. Compared to the existing wavelets, it provides one more new parameter, namely, the fractional order, to balance or enhance the transform coefficients among different shape vertices, enabling more flexible local shape analysis and more hidden shape structural information explored. Due to the advantages of this added parameter and the capability of analyzing shape features from multiple scales and orientations, the AFSMW allows us to construct the powerful descriptor AFSMWD just using the AFSMW transform coefficients of a very simple function. The proposed descriptor appears to be especially localizable, discriminative, and robust to noises. Extensive experiments have demonstrated that our descriptor has outperformed the state-of-the-art descriptors, nearly achieving 22% improvements to the most related work ASMWD and 69% to the recent popular work WEDS on the FAUST dataset. Its superiorities are also announced in some challenging occasions such as shapes with large deformation or topological partiality. [ABSTRACT FROM AUTHOR]

Volume reconstruction techniques are gaining increasing interest in medical domains due to their potential to learn complex 3D structural information from sparse 2D images. Recently, neural radiance fields (NeRF), which implicitly model continuous radiance fields based on multi-layer perceptrons to enable volume reconstruction of objects at arbitrary resolution, have gained traction in natural image volume reconstruction. However, the direct application of NeRF to medical volume reconstruction presents unique challenges due to differences in imaging principles, internal structure requirements, and boundary delineation. In this paper, we evaluate different NeRF techniques developed for natural images, including sampling strategies, feature encoding, and the use of complimentary features, by applying them to medical images. We evaluate three state-of-the-art NeRF techniques on four datasets of medical images of different complexity. Our goal is to identify the strengths, limitations, and future directions for integrating NeRF into the medical domain. [ABSTRACT FROM AUTHOR]

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

In this paper, we present a new encoding and decoding method based on the recently introduced Minesweeper Model. Here, the main idea is to use Fibonacci–Lucas tree diagrams, and this proposed method uses a public key and a private key. To test this method, a new algorithm is constructed to enable a faster and more accurate control. [ABSTRACT FROM AUTHOR]

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

In this paper, we first study the maximally commutative set in prime dimensional systems, which is a set of generalized Pauli matrices, and it can be used to detect the local discrimination of generalized Bell states. We give a simple characterization of prime dimensional maximally commutative sets, that is, a subset of a set of generalized Bell states, whose second subscript is a multiple of the first subscript. Furthermore, some sets of generalized Bell states which can be locally distinguishable by one-way local operation and classical communication (LOCC) are constructed by using the structural characteristics of prime dimensional maximally commutative sets. Based on these distinguishable generalized Bell states, we propose a (t, n)-threshold quantum secret sharing scheme. Compared with the existing quantum secret sharing scheme, it can be found that there are enough distinguishable states to encode classical information in our scheme, the dealer only needs to send entangled particles once to make the participants get their secret share, which makes the secret sharing process more efficient than the existing schemes. Finally, we prove that this protocol is secure under dishonest participant attack, interception-and-resend attack and entangle-and-measure attack. [ABSTRACT FROM AUTHOR]

Pseudorandom encoders represent a significant type of absolute position encoders that can be implemented using only a single code track on a code disk and a single reading head, making them especially suitable for high-resolution position measurements. Since pseudorandom code words used for encoding positions are not compatible with other electronics, pseudorandom encoders include a converter that translates the pseudorandom code into the natural binary code. This paper proposes an improved implementation of the serial Fibonacci pseudorandom/natural code converter used in pseudorandom position encoders. The improvement is based on modifying the way bits are written into the converter's shift register, which reduces the propagation delay inside the code converter, thus enabling an increase in the clock frequency. Simulations of the proposed solution, performed in Multisim software, provide a realistic picture of its behaviour and performance. Based on the simulation results, the proposed solution enables a significant increase in the clock frequency of the code converter by 180.54% or 57.56%, depending on the encoder resolution. This significantly boosts the speed of code conversion and enhances the overall performance of the position encoder. The proposed solution is highly valuable for numerous position measurement applications, especially in the increasingly relevant high-resolution applications. [ABSTRACT FROM AUTHOR]

