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"parallelism"
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A Comparative Cognitive Analysis of Repartee in Persian, Arabic, and English
This study presents a comparative cognitive analysis of repartee—a strategic, adversarial form of verbal interaction characterized by rapid response, subversion, and parallelism—across Persian, Arabic, and English. Repartee is conceptualized as a linguistic duel wherein the responder (H) reconfigures the initiator’s (S) utterance through deliberate mirroring, thereby inverting its intended meaning. This research addresses a notable gap in the literature, as most prior studies have focused primarily on European languages, leaving the cognitive and structural underpinnings in non-European traditions underexplored. Focusing on two core mechanisms, the study examines Structural Parallelism—defined as the replication of the syntactic or logical framework of the initiating utterance to produce a counter-response with an inverted evaluative focus—and Polysemous Parallelism, which exploits inherent lexical ambiguity to reassign meaning and subvert the original communicative intent. Employing Langacker’s (2001) Current Discourse Space (CDS) model, the analysis demonstrates how hyper-understanding and figure-ground reversal operate within the CDS to reconfigure shared conceptual structures. Qualitative analysis of 150 repartee instances (50 per language) reveals universal cognitive strategies: (1) responders detect and subvert implicit assumptions via syntactic/lexical mirroring, and (2) reorient evaluative hierarchies by shifting the figure (salient focus) and ground (contextual assumptions) within the CDS.
Journal Article
Adding parallelism to sequential programs – a combined method
The article outlines a contemporary method for creating software for multi-processor computers. It describes the identification of parallelizable sequential code structures. Three structures were found and then carefully examined. The algorithms used to determine whether or not certain parts of code may be parallelized result from static analysis. The techniques demonstrate how, if possible, existing sequential structures might be transformed into parallel-running programs. A dynamic evaluation is also a part of our process, and it can be used to assess the efficiency of the parallel programs that are developed. As a tool for sequential programs, the algorithms have been implemented in C#. All proposed methods were discussed using a common benchmark.
Journal Article
Optimal distributed parallel algorithms for deep learning framework Tensorflow
Since its release, the Tensorflow framework has been widely used in various fields due to its advantages in deep learning. However, it is still at its early state. Its native distributed implementation has difficulty in expanding for large models because it has issues of low utilization of multiple GPUs and slow distribution compared with running on single machine. It is of great significance to reduce the training time through parallel models. In view of this, we firstly provided an in-depth analysis of the implementation principle of Tensorflow and identify the bottlenecks of its native distributed parallel models to improve. Then, two optimal algorithms are designed and implemented based on data parallelism and model parallelism modes of Tensorflow. For data parallelism, the proposed algorithm is implemented to replace the native linear execution mode with pipeline execution mode. As for model parallelism, the native random partitioning mode is replaced by our proposed novel greedy algorithm. Finally, we built a homogeneous distributed cluster and a heterogeneous distributed cluster respectively to verify the effectiveness of the proposed algorithms. Through a number of comparative experiments, we showed that the proposed optimal parallel algorithms can effectively reduce model training time by an average of 26.5%(or average 1.5x speedup than native distributed algorithms) and improve the utilization of the cluster while keeping the same accuracy level of native Tensorflow.
Journal Article
Study on the Moving Target Tracking Based on Vision DSP
The embedded visual tracking system has higher requirements for real-time performance and system resources, and this is a challenge for visual tracking systems with available hardware resources. The major focus of this study is evaluating the results of hardware optimization methods. These optimization techniques provide efficient utilization based on limited hardware resources. This paper also uses a pragmatic approach to investigate the real-time performance effect by implementing and optimizing a kernel correlation filter (KCF) tracking algorithm based on a vision digital signal processor (vision DSP). We examine and analyze the impact factors of the tracking system, which include DP (data parallelism), IP (instruction parallelism), and the characteristics of parallel processing of the DSP core and iDMA (integrated direct memory access). Moreover, we utilize a time-sharing strategy to increase the system runtime speed. These research results are also applicable to other machine vision algorithms. In addition, we introduced a scale filter to overcome the disadvantages of KCF for scale transformation. The experimental results demonstrate that the use of system resources and real-time tracking speed also satisfies the expected requirements, and the tracking algorithm with a scale filter can realize almost the same accuracy as the DSST (discriminative scale space tracking) algorithm under a vision DSP environment.
Journal Article
A CHARACTERIZATION OF ENERGY-PRESERVING METHODS AND THE CONSTRUCTION OF PARALLEL INTEGRATORS FOR HAMILTONIAN SYSTEMS
High order energy-preserving methods for Hamiltonian systems are presented. For this aim, an energy-preserving condition of continuous stage Runge-Kutta methods is proved. Order conditions are simplified and parallelizable conditions are also given. The computational cost of our high order methods is comparable to that of the average vector field method of order two.
