An empirical assessment of quality metrics for diversified similarity searching

A diversified similarity search retrieves elements that are simultaneously similar to a query object and akin to the different collections within the explored data. While several methods in information retrieval, data clustering, and similarity searching have tackled the problem of adding diversity into result sets, the experimental comparison of their performances is still an open issue mainly because the quality metrics are “borrowed” from those different research areas, bringing their biases alongside. In this manuscript, we investigate a series of such metrics and experimentally discuss their trends and limitations. We conclude diversity is better addressed by a set of measures rather than a single quality index and introduce the concept of Diversity Features Model (DFM), which combines the viewpoints of biased metrics into a multidimensional representation. Experimental evaluations indicate (i) DFM enables comparing different result diversification algorithms by considering multiple criteria, and (ii) the most suitable searching methods for a particular dataset are spotted by combining DFM with ranking aggregation and parallel coordinates maps.

Continuous, multidimensional coding of 3D complex tactile stimuli by primary sensory neurons of the vibrissal system

Across all sensory modalities, first-stage sensory neurons are an information bottleneck: they must convey all information available for an animal to perceive and act in its environment. Our understanding of coding properties of primary sensory neurons in the auditory and visual systems has been aided by the use of increasingly complex, naturalistic stimulus sets. By comparison, encoding properties of primary somatosensory afferents are poorly understood. Here, we use the rodent whisker system to examine how tactile information is represented in primary sensory neurons of the trigeminal ganglion (Vg). Vg neurons have long been thought to segregate into functional classes associated with separate streams of information processing. However, this view is based on Vg responses to restricted stimulus sets which potentially underreport the coding capabilities of these neurons. In contrast, the current study records Vg responses to complex three-dimensional (3D) stimulation while quantifying the complete 3D whisker shape and mechanics, thereby beginning to reveal their full representational capabilities. The results show that individual Vg neurons simultaneously represent multiple mechanical features of a stimulus, do not preferentially encode principal components of the stimuli, and represent continuous and tiled variations of all available mechanical information. These results directly contrast with proposed codes in which subpopulations of Vg neurons encode select stimulus features. Instead, individual Vg neurons likely overcome the information bottleneck by encoding large regions of a complex sensory space. This proposed tiled and multidimensional representation at the Vg directly constrains the computations performed by more central neurons of the vibrissotrigeminal pathway.

Learning Ordinal Embedding from Sets

Ordinal embedding is the task of computing a meaningful multidimensional representation of objects, for which only qualitative constraints on their distance functions are known. In particular, we consider comparisons of the form “Which object from the pair (j, k) is more similar to object i?”. In this paper, we generalize this framework to the case where the ordinal constraints are not given at the level of individual points, but at the level of sets, and propose a distributional triplet embedding approach in a scalable learning framework. We show that the query complexity of our approach is on par with the single-item approach. Without having access to features of the items to be embedded, we show the applicability of our model on toy datasets for the task of reconstruction and demonstrate the validity of the obtained embeddings in experiments on synthetic and real-world datasets.

Challenges and Barriers for Net‐Zero/Positive Energy Buildings and Districts—Empirical Evidence from the Smart City Project SPARCS

Without decarbonizing cities energy and climate objectives cannot be achieved as cities account for approximately two thirds of energy consumption and emissions. This goal of decarbonizing cities has to be facilitated by promoting net-zero/positive energy buildings and districts and replicating them, driving cities towards sustainability goals. Many projects in smart cities demonstrate novel and groundbreaking low-carbon solutions in demonstration and lighthouse projects. However, as the historical, geographic, political, social and economic context of urban areas vary greatly, it is not always easy to repeat the solution in another city or even district. It is therefore important to look for the opportunities to scale up or repeat successful pilots. The purpose of this paper is to explore common trends in technologies and replication strategies for positive energy buildings or districts in smart city projects, based on the practical experience from a case study in Leipzig—one of the lighthouse cities in the project SPARCS. One of the key findings the paper has proven is the necessity of a profound replication modelling to deepen the understanding of upscaling processes. Three models analyzed in this article are able to provide a multidimensional representation of the solution to be replicated.

