123 research outputs found

    Post-apartheid South Africa: An economic success story?

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    The paper provides an evaluation of the development of the South African economy since the end of apartheid in 1994. Taking the 1993 situation as the point of departure, it gives an account of the path leading to the formulation of the major policy documents, and examines to what extent their objectives have been met. It highlights poverty reduction and redistribution, stabilization (fiscal and monetary policy, liberalization of the capital account of the balance of payments, exchange rate policy), trade and competition policy, investment strategies and labor market policy

    Object Detection as Probabilistic Set Prediction

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    Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems. The development and evaluation of probabilistic object detectors have been hindered by shortcomings in existing performance measures, which tend to involve arbitrary thresholds or limit the detector’s choice of distributions. In this work, we propose to view object detection as a set prediction task where detectors predict the distribution over the set of objects. Using the negative log-likelihood for random finite sets, we present a proper scoring rule for evaluating and training probabilistic object detectors. The proposed method can be applied to existing probabilistic detectors, is free from thresholds, and enables fair comparison between architectures. Three different types of detectors are evaluated on the COCO dataset. Our results indicate that the training of existing detectors is optimized toward non-probabilistic metrics. We hope to encourage the development of new object detectors that can accurately estimate their own uncertainty. Code at\ua0https://github.com/georghess/pmb-nll

    LidarCLIP or: How I Learned to Talk to Point Clouds

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    Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection between text and other visual modalities, such as lidar data, has received less attention, prohibited by the lack of text-lidar datasets. In this work, we propose LidarCLIP, a mapping from automotive point clouds to a pre-existing CLIP embedding space. Using image-lidar pairs, we supervise a point cloud encoder with the image CLIP embeddings, effectively relating text and lidar data with the image domain as an intermediary. We show the effectiveness of LidarCLIP by demonstrating that lidar-based retrieval is generally on par with image-based retrieval, but with complementary strengths and weaknesses. By combining image and lidar features, we improve upon both single-modality methods and enable a targeted search for challenging detection scenarios under adverse sensor conditions. We also explore zero-shot classification and show that LidarCLIP outperforms existing attempts to use CLIP for point clouds by a large margin. Finally, we leverage our compatibility with CLIP to explore a range of applications, such as point cloud captioning and lidar-to-image generation, without any additional training. Code and pre-trained models are available at https://github.com/atonderski/lidarclip

    Work productivity in a population-based cohort of patients with spondyloarthritis.

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    Objective. To assess work productivity and associated factors in patients with SpA.Methods. This cross-sectional postal survey included 1773 patients with SpA identified in a regional health care register. Items on presenteeism (reduced productivity at work, 0-100%, 0 = no reduction) were answered by 1447 individuals. Absenteeism was defined as register-based sick leave using data from a national register. Disease duration, disease activity (BASDAI), physical function (BASFI), health-related quality of life (EQ-5D), anxiety (HAD-a), depression (HAD-d), self-efficacy [Arthritis Self-efficacy Scale (ASES) pain and symptom], physical activity and education were also measured.Results. Forty-five per cent reported reduced productivity at work with a mean reduction of 20% (95% CI 18, 21) and women reported a higher mean reduction than men (mean 23% vs 17%, P < 0.001). Worse quality of life, disease activity, physical function and anxiety all correlated with reduced productivity (r = 0.52-0.66, P < 0.001), while sick leave did not. Worse outcomes on the EQ-5D (β-est -9.6, P < 0.001), BASDAI (β-est 7.8, P < 0.001), BASFI (β-est 7.3, P < 0.001), ASES pain (β-est -0.5, P < 0.001) and HAD-d (β-est 3.4, P < 0.001) were associated with reduced productivity at work in patients with SpA regardless of age, gender and disease subgroup. ASES symptoms, HAD-a and education level <12 years were associated with reduced productivity but were not significant in all strata for age, gender and disease subgroup.Conclusion. Work productivity was reduced in patients with SpA and more so in women. Worse quality of life, disease activity, physical function, self-efficacy and depression were all associated with reduced productivity at work in patients with SpA

