A Compact Representation of Measured BRDFs Using Neural Processes

In this article, we introduce a compact representation for measured BRDFs by leveraging Neural Processes (NPs). Unlike prior methods that express those BRDFs as discrete high-dimensional matrices or tensors, our technique considers measured BRDFs as continuous functions and works in corresponding function spaces . Specifically, provided the evaluations of a set of BRDFs, such as ones in MERL and EPFL datasets, our method learns a low-dimensional latent space as well as a few neural networks to encode and decode these measured BRDFs or new BRDFs into and from this space in a non-linear fashion. Leveraging this latent space and the flexibility offered by the NPs formulation, our encoded BRDFs are highly compact and offer a level of accuracy better than prior methods. We demonstrate the practical usefulness of our approach via two important applications, BRDF compression and editing. Additionally, we design two alternative post-trained decoders to, respectively, achieve better compression ratio for individual BRDFs and enable importance sampling of BRDFs.

Janus

Proximity detection is at the core of several mobile and ubiquitous computing applications. These include reactive use cases, e.g., alerting individuals of hazards or interaction opportunities, and others concerned only with logging proximity data, e.g., for offline analysis and modeling. Common approaches rely on Bluetooth Low Energy (BLE) or ultra-wideband (UWB) radios. Nevertheless, these strike opposite tradeoffs between the accuracy of distance estimates quantifying proximity and the energy efficiency affecting system lifetime, effectively forcing a choice between the two and ultimately constraining applicability. Janus reconciles these dimensions in a dual-radio protocol enabling accurate and energy-efficient proximity detection, where the energy-savvy BLE is exploited to discover devices and coordinate their distance measurements, acquired via the energy-hungry UWB. A model supports domain experts in configuring Janus for their use cases with predictable performance. The latency, reliability, and accuracy of Janus are evaluated experimentally, including realistic scenarios endowed with the mm-level ground truth provided by a motion capture system. Energy measurements show that Janus achieves weeks to months of autonomous operation, depending on the use case configuration. Finally, several large-scale campaigns exemplify its practical usefulness in real-world contexts.

Robust Estimation of Deformation from Observation Differences Using Some Evolutionary Optimisation Algorithms

In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth’s crust surface.

Method of Air Temperature Forecast in Mechanized Longwall Workings in the Conditions of Vietnamese Mines

In the most recent years, the Vietnam National Coal - Mineral Industries Holding CorporationLimited (VINACOMIN) has been dynamically developing mechanization technologies in undergroundcoal mines. The climatic conditions of Vietnam, as well as increasing the depth of the coal seams and theproduction capacity, contribute to an air temperature increasing in mining excavations. The articlepresents statistical equations enabling air temperature forecasting at the outlet of mechanized longwallworkings. The results of numerical calculations, obtained from the solutions of the adopted mathematicaldescriptions, were compared with the measurement results and the statistical significance of the obtaineddeviations was determined. The performed analysis allowed to assess the practical usefulness of theadopted model for the air temperature forecasting in the workings of mechanized underground mines inVietnam. The presented method can be used as a tool for mining services in the fight against the climatethreat in underground excavations.

Enhancing the benefits of group involvement in research

Research can facilitate mutual learning, allow participants’ voices to be heard, increase practical usefulness of studies and foster empowerment. This paper discusses ways that groups can take part in research, outlines advantages and limits of each and explores strategies for enhancing benefits. This content is illustrated with brief examples from recent research publications and from two longer case studies. Groups and members can be involved as participants or co-producers of research. As participants, they either act as research subjects by contributing data, or as collaborators who are consulted at various times to help keep a study relevant to community issues. Being a subject offers an opportunity to reflect and share views, while collaborators and researchers can learn from working together. Though collaborators can exert influence, they have little control over decisions around focus, design, methods or dissemination of a study. Co-producing knowledge offers community groups more power, learning and empowerment but requires high levels of mutual trust, commitment and persistence. Potential gains and risks increase as involvement intensifies. However, researchers can enhance benefits at any level, by keeping this goal in mind when planning studies.

