Information theoretic methods for stochastic model reduction based on state projection

With the growing technology and amount of data, the use of cloud and centralized servers is increasing. When the data of the individuals is stored by the third party it leads to the insecurity of misuse of their information in the individuals. Data Mining is carried on this data stored over cloud to get useful information, analysis and decision making purposes. But along with the process of mining, privacy preservation is the key concern. While performing data mining over large set of data there are chances of privacy violation, loss of information, loss of privacy of individuals. While concentrating only on preserving privacy can increase inefficiency and complexity of data mining techniques. Techniques used for achieving privacy lead to a significant information loss. The methods concerned about information loss generally lack behind in achieving a good level of privacy. Thus it is important to find the efficient data mining technique which can preserve privacy without causing high loss of information. This paper analyses various privacy preserving data mining techniques for their pros and cons and then gives a proposed concept to achieve privacy with minimum information loss.

A reconstruction decoder for computing with words

The new members of mathematical physics of heat conduction in stationary state with their subharmonic solution originate from a real variational procedure named as “recency principle”. As this variational principle is not a so‐called “quasivariational” one therefore it is suitable to show irreversibility in stationary state process. The physical meaning is here the minimum production of entropy (the minimum information loss) as the internal irreversible process source. We show for the dissipation of information explanations from the set theory. Neighborhood, closure, boundary, cluster point, and accumulation point etc. are necessary to form well‐posed boundary conditions. Irreversible non‐equilibrium process causes energy degradation which can be interpreted both geometrically and mathematically as compared with the Laplace potential equation for conservative field. The energy degradation comes from information loss equivalence. For subharmonic solution we use Perron theorem.

In 2018, EU Member States adopted a 17-item scale to measure child deprivation and monitor progress in their fight against child poverty. This indicator will be collected in future every three years via an ad hoc module of the European Union Statistics on Income and Living Conditions (EU-SILC). Previous research has shown how deprivation measures can be implemented more efficiently and with minimal information loss using adaptive testing, at least in the context of a single country. This paper examines the scope to implement the adaptive approach in a multi-national context with wide variations in deprivation levels and potentially in cultural preferences for consumption. The paper shows that the adaptive approach works effectively in this context. Time savings of around 40 per cent can be achieved with very minimal information losses both at the EU level and at the level of each individual country. Time savings are much greater in countries with lower deprivation. The adaptive approach may therefore offer particular advantages in a multi-national context as it can provide a consistent measure for all participating countries while targeting survey time and resources where they are most needed.

Accuracy-enhancing methods for balancing-related frequency-weighted model and controller reduction

We examine the evaluation of the minimum information loss due to an unread local measurement in mixed states of bipartite systems, for a general entropic form. Such quantity provides a measure of quantum correlations, reducing for pure states to the generalized entanglement entropy, while in the case of mixed states it vanishes just for classically correlated states with respect to the measured system, as the quantum discord. General stationary conditions are provided, together with their explicit form for general two-qubit states. Closed expressions for the minimum information loss as measured by quadratic and cubic entropies are also derived for general states of two-qubit systems. As application, we analyze the case of states with maximally mixed marginals, where a general evaluation is provided, as well as X states and the mixture of two aligned states.

The technique of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -anonymization allows the releasing of databases that contain personal information while ensuring some degree of individual privacy. Anonymization is usually performed by generalizing database entries. We formally study the concept of generalization, and propose three information-theoretic measures for capturing the amount of information that is lost during the anonymization process. The proposed measures are more general and more accurate than those that were proposed by Meyerson and Williams and Aggarwal et al. We study the problem of achieving <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -anonymity with minimal loss of information. We prove that it is NP-hard and study polynomial approximations for the optimal solution. Our first algorithm gives an approximation guarantee of O(ln <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ) for two of our measures as well as for the previously studied measures. This improves the best-known O( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> )-approximation in. While the previous approximation algorithms relied on the graph representation framework, our algorithm relies on a novel hypergraph representation that enables the improvement in the approximation ratio from O( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ) to O(ln <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ). As the running time of the algorithm is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k</sup> }), we also show how to adapt the algorithm in in order to obtain an O( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> )-approximation algorithm that is polynomial in both <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> .

