Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.Comment: Accepted to CVPR 2018 as a spotligh

Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization

Long-term visual localization is the problem of estimating the camera pose of a given query image in a scene whose appearance changes over time. It is an important problem in practice, for example, encountered in autonomous driving. In order to gain robustness to such changes, long-term localization approaches often use segmantic segmentations as an invariant scene representation, as the semantic meaning of each scene part should not be affected by seasonal and other changes. However, these representations are typically not very discriminative due to the limited number of available classes. In this paper, we propose a new neural network, the Fine-Grained Segmentation Network (FGSN), that can be used to provide image segmentations with a larger number of labels and can be trained in a self-supervised fashion. In addition, we show how FGSNs can be trained to output consistent labels across seasonal changes. We demonstrate through extensive experiments that integrating the fine-grained segmentations produced by our FGSNs into existing localization algorithms leads to substantial improvements in localization performance

Feasibility Study of FPGA-Based Equalizer for 112-Gbit/s Optical Fiber Receivers

With ever increasing demands on spectral efficiency, complex modulation schemes are being introduced in fiber communication. However, these schemes are challenging to implement as they drastically increase the computational burden at the fiber receiver’s end. We perform a feasibility study of implementing a 16-QAM 112-Gbit/s decision directed equalizer on a state-of-the-art FPGA platform. An FPGA offers the reconfigurability needed to allow for modulation scheme updates, however, its clock rate is limited. For this purpose, we introduce a new phase correction technique to significantly relax the delay requirement on the critical phase-recovery feedback loop

Long-Term Visual Localization Revisited

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server

Long-Term Visual Localization Revisited

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server

Истмико-цервикальная недостаточность и беременность: диагностика, лечение, профилактика

МЕТОДИЧЕСКИЕ РЕКОМЕНДАЦИИШЕЙКИ МАТКИ ФУНКЦИОНАЛЬНАЯ НЕДОСТАТОЧНОСТЬБЕРЕМЕННОСТИ ОСЛОЖНЕНИЯАБОРТ ПРИВЫЧНЫЙСЕРКЛЯЖ ШЕЙКИ МАТКИПРОГЕСТЕРОНПЕССАРИИИНТЕРНЫКЛИНИЧЕСКИЕ ОРДИНАТОРЫВ рекомендациях описаны современные методы диагностики и лечения пациентов с истмико-цервикальной недостаточностью, тактика ведения беременности при несостоятельности шейки матки. Издание предназначено для врачей акушеров-гинекологов, студентов медицинских вузов, врачей-интернов, клинических ординаторов

Personality Changes after Deep Brain Stimulation in Parkinson’s Disease

Objectives. Deep brain stimulation of the subthalamic nucleus (STN-DBS) is a recognized therapy that improves motor symptoms in advanced Parkinson’s disease (PD). However, little is known about its impact on personality. To address this topic, we have assessed personality traits before and after STN-DBS in PD patients. Methods. Forty patients with advanced PD were assessed with the Temperament and Character Inventory (TCI): the Urgency, Premeditation, Perseverance, Sensation Seeking impulsive behaviour scale (UPPS), and the Neuroticism and Lie subscales of the Eysenck Personality Questionnaire (EPQ-N, EPQ-L) before surgery and after three months of STN-DBS. Collateral information obtained from the UPPS was also reported. Results. Despite improvement in motor function and reduction in dopaminergic dosage patients reported lower score on the TCI Persistence and Self-Transcendence scales, after three months of STN-DBS, compared to baseline (P=0.006; P=0.024). Relatives reported significantly increased scores on the UPPS Lack of Premeditation scale at follow-up (P=0.027). Conclusion. STN-DBS in PD patients is associated with personality changes in the direction of increased impulsivity

Associations between the time of conception and the shape of the lactation curve in early lactation in Norwegian dairy cattle

<p>Abstract</p> <p>Background</p> <p>This study was carried out to determine if an association exists between the shape of the lactation curve before it is influenced by the event of conception and the time from calving to conception in Norwegian dairy cattle. Lactation curves of Norwegian Red cows during 5 to 42 days in milk (DIM) were compared between cows conceiving between 43 and 93 DIM and cows conceiving after 93 DIM.</p> <p>Methods</p> <p>Data from 23,049 cows, represented by one lactation each, with 219,538 monthly test days were extracted from the Norwegian Dairy Herd Recording System, which represents 97% of all Norwegian dairy cows. Besides veterinary treatments, these records also included information on daily milk yield at monthly test days. The data were stratified by parity groups (1, 2, and 3 and higher) and time to conception periods (43-93 DIM and >93 DIM). The sample was selected using the following selection criteria: conception later than 42 DIM, calving season July to September, no records of veterinary treatment and the level of energy fed as concentrates between 8.69 and 12.83 MJ. The shape of the lactation curves were parameterized using a modified Wilmink-model in a mixed model analysis. Differences in the parameters of the lactation curves with different conception times were evaluated using confidence intervals.</p> <p>Results</p> <p>Lactation curves characterized by a low intercept and a steep ascending slope and a steep descending slope were associated with early conception across all parities. The peak milk yield was not associated with time of conception.</p> <p>Conclusions</p> <p>A practical application of the study results is the use of the shape of the lactation curve in future herd management. Groups of cows with impaired reproductive performance may be identified due to an unfavorable shape of the lactation curve. Monitoring lactation curves and adjusting the feeding strategy to adjust yield therefore may be useful for the improvement of reproductive performance at herd level.</p

Addressing the climate challenge

In 2021, colleagues from across the University of Birmingham community were invited to write articles about topics relevant to the COP26 climate change summit. In this series of articles, experts from across many different disciplines provide new insight and evidence on how we might all understand and tackle climate change