Evaluation of PV technology implementation in the building sector

This paper presents a simulation case that shows the impact on energy consumption of a building applying photovoltaic shading systems. In order to make photovoltaic application more economical, the effect of a photovoltaic facade as a passive cooling system can result in a considerable energy cost reduction, with positive influence on the payback time of the photovoltaic installation. Photovoltaic shading systems can be applied to both refurbishment of old buildings and to new-build, offering attractive and environmentally integrated architectural solutions

Automatic Error Localization for Software using Deductive Verification

Even competent programmers make mistakes. Automatic verification can detect errors, but leaves the frustrating task of finding the erroneous line of code to the user. This paper presents an automatic approach for identifying potential error locations in software. It is based on a deductive verification engine, which detects errors in functions annotated with pre- and post-conditions. Using an automatic theorem prover, our approach finds expressions in the code that can be modified such that the program satisfies its specification. Scalability is achieved by analyzing each function in isolation. We have implemented our approach in the widely used Frama-C framework and present first experimental results. This is an extended version of [8], featuring an additional appendix.Comment: This is an extended version of [8], featuring an additional appendi

Refugee and Asylum Seeker Rights

I will approach my analysis through the lens of new social movement theory and evaluate the role of the Centre for Peace and Conflict Studies (CPACS) at the University of Sydney in this context, asking: How can CPACS successfully contribute to the existing web of collective action in pro-refugee advocacy? Furthermore, as CPACS‘ struggle in re-shaping the politics and policies of the current government is a microcosm of the pro-refugee movement‘s struggle to re-shape contemporary notions of citizenship, I will shift from a micro-evaluation of CPACS to a macro-evaluation of the pro-refugee movement as a whole in successfully challenging the existing system of citizenship and model of civic engagement in Australia

The Knowledge Graph for End-to-End Learning on Heterogeneous Knowledge

In modern machine learning,raw data is the preferred input for our models. Where a decade ago data scientists were still engineering features, manually picking out the details we thought salient, they now prefer the data in their raw form. As long as we can assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: information of different types and from different domains. If we can develop methods that operate on this form of knowledge, we can dispense with a great deal more ad-hoc feature engineering and train deep models end-to-end in many more domains. To accomplish this, we first need a data model capable of expressing heterogeneous knowledge naturally in various domains, in as usable a form as possible, and satisfying as many use cases as possible. We argue that the knowledge graph is a suitable candidate for this data model. We further discuss some of the promises and challenges of this approach, and how we are currently broadening our efforts to multi-modal knowledge graphs

Too hot to handle: heat stroke in multiple system atrophy

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Temporal Stream Logic: Synthesis beyond the Bools

Reactive systems that operate in environments with complex data, such as mobile apps or embedded controllers with many sensors, are difficult to synthesize. Synthesis tools usually fail for such systems because the state space resulting from the discretization of the data is too large. We introduce TSL, a new temporal logic that separates control and data. We provide a CEGAR-based synthesis approach for the construction of implementations that are guaranteed to satisfy a TSL specification for all possible instantiations of the data processing functions. TSL provides an attractive trade-off for synthesis. On the one hand, synthesis from TSL, unlike synthesis from standard temporal logics, is undecidable in general. On the other hand, however, synthesis from TSL is scalable, because it is independent of the complexity of the handled data. Among other benchmarks, we have successfully synthesized a music player Android app and a controller for an autonomous vehicle in the Open Race Car Simulator (TORCS.

The clinical heterogeneity of drug-induced myoclonus: an illustrated review

Contains fulltext : 177995.pdf (publisher's version ) (Open Access)A wide variety of drugs can cause myoclonus. To illustrate this, we first discuss two personally observed cases, one presenting with generalized, but facial-predominant, myoclonus that was induced by amantadine; and the other presenting with propriospinal myoclonus triggered by an antibiotic. We then review the literature on drugs that may cause myoclonus, extracting the corresponding clinical phenotype and suggested underlying pathophysiology. The most frequently reported classes of drugs causing myoclonus include opiates, antidepressants, antipsychotics, and antibiotics. The distribution of myoclonus ranges from focal to generalized, even amongst patients using the same drug, which suggests various neuro-anatomical generators. Possible underlying pathophysiological alterations involve serotonin, dopamine, GABA, and glutamate-related processes at various levels of the neuraxis. The high number of cases of drug-induced myoclonus, together with their reported heterogeneous clinical characteristics, underscores the importance of considering drugs as a possible cause of myoclonus, regardless of its clinical characteristics

Near-Optimal Scheduling for LTL with Future Discounting

We study the search problem for optimal schedulers for the linear temporal logic (LTL) with future discounting. The logic, introduced by Almagor, Boker and Kupferman, is a quantitative variant of LTL in which an event in the far future has only discounted contribution to a truth value (that is a real number in the unit interval [0, 1]). The precise problem we study---it naturally arises e.g. in search for a scheduler that recovers from an internal error state as soon as possible---is the following: given a Kripke frame, a formula and a number in [0, 1] called a margin, find a path of the Kripke frame that is optimal with respect to the formula up to the prescribed margin (a truly optimal path may not exist). We present an algorithm for the problem; it works even in the extended setting with propositional quality operators, a setting where (threshold) model-checking is known to be undecidable

Challenging Distributional Models with a Conceptual Network of Philosophical Terms

Computational linguistic research on language change through distributional semantic (DS) models has inspired researchers from fields such as philosophy and literary studies, who use these methods for the exploration and comparison of comparatively small datasets traditionally analyzed by close reading. Research on methods for small data is still in early stages and it is not clear which methods achieve the best results. We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case. We provide a ground truth for evaluation created by philosophy experts and a blueprint for using DS models in a sound methodological setup. We compare three methods for creating specialized models from small datasets. Though the models do not perform well enough to directly support philosophers yet, we find that models designed for small data yield promising directions for future work