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

Optical Wireless Communication [OWC] involves communication over a light media by a Transmit-Receive pair. In Optical Camera communication (OCC), a camera is involved in decoding the received information while transmitter transmits the information by encoding it in a spatially arranged colour codes. If there is information loss in the received image resulting in the incorrect decoding of the received information, the overall system throughput will decrease. Information loss could be due to long distance, low camera resolution, background illumination and opaqueness of the media between the transmitter and the receiver. This paper presents a rate adaptation mechanism to improve the communication throughput by minimizing information loss. Based on our literature survey, we find that our approach is novel. We have also presented a comparative performance with and without rate adaptation. While throughput improvement with rate adaptation varies in our experiment, the maximum improvement we have achieved is by ∼50%.

Abstract. Error metrics are useful for evaluating model performance and have been used extensively in climate change studies. Despite the abundance of error metrics in the literature, most studies use only one or two metrics. Since each metric evaluates a specific aspect of the relationship between the reference data and model data, restricting the comparison to just one or two metrics limits the range of insights derived from the analysis. This study proposes a new framework and composite error metrics called Bergen metrics to summarize the overall performance of climate models and to ease interpretation of results from multiple error metrics. The framework of Bergen metrics are based on the p norm, and the first norm is selected to evaluate the climate models. The framework includes the application of a non-parametric clustering technique to multiple error metrics to reduce the number of error metrics with minimum information loss. An example of Bergen metrics is provided through its application to the large ensemble of regional climate simulations available from the EURO-CORDEX initiative. This study calculates 38 different error metrics to assess the performance of 89 regional climate simulations of precipitation and temperature over Europe. The non-parametric clustering technique is applied to these 38 metrics to reduce the number of metrics to be used in Bergen metrics for eight different sub-regions in Europe. These provide useful information about the performance of the error metrics in different regions. Results show it is possible to observe contradictory behaviour among error metrics when examining a single model. Therefore, the study also underscores the significance of employing multiple error metrics depending on the specific use case to achieve a thorough understanding of the model behaviour.

Feature selection (FS) can be defined as the problem of finding the minimal number of features from an original set with the minimum information loss. Since FS problems are known as NP-hard problems, it is necessary to investigate a fast and an effective search algorithm to tackle this problem. In this paper, two incremental hill-climbing techniques (QuickReduct and CEBARKCC) are hybridized with the binary ant lion optimizer in a model called HBALO. In the proposed approach, a pool of solutions (ants) is generated randomly and then enhanced by embedding the most informative features in the dataset that are selected by the two filter feature selection models. The resultant population is then used by BALO algorithm to find the best solution. The proposed binary approaches are tested on a set of 18 well-known datasets from UCI repository and compared with the most recent related approaches. The experimental results show the superior performance of the proposed approaches in searching the feature space for optimal feature combinations.

Vietnamese feudal dynasty woodblocks are invaluable national treasures, but many have been lost or damaged due to wars and poor conditions of preservative environments. Fortunately, 2D-printed papers of the damaged or lost woodblocks have been well-preserved, allowing for reconstruction their 3D digital version. To ensure accurate reconstruction of 3D woodblocks, it is essential to have a reliable alignment method that closely matches human visual perception. In this paper, we introduce an automatic pipeline for objective visual quality evaluation of woodblock characters. The pipeline includes two components: the first shifts the quality evaluation from 3D domain to 2D domain by employing orthogonal projection to transform a 3D mesh woodblock character model into a 2D depth map image with minimum information loss. The second utilizes established 2D perceptual metrics, which closely align with human visual perception, to evaluate the 2D depth map. Our evaluation demonstrates that features of these proposed perceptual metrics employed in the pipeline can effectively characterize the visual appearance of the woodblock character based on our prepared dataset. Additionally, the experiments presented in the paper also demonstrate that these metrics can sensitively detect degradation levels in both the foreground and background components of a woodblock character.

