Performance Of Codes Research Articles

Faster-than-Clifford simulations of entanglement purification circuits and their full-stack optimization

Generating quantum entanglement is plagued by decoherence. Distillation and error-correction are employed against such noise, but designing a good distillation circuit, especially on today’s imperfect hardware, is challenging. We develop a simulation algorithm for distillation circuits with per-gate complexity of O(1), drastically faster than O(N) Clifford simulators or O(2N) wavefunction simulators over N qubits. This simulator made it possible to optimize distillation circuits much larger than previously feasible. We design distillation circuits from n raw Bell pairs to k purified pairs and study the use of these circuits in the teleportation of logical qubits. The resulting purification circuits are the best-known for finite-size noisy hardware and can be fine-tuned for specific error-models. Furthermore, we design purification circuits that shape the correlations of errors in the purified pairs such that the performance of potential error-correcting codes is greatly improved.

Testing Performance of Modeling and Simulation Code of Liquid Propellant Supply System Using Method of Characteristics

The primary objective of a liquid propellant supply system is to supply propellant to the engine, enabling stable mission performance. Therefore, the system design must aim at minimizing factors that may contribute to instability while satisfying the specified requirements. Modeling and simulation (M&S) techniques are typically used to achieve efficient design, offering advantages such as the ability to simulate various conditions without constraints, thereby reducing initial development costs and time. Consequently, the performance and accuracy of the M&S program can significantly impact the overall development process. In this study, the method of characteristics (MOC) is used to effectively calculate internal flow in pipes, thereby developing an M&S code with excellent accuracy and performance. The efficiency of the developed in-house code is evaluated by comparing the simulation results with those of a commercial program. Additionally, a water supply experiment is conducted, and the experimental results are compared with the two simulation outcomes for validation. The comparative analysis highlights the superior performance and accuracy of the in-house code over the commercial program, with the in-house code demonstrating an average accuracy improvement of approximately 2% to 3% and with computational performance being approximately 1.7 times faster.

Large language models vs human for classifying clinical documents.

Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and Systematized Nomenclature of Medicine (SNOMED) mapping has shown promise in performing these classifications. However, challenges remain, particularly in reducing false negatives, where certain diagnoses are not correctly identified by either approach. This study explores the potential of leveraging advanced large language models to improve the accuracy of ICD-10 classifications in challenging cases of medical records where machine learning and SNOMED mapping fail. We evaluated the performance of ChatGPT 3.5 and ChatGPT 4 in classifying ICD-10 codes from discharge summaries within selected records of the Medical Information Mart for Intensive Care (MIMIC) IV dataset. These records comprised 802 discharge summaries identified as false negatives by both machine learning and SNOMED mapping methods, showing their challenging case. Each summary was assessed by ChatGPT 3.5 and 4 using a classification prompt, and the results were compared to human coder evaluations. Five human coders, with a combined experience of over 30 years, independently classified a stratified sample of 100 summaries to validate ChatGPT's performance. ChatGPT 4 demonstrated significantly improved consistency over ChatGPT 3.5, with matching results between runs ranging from 86% to 89%, compared to 57% to 67% for ChatGPT 3.5. The classification accuracy of ChatGPT 4 was variable across different ICD-10 codes. Overall, human coders performed better than ChatGPT. However, ChatGPT matched the median performance of human coders, achieving an accuracy rate of 22%. This study underscores the potential of integrating advanced language models with clinical coding processes to improve documentation accuracy. ChatGPT 4 demonstrated improved consistency and comparable performance to median human coders, achieving 22% accuracy in challenging cases. Combining ChatGPT with methods like SNOMED mapping could further enhance clinical coding accuracy, particularly for complex scenarios.

Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review.

