scholarly journals THE SCALE OF THE SPREAD OF COVID-19 IN GEORGIA AND EFFECTIVENESS OF PREVENTIVE MEASURES IMPLIMENTED BY THE GOVERNMENT – WHAT DO MODELS SAY?

2020 ◽  
pp. 49-57
Author(s):  
IURI ANANIASHVILI ◽  
LEVAN GAPRINDASHVILI
Keyword(s):  
Machine Learning ◽  
Preventive Measures ◽  
Herd Immunity ◽  
Optimal Level ◽  
Short Term ◽  
Machine Learning Methods ◽  
Data Limitations ◽  
The Government ◽  
Different Characteristics

. In this article we present forecasts of the spread of COVID-19 virus, obtained by econometric and machine learning methods. Furthermore, by employing modelling method, we estimate effectiveness of preventive measures implemented by the government. Each of the models discussed in this article is modelling different characteristics of the COVID-19 epidemic’s trajectory: peak and end date, number of daily infections over different forecasting horizons, total number of infection cases. All these provide quite clear picture to the interested reader of the future threats posed by COVID-19. In terms of existing models and data, our research indicates that phenomenological models do well in forecasting the trend, duration and total infections of the COVID- 19 epidemic, but make serious mistakes in forecasting the number of daily infections. Machine learning models, deliver more accurate short –term forecast of daily infections, but due to data limitations, they struggle to make long-term forecasts. Compartmental models are the best choice for modelling the measures implemented by the government for preventing the spread of COVID-19 and determining optimal level of restrictions. These models show that until achieving herd immunity (i.e. without any epidemiological or government implemented measures), approximate number of people infected with COVID-19 would be 3 million, but due to preventive measures, expected total number of infections has reduced to several thousand (1555-3189) people. This unequivocally indicates the effectiveness of the preventive measures.

Italy's politics without policy: Balancing Atlanticism and Europeanism in the Middle East

Modern Italy ◽  
2008 ◽  
Vol 13 (2) ◽  
pp. 135-153 ◽  
Author(s):  
Raffaella A. Del Sarto ◽  
Nathalie Tocci
Keyword(s):  
Foreign Policy ◽  
Middle East ◽  
The United States ◽  
The European Union ◽  
Short Term ◽  
The Government ◽  
Specific Interests ◽  
Policy Shifts ◽  
The Eu

Focusing on Italy's Middle East policies under the second Berlusconi (2001–2006) and the second Prodi (2006–2008) governments, this article assesses the manner and extent to which the observed foreign policy shifts between the two governments can be explained in terms of the rebalancing between a ‘Europeanist’ and a transatlantic orientation. Arguing that Rome's policy towards the Middle East hinges less on Italy's specific interests and objectives in the region and more on whether the preference of the government in power is to foster closer ties to the United States or concentrate on the European Union, the analysis highlights how these swings of the pendulum along the EU–US axis are inextricably linked to a number of underlying structural weaknesses of Rome's foreign policy. In particular, the oscillations can be explained by the prevalence of short-term political (and domestic) considerations and the absence of long-term, substantive political strategies, or, in short, by the phenomenon of ‘politics without policy’ that often characterises Italy's foreign policy.

scholarly journals Implikasi Penerapan Undang-Undang Nomor 11 Tahun 2016 Tentang Pengampunan Pajak (Tax Amnesty) Dalam Meningkatkan Kepatuhan Wajib Pajak

2017 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Agus Saiful Abib ◽  
Efi Yulistyowati ◽  
Amri Panahatan Sihotang
Keyword(s):  
Secondary Data ◽  
Fiscal Year ◽  
Tax Revenue ◽  
Short Term ◽  
Descriptive Research ◽  
Tax Amnesty ◽  
The Government ◽  
To Come ◽  
National Tax

