Coding Efficiency Research Articles

A memory access number constraint-based string prediction technique for high throughput SCC implemented in AVS3

String prediction (SP) is a highly efficient screen content coding (SCC) tool that has been adopted in international and Chinese video coding standards. SP exhibits a highly flexible and efficient ability to predict repetitive matching patterns. However, SP also suffers from low throughput of decoded display output pixels per memory access, which is synchronized with the decoder clock, due to the high number of memory accesses required to decode an SP coding unit for display. Even in state-of-the-art (SOTA) SP, the worst-case scenario involves two memory accesses for decoding each 4-pixel basic string unit across two memory access units, resulting in a throughput as low as two pixels per memory access (PPMA). To solve this problem, we are the first to propose a technique called memory access number constraint-based string prediction (MANC-SP) to achieve high throughput in SCC. First, a novel MANC-SP framework is proposed, a well-designed memory access number constraint rule is established on the basis of statistical data, and a constrained RDO-based string searching method is presented. Compared with the existing SOTA SP, the experimental results demonstrate that MANC-SP can improve the throughput from 2 to 2.67 PPMA, achieving a throughput improvement of 33.33% while maintaining a negligible impact on coding efficiency and complexity.

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

Android Application for : Secure Employee Track Vision

This project presents an Android-based application designed to enhance security and monitoring in restricted areas such as factories, warehouses, colleges, companies, and godowns through the use of QR code technology. The application enables the registration of authorized personnel by generating unique QR codes for each individual. Upon entering a restricted area, users can scan their QR code, which is then verified in real-time against a central database, ensuring that only authorized individuals gain access while maintaining a log of entries for security purposes. Key functionalities include user registration with QR code generation, real-time access control through scanning, a monitoring dashboard displaying real-time data on entries and exits, and an alert system that notifies security personnel of unauthorized access attempts. This project aims to improve security measures in sensitive environments, reduce human error in monitoring access, and streamline the tracking of personnel in restricted zones. By leveraging the simplicity and efficiency of QR codes, this application offers a practical solution to modern security challenges.

Block-Correlated Coupled Cluster Theory Based on the Generalized Valence Bond Reference for Singlet-Triplet Energy Gaps of Strongly Correlated Systems.

A block-correlated coupled cluster (BCCC) method based on the triplet generalized valence bond (GVB) wave function (GVB-BCCC) has been implemented for the first time. By introducing several techniques, we have developed a practical and efficient GVB-BCCC code. The GVB-BCCC3 method (with up to three-pair correlation) can be used to deal with strongly correlated (SC) systems with triplet or singlet ground states, allowing singlet-triplet (S-T) energy gaps in the active space of SC systems computationally available. For selected SC systems, our calculations show that GVB-BCCC3 can always provide correct ground-state spin multiplicity as the complete active space configuration interaction (CASCI) or density matrix renormalization group (DMRG). Furthermore, we found that the S-T energy gaps from GVB-BCCC3 are quite consistent with CASCI or DMRG results. This work demonstrates that GVB-BCCC3 is a promising theoretical tool for describing S-T energy gaps within the active space of SC systems with large active spaces.

Detecting Autism from Head Movements using Kinesics.

Head movements play a crucial role in social interactions. The quantification of communicative movements such as nodding, shaking, orienting, and backchanneling is significant in behavioral and mental health research. However, automated localization of such head movements within videos remains challenging in computer vision due to their arbitrary start and end times, durations, and frequencies. In this work, we introduce a novel and efficient coding system for head movements, grounded in Birdwhistell's kinesics theory, to automatically identify basic head motion units such as nodding and shaking. Our approach first defines the smallest unit of head movement, termed kine, based on the anatomical constraints of the neck and head. We then quantify the location, magnitude, and duration of kines within each angular component of head movement. Through defining possible combinations of identified kines, we define a higher-level construct, kineme, which corresponds to basic head motion units such as nodding and shaking. We validate the proposed framework by predicting autism spectrum disorder (ASD) diagnosis from video recordings of interacting partners. We show that the multi-scale property of the proposed framework provides a significant advantage, as collapsing behavior across temporal scales reduces performance consistently. Finally, we incorporate another fundamental behavioral modality, namely speech, and show that distinguishing between speaking- and listening-time head movementsments significantly improves ASD classification performance.

