bluuebunny/crossref_metadata_2025 huggingface dataset:
OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning
Authors: Henderson Peter , Chang Wei-Di , Bacon Pierre-Luc , Meger David , Pineau Joelle , Precup Doina
Published: June 2022

Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition. The options framework in reinforcement learning is specifically designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states. We show that this approach works well in both simple and complex continuous control tasks and shows significant performance increases in one-shot transfer learning.
A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
Authors: Zhu Wanzheng , Zhang Chao , Yao Shuochao , Gao Xiaobin , Han Jiawei
Published: June 2022

We study the problem of modeling human mobility from semantic trace data, wherein each GPS record in a trace is associated with a text message that describes the user's activity. Existing methods fall short in unveiling human movement regularities for such data, because they either do not model the text data at all or suffer from text sparsity severely. We propose SHMM, a multi-modal spherical hidden Markov model for semantics-rich human mobility modeling. Under the hidden Markov assumption, SHMM models the generation process of a given trace by jointly considering the observed location, time, and text at each step of the trace. The distinguishing characteristic of SHMM is the text modeling part. We use fixed-size vector representations to encode the semantics of the text messages, and model the generation of the l2-normalized text embeddings on a unit sphere with the von Mises-Fisher (vMF) distribution. Compared with other alternatives like multi-variate Gaussian, our choice of the vMF distribution not only incurs much fewer parameters, but also better leverages the discriminative power of text embeddings in a directional metric space. The parameter inference for the vMF distribution is non-trivial since it involves functional inversion of ratios of Bessel functions. We theoretically prove, for the first time, that: 1) the classical Expectation-Maximization algorithm is able to work with vMF distributions; and 2) while closed-form solutions are hard to be obtained for the M-step, Newton's method is guaranteed to converge to the optimal solution with quadratic convergence rate. We have performed extensive experiments on both synthetic and real-life data. The results on synthetic data verify our theoretical analysis; while the results on real-life data demonstrate that SHMM learns meaningful semantics-rich mobility models, outperforms state-of-the-art mobility models for next location prediction, and incurs lower training cost.
MDP-Based Cost Sensitive Classification Using Decision Trees
Authors: Maliah Shlomi , Shani Guy
Published: June 2022

In classification, an algorithm learns to classify a given instance based on a set of observed attribute values. In many real world cases testing the value of an attribute incurs a cost. Furthermore, there can also be a cost associated with the misclassification of an instance. Cost sensitive classification attempts to minimize the expected cost of classification, by deciding after each observed attribute value, which attribute to measure next. In this paper we suggest Markov Decision Processes as a modeling tool for cost sensitive classification. We construct standard decision trees over all attribute subsets, and the leaves of these trees become the state space of our MDP. At each phase we decide on the next attribute to measure, balancing the cost of the measurement and the classification accuracy. We compare our approach to a set of previous approaches, showing our approach to work better for a range of misclassification costs.
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
Authors: Chen Yuntao , Wang Naiyan , Zhang Zhaoxiang
Published: June 2022

We have witnessed rapid evolution of deep neural network architecture design in the past years. These latest progresses greatly facilitate the developments in various areas such as computer vision and natural language processing. However, along with the extraordinary performance, these state-of-the-art models also bring in expensive computational cost. Directly deploying these models into applications with real-time requirement is still infeasible. Recently, Hinton et al. have shown that the dark knowledge within a powerful teacher model can significantly help the training of a smaller and faster student network. These knowledge are vastly beneficial to improve the generalization ability of the student model. Inspired by their work, we introduce a new type of knowledge---cross sample similarities for model compression and acceleration. This knowledge can be naturally derived from deep metric learning model. To transfer them, we bring the "learning to rank" technique into deep metric learning formulation. We test our proposed DarkRank method on various metric learning tasks including pedestrian re-identification, image retrieval and image clustering. The results are quite encouraging. Our method can improve over the baseline method by a large margin. Moreover, it is fully compatible with other existing methods. When combined, the performance can be further boosted.
Counterfactual Multi-Agent Policy Gradients
Authors: Foerster Jakob , Farquhar Gregory , Afouras Triantafyllos , Nardelli Nantas , Whiteson Shimon
Published: June 2022

Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.
Accelerated Method for Stochastic Composition Optimization With Nonsmooth Regularization
Authors: Huo Zhouyuan , Gu Bin , Liu Ji , Huang Heng
Published: June 2022

Stochastic composition optimization draws much attention recently and has been successful in many emerging applications of machine learning, statistical analysis, and reinforcement learning. In this paper, we focus on the composition problem with nonsmooth regularization penalty. Previous works either have slow convergence rate, or do not provide complete convergence analysis for the general problem. In this paper, we tackle these two issues by proposing a new stochastic composition optimization method for composition problem with nonsmooth regularization penalty. In our method, we apply variance reduction technique to accelerate the speed of convergence. To the best of our knowledge, our method admits the fastest convergence rate for stochastic composition optimization: for strongly convex composition problem, our algorithm is proved to admit linear convergence; for general composition problem, our algorithm significantly improves the state-of-the-art convergence rate from O(T–1/2) to O((n1+n2)2/3T-1). Finally, we apply our proposed algorithm to portfolio management and policy evaluation in reinforcement learning. Experimental results verify our theoretical analysis.
High Rank Matrix Completion With Side Information
Authors: Wang Yugang , Elhamifar Ehsan
Published: June 2022

