Rate Of Information Loss Research Articles

Aging drives a program of DNA methylation decay in plant organs.

How organisms age is a question with broad implications for human health. In mammals, DNA methylation is a biomarker for biological age, which may predict age more accurately than date of birth. However, limitations in mammalian models make it difficult to identify mechanisms underpinning age-related DNA methylation changes. Here, we show that the short-lived model plant Arabidopsis thaliana exhibits a loss of epigenetic integrity during aging, causing heterochromatin DNA methylation decay and the expression of transposable elements. We show that the rate of epigenetic aging can be manipulated by extending or curtailing lifespan, and that shoot apical meristems are protected from this aging process. We demonstrate that a program of transcriptional repression suppresses DNA methylation maintenance pathways during aging, and that mutants of this mechanism display a complete absence of epigenetic decay. This presents a new paradigm in which a gene regulatory program sets the rate of epigenomic information loss during aging.

Memory compromise at extended delays in people with new-onset epilepsy.

Memory is one of the most sensitive markers of cognitive compromise in people with new-onset epilepsy. Nonetheless, around half of these cases score within the normal range on standard memory testing. Here we explore whether memory retention at a 1-week delay reveals otherwise undetected memory compromise in such individuals, and how it relates to subjective memory complaints and mood. Using a prospective design, 38 adults with new-onset epilepsy underwent baseline memory screening via telephone using an abbreviated Rey Auditory Verbal Learning Test (RAVLT). Psychological screening occurred via online questionnaires. One week later, without forewarning, participants completed three follow-up memory tasks. Of particular focus, we explored longer-term memory performances and forgetting trajectories in those individuals (n = 23) who demonstrated normal memory performances (scores >10th percentile) at baseline (30-min delay). Outcomes were compared to 32 healthy controls, matched for age, sex, and education. As a group, people with epilepsy performed worse than controls on all memory measures, with 44 percent impaired at baseline testing. Of those unimpaired at baseline, the rate and volume of information loss over 1 week was significantly greater than for controls. Contextual memory performance at 1 week was also significantly poorer for people with epilepsy. At the individual level, the prevalence of impaired forgetting was not significantly different between patients and controls. Subjective memory complaints were not related to any objective tests but were strongly related to self-reported mood and anxiety symptoms. People with new-onset epilepsy show reduced memory at short and extended intervals. For those showing normal memory at baseline, information does appear to be forgotten more quickly than in healthy controls, though the effect is not large. The findings indicate that while extended delay memory testing is not essential in all new epilepsy cases, it could provide useful information for particular individuals. Memory problems are common in people with epilepsy shortly after seizure onset, however, many individuals still show normal memory performances on standard neuropsychological testing. Through testing memory at an extended timepoint (1 week), our study found that on average, these individuals showed a slightly quicker rate of forgetting over a 1-week period than people without a brain condition. Self-reported memory complaints in people with new epilepsy were unrelated to their actual memory skills on testing at short and long timepoints but were rather linked to lower mood and quality of life.

Joint resource scheduling and flight path planning of UAV-assisted IoTs in response to emergencies

In unmanned aerial vehicles (UAV)-assisted Internet of Things (IoT), emergencies can lead to changes in the status of ground sensor networks. This necessitates UAV-assisted IoT systems to possess the capability to dynamically respond to changes in the status of the ground sensor network. Therefore, this paper proposes a UAV scheduling scheme based on regional coordination (USRC). In this scheme, we divide the ground sensor network into task sub-regions according to the deployment of base stations. Then, we introduce a scheduling relationship pairing algorithm to determine the scheduling relationships between task sub-regions and UAV resources that need to be scheduled for each sub-region. Based on this, a dynamic path planning algorithm is designed to synchronize the planning of cross-region flight paths and intra-region flight paths for UAVs. Experimental results have demonstrated that the proposed scheme can efficiently respond to changes in the status of the ground sensor network by scheduling UAVs from sub-regions with abundant resources to those with scarce resources. Compared to other schemes, our scheme exhibits superior performance in reducing the age of information (AoI) and packet loss rate.

