Loss tolerance in one-way quantum computation via counterfactual error correction

We introduce a scheme for fault tolerantly dealing with losses (or other "leakage" errors) in cluster state computation that can tolerate up to 50% qubit loss. This is achieved passively using an adaptive strategy of measurement - no coherent measurements or coherent correction is required. Since the scheme relies on inferring information about what would have been the outcome of a measurement had one been able to carry it out, we call this "counterfactual" error correction.Comment: Published version - much revised and with a new title. Here we now focus solely on the general aspects of the protocol - a much expanded and improved discussion of its application in linear optical quantum computation can now be found in quant-ph/070204

Transport in developing countries and climate policy: suggestions for a Copenhagen agreement and beyond

Also in the global South, transport already significantly contributes to climate change and has high growth rates. Further rapid motorisation of countries in Asia and Latin America could counteract any climate efforts and aggravate problems of noxious emissions, noise and congestion. This Paper aims at connecting the need for transport actions in developing countries to the international negotiations on a post-2012 climate change agreement. It outlines the decisions to be taken in Copenhagen and the preparations to adequately implement these decisions from 2013. Arguing, that a sustainable transport approach needs to set up comprehensive policy packages, the paper assesses the substance of current climate negotiations against the fit to sustainable transport. It concludes that the transport sector's importance should be highlighted and a significant contribution to mitigation efforts required. Combining the two perspectives lead to several concrete suggestions: Existing elements of the carbon market should be improved (e.g. discounting), but an upscale of the carbon market would not be an appropriate solution. Due to a lack of additionality, offsetting industrialised countries' targets would finally undermine the overall success of the climate agreement. Instead, a mitigation fund should be established under the UNFCCC and financed by industrialised countries. This fund should explicitly enable developing countries to implement national sustainable development transport and mobility policies as well as local projects. While industrialized countries would set up target achievement plans, developing countries should outline low carbon development strategies, including a section on transport policy. -- Die rasante Motorisierung Asiens und Lateinamerikas könnte die Klimaschutzerfolge konterkarieren. Bis 2030, so Prognosen der IEA, werden im Verkehrssektor 2,5 Gigatonnen CO2 mehr emitiert als heute; 80 Prozent davon in den Ländern des Südens. Das Papier soll die Notwendigkeit verdeutlichen, dass in den Entwicklungsländern im Verkehrssektor heute schon Maßnahmen ergriffen werden müssen und die Klimaverhandlungen für die Post-Kyoto-Phase eine wichtige Gelegenheit sind. Die Ansätze in den gegenwärtigen Klimaverhandlungen werden den Anforderungen einer nachhaltigen Verkehrspolitik gegenübergestellt und dafür plädiert, den Stellenwert des Verkehrssektors zu den Klimaschutzanstrengungen zu erhöhen. Dafür werden mehrere konkrete Vorschläge gemacht: So sollten vorhandene Elemente des Emissionshandels verbessert werden, die eigentlich angemessene Lösung sei jedoch ein neues Instrument: Um die Entwicklungsländer in die Lage zu versetzen Maßnahmen in der Verkehrspolitik umzusetzen und Politiken und Projekte vor Ort zu fördern, sollte ein von den Industrieländern finanzierter Klimaschutzfonds unter dem UN-Klimaregime eingerichtet werden. In Strategien für eine kohlenstoffarme Entwicklung sind dabei die Politikinstrumente einer nachhaltigen Verkehrsentwicklung zu integrieren.

