The Finnish Development Cooperation in the Water Sector

This report evaluates Finland's participation in the water and sanitation sector from 1995 through 2009. Drawing from previous assessments as well as original research, the authors evaluate the country's performance in the water sector of its partner countries Ethiopia, Kenya, Nepal and Vietnam through an evaluation matrix. Most of the projects in these countries are water and sanitation for rural and small towns, though a few more recent projects in Ethiopia and Nepal focus on water resource management. The evaluation urges the Ministry of Foreign Affairs of Finland (MFA) to better globally disseminate information on a few of its projects that they consider to be "jewels in the crown", particularly Community Development Funds in Ethiopia and Rural Water Supply and Sanitation (Support) Programme in Nepal. Scaling up and replication present important challenges

The Dynamic Impact of Monetary Policy on Regional Housing Prices in the United States

This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high-frequency surprises around policy announcements as an external instrument. Impulse response functions reveal differences in regional housing price responses, which in some cases are substantial. The heterogeneity in policy responses is found to be significantly related to local regulatory environments and housing supply elasticities. Moreover, housing prices responses tend to be similar within states and adjacent regions in neighboring states

Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data

The embedded sensors in widely used smartphones and other wearable devices make the data of human activities more accessible. However, recognizing different human activities from the wearable sensor data remains a challenging research problem in ubiquitous computing. One of the reasons is that the majority of the acquired data has no labels. In this paper, we present an unsupervised approach, which is based on the nature of human activity, to project the human activities into an embedding space in which similar activities will be located closely together. Using this, subsequent clustering algorithms can benefit from the embeddings, forming behavior clusters that represent the distinct activities performed by a person. Results of experiments on three labeled benchmark datasets demonstrate the effectiveness of the framework and show that our approach can help the clustering algorithm achieve improved performance in identifying and categorizing the underlying human activities compared to unsupervised techniques applied directly to the original data set.Comment: The Thirty-Third International Flairs Conference. 202

Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables

Sensor data streams from wearable devices and smart environments are widely studied in areas like human activity recognition (HAR), person identification, or health monitoring. However, most of the previous works in activity and sensor stream analysis have been focusing on one aspect of the data, e.g. only recognizing the type of the activity or only identifying the person who performed the activity. We instead propose an approach that uses a weakly supervised multi-output siamese network that learns to map the data into multiple representation spaces, where each representation space focuses on one aspect of the data. The representation vectors of the data samples are positioned in the space such that the data with the same semantic meaning in that aspect are closely located to each other. Therefore, as demonstrated with a set of experiments, the trained model can provide metrics for clustering data based on multiple aspects, allowing it to address multiple tasks simultaneously and even to outperform single task supervised methods in many situations. In addition, further experiments are presented that in more detail analyze the effect of the architecture and of using multiple tasks within this framework, that investigate the scalability of the model to include additional tasks, and that demonstrate the ability of the framework to combine data for which only partial relationship information with respect to the target tasks is available

A Markov switching factor-augmented VAR model for analyzing US business cycles and monetary policy

This paper develops a multivariate regime switching monetary policy model for the US economy. To exploit a large dataset we use a factor-augmented VAR with discrete regime shifts, capturing distinct business cycle phases. The transition probabilities are modelled as time-varying, depending on a broad set of indicators that influence business cycle movements. The model is used to investigate the relationship between business cycle phases and monetary policy. Our results indicate that the effects of monetary policy are stronger in recessions, whereas the responses are more muted in expansionary phases. Moreover, lagged prices serve as good predictors for business cycle transitions.Series: Department of Economics Working Paper Serie