The Compressed Baryonic Matter (CBM) experiment is designed to handle interaction rates of up to 10 MHz and up to 1 TB/s of raw data generated. With triggerless streaming data acquisition in the experiment and beam intensity fluctuations, it is expected that occasional data bursts will surpass bandwidth capabilities of the Data Acquisition System (DAQ) system. In order to preserve integrity of event data, the bandwidth of DAQ must be throttled in an organised way with minimum information loss. The Timing and Fast Control (TFC) system provides a latency-optimised datapath for throttling commands and distributes a system clock together with a global timestamp. This paper describes a prototype design of the system with focus on synchronisation and its evaluation.

Ginger (Zingiber officinale) is a herbaceous perennial plant widely recognized as a popular spice across the world. India ranks as the largest producer and consumer of ginger globally. However, ginger cultivation faces several production constraints, primarily due to variability in weather conditions. The uncertainty associated with weather patterns poses significant challenges for both farmers and policymakers, hindering timely decision-making at the field and market levels. Crop weather models serve as a reliable statistical tool that represents complex relationship between crop and weather parameters. A field experiment conducted at Kerala Agricultural University, Vellanikkara, Thrissur district using Maran variety in 2021-2022. The experiment was conducted using a split plot design. The main plot treatments consisted of four planting dates: 1st June (D1), 15th June (D2), 1st July (D3), and 15th July (D4). The subplot treatments comprised three types of organic mulches: green leaves (M1), paddy straw (M2), and dry coconut leaves (M3). The June 1st planted crop yielded more fresh ginger (19957.81 kg ha-1) than the July 15th planted crop. A consistent decline in observed yield was noted from D1 to D4 across all mulch types. Early planting (D1) showed the highest yields, (15,276.74 kg ha⁻¹), whereas the latest planting (D4) yielded the lowest (1,589.5 kg ha⁻¹). Paddy straw mulch produced high yield (fresh yield of 16941.36 kg ha-1) which was on par with and green leaves mulch (fresh yield of 15798.1 kg ha-1). PCA is an adaptive data analysis technique for reducing the dimensionality of large data sets like weather parameters hence increasing the interpretability with minimum information loss. Regression equations were fitted estimating yield for green leaves; paddy straw and dry coconut leaves mulch by performing Principal Component Analysis (PCA). From analysis both the observed and estimated yields were comparable. This study investigates the effects of planting dates and mulching types on ginger yield in the face of fluctuating weather patterns, as well as the usefulness of statistical modelling like PCA and regression in yield prediction. This yield prediction models can be utilised by farmers and policy makers to predict yield in advance.

In this study an attempt is made for studying the Himalayan glacier features using TerraSAR-X and Indian Remote Sensing Satellite, Linear Imaging Self Scanning System III (IRS LISS –III) images. New generation, synthetic aperture radar (SAR) data from TerraSAR-X (TS-X) sensor provide opportunity for glacier feature studies in Himalayan rugged terrain. Spot Light High resolution mode TS-X data give idea about glacial features which remained untraceable from other existing SAR system. However, presence of speckle noise in SAR images degrades the interpretability of the glacier features. Speckle suppression filters (Lee, Frost, Enhanced Lee, Gamma-Map) are applied on SAR intensity images. On the basis of field sight seeing and insitu observations it is observed that still features are not clear. Hence attempt has been made for fusing multitemporal multispatial speckle reduced TS-X SAR data and multispectral IRS LISS-III data for extracting the glacial features such as crevasses, exposed ice and superaglacier lakes. Principal component analysis (PCA) represents the high spectral resolution data in a linear subspace with minimum information loss. Herein, PCA based image fusion technique is adopted for this study and comparison is made between IHS fusion technique and PCA based technique for glacier studies in the Himalayan region.