We address the problem of high-rank matrix completion with side information. In contrast to existing work dealing with side information, which assume that the data matrix is low-rank, we consider the more general scenario where the columns of the data matrix are drawn from a union of low-dimensional subspaces, which can lead to a high rank matrix. Our goal is to complete the matrix while taking advantage of the side information. To do so, we use the self-expressive property of the data, searching for a sparse representation of each column of matrix as a combination of a few other columns. More specifically, we propose a factorization of the data matrix as the product of side information matrices with an unknown interaction matrix, under which each column of the data matrix can be reconstructed using a sparse combination of other columns. As our proposed optimization, searching for missing entries and sparse coefficients, is non-convex and NP-hard, we propose a lifting framework, where we couple sparse coefficients and missing values and define an equivalent optimization that is amenable to convex relaxation. We also propose a fast implementation of our convex framework using a Linearized Alternating Direction Method. By extensive experiments on both synthetic and real data, and, in particular, by studying the problem of multi-label learning, we demonstrate that our method outperforms existing techniques in both low-rank and high-rank data regimes.
Interpreting CNN Knowledge via an Explanatory Graph
Authors: Zhang Quanshi , Cao Ruiming , Shi Feng , Wu Ying Nian , Zhu Song-Chun
Published: June 2022

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph. In the explanatory graph, each node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns. More importantly, we learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts. Experiments show that each graph node consistently represents the same object part through different images. We transfer part patterns in the explanatory graph to the task of part localization, and our method significantly outperforms other approaches.
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Authors: Levy Daniel , Ermon Stefano
Published: June 2022

Policy optimization methods have shown great promise in solving complex reinforcement and imitation learning tasks. While model-free methods are broadly applicable, they often require many samples to optimize complex policies. Model-based methods greatly improve sample-efficiency but at the cost of poor generalization, requiring a carefully handcrafted model of the system dynamics for each task. Recently, hybrid methods have been successful in trading off applicability for improved sample-complexity. However, these have been limited to continuous action spaces. In this work, we present a new hybrid method based on an approximation of the dynamics as an expectation over the next state under the current policy. This relaxation allows us to derive a novel hybrid policy gradient estimator, combining score function and pathwise derivative estimators, that is applicable to discrete action spaces. We show significant gains in sample complexity, ranging between 1.7 and 25 times, when learning parameterized policies on Cart Pole, Acrobot, Mountain Car and Hand Mass. Our method is applicable to both discrete and continuous action spaces, when competing pathwise methods are limited to the latter.
Latent Discriminant Subspace Representations for Multi-View Outlier Detection
Authors: Li Kai , Li Sheng , Ding Zhengming , Zhang Weidong , Fu Yun
Published: June 2022

Identifying multi-view outliers is challenging because of the complex data distributions across different views. Existing methods cope this problem by exploiting pairwise constraints across different views to obtain new feature representations,based on which certain outlier score measurements are defined. Due to the use of pairwise constraint, it is complicated and time-consuming for existing methods to detect outliers from three or more views. In this paper, we propose a novel method capable of detecting outliers from any number of dataviews. Our method first learns latent discriminant representations for all view data and defines a novel outlier score function based on the latent discriminant representations. Specifically, we represent multi-view data by a global low-rank representation shared by all views and residual representations specific to each view. Through analyzing the view-specific residual representations of all views, we can get the outlier score for every sample. Moreover, we raise the problem of detectinga third type of multi-view outliers which are neglected by existing methods. Experiments on six datasets show our method outperforms the existing ones in identifying all types of multi-view outliers, often by large margins.
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Authors: Grover Aditya , Dhar Manik , Ermon Stefano
Published: June 2022

Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood. Yet, GANs sidestep the characterization of an explicit density which makes quantitative evaluations challenging. To bridge this gap, we propose Flow-GANs, a generative adversarial network for which we can perform exact likelihood evaluation, thus supporting both adversarial and maximum likelihood training. When trained adversarially, Flow-GANs generate high-quality samples but attain extremely poor log-likelihood scores, inferior even to a mixture model memorizing the training data; the opposite is true when trained by maximum likelihood. Results on MNIST and CIFAR-10 demonstrate that hybrid training can attain high held-out likelihoods while retaining visual fidelity in the generated samples.
Gated-Attention Architectures for Task-Oriented Language Grounding
Authors: Chaplot Devendra Singh , Mysore Sathyendra Kanthashree , Pasumarthi Rama Kumar , Rajagopal Dheeraj , Salakhutdinov Ruslan
Published: June 2022

To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment. This problem is called task-oriented language grounding. We propose an end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input. The proposed model combines the image and text representations using a Gated-Attention mechanism and learns a policy to execute the natural language instruction using standard reinforcement and imitation learning methods. We show the effectiveness of the proposed model on unseen instructions as well as unseen maps, both quantitatively and qualitatively. We also introduce a novel environment based on a 3D game engine to simulate the challenges of task-oriented language grounding over a rich set of instructions and environment states.
Visual Recognition in Very Low-Quality Settings: Delving Into the Power of Pre-Training
Authors: Cheng Bowen , Liu Ding , Wang Zhangyang , Zhang Haichao , Huang Thomas
Published: June 2022

