Redundant Code Research Articles

Robust structured light with efficient redundant codes

Structured light (SL) systems acquire high-fidelity 3D geometry with active illumination projection. Conventional systems exhibit challenges when working in environments with strong ambient illumination. This paper studies a general-purposed solution to improve the robustness of SL by projecting a redundant number of patterns. Despite sacrificing the signal-noise-ratio at each frame, projected signals become more distinguishable from errors. Thus, the geometry can be recovered easily. We systematically analyze the redundant SL code design rules to achieve high accuracy with minimum redundancy. Based on the more reliable correspondence cost volume and the natural image prior, we integrate spatial context-aware disparity estimators into our system to further boost performance. We also demonstrate the application of such techniques in iterative error detection and refinement. We demonstrate significant performance improvements of efficient redundant code SL systems in both simulations and challenging real-world scenes.

Invertible Bloom Lookup Tables with Listing Guarantees

The Invertible Bloom Lookup Table (IBLT) is a probabilistic concise data structure for set representation that supports a listing operation as the recovery of the elements in the represented set. Its applications can be found in network synchronization and traffic monitoring as well as in error-correction codes. IBLT can list its elements with probability affected by the size of the allocated memory and the size of the represented set, such that it can fail with small probability even for relatively small sets. While previous works only studied the failure probability of IBLT, this work initiates the worst case analysis of IBLT that guarantees successful listing for all sets of a certain size. The worst case study is important since the failure of IBLT imposes high overhead. We describe a novel approach that guarantees successful listing when the set satisfies a tunable upper bound on its size. To allow that, we develop multiple constructions that are based on various coding techniques such as stopping sets and the stopping redundancy of error-correcting codes, and Steiner systems as well as new methodologies we develop. We analyze the sizes of IBLTs with listing guarantees obtained by the various methods as well as their mapping memory and runtime overheads. Lastly, we study lower bounds on the achievable sizes of IBLT with listing guarantees and verify the results in the paper by simulations.

Confix: Combining node-level fix templates and masked language model for automatic program repair

Automatic program repair (APR) is a promising technique to fix program defects by generating patches. In the current APR techniques, template-based and learning-based techniques have demonstrated different advantages. Template-based APR techniques rely on pre-defined fix templates, providing higher controllability but limited by the variety of templates and edit expressiveness. In contrast, learning-based APR techniques treat repair as a neural machine translation task, improving the edit expressiveness through training neural networks. However, this technique also faces the influence of quality and variety of training data, leading to numerous errors and redundant code generation. To overcome their limitations, this paper proposes an innovative APR technique called Confix. Confix first constructs a code information tree to assist in mining edit changes during historical repair. It then further enriches the types of fix templates using node information in the tree. Afterward, Confix defines masked lines based on node-level fix templates to control the scope of patch generation, avoiding redundant semantic code generation. Finally, Confix leverages the powerful edit expressiveness of the masked language model and combines it with fix strategies to generate correct patches more efficiently and accurately. Experimental results show that Confix exhibits state-of-the-art performance on the Defects4J 1.2 and QuixBugs benchmarks.

CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny

BackgroundThe rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called “gating”, and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data.ResultsWe applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations.ConclusionsCITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

On the First Separating Redundancy of Array LDPC Codes

On the First Separating Redundancy of Array LDPC Codes

Codon-optimization in gene therapy: promises, prospects and challenges.

Codon optimization has evolved to enhance protein expression efficiency by exploiting the genetic code's redundancy, allowing for multiple codon options for a single amino acid. Initially observed in E. coli, optimal codon usage correlates with high gene expression, which has propelled applications expanding from basic research to biopharmaceuticals and vaccine development. The method is especially valuable for adjusting immune responses in gene therapies and has the potenial to create tissue-specific therapies. However, challenges persist, such as the risk of unintended effects on protein function and the complexity of evaluating optimization effectiveness. Despite these issues, codon optimization is crucial in advancing gene therapeutics. This study provides a comprehensive review of the current metrics for codon-optimization, and its practical usage in research and clinical applications, in the context of gene therapy.