Landscape design of urban culture transmission based on the regional information security of Internet of Things

In the context of the rapid development of Internet of Things technology, urban cultural communication and information security have become a new focus in the field of landscape design. This paper innovatively discusses the landscape design of urban cultural communication based on Internet of Things regional information security, aiming at building a safe and culturally rich urban landscape environment. Taking the unique regional culture of Zhangjiajie as an example, this study evaluated the cultural communication effect of landscape design under information security guarantee through in-depth case analysis and field investigation, combined with Internet of Things information security technology. The results show that the cluster head node strategy has significant advantages in resisting physical capture attacks, especially when the number of sensor nodes captured is less than 2000, the information loss rate is less than 0.1. This discovery not only improves the level of information security in the Internet of Things environment, but also provides technical support for the effective dissemination of urban culture. In addition, through the detailed analysis and evaluation of landscape, this study further reveals the important role of landscape design in regional cultural inheritance. To sum up, this study not only provides a new perspective for urban landscape design, but also provides practical guidance for the protection and dissemination of urban culture in the era of Internet of Things.

Closed-Loop Supply Chain Decision Making For Waste Electrical Appliances in the Context of Blockchain

WEEE recycling not only effectively reduces environmental pollution and resource waste, but also creates economic benefits, which is in line with the concept of green development. However, the recycling market for used and end-of-life electrical products is characterized by problems such as asymmetric information and easy manipulation or loss of data, which may bring losses to enterprises. In this paper, by constructing a game model of the government, producers, and third-party recyclers, We delve into the optimal strategies of individual entities within the supply chain, contemplating both scenarios: the incorporation of blockchain technology and its absence, to determine the most logical and effective approach for each. The results show that the optimal strategies of the supply chain actors change in the case of adopting blockchain technology. At the same time, the profitability of each entity within the supply chain is predominantly influenced by the rate of inventory availability, information loss rate and input technology cost, and each subject can choose whether to put into use blockchain technology according to the actual situation.

Expecting the Unexpected: Entropy and Multifractal Systems in Finance.

Entropy serves as a measure of chaos in systems by representing the average rate of information loss about a phase point's position on the attractor. When dealing with a multifractal system, a single exponent cannot fully describe its dynamics, necessitating a continuous spectrum of exponents, known as the singularity spectrum. From an investor's point of view, a rise in entropy is a signal of abnormal and possibly negative returns. This means he has to expect the unexpected and prepare for it. To explore this, we analyse the New York Stock Exchange (NYSE) U.S. Index as well as its constituents. Through this examination, we assess their multifractal characteristics and identify market conditions (bearish/bullish markets) using entropy, an effective method for recognizing fluctuating fractal markets. Our findings challenge conventional beliefs by demonstrating that price declines lead to increased entropy, contrary to some studies in the literature that suggest that reduced entropy in market crises implies more determinism. Instead, we propose that bear markets are likely to exhibit higher entropy, indicating a greater chance of unexpected extreme events. Moreover, our study reveals a power-law behaviour and indicates the absence of variance.

Automatic Face Recognition of Target Images Based on Deep Learning Algorithms

The current research results of face recognition have poor recognition accuracy, time-consuming and high information loss rate. A method of automatic face recognition based on depth learning algorithm is proposed. The three primary RGB images are transformed into color saturated HSV images, and the dynamic range is extended by changing the non-linearity according to the conversion results. Based on the extended situation, the membership function is used to map the target image to the blurred plane, and the contrast enhancement of the image is completed. The enhanced image is substituted into the image segmentation, and the image eigenvalues are extracted preliminarily. The seed of image segmentation is generated by the eigenvalues. The growing seed regions are merged by the color distance and the texture distance to achieve the target image segmentation. Based on image segmentation, LBP operator is used to extract the local texture features of face twice, and then a deep convolution network model is constructed. The shared weights of convolution network model and pooling down-sampling technique are used to reduce the complexity of the model. The top layer of the model forms a feature classification surface of face image, fuses the constraint conditions, and obtains the trained face. The deep convolution network model is used to extract features from face images and complete face recognition. The experimental results show that the method has good accuracy, high efficiency and low information loss rate.

