Semantic Information Research Articles

PSR-GAN: a product concept sketch rendering method based on generative adversarial network and colour tags

Looking for the fit of colour and form is an important goal of product design, but conceptual visualisation based on hand-drawn sketches, a necessary way in the early stage, consumes a lot of designers’ time and energy, resulting in missing fleeting design inspirations and clues. We propose a product concept sketch rendering method based on generative adversarial network and colour tags to assist designers in quickly capturing the harmony and balance between product colour and form, and improving design efficiency. This method encodes colour semantic information as the condition and combines ACGAN to achieve controllable rendering of line sketches based on specified colour tags. The generator is a hybrid of CNN and Transformer, further guided to optimise by combining pixel-wise loss and perceptual loss, while the discriminator adopts a convolution-based spatial-channel attention structure. Results show that PSR-GAN outperforms existing methods in terms of generation quality, and it also demonstrates excellent rendering results compared to professional manuscripts. Designers can use this method not only to obtain real-time comprehensive conceptual feedback but also to effectively narrow the colour search space for product details, accelerating the convergence of their design ideas during the sketch phase.

Neural processing of naturalistic audiovisual events in space and time

Our brain seamlessly integrates distinct sensory information to form a coherent percept. However, when real-world audiovisual events are perceived, the specific brain regions and timings for processing different levels of information remain less investigated. To address that, we curated naturalistic videos and recorded functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data when participants viewed videos with accompanying sounds. Our findings reveal early asymmetrical cross-modal interaction, with acoustic information represented in both early visual and auditory regions, while visual information only identified in visual cortices. The visual and auditory features were processed with similar onset but different temporal dynamics. High-level categorical and semantic information emerged in multisensory association areas later in time, indicating late cross-modal integration and its distinct role in converging conceptual information. Comparing neural representations to a two-branch deep neural network model highlighted the necessity of early cross-modal connections to build a biologically plausible model of audiovisual perception. With EEG-fMRI fusion, we provided a spatiotemporally resolved account of neural activity during the processing of naturalistic audiovisual stimuli.

CuTCP: Custom Text Generation-based Class-aware Prompt Tuning for visual-language models

Visual-language models (VLMs) excel in cross-modal reasoning by synthesizing visual and linguistic features. Recent VLMs use prompt learning for fine-tuning, allowing adaptation to various downstream tasks. TCP applies class-aware prompt tuning to improve VLMs generalization, yet its reliance on fixed text templates as prior knowledge can limit adaptability to fine-grained category distinctions. To address this, we propose Custom Text Generation-based Class-aware Prompt Tuning (CuTCP). CuTCP leverages large language models to generate descriptive, category-specific prompts, embedding richer semantic information that enhances the model’s ability to differentiate between known and unseen categories. Compared with TCP, CuTCP achieves an improvement of 0.74% on new classes and 0.44% on overall harmonic mean, averaged over 11 diverse image datasets. Experimental results demonstrate that CuTCP addresses the limitations of general prompt templates, significantly improving model adaptability and generalization capability, with particularly strong performance in fine-grained classification tasks.

Supporting vision-language model few-shot inference with confounder-pruned knowledge prompt.

Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. Recent works adopt fixed or learnable prompts, i.e., classification weights are synthesized from natural language descriptions of task-relevant categories, to reduce the gap between tasks during the pre-training and inference phases. However, how and what prompts can improve inference performance remains unclear. In this paper, we explicitly clarify the importance of incorporating semantic information into prompts, while existing prompting methods generate prompts without sufficiently exploring the semantic information of textual labels. Manually constructing prompts with rich semantics requires domain expertise and is extremely time-consuming. To cope with this issue, we propose a knowledge-aware prompt learning method, namely Confounder-pruned Knowledge Prompt (CPKP), which retrieves an ontology knowledge graph by treating the textual label as a query to extract task-relevant semantic information. CPKP further introduces a double-tier confounder-pruning procedure to refine the derived semantic information. Adhering to the individual causal effect principle, the graph-tier confounders are gradually identified and phased out. The feature-tier confounders are eliminated by following the maximum entropy principle in information theory. Empirically, the evaluations demonstrate the effectiveness of CPKP in few-shot inference, e.g., with only two shots, CPKP outperforms the manual-prompt method by 4.64% and the learnable-prompt method by 1.09% on average.

