Perspective Of Representation Research Articles

Spatial–Spectral Total Variation-Regularized Low-Rank Tensor Representation for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is a vital aspect of remote sensing as it focuses on identifying pixels with distinct spectral–spatial properties in comparison to their background representations. However, existing methods for anomaly detection in HSIs often overlook the spatial correlation between pixels by converting the three-dimensional tensor data into its folded form of independent signatures, which may lead to insufficient detection performance. To address this limitation, we develop an anomaly detection algorithm from a tensor representation perspective, which begins by separating the observed hyperspectral image into background and anomaly cubes. We leverage the tensor nuclear norm (TNN) to capture the inherent low-rank structure of background cube globally. This allows us to effectively model and represent the background information. To further improve the detection performance, we introduce spatial–spectral total variation (SSTV) for effectively promoting piecewise smoothness of the background tensor, aiding in the identification of anomalies. Additionally, we incorporate RX-derived attention weights-guided [Formula: see text] norm. This encourages group sparsity of anomalous pixels, improving the precision of anomaly detection. To solve our proposed method, we employ the alternating direction method of multipliers (ADMM), ensuring guaranteed convergence and efficient computation. Through experiments on different kinds of hyperspectral real datasets, we have demonstrated that our method surpasses several state-of-the-art detectors.

Exploring social cognition in an international context: insights from the social representations perspective on legitimacy among French entrepreneurs

ABSTRACT In the field of international entrepreneurship, there are increasing calls for the study of cognition in its social context. In this regard, this article draws on social representations theory to explore and better understand the cognition of entrepreneurs. The objective of the present study is to explore the social representation of legitimacy among different social groups and show how these collective images vary depending on the context in which entrepreneurs operate. By applying social representations theory to data gathered from a sample of 150 French entrepreneurs, we construct a framework that elucidates social development and the changes that influence entrepreneurs’ decision-making. In addition, this article reveals a new avenue of study in relation to legitimacy from a collective perspective.

Degressively proportional representation in the European Parliament: European Union law versus reality

ABSTRACT As the European Parliament is the first transnational representative body based on the Member States representation, the apportionment of seats among the European Union Member States may be a relevant issue. This article is conceived as an idiographic case study and presents an analysis of the composition of the European Parliament in the territorial representation perspective. According to the 2007 Lisbon Treaty, there should be degressively proportional representation in the European Parliament. Following that existing EU legislation implies an uneven representation of citizens in the European Parliament across the EU Member States, this study pursues the following two goals. Firstly, it seeks to quantify malapportionment (the degressive proportionality principle), i.e. distortion of the proportional representation of the Member States (malapportionment) and under-/over-representation of individual Member States, in European elections since 1979 when the direct elections of MEPs were introduced. Secondly, it aims to answer whether there has in fact been a degressively proportional representation in the European Parliament. This article concludes that malapportionment has stabilized at fourteen to fifteen per cent of the total seats since the 2004 European election, but there has been no degressively proportional representation in the European Parliament since 2004.

Representations of Post-Industrial Shanghai

This article explores how vernacular aesthetics have been re-appropriated from pictorial to modern documentary photography over the past century to instigate a modern collective imagination of the industrial disintegration in the Chinese urban milieu. Within the scope of a discursive visual process, Jean Philippe Gauvrit (b.1963) documents the departure of the industrial urban society in his photo-essay Shanghai in JP Gauvrit (2008). The paper claims that Gauvrit’s documentary photography can be understood as a visual critical discourse of several representational perspectives of time that render visible anachronistic and new social structures that come into being: between utopias of the past and visions of the future in an alternative chronotopic ‘present’ cartography. Drawing on Bakhtin’s (1981) conception of chronotope, in this study, the sublime industrial comes to be represented as an intelligible reconfiguration of linear and cyclical time. By linking that socio-economic reality of that time with a collective consciousness, documentary photography can serve as a chronotope that reveals both the tension and the assimilation relating to the historical myths that lie between the fall of an industrial mode of production and the birth of a post-industrial cultural city in an era of de-industrialization.