FSD-CLCD: Functional semantic distillation graph learning for cross-language code clone detection

Code clone detection can find similar or the same code snippets, which is important in analyzing homologous components, discovering redundant code, and improving software system development and maintenance efficiency. A crucial challenge is to extract more functional semantic similarity from code in heterogeneous conditions, such as a cross-language scenario. Existing methods mainly exploit sequence models with only lexical and statistical features to compare code pairs, which are susceptible to linguistic feature noise and misclassify code pairs that have similar structure dependencies such as control flow. Meanwhile, there are issues with inconsistent node types and a great variation of node numbers while capturing structure-dependent features, resulting in a misaligned distribution of clone pairs, and weakening the detection precision. This work presents a novel cross-language code clone detection method. It represents code with a graph structure based on abstract syntax trees and introduces a global node to strengthen the connection between control flows. Prune the graph structure based on key node protection rules to reduce the impact of linguistic feature noise. Besides, optimize graph matching networks for cross-language abstract syntax trees by using contrastive loss to align the functional semantic distribution of clone pairs. The method distills the invariant functional semantic similarity with a huge discrepancy of the code graph in heterogeneous cross-language conditions. Experiment results show that the proposed method achieves scores of 0.95, 0.98, and 0.96 in terms of precision, recall and F1-score and substantially outperforms the state-of-the-art baselines.

Custom static analysis to enhance insight into the usage of in-house libraries

For software maintenance and evolution, insight into the codebase is crucial. One way to enhance insight is the application of static analysis to extract and visualize program-specific relations from the code itself, such as call graphs and inheritance trees. Yet, software often contains in-house libraries: unique, domain-specific libraries whose usage is typically scattered throughout the codebase. To provide sufficient insight into the usage of those libraries, the static analysis must be customized with domain-specific information. In this paper, we propose a method to enhance insight into the usage of in-house libraries by producing custom overviews. Furthermore, we describe three exploratory case studies targeting industrial C++ and Ada codebases, in which the method was developed, evolved, and validated. The method prescribes how to create custom overviews using static analysis iteratively, starting from a user-provided, initial specification of proper library usage using code patterns. As a safeguard, the method includes cross-checks to detect code fragments that deviate from proper library usage. Whenever such a deviating library usage is found, the code owners determine whether that deviating library usage should be added to the specification of proper library usage or the code fragment should be made compliant. The latter alternative makes both the codebase more regular and keeps the custom static analysis simpler. The method creates custom overviews that reveal opportunities to improve the usage of the in-house libraries, e.g., the removal of domain-specific redundant code which cannot be detected using generic tools, such as compilers and linters.We observed that industrial codebases are regular enough to create custom overviews using static analysis in the three exploratory case studies. Furthermore, we observed that the cross-checks, which detect deviating library usage, ensure the validity and completeness of the custom overviews. We conclude that producing custom overviews for in-house libraries using the method is valuable and feasible.

Robust Soliton Distribution-Based Zero-Watermarking for Semi-Structured Power Data

To ensure the security of online-shared power data, this paper adopts a robust soliton distribution-based zero-watermarking approach for tracing semi-structured power data. The method involves extracting partial key-value pairs to generate a feature sequence, processing the watermark into an equivalent number of blocks. Robust soliton distribution from erasure codes and redundant error correction codes is utilized to generate an intermediate sequence. Subsequently, the error-corrected watermark information is embedded into the feature sequence, creating a zero-watermark for semi-structured power data. In the tracking process, the extraction and analysis of the robust zero-watermark associated with the tracked data facilitate the effective identification and localization of data anomalies. Experimental and simulation validation demonstrates that this method, while ensuring data security, achieves a zero-watermark extraction success rate exceeding 98%. The proposed approach holds significant application value for data monitoring and anomaly tracking in power systems.

Method for changing the redundancy of sequential concatenated code

During the operation of information transmission systems errors occur in the transmitted data due to the influence of various negative factors. To correct errors the codes are currently used that can withstand both single and multiple errors. From the point of view of the ability to correct multiple errors, concatenated codes are highly efficient. When constructing a sequential concatenated code, coding is carried out first by the external, then by the internal code. A significant disadvantage of such coding is a significant increase in redundancy. It is proposed to regulate redundancy by encoding with the internal code only a certain part of the bits from the output of the external code encoder. The study showed the effectiveness of this method, which made it possible to change the parameters of the cascade code within a wide range. A method was proposed and tested to increase the corrective ability of selective coding by multiplying symbols decoded by an external code by coefficients that are different for symbols that have and have not been encoded by an internal code. Decoding of the external code was carried out according to the Viterbi algorithm. The performance of the proposed method has been confirmed experimentally.

