Elementary trapping sets (ETSs) are the main culprits of the performance of low-density parity-check (LDPC) codes in the error floor region. Due to their large quantities and complex structures, ETSs are difficult to analyze. This paper studies the impact of the distance between cycles on ETSs, focusing on two special graph classes: theta graphs and dumbbell graphs, which correspond to cycles with negative and non-negative distances, respectively. We determine the Turán numbers of these graphs and prove that increasing the distance between cycles can eliminate more ETSs. Additionally, using the linear state-space model and spectral theory, we prove that increasing the length of cycles or distance between cycles decreases the spectral radius of the system matrix, thereby reducing the harmfulness of ETSs. This is consistent with the conclusion obtained using Turán numbers. For specific cases when removing two 6-cycles with distance of -1, 0 and 1, respectively, we calculate the sizes, spectral radii, and error probabilities of ETSs. These results confirm that the performance of LDPC codes improves as the distance between cycles increases. Furthermore, we design the PEG-CYCLE algorithm, which greedily maximizes the distance between cycles in the Tanner graph. Numerical results show that the QC-LDPC codes constructed by our method achieve performance comparable to or even superior to state-of-the-art construction methods.

Temperature and emissivity separation (TES) is a crucial process for converting thermal infrared (TIR) hyperspectral data into actionable information. Since the development of thermal infrared hyperspectral imagers is still in its nascent stage, most TES algorithms have been validated primarily using simulated data within the context of land resource remote sensing. However, there has been insufficient focus on urban environments with complex underlying surfaces, particularly on low emissivity targets. In this study, we developed a TES algorithm capable of adapting to a broader emissivity range. The performance of the algorithm in retrieving temperature and emissivity was evaluated against several typical TES algorithms, using data cubes acquired by the airborne thermal infrared hyperspectral imaging system (ATHIS) as test data. The experimental results revealed that existing algorithms exhibited relatively high retrieval errors for low emissivity ground objects. In contrast, the developed algorithm significantly improved the accuracy of retrieval for such objects while maintaining comparable accuracy for non-low-emissivity targets. These findings suggest that the proposed algorithm enhances TES accuracy and expands the applicability of thermal infrared hyperspectral imaging in environmental remote sensing of urban environments with complex underlying surfaces.

In recent years, Multimodal Sentiment Analysis (MSA) leveraging deep learning has demonstrated exceptional performance in a wide range of domains. Its success lies in effectively utilizing information from multiple modalities to analyze sentiments. Despite these advancements, MSA is confronted with two significant challenges. Firstly, each modality often has a surplus of unimportance data, which can overshadow the essential information. Secondly, the crucial cues for sentiment analysis may conflict across different modalities, thereby complicating the analysis process. These issues have a certain impact on the model’s effectiveness in MSA tasks. To address these challenges, this paper introduces a novel method tailored for MSA, termed InfoEnh. This approach utilizes a masking technique as the bottleneck for information filtering, simultaneously maximizing mutual information to retain crucial data. Furthermore, the method integrates all modalities into a common feature space via domain adaptation, which is enhanced by the application of optimal transport. Extensive experiments conducted on two benchmark MSA datasets demonstrate the effectiveness of our proposed approach. Further analyzes indicate significant improvements over the baselines.

Elementary trapping sets (ETSs) are the principal culprits for the performance of LDPC codes in the error floor region. Due to their large quantity, intricate structures, and high computational complexity, determining how to eliminate dominant ETSs in the design of LDPC codes has become a critical issue in improving error floor behavior. In this paper, we address this problem by avoiding particular theta graphs$(\theta(1,2,2)$and$\theta(2,2,2))$in the Tanner graph to eliminate specific ETSs. These can be characterized by a pivotal tool in graph theory - Turán numbers. Theoretically, we derive the exact Turán number for$\theta(1,2,2)$and demonstrate that all$(a, b)$-ETSs in a Tanner graph with variable-reaular degree$d_{L}(v)=\gamma$must satisfy the inequality$b\geq a\gamma-\frac{1}{2}a^{2}$. This result improves the lower bound previously obtained by Amirzade when the girth is 6. For girth 8, by constraining the relationship between any two 8-cycles in the Tanner graph, we establish a similar inequality$b\geq a\gamma-\frac{a(\sqrt{8a-7}-1)}{2}$. Our simulation results indicate that codes designed with these considerations exhibit improved performance and a lower error floor over additive white Gaussian noise channels.

Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle these drawbacks, we leverage inherent recurrent patterns in time series, analogize them as fixed combinations in textual data, and incorporate the concept of perplexity. The cleaning problem is thus transformed to minimize the perplexity of the time series under a given cleaning cost, and we design a four-phase algorithmic framework to tackle this problem. To ensure the framework's feasibility, we also conduct a brief analysis of the impact of dirty data and devise an automatic budget selection strategy. Moreover, to make it more generic, we additionally introduce advanced solutions, including an ameliorative probability calculation method grounded in the homomorphic pattern aggregation and a greedy-based heuristic algorithm for resource savings. Experiments on 12 real-world datasets demonstrate the superiority of our methods.

Nowadays the demands for managing and analyzing substantially increasing collections of time series are becoming more challenging. Subsequence matching, as a core subroutine in time series analysis, has drawn significant research attention. Most of the previous works only focus on matching the subsequences with equal length to the query. However, many scenarios require support for efficient variable-length subsequence matching. In this paper, we propose a new representation, Uniform Piecewise Aggregate Approximation (UPAA) with the capability of aligning features for variable-length time series while remaining the lower bounding property. Based on UPAA, we present a compact index structure by grouping adjacent subsequences and similar subsequences respectively. Moreover, we propose an index pruning algorithm and a data filtering strategy to efficiently support variable-length subsequence matching without false dismissals. The experiments conducted on both real and synthetic datasets demonstrate that our approach achieves considerably better efficiency, scalability, and effectiveness than existing approaches.

Understanding the reasons behind differences between aggregated sequences derived from SQL queries is crucial for data scientists. However, existing methods often suffer from being labor-intensive, lacking scalability, providing only approximate solutions, and inadequately supporting sequence difference explanations. In response, we introduce SDEcho, a novel framework designed to automate the explanation searching for sequence differences in high-dimensional and high-volume datasets. SDEcho utilizes advanced pruning techniques, considering pattern, order, and dimension perspectives, as well as their interactions, to prune the entire explanation space while maintaining explanations accurate and concise. This hybrid pruning approach significantly accelerates the explanation searching process, making SDEcho a valuable tool for data analysis tasks. Extensive experiments on synthetic and real-world datasets, along with a case study, demonstrate that SDEcho outperforms existing methods in terms of both effectiveness and efficiency.