Web-Based Geographic Information System to Find Viral Culinary Tourist Spots

The development and implementation of a Web-Based Geographic Information System (GIS) designed to help users discover viral culinary tourist spots, focusing on promoting local food culture. The system is built using the PHP programming language, leveraging its robust server-side scripting capabilities for dynamic web development. The GIS platform integrates various functionalities, including real-time mapping, geolocation services, and user-generated content, to offer an interactive experience for tourists. The platform allows users to search for culinary hotspots, view their locations on an interactive map, get directions, and read reviews from other users. Business owners can register and update information about their culinary spots, contributing to an up-to-date, community-driven database. The system employs a MySQL database to store location data, user profiles, and reviews, while Google Maps API is used for map visualization and geolocation services. The backend structure is built to handle high-traffic environments, using PHP's object-oriented features for efficient and scalable code management. Administrators can moderate content, ensuring the reliability and quality of the information provided. Implementing this system in PHP highlights the language’s flexibility in creating web-based GIS platforms, demonstrating how culinary tourism can be enhanced through modern web technologies. This paper discusses the technical aspects of the system’s architecture, database management, and frontend-backend integration, offering insights into the benefits of using PHP for developing similar GIS applications.

A QUALITATIVE STUDY OF EFFECTIVE PEDAGOGICAL PRACTICES TO FOSTER JOY, CURIOSITY, AND WONDER IN MATHEMATICS AT ELEMENTARY STAGE

This Study Aims To Identify And Evaluate Effective Pedagogical Practices That Foster Joy, Curiosity, And Wonder Among Students In Mathematics Education At The Elementary Stage. The Study Employs A Qualitative Approach To Investigate The Impact Of These Practices On Students' Engagement And Enthusiasm For Mathematics. A Sample Of Mathematics Teachers From 10 Schools (20, Teachers) In The Varanasi District, India, Was Selected Randomly. Intelligent Verbatim Transcription Was Used To Convert Audio Recordings Into Text, Balancing Accuracy With Clarity By Omitting Unnecessary Fillers While Preserving The Essence Of The Conversations And Using NVIVO Employed To Facilitate Efficient Coding And Visualisation Of Connections Between Themes. The Analysis Process Will Involve Organizing The Transcribed Data, Qualitatively Identifying Themes And Patterns Related To Effective Pedagogical Practices To Foster 'Joy, Curiosity & Wonder' Among The Students In Mathematics At Elementary Stage, Combining The Benefits Of Observational Data With In-Depth Teacher Perspectives. The Findings Reveal A Positive Trend Towards More Student-Centered, Engaging, And Effective Teaching Strategies In Mathematics, While Also Indicating Opportunities For Further Development In Certain Areas. These Findings Have Significant Implications For Enhancing Student Engagement, Promoting Conceptual Understanding, And Encouraging A Growth Mind Set In Mathematics. This Study Emphasizes The Need For Policy Makers To Address Resource Disparities Between Schools And For Educators To Adopt Practices That Create Joyful, Curiosity-Driven Learning Environments, Ultimately Leading To Improved Student Outcomes And A More Positive Attitude Towards The Subject. KEYWORD: Mathematics Education, Pedagogy, Joy, Curiosity, Wonder, Student Engagement, Innovative Teaching Practices

A Comparative Study on Fault-Tolerant Coding Techniques in RAID6 Analyzing the Efficiency and Limitation of EVENODD and RDP Codes

With the development of computer science, the application of information storage in various fields such as business, military, gaming, and more has been playing a important role in our day-to-day life. This paper provides a systematic analysis of the current academic literature on data storage, offering an overview of RAID storage technology. It discusses the historical development of RAID technology, including real-world application examples, and highlights some researchers’ innovative upgrades to the technology. The paper elucidates the details and applications of the EVENODD and RDP algorithms, which are closely related to RAID technology, and analyzes the error correction performance of these two algorithms in scenarios where only a single device fails, supported by relevant calculations. The analysis concludes that the RDP algorithm has an advantage in recovering from single device failures when comparing EVENODD and RDP. Finally, the future prospects and some new application scenarios of RAID data storage technology are discussed.

