Reasons for Unjust Enrichment

Birks’ unjust enrichment formula was intended to provide a common descriptive structure to all the instances where there was recovery. He did not, however, engage in an analysis of the various reasons why courts awarded restitution. My thesis seeks to fill this gap. I argue that without such an account Birks work is incomplete. According to Birks, for example, money and services both amounted to enrichments and so should be considered together. But there are some differences and similarities between money and services. In order to be able to group them together Birks needs to be able to say that the reasons for giving recovery in money and service cases are similar enough that they can be grouped together. The same goes for all the unjust factors. The point is, the generalisation that Birks sought to do, can only properly be done if one is attuned to the reasons why recovery is granted in each of those cases. If the reasons are similar then the generalisation makes sense. But if they are not then it does not make sense to so generalise. The argument of the thesis is that there three relevant principles to justifying unjust enrichment: the Property Principle, the Benefit-Burden Principle and the Autonomy Principle. The Property Principle states that one should not have property belonging to another. The Benefit-Burden Principle states that if one takes a benefit then one must bear the associated burdens; to put it more colloquially: you have to take the rough with the smooth. These first two principles provide reasons for considering a situation to be defective and the last principle provides a constraint for the operation of the first two. It is there to ensure that the imposition of liability will not unduly affect the autonomy of the defendant. Based on that the thesis proposes that the scope of the unjust enrichment formula be trimmed down to only cover defective transfers of money and other assets. For the other cases, a different analytical structure is needed. This is because the reasons for recovery in those cases are different.Travers Smith Studentship in Private Law, Faculty of Law, University of Cambridg

Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction

Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data. In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories

Shaping Code

To allow society to intervene and proactively shape code (i.e., the software and hardware of information technologies), we analyze a number of mechanisms and schemes concerning how society can shape the development of code. These recommendations include regulatory and fiscal actions by the government, as well as actions that public interest organizations can take to shape code. These recommendations also include a number of specific policy prescriptions, such as prohibitions on code, using standards or market-based incentives, modifying liability, requiring disclosure, governmental funding for the development of code, government\u27s use of its procurement power to favor open source code, export prohibitions on encryption code, developing an insurance regime for cybersecurity, and fashioning technology transfer policy for code. For each measure, we identify and discuss regulatory and technological issues that affect its effectiveness. The result is a more informed approach in weighing the alterative approaches to shaping code. We do not attempt to determine the comparative efficiency of different approaches to shaping code, because, in part, that analysis is a factually laden inquiry depending on the specific characteristics and issues related to the particular type of code in question. These recommendations will allow policymakers to better anticipate and guide the development of code that contributes to our society and reflects its values and preferences

Mind Your Language: Abuse and Offense Detection for Code-Switched Languages

In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users. In this paper, we study offense and abuse detection in the code-switched pair of Hindi and English (i.e. Hinglish), the pair that is the most spoken. The task is made difficult due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish language. We apply transfer learning and make a LSTM based model for hate speech classification. This model surpasses the performance shown by the current best models to establish itself as the state-of-the-art in the unexplored domain of Hinglish offensive text classification.We also release our model and the embeddings trained for research purpose

IceBreaker: Solving Cold Start Problem for Video Recommendation Engines

Internet has brought about a tremendous increase in content of all forms and, in that, video content constitutes the major backbone of the total content being published as well as watched. Thus it becomes imperative for video recommendation engines such as Hulu to look for novel and innovative ways to recommend the newly added videos to their users. However, the problem with new videos is that they lack any sort of metadata and user interaction so as to be able to rate the videos for the consumers. To this effect, this paper introduces the several techniques we develop for the Content Based Video Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM Multimedia Conference 2018. We employ different architectures on the CBVRP dataset to make use of the provided frame and video level features and generate predictions of videos that are similar to the other videos. We also implement several ensemble strategies to explore complementarity between both the types of provided features. The obtained results are encouraging and will impel the boundaries of research for multimedia based video recommendation systems