Visual recognition from very low-quality images is an extremely challenging task with great practical values. While deep networks have been extensively applied to low-quality image restoration and high-quality image recognition tasks respectively, few works have been done on the important problem of recognition from very low-quality images.This paper presents a degradation-robust pre-training approach on improving deep learning models towards this direction. Extensive experiments on different datasets validate the effectiveness of our proposed method.
Fast Approximate Nearest Neighbor Search via k-Diverse Nearest Neighbor Graph
Authors: Xiao Yan , Guo Jiafeng , Lan Yanyan , Xu Jun , Cheng Xueqi
Published: June 2022

Approximate nearest neighbor search is a fundamental problem and has been studied for a few decades. Recently graph-based indexing methods have demonstrated their great efficiency, whose main idea is to construct neighborhood graph offline and perform a greedy search starting from some sampled points of the graph online. Most existing graph-based methods focus on either the precise k-nearest neighbor (k-NN) graph which has good exploitation ability, or the diverse graph which has good exploration ability. In this paper, we propose the k-diverse nearest neighbor (k-DNN) graph, which balances the precision and diversity of the graph, leading to good exploitation and exploration abilities simultaneously. We introduce an efficient indexing algorithm for the construction of the k-DNN graph inspired by a well-known diverse ranking algorithm in information retrieval (IR). Experimental results show that our method can outperform both state-of-the-art precise graph and diverse graph methods.
METEOROLOGICAL CONDITIONS OF THE STORM WIND AND DUST STORM OCCURRENCE IN THE ATYRAU REGION IN JANUARY 2021
Authors:   Al Farabi Kazakh National University, Stambekov Mukhtar D. , Polyakova Svetlana E. ,   Al Farabi Kazakh National University
Published: June 2022

The article discusses the atmospheric processes that caused the occurrence of storm wind and a dust storm in the Atyrau region on January 14, 2021. We provide synoptic maps and satellite images, actual and forecast data, analyze in detail the synoptic situation that led to the occurrence of the dangerous phenomena, as well as the general characteristics of the formation of dust storms in the western part of Kazakhstan. In the winter period, the north-eastern part of the Caspian Sea and the entire Atyrau region are under the influence of the western ridge of the Asian (Siberian) high pressure area. The center of high pressure in the winter is located over the central part of Mongolia, in the summer - over the European territory of Russia, therefore winds with a northerly component are a frequent phenomenon in the Atyrau region. We have established that the occurrence and development of dust storms were conditioned by a complex of meteorological factors, including a strong gale wind (more than 15 m/s), shallow freezing of the soil, desiccation and dispersion of the upper layer of the soil, absence of snow cover and ice crusts, weak vegetation cover, and presence of vast open spaces. The total material damage from the observed complex of dangerous phenomena in the territory of the Atyrau region amounted to 14.5 million tenge (34 thousand dollars).
SPATIAL ANALYSIS OF STRUCTURAL AND SECTORAL SHIFTS IN THE RUSSIAN-KAZAKH CROSS-BORDER REGION
Authors:   Orenburg Federal Research Center, Ural Branch of the Russian Academy of Sciences, Sokolov Alexandr A. , Rudneva Oxana S. ,   Orenburg Federal Research Center, Ural Branch of the Russian Academy of Sciences
Published: June 2022

A well-balanced sectoral structure of the economy is the most important aspect of the sustainable development of cross-border territories. In the Russian-Kazakh cross-border region, the processes of transformation and restructuring of the economy are currently taking place. As a result, there occur structural and sectoral shifts in the economy, which expand the geographical division of labor and determine further specialization of the region. The paper studies the transformation of the sectoral structure of border regions of Russia and Kazakhstan by the main types of economic activity, including various components of industrial and non-industrial sectors (manufacturing, mining, energy, trade, transport, agriculture, etc.). Our analysis is special in that we apply different methodological approaches to studying the problem (by specialization, by types of industries, and by coverage). We have established that the structural and sectoral restructuring of the economy taking place in the border regions affects a wide range of different industries, with the economy of the regions undergoing a catch-up stage of development, the share of the non-manufacturing sector steadily growing and that of industry and agriculture declining, but still remaining high. The regional features are a widespread increase in the share of the non-manufacturing sector, a significant degradation of old traditional agricultural industries, as well as minor changes in the regions with the dominant role of the fuel and energy complex.
ART LANDSCAPE SCIENCE IN GEOGRAPHICAL DESCRIPTION
Authors:   Institute for Evolutionary Ecology of the National Academy of Sciences of Ukraine, Tyutyunnik Yulian G.
Published: June 2022

The paper deals with the history, current state and prospects of using the descriptive method in geographical science. We identify the goals, classify the methods, analyze the conditions of the application of literary techniques in geographical description; the transformations of these techniques, both occurring today and possible in the future, are also analyzed. The subject of geographical description is characterized, its distinctive features are specified. Based on the ideas of Gilles Deleuze, we offer a classification of the methods of obtaining geographical images using the techniques of fiction (image-picture, image-movement, image-crystal). Specific examples show the philosophical and theoretical potential of geographical description. The paper argues that geographical description, using literary techniques, not only has not outlived its usefulness but also has the prerequisites for further development as one of the methods of geographical science.
THE SOUTHERN MAN IN SIBERIA: A LITERARY AND GEOGRAPHICAL STUDY OF ANTON CHEKHOV'S JOURNEY (BASED ON CHEKHOV'S ESSAYS ‘FROM SIBERIA’ AND HIS TRAVEL LETTERS)
Authors:   Moscow State University, Kalutskov Vladimir N.
Published: June 2022

