Information Transfer Scheme Research Articles

Modern graphic design application innovation based on graphic abstract processing method

Abstract The art of abstraction reflects the rich imagination of human beings to a certain extent and is now widely used in movies, games, advertisements, and other modern design fields. In this paper, a new edge tangential flow algorithm applicable to image abstraction is proposed, which first estimates the gradient strength and gradient direction of each point in a given input image by using the Sobel operator and then establishes the edge graph of the input image by using the edge information transfer strategy. On this basis, the application and value of abstract art and this graphical abstract processing method in modern graphic design are discussed, and the essence of abstract form art is perfectly embodied so as to make the two perfectly integrated. The results show that in the application of graphic abstract processing in graphic design, the average value of user attraction, understanding, and memory dimension scores are 0.53, 0.675, and 0.683, respectively, which verifies the effectiveness of this graphic design practice through users.

Multifunctional Thermoelectric Temperature Sensor for Noncontact Information Transfer and Tactile Sensing in Human‐Machine Interaction

AbstractUsing sensing devices for noncontact information transfer enhances data security in human‐machine interaction by eliminating direct contact between the information carrier and the communication interface. However, current contactless information transfer strategies struggle with complex application scenarios due to limitations in sensing mechanisms. Here, this work proposes an innovative noncontact information transfer approach that leverages invisible thermal radiation and the Seebeck effect, and demonstrates its feasibility by designing a high‐resolution temperature sensor based on a laminar thermoelectric aerogel of carbon nanotube/PEDOT:PSS/nanocellulose. The sensor exhibits exceptional sensitivity, ultralow detection limits (0.02 K), rapid response time, and reliable cycling stability in temperature detection without interference from pressure stimuli. This work further presents a sensor array‐based communication interface capable of extracting, decoding, and transmitting high‐capacity encrypted information contactlessly. The interface also serves as a contactless input terminal for precisely monitoring finger movements. Additionally, the sensor can convert pressure into resistance signals, functioning as tactile electronic skin in contact scenarios. This study presents a dependable strategy for transmitting information without physical contact, contributing to the development of future integrated devices.

Construction information model as a tool to reduce information uncertainty in the operational management of construction

The construction of capital construction projects is a complex probabilistic dynamic system subject to numerous external and internal factors. With the complexity, uniqueness of the construction object or constrained conditions for the production of work, the number of such factors increases, as a result, information uncertainty in construction management increases, which leads to a decrease in the effectiveness of the use of traditional organizational and technological models. It is required to search for new and improve existing models. One such model is the construction information model. The purpose of the article is to substantiate the reduction of information uncertainty in the operational management of construction by changing the structural links in the construction management system and the use of a construction information model as a dynamic organizational and technological model. The transition to information modeling technologies requires the main participants in the investment and construction project to change the form of interaction, as well as changes in communication patterns in the structures of individual organizations. For example, the management of construction organizations should continuously manage construction using a building information model. The organizational structure in which the building information model is used positively affects the following aspects of the control system: reduces transaction costs between departments and services; provides high information content; improves the consistency of information between services by analyzing data from a single source; speeds up the decision-making process; increases the stability of the management system, taking into account external and internal factors; enhances organizational reliability. The significance of the presented results for the construction industry is to reduce information uncertainty in the operational management of construction by changing the structural links and information transfer scheme, as well as the continuous collection, storage and analysis of information coming from the construction site into a single construction information model.

Biomedical Argument Mining Based on Sequential Multi-Task Learning.

Biomedical argument mining aims to automatically identify and extract the argumentative structure in biomedical text. It helps to determine not only what positions people adopt, but also why they hold such opinions, which provides valuable insights into medical decision making. Generally, biomedical argument mining consists of three subtasks: argument component identification, argument component classification and relation identification. Current approaches employ conventional multi-task learning framework for jointly addressing the latter two subtasks, and achieve some success. However, explicit sequential dependency between these two subtasks is ignored, which is crucial for accurate biomedical argument mining. Moreover, relation identification is conducted solely based on the argument component pair without considering its potentially valuable context. Therefore, in this paper, a novel sequential multi-task learning approach is proposed for biomedical argument mining. Specifically, to model explicit sequential dependency between argument component classification and relation identification, an information transfer strategy is employed to capture the information of argument component type that is transferred to relation identification. Furthermore, graph convolutional network is employed to model dependency relation among the related argument component pairs. The proposed method has been evaluated on a benchmark dataset and the experimental results show that the proposed method outperforms the state-of-the-art methods.