Improved printed circuit board defect detection scheme

In this paper, an improved printed circuit board(PCB)defect detection scheme named PD-YOLOv8 is proposed, which is specialized in the common and challenging problem of small target recognition in PCB inspection. This improved scheme mainly relies on the basic framework of YOLOv8n, and effectively enhances the detection performance of PCB small defects through multiple innovative designs. First, we incorporate the Efficient Channel Attention Network (ECANet) attention mechanism into the backbone network of YOLOv8, which improves the performance of small-target detection by adaptively enhancing the expressiveness of key features, so that the network possesses higher sensitivity and focus on tiny details in PCB images. Second, we optimize and upgraded the neck structure. On the one hand, the module is introduced to facilitate cross-layer feature fusion to ensure that the rich texture information at the lower layer and the abstract semantic information at the higher layer complement each other, which is conducive to improving the contextual understanding of small target detection; on the other hand, a detection head specialized for small targets is designed and added to enhance the ability of locating and identifying tiny defects. Furthermore, in order to further enhance the interaction and fusion of multi-scale features, we also add a SlimNeck module to the neck structure, which realizes efficient information transfer through streamlined design and reduces computational complexity at the same time. In addition, we draw on the advanced BiFPN structure, which enables the bidirectional flow of feature information between multiple layers and greatly improves the capture and integration of small target features. Compared to the original YOLOv8 algorithm, this algorithm improves the average accuracy on small targets by for mAP50.

MDA-DETR: Enhancing Offending Animal Detection with Multi-Channel Attention and Multi-Scale Feature Aggregation.

Conflicts between humans and animals in agricultural and settlement areas have recently increased, resulting in significant resource loss and risks to human and animal lives. This growing issue presents a global challenge. This paper addresses the detection and identification of offending animals, particularly in obscured or blurry nighttime images. This article introduces Multi-Channel Coordinated Attention and Multi-Dimension Feature Aggregation (MDA-DETR). It integrates multi-scale features for enhanced detection accuracy, employing a Multi-Channel Coordinated Attention (MCCA) mechanism to incorporate location, semantic, and long-range dependency information and a Multi-Dimension Feature Aggregation Module (DFAM) for cross-scale feature aggregation. Additionally, the VariFocal Loss function is utilized to assign pixel weights, enhancing detail focus and maintaining accuracy. In the dataset section, this article uses a dataset from the Northeast China Tiger and Leopard National Park, which includes images of six common offending animal species. In the comprehensive experiments on the dataset, the mAP50 index of MDA-DETR was 1.3%, 0.6%, 0.3%, 3%, 1.1%, and 0.5% higher than RT-DETR-r18, yolov8n, yolov9-C, DETR, Deformable-detr, and DCA-yolov8, respectively, indicating that MDA-DETR is superior to other advanced methods.

Acoustic features of instrumental movie soundtracks elicit distinct and mostly non-overlapping extra-musical meanings in the mind of the listener

Music can evoke powerful emotions in listeners. However, the role that instrumental music (music without any vocal part) plays in conveying extra-musical meaning, above and beyond emotions, is still a debated question. We conducted a study wherein participants (N = 121) listened to twenty 15-second-long excerpts of polyphonic instrumental soundtrack music and reported (i) perceived emotions (e.g., happiness, sadness) as well as (ii) movie scene properties imagined during listening (e.g., scene brightness, character role). We systematically investigated how acoustic features of instrumental soundtrack excerpts (e.g., tempo, loudness) contributed to mental imagery of movie scenes. We show distinct and mostly non-overlapping contributions of acoustic features to the imagination of properties of movie scene settings, characters, actions, and objects. Moreover, we find that negatively-valenced emotions fully mediate the relation between a subset of acoustic features and movie scene properties, providing evidence for the importance of emotional valence in evoking mental imagery. The data demonstrate the capacity of music to convey extra-musical semantic information through audition.

Contrastive independent subspace analysis network for multi-view spatial information extraction.

Multi-view classification integrates features from different views to optimize classification performance. Most of the existing works typically utilize semantic information to achieve view fusion but neglect the spatial information of data itself, which accommodates data representation with correlation information and is proven to be an essential aspect. Thus robust independent subspace analysis network, optimized by sparse and soft orthogonal optimization, is first proposed to extract the latent spatial information of multi-view data with subspace bases. Building on this, a novel contrastive independent subspace analysis framework for multi-view classification is developed to further optimize from spatial perspective. Specifically, contrastive subspace optimization separates the subspaces, thereby enhancing their representational capacity. Whilst contrastive fusion optimization aims at building cross-view subspace correlations and forms a non overlapping data representation. In k-fold validation experiments, MvCISA achieved state-of-the-art accuracies of 76.95%, 98.50%, 93.33% and 88.24% on four benchmark multi-view datasets, significantly outperforming the second-best method by 8.57%, 0.25%, 1.66% and 5.96% in accuracy. And visualization experiments demonstrate the effectiveness of the subspace and feature space optimization, also indicating their promising potential for other downstream tasks. Our code is available at https://github.com/raRn0y/MvCISA.