A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation

A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation

Overview of image-based 3D reconstruction technology

Three-dimensional (3D) reconstruction technology is the key technology to establish and express the objective world by using computer, and it is widely used in real 3D, automatic driving, aerospace, navigation and industrial robot applications. According to different principles, it is mainly divided into methods based on traditional multi-view geometry and methods based on deep learning. This paper introduces the above methods from the perspective of three-dimensional space representation. The feature extraction and stereo matching theory of traditional 3D reconstruction methods are the theoretical basis of 3D reconstruction methods based on deep learning, so the paper focuses on them. With the development of traditional 3D reconstruction methods and the development of deep learning related theories, the explicit deep learning 3D reconstruction method represented by MVSNet and the implicit 3D reconstruction method represented by NeRF have been gradually developed. At the same time, the dataset and evaluation indicators for 3D reconstruction were introduced. Finally, a summary of image based 3D reconstruction was provided.

젊은 세대의 정치적 대표성의 관점에서 바라본 공직선거법과 정당법 개정의 한계와 개선방안

Recently amended legislation related to political participation has a positive aspect in that it has legally and institutionally expanded the political participation of the young generation, including the youth. however, there is a limit to the fact that the political party law still stipulates the grounds for party membership and activities that can embody youth's political responsiveness. In other words, Article 22, Paragraph 1 of the Political Party Act sets an age limit of “16 years of age or older” as a condition for joining a political party. As mentioned above, this has a positive aspect in that the existing voting age was lowered to 16 through revision. however, there is a limitation in restricting party membership and activities by stipulating membership in political parties in the Political Party Act and preventing youth under the age of 16 from becoming party members.
 And in the case of deposits, the youth who have not achieved economic independence are actually restricted in exercising their right to hold public office, which has lowered the age of eligibility for election. Ultimately, follow-up legislative improvements are needed, such as setting the age for joining a political party by the party's voluntary choice, not a regulation in the Political Party Act, or abolishing the age for joining a party, and abolishing the deposit system that restricts the exercise of the right to hold public office by the younger generation.
 Therefore, from the perspective of democracy and generational equality, this paper emphasizes that improvement should be made in the direction of expanding 'representation' by analyzing not only the expansion of the electorate but also the legal and institutional factors that restrict the right to be elected. In order to achieve the purpose of the study, after analyzing the limitations of the recently revised public official election law, Article 56, Paragraph 1 of the deposit system and the political party membership age regulation of Article 22, Paragraph 1 of the Political Party Act, we propose ways to improve them.

Model of cognitive activity of the human brain based on the mathematical apparatus of quantum mechanics

Describing the transmission and processing of information by an individual is a fundamental issue in modern cognitive science. Previously, unique scientific models for information transfer between individuals [1], as well as models for cognitive activity [2], were developed. However, numerous presented models lack scalability and formalization, hindering their ability to provide a comprehensive explanation for information transfer processes and their distortion due to interaction with the external communicative environment. One aspect of human cognitive activity is that individuals think not based on a code, as a computer does, but through the interaction of various mental images. Despite the fact that these images have a specific material foundation in the form of electrical and chemical activity in the human brain, describing them using conventional mathematical models presents several difficulties [3]. Therefore, this paper suggests novel techniques for describing information images/representations’ functioning, which simulate an individual’s cognitive activity. These methods rely on the mathematical models of self-oscillatory and conventional quantum physics (including potential wells and virtual particles conventionally used in the physical domain) for characterizing basic interactions [4]. The authors adopt a phenomenological perspective and do not regard cognitive systems as quantum. The aim of this study ject is to establish a model for cognitive function in the human brain using the mathematical principles of self-oscillating quantum mechanics from the perspective of information imaging and representation. Methods. The theory proposes [5] that information images/representations (IR) share characteristic properties with virtual Feymann particles and other elementary particles. The human mind is presented as a one-dimensional potential well with finite walls of varying sizes and an internal potential barrier that simulates the boundary between consciousness and subconsciousness. The authors have applied parametrization based on this theory. As a result, we propose the foundations of the mathematical apparatus based on classical quantum mechanics, followed by the mathematical apparatus of self-oscillating quantum mechanics. The latter, though little known, may allow to predict certain cognitive functions of the human brain by modifying and applying it to non-quantum settings. An equation is derived for the state function of the information image of an individual engaging in cognitive activity. Primary calculations were conducted on the state functions of information images using a computer model. The movement patterns of the information image within and outside were then deduced.