Memory built-in self-repair and correction for improving yield: a review

Nanometer memories are highly prone to defects due to dense structure, necessitating memory built-in self-repair as a must-have feature to improve yield. Today’s system-on-chips contain memories occupying an area as high as 90% of the chip area. Shrinking technology uses stricter design rules for memories, making them more prone to manufacturing defects. Further, using 3D-stacked memories makes the system vulnerable to newer defects such as those coming from through-silicon-vias (TSV) and micro bumps. The increased memory size is also resulting in an increase in soft errors during system operation. Multiple memory repair techniques based on redundancy and correction codes have been presented to recover from such defects and prevent system failures. This paper reviews recently published memory repair methodologies, including various built-in self-repair (BISR) architectures, repair analysis algorithms, in-system repair, and soft repair handling using error correcting codes (ECC). It provides a classification of these techniques based on method and usage. Finally, it reviews evaluation methods used to determine the effectiveness of the repair algorithms. The paper aims to present a survey of these methodologies and prepare a platform for developing repair methods for upcoming-generation memories.

VDTriplet: Vulnerability detection with graph semantics using triplet model

This study presents VDTriplet, a novel learning framework for building vulnerability detection models. VDTriplet is the first attempt using deep learning to avoid the potential known vulnerability function misjudgment due to the small difference between vulnerability and its fixed vulnerability function. Unlike prior work that treats the program as sequential tokens or randomly initialized graphs for supervised binary classification detection tasks, our model not only fuses rich syntactic and semantic information to obtain the most accurate program representation, but also utilizes the TripletNN model to reduce misjudgment of potential known vulnerabilities. VDTriplet first extracts the subgraphs that causes the vulnerability through the typical programming errors to reduce redundant code. Then, it uses the pre-trained model and unsupervised model for the graph encoding of subgraphs, thereby minimizing the influence of randomly initialized graph nodes and avoiding the need for supervised labeling. Finally, TripletNN model minimizes the distance between potential vulnerabilities and vulnerabilities with the same vulnerability type, and maximizes the distance between potential vulnerabilities and fixed vulnerabilities to reduce false positives. The results show that the performance of VDTriplet is significantly better than the studied baselines. Compared with the best performing model in the literature, our model achieves a total of 4.89%, 4.23%, 4.56% and 5.34% improvement in Accuracy, Precision, Recall and F1-Score in the test results respectively. Moreover, it exhibits well generalization in detecting new eight applications, demonstrating that it is potentially valuable in practical usage. Overall, this is indeed an outstanding improvement.

The Stigma of Burnout Impeding Formal Help: A Qualitative StudyExploring Residents' Experiences During Training.

Burnout is an occupational stress syndrome that gives rise to emotional exhaustion (EE) depersonalization (DP) and reduced personal accomplishment (PA). Increasing rates of burnout among health care professionals has been reported globally. Saudi Arabia appears to be among the highest in prevalence with reports of higher than 70%. Medical residents in training are the highest group at risk. The literature has repeatedly linked burnout among residents with poor academic performance on training exams, impaired quality of life, career choice regret and intentions to abandon medicine. In this study, we explore the factors that contribute to resident burnout, their experiences with burnout and how they choose to mitigate it. A qualitative design was used to conduct this study in the city of Riyadh, Saudi Arabia. A total of 14 residents from surgical and non-surgical programs were interviewed through in-depth interviews. Interpretive thematic analysis was used in coding and generated coding templates. Categories were repetitively reviewed and revised, expanding to include new data as it emerged and collapsing to remove redundant codes. Categories were organized into the final themes and sub-themes. All participants demonstrated a shared thread of shame in reaching the level of burnout. Three main interlinked themes were identified: Burnout stigma cycle, amalgamated causes of burnout and self-coping with burnout. One of the concerning findings in our study is the participants' pursuit of self-coping strategies and the avoidance of formal help, creating a cycle of suffering in silence. The literature has repeatedly reported high levels of burnout among residents in training. This study has added another dimension to those findings through the exploration of residents actual accounts and appears to link burnout with suboptimal training and working conditions. We have highlighted the pivotal role stigma and shame play in completely preventing residents from seeking professional help.

