Trapped in inactivity? Social assistance and labour supply in Austria

Financial incentives affect the labour supply decisions of households. However, the impact usually varies significantly across household types. Whilst there is a substantial amount of literature on the labour supply effects of tax reforms and in-work benefits, the impact of changes in social assistance benefits has received less attention. This paper analyses labour supply responses to changes in social assistance. We show that labour supply elasticities vary substantially across gender and household type. Women exhibit higher labour supply elasticities, both on the intensive and the extensive margins. Additionally, labour supply elasticities are typically higher for singles and for households with children. Using these results, we analyse the impact of the Austrian reform proposal “Neue Sozialhilfe” (New Social Assistance), which was introduced in 2019 and substantially cut social assistance benefits for migrants and families with children. The overall effects of the reform are especially strong for men and migrants. Migrants and couples with children, that is, the groups hardest hit by the reform’s social assistance reductions, show the strongest labour supply reactions to the New Social Assistance. Furthermore, we show that overall, the reform is expected to have a positive, but small, effect on the intensive margin of labour supply

WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series

Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwards (NDF), a derivatives contract used in foreign exchange (FX) trading, presents additional difficulty in the form of long-term planning required for an effective selection of start and end date of the contract. In this work, we focus on tackling the problem of NDF tenor selection by leveraging high-dimensional sequential data consisting of spot rates, technical indicators and expert tenor patterns. To this end, we construct a dataset from the Depository Trust & Clearing Corporation (DTCC) NDF data that includes a comprehensive list of NDF volumes and daily spot rates for 64 FX pairs. We introduce WaveATTentionNet (WATTNet), a novel temporal convolution (TCN) model for spatio-temporal modeling of highly multivariate time series, and validate it across NDF markets with varying degrees of dissimilarity between the training and test periods in terms of volatility and general market regimes. The proposed method achieves a significant positive return on investment (ROI) in all NDF markets under analysis, outperforming recurrent and classical baselines by a wide margin. Finally, we propose two orthogonal interpretability approaches to verify noise stability and detect the driving factors of the learned tenor selection strategy.Comment: Submitted to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 20