Supervised adaptive similarity consistent latent representation hashing

Cross-modal hashing has attracted significant attention in multimedia data similarity given its appealing computational cost and retrieval performance. Supervised hashing benefits from the auxiliary learning of a similarity matrix, which is usually predefined by inner product features or category labels. However, a predefined similarity matrix fails to reflect the real similarity relationship between image-text pairs. In addition, existing methods fix the weights to a value or update them by introducing sensitive dataset-related hyper-parameters. To overcome these problems, we propose a method to perform supervised adaptive similarity consistent latent representation hashing (SCLRH) that adaptively learns the similarity matrix during hashing learning. In SCLRH, we assume that multimodal data are observed and reconstructed from different perspectives of a common consistent latent representation. Instead of using a predefined similarity matrix, SCLRH adaptively learns this matrix to reflect the underlying manifold structure and describes the fine-grained similarity between consistent latent representations. In addition, SCLRH introduces a self-weighted learning strategy to update the weights based on the contributions of different modalities without involving additional hyper-parameters. Experimental results on three benchmark datasets demonstrate the superiority of the proposed SCLRH for cross-modal retrieval.

Africans in London: Chronicle of the Union of West African Students

Research interest in the problems of the young generation of the countries of the African continent, including students, has noticeably increased over the last decades. The student youth, called the “conscience of the nation” in African countries, for a long time has been one of the most organized streams of the democratic movement and continues to play a prominent role in national processes. However, the student movement is a very complex and contradictory phenomenon, feeding numerous hotbeds and centers of intense search for national identity and struggle for their rights and freedoms. The authors of this article saw their task in an objective examination of the experience of one of the most famous and authoritative student organizations, mainly in the historical and cultural context. The article is dedicated to the history of the Union of West African Students’ Union (WASU) – one of the largest organizations of African students who lived and studied in Great Britain in the first half of the 20th century. The historical and cultural perspective of representation was not chosen by the authors of the article by chance. The activities of the WASU are closely connected with the problems of the national liberation movement in West Africa, though it also performed various tasks to protect the interests and rights of students in London and in the UK and therefore holds a key place in the history of the African Diaspora. The authors examine in detail the stages of the ideological and political formation and development of the WASU – from the Union of Students of African Descent, which existed in London at the turn of the second and the third decades of the 20th century, to the extinction of the WASU in the early 1960s. The authors show that for all its weaknesses, mistakes, theoretical immaturity and eclecticism of ideological attitudes, it was the WASU that had formed the anti-colonial agenda in Africa implemented during the national liberation struggle at the turn of 1950–60s in most countries on the continent. In a number of countries in West Africa, after the declaration of independence, former members and leaders of the Union came to power. Among them are Kwame Nkrumah (1909–1972), Benjamin Nnamdi Azikiwe (1904–1996), etc. The authors offer a new look at the phenomenon of African students, who were equally capable to fight for their interests and the interests of the nation and at the same time expressed conformist sentiments to the colonial authorities and post-colonial reality.

A cascade information diffusion prediction model integrating topic features and cross-attention

Information cascade prediction is a crucial task in social network analysis. However, previous research has only focused on the impact of social relationships on cascade information diffusion, while ignoring the differences caused by the characteristics of cascade information itself, which limits the performance of prediction results. We propose a novel cascade information diffusion prediction model (Topic-HGAT). Firstly, we extract features from different topic features to enhance the learned cascade information representation. To better implement this method, we use hypergraphs to better characterize cascade information and dynamically learn multiple diffusion sub-hypergraphs according to the time process; secondly, we introduce cross-attention mechanisms to learn each other's feature representations from the perspectives of both user representation and cascade representation, thereby achieving deep fusion of the two features. This solves the problem of poor feature fusion effect caused by simply calculating self-attention on learned user representation and cascade representation in previous studies; finally, we conduct comparative experiments on four real datasets, including Twitter and Douban. Experimental results show that the proposed Topic-HGAT model achieves the highest improvements of 2.91% and 1.59% on Hits@100 and MAP@100 indicators, respectively, compared to other 8 baseline models, verifying the rationality and effectiveness of the proposed Topic-HGAT model.

Link prediction in bipartite networks via effective integration of explicit and implicit relations

Link prediction in bipartite networks aims to identify or predict possible links between nodes of different types based on known network information. However, most existing studies predominantly focus on monopartite networks, neglecting the intrinsic properties unique to bipartite networks, such as the intricate high-order relationships between nodes. Both explicit relations (representing low-order information) and implicit relations (representing high-order information) play essential roles in predicting the evolution of bipartite networks, and they are indispensable and mutually reinforce each other. To fully leverage their potential in addressing the link prediction problem, we propose a novel framework from the perspective of network representation. This framework not only effectively integrates explicit relations and implicit relations, but also preserves the local and global structure of bipartite networks. Specifically, the probability of a link between two nodes of different types is determined by the linear sum of the contribution values of the mutually connected nodes in their respective common neighbors. Implicit relations are then used to preserve the local structure during the network representation. Furthermore, we implement optimization using a relaxed majorization-minimization algorithm, offering the advantage of uncovering high-quality local minima. Our proposed framework has undergone extensive testing on eight real-world datasets, and the results unequivocally demonstrate its significant superiority over state-of-the-art methods.