    You can have your ensemble and run it too -- Deep Ensembles Spread Over Time

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    Ensembles of independently trained deep neural networks yield uncertainty estimates that rival Bayesian networks in performance. They also offer sizable improvements in terms of predictive performance over single models. However, deep ensembles are not commonly used in environments with limited computational budget -- such as autonomous driving -- since the complexity grows linearly with the number of ensemble members. An important observation that can be made for robotics applications, such as autonomous driving, is that data is typically sequential. For instance, when an object is to be recognized, an autonomous vehicle typically observes a sequence of images, rather than a single image. This raises the question, could the deep ensemble be spread over time? In this work, we propose and analyze Deep Ensembles Spread Over Time (DESOT). The idea is to apply only a single ensemble member to each data point in the sequence, and fuse the predictions over a sequence of data points. We implement and experiment with DESOT for traffic sign classification, where sequences of tracked image patches are to be classified. We find that DESOT obtains the benefits of deep ensembles, in terms of predictive and uncertainty estimation performance, while avoiding the added computational cost. Moreover, DESOT is simple to implement and does not require sequences during training. Finally, we find that DESOT, like deep ensembles, outperform single models for out-of-distribution detection

    Decrease in the incidence of total hip arthroplasties in patients with rheumatoid arthritis - results from a well defined population in south Sweden

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    To access publisher full text version of this article. Please click on the hyperlink in Additional Links field.INTRODUCTION: One aim of modern pharmacologic treatment in rheumatoid arthritis (RA) is to prevent joint destruction and reduce the need for surgery. Our purpose was to investigate secular trends in the incidence of primary total hip and knee arthroplasties in a well defined sample of patients with RA. METHODS: Prevalent cases with RA in 1997 and incident cases from 1997 to 2007 in a community based register in Malmö, south Sweden, were included. Based on a structured review of the medical records, patients were classified according to the 1987 ACR criteria for RA. This cohort was linked to the Swedish Hip Arthroplasty Register (through December 2006) and the Swedish Knee Arthroplasty Register (through October 2007). Patients with a registered total hip or knee arthroplasty before 1997 or before RA diagnosis were excluded. Incidence rates for the period of introduction of TNF inhibitors (1998 to 2001) were compared to the period when biologics were part of the established treatment for severe RA (2002 to 2006/2007). RESULTS: In the cohort (n = 2,164; 71% women) a primary hip arthroplasty was registered for 115 patients and a primary knee arthroplasty for 82 patients. The incidence of primary total hip arthroplasties decreased from the period 1998 to 2001 (12.6/1,000 person-years (pyr)) to 2002 to 2006 (6.6/1,000 pyr) (rate ratio (RR) 0.52; 95% confidence interval (CI) 0.35 to 0.76). There was a trend towards an increase of primary knee arthroplasties (incidence 4.8/1,000 pyr vs. 6.8/1,000 pyr; RR 1.43; 95% CI 0.89 to 2.31). CONCLUSIONS: Our investigation shows a significant decrease in the incidence of total hip arthroplasties in patients with RA after 2001. Possible explanations include a positive effect on joint damage from more aggressive pharmacological treatment.The Swedish Research Council Lund University Crafoord Foundation Swedish Rheumatism Associatio

    Decrease in sick leave among patients with rheumatoid arthritis in the first 12 months after start of treatment with tumour necrosis factor antagonists: a population-based controlled cohort study.

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    OBJECTIVE: /st> To investigate the effect of tumour necrosis factor (TNF) antagonist treatment of patients with rheumatoid arthritis (RA) on sick leave (SL) and disability pension (DP) in a population-based setting in southern Sweden. METHODS: /st> All patients with RA in the South Swedish Arthritis Treatment Group register living in the county of Skåne (population 1.2 million), who started their first treatment with a TNF antagonist between January 2004 and December 2007 and were 18-58 years at treatment start (n=365), were identified. For each patient with RA, four matched reference subjects from the general population were randomly selected. Data were linked to the Swedish Social Insurance Agency register and the point prevalence of SL and DP as well as days of SL and DP per month were calculated from 360 days before until 360 days after treatment start. RESULTS: /st> At treatment start 38.6% of the patients with RA were registered for SL. During the first 6 months this share dropped to 28.5% (decrease by 26.2%, p There was a marked decline in SL during the first 6 months of TNF antagonist treatment in patients with RA in southern Sweden, maintained throughout the first year, which was not offset by a corresponding increase in DP