Hardness results for decoding the surface code with Pauli noise

Real quantum computers will be subject to complicated, qubit-dependent noise, instead of simple noise such as depolarizing noise with the same strength for all qubits. We can do quantum error correction more effectively if our decoding algorithms take into account this prior information about the specific noise present. This motivates us to consider the complexity of surface code decoding where the input to the decoding problem is not only the syndrome-measurement results, but also a noise model in the form of probabilities of single-qubit Pauli errors for every qubit.In this setting, we show that quantum maximum likelihood decoding (QMLD) and degenerate quantum maximum likelihood decoding (DQMLD) for the surface code are NP-hard and #P-hard, respectively. We reduce directly from SAT for QMLD, and from #SAT for DQMLD, by showing how to transform a boolean formula into a qubit-dependent Pauli noise model and set of syndromes that encode the satisfiability properties of the formula. We also give hardness of approximation results for QMLD and DQMLD. These are worst-case hardness results that do not contradict the empirical fact that many efficient surface code decoders are correct in the average case (i.e., for most sets of syndromes and for most reasonable noise models). These hardness results are nicely analogous with the known hardness results for QMLD and DQMLD for arbitrary stabilizer codes with independent X and Z noise.

A complex acoustical environment during development enhances auditory perception and coding efficiency in the zebra finch.

Sensory experience during development has lasting effects on perception and neural processing. Exposing juvenile animals to artificial stimuli influences the tuning and functional organization of the auditory cortex, but less is known about how the rich acoustical environments experienced by vocal communicators affect the processing of complex vocalizations. Here, we show that in zebra finches (Taeniopygia guttata), a colonial-breeding songbird species, exposure to a naturalistic social-acoustical environment during development has a profound impact on auditory perceptual behavior and on cortical-level auditory responses to conspecific song. Compared to birds raised by pairs in acoustic isolation, male and female birds raised in a breeding colony were better in an operant discrimination task at recognizing conspecific songs with and without masking colony noise. Neurons in colony-reared birds had higher average firing rates, selectivity, and discriminability, especially in the narrow-spiking, putatively inhibitory neurons of a higher-order auditory area, the caudomedial nidopallium (NCM). Neurons in colony-reared birds were also less correlated in their tuning and more efficient at encoding the spectrotemporal structure of conspecific song, and better at filtering out masking noise. These results suggest that the auditory cortex adapts to noisy, complex acoustical environments by strengthening inhibitory circuitry, functionally decoupling excitatory neurons while maintaining overall excitatory-inhibitory balance.

Real-Time Collaborative Programming in Undergraduate Education: A Comprehensive Empirical Analysis of Its Impact on Knowledge, Behaviors, and Attitudes

Real-time collaborative programming (RCP), which allows multiple programmers to work concurrently on the same codebase with changes instantly visible to all participants, has garnered considerable popularity in higher education. Despite this trend, little work has rigorously examined how undergraduates engage in collaborative programming when using RCP. This study utilized a quasi-experimental design to investigate the impact of RCP on undergraduates’ programming knowledge and skills, behaviors, and attitudes toward collaboration. Eighty-two participants were randomly assigned to either the RCP group or the Traditional Collaborative Programming (TCP) group. The data analysis yielded noteworthy findings. First, RCP significantly improved learners’ problem-solving skills and led to higher knowledge test scores compared to TCP. Second, there were no significant differences in attitudes toward collaboration between the two groups. Third, analysis of programming behaviors indicated that the RCP group exhibited more efficient coding practices, including producing a greater volume of code and executing it more successfully. Additionally, the RCP group’s code-testing activities were predominantly concentrated at the first half of the class sessions. Based on the empirical research results, three major pedagogical implications were proposed. These findings highlight the benefits of integrating RCP into programming education and suggest avenues for future research into collaborative learning dynamics.

One-step sparse-matrix method for full wave equation depth migration

By completely solving the second-order full-acoustic wave equation, two-way/full wave equation migration generates images up to arbitrary angles in inhomogeneous media. Full wave equation depth migration (FWDM) necessitates less wavefield storage during imaging, generates clearer imaging results, and has more computational flexibility compared with conventional reverse time migration. Conventionally, FWDM uses Runge-Kutta or two explicit one-way operators to extrapolate the data, which involves either a multirecursive process or complex square root computations of the Helmholtz operator, causing implementation complexities and inconveniences. To overcome these challenges, a novel algorithm called the one-step sparse-matrix method for FWDM (OSM-FWDM) is developed and executed. This method involves a straightforward calculation of the full wave equation propagator through a Taylor expansion and using the so-called precise integration method. The computational efficiency can be significantly improved by using sparse-matrix multiplication in the implementation of wavefield extrapolation. Building upon that, the method serves as a basic solution complemented by FWDM, offering straightforward, efficient, and easy coding characteristics. Our OSM-FWDM method is simpler and faster compared with conventional FWDM, all while maintaining imaging accuracy. In addition, a new solution that uses an extended velocity model to remove source-related artifacts in FWDM is developed and illustrated. These improvements are demonstrated through a series of numerical examples and a real data application.