Journal Article
Genomic basis of parallel adaptation varies with divergence in Arabidopsis and its relatives
Parallel adaptation provides valuable insight into the predictability of evolutionary change through replicated natural experiments. A steadily increasing number of studies have demonstrated genomic parallelism, yet the magnitude of this parallelism varies depending on whether populations, species, or genera are compared. This led us to hypothesize that the magnitude of genomic parallelism scales with genetic divergence between lineages, but whether this is the case and the underlying evolutionary processes remain unknown. Here, we resequenced seven parallel lineages of two Arabidopsis species, which repeatedly adapted to challenging alpine environments. By combining genome-wide divergence scans with model-based approaches, we detected a suite of 151 genes that show parallel signatures of positive selection associated with alpine colonization, involved in response to cold, high radiation, short season, herbivores, and pathogens. We complemented these parallel candidates with published gene lists from five additional alpine Brassicaceae and tested our hypothesis on a broad scale spanning ∼0.02 to 18 My of divergence. Indeed, we found quantitatively variable genomic parallelism whose extent significantly decreased with increasing divergence between the compared lineages. We further modeled parallel evolution over the Arabidopsis candidate genes and showed that a decreasing probability of repeated selection on the same standing or introgressed alleles drives the observed pattern of divergence-dependent parallelism. We therefore conclude that genetic divergence between populations, species, and genera, affecting the pool of shared variants, is an important factor in the predictability of genome evolution.
Journal Article
The probability of genetic parallelism and convergence in natural populations
Genomic and genetic methods allow investigation of how frequently the same genes are used by different populations during adaptive evolution, yielding insights into the predictability of evolution at the genetic level. We estimated the probability of gene reuse in parallel and convergent phenotypic evolution in nature using data from published studies. The estimates are surprisingly high, with mean probabilities of 0.32 for genetic mapping studies and 0.55 for candidate gene studies. The probability declines with increasing age of the common ancestor of compared taxa, from about 0.8 for young nodes to 0.1–0.4 for the oldest nodes in our study. Probability of gene reuse is higher when populations begin from the same ancestor (genetic parallelism) than when they begin from divergent ancestors (genetic convergence). Our estimates are broadly consistent with genomic estimates of gene reuse during repeated adaptation to similar environments, but most genomic studies lack data on phenotypic traits affected. Frequent reuse of the same genes during repeated phenotypic evolution suggests that strong biases and constraints affect adaptive evolution, resulting in changes at a relatively small subset of available genes. Declines in the probability of gene reuse with increasing age suggest that these biases diverge with time.
Journal Article
The Molecular Basis of Phenotypic Convergence
Understanding what aspects of evolution are predictable, and repeatable, is a central goal of biology. Studying phenotypic convergence (the independent evolution of similar traits in different organisms) provides an opportunity to address evolutionary predictability at different hierarchical levels. Here we focus on recent advances in understanding the molecular basis of convergence. Understanding when, and why, similar molecular solutions are used repeatedly provides insight into the constraints that shape biological diversity. We first distinguish between convergence as a phenotypic pattern and parallelism as a shared molecular basis for convergence. We then address the overarching question: What factors influence when parallel molecular mechanisms will underlie phenotypic convergence? We present four core determinants of convergence (natural selection, phylogenetic history, population demography, and genetic constraints) and explore specific factors that influence the probability of molecular parallelism. Finally, we address frontiers for future study, including integration across different systems, subfields, and hierarchical levels.
Journal Article
The application of improved densenet algorithm in accurate image recognition
Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume. The introduction of quantization error reduces the impact of gradient loss on model convergence. The test results show that the improvement strategy designed by the research improves the model parameter efficiency while ensuring the recognition effect. Narrowing the learning rate is conducive to refining the updating granularity of model parameters, and deepening the number of network layers can effectively improve the final recognition accuracy and convergence effect of the model. It is better than the existing state-of-the-art image recognition models, visual geometry group and EfficientNet. The parallel acceleration algorithm, which is improved by the gradient quantization, performs better than the traditional synchronous data parallel algorithm, and the improvement of the acceleration ratio is obvious. Compared with the traditional synchronous data parallel algorithm and stale synchronous parallel algorithm, the optimized parallel acceleration algorithm of the study ensures the image data training speed and solves the bottleneck problem of communication data. The model designed by the research improves the accuracy and training speed of image recognition technology and expands the use of image recognition technology in the field of computer vision.Please confirm the affiliation details of [1] is correct.The relevant detailed information in reference [1] has been confirmed to be correct.
Journal Article