Study of some abnormalities occurring in certain potato varieties in Colorado, A

June, 1935.Includes bibliographical references (pages 88-92)

Development and application of software and algorithms for network approaches to proteomics data analysis

The cells making up all living organisms integrate external and internal signals to carry out the functions of life. Dysregulation of signaling can lead to a variety of grave diseases, including cancer [Slamon et al., 1987]. In order to understand signal transduction, one has to identify and characterize the main constituents of cellular signaling cascades. Proteins are involved in most cellular processes and form the major class of biomolecules responsible for signal transduction. Post-translational modifications (PTMs) of proteins can modulate their enzymatic activity and their protein-protein interactions (PPIs) which in turn can ultimately lead to changes in protein expression. Classical biochemistry has approached the study of proteins, PTMs and interaction from a reductionist view. The abundance, stability and localization of proteins was studied one protein at a time, following the one gene-one protein-one function paradigm [Beadle and Tatum, 1941]. Pathways were considered to be linear, where signals would be transmitted from a gene to proteins, eventually resulting in a specific phenotype. Establishing the crucial link between genotype and phenotype remains challenging despite great advances in omics technologies, such as liquid chromatography (LC)-mass spectrometry (MS) that allow for the system-wide interrogation of proteins. Systems and network biology [Barabási and Oltvai, 2004, Bensimon et al., 2012, Jørgensen and Locard-Paulet, 2012, Choudhary and Mann, 2010] aims to transform modern biology by utilizing omics technologies to understand and uncover the various complex networks that govern the cell. The first detected large-scale biological networks have been found to be highly structured and non-random [Albert and Barabási, 2002]. Furthermore, these are assembled from functional and topological modules. The smallest topological modules are formed by the direct physical interactions within protein-protein and protein-RNA complexes. These molecular machines are able to perform a diverse array of cellular functions, such as transcription and degradation [Alberts, 1998]. Members of functional modules are not required to have a direct physical interaction. Instead, such modules also include proteins with temporal co-regulation throughout the cell cycle [Olsen et al., 2010], or following the circadian day-night rhythm [Robles et al., 2014]. The signaling pathways that make up the cellular network [Jordan et al., 2000] are assembled from a hierarchy of these smaller modules [Barabási and Oltvai, 2004]. The regulation of these modules through dynamic rewiring enables the cell to respond to internal an external stimuli. The main challenge in network biology is to develop techniques to probe the topology of various biological networks, to identify topological and functional modules, and to understand their assembly and dynamic rewiring. LC-MS has become a powerful experimental platform that addresses all these challenges directly [Bensimon et al., 2012], and has long been used to study a wide range of biomolecules that participate in the cellular network. The field of proteomics in particular, which is concerned with the identification and characterization of the proteins in the cell, has been revolutionized by recent technological advances in MS. Proteomics experiments are used not only to quantify peptides and proteins, but also to uncover the edges of the cellular network, by screening for physical PPIs in a global [Hein et al., 2015] or condition specific manner [Kloet et al., 2016]. Crucial for the interpretation of the large-scale data generated by MS experiments is the development of software tools that aid researchers in translating raw measurements into biological insights. The MaxQuant and Perseus platforms were designed for this exact purpose. The aim of this thesis was to develop software tools for the analysis of MS-based proteomics data with a focus on network biology and apply the developed tools to study cellular signaling. The first step was the extension of the Perseus software with network data structures and activities. The new network module allows for the sideby-side analysis of matrices and networks inside an interactive workflow and is described in article 1. We subsequently apply the newly developed software to study the circadian phosphoproteome of cortical synapses (see article 2). In parallel we aimed to improve the analysis of large datasets by adapting the previously Windows-only MaxQuant software to the Linux operating system, which is more prevalent in high performance computing environments (see article 3)

Optimal intrinsic descriptors for non-rigid shape analysis

We propose novel point descriptors for 3D shapes with the potential to match two shapes representing the same object undergoing natural deformations. These deformations are more general than the often assumed isometries, and we use labeled training data to learn optimal descriptors for such cases. Furthermore, instead of explicitly defining the descriptor, we introduce new Mercer kernels, for which we formally show that their corresponding feature space mapping is a generalization of either the Heat Kernel Signature or the Wave Kernel Signature. I.e. the proposed descriptors are guaranteed to be at least as precise as any Heat Kernel Signature or Wave Kernel Signature of any parameterisation. In experiments, we show that our implicitly defined, infinite-dimensional descriptors can better deal with non-isometric deformations than state-of-the-art methods