Card-based cryptography is a research field for realizing cryptographic protocols using a deck of physical cards. Shinagawa et al. proposed a regular n-sided polygon card, which can hold a value from 0 to n - 1 , and constructed an addition protocol over Z / n Z and a voting protocol with v voters and c candidates when v < n . In this paper, we propose an addition protocol over Z / m n Z using regular n-sided polygon cards. Technically, we introduce a cyclic integer encoding and a rot-and-shift shuffle to extend the modulus from n to mn. In addition, we construct two voting protocols with v voters and c candidates using regular n-sided polygon cards. Our first voting protocol requires c (⌈ v + 1 n ⌉ + v + 1) cards and v + 1 shuffles without restriction. Our second voting protocol reduces the number of cards to ⌈ v + 1 n ⌉ n + v + 1 when v < n and c ≤ n . [ABSTRACT FROM AUTHOR]

This paper proposes a scheme that adds XOR bit operations into the encoding and decoding process of the conventional non-orthogonal multiple access (NOMA) system to alleviate performance degradation caused by the power distribution of the original signal. Because the conventional NOMA combines and sends multiple data within limited resources, it has a higher data rate than orthogonal multiple access (OMA), at the expense of error performance. However, by using the proposed scheme, both error performance and sum rate can be improved. In the proposed scheme, the transmitter sends the original data and the redundancy data in which the exclusive OR (XOR) values of the data are compressed using the superposition coding (SC) technique. After this process, the data rate of users decreases due to redundancy data, but since the original data are sent without power allocation, the data rate of users with poor channel conditions increases compared to the conventional NOMA. As a result, the error performance and sum rate of the proposed scheme are better than those of the conventional NOMA. Additionally, we derive an exact closed-form bit error rate (BER) expression for the proposed downlink NOMA design over Rayleigh fading channels. [ABSTRACT FROM AUTHOR]

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

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

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

Recent experiments have shown that improper encoding in MR user interfaces exacerbates the individual cognitive symbol effect, leading to issues of excessive cognitive workload and elevated psychological stress levels in individual cognitive task methods. In light of this, this paper proposes a novel MR user interface information encoding method. By assessing the improvement level of object clues and action clues on information encoding quality, it identifies the optimal encoding forms for combining these two types of cues, thereby establishing a more intuitive and natural communication channel for information. The paper presents several human–machine interface usability test results and compares the proposed encoding forms with the MR interface encoding information currently used in significant factories like AVIC (Aviation Industry Corporation of China) to analyze the advantages and disadvantages of the proposed encoding forms. [ABSTRACT FROM AUTHOR]

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

This paper proposes an positional encoding-based attention mechanism model which can quantify the temporal correlation of ship maneuvering motion to predict the future ship motion in real sea state. To represent the temporal information of the sequential motion status, the positional encoding consisted by sine and cosine functions of different frequencies is chosen as the input of the model. First, the reasonableness of the improved architecture of the model is validated on the standard turning test datasets of an unmanned surface vehicle. Then, the absolute positional encoding based-scaled-dot product attention mechanism model is compared with other two attention mechanism models with different positional encoding and attention calculation methods and its superiority is verified. As demonstrated by exhaustive experiments, the model has the highest prediction accuracy when the input sequence length equals the output sequence length and the accuracy defined in this paper of the model will drop to less than 90% when the predicted length exceeds 45. Finally, the attention mechanism model is compared with the LSTM model with different lengths of input sequences to demonstrate that the attention mechanism model has a faster training speed when dealing with long sequences. [ABSTRACT FROM AUTHOR]

This paper studies the second-order coding rates for memoryless channels with a state sequence known non-causally at the encoder. In the case of finite alphabets, an achievability result is obtained using constant-composition random coding, and by using a small fraction of the block to transmit the empirical distribution of the state sequence. For error probabilities less than 0.5, it is shown that the second-order rate improves on an existing one based on independent and identically distributed random coding. In the Gaussian case (dirty paper coding) with an almost-sure power constraint, an achievability result is obtained using random coding over the surface of a sphere, and using a small fraction of the block to transmit a quantized description of the state power. It is shown that the second-order asymptotics are identical to the single-user Gaussian channel of the same input power without a state. [ABSTRACT FROM AUTHOR]

Recently, there has been a lot of success in using the deterministic approach to provide approximate characterization of Gaussian network capacity. In this paper, we take a deterministic view and revisit the problem of wiretap channel with side information. A precise characterization of the secrecy capacity is obtained for a linear deterministic model, which naturally suggests a coding scheme which we show to achieve the secrecy capacity of the degraded Gaussian model (dubbed as “secret writing on dirty paper”) to within half a bit. [ABSTRACT FROM PUBLISHER]

