The Employment Effects of Low-Wage Subsidies

Low-wage subsidies are often proposed as a solution to the unemployment problem among the low skilled. Yet the empirical evidence on the effects of low-wage subsidies is surprisingly scarce. This paper examines the employment effects of a Finnish payroll tax subsidy scheme, which is targeted at the employers of older, full-time, low-wage workers. The system’s clear eligibility criteria open up an opportunity for a reliable estimation of the causal impacts of the subsidy, using a difference-in-difference-in-differences approach. Our results indicate that the subsidy system had no effects on the employment rate. However, it appears to have increased the probability of part-time workers obtaining full-time employment.low-wage subsidies, employment, social security contributions

The Employment Effects of Low-Wage Subsidies

Low-wage subsidies are often proposed as a solution to the unemployment problem among the low skilled. Yet the empirical evidence on the effects of low-wage subsidies is surprisingly scarce. This paper examines the employment effects of a Finnish payroll tax subsidy scheme, which is targeted at the employers of older, full-time, low-wage workers. The system's clear eligibility criteria open up an opportunity for a reliable estimation of the causal impacts of the subsidy, using a difference-in-difference-in-differences approach. Our results indicate that the subsidy system had no effects on the employment rate. However, it appears to have increased the probability of part-time workers obtaining full-time employment.low-wage subsidies, employment, social security contributions

Designing a Bubbling Fluidized Bed (BFB) boiler for research purposes

This project is part of the efforts made by Savonia University of Applied Sciences to plan the future EU-funded combustion research laboratory that will be located on Varkaus Campus (Finland). The main objective of the present thesis was to carry out an optimal design, in technical, environmental and economical terms, of a small-sized bubbling fluidized bed (BFB) boiler, which will be used mainly for research purposes. This design takes as a reference a former BFB boiler that was located at Lappeenranta University of Technology. The efforts have been focused mainly on adapting this reference design to be used for research activities and bringing construction and operating costs down. All the drawings and virtual three-dimensional models of the components have been executed using SolidWords, a computer-aided design program. The result of the design process is an efficient and environmentally friendly boiler that is going to enable numerous testing services and lines of research

The New Finnish Dream Home? : Townhouse Living from a Resident's Perspective

The Finnish Dream Home study, which examines the Townhouse typology is based on large planning projects that have been launched and or are being planned in Helsinki Metropolitan area, a key target of which is increasing the diversity of housing solutions by means of urban housing typologies of different kinds. The Finnish Dream Home study examines the ability of the townhouse typology to meet the diversifying housing preferences, which are connected to the one-family houses. What needs lay behind the preferences for housing? What characteristics are residents willing to forgo

Uusi suomalainen unelmakoti? : Asukasnäkökulma townhouse-asumiseen

Townhouse-talotyyppiä käsittelevän Suomalainen unelmakoti -tutkimuksen taustana ovat Helsingin seudulla käynnistetyt ja suunnitteilla olevat mittavat kaavoitushankkeet, joiden yhtenä keskeisenä tavoitteena on asumisratkaisujen monimuotoisuuden lisääminen erityyppisten urbaanien talotyyppien avulla. Suomalainen unelmakoti –tutkimuksessa tarkastellaan townhouse-talotyypin mahdollisuuksia vastata pientaloasumiseen kytkeytyviin, mutta samalla erilaistuviin asumispreferensseihin: Mitä tarpeita asumisen toiveiden taakse kätkeytyy? Minkä ominaisuuksien osalta asukkaat ovat valmiita tinkimään? Siinä missä townhouse-asuminen on tyypillisesti mielletty ensisijaisesti perheille sopivaksi vaihtoehdoksi, on Suomalainen unelmakoti -tutkimuksen lähtökohta tunnistaa erilaisia asukasprofiileja ja asumistarpeita. Tutkimuksessa pureudutaan erityisesti siihen, millä ehdoilla townhouseasuminen voi vastata erilaisten kotitalouksien asumistarpeisiin. Aalto-yliopiston rinnalla hankkeen päärahoittaja on Innovatiivinen kaupunki -ohjelma. Muita rahoittajia ja yhteistyökumppaneita ovat Helsingin kaupunki, kaupunkisuunnitteluvirasto, rakennusvalvontavirasto, kaupunginkanslia sekä Asumisen rahoitus- ja kehittämiskeskus ARA

Effect of Label Noise on Robustness of Deep Neural Network Object Detectors

Label noise is a primary point of interest for safety concerns in previous works as it affects the robustness of a machine learning system by a considerable amount. This paper studies the sensitivity of object detection loss functions to label noise in bounding box detection tasks. Although label noise has been widely studied in the classification context, less attention is paid to its effect on object detection. We characterize different types of label noise and concentrate on the most common type of annotation error, which is missing labels. We simulate missing labels by deliberately removing bounding boxes at training time and study its effect on different deep learning object detection architectures and their loss functions. Our primary focus is on comparing two particular loss functions: cross-entropy loss and focal loss. We also experiment on the effect of different focal loss hyperparameter values with varying amounts of noise in the datasets and discover that even up to 50% missing labels can be tolerated with an appropriate selection of hyperparameters. The results suggest that focal loss is more sensitive to label noise, but increasing the gamma value can boost its robustness.acceptedVersionPeer reviewe

Loop-closure detection by LiDAR scan re-identification

In this work, loop-closure detection from LiDAR scans is defined as an image re-identification problem. Re-identification is performed by computing Euclidean distances of a query scan to a gallery set of previous scans. The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN). The network is trained using the triplet loss training strategy. In our experiments we compare different backbone networks, variants of the triplet loss and generic and LiDAR specific data augmentation techniques. With a realistic indoor dataset the best architecture obtains the mean average precision (mAP) above 0.94.acceptedVersionPeer reviewe