A 2-pyridyl-2,1-borazaronaphthalene derivative as forefather of a new class​ of bidentate ligands: synthesis and application in luminescent Ir(III) complexes

Borazaro compounds (or azaborines) are aromatic compounds in which a C=C unit is replaced by an isoelectronic B-N unit. The possibility to generate chemical diversity has led to an increasing interest in azaborines, especially in the fields of biomedical research and optoelectonics. In particular, Dewar’s synthesis of borazaronaphthalene is a common starting step to obtain different 1,2-azaborines via nucleophilic substitution on the boron atom. Here we present the synthesis of a novel 1,2-azaborine (i.e. 4-methyl-2-(pyridin-2-yl)-2,1-borazaronaphthalene, named FAAH) via functionalization of 2-chloro-4-methyl-2,1-borazaronaphthalene with a 2-pyridyl unit. FAAH can be used as an anionic bidentate ligand for transition metal complexes, since it can chelate the metal center with both the pyridine and the azaborine nitrogen atoms. FAAH was used for the synthesis of a series of neutral luminescent Ir(III) complexes (named FAV, FAB and FAR) of general formula [Ir(C^N )2(FAA)], where C^N indicates three different cyclometalating ligands: i.e. 2-phenylpyridine in the case of FAV; 2-(2,4-difluorophenyl)pyridine in the case of FAB; 2-methyl-3-phenylquinoxaline in the case of FAR. The reaction yields are quite low, however it was always possible to characterize all the compounds by means of NMR spectroscopy. A complete photophysical and theoretical characterization is also presented. FAAH displays a good chemical stability and a high photoluminescence quantum yield (up to 28 % in solution). On the contrary, the Iridium complexes undergo degradation over time in solution. Despite this stability problem, it was possible to get a good understanding of the photophysics of the three complexes: the emission of both FAV and FAB is observed around 500 nm and arises from a 3LC state centered on the azaborine ligand. In the case of FAR, the emitting state is basically 3MLCT/3LLCT in nature and the resulting broad and unstructured emission band is centered around 700 nm

Evaluation of the Oculus Rift S tracking system in room scale virtual reality

In specific virtual reality applications that require high accuracy it may be advisable to replace the built-in tracking system of the HMD with a third party solution. The purpose of this research work is to evaluate the accuracy of the built-in tracking system of the Oculus Rift S Head Mounted Display (HMD) in room scale environments against a motion capture system. In particular, an experimental evaluation of the Oculus Rift S inside-out tracking technology was carried out, compared to the performance of an outside-in tracking method based on the OptiTrack motion capture system. In order to track the pose of the HMD using the motion capture system the Oculus Rift S was instrumented with passive retro-reflective markers and calibrated. Experiments have been performed on a dataset of multiple paths including simple motions as well as more complex paths. Each recorded path contained simultaneous changes in both position and orientation of the HMD. Our results indicate that in room-scale environments the average translation error for the Oculus Rift S tracking system is about 1.83 cm, and the average rotation error is about 0. 77°, which is 2 orders of magnitude higher than the performance that can be achieved using a motion capture system

Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment

Background Individualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke. Methods A decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data. Results The model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA. Conclusions We have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent

Fractional graph Laplacian for image reconstruction

Image reconstruction problems, like image deblurring and computer tomography, are usually ill-posed and require regularization. A popular approach to regularization is to substitute the original problem with an optimization problem that minimizes the sum of two terms, an term and an term with . The first penalizes the distance between the measured data and the reconstructed one, the latter imposes sparsity on some features of the computed solution. In this work, we propose to use the fractional Laplacian of a properly constructed graph in the term to compute extremely accurate reconstructions of the desired images. A simple model with a fully automatic method, i.e., that does not require the tuning of any parameter, is used to construct the graph and enhanced diffusion on the graph is achieved with the use of a fractional exponent in the Laplacian operator. Since the fractional Laplacian is a global operator, i.e., its matrix representation is completely full, it cannot be formed and stored. We propose to replace it with an approximation in an appropriate Krylov subspace. We show that the algorithm is a regularization method under some reasonable assumptions. Some selected numerical examples in image deblurring and computer tomography show the performance of our proposal

Learning optical flow from still images

This paper deals with the scarcity of data for training optical flow networks, highlighting the limitations of existing sources such as labeled synthetic datasets or unlabeled real videos. Specifically, we introduce a framework to generate accurate ground-truth optical flow annotations quickly and in large amounts from any readily available single real picture. Given an image, we use an off-the-shelf monocular depth estimation network to build a plausible point cloud for the observed scene. Then, we virtually move the camera in the reconstructed environment with known motion vectors and rotation angles, allowing us to synthesize both a novel view and the corresponding optical flow field connecting each pixel in the input image to the one in the new frame. When trained with our data, state-of-the-art optical flow networks achieve superior generalization to unseen real data compared to the same models trained either on annotated synthetic datasets or unlabeled videos, and better specialization if combined with synthetic images.Comment: CVPR 2021. Project page with supplementary and code: https://mattpoggi.github.io/projects/cvpr2021aleotti