In this paper, we demonstrate for the first time that a 19-bit chipless tag based on a paper substrate can be realized using the flexography technique, which is an industrial high-speed printing process. The chipless tag is able to operate within the ultra-wide band (UWB) and has a reasonable size (7\times 3 cm^2) compared to state-of-the-art versions. Thus, it is possible to use this design for various identification applications that require a low unit cost of tags. Both the simulation and measurement results are shown, and performance comparisons are provided between several realization processes, such as classical chemical etching, flexography printing, and catalyst inkjet printing. [ABSTRACT FROM PUBLISHER]

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO + , and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy. [ABSTRACT FROM AUTHOR]

Recently, Siamese network based approaches show promising results on visual object tracking. These methods typically handle the tracking task by per-frame object detection and thus fail to fully exploit the rich temporal contexts among successive frames, which are important for accurate and robust object tracking. To benefit from the temporal information, in this paper, we investigate a per-clip tracking scheme in the Siamese-based approach and present a novel spatio-temporal SiamFC method for high-performance visual tracking. More specifically, we incorporate a non-local 3D fully convolutional network into a Siamese framework, which allows the model to act directly on the inputs of multiple templates and search video clips and to extract features from both spatial and temporal dimensions, thereby capturing the temporal information encoded in multiple video frames. We then propose a multi-template matching module to learn a representative tracking model using spatio-temporal template features and propagate informative target cues from the template set to the search clip using attention, which facilitate the object searching in clips. During inference, we employ a confident search region cropping and a dynamic multi-template update mechanism for stable and robust per-clip tracking. Experiments on six benchmark datasets show that our spatio-temporal SiamFC achieves competitive performance compared to state-of-the-art while running at approximatively 60 FPS on GPU. Codes are available at https://github.com/liangminstu/STSiamFC. [ABSTRACT FROM AUTHOR]

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

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

Accurate precipitation nowcasting is crucial for daily life, but the current forecasting models need further improvement in terms of accuracy. This paper proposed an innovative forecasting model called BTPN to address this issue. The model introduced bidirectional Transformer to extract features from both the forward and backward directions of spatiotemporal sequences, capturing key information and reducing spatiotemporal feature loss. It combined the convolutional Transformer block with the local encoding of convolution and the global encoding of Transformer to enhance spatiotemporal information extraction and correlation, alleviating the problem of long-term spatiotemporal information loss. The model also incorporated a detail extraction module to reduce the loss of local details and mitigate the issue of dissipation in high-value areas. Evaluation on the HKO-7 dataset shows that the BTPN model surpasses other advanced models in terms of MAE, SSIM, and CSI metrics, demonstrating excellent predictive capability in large-scale precipitation and extreme weather scenarios. The experiments demonstrate that the BTPN model possesses higher forecasting accuracy and promising applications. [ABSTRACT FROM AUTHOR]

Document level event extraction faces two major challenges: argument dispersion and multiple events. Most existing work adopts the method of extracting candidate arguments sentence by sentence, which makes it difficult to model contextual information across sentences. Therefore, this paper proposed a document level event extraction model based on multi granularity readers and graph attention networks. It used multi-granularity readers to achieve multi-level semantic encoding, and used the graph attention network to capture local and global relations between entity pairs. It constructed a pruned complete graph based on entity pair similarity as a pseudo trigger to comprehensively capture events and arguments in the document. Experiments conducted on the public datasets of ChFinAnn and DuEE-Fin show that the proposed method improves the problem of argument dispersion and enhances model s event extraction performance. [ABSTRACT FROM AUTHOR]

Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic neural radiance fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we perform an in-depth analysis of these artifacts and leverage the resulting insights to propose an improved representation. In particular, we present a novel density encoding that adapts the Fourier-based compression to the characteristics of the transfer function used by the underlying volume rendering procedure and leads to a substantial reduction of artifacts in the dynamic model. We demonstrate the effectiveness of our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative evaluations on synthetic and real-world scenes. [ABSTRACT FROM AUTHOR]

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation. [ABSTRACT FROM AUTHOR]