We have developed and applied a cultural and geographical approach in relation to one of the most famous literary trips on the territory of Russia – Anton Chekhov's trip. An important component of this approach is comprehensive geographical methodology of studying literary trips, which includes 3 methodological blocks: geographical contexts of the literary trip, a step-by-step description of the route with the detection of local and regional geographical images, and the identification and study of the main author's regional themes. The core of this method is a modular spatiotemporal periodization of a literary trip, which is based on objective geographical indicators and subjective author's assessments and perceptions. As a result of the periodization, the stages of the journey are distinguished, each of which is a spatiotemporal chronotope with objective measurable characteristics (the border of the territory, a certain landscape, the border of the season and weather, the vehicles used, etc.) and subjective criteria related to literary assessments of places and their modality. As a result of applying this technique, 6 stages of the Chekhov's literary journey were identified and mapped; a step-by-step description of the route was carried out, including the analysis of geographical images. The main Chekhov’s themes related to Siberia were also identified and commented on, among which are such as ‘Siberia and Russia: the problem of cultural and geographical positioning’, ‘Chekhov's Western and Chekhov's Eastern Siberia: two contrasting geographical images’, ‘On the attractiveness of Siberia’.
GEOGRAPHICAL ANALYSIS OF FOREIGN INVESTOR ACTIVITY (CASE STUDY OF AFRICA)
Authors:   Institute for African Studies, Russian Academy of Sciences, Sugakov Gleb K.
Published: June 2022

Today, there is a serious competition between foreign investors in Africa. Russian companies also have foreign economic interests in the continent, which requires studying the experience of other states, namely their country and sectoral priorities and the instruments used. Human geography lacks a generally accepted framework for analyzing the geography of investment from the perspective of donors rather than recipients of capital, which is why it is proposed in this paper. After specifying the relationship between the main terms in the field of investment (foreign investor activity, investment image, investment climate, etc.), the article provides a consistent scheme of geographical macroregional analysis of this sphere using tile maps as the most productive means of visualizing spatial information on investment at a given hierarchical level. After identifying the spatial and sectoral patterns of activity of different investment donor countries in Africa, an attempt is made to reconstruct the factors that have predetermined the spatial and sectoral differentiation of direct investment in the countries of the macro-region. A typology of capital-exporting states (‘ubiquitous’, ‘satellite’, ‘cultureoriented’, ‘European colonial powers’, ‘intra-regional leaders) that can be applied (both partially andfully) to other continents is proposed.
DEATH IN THE LANDSCAPE: A SPATIAL ASPECT OF THE MODERN TRADITION OF MARKING PLACES OF DEATH ON HIGHWAYS (A CASE STUDY OF THE RUSSIAN FEDERAL HIGHWAYS A 151, R 178, R 241)
Authors:   Ulyanovsk State Pedagogical University named after I. N. Ulyanov, Matlin Mikhail G.
Published: June 2022

An important aspect of the study of cultural space is the analysis of ways and forms of commemoration and memorialization of road accident victims. The placement of traditional cemetery monuments in it, installed at the place of people’s death, transfers the symbolism of death from strictly defined places of cemeteries into public space and turns it into sceneries of death or landscapes of death. It is no coincidence that many of the motorists and professional drivers who use such highways say that it is like driving in a cemetery. This article is the first in Russia to analyze this modern phenomenon based on the material obtained from a survey of part of three federal highways (A 151, R 241, R 178). It is shown that in Russia roadside monuments are not limited to the Christian cross, and their locations are much more diverse than in Europe and North America. In general, there are two trends – to place roadside monuments as close to the highway as possible (on the roadside, slope, behind the bump or on it) and in or near the green zone (in the forest or forest plantation) and a transitional one – to place them in the roadside lane. In the first case, the choice of the location is conditioned by the desire to approach the place of death of a loved one as close as possible, the second case demonstrates the desire to protect the memorial sign, to place it in accordance with the tradition of cemetery, primarily rural cemetery, where trees on the graves played an important role. Our analysis shows that different types of roadside monuments have different effects on the formation of the cultural landscape along the highways. Memorials are the most striking examples of ‘cemetery culture’, they mostly reformat the roadside space into landscapes or sceneries of death. In turn, gazebos, flowerpots with live or artificial flowers that do not have traditional plaques and photographs of the dead, i.e. do not reproduce the cemetery tradition, do this to the minimum extent. Crosses, steles and obelisks as the most typical cemetery structures occupy, to a certain extent, an intermediate position between memorials and flowerpots and gazebos.
The prickly problem of interwoven lineages: hybridization processes in Cactaceae
Authors: Granados-Aguilar Xochitl , Rosas Ulises , González-Rodríguez Antonio , Arias Salvador
Published: June 2022

