1,214 research outputs found
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
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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
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MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
While most approaches to semantic reasoning have fo-
cused on improving performance, in this paper we argue
that computational times are very important in order to en-
able real time applications such as autonomous driving. To-
wards this goal, we present an approach to joint classifi-
cation, detection and semantic segmentation via a unified
architecture where the encoder is shared amongst the three
tasks. Our approach is very simple, can be trained end-to-
end and performs extremely well in the challenging KITTI
dataset, outperforming the state-of-the-art in the road seg-
mentation task. Our approach is also very efficient, allow-
ing us to perform inference at more then 23 frames per sec-
ond.
Training scripts and trained weights to reproduce our
results can be found here: https://github.com/
MarvinTeichmann/MultiNetBegabtenstiftung Informatik Karlsruhe, ONR-N00014-
14-1-0232, Qualcomm, Samsung, NVIDIA, Google, EP-
SRC and NSER
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
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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
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