Localized errors, which occur in windows with bounded lengths, are common in a range of applications. Such errors can be modeled as k-substring edits, which replace one substring with another string, both with lengths upper bounded by k. This generalizes errors such as localized deletions or burst substitutions studied in the literature. In this paper, we show through statistical analysis of real data that substring edits better describe differences between related documents compared to independent edits, and thus commonly arise in problems related to data synchronization. We also show that for the dataset under study, assuming codes exist that can achieve the Gilbert-Varshamov (GV) bound, substring-edit-correcting codes can synchronize two documents with much lower overhead compared to general indel/substitution-correcting codes. Furthermore, given a constant k, we construct binary codes of length n for correcting a single k-substring edit that achieves the GV bound and subsequently has redundancy of asymptotically$2\log n$, compared to$4k\log n$, the lowest redundancy achievable by an existing code for this problem. The time complexities of both encoding and decoding are polynomial with respect to n.

The substring edit error replaces a substringuofxwith another stringv, where the lengths ofuandvare bounded by a given constantk. It encompasses localized insertions, deletions, and substitutions within a window. Codes correcting one substring edit have redundancy at least logn+k. In this paper, we construct codes correcting one substring edit with redundancy logn+Ok(log logn), which is almost optimal. We also study the average-case document-exchange problem under one substring edit and construct a hash with an expected length of approximately 2 logn+Ok(log logn) for any iid distribution for the documents.

Dueling bandits are widely used to model preferential feedback prevalent in many applications such as recommendation systems and ranking. In this paper, we study the Borda regret minimization problem for dueling bandits, which aims to identify the item with the highest Borda score while minimizing the cumulative regret. We propose a rich class of generalized linear dueling bandit models, which cover many existing models. We first prove a regret lower bound of order $\Omega(d^{2/3} T^{2/3})$ for the Borda regret minimization problem, where $d$ is the dimension of contextual vectors and $T$ is the time horizon. To attain this lower bound, we propose an explore-then-commit type algorithm for the stochastic setting, which has a nearly matching regret upper bound $\tilde{O}(d^{2/3} T^{2/3})$. We also propose an EXP3-type algorithm for the adversarial linear setting, where the underlying model parameter can change in each round. Our algorithm achieves an $\tilde{O}(d^{2/3} T^{2/3})$ regret, which is also optimal. Empirical evaluations on both synthetic data and a simulated real-world environment are conducted to corroborate our theoretical analysis.

The substring edit error is the operation of replacing a substring$\boldsymbol{u}$of$\boldsymbol{x}$with another string$\boldsymbol{v}$, where the lengths of$\boldsymbol{u}$and$\boldsymbol{v}$are bounded by a given constant$k$. It encompasses localized insertions, deletions, and substitutions within a window. Codes correcting one substring edit have redundancy at least$\log n+k$. In this paper, we construct codes correcting one substring edit with redundancy$\log n+O(\log\log n)$, which is asymptotically optimal. The full version of this paper is available online.1

Abstract Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assigning distinct penalties to errors based on their directions. We employ a genetic algorithm, combined with a swift nondominated sorting technique, for multiobjective optimization in the ELM-Pinball-Huber context. This method simultaneously reduces training errors while streamlining model structure. We practically apply the integrated model to forecast power load data in Taixing City, which is situated in the southern part of Jiangsu Province. The empirical findings confirm the method’s effectiveness.

Abstract Wind power forecasting techniques have been well developed over the last half-century. There has been a large number of research literature as well as review analyses. Over the past 5 decades, considerable advancements have been achieved in wind power forecasting. A large body of research literature has been produced, including review articles that have addressed various aspects of the subject. However, these reviews have predominantly utilized horizontal comparisons and have not conducted a comprehensive analysis of the research that has been undertaken. This survey aims to provide a systematic and analytical review of the technical progress made in wind power forecasting. To accomplish this goal, we conducted a knowledge map analysis of the wind power forecasting literature published in the Web of Science database over the last 2 decades. We examined the collaboration network and development context, analyzed publication volume, citation frequency, journal of publication, author, and institutional influence, and studied co-occurring and bursting keywords to reveal changing research hotspots. These hotspots aim to indicate the progress and challenges of current forecasting technologies, which is of great significance for promoting the development of forecasting technology. Based on our findings, we analyzed commonly used traditional machine learning and advanced deep learning methods in this field, such as classical neural networks, and recent Transformers, and discussed emerging technologies like large language models. We also provide quantitative analysis of the advantages, disadvantages, forecasting accuracy, and computational costs of these methods. Finally, some open research questions and trends related to this topic were discussed, which can help improve the understanding of various power forecasting methods. This survey paper provides valuable insights for wind power engineers.