Invertible Bloom Lookup Tables with Listing Guarantees

The Invertible Bloom Lookup Table (IBLT) is a probabilistic concise data structure for set representation that supports a listing operation as the recovery of the elements in the represented set. Its applications can be found in network synchronization and traffic monitoring as well as in error-correction codes. IBLT can list its elements with probability affected by the size of the allocated memory and the size of the represented set, such that it can fail with small probability even for relatively small sets. While previous works only studied the failure probability of IBLT, this work initiates the worst case analysis of IBLT that guarantees successful listing for all sets of a certain size. The worst case study is important since the failure of IBLT imposes high overhead. We describe a novel approach that guarantees successful listing when the set satisfies a tunable upper bound on its size. To allow that, we develop multiple constructions that are based on various coding techniques such as stopping sets and the stopping redundancy of error-correcting codes, and Steiner systems as well as new methodologies we develop. We analyze the sizes of IBLTs with listing guarantees obtained by the various methods as well as their mapping memory and runtime overheads. Lastly, we study lower bounds on the achievable sizes of IBLT with listing guarantees and verify the results in the paper by simulations.

Investigating the impact of treatment timing on urinary tract infections in dementia at the population level

AbstractBackgroundUrinary tract infections (UTIs) are common causes of avoidable emergency hospitalisation in people living with dementia (PLWD). PLWD are at higher risk of UTIs, which can often cause death. Previous research in older adults has suggested that survival following infection is reduced by delays in antibiotic treatment (Gharbi et al., 2019). Here we investigate the incidence of UTIs in people with and without dementia and test whether delays in antibiotic treatment particularly increase mortality in PLWD.MethodData were extracted for 2.47 million people aged 50 and living in Wales from 2000‐2021 using the SAIL Databank (Figure 1). UTI incidence was calculated as the number of cases identified in GP and hospital records using diagnostic codes. Temporal clustering was applied to remove redundant coding occurring within 90 days. Eligible PLWD and paired non‐dementia (ND) groups were selected monthly using propensity sampling, matching for age, sex, and socioeconomic deprivation. Cox regression was used to study the effects of immediate and deferred treatment (1‐7 days after diagnosis) on survival, focusing on the last recorded UTI in a subset of patients diagnosed and treated at the primary care level.ResultUTI incidence was higher in PLWD compared to ND (11.21.85 vs 4.30.75, 1:1000) over 22 years (Figure 2). We also observed an increase in UTIs leading to death within 60 days of the infection (1.560.4 vs 0.60.2, 1:1000). We performed Cox regression survival analysis on 76,283 last UTIs with 2,973 observed events (deaths). Age, sex, dementia, and treatment timing were used as predictors. Mortality risk was significantly increased in PLWD relative to ND (***HR = 2.06,[1.91,2.23],z = 18.2) (Figure 3). Mortality was also increased in older adults (***HR = 1.07,[1.07,1.08],z = 36.4) and in males (***HR = 1.50,[1.40,1.61],z = 11.3). Furthermore, deferred treatment was associated with increased mortality (***HR = 1.58,[1.43,1.74],z = 8.93), replicating previous findings. The interaction between dementia and treatment timing was not significant.ConclusionOur study found increased UTI incidence and mortality rates in PLWD. Deferred treatment was associated with higher mortality, regardless of dementia diagnosis. Early intervention and frequent screening may reduce adverse health outcomes. Further research is needed to determine the best strategies to prevent and manage UTIs in PLWD.

Design and implementation of ternary cyclic redundancy code based on ternary optical computer

In order to meet the multiple requirements of error checking in ternary optical computer (TOC), cyclic redundancy check (CRC) is introduced into ternary optical computers and the research on ternary cyclic redundancy check (T-CRC) is carried out. Combining the characteristics of MSD (Modefied Signed-Digit) digital system and the concept of cyclic redundancy check, this paper studies T-CRC based on ternary optical computer, and proposes the operation rules of modular 3 division and two basic operations to assist in calculating the check bits of T-CRC. In order to avoid the complicated one-step iterative modular 3 division and realize the fast computation of check bits of T-CRC, a two-step parallel computation scheme of T-CRC is proposed. In the parallel computing scheme, the original single step iterative division operation is replaced by table look-up and special parity check code splicing, and the optical path structure of auxiliary operation is designed, which greatly reduces the computational difficulty of check bit in T-CRC. Finally, the parallel algorithm experiment is carried out on the ternary optical computer platform, and the experimental results are completely consistent with the theoretical calculation results, proving that it is completely feasible.