Background: Hybridization in nature occurs in numerous botanical families. In particular, the Cactaceae family contains lots of genera in which hybridization is reported. Questions: What are the patterns of reported natural hybridization in Cactaceae and their probable causes? Are there phylogenetic and evolutionary implications related to hybridization, particularly in Opuntioideae? Data description: A total of 62 articles about natural hybridization and classical Cactaceae literature were reviewed. Study site and dates: From 1900 to June 2021 Methods: A search for articles was performed in Web of Science and Google Scholar with the keywords "Cactaceae hybridization", for time span "1900 to 2021" and included information from classic family-specific monographs. Results: Natural hybrids in Cactaceae occur in subfamilies, Cactoideae and Opuntioideae. There is evidence of nonselective mechanisms of reproductive isolation, but only for few taxa. For Cactoideae members the main approach used was morphological description, and the tribe with the highest number of natural hybrids was Trichocereeae. In Opuntioideae, the reviewed articles performed mostly chromosome counts, morphometric and phylogenetic analyses, and showed the highest number of natural hybrids. Conclusions: It has been suggested that hybridization impacts the evolution of Cactoideae and Opuntioideae, but few studies have formally tested this hypothesis. In Cactoideae, we found only descriptive evidences of hybridization; therefore, previous statements suggesting an important role of hybridization in the evolution of Cactoideae should be supported by performing formal analyses. For the postulation that hybridization impacts the evolution of Opuntioideae, we found formal evidence supporting hybridization hypothesis unlike what we found in Cactoideae.
MODERN DISCOURSE OF CIVIL SERVICE IN RUSSIA: A VIEW THROUGH THE PRISM OF ADMINISTRATIVE PARADIGMS
Authors:   Russian Academy of National Economy and Public Administration under the President of the Russian Federation, Borshhevskiy George
Published: June 2022

In this article we examine the features of the current stage of Russian civil service development through the prism of the basic administrative paradigms – Weberian rational bureaucracy, New Public Management (NPM) and Good Governance; and we identify the prevailing paradigms in scientific discourse, electoral political discourse and modern legal discourse. We use such research methods as follows: content analysis, thesaurus analysis, bibliometric analysis of publications, comparative legal analysis. We propose some criteria for identifying administrative paradigms using a set of thesaurus words, as well as methodological tools for comparing with paradigms: a) scientific articles on state bureaucracy, b) election programs of Russian political parties in terms of reforming the civil service, c) regulatory documents containing the goals of the current stage of the Russian civil service development. As a result of the study, we have found that the Weberian paradigm dominates in scientific publications on law, NPM paradigm – on management and economics, Good Governance paradigm – on sociology and political science, which indicates the multiparadigmality of modern research. During the 2016 and 2021 parliamentary election campaigns, political parties of the entire ideological spectrum worked out a request for social justice, which was due to the predominance of theses in their programs corresponding to Good Governance. Regulation of the Russian civil service development in the period is carried out at the intersection of managerial and Weberian models. The first is manifested in the rigid formalization of the civil servants’ activities, and the second – in attempts to make the bureaucracy efficient, compact and complaisant. The preservation of the civil service’ closeness is confirmed by a decrease in the share of program measures equivalent to Good Governance, which contradicts the attitude towards openness and justice, fixed in scientific and political discourse.
DOMINANTS OF NATIONAL STRATEGIES FOR THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE IN RUSSIA, GERMANY, AND THE USA
Authors:   Moscow State Institute of International Relations (University) of the Ministry of Foreign Affairs of Russia, Kamolov Sergei , Varos Anastasia ,   Moscow State Institute of International Relations (University) of the Ministry of Foreign Affairs of Russia, Kriebitz Aleksandr ,   Technische Universität München, Alashkevich Mikhail ,   State Development Corporation WEB.RF
Published: June 2022

The study based on multivariate comparative analysis identifies the dominants of national strategies for the development of artificial intelligence (AI) of the global superpowers. The article examines both structural similarities of strategic documents and fundamental differences in goal setting and functional expectations of sovereign AI technologies. Projecting fundamental ideas of technical and economic paradigms onto national strategies for the development of AI allows the authors to put forward a hypothesis concerning the formation of a common European, Russian and American proto-models of the AI development state policy. The dialectical nature of AI technologies demands consideration of the ethical aspects of the AI technologies application in Germany, Russia and the United States. As a result of the study, the foundations of various fundamental approaches of states to the development of AI technologies were established. Possible scenarios for the development of national strategic programs in the medium term globally were assessed.
Metanın kuru reformlanma reaksiyonunda karbon oluşumunu azaltan Zr-SBA-15 destekli Ni katalizörlerin geliştirilmesi: Sentez ortamının etkisi
Authors: OKUTAN Çiğdem , ARBAĞ Hüseyin , YAŞYERLİ Nail , YAŞYERLİ Sena
Published: June 2022

