The Political Economy of the Investment Treaty Regime

Investment treaties are some of the most controversial but least understood instruments of global economic governance. Public interest in international investment arbitration is growing and some developed and developing countries are beginning to revisit their investment treaty policies. The Political Economy of the Investment Treaty Regime synthesises and advances the growing literature on this subject by integrating legal, economic, and political perspectives. Based on an analysis of the substantive and procedural rights conferred by investment treaties, it asks four basic questions. What are the costs and benefits of investment treaties for investors, states, and other stakeholders? Why did developed and developing countries sign the treaties? Why should private arbitrators be allowed to review public regulations passed by states? And what is the relationship between the investment treaty regime and the broader regime complex that governs international investment? Through a concise, but comprehensive, analysis, this book fills in some of the many "blind spots" of academics from different disciplines, and is the first port of call for lawyers, investors, policy-makers, and stakeholders trying to make sense of these critical instruments governing investor-state relations

Technical guidelines for economic valuation of inland small-scale fisheries in developing countries

These ôTechnical Guidelines for Economic Valuation of Inland Small-scale Fisheries in Developing Countriesö are one of the outputs of the project on ôFood security and poverty alleviation through improved valuation and governance of river fisheries in Africaö. The guidelines draw upon research results and experience gained during the course of the project. The project was coordinated and implemented by the WorldFish Center and was carried out in cooperation with the National Agricultural Research Institutes (NARs) from the participating countries: the Nigeria Institute for Freshwater Fisheries Research, the Departments of Fishery of Niger, Malawi and Zambia, and the Cameroonian MinistΦre de lÆElevage, des PΩches et de lÆIndustrie Animale; and three advanced research institutes (ARIs): the Leibniz University of Hannover in Germany, the Institute for Sustainable Development and Aquatic Resources in UK, and the University of Cape Town in South Africa.Rural development, Sustainable development, Livelihoods, Economic analysis, Research, Artisanal fishing

Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

DaCToR: A data collection tool for the RELATER project

Collecting domain-specific data for under-resourced languages, e.g., dialects of languages, can be very expensive, potentially financially prohibitive and taking long time. Moreover, in the case of rarely written languages, the normalization of non-canonical transcription might be another time consuming but necessary task. In order to collect domain-specific data in such circumstances in a time and cost-efficient way, collecting read data of pre-prepared texts is often a viable option. In order to collect data in the domain of psychiatric diagnosis in Arabic dialects for the project RELATER, we have prepared the data collection tool DaCToR for collecting read texts by speakers in the respective countries and districts in which the dialects are spoken. In this paper we describe our tool, its purpose within the project RELATER and the dialects which we have started to collect with the tool

Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction

Reconstructing 3D objects from 2D images is both challenging for our brains and machine learning algorithms. To support this spatial reasoning task, contextual information about the overall shape of an object is critical. However, such information is not captured by established loss terms (e.g. Dice loss). We propose to complement geometrical shape information by including multi-scale topological features, such as connected components, cycles, and voids, in the reconstruction loss. Our method uses cubical complexes to calculate topological features of 3D volume data and employs an optimal transport distance to guide the reconstruction process. This topology-aware loss is fully differentiable, computationally efficient, and can be added to any neural network. We demonstrate the utility of our loss by incorporating it into SHAPR, a model for predicting the 3D cell shape of individual cells based on 2D microscopy images. Using a hybrid loss that leverages both geometrical and topological information of single objects to assess their shape, we find that topological information substantially improves the quality of reconstructions, thus highlighting its ability to extract more relevant features from image datasets.Comment: Accepted at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI

A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images

Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the inverse problem of three-dimensional (3D) cell shape prediction from two-dimensional (2D) single cell microscopy images. Using the 2D microscopy image as a prior, DISPR is conditioned to predict realistic 3D shape reconstructions. To showcase the applicability of DISPR as a data augmentation tool in a feature-based single cell classification task, we extract morphological features from the red blood cells grouped into six highly imbalanced classes. Adding features from the DISPR predictions to the three minority classes improved the macro F1 score from $F1_\text{macro} = 55.2 \pm 4.6\%$ to $F1_\text{macro} = 72.2 \pm 4.9\%$. We thus demonstrate that diffusion models can be successfully applied to inverse biomedical problems, and that they learn to reconstruct 3D shapes with realistic morphological features from 2D microscopy images

A brief description of geological and geophysical exploration of the Marysville geothermal area

Extensive geological and geophysical surveys were carried out at the Marysville geothermal area during 1973 and 1974. The area has high heat flow (up to microcalories per square centimeter-second, a negative gravity anomaly, high electrical resistivity, low seismic ground noise, and nearby microseismic activity. Significant magnetic and infrared anomalies are not associated with the geothermal area. The geothermal anomaly occupies the axial portion of a dome in Precambrian sedimentary rocks intruded by Cretaceous and Cenozoic granitic rocks. The results from a 2.4-km-deep test well indicate that the cause of the geothermal anomaly is hydrothermal convection in a Cenozoic intrusive. A maximum temperature of 95 C was measured at a depth of 500 m in the test well