The Effects of Visual Complexity and Task Difficulty on the Comprehensive Cognitive Efficiency of Cluster Separation Tasks.

Cluster separation is required to perform multi-class visual statistics tasks and plays an essential role in information processing in visualization. This cognition behavioral study investigated the cluster separation task and the effects of visual complexity and task difficulty. A total of 32 college students (18 men and 14 women, with ages ranging from 18 to 25 years; mean = 21.2, SD = 3.9) participated in this study. The observers' average response accuracy, reaction time, mental effort, and comprehensive cognitive efficiency were measured as functions of three levels of visual complexity and task difficulty. The levels of visual complexity and task difficulty were quantified via an optimized complexity evaluation method and discrimination judgment task, respectively. The results showed that visual complexity and task difficulty significantly influenced comprehensive cognitive efficiency. Moreover, a strong interaction was observed between the effects of visual complexity and task difficulty. However, there was no positive linear relationship between the mental effort and the complexity level. Furthermore, two-dimensional color × shape redundant coding showed higher cognitive efficiency at low task difficulty levels. In contrast, the one-dimensional color encoding approach showed higher cognitive efficiency at increased task difficulty levels. The findings of this study provide valuable insights into designing more efficient and user-friendly visualization in the future.

Optimal Codes Detecting Deletions in Concatenated Binary Strings Applied to Trace Reconstruction

Consider two or more strings x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ,..., that are concatenated to form x = 〈x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ,...〉. Suppose that up to δ deletions occur in each of the concatenated strings. Since deletions alter the lengths of the strings, a fundamental question to ask is: how much redundancy do we need to introduce in x in order to recover the boundaries of x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ,...? This boundary problem is equivalent to the problem of designing codes that can detect the exact number of deletions in each concatenated string. In this work, we answer the question above by first deriving converse results that give lower bounds on the redundancy of deletion-detecting codes. Then, we present a marker-based code construction whose redundancy is asymptotically optimal in δ among all families of deletion-detecting codes, and exactly optimal among all block-by-block decodable codes. To exemplify the usefulness of such deletion-detecting codes, we apply our code to trace reconstruction and design an efficient coded reconstruction scheme that requires a constant number of traces.

WELDP: Weighed Edge Local Directional Pattern for expression recognition from facial images

At present, the local appearance-based texture descriptors in Facial Expression Recognition have limited accuracy due to the inability to encode the discriminative edges. The major cause is the presence of distorted and weak edges due to noise. Hence, this paper proposes new Expression Descriptor called as Weighted Edge Local Directional Pattern (WELDP) which can discriminate the weak and strong edges. Unlike the conventional local descriptors, WELDP searches for the support of neighbor pixels in the determination of Facial expression attributes such as Edges, Corners, Lines, and Curved Edges. WELDP encodes only Strong edge responses and discards weaker edge responses after extracting them through edge detection masks. This work adapted two masks for edge detection: they are Robinson Compass Mask and Kirsch Compass Mask. Moreover, the WELDP aims at code redundancy and encode each pixel only with seven bits (one sign bit and six directional bits). Then the WELDP image is described by a histogram and then processed through SVM (Support Vector Machine) for expression identification. From the simulation experiments, the proposed WELDP is found as better than several existing methods.

Reconstruction of Sequences Distorted by Two Insertions

Reconstruction codes are generalizations of error-correcting codes that can correct errors by a given number of noisy reads. The study of such codes was initiated by Levenshtein in 2001 and developed recently due to applications in modern storage devices such as racetrack memories and DNA storage. The central problem on this topic is to design codes with redundancy as small as possible for a given number <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> of noisy reads. In this paper, the minimum redundancy of such codes for binary channels with exactly two insertions is determined asymptotically for all values of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> ≥ 5. Previously, such codes were studied only for channels with single edit errors or two-deletion errors.