The Dimensionality of Genetic Information

This paper investigates the dimensionality of genetic information from the perspective of optimal representation. Recently it has been shown that optimal coding of information is in terms of the noninteger dimension of e, which is accompanied by the property of scale invariance. Since Nature is optimal, we should see this dimension reflected in the organization of the genetic code. With this as background, this paper investigates the problem of the logic behind the nature of the assignment of codons to amino acids, for they take different values that range from 1 to 6. It is shown that the non-uniformity of this assignment, which goes against mathematical coding theory that demands a near uniform assignment, is consistent with noninteger dimensions. The reason why the codon assignment for different amino acids varies is because uniformity is a requirement for optimality only in a standard vector space, and is not so in the noninteger dimensional space. It is noteworthy that there are 20 different covering regions in an e-dimensional information space, which is equal to the number of amino acids. The problem of the visualization of data that originates in an e-dimensional space but examined in a 3-dimensional vector space is also discussed. It is shown that the assignment of the codons to the amino acids is fractal-like that is well modeled by the Zipf distribution which is a power law. It is remarkable that the Zipf distribution that holds for the letter frequencies of words in a natural language also applies to the rank order of triplets in the code for amino acids.

Epistemology of Feelies. Aldous Huxley’s Brave New World and the Dream of Smell Media

The essay analyses from an epistemological point of view the functions of an imaginary medium, the feelies, namely some stereoscopic, tactile, and especially olfactory movies imagined by Aldous Huxley in the futuristic dystopia Brave New World (1932). Its main goal is to comprehend the identity, functions, and objectives of yesterday’s and today’s multisensory media. In this perspective, the essay reconstructs the cultural-historical horizon which produced Huxley’s imaginary entertainment, considering the three polarities which constitute a media dispositive in the perspective of media epistemology, i.e. machinery, representation, and spectator. With regard to the mechanical function of the fictional dispositive, its ability to catch sensory spheres such as smell and touch reflects the contemporary debate on media specificity, a discussion in which Huxley himself participated with a famous 1929 article. In the same way, the fictional feely Three Weeks in a Helicopter appears as a parodic pastiche which bears the marks of various cinematic paradigms of the time: especially early “cinema of attractions”, as well as film genres which communicate directly to the spectator’s unconscious. Finally, focusing on the imaginary spectator’s experience, the essay reconnects feelies to the cultural history of olfaction, a repressed sense whose media conquest coincides with a precise form of colonization of human subjectivity.

Conversations on Cannabis and Mental Health: Recommendations for Health and Social Care Providers from Indigenous 2SLGTBQQIA+ People in Canada

With the recent legalization of cannabis in Canada, there is an urgent need for information about its effects on Indigenous populations due to the impact of cannabis on the mental health of Indigenous Peoples in Canada being largely unknown. Using the guiding principles of Etuaptmumk (Two-Eyed Seeing), Sharing Circles were held to hear the needs and experiences of Indigenous People in relation to their mental health and cannabis use. From these engagements and using gender-based and distinctions-based analysis, four recommendations were developed for academic institutions, medical regulatory authorities and health and social care providers (HSCPs) to consider when caring for Indigenous People living with mental health issues. The findings point to the disconnection between recent research on medical cannabis and its availability to Indigenous People through accessible mediums, HSCPs, and the lack of cultural safety in health and social services. The four recommendations provided are helpful to both educate frontline HSCPs about the needs and experiences of Indigenous People and improve access to current information and best practices for Indigenous People who use cannabis for mental health from the regulatory and representation perspective.

Fan perspectives of queer representation in DC's Legends of Tomorrow on Tumblr and AO3

DC's Legends of Tomorrow (2016–2022) is a television show with an ensemble cast of existing characters from other DC TV shows, formed into a single, time-traveling team of antiheroes. The show is known for its inclusion of multiple queer characters, particularly Sara Lance, the show's long-running central character and captain of the Legends team and an openly bisexual woman. Fans use Tumblr and Archive of Our Own to discuss queer characters, canonical same-sex pairings, and noncanonical slash pairings. Queer representation in television matters to the show's fan community, and users are appreciative of and invested in canonically queer characters and pairings. Beyond the canon content, users are perhaps equally invested in noncanonical queer pairings and utilize manipulation of the source text in order to appreciate these pairings. Fans simultaneously enjoy watching and blogging about the show while also forming their own interpretations of queer characters and relationships. However, the central canon queer couple of the show is still by far the most popular with fans, and fans pay more attention to white characters and relationships, regardless of canonicity, than to the show's characters of color.