A New Approach for Anonymizing Transaction Data with Set Values

This article proposes a new method that can guarantee strong privacy while minimizing information loss in transactional data composed of a set of each attribute value in a relational database, which is not generally well-known structured data. The proposed scheme adopts the same top-down partitioning algorithm as the existing k-anonymity model, using local generalization to optimize safety and CPU execution time. At the same time, the information loss rate, which is a disadvantage of the existing local generalization, is further improved by reallocating transactions through an additional bottom-up tree search process after the partitioning process. Our scheme shows a very fast processing time compared to the HgHs algorithm using generalization and deletion techniques. In terms of information loss, our scheme shows much better performance than any schemes proposed so far, such as the existing local generalization or HgHs algorithm. In order to evaluate the efficiency of our algorithm, the experiment compared its performance with the existing local generalization and the HgHs algorithm, in terms of both execution time and information loss rate. As a result of the experiment, for example, when k is 5 in k-anonymity for the dataset BMS-WebView-2, the execution time of our scheme is up to 255 times faster than the HgHs algorithm, and with regard to the information loss rate, our method showed a maximum rate of 62.37 times lower than the local generalization algorithm.

A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder

The driving fatigue state of shield machine drivers directly affects the safe operation and tunneling efficiency of shield machines during metro construction. To cope with the problem that it is challenging to simulate the working conditions and operation process of shield machine drivers using driving simulation platforms and that the existing fatigue feature fusion methods usually show low recognition accuracy, shield machine drivers at Shenyang metro line 4 in China were taken as the research subjects, and a multi-modal physiological feature fusion method based on an L2-regularized stacked auto-encoder was designed. First, the ErgoLAB cloud platform was used to extract the combined energy feature (E), the reaction time, the HRV (heart rate variability) time-domain SDNN (standard deviation of normal-to-normal intervals) index, the HRV frequency-domain LF/HF (energy ratio of low frequency to high frequency) index and the pupil diameter index from EEG (electroencephalogram) signals, skin signals, pulse signals and eye movement data, respectively. Second, the physiological signal characteristics were extracted based on the WPT (wavelet packet transform) method and time–frequency analysis. Then, a method for driving fatigue feature fusion based on an auto-encoder was designed aiming at the characteristics of the L2-regularization method to solve the over-fitting problem of small sample data sets in the process of model training. The optimal hyper-parameters of the model were verified with the experimental method of the control variable, which reduces the loss of multi-modal feature data in compression fusion and the information loss rate of the fused index. The results show that the method proposed outperforms its competitors in recognition accuracy and can effectively reduce the loss rate of deep features in existing decision-making-level fusion.

Application of Intelligent Image Recognition and Digital Media Art in the Inheritance of Black Pottery Intangible Cultural Heritage

With the development of science and technology, intelligent image recognition and digital media art are gradually applied to the inheritance of intangible cultural heritage. In the inheritance of black pottery, intelligent image recognition can help identify the authenticity and age of black pottery, and digital media art can inject new vitality into black pottery culture. Digital media, which combines digitalization and mediaization, breaks the limitations of traditional media and provides ideological and technical guarantees for the reconstruction, dissemination and promotion of intangible cultural heritage protection. Image recognition technology is no longer limited to the auxiliary detection of images, on the contrary, it has become a recording and collection tool. With the promotion and development of science and technology, it can be more widely shared and disseminated in time and space. In this paper, the traditional machine learning algorithm and the intelligent learning algorithm are compared in image recognition, and the image information recognition rate, the number of iterations, the training time, and the information loss rate are compared in four aspects. The results have found that compared with the traditional learning algorithm, the number of iterations of the intelligent learning algorithm is reduced by 20%, the training time is reduced by 40%, the information loss rate is reduced by 6.3%, and the recognition accuracy rate is improved by 6.1%, which showed that under the current technology, intangible cultural heritage can be better inherited, and Chinese culture can be better promoted, so that more people can understand and join in the protection of intangible cultural heritage.

