Towards feasible, machine-assisted verification of object-oriented programs

This thesis provides an account of a development of tools towards making verification of object-oriented programs more feasible. We note that proofs in program verification logics are typically long, yet, mathematically, not very deep; these observations suggest the thesis that computers can significantly ease the burden of program verification. We give evidence supporting this by applying computers to (1) automatically check and (2) automatically infer large parts of proofs. Taking the logic (AL) of Abadi and Leino as our starting point, we initially show how the logic can be embedded into a higher-order logic theorem prover, by way of introducing axioms, using a mix of both higher-order abstract syntax (HOAS) and a direct embedding of the assertion logic. The tenacity and exactness of the theorem prover ensures that no proof obligation is inadvertently lost during construction of a proof; we inherit any automatic facilities such as tactics which take us part way towards goal (2); and moreover, we achieve goal (1), since we inherit machine proofs which can be checked automatically. We present som

Semantic Augmented Reality Environment with Material-Aware Physical Interactions

© 2017 IEEE. In Augmented Reality (AR) environment, realistic interactions between the virtual and real objects play a crucial role in user experience. Much of recent advances in AR has been largely focused on developing geometry-aware environment, but little has been done in dealing with interactions at the semantic level. High-level scene understanding and semantic descriptions in AR would allow effective design of complex applications and enhanced user experience. In this paper, we present a novel approach and a prototype system that enables the deeper understanding of semantic properties of the real world environment, so that realistic physical interactions between the real and the virtual objects can be generated. A material-aware AR environment has been created based on the deep material learning using a fully convolutional network (FCN). The state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) has been used for the semantic mapping. Together with efficient accelerated 3D ray casting, natural and realistic physical interactions are generated for interactive AR games. Our approach has significant impact on the future development of advanced AR systems and applications

A Ly-alpha nebula at z~3.3

Context. Searching for high-redshift galaxies is a field of intense activity in modern observational cosmology that will continue to grow with future ground-based and sky observatories. Over the last few years, a lot has been learned about the high-z Universe. Aims. Despite extensive Ly-alpha Blobs (LAB) surveys from low to high redshifts, giant LABs over 100 kpc have been found mostly at z~2-4. This redshift range is coincident with the transition epoch of galactic gas-circulation processes from inflows to outflows at z~2.5-3. This suggests that the formation of giant LABs may be related to a combination of gas inflows and outflows. Their extreme youth makes them interesting objects in the study of galaxy formation as they provide insight into some of the youngest known highly star forming galaxies, with only modest time investments using ground-based telescopes. Methods. Systematic narrow-band Ly-alpha nebula surveys are ongoing, but they are limited in their covered redshift range and their comoving volume. This poses a significant problem when searching for such rare sources. To address this problem, we developed a systematic searching tool, ATACAMA (A Tool for seArChing for lArge LyMan Alpha nebulae) designed to find large Ly-alpha nebulae at any redshift within deep multi-wavelength broad-band imaging. Results. We identified a Ly-alpha nebula candidate at zphot~3.3 covering an isophotal area of 29.4sq.arcsec. Its morphology shows a bright core and a faint core which coincides with the morphology of previously known Ly-alpha blobs. A first estimation of the Ly-alpha equivalent width and line flux agree with the values from the study led by several groups.Comment: Accepted to A&A, 8 pages, 4 figures. arXiv admin note: text overlap with arXiv:1708.00447 by other author

Large magnetoresistance at room-temperature in small molecular weight organic semiconductor sandwich devices

We present an extensive study of a large, room temperature negative magnetoresistance (MR) effect in tris-(8-hydroxyquinoline) aluminum sandwich devices in weak magnetic fields. The effect is similar to that previously discovered in polymer devices. We characterize this effect and discuss its dependence on field direction, voltage, temperature, film thickness, and electrode materials. The MR effect reaches almost 10% at fields of approximately 10 mT at room temperature. The effect shows only a weak temperature dependence and is independent of the sign and direction of the magnetic field. Measuring the devices' current-voltage characteristics, we find that the current depends on the voltage through a power-law. We find that the magnetic field changes the prefactor of the power-law, whereas the exponent remains unaffected. We also studied the effect of the magnetic field on the electroluminescence (MEL) of the devices and analyze the relationship between MR and MEL. We find that the largest part of MEL is simply a consequence of a change in device current caused by the MR effect.Comment: 8 figure

Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes

We propose a structure-adaptive variant of the state-of-the-art stochastic variance-reduced gradient algorithm Katyusha for regularized empirical risk minimization. The proposed method is able to exploit the intrinsic low-dimensional structure of the solution, such as sparsity or low rank which is enforced by a non-smooth regularization, to achieve even faster convergence rate. This provable algorithmic improvement is done by restarting the Katyusha algorithm according to restricted strong-convexity constants. We demonstrate the effectiveness of our approach via numerical experiments

Measurement of teicoplanin by liquid chromatography-tandem mass spectrometry:development of a novel method

Teicoplanin is an antibiotic used for the treatment of endocarditis, osteomyelitis, septic arthritis and methicillin-resistant Staphylococcus aureus. Teicoplanin is emerging as a suitable alternative antibiotic to vancomycin, where their trough serum levels are monitored by immunoassay routinely. This is the first report detailing the development of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for measuring teicoplanin in patients' serum