Representation Of Information Research Articles

Deciphering the chemical language of inbred and wild mouse conspecific scents.

In most mammals, conspecific chemosensory communication relies on semiochemical release within complex bodily secretions and subsequent stimulus detection by the vomeronasal organ (VNO). Urine, a rich source of ethologically relevant chemosignals, conveys detailed information about sex, social hierarchy, health, and reproductive state, which becomes accessible to a conspecific via vomeronasal sampling. So far, however, numerous aspects of social chemosignaling along the vomeronasal pathway remain unclear. Moreover, since virtually all research on vomeronasal physiology is based on secretions derived from inbred laboratory mice, it remains uncertain whether such stimuli provide a true representation of potentially more relevant cues found in the wild. Here, we combine a robust low-noise VNO activity assay with comparative molecular profiling of sex- and strain-specific mouse urine samples from two inbred laboratory strains as well as from wild mice. With comprehensive molecular portraits of these secretions, VNO activity analysis now enables us to (i) assess whether and, if so, how much sex/strain-selective 'raw' chemical information in urine is accessible via vomeronasal sampling; (ii) identify which chemicals exhibit sufficient discriminatory power to signal an animal's sex, strain, or both; (iii) determine the extent to which wild mouse secretions are unique; and (iv) analyze whether vomeronasal response profiles differ between strains. We report both sex- and, in particular, strain-selective VNO representations of chemical information. Within the urinary 'secretome', both volatile compounds and proteins exhibit sufficient discriminative power to provide sex- and strain-specific molecular fingerprints. While total protein amount is substantially enriched in male urine, females secrete a larger variety at overall comparatively low concentrations. Surprisingly, the molecular spectrum of wild mouse urine does not dramatically exceed that of inbred strains. Finally, vomeronasal response profiles differ between C57BL/6 and BALB/c animals, with particularly disparate representations of female semiochemicals.

Reporting and representation of participant demographic information in completed Alzheimer's disease clinical trials.

Reporting and representation of participant demographic information in completed Alzheimer's disease clinical trials.

Nonuniform and pathway-specific laminar processing of spatial frequencies in the primary visual cortex of primates

The neocortex comprises six cortical layers that play a crucial role in information processing; however, it remains unclear whether laminar processing is consistent across all regions within a single cortex. In this study, we demonstrate diverse laminar response patterns in the primary visual cortex (V1) of three male macaque monkeys when exposed to visual stimuli at different spatial frequencies (SFs). These response patterns can be categorized into two groups. One group exhibit suppressed responses in the output layers for all SFs, while the other type shows amplified responses specifically at high SFs. Further analysis suggests that both magnocellular (M) and parvocellular (P) pathways contribute to the suppressive effect through feedforward mechanisms, whereas amplification is specific to local recurrent mechanisms within the parvocellular pathway. These findings highlight the non-uniform distribution of neural mechanisms involved in laminar processing and emphasize how pathway-specific amplification selectively enhances representations of high-SF information in primate V1.

A Semiotic Analysis of Social Class Representation in Pakistani Television Commercials

Semiotics interprets that how different signs are used in everyday lives to represent different things. Different verbal and non-verbal signs are used to reflect different ideologies and hidden agendas in the field of advertisements. The meanings and hidden agendas of TV commercials can be analysed in a more appropriate and effective way by considering semiotics as an influential tool of investigation. The enterprises of different domains as semiotics, linguistics and sociocultural studies make the current study as a multidisciplinary study. Media has become one of the most influential and feasible tool in the diffusion and representation of information and ideas in this globalised world with the advancement of science and technology. It encompasses strength and power to present different ideologies and agendas with the help of different verbal and non-verbal signs. The current study aims to analyse the demonstration of social class distinctive patterns in Pakistani television commercials. The study examines the representation of different social classes in the commercials by keeping in view the agendas and ideologies of advertisers. The nature of the study is qualitative. The theory of semiotics proposed by Dyer (1982) in the book advertising as Communication is the basis of the theoretical framework of the study. The commercials of renowned Pakistani television channels are collected as the data of the study. Non probability sampling technique is used to collect the data which comprises only those commercials which reflect social class representation. The analysis of the study highlights that different social classes as upper and lower classes are represented in different spheres of lives at symbolic level in the selected advertisements. The social class presentation in commercials represent the domination and superiority of upper class. The images of both upper and lower classes presented in the commercials support ideological beliefs about how social class biased practices are prevailed in the Pakistani society.

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Knowledge graphs have been extensively studied and applied, but most of these studies assume that the relationship facts in the knowledge graph are correct and deterministic. However, in the objective world, there inevitably exist uncertain relationship facts. The existing research lacks effective representation of such uncertain information. In this regard, we propose a novel representation learning framework called CosUKG, which is specifically designed for uncertain knowledge graphs. This framework models uncertain information by measuring the cosine similarity between transformed vectors and actual target vectors, effectively integrating uncertainty into the embedding process of the knowledge graph while preserving its structural information. Through multiple experiments on three public datasets, the superiority of the CosUKG framework in representing uncertain knowledge graphs is demonstrated. It achieves improved representation accuracy of uncertain information without increasing model complexity or weakening structural information.