The predictive coding framework postulates that the human brain continuously generates predictions about the environment, maximizing successes and minimizing failures based on prior experiences and beliefs. This PRISMA-compliant systematic review aims to comprehensively and transdiagnostically examine the differences in predictive coding between individuals with neuropsychiatric disorders and healthy controls. We included 72 case-control studies investigating predictive coding as the primary outcome and reporting behavioral, neuroimaging, or electrophysiological findings. Thirty-three articles investigated predictive coding in the schizophrenia spectrum, 33 in neurodevelopmental disorders, 5 in mood disorders, 4 in neurocognitive disorders, 1 in post-traumatic stress disorder, and 1 in substance use disorders. Oddball and oddball-like paradigms were most frequently used to quantify predictive coding performance. Evidence showed heterogeneous impairments in the predictive coding abilities of the brain across neuropsychiatric disorders, particularly in schizophrenia and autism. Patients within the schizophrenia spectrum showed a consistent pattern of impaired non-social predictive coding. Conversely, predictive coding deficits were more selective for social cues in the autism spectrum. Predictive coding impairments were correlated with clinical symptom severity. These findings underscore the potential utility of predictive coding as a framework for understanding cognitive dysfunctions in the neuropsychiatric population, even though more evidence is needed on underexplored conditions, also considering potential confounders such as medication use and sex/gender. The potential role of predictive coding as a determinant of treatment response may also be considered to tailor personalized interventions.

Hybrid Data Pipelines with Beam, Spark, and Flink: Selecting the Right Framework for Your Workloads

As data volumes and velocity continue to grow, hybrid data pipelines encompassing both batch and streaming modes have become a cornerstone of modern analytics. Engineers and architects often face a critical question: Which data processing framework Apache Beam, Apache Spark, or Apache Flink best fits their workloads? Each technology offers unique strengths in programming model, runtime efficiency, ecosystem integration, and operational overhead. This paper presents an in-depth exploration of Beam, Spark, and Flink for building hybrid pipelines, examining their respective architectural designs, performance characteristics, fault-tolerance mechanisms, and developer ergonomics. We propose a systematic approach to framework selection by outlining typical data pipeline patterns: pure batch, streaming, micro-batching, unified batch-stream, and continuous dataflows. Through code snippets, architecture diagrams, and performance benchmarks, we reveal how each framework can address scenario-specific constraints such as low-latency ingestion, high-volume batch transformations, or advanced streaming analytics. Finally, we discuss real-world deployment stories, best practices for orchestrating multi-framework data platforms, and relevant anti-patterns that hamper pipeline scalability or maintainability. By combining theory, empirical results, and practical guidelines, this paper aims to equip data engineers, architects, and DevOps teams with the insights necessary to choose and implement a robust, cost-effective hybrid data pipeline strategy. Keywords Data Pipelines, Apache Beam, Apache Spark, Apache Flink, Hybrid Workloads, Batch Processing, Streaming Analytics, Scalability, Performance, Cloud Data Engineering

GPU-Accelerated Full-Wheel Large-Eddy Simulations of a Transonic Fan Stage

Abstract A single-stage transonic axial-flow fan is simulated using graphic processing unit (GPU)-accelerated wall-modeled large-eddy simulations. The computational meshes are generated using Voronoi diagram-based approach. Two types of mesh are employed for grid sensitivity study. The first type uses isotropic hexagonal close-packed (HCP) seeding whereas the second combines the background HCP mesh with anisotropic boundary-layer type of mesh around the blade surfaces. The effect of the rotor tip clearance is examined using a rotor-only configuration. The sweep of the performance curve at full rotation speed is carried out for both the rotor and the stage. The computational results are compared against the experimental measurement and excellent agreement is observed when adequate near-wall resolution is employed. The code performance of the GPU-accelerated solver is compared to the CPU-based version. The high throughput of the GPU solver significantly reduces the computational cost. On a mesh with 84 million control volumes, 10 rotor revolutions can be computed within 1 day using 20 GPUs.

Association of abnormal cortical inhibition and clinical outcomes in patients at clinical high risk for psychosis

ObjectiveCortical inhibition (CI) can be in-vivo measured using transcranial magnetic stimulation (TMS), and patients with schizophrenia had abnormal CI. However, whether the abnormal CI occur early in patients with clinical high risk for psychosis (CHR) or could predict their clinical outcomes remains less known. MethodsWe measured short-interval cortical inhibition (SICI), cortical silent period (CSP), and intra-cortical facilitation (ICF) over the motor cortex and neurocognitive performances in 55 CHR, 35 first-episode schizophrenia (FES), and 35 healthy controls (HC). We divided CHR patients into CHR converters (CHR-C) and CHR non-converters (CHR-NC) according to their clinical outcomes within the two-year follow-up. ResultsCSP was longer in CHR-C (P = 0.033) and FES (P = 0.047) than in HC, while CSP was comparable between CHR-NC and HC. In CHR, CSP was negatively related to their performances in symbol coding and maze tasks. There was no significant between-group difference for either SICI or ICF. ConclusionsOur findings suggested GABAB-mediated CSP was prolonged in CHR, who later converted into schizophrenia, and was associated with poor neurocognitive functions. SignificanceCSP is prolonged before the onset of psychosis, particularly in CHR-C patients, suggesting that CSP could be a potential biomarker for predicting transition to schizophrenia.

