Generation of mesoscopic superpositions of a binary Bose-Einstein condensate in a slightly asymmetric double well

A previous publication [Europhysics Letters 78, 10009 (2007)] suggested to coherently generate mesoscopic superpositions of a two-component Bose-Einstein condensate in a double well under perfectly symmetric conditions. However, already tiny asymmetries can destroy the entanglement properties of the ground state. Nevertheless, even under more realistic conditions, the scheme is demonstrated numerically to generate mesoscopic superpositions.Comment: 5 pages, 4 figures, preprint-versio

Convolutional CRFs for semantic segmentation

For the challenging semantic image segmentation task the best performing models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs. Doing so speeds up inference and training by two orders of magnitude. All parameters of the convolutional CRFs can easily be optimized using backpropagation. Towards the goal of facilitating further CRF research we have made our implementations publicly available

Large scale joint semantic re-localisation and scene understanding via globally unique instance coordinate regression

In this work we present a novel approach to joint semantic localisation and scene understanding. Our work is motivated by the need for localisation algorithms which not only predict 6-DoF camera pose but also simultaneously recognise surrounding objects and estimate 3D geometry. Such capabilities are crucial for computer vision guided systems which interact with the environment: autonomous driving, augmented reality and robotics. In particular, we propose a two step procedure. During the first step we train a convolutional neural network to jointly predict per-pixel globally unique instance labels and corresponding local coordinates for each instance of a static object (e.g. a building). During the second step we obtain scene coordinates by combining object center coordinates and local coordinates and use them to perform 6-DoF camera pose estimation. We evaluate our approach on real world (CamVid-360) and artificial (SceneCity) autonomous driving datasets. We obtain smaller mean distance and angular errors than state-of-the-art 6-DoF pose estimation algorithms based on direct pose regression and pose estimation from scene coordinates on all datasets. Our contributions include: (i) a novel formulation of scene coordinate regression as two separate tasks of object instance recognition and local coordinate regression and a demonstration that our proposed solution allows to predict accurate 3D geometry of static objects and estimate 6-DoF pose of camera on (ii) maps larger by several orders of magnitude than previously attempted by scene coordinate regression methods, as well as on (iii) lightweight, approximate 3D maps built from 3D primitives such as building-aligned cuboids.Toyota Corporatio

Digit-colour synaesthesia only enhances memory for colours in a specific context:A new method of duration thresholds to measure serial recall

For digit-color synaesthetes, digits elicit vivid experiences of color that are highly consistent for each individual. The conscious experience of synaesthesia is typically unidirectional: Digits evoke colors but not vice versa. There is an ongoing debate about whether synaesthetes have a memory advantage over non-synaesthetes. One key question in this debate is whether synaesthetes have a general superiority or whether any benefit is specific to a certain type of material. Here, we focus on immediate serial recall and ask digit-color synaesthetes and controls to memorize digit and color sequences. We developed a sensitive staircase method manipulating presentation duration to measure participants' serial recall of both overlearned and novel sequences. Our results show that synaesthetes can activate digit information to enhance serial memory for color sequences. When color sequences corresponded to ascending or descending digit sequences, synaesthetes encoded these sequences at a faster rate than their non-synaesthetes counterparts and faster than non-structured color sequences. However, encoding color sequences is approximately 200 ms slower than encoding digit sequences directly, independent of group and condition, which shows that the translation process is time consuming. These results suggest memory advantages in synaesthesia require a modified dual-coding account, in which secondary (synaesthetically linked) information is useful only if it is more memorable than the primary information to be recalled. Our study further shows that duration thresholds are a sensitive method to measure subtle differences in serial recall performance

The Coagulation Box and a New Hemoglobin-Driven Algorithm for Bleeding Control in Patients with Severe Multiple Traumas