    TNF inhibitors and extraarticular RA Personal non-commercial use only

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    ABSTRACT. Objective. The aims of this study were to evaluate whether treatment with tumor necrosis factor (TNF) inhibitors in patients with rheumatoid arthritis (RA) affects the risk of developing severe extraarticular rheumatoid arthritis (ExRA) manifestations and to investigate potential predictors for developing ExRA. Methods. A dynamic community-based cohort of patients with RA was studied (n = 1977). Clinical records were reviewed and cases of severe ExRA were identified. Information on exposure to TNF inhibitors was obtained from a regional register. Exposure to TNF inhibitors was analyzed in a time-dependent fashion and the incidence of severe ExRA in exposed patients was compared with the incidence in unexposed patients. Cox regression models were used to assess potential predictors of severe ExRA. Results. During treatment with TNF inhibitors, there were 17 patients with new onset of severe ExRA in 2400 person-years at risk (PY; 0.71/100 PY, 95% CI 0.41-1.13) compared with 104 in 15,599 PY (0.67/100 PY, 95% CI 0.54-0.81) in patients without TNF inhibitors. This corresponded to an incidence rate ratio of 1.06 (95% CI 0.60-1.78). The age-and sex-adjusted HR for ExRA in anti-TNF-treated patients was 1.21 (95% CI 1.02-1.43), with similar findings in models adjusted for time-dependent Health Assessment Questionnaire and propensity for anti-TNF treatment. Male sex, positive rheumatoid factor (RF), long disease duration, and greater disability were predictors for ExRA. Conclusion. This study suggests that patients treated with TNF inhibitors are at a slightly increased risk of developing severe ExRA. RF-positive patients with disabling disease of long duration were more likely to develop sever

    Influence of Ocean Acidification on a Natural Winter-to-Summer Plankton Succession : First Insights from a Long-Term Mesocosm Study Draw Attention to Periods of Low Nutrient Concentrations

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    Every year, the oceans absorb about 30% of anthropogenic carbon dioxide (CO2) leading to a re-equilibration of the marine carbonate system and decreasing seawater pH. Today, there is increasing awareness that these changes-summarized by the term ocean acidification (OA)-could differentially affect the competitive ability of marine organisms, thereby provoking a restructuring of marine ecosystems and biogeochemical element cycles. In winter 2013, we deployed ten pelagic mesocosms in the Gullmar Fjord at the Swedish west coast in order to study the effect of OA on plankton ecology and biogeochemistry under close to natural conditions. Five of the ten mesocosms were left unperturbed and served as controls (similar to 380 mu atm pCO(2)), whereas the others were enriched with CO2-saturated water to simulate realistic end-of-the-century carbonate chemistry conditions (mu 760 mu atm pCO(2)). We ran the experiment for 113 days which allowed us to study the influence of high CO2 on an entire winter-to-summer plankton succession and to investigate the potential of some plankton organisms for evolutionary adaptation to OA in their natural environment. This paper is the first in a PLOS collection and provides a detailed overview on the experimental design, important events, and the key complexities of such a "long-term mesocosm" approach. Furthermore, we analyzed whether simulated end-of-the-century carbonate chemistry conditions could lead to a significant restructuring of the plankton community in the course of the succession. At the level of detail analyzed in this overview paper we found that CO2-induced differences in plankton community composition were non-detectable during most of the succession except for a period where a phytoplankton bloom was fueled by remineralized nutrients. These results indicate: (1) Long-term studies with pelagic ecosystems are necessary to uncover OA-sensitive stages of succession. (2) Plankton communities fueled by regenerated nutrients may be more responsive to changing carbonate chemistry than those having access to high inorganic nutrient concentrations and may deserve particular attention in future studies.Peer reviewe
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