Localized errors, which occur in windows with bounded lengths, are common in a range of applications. Such errors can be modeled as k-substring edits, which replace one substring with another string, both with lengths upper bounded by k. This generalizes errors such as localized deletions or burst substitutions studied in the literature. In this paper, we show through statistical analysis of real data that substring edits better describe differences between related documents compared to independent edits, and thus commonly arise in problems related to data synchronization. We also show that for the dataset under study, assuming codes exist that can achieve the Gilbert-Varshamov bound, substring-edit-correcting codes can synchronize two documents with much lower overhead compared to general indel/substitution-correcting codes. Furthermore, given a constant k, we construct binary codes of length n for correcting a k-substring edit with redundancy of roughly 2logn, compared to 8logn, the lowest redundancy achievable by an existing code for this problem. The time complexities of both encoding and decoding are polynomial with respect to n.

Due to its higher data density, longevity, energy efficiency, and ease of generating copies, DNA is considered a promising technology for satisfying future storage needs. However, a diverse set of errors including deletions, insertions, duplications, and substitutions may arise in DNA at different stages of data storage and retrieval. The current paper constructs error-correcting codes for simultaneously correcting short (tandem) duplications and at most$p$edits, where a short duplication generates a copy of a substring with length$\leq 3$and inserts the copy following the original substring, and an edit is a substitution, deletion, or insertion. Compared to the state-of-the-art codes for duplications only, the proposed codes correct up to$p$edits (in addition to duplications) at the additional cost of roughly$8p(\log _{q} n) (1+o(1))$symbols of redundancy, thus achieving the same asymptotic rate, where$q\ge 4$is the alphabet size and$p$is a constant. Furthermore, the time complexities of both the encoding and decoding processes are polynomial when$p$is a constant with respect to the code length.

In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items. Thus, a uniform querying strategy over users may not be optimal. To address this issue, we propose an elimination-based active sampling strategy, which estimates the ranking of items via noisy pairwise comparisons from multiple users and improves the users’ average accuracy by maintaining an active set of users. We prove that our algorithm can return the true ranking of items with high probability. We also provide a sample complexity bound for the proposed algorithm, which outperforms the non-active strategies in the literature and close to oracle under mild conditions. Experiments are provided to show the empirical advantage of the proposed methods over the state-of-the-art baselines.

Over unknown, possibly large, alphabets, one approach for compressing sequences is to separately convey their symbols and patterns (sequences of integers representing orders in which the symbols appear). It has been shown that patterns generated by i.i.d. sources can be compressed with diminishing redundancy using compressors that know the number of occurrences of each integer symbol. Motivated by applications with resource restrictions, e.g., data deduplication, we study universal compression of patterns using compressors under constraints. A characterization of constrained compressors is given and general results for computing redundancies are derived. We also show that for patterns generated by i.i.d. sources over an alphabet of size k, the per-symbol average- and worst-case redundancies are at least Θ(log(min(k, n/ logn))) bits (n is the sequence length), under the constraint that compressors only know the number of distinct integer symbols in the pattern. A simple sequential compressor satisfying this constraint is also analyzed and shown to achieve this redundancy in the first order term.

Ranking from noisy comparisons is of great practical interest in machine learning. In this paper, we consider the problem of recovering the exact full ranking for a list of items under ranking models that do *not* assume the Strong Stochastic Transitivity property. We propose a $$\delta$$-correct algorithm, Probe-Rank, that actively learns the ranking of the items from noisy pairwise comparisons. We prove a sample complexity upper bound for Probe-Rank, which only depends on the preference probabilities between items that are adjacent in the true ranking. This improves upon existing sample complexity results that depend on the preference probabilities for all pairs of items. Probe-Rank thus outperforms existing methods over a large collection of instances that do not satisfy Strong Stochastic Transitivity. Thorough numerical experiments in various settings are conducted, demonstrating that Probe-Rank is significantly more sample-efficient than the state-of-the-art active ranking method.

This study investigates antecedents to and outcomes of two stress reactions, telework distress (detrimental stress), and telework eustress (beneficial stress) using a model derived from an integration of the transactional model of stress with the job-demands and resources model. The model includes a person antecedent (resilience), and three environment antecedents (work-family conflict, work overload, and autonomy). These factors should influence experienced distress and eustress, which, in turn, affect telework outcomes (telework satisfaction, exhaustion, perceived performance, and perceived productivity. The model is evaluated using a sample of 329 Chinese teleworkers. This study findings indicate that resilience, work-family conflict, and work overload affect experienced distress, while resilience and autonomy affect experienced eustress. Experienced distress influenced satisfaction, exhaustion, and perceived performance; eustress had effects on all four outcomes. Interestingly, resilience had the largest total effect sizes on telework outcomes.

