Performance Of Codes Research Articles

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.

SparseAuto: An Auto-scheduler for Sparse Tensor Computations using Recursive Loop Nest Restructuring

Automated code generation and performance enhancements for sparse tensor algebra have become essential in many real-world applications, such as quantum computing, physical simulations, computational chemistry, and machine learning. General sparse tensor algebra compilers are not always versatile enough to generate asymptotically optimal code for sparse tensor contractions. This paper shows how to generate asymptotically better schedules for complex sparse tensor expressions using kernel fission and fusion. We present generalized loop restructuring transformations to reduce asymptotic time complexity and memory footprint. Furthermore, we present an auto-scheduler that uses a partially ordered set (poset)-based cost model that uses both time and auxiliary memory complexities to prune the search space of schedules. In addition, we highlight the use of Satisfiability Module Theory (SMT) solvers in sparse auto-schedulers to approximate the Pareto frontier of better schedules to the smallest number of possible schedules, with user-defined constraints available at compile-time. Finally, we show that our auto-scheduler can select better-performing schedules and generate code for them. Our results show that the auto-scheduler provided schedules achieve orders-of-magnitude speedup compared to the code generated by the Tensor Algebra Compiler (TACO) for several computations on different real-world tensors.

Error rates of optically pre-amplified PPM wireless systems with coding and arbitrary optical filter response

We present novel results for the uncoded and coded bit-error probability (BEP) of optically pre-amplified pulse-position modulation (PPM) wireless systems. For uncoded systems, a novel analytic method for the evaluation of the BEP is derived. The method takes into account the non-ideal optical filter response and utilizes a finite Karhunen–Loève series expansion to calculate the BEP. Using the proposed approach, it is possible to accurately evaluate the PPM BEP for arbitrarily shaped filters where the well-established χ2 method only provides approximate results. Considering a Lorentzian filter response, the discrepancy between the two methods amounts to 0.5 dB in a variety of filter bandwidths and PPM modulation orders. The Lorentzian filter response was chosen as an illustrative practical example whose series can be calculated analytically. The proposed method is also valid for any type of optical filter for which the Karhunen–Loève series expansion can be calculated analytically or numerically. Due to the finite number of terms that are required irrespective of the signal energy level, the proposed method can also be applied without loss of accuracy to assess the system performance under the effects of turbulence and adverse weather conditions. For coded systems with Lorentzian filters, Monte-Carlo simulations are utilized to evaluate the BEP performance of the 5G LDPC codes, and it is demonstrated that they impart an energy gain up to 3.3 dB for 4–PPM and 2.3 dB for 16–PPM at a target BEP of 10−5. The optimal code rates are also discussed for several combinations of the optical filter bandwidth and PPM modulation order and it is shown that in almost all of the cases the optimal code rate is 11/13. Moreover, the sum-product and min-sum decoders perform within 0.1 dB from each other for the best code rates, which points towards the utilization of the min-sum decoder in all settings, since its operation does not require knowledge of the filter parameters. Finally, the comparison between the coded systems with Lorentzian and ideal passband filters exhibits the same 0.5 dB discrepancy that was observed for uncoded systems.

Weight-Reduced Stabilizer Codes with Lower Overhead

Stabilizer codes are the most widely studied class of quantum error-correcting codes and form the basis of most proposals for a fault-tolerant quantum computer. A stabilizer code is defined by a set of parity-check operators, which are measured in order to infer information about errors that may have occurred. In typical settings, measuring these operators is itself a noisy process and the noise strength scales with the number of qubits involved in a given parity check, or its weight. Hastings has proposed a method for reducing the weights of the parity checks of a stabilizer code, though it has previously only been studied in the asymptotic regime. Here, we instead focus on the regime of small to medium-size codes suitable for quantum computing hardware. We both provide a fully explicit description of Hastings’s method and propose a substantially simplified weight-reduction method that is applicable to the class of quantum product codes. Our simplified method allows us to reduce the check weights of hypergraph- and lifted-product codes to at most six, while preserving the number of logical qubits and at least retaining (in fact, often increasing) the code distance. The price we pay is an increase in the number of physical qubits by a constant factor but we find that our method is much more efficient than Hastings’s method in this regard. We benchmark the performance of our codes on a simulated photonic quantum computing architecture based on Gottesman-Kitaev-Preskill qubits and passive linear optics, finding that our weight-reduction method substantially improves code performance. Published by the American Physical Society 2024