Metanın kuru reformlanması ile sentez gazı üretimi için karbon oluşumunu azaltabilecek Zr içerikli SBA-15 destekli Ni katalizörlerin geliştirilmesi amaçlanmıştır. Bu çalışmada, Zr içerikli SBA-15 destek malzemeleri HCl (A), NaCl (S) ve HCl ile NaCl’ün (B) birlikte ilave edildiği farklı koşullardaki tek-kap hidrotermal sentez yöntemiyle hazırlanmıştır. Zr-SBA-15 malzemelerinin destek olarak kullanıldığı Ni katalizörleri emdirme yöntemi ile hazırlanmıştır. Farklı sentez koşullarında hazırlanan Zr-SBA-15 destekli Ni katalizörlerinin aktiviteleri metanın kuru reformlanma reaksiyonunda sabit yatak reaktör sisteminde 750 °C’de test edilmiştir. Katalitik aktivite ve katalizör özellikleri arasındaki ilişkinin açıklanabilmesi amacıyla reaksiyon öncesi ve/veya sonrası XRD, N2 adsorpsiyon-desorpsiyon, ICP-OES, DRIFTS, SEM, TEM ve TG analizleri gerçekleştirilmiştir. HCl varlığında sentezlenen Zr-SBA-15 destekli Ni (5Ni@10Zr ̶ A) katalizörde diğer iki katalizörden (5Ni@10Zr ̶ B ve 5Ni@10Zr ̶ S) farklı olarak kısa ve hekzagonal şeklinde yapıların oluşumu gözlenmiştir. Farklı morfolojiye sahip olan 5Ni@10Zr ̶ A katalizörünün yapısında bulunan kütlece % 4,5 Zr miktarının katalizörün aktivitesini artırdığı (%85 CH4 ve %88 CO2 dönüşümü) ve karbon birikimini önemli derecede azalttığı belirlenmiştir. Bunun yanı sıra en yüksek H2/CO oranı ve H2 verimi sırasıyla 0,79 ve 1,37 olarak 5Ni@10Zr ̶ A katalizörü ile elde edilmiştir.
Soğuk depolarda ekonomik ve pratik olarak homojen hava ve sıcaklık dağılımının HAD analizi ile incelenmesi
Authors: YAMANKARADENİZ Nurettin , KAYNAKLI Faruk
Published: June 2022

Meyvelerin depolama maliyetlerini düşürmek adına soğuk depolarda mümkün oldukça fazla ürün konmaktadır. Aşırı yüklemeden dolayı ortamda homojen bir sıcaklık ve hız dağılımı sağlanamadığında gıdalarda kısmi zarar ve kayıplar meydana gelmektedir. Homojen bir hava ve sıcaklık dağılımı için uygulanan metotların ilk yatırım maliyeti yüksek olduğundan pek tercih edilmemektedir.

Bu çalışmada granny smith cinsi elma dolu bir soğuk depoda akış karakteristiği açısından kritik noktalarda ölçümler yapılarak sıcaklık ve hız dağılımları çıkarılmıştır. Daha sonra Ansys Fluent programında söz konusu deponun üç boyutlu modeli oluşturulmuş ve modelin doğruluğu gerçek depodan alınan ölçümler ile teyit edilmiştir. Model üzerinde yapılan analizler ile hava ve sıcaklık dağılımı açısından aksayan yönler tespit edilerek bu aksaklıkları gidermek üzere hava saptırma paneli yerleştirilmiştir. Son olarak da farklı panel açıları analiz edilerek soğuk depo içerisindeki hava ve sıcaklık dağılımını daha homojen duruma getirecek panel açısının 63° olduğu tespit edilmiştir.

Önerilen çözüm yöntemi; boyut, depolanan ürün ve yerleşim fark etmeksizin, doğrudan depo içerisine üfleme yapılan tüm soğuk depolara uygulanabilmektedir. Bu sayede pratik, kolay uygulanabilir ve ekonomik bir şekilde mevcut tüm soğuk depolardaki ürün zararlarında azalma sağlanabilecektedir.
GSM-1800 ve GPS uygulamaları için çift bantlı ve çift modlu yarıklı halka mikroşerit yama anten
Authors: DOGUSGEN ERBAS Cihan
Published: June 2022

Bu çalışmada, toprak düzleminde 6 eşit dikdörtgensel yarık bulunduran özgün, çift bantlı ve çift modlu bir halka mikroşerit yama anten tasarlanmış, üretilmiş ve incelenmiştir. Ardışık yarıklar arasındaki açı 60ᴼ değerindedir. Anten, GSM-1800 (1,710–1,880 GHz) ve L2 frekans bandında çalışan GPS (1,215–1,240 GHz) uygulamaları için aynı anda kullanılabilirdir ve bahsedilen uygulamalar için sırasıyla TM21 ve TM11 modlarında çalışmaktadır. Ölçülen -10 dB dönüş kaybına ait oransal bant genişlikleri GSM-1800 ve GPS frekans bantları için sırasıyla %33,510 (1,540–2,160 GHz) ve %15,130 (1,100–1,280 GHz) biçimindedir. Anten GSM-1800 uygulaması için konik ışıma örüntüsüne sahipken GPS uygulaması için tek yönlü ışıma örüntüsüne sahiptir. 1,227 GHz çalışma frekansı için ölçülen maksimum kazanç değeri 7,410 dBi ve1,800 GHz çalışma frekansı için bu parametre 5,600 dBi değerindedir. GPS uygulaması için anten dairesel polarizasyona sahiptir ve 3 dB eksensel orana ait oransal bant genişliği %4,590 (1,192–1,248 GHz) değerindedir. Ölçümler benzetim sonuçlarıyla uyumludur.
Hukuk sistemlerinde personel çizelgeleme problemi: Kamulaştırma davaları örneği
Authors: YAZICI Emre , EREN Tamer , ALAKAŞ Hacı Mehmet
Published: June 2022