Down-Looking Airborne Radar Imaging Performance: The Multi-Line and Multi-Frequency

The paper proposes an analytical study regarding airborne radar imaging performances and accounts for a down-looking radar system moving along parallel lines far, in terms of probing wavelength, from the investigated domain and collecting multi-frequency and multi-monostatic data. The imaging problem is formulated in a constant depth plane by exploiting the Born approximation. Hence, a linear inverse scattering problem is faced by considering both the Adjoint and the Truncated Singular Value Decomposition reconstruction schemes. Analytical and simulated results are provided to state how the achievable performances depend on the measurement configuration. These results are of practical usefulness because, in operative conditions, it is unfeasible to plan a flight grid made up by a high number of closely (in terms of probing wavelength) spaced lines. Hence, the understanding of how the availability of under-sampled data affects the radar imaging allows for a trade-off between operative data collection constrains and reliable reconstructions of the scenario under test.

Kardynał Stefan Wyszyński w obronie praw narodu

Cardinal Stefan Wyszynski in Defense of the Rights of the Nation The impulse to refer to the personal exemplar of Primate Stefan Cardinal Wyszyński and his teachings about the nation and family are the tendencies of moral relativism present in social life, resulting from the belief that only effectiveness and practical usefulness determine what is true and what is good. Seeing in this the threat of a return to „the only right and true doctrine”, which in truth is no longer the ideology of Marxism-Leninism, but destructive views opposing national culture, I consider it justified to recall one of the most outstanding Poles of the 20th century, for whom the good of Poland and Poles were the primary defense of human rights and the right of the nation to independence and justice. He tried to bring to light those values inherent in the nation that could effectively resist these growing threats. These defenses were cited as particularly valuable for the life of the nation and the Church in a totalitarian state that is striving to objectify and incapacitate the individual. However, faith combined with the morality of the Nation formed by the Church contributed to the maintenance of national unity and allowed it to survive in the conditions of communist restrictions. In order to release the Nation from the supremacy of communist ideology, the Primate of the Millennium did not hesitate to confront this system. Even his arrest and imprisonment did not discourage him from serving God and the Homeland. His nobility of heart, bravery of spirit and determination to defend the rights of the nation made him a hero who will not be forgotten!

Urban Sustainability Deficits: The Urban Non-Sustainability Index (UNSI) as a Tool for Urban Policy

Urban sustainability has been revealed as one of the key elements in achieving global sustainability. There is a wide range of indicators in this field; however, sustainability indicators have not been exempt from criticism, in both their formal aspects and practical usefulness. If measuring sustainability is “measuring the immeasurable”, then the objective of this article is to propose a composite indicator that evaluates only the deficits of sustainability or, which is the same thing, the unsustainability of cities. This focus has the advantage of showing up the particular deficiencies and thus the priorities that each city must attend to. For this purpose, only unsustainability, defined as the distance to a sustainability target, was considered. Aggregation was carried out through generalized means, which lead to a proper balance between compensatory and non-compensatory aggregation. The results emerging from the application of our methodology to a sample of fifty different cities suggest that all cities should attend to some aspect concerning sustainability, and that no city is sustainable, but only less unsustainable, showing both significant differences between cities in terms of the degree of unsustainability and a common underlying structure of unsustainability in which the environmental dimension contributes the most.

Drug target inference by mining transcriptional data using a novel graph convolutional network framework

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.

DEVELOPMENT OF PERFORMANCE EVALUATION INDICATORS FOR SOCIAL ENTERPRISES: THE USE OF DELPHI TECHNIQUE

Social enterprises have become a natural part of the economy. Their importance is significantly increasing, as well as the need to evaluate their performance. There are different points of view on the indicators and methods used to evaluate their performance and they often lack simplicity and are costly and time consuming. Thus, this study aims to identify and discuss performance evaluation indicators for social enterprises with particular focus on identifying a set of indicators that might be applied without placing high demands on each social enterprise. A two-phase approach was adopted and applied in the Czech social entrepreneurship environment. First, the Delphi technique was used to get a set of indicators for each of the areas of the triple bottom line principles. Consequently, an empirical examination of these indicators using a set of social enterprises was conducted. Final results were determined using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). As a result, a set of twelve indicators that cover social, economic, environmental and local areas are proposed. These indicators reflect the opinions of experts in the field as well as their practical usefulness, which is discussed at the end of the paper.