On the Performance of Polar Codes over RIS-aided Wireless Communication Channels

On the Performance of Polar Codes over RIS-aided Wireless Communication Channels

THE RELIGIOUS AND IDEOLOGICAL FOUNDATION OF THE TALIBAN MOVEMENT AND ITS MAIN PRINCIPLES

Since the fall of Kabul to the Taliban in August 2021, numerous qualitative changes have occurred in Afghanistan’s domestic and regional influence. The Taliban have reinvented their The Islamic Emirate idea of national governance. The hasty withdrawal of the U.S. and other international forces in August 2021 took place amidst a monopolizing power strategy of the Taliban and efforts by national and regional spoilers to destabilize the peace process. In this context, the paper will comprehensively analyze the power structure of the “Taliban”, interpretation and regional policies in framing their governmental policies and authorities in more than 34 months of their government. In exploring this interaction, this research paper will attempt to answer the questions, «How much are Taliban correct in representing the Hanafi Jurisprudence and Deobandi interpretation of Islam, and how much are profound in influencing the regional economic and religious?». To respond to this question, the paper will initially provide a theoretical foundation describing political Islam and traditions in Afghanistan’s political history. The article provides a historical account of the Islamic governments in Afghanistan and their different interpretation throughout history and its code of conduct. Then, the paper will apply these perspectives to comparatively analyze and understand the political performance of these Islamic codes in the view of the Taliban’s governance in two eras: First Islamic Emirate from 1996 to 2001 and then the return of the Islamic Emirate since August 15, 2021, in Afghanistan.

A Mutation-Based Data Enhancement Approach for Software Vulnerability Detection

Effective software vulnerability detection is paramount for ensuring the security of software systems. However, the presence of imbalanced data in extensive datasets often leads to overfitting on non-vulnerable code and suboptimal performance on vulnerable code. Traditional methods of collecting vulnerable data frequently fall short in capturing the complexities of real-world scenarios. This paper proposes a mutation-based data enhancement approach to tackle this challenge, with a focus on capturing essential traits of vulnerable source code. Our approach systematically extracts traits from extensive vulnerable source code and employs mutation operators to introduce high-level alterations. We evaluate the convergence of multiple mutation rounds using a diversity index to ensure consistent enhancements. By selecting the most effective mutation operators for subsequent model training, our approach achieves substantial accuracy improvements across diverse datasets and deep neural network models. This work represents the initial version of our approach, with continuous refinements underway to facilitate practical implementation and address real-world security challenges.

The performance of the LSTM-based code generated by Large Language Models (LLMs) in forecasting time series data

Generative AI, and in particular Large Language Models (LLMs), have gained substantial momentum due to their wide applications in various disciplines. While the use of these game changing technologies in generating textual information has already been demonstrated in several application domains, their abilities in generating complex models and executable codes need to be explored. As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep learning models and codes and thus may opt to leverage LLMs to generate the required models. This paper investigates and compares the performance of the mainstream LLMs, such as ChatGPT, PaLM, LLama, and Falcon, in generating deep learning models for analyzing time series data, an important and popular data type with its prevalent applications in many application domains including financial and stock market. This research conducts a set of controlled experiments where the prompts for generating deep learning-based models are controlled with respect to sensitivity levels of four criteria including (1) Clarify and Specificity, (2) Objective and Intent, (3) Contextual Information, and (4) Format and Style. While the results are relatively mix, we observe some distinct patterns. We notice that using LLMs, we are able to generate deep learning-based models with executable codes for each dataset separately whose performance are comparable with the manually crafted and optimized LSTM models for predicting the whole time series dataset. We also noticed that ChatGPT outperforms the other LLMs in generating more accurate models. Furthermore, we observed that the goodness of the generated models vary with respect to the “temperature” parameter used in configuring LLMS. The results can be beneficial for data analysts and practitioners who would like to leverage generative AIs to produce good prediction models with acceptable goodness.