ExtendedSketch+: Super host identification and network host trust evaluation with memory efficiency and high accuracy

Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host cardinality estimation is the premise of super host identification and it can be used to measure the trust degree of a network. Sketches have been widely used in super host identification. However, expanding network scale and increasing link rate bring challenges to the efficiency and accuracy of sketch-based super host identification. The size of the counters in most sketches requires a trade-off between memory resources and accuracy. To meet the need of measuring high cardinalities, large size counters are applied, which leads to memory waste in monitoring low cardinality hosts. On the other hand, most sketches have high computational overhead when tracking superhosts, resulting in inefficiency. In order to address these issues, we propose a novel memory efficient and reversible sketch, named ExtendedSketch+, to provide accurate host cardinality estimation with the purpose of super host identification and network host trust evaluation. ExtendedSketch+ achieves both high memory utilization efficiency and high accuracy, in addition to high super host identification efficiency. It applies extensible counters to record unbalanced host cardinality distribution, by adaptively expanding the counters with the increase of cardinality. It adopts a lossless traffic information transfer strategy during counter extension to ensure the accuracy of cardinality estimation. It can directly tracks the host with the highest cardinality in each bucket, which greatly improves the identification efficiency of super hosts. Based on accurate cardinality estimation of ExtendedSketch+, we can further accurately evaluate host trust degree. We theoretically analyze ExtendedSketch+ with regard to its space and time complexities and estimation accuracy. We conduct performance evaluation based on real network traffic traces. Experimental results show that ExtendedSketch+ performs better than state-of-the-art sketches regarding super host identification and provides accurate host trust evaluation.

SorTEE: Service-Oriented Routing for Payment Channel Networks With Scalability and Privacy Protection

Payment channel networks (PCNs) are emerged as the most widely deployed solution to mitigate the scalability problem of permissionless cryptocurrencies, allowing vast payments to be carried out off-chain. Routing, which finds feasible paths between the senders and receivers, is critical for PCNs. However, existing solutions either fail to achieve high scalability that can maintain low storage/computation/network communication overhead, or they are susceptible to privacy disclosure. In this paper, we propose SorTEE, a service-oriented routing solution for PCNs, which adopts a set of service nodes to alleviate the per-user burden of routing and achieves more comprehensive privacy guarantees than the state-of-the-art by leveraging trusted execution environments (TEEs). SorTEE demands users communicate with the TEE by the secure channel to protect the privacy of transaction value. Then, an oblivious path mechanism is designed to construct redundant paths with the pseudo senders and receivers generated by TEEs to confuse its untrusted controller. Further, we report a novel attack that allows malicious service nodes to drop the valid paths for profit, and design a feedback mechanism to relieve it. Moreover, SorTEE hides the identities of the senders/receivers for the intermediate nodes of payment paths by introducing a novel identity information transfer scheme called encrypted identity chain. Based on security analysis and performance evaluation, our results demonstrate that SorTEE is able to achieve sufficient privacy-preserving payment and low per-user overhead.

Cultural transmission based multi-objective evolution strategy for evolutionary multitasking

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller population size and fewer computing resources. As a result, the proposed algorithm can effectively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative transfer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms.

Informational Lithography Approach Based on Source and Mask Optimization

Optical lithography is a critical technique to fabricate nano-scale semiconductor devices by replicating the layouts of integrated circuits from the lithography mask onto the silicon wafer. As the critical dimension of integrated circuits continuously shrinks, source and mask optimization (SMO) methods are extensively used to improve the resolution and image fidelity of lithography patterning. However, the theoretical lower bound of the lithography pattern error in the SMO framework is not yet understood. This paper introduces an informational lithography approach to unveil the information transmission mechanism in lithography systems under freeform illumination configurations. The lithography system is regarded as an information channel, where the mask pattern and the print image on the wafer are modeled as the statistical input and output signals, respectively. Subsequently, we derive the optimal information transfer (OIT) of the lithography system, which represents the best information transfer strategy rendering the least image distortion. Based on the OIT, we derive a lower bound for the lithography pattern error, and the corresponding optimal source pattern and optimal mask probability distribution. Finally, we propose a new SMO algorithm based on the information theoretical framework to effectively improve the lithography image fidelity compared to the existing gradient-based SMO algorithm.