<p>Tahun 2016, pemerintah mengeluarkan kembali kebijakan <em>Tax</em> <em>Amnesty </em>yang dituangkan dalam Undang-Undang Nomor 11 Tahun 2016 tentang Pengampunan Pajak. Pengampunan Pajak (<em>Tax</em> <em>Amnesty)</em> ini diharapkan dapat meningkatkan penerimaan pajak dalam jangka pendek melalui pembayaran uang tebusan, meningkatkan penerimaan pajak dalam jangka panjang melalui perluasan basis data pemajakan, meningkatkan kepatuhan Wajib Pajak, transisi ke sistem perpajakan baru yang lebih kuat dan adil, dan mendorong rekonsiliasi perpajakan nasional. Sehubungan dengan hal tersebut, untuk mengetahui apakah program <em>Tax</em> <em>Amnesty</em> Indonesia Tahun 2016 berhasil atau tidak, khususnya dalam meningkatkan kepatuhan wajib pajak, maka perlu dilakukan penelitian tentang : “Implikasi Penerapan Undang-Undang Nomor 11 Tahun 2016 tentang Pengampunan Pajak (<em>Tax</em> <em>Amnesty</em>) dalam Meningkatkan Kepatuhan Wajib Pajak”. Adapun permasalahan yang akan dibahas adalah bagaimana implikasi penerapan Undang-Undang Nomor 11 Tahun 2016 tentang Pengampunan Pajak<em> (Tax</em> <em>Amnesty)</em> dalam meningkatkan kepatuhan Wajib Pajak ? Berdasarkan implikasi tersebut, maka bagaimana sebaiknya pengaturan perpajakan yang akan datang ? Berdasarkan permasalahan tersebut jenis penelitian ini adalah yuridis normatif yang akan dikaji dengan pendekatan perundang-undangan, spesifikasi penelitiannya diskriptif analitis, data yang dipergunakan data sekunder, yang dianalisis secara kualitatif. Hasil penelitian menunjukkan bahwa implikasi penerapan Undang-Undang Nomor 11 Tahun 2016 tentang Pengampunan Pajak<em> (Tax</em> <em>Amnesty)</em> dapat meningkatkan kepatuhan Wajib Pajak, dan berdasarkan implikasi tersebut SE Dirjen Pajak No. SE - 06/PJ/2017 seharusnya tidak hanya untuk tahun pajak 2017 saja, tetapi juga untuk tahun-tahun yang akan datang. Di samping itu perlu ada peraturan yang mengatur tentang pengawasan terhadap pelaksanaan hak Wajib Pajak.</p><pre>In 2016, the government re-issue the Tax Amnesty policy as outlined in Law Number 11 Year 2016 on Tax Amnesty. The Tax Amnesty is expected to increase tax revenue in the short term through ransom payments, increase tax revenues over the long term through the expansion of taxation databases, increase taxpayer compliance, transition to a stronger and more just tax system, and encourage national tax reconciliation. In relation to this matter, to find out whether the program of Tax Amnesty Indonesia Year 2016 succeed or not, especially in increasing taxpayer compliance, it is necessary to do research on: "Implications Implementation of Law Number 11 Year 2016 on Tax Amnesty in Improving Taxpayer Compliance ". The problem to be discussed is how the implications of the implementation of Law Number 11 Year 2016 on Tax Amendment (Tax Amnesty) in improving taxpayer compliance? Based on these implications, then how should the taxation arrangements to come? Based on the problem, this type of research is normative juridical which will be studied with the approach of legislation, the analytical descriptive research specification, the data used secondary data, which analyzed qualitatively. The result of the research shows that the implication of the implementation of Law Number 11 Year 2016 on Tax Amnesty can improve Taxpayer compliance, and based on the implication of SE Dirjen Pajak No. SE - 06 / PJ / 2017 should not only be for the fiscal year 2017 alone, but also for the years to come. In addition, there should be a regulation that regulates the supervision of the implementation of taxpayers' rights.</pre>

scholarly journals "Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

2021 ◽  
Author(s):  
Yongmin Cho ◽  
Rachael A Jonas-Closs ◽  
Lev Y Yampolsky ◽  
Marc W Kirschner ◽  
Leonid Peshkin
Keyword(s):  
Machine Learning ◽  
Chronological Age ◽  
Complex Behavior ◽  
Learning Models ◽  
Short Term ◽  
Health Span ◽  
Response Profile ◽  
Automated Phenotyping ◽  
Machine Learning Models

We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

scholarly journals Modelling of Traffic Flows and Supply Chains Based on Geospatial Knowledge