Voice coding experiences for developers with physical impairments

PurposeThis research explores the current working practices of voice coders with physical impairments and investigates their perceptions of a research prototype utilising a fixed grammar speech coding approach.Design/methodology/approachSemi-structured interviews were initially conducted with seven voice coders with physical impairments to understand their development practices, followed by an exploratory user study with five disabled voice coders to obtain their feedback on a fixed grammar voice coding system.FindingsInterviews provided new insights around the tools voice coders utilise, the need for multimodal coding approaches, as well as experiences in working within mixed ability development teams. Findings from the user evaluation elicited views around the need for distinct monosyllable voice commands, as well as other requirements to support efficient voice coding (e.g. command chaining, intuitive navigation, and custom command definition).Research limitations/implicationsThe research was conducted with experienced voice coders which may have influenced perceptions of the voice coding prototype. The system was also evaluated over a single evaluation session, whereas longitudinal research with novice voice coders could also present additional insights.Practical implicationsFurther research is required into customisable multimodal voice coding approaches for developers with physical impairments who may have varying levels of experience in coding via alternative methods.Originality/valueThe findings present new insights around the working practices and unique requirements of voice coders with physical impairments and highlights important new avenues for future research.

Multi-feature enhancement based on sparse networks for single-stage 3D object detection

In the field of autonomous driving, the accuracy and real-time requirements for 3D object detection technology continue to improve, which is directly related to the commercialization process and market popularity of autonomous vehicles. Despite the efficiency of pillar-based coding for onboard systems, it falls short in terms of accuracy and the reduction of incorrect positives. In this paper, we will examine how to solve the problem of high incorrect rate and low accuracy of existing methods. Firstly, a MAP coding module is introduced to optimize previous point cloud feature coding modules, allowing for the efficient extraction of fine-grained features from point cloud data. Then, we introduce an innovative sparse dual attention (SDA) to efficiently filter out irrelevant details in feature extraction, thereby improving the pertinence and efficiency of information extraction. Finally, to address the potential loss of information from single local feature extraction, a local and global fusion module (CTGC) is introduced. Our method has proactively demonstrated its efficiency and accuracy through rigorous experimentation across diverse datasets. Analysis of the results leads to the conclusion that our solutions provide accurate and robust detection results. Code will be available at https://github.com/lcy199905/MyOpenPCDet.git.

E-code: Mastering efficient code generation through pretrained models and expert encoder group

Context:With the waning of Moore’s Law, the software industry is placing increasing importance on finding alternative solutions for continuous performance enhancement. The significance and research results of software performance optimization have been on the rise in recent years, especially with the advancement propelled by Large Language Models (LLMs). However, traditional strategies for rectifying performance flaws have shown significant limitations at the competitive code efficiency optimization level, and research on this topic is surprisingly scarce. Objective:This study aims to address the research gap in this domain, offering practical solutions to the various challenges encountered. Specifically, we have overcome the constraints of traditional performance error rectification strategies and developed a Language Model (LM) tailored for the competitive code efficiency optimization realm. Methods:We introduced E-code, an advanced program synthesis LM. Inspired by the recent success of expert LMs, we designed an innovative structure called the Expert Encoder Group. This structure employs multiple expert encoders to extract features tailored for different input types. We assessed the performance of E-code against other leading models on a competitive dataset and conducted in-depth ablation experiments. Results:Upon systematic evaluation, E-code achieved a 54.98% improvement in code efficiency, significantly outperforming other advanced models. In the ablation experiments, we further validated the significance of the expert encoder group and other components within E-code. Conclusion:The research findings indicate that the expert encoder group can effectively handle various inputs in efficiency optimization tasks, significantly enhancing the model’s performance. In summary, this study paves new avenues for developing systems and methods to assist programmers in writing efficient code.