PELAKSANAAN FUNGSI PENGAWASAN PEMBANGUNAN DEWAN PERWAKILAN RAKYAT DAERAH KABUPATEN MINAHASA SELATAN PERIODE 2019 -2024

Fungsi pengawasan merupakan fungsi dari DPRD, dimana DPRD memiliki fungsi utama sebagai pengawas dan juga pemantau setiap pelaksanaan peraturan daerah yang sudah disepakati bersama dengan pimpinan daerah, serta mengawasi penggunaan anggaran yang sudah disahkan sebelumnya dalam APBD, begitu pula pengawasan yang harus dilakukan oleh anggota DPRD Kabupaten Minahasa Selatan periode 2019-2024 pertumbuhan ekonomi kabupaten Minahasa Selatan dalam 3 tahun terakhir (2018-2020) terus menurun meskipun berada di urutan ke 7 tingkat kabupaten/kota. Sementara angka kemiskinan yang menunjukan angka rata-rata di atas 9% selama 3 tahun (2018–2020) juga menunjukan belum terjadinya peningkatan kualitas kehidupan masyarakat yang signifikan. Hal ini juga merepresentasikan pembangunan di kabupaten ini tidak mengalami perkembangan yang signifikan. Terkait dengan persoalan tugas, fungsi dan tanggung jawab DPRD, maka keadaan ini tidaklah lepas dari tanggungjawab DPRD Kabupaten Minahasa Selatan sebagaimana yang dijelaskan di dalam peraturan perundang-undangan. Tujuan penelitian adalah untuk mengetahui pelaksanaan fungsi pelaksanaan pembangunan yang dilakukan oleh Komisi II DPRD Kabupaten Minahasa Selatan periode 2019–2024. Penelitian ini menggunakan metode penelitian kualitatif. Penelitian ini berlokasi di DPRD Kabupaten Minahasa Selatan. Hasil penelitian penunjukan bahwa pengawasan yang dilakukan oleh Komisi II DPRD Minsel sudah dilakukan secara objektif, jujur dan mendahulukan kepentingan umum. Dalam melakukan pengawasan, Komisi II DPRD Minsel tidak memandang bulu. Pengawasan pembangunan dilakulan secara terbuka kepada masyarakat dengan menginformasikan lewat media-media alternative (media sosial) yang ada, agar supaya masyarakat dapat mengetahui kerja-kerja mereka. Terdapat beberapa factor yang mempengaruhi lemahnya fungsi pengawasan pembangunan dari komisi II DPRD Minsel, diantaranya: faktor organisasi, faktor latar belakang politik anggota, dan faktor pengetahuan tentang teknik pengawasan dari anggota. Kata Kunci : Fungsi Pengawasan; Dewan Perwakilan Rakyat Daerah;  Pembangunan  ABSTRACTThe supervisory function is a function of the DPRD, where the DPRD has the main function as a supervisor and also monitors the implementation of every regional regulation that has been agreed with regional leaders, as well as supervising the use of the budget that has been previously approved in the APBD, as well as supervision that must be carried out by members of the Regency DPRD. South Minahasa for the 2019-2024 period, South Minahasa district's economic growth in the last 3 years (2018-2020) continued to decline even though it was in 7th place at the district/city level. Meanwhile, the poverty rate which shows an average rate of above 9% for 3 years (2018–2020) also shows that there has not been a significant improvement in the quality of people's lives. This also represents that development in this district has not experienced significant progress. Regarding the issue of the duties, functions and responsibilities of the DPRD, this situation cannot be separated from the responsibility of the DPRD of South Minahasa Regency as described in the legislation. The purpose of the study was to determine the implementation of the development implementation function carried out by Commission II of the South Minahasa Regency DPRD for the 2019-2024 period. This study used qualitative research methods. This research is located in the DPRD of South Minahasa Regency. The results of the research indicate that the supervision carried out by Commission II of the Minsel DPRD has been carried out objectively, honestly and puts the public interest first. In carrying out supervision, Commission II of the Minsel DPRD does not give a damn. Supervision of development is carried out openly to the public by informing them through alternative media (social media) that exist, so that people can find out about their work. There are several factors that influence the weakness of the development oversight function of Commission II of the Minsel DPRD, including: organizational factors, members' political background factors, and members' knowledge of supervisory techniques. Keywords: Supervision Function; DPRD; Developmen

Deep Latent State Space Models for Time-Series Generation

Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences

Learning Efficient Surrogate Dynamic Models with Graph Spline Networks

While complex simulations of physical systems have been widely used in engineering and scientific computing, lowering their often prohibitive computational requirements has only recently been tackled by deep learning approaches. In this paper, we present GraphSplineNets, a novel deep-learning method to speed up the forecasting of physical systems by reducing the grid size and number of iteration steps of deep surrogate models. Our method uses two differentiable orthogonal spline collocation methods to efficiently predict response at any location in time and space. Additionally, we introduce an adaptive collocation strategy in space to prioritize sampling from the most important regions. GraphSplineNets improve the accuracy-speedup tradeoff in forecasting various dynamical systems with increasing complexity, including the heat equation, damped wave propagation, Navier-Stokes equations, and real-world ocean currents in both regular and irregular domains.Comment: Published as a conference paper in NeurIPS 202

Port-Hamiltonian Approach to Neural Network Training

Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes networks with layer outputs which are no longer quantized but are solutions of an ordinary differential equation (ODE); however, these networks are still optimized via discrete methods (e.g. gradient descent). In this paper, we explore a different direction: namely, we propose a novel framework for learning in which the parameters themselves are solutions of ODEs. By viewing the optimization process as the evolution of a port-Hamiltonian system, we can ensure convergence to a minimum of the objective function. Numerical experiments have been performed to show the validity and effectiveness of the proposed methods.Comment: To appear in the Proceedings of the 58th IEEE Conference on Decision and Control (CDC 2019). The first two authors contributed equally to the wor