The segmentation of nanoparticles with a novel approach of HRU2-Net†

Nanoparticles have great potential for the application in new energy and aerospace fields. The distribution of nanoparticle sizes is a critical determinant of material properties and serves as a significant parameter in defining the characteristics of zero-dimensional nanomaterials. In this study, we proposed HRU2-Net†, an enhancement of the U2-Net† model, featuring multi-level semantic information fusion. This approach exhibits strong competitiveness and refined segmentation capabilities for nanoparticle segmentation. It achieves a Mean intersection over union (MIoU) of 87.31%, with an accuracy rate exceeding 97.31%, leading to a significant improvement in segmentation effectiveness and precision. The results show that the deep learning-based method significantly enhances the efficacy of nanomaterial research, which holds substantial significance for the advancement of nanomaterial science.

Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review

Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients.These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support. Objective: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes. Design: A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis. Results: The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings. Conclusions: Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding.

The semiotics of culture in light of commonalities and conflicts in Lotmanian and Peircean theories

This article will outline in general lines the tenets of Lotmanian and Peircean semiotics, observing their possible intersections and interactions as well as their theoretical incompatibilities. As history would have it, both traditions stem from different roots, and their playing along cannot be taken for granted. In examining these historical roots in light of the naturalizing project of current (bio)semiotics, we can hope to find some points to connect the more general points of the theories, despite potential conflicts in their views. It will be argued that the more mainstream accounts of biosemiotics can offer a productive prospect for the interface in the analysis of culture and cognition, while a rereading of Lotmanian commitments to a specific form of cultural semantics can bridge theoretical positions held within the Peircean paradigm of biosemiotics.

Implementation and evaluation of a solution for automated documentation of the doctor-patient dialogue (ADAPI) in ophthalmology using the example of the IVI routine

The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide asolution. The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization. In aprospective monocentric study at the Sulzbach Eye Clinic, 50patients were included in the period from 2020 to 2022. As part of aproject funded by the "Zentrale Initiative des Mittelstandes" (ZIM), ademonstrator was developed with the consortium partners and integrated into the hospital information system (HIS). For qualitative and quantitative evaluation, asurvey of patients was carried out using aquestionnaire before and after implementation of the ADAPI module, supplemented by adetermination of acceptance and possible time saving for users. The ADAPI module was successfully integrated into the HIS. The documentation of the findings and connection of the subsequent processes could be automated. An improvement in the DPC was reported for 13/50patients (26%). The majority 35/50 (70%) did not notice any subjective change. The average duration of the conversation in the area of conventional documentation was 4.46 min (1-10 min) and 3.9 min with ADAPI (1-10 min). With respect to the quality of the conversation within the DPC, no relevant differences could be shown with both types of documentation; however, larger case numbers and other areas of application still need to be evaluated. In the long term, the use of automated documentation solutions will sustainably improve the efficiency, completeness and consistency of clinical documentation.

Advances and Challenges in Small Object Detection: A Comparative Analysis of State-of-the-Art Models and Future Directions

Object detection involves the precise and efficient identification and localization of multiple predefined object categories within images. With the advent of deep learning, both the accuracy and efficiency of object detection have significantly improved. Nevertheless, challenges remain in optimizing the performance of mainstream detection algorithms, improving the accuracy of small object detection, enabling multi-class detection, and developing lightweight models. In response to these challenges, this paper provides a comprehensive literature review, analyzing approaches to enhance mainstream object detection by exploring advancements in backbone networks, expanding the visual receptive field, feature fusion, and various training strategies. We also evaluate the performance of leading detection models across established datasets, identifying current limitations and proposing future research directions. These directions include enhancing small object representation in datasets, enriching semantic information, and improving model interpretability. Small object detection remains a critical focus in computer vision, and we anticipate the continued development of algorithms with higher accuracy and efficiency

A Novel Approach to Optimize Key Limitations of Azure Kinect DK for Efficient and Precise Leaf Area Measurement