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summaryProgram Title: VAN-DAMMECPC Library link to program files::https://doi.org/10.17632/zcgw2n5bjf.1Licensing provisions: GNU General Public License 3Programming language: C++ and CUDANature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability.Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Efficient Coding in Motor Planning.

A paramount challenge for the brain is to precisely model the world and control behavior within the confines of limited encoding capacities. Efficient coding theory posits a unified framework for understanding how neural systems enhance encoding accuracy by tuning to environmental statistics. While this theory has been thoroughly explored within the perceptual realm, it is less clear how efficient coding applies to the motor system. Here, we probe the core principles of efficient coding theory through center-out reaching tasks. Our results reveal novel sequential effects in motor planning. Specifically, current movements are biased in a direction opposite to recent movements, and movement variance increases with the angular divergence between successive actions. These effects are modulated by the variability within the motor system: a larger repulsive bias is observed when movements are performed with the nondominant hand compared to the dominant hand, and in individuals exhibiting higher motor variance compared to those with lower variance. These behavioral findings align with the predictions of an efficient coding model, suggesting that the motor system rapidly adapts to the context to enhance accuracy in motor planning.

Measuring code efficiency optimization capabilities with ACEOB

As Moore’s Law gains diminish, software performance and efficiency become increasingly vital. Optimizing code efficiency is challenging, even for professional programmers. However, related research remains relatively scarce, and rigorously assessing models’ abilities to optimize code efficiency is fraught with difficulties. In response to this challenge, we first conduct an in-depth analysis of “code patterns” in the model training dataset, meticulously exploring human-written code. Secondly, we define a task for optimizing code efficiency and introduce the Automatic Code Efficiency Optimization Benchmark (ACEOB), which consists of 95,359 pairs of efficient–inefficient code aimed at assessing code efficiency optimization capabilities. To our knowledge, ACEOB is the first dataset specifically targeting Python code efficiency optimization. To evaluate models’ ability in optimizing code efficiency, we propose two new metrics: the Isomorphic Optimal Comparison CodeBLEU (IOCCB) metric and the Normalized Performance Index (NPI) metric, to assess the efficiency of model-generated code. We also evaluate several advanced code models, such as PolyCoder and CodeT5, after fine-tuning them on ACEOB and demonstrate that the efficiency of each model improves after introducing the NPI filter. However, it was observed that even ChatGPT does not perform optimally in code efficiency optimization tasks. Our dataset and models are available at: https://github.com/CodeGeneration2/ACEOB.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Lexical Effect Handlers, Directly

Lexically scoping effect handlers is a language-design idea that equips algebraic effects with a modular semantics: it enables local-reasoning principles without giving up on the control-flow expressiveness that makes effect handlers powerful. However, we observe that existing implementations risk incurring costs akin to the run-time search for dynamically scoped handlers. This paper presents a compilation strategy for lexical effect handlers, adhering to the lexical scoping principle and targeting a language with low-level control over stack layout. Key aspects of this approach are formalized and proven correct. We embody the ideas in a language called Lexa: the Lexa compiler translates high-level effect handling to low-level stack switching. We evaluate the Lexa compiler on a set of benchmarks; the results suggest that it generates efficient code, reducing running-time complexity from quadratic to linear in some cases.

Compiler Support for Sparse Tensor Convolutions

This paper extends prior work on sparse tensor algebra compilers to generate asymptotically efficient code for tensor expressions with affine subscript expressions. Our technique enables compiler support for a wide range of sparse computations, including sparse convolutions and pooling that are widely used in ML and graphics applications. We propose an approach that gradually rewrites compound subscript expressions to simple subscript expressions with loops that exploit the sparsity pattern of the input sparse tensors. As a result, the time complexity of the generated kernels is bounded by the number of stored elements and not by the shape of the tensors. Our approach seamlessly integrates into existing frameworks and is compatible with recent advances in compilers for sparse computations, including the flexibility to efficiently handle arbitrary combinations of different sparse tensor formats. The implementation of our algorithm is open source and upstreamed to the MLIR sparse compiler. Experimental results show that our method achieves 19.5x speedup when compared with the state-of-the-art compiler-based method at 99.9% sparsity. The generated sparse kernels start to outperform dense convolution implementations at about 80% sparsity.