Eye movement intervention facilitates concurrent perception and memory processing.

A widely used psychotherapeutic treatment for post-traumatic stress disorder (PTSD) involves performing bilateral eye movement (EM) during trauma memory retrieval. However, how this treatment-described as eye movement desensitization and reprocessing (EMDR)-alleviates trauma-related symptoms is unclear. While conventional theories suggest that bilateral EM interferes with concurrently retrieved trauma memories by taxing the limited working memory resources, here, we propose that bilateral EM actually facilitates information processing. In two EEG experiments, we replicated the bilateral EM procedure of EMDR, having participants engaging in continuous bilateral EM or receiving bilateral sensory stimulation (BS) as a control while retrieving short- or long-term memory. During EM or BS, we presented bystander images or memory cues to probe neural representations of perceptual and memory information. Multivariate pattern analysis of the EEG signals revealed that bilateral EM enhanced neural representations of simultaneously processed perceptual and memory information. This enhancement was accompanied by heightened visual responses and increased neural excitability in the occipital region. Furthermore, bilateral EM increased information transmission from the occipital to the frontoparietal region, indicating facilitated information transition from low-level perceptual representation to high-level memory representation. These findings argue for theories that emphasize information facilitation rather than disruption in the EMDR treatment.

Differential representation of sensory information and behavioral choice across layers of the mouse auditory cortex.

The activity of neurons in sensory areas sometimes covaries with upcoming choices in decision-making tasks. However, the prevalence, causal origin, and functional role of choice-related activity remain controversial. Understanding the circuit-logic of decision signals in sensory areas will require understanding their laminar specificity, but simultaneous recordings of neural activity across the cortical layers in forced-choice discrimination tasks have not yet been performed. Here, we describe neural activity from such recordings in the auditory cortex of mice during a frequency discrimination task with delayed report, which, as we show, requires the auditory cortex. Stimulus-related information was widely distributed across layers but disappeared very quickly after stimulus offset. Choice selectivity emerged toward the end of the delay period-suggesting a top-down origin-but only in the deep layers. Early stimulus-selective and late choice-selective deep neural ensembles were correlated, suggesting that the choice-selective signal fed back to the auditory cortex is not just action specific but develops as a consequence of the sensory-motor contingency imposed by the task.

A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks.

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems.

Semantically enhanced attention map‐driven occluded person re‐identification

AbstractOccluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID.

DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs

Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which have played crucial roles in various applications. Recently, many graph neural networks based approaches are proposed to model the graph structure information in TKGs. However, these approaches only construct graphs based on quadruplets and model the pairwise correlation between entities, which fail to capture the high-order correlations among entities. To this end, we propose DHyper, a recurrent Dual Hypergraph neural network for event prediction in TKGs, which simultaneously models the influences of the high-order correlations among both entities and relations. Specifically, a dual hypergraph learning module is proposed to discover the high-order correlations among entities and among relations in a parameterized way. A dual hypergraph message passing network is introduced to perform the information aggregation and representation fusion on the entity hypergraph and the relation hypergraph. Extensive experiments on six real-world datasets demonstrate that DHyper achieves the state-of-the-art performances, outperforming the best baseline by an average of 13.09%, 4.26%, 17.60%, and 18.03% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

A Method for Measuring Spatial Information of Area Maps Considering the Diversity of Node–Edge and Gestalt Principles

Existing methods for measuring the spatial information of area maps fail to take into account the diversity of adjacency relations and the heterogeneity of adjacency distances among area objects, resulting in insufficient measurement information. This article proposes a method for measuring area map information that considers the diversity of the node–edge and Gestalt principles. Firstly, this method utilizes the adjacency relations between the Voronoi diagram of area objects to construct an adjacency graph that characterizes the spatial distribution of area objects in area maps. This adjacency graph serves as the information representation of area maps. Secondly, the method selects four characteristic indicators, namely geometric information, node degree, adjacency distance, and adjacency strength, to represent the diversity of nodes and edges in the graph that affect spatial information. Finally, nodes in the adjacency graph are taken as the basic units, and the spatial information of area maps is comprehensively calculated by integrating the four characteristics that represent spatial information. To verify the validity and rationality of the proposed method, a dataset of continuously simplified area maps and a dataset of artificially simulated degrees of randomness were designed to evaluate the performance of the existing method and the method proposed in this paper. The results indicate that the correlation between the measurement results obtained by the method proposed in this paper and the degree of disorder is as high as 0.94, outperforming the existing representative methods. Additionally, the correlation between the measurement results of this method and the degree of simplification reaches 1, indicating that the variation range of the measured values is more consistent with the cognitive assumptions based on artificial simulations compared to the existing methods. The experimental results show that the method proposed in this paper is an effective metric approach for representing spatial information in area maps.