Evolution of biocoenosis through symbiosis with fitness approximation for many-tasking optimization

Memetic computing is a blooming research area, which treats memes as the fundamental building blocks of information transfer. Evolutionary multitasking is an emerging topic in memetic computation, which applies evolutionary algorithm to optimize multiple tasks at a time. A famous class of algorithms for evolutionary multitasking is the multi-factorial evolutionary algorithm (MFEA). Nevertheless, current MFEAs only consider problems with small number of tasks, resulting in a lack of effective information transfer strategy. This study proposes a framework for evolutionary multitasking, called the evolution of biocoenosis through symbiosis with fitness approximation (EBSFA). The EBSFA incorporates evolution of biocoenosis through symbiosis (EBS) with fitness approximation to ameliorate the information transfer. The improvement of EBSFA is three-fold, including (1) the adaptive control of information transfer among tasks, (2) the selection of individuals from the universal offspring pool for evaluation based on fitness approximation, and (3) an ensemble method for improving the accuracy of fitness approximation through k nearest neighbors. Experimental analysis verifies the effectiveness and efficiency of the proposed EBSFA, by comparison with an advanced single-tasking method, the covariance matrix adaptation evolution strategy (CMAES), an illustrious multitasking optimization method, the MFEA-II, and an evolutionary many-tasking method, the EBS on a set of many-tasking benchmark problems. The results show that EBSFA can gain nice solution quality and fast convergence speed. Further analysis validates the effectiveness of the proposed components on improving the information transfer.

Regularized Evolutionary Multitask Optimization: Learning to Intertask Transfer in Aligned Subspace

This article proposes a novel and computationally efficient explicit intertask information transfer strategy between optimization tasks by aligning the subspaces. In evolutionary multitasking, the tasks might have biases embedded in function landscapes and decision spaces, which often causes the threat of predominantly negative transfer. However, the complementary information among different tasks can give an enhanced performance of solving complicated problems when properly harnessed. In this article, we distill this insight by introducing an intertask knowledge transfer strategy implemented in the low-dimension subspaces via a learnable alignment matrix. Specifically, to unveil the significant features of the function landscapes, the task-specific low-dimension subspaces is established based on the distribution information of subpopulations possessed by tasks, respectively. Next, the alignment matrix between pairwise subspaces is learned by minimizing the discrepancies of the subspaces. Given the aligned subspaces by applying the alignment matrix to subspaces' base vectors, the individuals from different tasks are then projected into aligned subspaces and reproduce therein. Moreover, since this method only considers the leading eigenvectors, it turns out to be intrinsically regularized and noise-insensitive. Comprehensive experiments are conducted on the synthetic and practical benchmark problems so as to assess the efficacy of the proposed method. According to the experimental results, the proposed method exhibits a superior performance compared with existing evolutionary multitask optimization algorithms.

다중 사용자 다중 안테나 시스템을 위한 유사 최적 무선 충전 정보 전송 기법

본 논문에서는 다중사용자 다중안테나 (MU-MIMO; Multiuser Multi-Input Multi-Output) 채널에서 유사 최적무선 충전 정보 전송 기법을 제안한다. 공유기는 하향링크 빔포밍을 통해 다수의 단말에 전력을 전송하고, 단말은 충전된 전력을 이용하여 ZF (Zero-forcing) 기반의 공간 다중화 기법을 이용하여 상향링크로 정보를 전송한다. 이때 최대-최소 전송률을 최적화하기 위해 Gradient 알고리즘 기반 하향링크 에너지 빔포밍 기법을 유도하고, 단말별 데이터 스트림 수 선정 기법을 적용한 상향링크 정보 전송 기법을 제안한다. 모의실험을 통해 제안된 기법이 기존 부최적 기법에 비해 성능이 우수하고, 최적 전송 기법과 유사한 성능을 보이면서 복잡도가 현저히 낮음을 보인다.