Data deduplication saves storage space by identifying and removing repeats in the data stream. Compared with traditional compression methods, data deduplication schemes are more computationally efficient and are thus widely used in large scale storage systems. In this paper, we provide an information-theoretic analysis of the performance of deduplication algorithms on data streams in which repeats are not exact. We introduce a source model in which probabilistic substitutions are considered. More precisely, each symbol in a repeated string is substituted with a given edit probability. Deduplication algorithms in both the fixed-length scheme and the variable-length scheme are studied. The fixed-length deduplication algorithm is shown to be unsuitable for the proposed source model as it does not take into account the edit probability. Two modifications are proposed and shown to have performances within a constant factor of optimal for a specific class of source models with the knowledge of model parameters. We also study the conventional variable-length deduplication algorithm and show that as source entropy becomes smaller, the size of the compressed string vanishes relative to the length of the uncompressed string, leading to high compression ratios.

In the tasks of image aesthetic quality assessment, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets. To reduce the error in labelling and solve the problem of normal data distribution, we propose a new aesthetic mixed dataset with classification and regression called AMD-CR, and we train a meta reweighting network to reweight the loss of training data differently. In addition, we provide a training strategy according to different stages based on pseudo labels, and then we use it for aesthetic training according to different stages in classification and regression tasks. In the construction of the network structure, we construct an aesthetic adaptive block (AAB) structure that can adapt to any size of the input images. Besides, we also use the efficient channel attention (ECA) to strengthen the feature extracting ability of each task. The experimental result shows that our method improves 0.1112 compared with the conventional method in SROCC. The method can also help to find best aesthetic path planning for unmanned aerial vehicles (UAV) and vehicles.

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this article, we construct an new image attribute dataset called aesthetic mixed dataset with attributes (AMD-A) and design external attribute features for fusion. Besides, we propose an efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring, and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment.

Data deduplication has gained attention in large-scale storage systems due to the explosive growth in digital data. Recently, the information-theoretic aspects of conventional deduplication algorithms have been studied and novel algorithms with better performance have been proposed. In this paper, we study the performances of variable-length deduplication and multi-chunk deduplication algorithms from the point of view of information theory. We consider a source model in which source strings are composed of repeated blocks with each data block containing a constant number of substitution edits. We show that over the proposed source model, the variable-length deduplication algorithm can achieve asymptotically arbitrarily large compression ratio and the multi-chunk deduplication algorithm is order optimal under mild conditions.

Compared to conventional data storage media, DNA has several advantages, including high data density, energy efficiency, longevity, and ease of generating copies. However, challenges arising from the prevalence and variety of errors in the DNA data storage pipeline, which include substitutions, duplications, insertions, and deletions, must be addressed. This paper focuses on simultaneously correcting an arbitrary number of short tandem duplications and at most$p$substitutions, where a short tandem duplication error consists of inserting a copy of a substring of length at most 3 immediately after it. Interacting with tandem duplications, the substitutions may affect segments of unbounded lengths in the stored sequence. However, if the codewords are irreducible, i.e., they do not have any short tandem repeats, the problem can be cast as correcting edits in at most$p$substrings of bounded lengths. We construct irreducible codes with a structure that allows identifying where edit errors have occurred, which are then corrected using an MDS code. The rate of the proposed code correcting duplications and at most$p$substitutions, when$\log p=o(\log n)$, is shown to be at least$\log(q-2)(1-o(1)$, where$q$is the alphabet size and$n$is the length of the code.

Nowadays, almost everyone can shoot photos using smart phones. However, not everyone can take good photos. We propose to use computational aesthetics to automatically teach people without photography training to take excellent photos. We present Aesthetic Dashboard: a system of rich aesthetic evaluation and guidance for mobile photography. We take 2 most used types of photos: landscapes and portraits into consideration. When people take photos in the preview mode, for landscapes, we show the overall aesthetic score and scores of 3 basic attributes: light, composition and color usage. Meanwhile, the matching scores of the 3 basic attributes of current preview to typical templates are shown, which can help users to adjust 3 basic attributes accordingly. For portraits, besides the above basic attributes, the facial appearance, the guidance of face light, body pose and the garment color are also shown to the users. This is the first system that can teach mobile users to shoot good photos in the form of aesthetic dashboard, through which, users can adjust several aesthetic attributes to take good photos easily.