Hierarchical reinforcement learning with unlimited option scheduling for sparse rewards in continuous spaces

The fundamental concept behind option-based hierarchical reinforcement learning (O-HRL) is to obtain temporal coarse-grained actions and abstract complex situations. Although O-HRL is intended for sparse rewards, it remains difficult to extend it to sparse reward problems in continuous spaces. In this paper, we provide a fresh perspective on option technology to comprehend different options based on knowledge representation. The hierarchical reinforcement learning with the unlimited option scheduling (UOS) algorithm is proposed. Unlike conventional O-HRL algorithms that apply a limited set of options with specific meanings, UOS encourages an infinite number of options to correlate with trajectories while maintaining a correlation with each other, thus representing more abundant knowledge. These unlimited options can guide infinite and diverse trajectories to cover fine-grained state spaces. Further, a composite scheduling mode is proposed to generate arbitrary-length trajectories with intrinsic characteristics, providing both flexibility and concentration for unlimited options. It significantly improves the performance and robustness of UOS. Finally, a new comprehensive experimental system is developed, and the experimental results demonstrate the notable success of UOS on sparse reward tasks in continuous spaces. It also identifies the root cause of UOS superiority from the perspective of knowledge representation.

Intersubject similarity in neural representations underlies shared episodic memory content

Individuals generally form their unique memories from shared experiences, yet the neural representational mechanisms underlying this subjectiveness of memory are poorly understood. The current study addressed this important question from the cross-subject neural representational perspective, leveraging a large functional magnetic resonance imaging dataset (n = 415) of a face-name associative memory task. We found that individuals' memory abilities were predicted by their synchronization to the group-averaged, canonical trial-by-trial activation level and, to a lesser degree, by their similarity to the group-averaged representational patterns during encoding. More importantly, the memory content shared between pairs of participants could be predicted by their shared local neural activation pattern, particularly in the angular gyrus and ventromedial prefrontal cortex, even after controlling for differences in memory abilities. These results uncover neural representational mechanisms for individualized memory and underscore the constructive nature of episodic memory.

Event fusion photometric stereo network

Photometric stereo methods typically rely on RGB cameras and are usually performed in a dark room to avoid ambient illumination. Ambient illumination poses a great challenge in photometric stereo due to the restricted dynamic range of the RGB cameras. To address this limitation, we present a novel method, namely Event Fusion Photometric Stereo Network (EFPS-Net), which estimates the surface normals of an object in an ambient light environment by utilizing a deep fusion of RGB and event cameras. The high dynamic range of event cameras provides a broader perspective of light representations that RGB cameras cannot provide. Specifically, we propose an event interpolation method to obtain ample light information, which enables precise estimation of the surface normals of an object. By using RGB-event fused observation maps, our EFPS-Net outperforms previous state-of-the-art methods that depend only on RGB frames, resulting in a 7.94% reduction in mean average error. In addition, we curate a novel photometric stereo dataset by capturing objects with RGB and event cameras under numerous ambient light environments.

Image Intensity Variation Information for Interest Point Detection.

Interest point detection methods are gaining more attention and are widely applied in computer vision tasks such as image retrieval and 3D reconstruction. However, there still exist two main problems to be solved: (1) from the perspective of mathematical representations, the differences among edges, corners, and blobs have not been convincingly explained and the relationships among the amplitude response, scale factor, and filtering orientation for interest points have not been thoroughly explained; (2) the existing design mechanism for interest point detection does not show how to accurately obtain intensity variation information on corners and blobs. In this paper, the first- and second-order Gaussian directional derivative representations of a step edge, four common genres of corners, an anisotropic-type blob, and an isotropic-type blob are analyzed and derived. Multiple interest point characteristics are discovered. The characteristics for interest points that we obtained help us describe the differences among edges, corners, and blobs, explain why the existing interest point detection methods with multiple scales cannot properly obtain interest points from images, and present novel corner and blob detection methods. Extensive experiments demonstrate the superiority of our proposed methods in terms of detection performance, robustness to affine transformations, noise, image matching, and 3D reconstruction.