Improving the Ability to Extract Novel Text with Information Transfer Strategies and Project-based Learning Models

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Hybrid Precoder Design for Cache-Enabled Millimeter-Wave Radio Access Networks

In this paper, we study the design of a hybrid precoder, consisting of an analog and a digital precoder, for the delivery phase of downlink cache-enabled millimeter wave (mmWave) radio access networks (CeMm-RANs). In CeMm-RANs, enhanced remote radio heads (eRRHs), which are equipped with local cache and baseband signal processing capabilities in addition to the basic functionalities of conventional RRHs, are connected to the baseband processing unit via fronthaul links. Two different fronthaul information transfer strategies are considered, namely, hard fronthaul information transfer, where hard information of uncached requested files is transmitted via the fronthaul links to a subset of eRRHs, and soft fronthaul information transfer, where the fronthaul links are used to transmit quantized baseband signals of uncached requested files. The hybrid precoder is optimized for maximization of the minimum user rate under a fronthaul capacity constraint, an eRRH transmit power constraint, and a constant-modulus constraint on the analog precoder. The resulting optimization problem is non-convex, and hence the global optimal solution is difficult to obtain. Therefore, convex approximation methods are employed to tackle the non-convexity of the achievable user rate, the fronthaul capacity constraint, and the constant modulus constraint on the analog precoder. Then, an effective algorithm with provable convergence is developed to solve the approximated optimization problem. Simulation results are provided to evaluate the performance of the proposed algorithms, where fully digital precoding is used as benchmark. The results reveal that except for the case of a large fronthaul link capacity, soft fronthaul information transfer is preferable for CeMm-RANs.

Research on Scalable Zigbee Wireless Sensor Network Expansion Solution

Zigbee technology has a wide range of applications in constructing wireless sensor networks which can increase the number of nodes in the Zigbee network that can be accommodated and expand the wireless sensor network’s network size and monitoring range. TI’s Z-Stack network protocol stack is used as the carrier for Zigbee technology. On the one hand, the information transfer strategy of the network is improved to increase the capacity of a single network. On the other hand, a multiple Zigbee network cooperative communication scheme is proposed which jointly build a scalable, high-capacity wireless sensor network. Test results on the CC2530 chip show that the actual capacity of a single Zigbee network can be improved and multiple Zigbee networks can be collaborated with each other. It is verified that the scheme not only improves the Zigbee network’s overall capacity, but also can flexibly expand, increase or delete Zigbee network size and capacity.

Non-linear amplification of graded voltage signals in the first-order visual interneurons of the butterfly Papilio xuthus.

Lamina monopolar cells (LMCs) are the first-order visual interneurons of insects and crustacea, primarily involved in achromatic vision. Here, we investigated morphological and electrophysiological properties of LMCs in the butterfly Papilio xuthus Using intracellular recording coupled with dye injection, we found two types of LMCs. Cells with roundish terminals near the distal surface of the medulla demonstrating no or small depolarizing spikes were classified as L1/2. Cells with elongated terminals deep in the medulla that showed prominent spiking were classified as L3/4. The majority of LMCs of both types had broad spectral sensitivities, peaking between 480 and 570 nm. Depending on the experimental conditions, spikes varied from small to action potential-like events, with their amplitudes and rates decreasing as stimulus brightness increased. When the eye was stimulated with naturalistic contrast-modulated time series, spikes were reliably triggered by high-contrast components of the stimulus. Spike-triggered average functions showed that spikes emphasize rapid membrane depolarizations. Our results suggest that spikes are mediated by voltage-activated Na+ channels, which are mainly inactivated at rest. Strong local minima in the coherence functions of spiking LMCs indicate that the depolarizing conductance contributes to the amplification of graded responses even when detectable spikes are not evoked. We propose that the information transfer strategies of spiking LMCs change with light intensity. In dim light, both graded voltage signals and large spikes are used together without mutual interference, as a result of separate transmission bandwidths. In bright light, signals are non-linearly amplified by the depolarizing conductance in the absence of detectable spikes.