Quality assessment of shrub observation data based on TLS: A case of revegetated shrubland, Southern Qinghai‐Tibetan Plateau

AbstractVegetation restoration is one of the effective measures to deal with land degradation, and shrubs, as the pioneer plants, can improve soil conditions and accelerate the recovery of desertification. In recent decades, single‐scan (SS) and multiple‐scan (MS) modes have been widely used for forest inventories by terrestrial laser scanner (TLS) but TLS‐based shrub observation, and data quality uncertainties are less studied, especially in eco‐fragile regions with high survey costs and harsh environmental conditions. In this study, two shrub sample plots with different topographic characteristics were selected as the study area in the southern Qinghai‐Tibetan Plateau. We compared the basic phenotypic parameters (BPPs) under SS and MS mode, including number (N), height (H) and crown width (CW), to evaluate the potential of SS mode in extracting vegetation attributes. Through the distribution of shrub information loss rate (L) with distance and shrub height, the limitations of SS mode in obtaining data were discussed. By combining the topographic features of scanning spots, topographic relief (T) and L, we provided reasonable opinions on TLS collection mode, setting of scanning spots, and other operational aspects. The results indicated that: (1) SS mode has good applicability in shrub observation under different terrain characteristics. (2) The height distribution of the shrub is the main factor affecting the quality of data collection, and the information loss bias caused by distance should be considered only in the shrub plots with uniform height distribution. (3) T has an impact on shrub growth but has no effect on the quality of data acquisition (no significant correlation between T and L). These findings will provide technical support for TLS data collection on ecological restoration of degraded grassland dunes.

Dynamic signal recovery in distribution grids using compressive lossy measurements

Distribution system state estimation requires reliable aggregation of the measured data. However, the large volume of the measured data imposes a significant stress on the underlying communication infrastructure. With the challenges associated with measurement availability, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. In this paper, we evaluate the robustness of an updated Kalman filtered modified compressive sensing (KF-ModCS) technique that dynamically estimates the grid states using a small fraction of measured data. In practice, measurements used for sparsity based state estimation may also be intermittent due to communication network induced losses. To understand the effect of packet losses on KF-ModCS, we provide an upper bound for the expected variances of the state estimation error for a given rate of information loss. This upper bound is further improved if the support set of the sparse signal that characterizes the state dynamics does not change over time and/or the reduced model is observable. Simulations based on two practical data sets collected from actual customers in a distribution grid validate the theoretical results.

Comprehensive Evaluation of College Students' Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model.

Nowadays, more and more college students' physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information loss rate is 4.136%, and the accuracy of motion mode decision is 92.58%. The average relative time is 12.7, and the accuracy of physical health decision making is 90.02%. The above two values are not significant compared with the traditional ID3 algorithm. The experimental results show that the improved ID3 algorithm has significant optimization in the stability of information transmission and the accuracy of sports recommendation decision making, and can be applied to the physical health evaluation and sports recommendation of college students in a certain range.

Waveform Learning for Next-Generation Wireless Communication Systems

We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-based detector. The method maximizes an achievable information rate, while simultaneously satisfying constraints on the adjacent channel leakage ratio (ACLR) and peak-to-average power ratio (PAPR). This allows control of the tradeoff between spectral containment, peak power, and communication rate. Evaluation on an additive white Gaussian noise (AWGN) channel shows significant reduction of ACLR and PAPR compared to a conventional baseline relying on quadrature amplitude modulation (QAM) and root-raised-cosine (RRC), without significant loss of information rate. When considering a 3rd Generation Partnership Project (3GPP) multipath channel, the learned waveform and neural receiver enable competitive or higher rates than an orthogonal frequency division multiplexing (OFDM) baseline, while reducing the ACLR by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {10~ \text {dB}}$ </tex-math></inline-formula> and the PAPR by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {2~ \text {dB}}$ </tex-math></inline-formula> . The proposed method incurs no additional complexity on the transmitter side and might be an attractive tool for waveform design of beyond-5G systems.

Performance enhanced probabilistically shaped PAM4 signal based on nonlinear differential coding for IM/DD system