Efficient Large-Scale IoT Botnet Detection through GraphSAINT-Based Subgraph Sampling and Graph Isomorphism Network

In recent years, with the rapid development of the Internet of Things, large-scale botnet attacks have occurred frequently and have become an important challenge to network security. As artificial intelligence technology continues to evolve, intelligent detection solutions for botnets are constantly emerging. Although graph neural networks are widely used for botnet detection, directly handling large-scale botnet data becomes inefficient and challenging as the number of infected hosts increases and the network scale expands. Especially in the process of node level learning and inference, a large number of nodes and edges need to be processed, leading to a significant increase in computational complexity and posing new challenges to network security. This paper presents a novel approach that can accurately identify diverse intricate botnet architectures in extensive IoT networks based on the aforementioned circumstance. By utilizing GraphSAINT to process large-scale IoT botnet graph data, efficient and unbiased subgraph sampling has been achieved. In addition, a solution with enhanced information representation capability has been developed based on the Graph Isomorphism Network (GIN) for botnet detection. Compared with the five currently popular graph neural network (GNN) models, our approach has been tested on C2, P2P, and Chord datasets, and higher accuracy has been achieved.

A review on electroencephalogram based channel selection

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment

Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model’s superior performance on segmentation and clinical function assessment.

A Span-based Multivariate Information-aware Embedding Network for joint relational triplet extraction of threat intelligence

The extraction of relational triplets in threat intelligence is a critical aspect of constructing a knowledge graph. However, the field encounters challenges like high semantic similarity among entities, limited relevance of entities, and a heavy reliance on experts, leading to low extraction efficiency. Currently, there is a lack of research on extracting threat intelligence relational triplets, which necessitates the development of an efficient extraction model. This study proposes a Span-based Multivariate Information-aware Embedding Network (SMIEN) for the joint extraction of threat intelligence relation triplets. SMIEN introduces aware embedding modules to capture fine-grained features of multivariate information, including semantic, temporal order, dependency, spans, span pairs, entity labels, and relational labels, and to enhance their interaction. Designed a Type-Aware Graph Convolutional Network (TA-GCN) to enhance the representation of key information between less relevant tokens in threat intelligence text sentences within the Dependency-Aware Embedding layer. The Entity-Aware Embedding module is designed to enhance fine-grained interaction between span information and other multivariate information, while the Relationship-Aware Embedding module is designed to determine correlations between span pairs and enhance interaction with relationship labels. Experimental results on the HACKER, RE-DNRTI, and RE-IVTI datasets demonstrate micro F1 scores of 44.73%, 81.74%, and 44.15%, respectively, highlighting the method’s effectiveness in extracting cyber threat intelligence and Internet of Vehicle (IoV) threat intelligence information. In addition, we have constructed a threat intelligence ontology (CVTIO) on which we have built a preliminary threat intelligence knowledge graph (CVTIKG) with 23,636 relational triplets. The CVTIKG can be found at https://github.com/wangxtz/CVTIKG.

Object pose estimation method for robotic arm grasping

To address the issue of target detection in the planar grasping task, a position and attitude estimation method based on YOLO-Pose is proposed. The aim is to detect the three-dimensional position of the spacecraft’s center point and the planar two-dimensional attitude in real time. First, the weight is trained through transfer learning, and the number of key points is optimized by analyzing the shape characteristics of the spacecraft to improve the representation of pose information. Second, the CBAM dual-channel attention mechanism is integrated into the C3 module of the backbone network to improve the accuracy of pose estimation. Furthermore, the Wing Loss function is used to mitigate the problem of random offset in key points. The incorporation of the bi-directional feature pyramid network (BiFPN) structure into the neck network further improves the accuracy of target detection. The experimental results show that the average accuracy value of the optimized algorithm has increased. The average detection speed can meet the speed and accuracy requirements of the actual capture task and has practical application value.

Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection

The exchange of sensitive information between power distribution networks (PDNs) and urban transport networks (UTNs) presents a difficulty in ensuring privacy protection. This research proposes a new collaborative operation method for a coupled system. The scheme takes into account the schedulable capacity of electric vehicle charging stations (EVCSs) and locational marginal prices (LMPs) to handle the difficulty at hand. The EVCS hosting capacity model is built and expressed as the feasible area of charging power, based on AC power flow. This model is then used to offer information on the real schedulable capacity. By incorporating the charging loads into the coupling nodes between PDNs and UTNs, the issue of coordinated operation is separated and becomes equal to the optimal problem involving charging loads. Based on this premise, the most efficient operational cost of PDNs is transformed into a comparable representation of cost information in PDNs. This representation incorporates LMP information that guides charging decisions in UTNs. The suggested collaborative scheduling methodology in UTNs utilises the collected projection information from the static traffic assignment (STA) to ensure data privacy protection and achieve non-iterative calculation. Numerical experiments are conducted to illustrate that the proposed method, which uses a smaller amount of data, achieves the same level of optimality as the coordinated optimisation.