Low Complexity Wireless Powered Information Transfer Strategy for Multiuser MIMO Systems

In this paper, the minimum achievable rate maximization problem for wireless-powered multiple users is investigated. Zero-forcing (ZF)-based precoding is considered for information transmission (IT) in the uplink from multi-antenna users to a multi-antenna access point that charges the users through wireless power transmission (PT) in the downlink. A max–min user rate optimization problem is formulated when a ZF-based transmission is used for spatial division multiplexing in the IT phase. We propose a low-complexity method to design the energy beamformer for the PT phase, the transmit power allocation for multi-stream transmission for the IT phase, and the time ratio between PT and IT phases in terms of maximizing the minimum achievable rate. The validity of the proposed scheme is shown through complexity analysis and numerical simulations.

Information transfer via implicit encoding with delay time modulation in a time-delay system

A new encoding scheme for information transfer with modulated delay time in a time-delay system is proposed. In the scheme, the message is implicitly encoded into the modulated delay time. The information transfer rate as a function of encoding redundancy in various noise scales is presented and it is analyzed that the implicit encoding scheme (IES) has stronger resistance against channel noise than the explicit encoding scheme (EES). In addition, its advantages in terms of secure communication and feasible applications are discussed.

Extension of a Modular Particle-Continuum Method to Vibrationally Excited, Hypersonic Flows

A modular particle-continuum method is extended to include vibrationally excited energy modes to simulate hypersonic steady-state flows that exhibit small regions of translational nonequilibrium in a mainly continuum flowfield. This method loosely couples an existing direct simulation Monte Carlo code to a Navier–Stokes solver (computational fluid dynamics) while allowing both time step and cell size to be completely decoupled between each method. A new information-transfer scheme that controls the inherently large statistical scatter of vibrational energies in low-temperature regions is described and tested. Two vibrational-relaxation models are implemented to test the sensitivity in agreement between direct simulation Monte Carlo and the modular particle-continuum method.By limiting the size of the direct simulationMonteCarlo region to only areas in translational nonequilibrium andmaintaining consistent physicalmodels in both computationalfluid dynamics anddirect simulationMonteCarlo modules, the modular particle-continuummethod is able to reproduce full direct simulationMonte Carlo results for flowwith globalKnudsen number of 0.01while decreasing the computational time required by a factor of about four.

Competition-based transfer of carbohydrate expression information from a cell-adhered surface to a secondary surface

An information transfer strategy was developed for the visualization of carbohydrate expression by the competition of a primary cell-adhered solid surface with a carbohydrate assembled surface as an artificial secondary surface for one species. The strategy could be effectively utilized for in situ monitoring of dynamic carbohydrate expression on an adhesive cell surface.

Coevolution of honest signaling and cooperative norms by cultural group selection

Evolution of cooperative norms is studied in a population where individual and group level selection are both in operation. Individuals play indirect reciprocity game within their group and follow second order norms. Individuals are norm-followers, and imitate their successful group mates. Aside from direct observation individuals can be informed about the previous actions and reputations by information transferred by others. A potential donor estimates the reputation of a potential receiver either by her own observation or by the opinion of the majority of others (indirect observation). Following a previous study ( Scheuring, 2009) we assume that norms determine only the probabilities of actions, and mutants can differ in these probabilities. Similarly, we assume that individuals follow a stochastic information transfer strategy. The central question is whether cooperative norm and honest social information transfer can emerge in a population where initially only non-cooperative norms were present, and the transferred information was not sufficiently honest. It is shown that evolution can lead to a cooperative state where information transferred in a reliable manner, where generous cooperative strategies are dominant. This cooperative state emerges along a sharp transition of norms. We studied the characteristics of actions and strategies in this transition by classifying the stochastic norms, and found that a series of more and more judging strategies invade each other before the stabilization of the so-called generous judging strategy. Numerical experiments on the coevolution of social parameters (e.g. probability of direct observation and the number of indirect observers) reveal that it is advantageous to lean on indirect observation even if information transfer is much noisier than for direct observation, which is because to follow the majorities’ opinion suppresses information noise meaningfully.