Kamusal hizmetlerin yerine getirilmesinde kamu kurumları bireylere hizmet ulaşımını sağlamak için kamulaştırma yapmaktadır. Hizmet alanlarının genişliği ve ihtiyaçlar nedeniyle ülkemizde kamulaştırma işlemine ilişkin yılda ortalama 50000 dava açılmaktadır. Davalarda kamu kurumlarını temsilen görev alan avukatlara dava dosyalarının atanmasında dosyaların iş yükleri hesaplanmadan, dosya sayısı veya bölgeye göre bir atama yapılması avukatlar arasında dengesiz bir iş yükü dağılımına neden olmaktadır. Avukatların iş yükünü oluşturan faaliyetler her ne kadar duruşma ve keşiflere katılmak gibi görünse de dava dosyasının hazırlanması, duruşmalara ve keşiflere hazırlık ve ilgili günlerde adliyelere ulaşım, yargılama esnasında beyan dilekçelerinin hazırlanması ve dosyaların incelenmesi işlemleri de iş yükleri arasında yer almaktadır. Bu iş yükleri dosya atamalarında dikkate alınmamaktadır ve avukatların iş yüklerinin dengesiz dağılımına sebep olmaktadır. Bu çalışma ile bir kamu kurumunda kamulaştırma işlemlerine ilişkin dava dosyalarının avukatlara atanması problemi ele alınmıştır. Kurumda bir aylık dönem içerisinde 15 farklı bölgede üç farklı türde 724 dava dosyası açılmaktadır ve kurumda altı avukat çalışmaktadır. Davaların iş yükleri hesaplanarak avukatların iş yüklerinin dengelenmesi için bir hedef programlama modeli önerilmiştir. Dosyaların iş yükünün hesaplanmasında hazırlık, duruşma, keşif, dosya inceleme ve yol süreleri dikkate alınmıştır. Önerilen model diğer kamu kurumlarında ve farklı dava dosyalarının atanmasında da kullanılabilir.
Tarihi konutların yeniden kullanımında geleneksel ocakların güncel ısıtma yüklerini karşılama etkinliği: Güneybatı Anadolu’dan örnekler
Authors: TİMUR Barış , BAŞARAN Tahsin , İPEKOĞLU Başak
Published: June 2022

Anadolu tarihi konutlarında ısıtma elemanı olarak yaygın kullanılan geleneksel ocaklar, estetik önemleri yanında fonksiyonel değerler taşır. Bu elemanların ısıl özelliklerinin belirlenmesi, tarihi konutların yeniden kullanımlarında ısıl konforun sağlanması için uygulanacak olası müdahalelerin kapsamının saptanması bakımından belirleyici bir değerlendirme alanıdır. Bu çalışmanın amacı, geleneksel ocakların ısıl kapasitelerinin güncel kullanım senaryolarına bağlı ısıl ihtiyaçlara cevap verme potansiyelinin belirlenmesidir. İncelenen yapı türü, açık sofalı tarihi konutlardır. Çalışmanın yöntemi, kırsal ve kentsel yerleşmelerden seçilmiş örnek iki konutta yıllık ısıl ölçümlerin alınması, yapılarda kullanılan yapım malzemelerinin termofiziksel özelliklerinin laboratuvar ölçümleriyle belirlenmesi ve yapıların DesignBuilder v5.4.0.21 yazılımında modellenerek zamana bağlı ısıl analizlerinin, modelin doğrulanması yapıldıktan sonra gerçekleştirilmesi aşamalarını kapsamaktadır. Analizlerle belirlenen mekânsal birim zamandaki ısınma enerjisi ihtiyacının zamana bağlı değişim değerleri, örnek konutlarda bulunan ocakların maksimum ısıtma güçleriyle karşılaştırılmış ve ocakların ısıl ihtiyaçları karşılama oranları bulunmuştur. Çalışma sonucunda, kentsel konutta bulunan üç ocaktan ikisinin bulundukları mekânlar için yüksek düzeyde yeterli olduğu, bir ocağın ise belirgin daha yetersiz kaldığı; kırsal konutta ise analizi yapılan her iki ocağın da yeterli ısıtma düzeyine sahip olduğu belirlenmiştir. Böylece, olası bir ısıl iyileştirme uygulaması kapsamında, hangi mekânlarda güncel ısıtma sistemi müdahalelerine ihtiyaç duyulup / duyulmayacağı saptanmıştır. Sunulan analiz süreçlerinin, tarihi konutlar için kurgulanacak ısıtma sistemi değerlendirmelerine yöntem açısından katkı sağlaması hedeflenmiştir.
Eriyik yığma modelleme (EYM) ile üretilen çeşitli hücresel yapıların mekanik performanslarının incelenmesi
Authors: ERGENE Berkay , YALÇIN Bekir
Published: June 2022