Trio/hybrid fiber effect on the geopolymer reinforced concrete for flexural and shear behavior

AbstractGeopolymer concrete (GC) is an innovative and sustainable type of composite resulting from the chemical reaction between waste materials containing and alkaline activators. The researchers have been focused to reduction of the greenhouse‐gas release, especially during the production of cement. Although numerous studies have been conducted on alkali‐activated cement or GC at the material level, there is limited research in the literature on the structural performance of reinforced GC members. The aim of this study is to investigate the effect of fiber combination on the shear and flexural performance of RC beams with maximum and minimum reinforcement ratio. To investigate the shear and flexural behavior of GC beam, four‐point and three‐point tests were performed on 12 GC with hybrid fibers, 4 GC with trio fibers, and 2 GC fiber‐free as references beams. The test parameters were the combination of fibers (steel, glass, carbon, and basalt), the longitudinal reinforcement ratio, and loading type. The key test results include the load‐deflection behavior, characteristics of the cracks, the effect of fiber type on the shear and flexural performance, ductility and stiffness properties, microstructure, the strain in the concrete, and the bars and code predictions. The test results showed that as expected, reducing the longitudinal reinforcement ratio decreased the strength, but the decrease in strength was tolerated by using different types of fibers. The use of trio fibers in beams under bending increased the strength capacity, considerably. Finally, the capacity prediction performance of current codes, that is, GB50010, EuroCode‐2, TS500, and ACI318, were also examined, and the calculations resulted that the current code equations had a percentage error of approximately 25% and 82% on average for flexural and shear, respectively, although EuroCode‐2 equations performed slightly better.

Mimetic Possibilities: Collaboration through movement in multimedia opera

Abstract In the opera Act I: Invocation – The Ghost Trilogy, artists from music composition, Greek tragedy, creative coding, and opera performance collaborated to create an artwork that engages with philosophy and technology. A light sculpture with a novel Internet of Things (IoT) architecture was adapted for the opera’s stage, where both the sculpture and its controlling interface were programmed to visually represent musical processes within the composer’s approach. While filming, the light sculpture contributed to the dramatic context through movement and gestural interaction between the sculpture and the soprano. Drawing upon Human-Computer Interaction (HCI) research and its inquiry into movement for engaging with new technology expressively, movement and gesture, transpiring from mimetic techniques in the opera, were central in this interdisciplinary collaboration.

Comparative Analysis of ChatGPT-Generated Code and Kaggle Champion Performance in Water Potability Prediction: A Few-Shot Learning prompts Approach

Comparative Analysis of ChatGPT-Generated Code and Kaggle Champion Performance in Water Potability Prediction: A Few-Shot Learning prompts Approach

Can GPT-3.5 generate and code discharge summaries?

The aim of this study was to investigate GPT-3.5 in generating and coding medical documents with International Classification of Diseases (ICD)-10 codes for data augmentation on low-resource labels. Employing GPT-3.5 we generated and coded 9606 discharge summaries based on lists of ICD-10 code descriptions of patients with infrequent (or generation) codes within the MIMIC-IV dataset. Combined with the baseline training set, this formed an augmented training set. Neural coding models were trained on baseline and augmented data and evaluated on an MIMIC-IV test set. We report micro- and macro-F1 scores on the full codeset, generation codes, and their families. Weak Hierarchical Confusion Matrices determined within-family and outside-of-family coding errors in the latter codesets. The coding performance of GPT-3.5 was evaluated on prompt-guided self-generated data and real MIMIC-IV data. Clinicians evaluated the clinical acceptability of the generated documents. Data augmentation results in slightly lower overall model performance but improves performance for the generation candidate codes and their families, including 1 absent from the baseline training data. Augmented models display lower out-of-family error rates. GPT-3.5 identifies ICD-10 codes by their prompted descriptions but underperforms on real data. Evaluators highlight the correctness of generated concepts while suffering in variety, supporting information, and narrative. While GPT-3.5 alone given our prompt setting is unsuitable for ICD-10 coding, it supports data augmentation for training neural models. Augmentation positively affects generation code families but mainly benefits codes with existing examples. Augmentation reduces out-of-family errors. Documents generated by GPT-3.5 state prompted concepts correctly but lack variety, and authenticity in narratives.

Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework

With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets.

Evaluating the Efficacy of Large Language Models in CPT Coding for Craniofacial Surgery: A Comparative Analysis.