Game-theory-based complexity allocation for 360-degree video coding

360-degree video applications with immersive experiences have been well spread in our daily life. However, 360-degree video with high resolution (e.g., 8192×4096,6144× 3072, and 3840 × 1920) leads to high coding computational complexity. To further optimize the complexity allocation and obtain the optimal coding performance, this paper proposes a game-theory-based complexity allocation algorithm for 360-degree video coding. The proposed method first constructs the latitude-level complexity allocation model by introducing game theory. Second, the optimal Lagrange coefficient λ∗ value is obtained by the Newton method, and then, the complexity of the latitude can be further obtained. Finally, the overall complexity allocation algorithm is also designed. Experimental results indicate our method obtains Time Saving (TS) with 18.44%∼67.08% and BDBR performance with 0.10%∼3.11%. The proposed method also achieves the optimal Coding Gain (CG) values for the most global target complexity.

Reliability of online visual and proprioceptive feedback: impact on learning and sensorimotor coding.

Multisensory integration is essential for learning and sensorimotor coding, facilitating learners' adaptation to environmental changes. Recent findings confirm that introducing unreliability into visual feedback enhances the use of motor coding, probably because proprioceptive cues are given greater weight. The present study was designed to test this hypothesis and, more generally, to explore the impact of visual versus proprioceptive cue reliability on learning processes. Participants performed a 12-target pointing sequence 100 times with different combinations of visual and proprioceptive feedback: reliable versus unreliable. Retention tests and intermanual transfer tests were administered 24h later. Results showed that learning and sensorimotor coding were both affected by the different combinations of visual and proprioceptive cue reliability. Fully reliable feedback allowed for the best retention, while fully unreliable feedback resulted in the worst retention. Visual reliability alone mediated the level of visuospatial coding performance in visuospatial transfer, regardless of the level of proprioceptive reliability, and conversely, reliable proprioception combined with unreliable vision provided the optimum sensory environment for motor coding in the motor transfer test. Overall, our study highlighted the essential role of both visual cue reliability and proprioceptive cue reliability -and their interactions- in motor learning and its generalization.

Stopping Sets of Algebraic Geometry Codes over Hyperelliptic Curves of Genus Two

Stopping sets are useful for analyzing the performance of a linear code under an iterative decoding algorithm over an erasure channel. In this paper, we consider stopping sets of one-point algebraic geometry codes defined by a hyperelliptic curve of genus g=2 defined by the plane model y2=f(x), where the degree of f(x) was 5. We completely classify the stopping sets of the one-point algebraic geometric codes C=CΩ(D,mP∞) defined by a hyperelliptic curve of genus 2 with m≤4. For m=3, we proved in detail that all sets S⊆{1,2,⋯,n} of a size greater than 3 are stopping sets and we give an example of sets of size 2,3 that are not.

Multi-label text classification via secondary use of large clinical real-world data sets.

Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This study seeks to create an application capable of predicting procedure codes by analysing clinicians' operative notes, aiming to streamline their workflow and enhance efficiency. We downstreamed an existing and a native German medical BERT model in a secondary use scenario, utilizing already coded surgery notes to model the coding procedure as a multi-label classification task. In comparison to the transformer-based architecture, we were levering the non-contextual model fastText, a convolutional neural network, a support vector machine and logistic regression for a comparative analysis of possible coding performance. About 350,000 notes were used for model adaption. By considering the top five suggested procedure codes from medBERT.de, surgeryBERT.at, fastText, a convolutional neural network, a support vector machine and a logistic regression, the mean average precision achieved was 0.880, 0.867, 0.870, 0.851, 0.870and0.805 respectively. Support vector machines performed better for surgery reports with a sequence length greater than 512, achieving a mean average precision of 0.872 in comparison to 0.840 for fastText, 0.837 for medBERT.de and 0.820 for surgeryBERT.at. A prototypical front-end application for coding support was additionally implemented. The problem of predicting procedure codes from a given operative report can be successfully modelled as a multi-label classification task, with a promising performance. Support vector machines as a classical machine learning method outperformed the non-contextual fastText approach. FastText with less demanding hardware resources has reached a similar performance to BERT-based models and has shown to be more suitable for explaining the predictions efficiently.