In this paper, the probabilistically shaped pulse amplitude modulation (PS-PAM) signal is proposed in intensity modulation and direct detection (IM/DD) system. For the traditional shaping scheme with constant composition distribution matcher (CCDM), there are several problems that high-precision algorithm for long block coding, the loss of information rate for short block coding. In addition, it can increase nonlinear interference. In order to reduce computational complexity and alleviate the influence of nonlinear interference on the system, the nonlinear differential coding (NLDC) scheme is used to realize PS, which can be better integrated with the cost-effective optical access network. This paper evaluates seven shaping spectrums obtained by different NLDC coding coefficients. As the coding coefficient increases, the performance of the system gradually improves. Moreover, this paper proposed the machine learning includes neural network (NN) and improved K-means clustering algorithm at the receiver. The results show that the PS-PAM4 signal is significantly better than the ordinary PAM4 signal in improving the channel bandwidth tolerance and signal transmission rate. Under the same data rate, using NLDC can save 5.8 GHz bandwidth for the system. For the bandwidth-limited system, the PS-PAM4 signal can obtain 19Gbit/s bit rate gain compared with the ordinary PAM4 signal. Compared with the typical shaping scheme using CCDM, this scheme can achieve 2.2 GHz band tolerance gain and 5Gbit/s rate gain in nonlinear fiber channel.

Experimental Demonstration of Rate-Adaptive Concatenated Codes for Flexible Optical Networks

We experimentally demonstrate a rate-adaptive concatenated forward error correction (FEC) scheme to minimize the information rate loss due to varying channel conditions. The concatenated coding scheme is based on inner soft-decision polar code and outer hard-decision staircase code. An experiment in a wavelength-division multiplexed (WDM) system with different SNR per WDM channel is performed by employing concatenated codes with various coded modulation schemes over a target distance of 140km which is relevant for extending the reach of data center interconnects. Experimental results show that the achieved data rate of the WDM system can be improved up to 37.6% with the proposed coding scheme w.r.t. the fixed code rate ZR FEC.

Research on information classification and storage in cloud computing data center based on group collaboration intelligent clustering

In order to overcome the problems of long time and low accuracy of traditional methods, a cloud computing data center information classification and storage method based on group collaborative intelligent clustering was proposed. The cloud computing data center information is collected in real time through the information acquisition terminal, and the collected information is transmitted. The optimization function of information classification storage location was constructed by using the group collaborative intelligent clustering algorithm, and the optimal solutions of all storage locations were evolved to obtain the elite set. According to the information attribute characteristics, different information was allocated to different elite sets to realize the classified storage of information in the cloud computing data center. The experimental results show that the longest time of information classification storage is only 0.6 s, the highest information loss rate is 10.0%, and the highest accuracy rate is more than 80%.

Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

PurposeThe purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback.Design/methodology/approachIn this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data.FindingsAnalysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated.Originality/valueThe paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.

Postural stability assessment in flatfoot subjects trough lyapunov exponent analysis

Abstract Background Flatfoot subjects presents an increase plantar foot area related to a plantar pressure feedback impairment. The Lyapunov Exponent is considered a nonlinear parameter used to characterize a signal chaotic behavior measuring the information rate loss from time series, i.e., for the Center of Pressure data. This exponent is used to quantify and measure the capability and resistance of subjects to several perturbations. The study purpose was to investigate the postural stability differences among foot posture condition through the Lyapunov Exponent analysis. Methods The sample of the observation descriptive study consisted of 31 participants (23.26 yo ± 4.43 SD) comprising a total of 62 feet, where 15 integrated into the experimental group with bilateral flatfoot condition and the remaining 16 in the control group with the bilateral neutral foot condition. Subjects were screened, before posture analysis, using the Navicular Drop Test and Resting Calcaneal Stance Position test, to characterize each group. All participants were subjected to a bipedal weight-bearing stance posture stability analysis a force platform, both in eyes-open and closed condition. The Lyapunov Exponent was calculated using the Matlab-R2020b (MathWorks Inc., USA) software. Results Regarding the Center of Pressure outcomes, only the Lyapunov Exponent value upon the Antero-Posterior component regarding groups in the eyes closed condition (diff = 3.09°, p = .016) presented a significant result. Conclusion Flatfoot subjects present a significant difference compared to neutral foot participants, in bipedal weight-bearing stance, in the eyes closed condition regarding the Lyapunov Exponent. This relates to increase variability and decrease stability regarding the Antero-Posterior component.

A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest

The selection of a suitable discretization method (DM) to discretize spatially continuous variables (SCVs) is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate (ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate (ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest (training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, China. The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small (<1%), whereas the ICR values are obviously more in line with general recognition (usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the ML-based natural hazard susceptibility assessments.