Background: Extensive hemorrhage is the leading cause of death in the first few hours following multiple traumas. Therefore, early and aggressive treatment of clotting disorders could reduce mortality. Unfortunately, the availability of results from commonly performed blood coagulation studies are often delayed whereas hemoglobin (Hb) levels are quickly available. Objectives: In this study, we evaluated the use of initial hemoglobin (Hb) levels as a guide line for the initial treatment of clotting disorders in multiple trauma patients. Patients and Methods: We have developed an Hb-driven algorithm to initiate the initial clotting therapy. The algorithm contains three different steps for aggressive clotting therapy depending on the first Hb value measured in the shock trauma room, (SR) and utilizes fibrinogen, prothrombin complex concentrate (PCC), factor VIIa, tranexamic acid and desmopressin. The above-mentioned drugs were stored in a special “coagulation box” in the hospital pharmacy, and this box could be immediately brought to the SR or operating room (OR) upon request. Despite the use of clotting factors, transfusions using red blood cells (RBC) and fresh frozen plasma (FFP) were performed at an RBC-to-FFP ratio of 2:1 to 1:1. Results: Over a 12-month investigation period, 123 severe multiple trauma patients needing intensive care therapy were admitted to our trauma center (mean age 48 years, mean ISS (injury severity score) 30). Fourteen (11%) patients died; 25 (mean age 51.5 years, mean ISS 53) of the 123 patients were treated using the “coagulation box,” and 17 patients required massive transfusions. Patients treated with the “coagulation box” required an average dose of 16.3 RBC and 12.9 FFP, whereas 17 of the 25 patients required an average dose of 3.6 platelet packs. According to the algorithm, 25 patients received fibrinogen (average dose of 8.25 g), 24 (96%) received PCC (3000 IU.), 14 (56%) received desmopressin (36.6 µg), 13 (52%) received tranexamic acid (2.88 g), and 11 (44%) received factor VIIa (3.7 mg). The clotting parameters markedly improved between SR admission and ICU admission. Of the 25 patients, 16 (64%) survived. The revised injury severity classification (RISC) predicted a survival rate of 41%, which corresponds to a standardized mortality ratio (SMR) of 0.62, which implies a higher survival rate than predicted. Conclusions: An Hb-driven algorithm, in combination with the “coagulation box” and the early use of clotting factors, could be a simple and effective tool for improving coagulopathy in multiple trauma patients

Bose-Einstein condensates in a double well: mean-field chaos and multi-particle entanglement

A recent publication [Phys. Rev. Lett. 100, 140408 (2008)] shows that there is a relation between mean-field chaos and multi-particle entanglement for BECs in a periodically shaken double well. 'Schrodinger-cat' like mesoscopic superpositions in phase-space occur for conditions for which the system displays mean-field chaos. In the present manuscript, more general highly-entangled states are investigated. Mean-field chaos accelerates the emergence of multi-particle entanglement; the boundaries of stable regions are particularly suited for entanglement generation.Comment: 5 Pages, 5 jpg-figures, to be published in the proceedings of the LPHYS0

INSIGHT: A population-scale COVID-19 testing strategy combining point-of-care diagnosis with centralized high-throughput sequencing.

We present INSIGHT [isothermal NASBA (nucleic acid sequence-based amplification) sequencing-based high-throughput test], a two-stage coronavirus disease 2019 testing strategy, using a barcoded isothermal NASBA reaction. It combines point-of-care diagnosis with next-generation sequencing, aiming to achieve population-scale testing. Stage 1 allows a quick decentralized readout for early isolation of presymptomatic or asymptomatic patients. It gives results within 1 to 2 hours, using either fluorescence detection or a lateral flow readout, while simultaneously incorporating sample-specific barcodes. The same reaction products from potentially hundreds of thousands of samples can then be pooled and used in a highly multiplexed sequencing-based assay in stage 2. This second stage confirms the near-patient testing results and facilitates centralized data collection. The 95% limit of detection is <50 copies of viral RNA per reaction. INSIGHT is suitable for further development into a rapid home-based, point-of-care assay and is potentially scalable to the population level