Analysis of Deep Generative Model Impact on Feature Extraction and Dimension Reduction for Short Utterance Text-Independent Speaker Verification

AbstractSpeaker verification is a biometric-based method for individual authentication. However, there are still several challenging problems in achieving high performance in short utterance text-independent conditions, maybe for weak speaker-specific features. Recently, deep learning algorithms have been used extensively in speech processing. This manuscript uses a deep belief network (DBN) as a deep generative method for feature extraction in speaker verification systems. This study aims to show the impact of using the proposed method in various challenging issues, including short utterances, text independence, language variation, and large-scale speaker verification. The proposed DBN uses MFCC as input and tries to extract more efficient features. This new representation of speaker information is evaluated in two popular speaker verification systems: GMM-UBM and i-vector-PLDA methods. The results show that, for the i-vector-PLDA system, the proposed feature decreases the EER considerably from 15.24 to 10.97%. In another experiment, DBN is used to reduce feature dimension and achieves significant results in decreasing computational time and increasing system response speed. In a case study, all the evaluations are performed for 1270 speakers of the NIST SRE2008 dataset. We show deep belief networks can be used in state-of-the-art acoustic modeling methods and more challenging datasets.

EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework

Chronic Obstructive Pulmonary Disease (COPD) poses a significant global healthcare challenge. It is a progressive lung disease that causes breathing difficulties and can significantly impact a person's quality of life. COPD is primarily caused by smoking, but other factors, such as air pollution and genetic predisposition, can also contribute to its development. This paper introduces a novel Context-Aware Framework for the Diagnosis and Personalized Management of COPD. We discuss the limitations of traditional COPD management, highlighting the importance of early detection and remote monitoring. Early detection and remote monitoring are crucial in managing COPD as they allow for timely interventions and better disease management. In this paper, we propose a framework based mostly on contextual data and other parameters of COPD as put forth by the World Health Organization (WHO) in the form of the International Classification of Functioning, Disability, and Health. Ontologies drive this architecture and incorporate dynamic contextual information from patient environments, user profiles, and sensor data. In addition to the various obvious data items like patient personal details (gender, contact, medical history) and COPD risks and symptoms, the COPD ontology also considers the details about the caregiver and healthcare professional. This is in addition to the contextual data processed separately using the Context Ontology. The ontology we constructed using Protégé serves as the framework for the structured representation and logical inference of contextual information. By harnessing dynamic contextual data, our ontology enables real-time decision-making tailored to individual patient requirements. It empowers healthcare professionals to make informed choices and deliver timely interventions, enhancing healthcare services by offering proactive care to detect early signs of health deterioration and suggest preventive measures. This approach improves patient experiences and optimizes resource allocation within the healthcare system. To uphold ethical standards and prioritize the needs of patients, we emphasize the significance of safeguarding data, obtaining informed permission, and recognizing data ownership. The ontology-based approach presented in this study offers a scalable and flexible framework that can be readily incorporated into existing healthcare systems, redefining the management of COPD in response to evolving demands. Security poses one of the biggest threats in context-based environments due to the different data formats acquired by the diverse sensors. Another essential consideration is confidentiality because the data in hand is sensitive patient information.

An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder.

Existing industrial image anomaly detection techniques predominantly utilize codecs based on convolutional neural networks (CNNs). However, traditional convolutional autoencoders are limited to local features, struggling to assimilate global feature information. CNNs' generalizability enables the reconstruction of certain anomalous regions. This is particularly evident when normal and abnormal regions, despite having similar pixel values, contain different semantic information, leading to ineffective anomaly detection. Furthermore, collecting abnormal image samples during actual industrial production poses challenges, often resulting in data imbalance. To mitigate these issues, this study proposes an unsupervised anomaly detection model employing the Vision Transformer (ViT) architecture, incorporating a Transformer structure to understand the global context between image blocks, thereby extracting a superior representation of feature information. It integrates a memory module to catalog normal sample features, both to counteract anomaly reconstruction issues and bolster feature representation, and additionally introduces a coordinate attention (CA) mechanism to intensify focus on image features at both spatial and channel dimensions, minimizing feature information loss and thereby enabling more precise anomaly identification and localization. Experiments conducted on two public datasets, MVTec AD and BeanTech AD, substantiate the method's effectiveness, demonstrating an approximate 20% improvement in average AUROC% at the image level over traditional convolutional encoders.