Maize leaf area offers valuable insights into physiological processes, playing a critical role in breeding and guiding agricultural practices. The Azure Kinect DK possesses the real-time capability to capture and analyze the spatial structural features of crops. However, its further application in maize leaf area measurement is constrained by RGB–depth misalignment and limited sensitivity to detailed organ-level features. This study proposed a novel approach to address and optimize the limitations of the Azure Kinect DK through the multimodal coupling of RGB-D data for enhanced organ-level crop phenotyping. To correct RGB–depth misalignment, a unified recalibration method was developed to ensure accurate alignment between RGB and depth data. Furthermore, a semantic information-guided depth inpainting method was proposed, designed to repair void and flying pixels commonly observed in Azure Kinect DK outputs. The semantic information was extracted using a joint YOLOv11-SAM2 model, which utilizes supervised object recognition prompts and advanced visual large models to achieve precise RGB image semantic parsing with minimal manual input. An efficient pixel filter-based depth inpainting algorithm was then designed to inpaint void and flying pixels and restore consistent, high-confidence depth values within semantic regions. A validation of this approach through leaf area measurements in practical maize field applications—challenged by a limited workspace, constrained viewpoints, and environmental variability—demonstrated near-laboratory precision, achieving an MAPE of 6.549%, RMSE of 4.114 cm², MAE of 2.980 cm², and R² of 0.976 across 60 maize leaf samples. By focusing processing efforts on the image level rather than directly on 3D point clouds, this approach markedly enhanced both efficiency and accuracy with the sufficient utilization of the Azure Kinect DK, making it a promising solution for high-throughput 3D crop phenotyping.

Examination Process Modeling for Intelligent Patent Management: A Multi-aspect Neural Sequential Approach

Recently, the booming growth of patent applications has brought an unprecedented challenge in performing efficient intellectual property management. Therefore, intelligent approaches are urgently needed to analyze intrinsic patterns of patents. However, a long-standing obstacle is the lack of effective methods for modeling the dynamic and diverse examination process of patent applications, which can benefit a wide range of downstream tasks for patent management. In fact, the major challenges lie in how to discover and integrate domain-specific properties from large-scale unlabeled examination data. To this end, in this paper, we propose a Self-supervised Examination Process Modeling (SEPM) framework to learn the contextualized embedding for patents through modeling their examination processes. Specifically, we first design a multi-aspect event embedding layer, which leverages the fine-tuned language model, frequent-pattern embedding, and time encoding to capture the semantic, frequent-pattern and temporal information of examination events, respectively. Then, a mutual-information-aware integration layer is applied to fuse the extracted features into multi-aspect embedding considering their mutual interactions. Further, we develop a multi-objective sequential neural network for learning the contextualized patent representation, which is achieved through jointly learning two self-supervised objectives, namely event code and event lag auto-regression. To explore the application potential of SEPM, we fine-tune the well-trained model for three important downstream tasks of patent management, including the prediction of next events, patent classification, and grant prediction. In the end, extensive experiments with real-world data from the US Patent and Trademark Office verify the effectiveness and application prospects of the proposed framework.

Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.

When we listen to speech, our brain's neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography (MEG) responses while subjects of both sexes listened to four types of continuous-speech-like passages: speech-envelope modulated noise, English-like non-words, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing. Critically, we show a stepwise hierarchical progression of progressively higher order features over time, reflected in both bottom-up (early) and top-down (late) processing stages. Linguistically driven top-down mechanisms take the form of late N400-like responses, suggesting a central role of predictive coding mechanisms at multiple levels. As expected, the neural processing of lower-level acoustic feature responses is bilateral or right lateralized, with left lateralization emerging only for lexical-semantic features. Finally, our results identify potential neural markers, linguistic level late responses, derived from TRF components modulated by linguistic content, suggesting that these markers are indicative of speech comprehension rather than mere speech perception.Significance Statement We investigate neural processing mechanisms as speech evolves from acoustic signals to meaningful language, using stimuli ranging from without any linguistic information to fully well-formed linguistic content. Computational models based on speech and linguistic hierarchy reveal that cortical responses time-lock to emergent features from acoustics to linguistic processes at the sentence level, with increasing the semantic information in the acoustic input. Temporal response functions (TRFs) uncovered millisecond-level processing dynamics as speech and language stages unfold. Each speech feature undergoes early and late processing stages, with the former driven by bottom-up activation and the latter influenced by top-down mechanisms. These insights enhance our understanding of the hierarchical nature of auditory language processing.