The advent of Large Language Models (LLMs) like ChatGPT has introduced significant advancements in various surgical disciplines. These developments have led to an increased interest in the utilization of LLMs for Current Procedural Terminology (CPT) coding in surgery. With CPT coding being a complex and time-consuming process, often exacerbated by the scarcity of professional coders, there is a pressing need for innovative solutions to enhance coding efficiency and accuracy. This observational study evaluated the effectiveness of 5 publicly available large language models-Perplexity.AI, Bard, BingAI, ChatGPT 3.5, and ChatGPT 4.0-in accurately identifying CPT codes for craniofacial procedures. A consistent query format was employed to test each model, ensuring the inclusion of detailed procedure components where necessary. The responses were classified as correct, partially correct, or incorrect based on their alignment with established CPT coding for the specified procedures. The results indicate that while there is no overall significant association between the type of AI model and the correctness of CPT code identification, there are notable differences in performance for simple and complex CPT codes among the models. Specifically, ChatGPT 4.0 showed higher accuracy for complex codes, whereas Perplexity.AI and Bard were more consistent with simple codes. The use of AI chatbots for CPT coding in craniofacial surgery presents a promising avenue for reducing the administrative burden and associated costs of manual coding. Despite the lower accuracy rates compared with specialized, trained algorithms, the accessibility and minimal training requirements of the AI chatbots make them attractive alternatives. The study also suggests that priming AI models with operative notes may enhance their accuracy, offering a resource-efficient strategy for improving CPT coding in clinical practice. This study highlights the feasibility and potential benefits of integrating LLMs into the CPT coding process for craniofacial surgery. The findings advocate for further refinement and training of AI models to improve their accuracy and practicality, suggesting a future where AI-assisted coding could become a standard component of surgical workflows, aligning with the ongoing digital transformation in health care.

Performance Comparison of AI Platforms in Solving Computer Science Problems

In the rapidly evolving landscape of artificial intelligence (AI), its significance and accelerated development are undeniable. AI has emerged as a cornerstone technology with profound implications across various domains, driving innovation and reshaping the way we approach complex problems. Particularly, the utilization of AI in coding tasks has garnered substantial attention, given its potential to streamline development processes and enhance the efficiency of software engineering practices. Against this backdrop, this paper presents a detailed comparative analysis of four different AI platforms, namely ChatGPT, Gemini, Blackbox, and Microsoft Copilot, in addressing key challenges within the realm of computer science, spanning natural language processing, image processing, and cybersecurity. The study focuses on leveraging the C++ programming language to develop solutions for these multifaceted problems across the aforementioned platforms. Each platform's outputs are meticulously evaluated on various parameters including accuracy, execution time, code size, and time complexity to provide a comprehensive understanding of their performance. Furthermore, an iterative optimization methodology is employed, entailing three rounds of refinement for the code produced by each platform, with the resultant outputs subjected to comparative analysis in each iteration. Through this rigorous approach, the paper not only elucidates the efficacy of different AI platforms in addressing diverse computational challenges but also underscores the iterative enhancement process on AI platforms for refining code quality and performance across multiple domains within computer science.

Experimental study on concrete-filled L-shaped steel tubular columns and beam-columns

Concrete-filled L-shaped steel tube (CFLST) is an efficient structural form for corner columns in high-rise buildings, which avoids column protrusion. This paper presents an experimental and theoretical study on the structural behavior of CFLST columns and beam-columns. A total of nine full-scale specimens (i.e., three stub columns, two slender columns, and four slender beam-columns) was tested under compressive loads and the specimen ends were free to rotate about a geometric axis. Plastic local buckling was observed for the stub columns and the confinement effect was not obvious. Flexural buckling occurred for the slender columns and beam-columns. With the increase in the applied load, the neutral axis shifted to the compressive side of the beam-column. Performance of current design codes, which apply for rectangular CFSTs, on predicting the ultimate capacities of CFLSTs was assessed. The prediction for section capacity was conservative and it was likely caused by the reduction in the design strength of concrete. The buckling curve in design codes for rectangular CFSTs was applicable for CFLST columns whereas the interaction relationship specified in design codes underestimates the ultimate capacities of CFLST beam-columns. Based on fiber element analysis, out-of-plane bending moment was found in the tested CFLST slender members and the values were quantified. A new interaction relationship was proposed for CFLST beam-columns, which exhibited good agreement with experimental and numerical results. Finally, design methods were recommended for CFLST columns and beam-columns.