Bu çalışmada eriyik yığarak modelleme metoduyla PLA malzemeden pozitif Poisson oranına (PO) sahip bal peteği yapı, negatif PO’na sahip re-entrant yapı, balpeteği ve re-entrant yapının bir kombinasyonu olan hibrit yapı ve son olarak da kiral yapılar üretilmiştir. Daha sonra, bu hücresel yapılarda kiriş kalınlığının ve birim hücrenin yönünün mekanik performanslarına etkisi çekme ve basma durumunda deneysel olarak incelenmiştir. EYM metoduyla üretilen hücresel yapıların çekme ve basma testleri sonucunda elastisite modül (EM) değerleri, çekme dayanımı (ÇD), basma dayanımı (BD), absorbe ettikleri enerji (AEE) ve spesifik absorbe ettikleri enerji (SAEE) miktarlarının yanı sıra Poisson oranları da tespit edilmiştir. Ayrıca, mekanik testler aracılığı ile elde edilen bu bulguların dışında, üretilmiş hücresel yapılı numunelerin sertlik değerleri ve yüzey pürüzlülük değerleri de ölçülmüştür. Elde edilen sonuçlara göre, hücresel yapılardaki birim hücrelerin yönünün ve kiriş kalınlıklarının hücresel yapıların mekanik performansını önemli düzeyde etkilediği tespit edilmiştir. Hücresel yapıların ÇD ve BD’ları artan kiriş kalınlığı ile birlikte artış gösterirken, yüzey pürüzlülük ve sertlik değerleri sırasıyla ortalama 14 µm ve 75 Shore D olarak ölçülmüştür. Re-entrant, kiral ve hibrit yapıda deformasyon miktarına bağlı olarak negatif PO gözlemlenmesine rağmen, balpeteği yapılarda beklendiği üzere pozitif PO görülmüştür. Ayrıca, çekme ve basma durumunda sırasıyla kiriş kalınlığı 0,5 mm ve birim hücreleri y yönüne bakan balpeteği yapı (Hy 0,5) ve re-entrant yapı (Ry 0,5) maksimum AEE ve SAEE sergilemektedirler.
Optimum parametreler yardımıyla performansı artırılmış KNN algoritması tabanlı kalp hastalığı tahmini
Authors: TAKCI Hidayet
Published: June 2022

Kalp hastalıkları ölüm oranı bakımından bütün hastalıklar arasında ilk sırada yer alır. Hastalığın kesin tedavisi olmamakla birlikte doğru teşhis hastaların hayatta kalma süresi ve yaşam kalitesine olumlu yönde etki eder. Bugüne kadar kalp hastalıklarının teşhisi için çeşitli klinik yöntemler kullanılmıştır. Son dönemde hastalığın teşhisi için makine öğrenmesi algoritmaları da kullanılmaktadır. Bu kapsamda yaptığımız çalışmada kalp hastalığı teşhisi için KNN sınıflayıcı kullanılmıştır. Algoritmanın sınıflandırma başarısını iyileştirmek için optimum parametreler bulunmaya çalışılmıştır. KNN algoritması için ilk parametre uzaklık yöntemidir ve bu parametre için Manhattan, Euclidean, Chebyshev ve Cosine ölçümleri tercih edilmiştir. Diğer parametre komşu sayısıdır ve en uygun komşu sayısını tespit edebilmek için 1…15 arasındaki tek sayılar denenmiştir. Kalp hastalıklarını sınıflandırmak için kullandığımız KNN algoritması C++ programlama dilinde kodlanmış ve çalıştırılmıştır. Model değerlendirme aşamasında UCI Statlog (Heart) veriseti kullanılmış ve sonuçlar doğruluk ve ROC analizine dayalı olarak elde edilmiştir. KNN algoritması ile elde edilen en yüksek sınıflandırma doğruluğu %100 ve en yüksek AUC değeri 1,00 olarak ölçülmüştür. Bu değer; Chebyshev uzaklık ölçümü ve komşu sayısının 7 olduğu durumda elde edilmiştir.
Development of the opinions on the effects of globalization in sports sciences scale
Authors: AKDAĞCIK İbrahim Umran , TEMEL Ahmet
Published: June 2022

This study aims to develop a standard scale by making the validity and reliability of the globalization survey developed for education faculties in the sample of sports sciences. A total of 878 people, including 81 academicians, 336 physical education and sports teachers, 118 trainers, and 343 sports sciences students, who were reached by a simple random method, participated in the study. The data was collected at 3 different times, Exploratory Factor Analysis (EFA) in the first group (n = 426), Confirmatory Factor Analysis (CFA) in the second group (n = 412), and test-retest analysis in the third group (n = 40) was performed. As a result of EFA, which was conducted to determine the construct validity, a structure consisting of 17 items and 3 factors was obtained, which explains 71.588% of the total variance. These factors are; teaching-learning processes are named as management and technology. The Cronbach Alpha internal consistency coefficient of the scale was calculated as .94. According to the DFA results (χ2 / df: 2.63; RMSEA: .06; PGFI: .69; PNFI: .79; GFI: .92; AGFI: .90; IFI: .96; TLI: .95; NFI:. 94; CFI: .96) the scale has acceptable fit values. In the test-retest analysis, a positive correlation was found .83. As a result, it can be said that the globalization scale can be used in sports science research and is a valid and reliable scale.
[RETRACTED] Derma PGX Face Cream Moisturizer v1
Authors: Logixxweb not provided
Published: June 2022

[RETRACTED]

Thus, as we progress in years, the skin starts to lose its flexibility, and we can't come down on it without it listing. This is on the grounds that our skin's capacity to clutch dampness diminishes, which makes the skin lose its versatility. As we progress in years, the skin starts to lose its flexibility and start to droop.