Research on financial fraud detection by integrating latent semantic features of annual report text with accounting indicators

Purpose This paper aims to integrate the latent semantic features of annual report text with accounting indicators to construct a financial fraud identification model, and quantitatively analyze the impact of different corporate risks on financial fraud behavior in different industries, providing a reference for identifying financial fraud. Design/methodology/approach This paper obtains 3,860 corporate annual report samples and accounting indicators from 2001 to 2020 through crawlers and the CSMAR database as our experimental subjects. By integrating latent semantic features with accounting indicators and textual language features, a new indicator system group is constructed. Based on this indicator system group, multiple model identification effects are compared and a stacking-based enterprise financial fraud identification model is constructed. In addition, an econometric model is established to verify the impact of latent semantic features related to enterprises on corporate financial fraud. Findings The experimental results show that the constructed stacking-based enterprise financial fraud identification model performs better than other machine learning models and can effectively identify financial fraud. The econometric model established for the latent semantic information of annual reports explains the impact of different corporate trends on fraud behavior in different industries. Originality/value This paper combines the textual latent semantic features of annual reports with accounting indicators, expands the scope of data analysis, introduces the idea of ensemble learning, updates the financial fraud identification algorithm and constructs an econometric model for further analysis, providing a reference for financial fraud identification.

Construction of 3D Indoor Topological Models Based on Improved Face Sorting

Indoor location-based services and applications need to obtain information about the indoor spatial layouts and topological relationships of indoor spaces. The 3D city modeling data standard CityGML describes the indoor geometric and semantic information of buildings, but the surfaces composing a volume are discrete, leading to invalid volumes. Moreover, the topological adjacency relationships of adjacent indoor spaces have not yet been described, which makes it difficult to realize effective queries and analyses for indoor applications. In this paper, we present a 3D topological data model for indoor spaces that adopts five topological primitives, namely, node, edge, loop, face, and solid, to describe the topological relationships of indoor spaces. Then, by improving the existing face-sorting method according to vector products in 3D space, a method for constructing 3D topological relationships for indoor spaces is proposed, which successively constructs the topological hierarchical combination of volume and the topological adjacency relationships of adjacent volumes. The experimental results show that by using the improved face-sorting method proposed in this work, the relative positions of faces are directly determined to sort the faces set, which avoids relatively cumbersome calculations and improves the efficiency of constructing 3D topological relationships for indoor spaces.

Enhanced Image Retrieval Using Multiscale Deep Feature Fusion in Supervised Hashing.

In recent years, deep-network-based hashing has gained prominence in image retrieval for its ability to generate compact and efficient binary representations. However, most existing methods predominantly focus on high-level semantic features extracted from the final layers of networks, often neglecting structural details that are crucial for capturing spatial relationships within images. Achieving a balance between preserving structural information and maximizing retrieval accuracy is the key to effective image hashing and retrieval. To address this challenge, we introduce Multiscale Deep Feature Fusion for Supervised Hashing (MDFF-SH), a novel approach that integrates multiscale feature fusion into the hashing process. The hallmark of MDFF-SH lies in its ability to combine low-level structural features with high-level semantic context, synthesizing robust and compact hash codes. By leveraging multiscale features from multiple convolutional layers, MDFF-SH ensures the preservation of fine-grained image details while maintaining global semantic integrity, achieving a harmonious balance that enhances retrieval precision and recall. Our approach demonstrated a superior performance on benchmark datasets, achieving significant gains in the Mean Average Precision (MAP) compared with the state-of-the-art methods: 9.5% on CIFAR-10, 5% on NUS-WIDE, and 11.5% on MS-COCO. These results highlight the effectiveness of MDFF-SH in bridging structural and semantic information, setting a new standard for high-precision image retrieval through multiscale feature fusion.

DGHR-YOLO: fabric defect detection based on High-level Screening -feature Pyramid Networks and deformable convolution

ABSTRACT Fabric defect detection is crucial for quality control in fabric manufacturing but remains a challenge due to the multi-scale characteristics of defects and their integration with the fabric background. To address this, we propose an efficient fabric defect detection model, DGHR-YOLO. First, the deformable convolution block (DCB) is introduced into the backbone network, leveraging its dynamic receptive field to effectively capture defect morphology and enhance feature extraction. Secondly, we propose the GS-SPPF module, designed to mitigate semantic information loss, optimize feature fusion, and improve both accuracy and inference speed. Thirdly, a lightweight High-level Screening Feature Pyramid Network (HS-FPN) is introduced, enabling effective multi-scale feature fusion while maintaining low complexity. Finally, a one-shot aggregation module based on channel shuffle and re-parameterized convolution is introduced, enhancing feature interaction across scales. Experimental results on the Tianchi textile dataset demonstrate that DGHR-YOLO achieves mAP0.5 and mAP0.5:0.95 scores of 85.8% and 72.7%, with respective improvements of 2.7% and 3.8% over YOLOv8m, while maintaining low parameter count and computational complexity.