A Method for Studying Inhomogeneous States of Induction Motors

The presented study is aimed at carrying out scientifically grounded assessments of the current technical state of induction motors (IMs). The existing classical theory of IMs has insufficient generality for solving this problem. It does not cover the state of IMs that feature manufacturing and technological deviations, as well as states with operational damages or ageing. The variety of IM operational states and design versions generates the need to widen the classical framework of analysis to the level of multidimensional representations. Such study was carried out within the framework of a topological approach. According to this approach, an IM is considered as a multidimensional inhomogeneous electromechanical system that can be in homogeneous or inhomogeneous states. From the methodological point of view, the topological approach is based on observing and analyzing the formal properties of a mathematical model with transferring their results to the real object. The rules and a systematized conceptual and terminological framework presented in the article play an essential role. In addition, the method of a topological study and its examples are given. Thus, the study covers all components of the scientific approach focused on a multidimensional representation of the IM operational states. The topological approach forms a scientific and methodological platform for setting up a system for monitoring the current technical state of IMs during their operation. It is pointed out that the existing diagnostic methods are focused on recording external manifestations of parametric heterogeneity. In view of this circumstance, they cannot serve as a reliable basis for drawing up conclusions on the current technical state of IMs. The topological method of operational diagnostics is focused on recording and analyzing changes in the internal properties of an object and is practically independent of the influence of external factors. This feature guarantees high reliability of the results obtained from applying the topological diagnostics method for estimating the current technical state of IMs.

Reevaluating Factor Models

Since the introduction of CAPM in the 1960s, the asset pricing literature has documented hundreds of characteristics that capture the cross-sectional variation in stock returns. Traditionally, multifactor models seek a multidimensional representation of common risks; this approach entails selecting a small number of representative characteristics from a set of candidate characteristics that, together, explain most of the cross-sectional variation in stock returns. Characteristics-based long-short portfolios are partially loaded on the true underlying risk factors and are at best noisy proxies for true latent factors. However, the expansive list of potential characteristics, along with developments in the field of dimensionality reduction, offers us an opportunity to seek better approximations of the unobservable latent risk factors. A recent stream of literature has investigated how to appropriately extract relevant features from the “factor zoo” while incorporating information from the expansive list of factors. This chapter aims to summarize this novel paradigm in factor modeling.

The development of a classification model of accounting in the digital economy

A multidimensional classification scheme and a semantic multidimensional accounting data model are defined in this article. Instead of accounts, multidimensional accounting uses categories of economic activity. The proposed multidimensional data model is more flexible than the traditional account model and allows you to expand the capabilities of accounting, taking into account the different needs of users of accounting information. The multidimensional data model allows you to expand the capabilities of accounting, taking into account the different needs of users of accounting information. To create a multidimensional accounting system, the categories of economic activity registered in accounting have been determined, the concept of double entry and balance in a multidimensional representation (probalance) has been formulated. The features of planning in a multidimensional accounting system have been described and the implementation of the financial results plan has been considered.

Reclaiming the narratives: Situated multidimensional representation of underserved Indigenous communities through citizen-driven reporting

Mainstream news outlets continue to ignore Indigenous people or cover them inadequately, resulting in mistrust and alienation by the former towards the latter. Yet, ways to meet Indigenous peoples’ needs for accurate media representation is understudied and undertheorized. Based on 16 in-depth interviews with Native and Indigenous citizens, we develop a conceptual framework of situated multidimensional representation to elucidate the agentic processes for citizen journalists to empower members of various tribal affiliations. Findings reveal that citizen journalists’ situated knowledge and expertise encourages humanizing Indigenous people, engenders media trust through evoking feelings of relatability and belonging, and strengthens Indigenous identity by foregrounding the focus on complex personhood. Our analysis highlights a need for transforming conventional journalistic values and relationship building practices to incorporate marginalized Indigenous perspectives. Theoretical and practical implications are discussed.