Plain Reading, Subtle Meaning: Rethinking the IOIA and the Immunity of International Organizations

Immunity is freedom from liability, and as such, it can quite literally provide a “get out of jail free” card. In the United States, international organizations face uncertainty about the scope of their immunity, which is provided by the International Organizations Immunities Act (IOIA). The D.C. Circuit has found that international organizations enjoy absolute immunity under the IOIA. Conversely, the Third Circuit recently held that international organizations are only entitled to restrictive immunity, which limits immunity to claims involving an organization’s public acts and does not exempt them from suits based on their commercial or private conduct. This Note contends that a plain reading of the IOIA, combined with a full understanding of the history and legislative purpose behind the immunity of international organizations, presents a third interpretation. It concludes that the IOIA requires judicial deference to immunity determinations by the executive branch, which provides the flexibility necessary to allow international organizations to operate without undue interference

Molecular self-organisation in a developmental model for the evolution of large-scale artificial neural networks

We argue that molecular self-organisation during embryonic development allows evolution to perform highly nonlinear combinatorial optimisation. A structured approach to architectural optimisation of large-scale Artificial Neural Networks using this principle is presented. We also present simulation results demonstrating the evolution of an edge detecting retina using the proposed methodology

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes

Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships, finding use in collaborative filtering, computational biology, and document analysis, among other areas. In many domains, there is additional information that can assist in prediction. For example, when modeling movie ratings, we might know when the rating occurred, where the user lives, or what actors appear in the movie. It is difficult, however, to incorporate this side information into the PMF model. We propose a framework for incorporating side information by coupling together multiple PMF problems via Gaussian process priors. We replace scalar latent features with functions that vary over the space of side information. The GP priors on these functions require them to vary smoothly and share information. We successfully use this new method to predict the scores of professional basketball games, where side information about the venue and date of the game are relevant for the outcome.Comment: 18 pages, 4 figures, Submitted to UAI 201

The analysis of animate object motion using neural networks and snakes

This paper presents a mechanism for analysing the deformable shape of an object as it moves across the visual field. An object’s outline is detected using active contour models, and is then re-represented as shape, location and rotation invariant axis crossover vectors. These vectors are used as input for a feedforward backpropagation neural network, which provides a confidence value determining how ‘human’ the network considers the given shape to be. The network was trained using simulated human shapes as well as simulated non-human shapes, including dogs, horses and inanimate objects. The network was then tested on unseen objects of these classes, as well as on an unseen object class. Analysis of the network’s confidence values for a given animated object identifies small, individual variations between different objects of the same class, and large variations between object classes. Confidence values for a given object are periodic and parallel the paces being taken by the object

Qualitative Data Analysis Challenges in Co-Designing Educational Technology Systems for Refugee Children

There is a growing interest in the potential for technology to facilitate emergency education of refugee children. But designing in this space requires knowledge of the displaced population and the contextual dynamics surrounding it. Design should therefore be informed by both existing research across relevant disciplines, and from those who are on the ground facing the problem in real life. This paper describes a process that is based on literature from emergency education, student engagement and motivation, educational technology, and participatory design. We describe how this process was implemented leading to the design of a digital learning space for children living in a refugee camp in Greece. The challenge of data analysis is critical, as the qualitative data in the process is elicited from activities of various natures and thus moving from qualitative data to designs is a critical challenge that we are looking to cover for our process to be complete and applicable. We discuss some of the challenges that can be expected in such context

Training Restricted Boltzmann Machines on Word Observations

The restricted Boltzmann machine (RBM) is a flexible tool for modeling complex data, however there have been significant computational difficulties in using RBMs to model high-dimensional multinomial observations. In natural language processing applications, words are naturally modeled by K-ary discrete distributions, where K is determined by the vocabulary size and can easily be in the hundreds of thousands. The conventional approach to training RBMs on word observations is limited because it requires sampling the states of K-way softmax visible units during block Gibbs updates, an operation that takes time linear in K. In this work, we address this issue by employing a more general class of Markov chain Monte Carlo operators on the visible units, yielding updates with computational complexity independent of K. We demonstrate the success of our approach by training RBMs on hundreds of millions of word n-grams using larger vocabularies than previously feasible and using the learned features to improve performance on chunking and sentiment classification tasks, achieving state-of-the-art results on the latter

A Study In Retention In Fundamental Operations In Algebra

The problem is to discover as nearly as possible, The retention of sophomores, who the preceding year had taken algebra, in the four fundamental arithmetic operations of positive and negative numbers.” The four fundamental arithmetic operations are namely: addition, subtraction, multiplication and division. In making computations with positive and negative numbers there are four possibilities involving cases concerning a positive and a negative , a negative and a positive , two negative , and two positive numbers . Negative numbers are numbers having a minus value and positive numbers are numbers which have a plus value

Development and application of deep learning and spatial statistics within 3D bone marrow imaging

The bone marrow is a highly specialised organ, responsible for the formation of blood cells. Despite 50 years of research, the spatial organisation of the bone marrow remains an area full of controversy and contradiction. One reason for this is that imaging of bone marrow tissue is notoriously difficult. Secondly, efficient methodologies to fully extract and analyse large datasets remain the Achilles heels of imaging-based research. In this thesis I present a pipeline for generating 3D bone marrow images followed by the large-scale data extraction and spatial statistical analysis of the resulting data. Using these techniques, in the context of 3D imaging, I am able to identify and classify the location of hundreds of thousands of cells within various bone marrow samples. I then introduce a series of statistical techniques tailored to work with spatial data, resulting in a 3D statistical map of the tissue from which multi-cellular interactions can be clearly understood. As an illustration of the power of this new approach, I apply this pipeline to diseased samples of bone marrow with a particular focus on leukaemia and its interactions with CD8+ T cells. In so doing I show that this novel pipeline can be used to unravel complex multi-cellular interactions and assist researchers in understanding the processes taking place within the bone marrow.Open Acces