MicroDNAs are a type of extrachromosomal circular DNAs found both in cell nuclei and as cell-free circulating DNA, with links to cancer and genetic mosaicism. Research suggests that microDNAs originate from chromosomal DNA. To better understand the evolutionary role of microDNAs, it is of interest to determine if and how they interact with the chromosomal DNA. In particular, do microDNAs re-integrate back into the chromosomal genome? Given their circular form, if they do, this will lead to a specific form of repeat in the genome, which we term circular repeat. Due to the presence of mutations, these repeats are expected to be approximate. Motivated by this question, we develop an efficient ab initio algorithm for finding approximate circular repeats in a given genome. The algorithm consists of two main components. First, it performs a two-stage search to locate candidate circular repeat patterns by identifying their substrings. Second, it checks the validity of each candidate by inspecting the flanking sequences of the substrings. By applying our method to human genome chromosomes 21, 22, and Y, we find hundreds of approximate circular repeats. Our simulation shows that the patterns found are unlikely to be purely the result of inherent repetitive structure of the genome, thus suggesting that microDNAs reintegrate back into the genome.

Data deduplication saves storage space by identifying and removing repeats in the data stream. In this paper, we provide an information-theoretic analysis of the performance of deduplication algorithms with data streams where repeats are not exact. We introduce a source model in which probabilistic substitutions are considered. Two modified versions of fixed-length deduplication are studied and proven to have performance within a constant factor of optimal with the knowledge of repeat length. We also study the variable-length scheme and show that as entropy becomes smaller, the size of the compressed string vanishes relative to the length of the uncompressed string.

Genomic evolution can be viewed as string-editing processes driven by mutations. An understanding of the statistical properties resulting from these mutation processes is of value in a variety of tasks related to biological sequence data, e.g., estimation of model parameters and compression. At the same time, due to the complexity of these processes, designing tractable stochastic models and analyzing them are challenging. In this paper, we study two kinds of systems, each representing a set of mutations. In the first system, tandem duplications and substitution mutations are allowed and in the other, interspersed duplications. We provide stochastic models and, via stochastic approximation, study the evolution of substring frequencies for these two systems separately. Specifically, we show that k-mer frequencies converge almost surely and determine the limit set. Furthermore, we present a method for finding upper bounds on entropy for such systems.

The ground-based microwave radiometer has a strong ability to observe the earth's surface throughout all-time and allweather conditions, which is widely used in the experiments of soil moisture, freeze-thaw and other surface parameters of microwave remote sensing. The observed data could not only be used to establish and verify microwave radiation model and interpret the transmission process and mechanism, but also could be used to improve the retrieval algorithm of surface parameters by optimizing different target parameters. Base on the observation experiment of soil moisture, the design of its experimental scheme was described, and the multi-frequency microwave radiation characteristics of soil moisture were analyzed under different land-cover types in this paper.

The driving force behind the generation of biological sequences are genomic mutations that shape these sequences throughout their evolutionary history. An understanding of the statistical properties that result from mutation processes is of value in a variety of tasks related to biological sequence data, e.g., estimation of model parameters and compression. At the same time, due to the complexity of these processes, designing tractable stochastic models and analyzing them are challenging. In this paper, we study two types of mutations, tandem duplication and substitution. These play a critical role in forming tandem repeat regions, which are common features of the genome of many organisms. We provide a stochastic model and, via stochastic approximation, study the behavior of the frequencies of N- grams in resulting sequences. Specifically, we show that N-gram frequencies converge almost surely to a set which we identify as a function of model parameters. From these frequencies, other statistics can be derived. In particular, we present a method for finding upper bounds on entropy.

Computer-based communication technologies are increasingly important to personal and organizational communication. One important factor related to the adoption and diffusion of communication innovations is critical mass. Critical mass influences the adoption and diffusion of interactive communication innovations, both through network externalities and through sustainability of the innovation. Unfortunately, critical mass is difficult to measure and is typically only demonstrable after the critical mass point has been reached. Potential adopters’ perceptions of critical mass also may be important to adoption decisions. In this paper, we extend this thinking using a synthesis of the Theory of Reasoned Action and Diffusion of Innovation theory by developing a research model. The model is empirically tested using survey data that are analyzed using partial least squares. The focal innovation is instant messaging. Results indicate that perceived critical mass influences use intentions directly and through perceptions of the characteristics of the innovation. The perceived innovation characteristics impact attitude toward use, which in turn impacts use intentions. The model predicts a sizable and significant portion of both attitudes and use intentions. Further, perceived critical mass is able to explain a significant portion of the variance in each perceived innovation characteristic. Implications for research and practice are discussed.

This study examines the impact of affiliation motivation on the intention to use a groupware system. Building on attachment theory, one personality attribute, affiliation motivation, was incorporated into the Technology Acceptance Model to explore groupware use. A study using a group of MBA students that were just beginning to use groupware was conducted to determine if affiliation motivation influences perceived usefulness, perceived ease of use, and intention to use. The results indicated that affiliation motivation was significantly associated with behavioral intention and perceived ease of use when use was voluntary. Implications for instructors who are using or planning to use groupware in their classes are discussed.