Deep video compression based on Long-range Temporal Context Learning

Video compression allows for efficient storage and transmission of data, benefiting imaging and vision applications, e.g. computational imaging, photography, and displays by delivering high-quality videos. To exploit more informative contexts of video, we propose DVCL, a novel Deep Video Compression based on Long-range Temporal Context Learning. Aiming at high coding performance, this new compression paradigm makes full use of long-range temporal correlations derived from multiple reference frames to learn richer contexts. Motion vectors (MVs) are estimated to represent the motion relations of videos. By employing MVs, a long-range temporal context learning (LTCL) module is presented to extract context information from multiple reference frames, such that a more accurate and informative temporal contexts can be learned and constructed. The long-range temporal contexts serve as conditions and generate the predicted frames by contextual encoder and decoder. To address the challenge of imbalanced training, we develop a multi-stage training strategy to ensure the whole DVCL framework is trained progressively and stably. Extensive experiments demonstrate the proposed DVCL achieves the highest objective and subjective quality, while maintaining relatively low complexity. Specifically, 25.30% and 45.75% bitrate savings on average can be obtained than x265 codec at the same PSNR and MS-SSIM, respectively.

Enhanced ICD-10 code assignment of clinical texts: A summarization-based approach

BackgroundAssigning International Classification of Diseases (ICD) codes to clinical texts is a common and crucial practice in patient classification, hospital management, and further statistics analysis. Current auto-coding methods mainly transfer this task to a multi-label classification problem. Such solutions are suffering from high-dimensional mapping space and excessive redundant information in long clinical texts. To alleviate such a situation, we introduce text summarization methods to the ICD coding regime and apply text matching to select ICD codes. MethodWe focus on the tenth revision of the ICD (ICD-10) coding and design a novel summarization-based approach (SuM) with an end-to-end strategy to efficiently assign ICD-10 code to clinical texts. In this approach, a knowledge-guided pointer network is purposed to distill and summarize key information in clinical texts precisely. Then a matching model with matching-aggregation architecture follows to align the summary result with code, tuning the one-vs-all scenario to one-vs-one matching so that the large-label-space obstacle laid in classification approaches would be avoided. ResultThe 12,788 ICD-10 coded discharge summaries from a Chinese hospital were collected to evaluate the proposed approach. Compared with existing methods, the purposed model achieves the greatest coding results with Micro AUC of 0.9548, MRR@10 of 0.7977, Precision@10 of 0.0944, and Recall@10 of 0.9439 for the TOP-50 Dataset. Results on the FULL-Dataset remain consistent. Also, the proposed knowledge encoder and applied end-to-end strategy are proven to facilitate the whole model to gain efficacy in selecting the most suitable code. ConclusionThe proposed automatic ICD-10 code assignment approach via text summarization can effectively capture critical messages in long clinical texts and improve the performance of ICD-10 coding of clinical texts.

NN-VVC: A Hybrid Learned-Conventional Video Codec Targeting Humans and Machines

Advancements in artificial intelligence have significantly increased the use of images and videos in machine analysis algorithms, predominantly neural networks. However, the traditional methods of compressing, storing and transmitting media have been optimized for human viewers rather than machines. Current research in coding images and videos for machine analysis has evolved in two distinct paths. The first is characterized by End-to-End (E2E) learned codes, which show promising results in image coding but have yet to match the performance of leading Conventional Video Codecs (CVC) and suffer from a lack of interoperability. The second path optimizes CVC, such as the Versatile Video Coding (VVC) standard, for machine-oriented reconstruction. Although CVC-based approaches enjoy widespread hardware and software compatibility and interoperability, they often fall short in machine task performance, especially at lower bitrates. This paper proposes a novel hybrid codec for machines named NN-VVC, which combines the advantages of an E2E-learned image codec and a CVC to achieve high performance in both image and video coding for machines. Our experiments show that the proposed system achieved up to − 43.20% and − 26.8% Bjøntegaard Delta rate reduction over VVC for image and video data, respectively, when evaluated on multiple different datasets and machine vision tasks according to the common test conditions designed by the VCM study group in MPEG standardization activities. Furthermore, to improve reconstruction quality, we introduce a human-focused branch into our codec, enhancing the visual appeal of reconstructions intended for human supervision of the machine-oriented main branch.