On the performance of optical codes for FSO systems using SAC-OCDMA technique to improve system performance

On the performance of optical codes for FSO systems using SAC-OCDMA technique to improve system performance

Two-Phase Globally Coupled Low-Density Parity Check Decoding Aided with Early Termination and Forced Convergence.

To enhance the decoding efficiency of Globally Coupled (GC) LDPC codes, we incorporated Early Termination (ET) and Forced Convergence (FC) into the local/global two-phase decoding algorithm to expedite the decoding process. The two-phase decoding scheme integrates the ET technique to halt unnecessary iterations in the local decoding phase while employing the FC technique to accelerate convergence in the global phase decoding. The application of ET technology in the local decoding of GC-LDPC codes will not cause performance loss as in traditional block codes and will cause considerable complexity gains. For a longer code length and larger convergence differences between nodes' global codes, the FC technique operates more efficiently in global code than local code. Two variants are proposed for the ET scheme in the local decoding, namely ET-1 and ET-2. The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. In the case of ET-2, the saved local iterations are transformed to global decoding equally. The results show that ET-1 saves considerable decoding time complexity and ET-2 improves the performance of the GC-LDPC code with the same decoding time complexity. The combined approach of ET-1 with FC reduces the decoding time complexity up to 42% at a low Signal Noise Rate region while maintaining its performance; ET-2-FC two-phase decoding saves approximately 25% decoding time complexity while improving the BER by about 0.18 dB and FER by about 0.23 dB.

Sorting Numbers in Ascending Order using Java

Sorting is a fundamental operation in programming that is widely used across various applications, from data analysis to organizing user inputs. This article explores the implementation of sorting numbers in ascending order using java programming language as Java's Arrays.sort().Through code examples and performance analysis, readers will gain an understanding of how different programming languages handle sorting operations, and which languages offer the best performance for specific scenarios. This comparative study aims to provide developers with the knowledge to choose the most appropriate sorting technique for their projects.

Cognition in Schizophrenia: Exploring the Dynamics of Improvement and the Influencing Determinants

Background: Cognitive impairments are fundamental characteristics of schizophrenia, significantly impacting the overall functioning of individuals with the disorder. However, the trajectory of cognitive deficits and the factors influencing their changes over the course of treatment remain poorly understood. Objectives: The present study aimed to investigate the changes in cognitive status of patients with schizophrenia and the factors that influence these changes following hospitalization and treatment. Methods: The study utilized a longitudinal design, enrolling thirty hospitalized patients (15 males and 15 females) from the psychiatric inpatient unit of a general educational hospital. Cognitive assessments were conducted upon admission and discharge. A Generalized Estimating Equation (GEE) model was used to analyze the data, accounting for demographic and clinical variables such as age, gender, Body Mass Index (BMI), family history, and smoking status. Results: The findings revealed a reduction in cognitive deficits following hospitalization and treatment. Significant improvements were observed in verbal memory, working memory, and executive function. However, older patients showed poorer performance in motor speed, verbal fluency, and symbol coding compared to younger patients. Male patients and smokers demonstrated lower cognitive functioning, particularly in verbal fluency, symbol coding, and executive function. Conclusions: This study highlighted the positive effect of comprehensive inpatient treatment on reducing cognitive impairments in individuals with schizophrenia. While improvements were noted in several cognitive domains, the persistence of certain deficits, particularly in relation to demographic factors (age, gender, and smoking status), underscores the need for further research and tailored cognitive rehabilitation interventions. A personalized, multidimensional approach to managing cognitive deficits in schizophrenia could lead to better functional outcomes and enhance the quality of life for affected individuals.

Finite-Blocklength Analysis of Coded Modulation with Retransmission.

The rapid developments of 5G and B5G networks have posed higher demands on retransmission in certain scenarios. This article reviews classical finite-length coding performance prediction formulas and proposes rate prediction formulas for coded modulation retransmission scenarios. Specifically, we demonstrate that a recently proposed model for correcting these prediction formulas also exhibits high accuracy in coded modulation retransmissions. To enhance the generality of this model, we introduce a range variable Pfinal to unify the predictions with different SNRs. Finally, based on simulation results, the article puts forth recommendations specific to retransmission with a high spectral efficiency.