An application of logistic capital management theory model to the economic growth cycle in Lithuania / Logistinės kapitalo valdymo teorijos modelio taikymas Lietuvos ekonominio augimo ciklui

The article analyses one of the most recent theories of economic growth prof. Girdzijauskas (2002, 2006, 2008, 2009, 2010) created logistic growth theory, whose main idea is based on market saturation and the concept of limitation. The essential claims of the theory are being tried to adjust analysing the economic growth cycle of Lithuania in 1995–2009 period. Performed logistic analysis of Lithuania's GDP (Loglet Lab2), macro–economic indicators’ and factors’ affecting them correlation regression analysis (SPSS15,0). The article concludes of the presentation of the created economic growth cycles and the bubble formation mechanism combining hypothetical Lithuanian economic growth cycle assessment model. Santrauka Straipsnyje analizuojama viena iš naujausių ekonominio augimo teorijų – prof. S. Girdzijausko (2006, 2008) sukurta logistine kapitalo augimo teorija, kurios pagrindine idėja remiasi rinkos prisisotinimo ir ribotumo sąvokomis. Teorijos esminius teiginius siekiama pritaikyti, vertinant Lietuvos ekonominio augimo ciklą 1995–2009 metų laikotarpiu. Atliekama Lietuvos BVP logistinė analizė (LogletLab2), makroekonominių rodiklių bei jiems įtaką darančių veiksnių koreliacinė regresinė analizė (SPSS 15,0). Straipsnio pabaigoje pateikimas sukurtas ekonominio augimo ciklus ir burbulo susiformavimo mechanizmą apjungiantis hipotetinis Lietuvos ekonominio augimo ciklo vertinimo modelis. Reikšminiai žodžiai: ekonominis, augimas, logistinis, kapitalo valdymas, ciklas, burbulas, modeli

Knowledge distillation on neural networks for evolving graphs

Graph representation learning on dynamic graphs has become an important task on several real-world applications, such as recommender systems, email spam detection, and so on. To efficiently capture the evolution of a graph, representation learning approaches employ deep neural networks, with large amount of parameters to train. Due to the large model size, such approaches have high online inference latency. As a consequence, such models are challenging to deploy to an industrial setting with vast number of users/nodes. In this study, we propose DynGKD, a distillation strategy to transfer the knowledge from a large teacher model to a small student model with low inference latency, while achieving high prediction accuracy. We first study different distillation loss functions to separately train the student model with various types of information from the teacher model. In addition, we propose a hybrid distillation strategy for evolving graph representation learning to combine the teacher’s different types of information. Our experiments with five publicly available datasets demonstrate the superiority of our proposed model against several baselines, with average relative drop 40.60 % in terms of RMSE in the link prediction task. Moreover, our DynGKD model achieves a compression ratio of 21:100, accelerating the inference latency with a speed up factor × 30 , when compared with the teacher model. For reproduction purposes, we make our datasets and implementation publicly available at https://github.com/stefanosantaris/DynGKD. © 2021, The Author(s)

Meta-reinforcement learning via buffering graph signatures for live video streaming events

In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated as a Markov Decision Process, performing meta-learning on reinforcement learning tasks. By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections. To ensure fast adaptation to new connections or changes among viewers during an event, we implement a prioritized replay memory buffer based on the Kullback-Leibler divergence of the reward/throughput of the viewers' connections. Moreover, we adopt a model-agnostic meta-learning framework to generate a global model from past events. As viewers scarcely participate in several events, the challenge resides on how to account for the low structural similarity of different events. To combat this issue, we design a graph signature buffer to calculate the structural similarities of several streaming events and adjust the training of the global model accordingly. We evaluate the proposed model on the link weight prediction task on three real-world datasets of live video streaming events. Our experiments demonstrate the effectiveness of our proposed model, with an average relative gain of 25% against state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/melanie © 2021 Owner/Author

BeaConvey: Co-Design of Overlay and Routing for Topic-basedPublish/Subscribe on Small-World Networks

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Domain expertise–agnostic feature selection for the analysis of breast cancer data

Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types and subtypes and the underlying interactions are not completely known. This work sheds light on the potential of the application of unsupervised learning in the analysis of the aforementioned data sets, namely in the detection of distinctive proteins for the identification of the cancer subtypes, in the absence of domain expertise. In the analyzed data set, the number of samples, or tumors, is significantly lower than the number of features, or proteins; consequently, the input data can be thought of as high-dimensional data. The use of high-dimensional data has already become widespread, and a great deal of effort has been put into high-dimensional data analysis by means of feature selection, but it is still largely based on prior specialist knowledge, which in this case is not complete. There is a growing need for unsupervised feature selection, which raises the issue of how to generate promising subsets of features among all the possible combinations, as well as how to evaluate the quality of these subsets in the absence of specialist knowledge. We hereby propose a new wrapper method for the generation and evaluation of subsets of features via spectral clustering and modularity, respectively. We conduct experiments to test the effectiveness of the new method in the analysis of the breast cancer data, in a domain expertise–agnostic context. Furthermore, we show that we can successfully augment our method by incorporating an external source of data on known protein complexes. Our approach reveals a large number of subsets of features that are better at clustering the samples than the state-of-the-art classification in terms of modularity and shows a potential to be useful for future proteomics research

ECONOMIC-LOGISTIC ANALYSIS: THE FUNDAMENTAL PREMISES OF CRISIS-RESISTANT GROWTH

Abstract: The article is aimed to explain the essence of the new economic paradigm, based on the conception of «common interest». It has been displayed that simulation of growth by using the common interest provides identifying new economic phenomena. This article discusses two of the latter: the phenomenon of scarce saturated markets and the phenomenon of rising profitability. They appear only in presence of market saturation with capital. As result of effect of these phenomena appear two new types of markets – self-regulating and not self-regulating. It has been displayed that, under control of the saturation, the profitability growth, the size of bubble and the depth of economic crisis can be influenced