2021 ◽  
Vol 2068 (1) ◽  
pp. 012042
Author(s):  
A Kolesnikov ◽  
P Kikin ◽  
E Panidi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chains ◽  
Inventory Management ◽  
Traffic Flows ◽  
Machine Learning Methods ◽  
Customer Interaction ◽  
Long Term Impact ◽  
Artificial Intelligence Systems

Abstract The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

scholarly journals Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

2020 ◽  
Vol 27 (3) ◽  
pp. 373-389 ◽  
Author(s):  
Ashesh Chattopadhyay ◽  
Pedram Hassanzadeh ◽  
Devika Subramanian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Short Term Memory ◽  
Data Driven ◽  
Reservoir Computing ◽  
Short Term ◽  
Term Memory ◽  
Machine Learning Methods ◽  
Long Short Term Memory ◽  
Lorenz 96

Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.

Long-Term Growth and Short-Term Policy

1964 ◽  
Vol 29 ◽  
pp. 26-38 ◽  
Author(s):  
W. A. H. Godley ◽  
J. R. Shepherd
Keyword(s):  
Economic Policy ◽  
Balance Of Payments ◽  
The Other ◽  
High Wage ◽  
Short Term ◽  
High Unemployment ◽  
The Government ◽  
Wage Increases

One of the main aims of short-term economic policy in Britain has been to regulate the pressure of demand for labour, and to keep the fluctuations of the unemployment percentage within fairly narrow limits. High unemployment is obviously undesirable; at the other end of the scale, if the pressure of demand for labour is too strong, this tends to lead to excessively high wage increases and to balance of payments difficulties. It is for the Government to decide at what pressure it wishes to run the economy, and to try to keep it there.

A trajectories' guide to the state space - learning missing terms in bifurcating ecological systems

2021 ◽  
Author(s):  
Rahel Vortmeyer-Kley ◽  
Pascal Nieters ◽  
Gordon Pipa
Keyword(s):  
Machine Learning ◽  
Differential Equations ◽  
Algal Blooms ◽  
Ecological Systems ◽  
Short Term ◽  
Specific System ◽  
Worst Case ◽  
System Components ◽  
Full Knowledge

&lt;p&gt;Ecological systems typically can exhibit various states ranging from extinction to coexistence of different species in oscillatory states. The switch from one state to another is called bifurcation. All these behaviours of a specific system are hidden in a set of describing differential equations (DE) depending on different parametrisations. To model such a system as DE requires full knowledge of all possible interactions of the system components. In practise, modellers can end up with terms in the DE that do not fully describe the interactions or in the worst case with missing terms.&lt;/p&gt;&lt;p&gt;The framework of universal differential equations (UDE) for scientific machine learning (SciML) [1] allows to reconstruct the incomplete or missing term from an idea of the DE and a short term timeseries of the system and make long term predictions of the system&amp;#8217;s behaviour. However, the approach in [1] has difficulties to reconstruct the incomplete or missing term in systems with bifurcations. We developed a trajectory-based loss metric for UDE and SciML to tackle the problem and tested it successfully on a system mimicking algal blooms in the ocean.&lt;/p&gt;&lt;p&gt;[1] Rackauckas, Christopher, et al. &quot;Universal differential equations for scientific machine learning.&quot; arXiv preprint arXiv:2001.04385 (2020).&lt;/p&gt;

Bond Risk Premiums with Machine Learning

2020 ◽  
Author(s):  
Daniele Bianchi ◽  
Matthias Büchner ◽  
Andrea Tamoni
Keyword(s):  
Machine Learning ◽  
Yield Curve ◽  
Return Predictability ◽  
Time Varying ◽  
Short Term ◽  
Risk Premiums ◽  
Machine Learning Methods ◽  
Yield Information ◽  
Bond Return ◽  
Bond Risk

Abstract We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.

scholarly journals Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

2018 ◽  
Vol 29 (3) ◽  
pp. 320-325 ◽  
Author(s):  
Mirac Baris Usta ◽  
Koray Karabekiroglu ◽  
Berkan Sahin ◽  
Muazzez Aydin ◽  
Abdullah Bozkurt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
Autism Spectrum ◽  
Short Term ◽  
Learning Methods ◽  
Term Outcome ◽  
Machine Learning Methods ◽  
Short Term Outcome ◽  
Spectrum Disorders
Sign in / Sign up

Export Citation Format

Share Document