Ziel der Studie: Analyse, ob teil- und vollqualifizierende Leistungen zur Teilhabe am Arbeitsleben vergleichbare Beschäftigungseffekte erreichen. Methodik: Für die Analysen wurden administrative Längsschnittdaten genutzt. Eingeschlossen wurden Personen im Alter von 18 bis 59 Jahren, die ihre Qualifizierung zwischen Januar und Juni 2005 begannen. Teilnehmer von Teil- und Vollqualifizierungen wurden mittels Propensity Scores gematcht. Ergebnisse: Die gematchten Gruppen waren hinsichtlich aller Ausgangswerte balanciert (Teilqualifizierung: n=514; Vollqualifizierung: n=514). 4 und 5 Jahre nach Beginn der Maßnahmen waren jährliches Entgelt, die Bezugsdauer von Transferleistungen und das Risiko erwerbsminderungsbedingter Renten für beide Gruppen gleich. Das für 2005–2009 kumulierte Einkommen war für Teilnehmer von Teilqualifizierungen um 9294 Euro höher (95%-KI: 3 656–14 932 Euro). Die kumulierte Bezugsdauer von Transferleistungen war für Teilnehmer von Teilqualifizierungen geringer. Schlussfolgerung: Die Festlegung einer beruflichen Rehabilitationsstrategie sollte den kumulativen Einkommensvorteil von Teilqualifizierungen berücksichtigen.
Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
Abstract A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models’ limitations in the domains of causal reasoning, intuitive physics and intuitive psychology. Yet recent advancements, namely the rise of large language models, particularly those designed for visual processing, have rekindled interest in the potential to emulate human-like cognitive abilities. This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning and intuitive psychology. Through a series of controlled experiments, we investigate the extent to which these modern models grasp complex physical interactions, causal relationships and intuitive understanding of others’ preferences. Our findings reveal that, while some of these models demonstrate a notable proficiency in processing and interpreting visual data, they still fall short of human capabilities in these areas. Our results emphasize the need for integrating more robust mechanisms for understanding causality, physical dynamics and social cognition into modern-day, vision-based language models, and point out the importance of cognitively inspired benchmarks.
Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.
Performance of functional capacity evaluation (FCE) may affect patients, self-efficacy to complete physical activity tasks. First evidence from a diagnostic before-after study indicates a significant increase of patient-reported functional ability. Our study set out to test the reproducibility of these results.Patients with musculoskeletal trauma and an unclear return to work prognosis were recruited in a trauma rehabilitation center in Lower Austria. We included patient cohorts of three consecutive years (2016: n = 161, 2017: n = 140; 2018: n = 151). Our primary outcome was patient-reported functional ability, measured using the Spinal Function Sort (SFS). SFS scores were assessed before and after performing an FCE to describe the change in patient-reported functional ability (cohort study). We investigated whether the change in SFS scores observed after performing an FCE in our first cohort could be replicated in subsequent cohorts.Demographic data (gender, age and time after trauma) did not differ significantly between the three patient cohorts. Correlation analysis showed highly associated before and after SFS scores in each cohort (2016: rs = 0.84, 95% CI: 0.79 to 0.89; 2017: rs = 0.85, 95% CI: 0.81 to 0.91; 2018: rs = 0.86, 95% CI: 0.82 to 0.91). Improvements in SFS scores were consistent across the cohorts, with overlapping 95% confidence intervals (2016: 14.8, 95% CI: 11.3 to 18.2; 2017: 14.8, 95% CI: 11.5 to 18.0; 2018: 15.2, 95% CI: 12.0 to 18.4). Similarity in SFS scores and SFS differences were also supported by non-significant Kruskal-Wallis H tests (before FCE: p = 0.517; after FCE: p = 0.531; SFS differences: p = 0.931).A significant increase in patient-reported functional ability after FCE was found in the original study and the results could be reproduced in two subsequent cohorts.
Abstract Background Effective care services for people whose work participation is at risk require low-threshold access, a comprehensive diagnostic clarification of intervention needs, a connection to the workplace and job demands, and interdisciplinary collaboration between key stakeholders at the interface of rehabilitation and occupational medicine. We have developed a comprehensive diagnostic service to clarify intervention needs for employees with health restrictions and limited work ability: this service is initiated by occupational health physicians. Methods/design Our randomized controlled trial tests the effectiveness of a comprehensive diagnostic service for clarifying intervention needs (GIBI: Comprehensive clarification of the need for intervention for people whose work participation is at risk). The comprehensive intervention comprises three elements: initial consultation, two-day diagnostics at a rehabilitation center and follow-up consultations. We will include 210 employees with health restrictions and limited work ability, who are identified by occupational health physicians. All individuals will receive an initial consultation with their occupational health physician to discuss their health, work ability and job demands. After this, half the individuals are randomly assigned to the intervention group and the other half to the waiting-list control group. Individuals in the intervention group start two-day diagnostics, carried out by a multi-professional rehabilitation team in a rehabilitation center, shortly after the initial consultation. The diagnostics will allow first recommendations for improving work participation. The implementation of these recommendations is supported by an occupational health physician in four follow-up consultations. The control group will receive the comprehensive two-day diagnostic service and subsequent follow-up consultations six months after the initial consultation. The primary outcome of the randomized controlled trial is self-rated work ability assessed using the Work Ability Score (0 to 10 points) six months after study inclusion. Secondary outcomes include a range of patient-reported outcomes regarding physical and mental health, impairment, and the physical and mental demands of jobs. Discussion This randomized controlled trial is designed to test the effects of a new complex intervention involving a comprehensive clarification of intervention needs in order to promote work participation and prevent the worsening of health and work disability. Trial registration German Clinical Trials Register (DRKS00027577, February 01, 2022).
The family of DeepGaze models comprises deep learning based computational models of freeviewing overt attention. DeepGaze II predicts freeviewing fixation locations (Kümmerer et al, ICCV 2017) and DeepGaze III (Kümmerer at al, CCN 2019) predicts freeviewing sequences of fixations. The models encode image information using deep features from pretrained deep neural networks to compute a spatial saliency map, which, in case of DeepGaze III, is then combined with information about the scanpath history to predict the next fixation. Both models have set the state of the art in their respective tasks in the last years. Here, we improve the performance of both models substantially. We replace the backbone deep neural network VGG-19 with better performing networks such as DenseNet. We also improve the architecture of the model and the training procedure. This results in a substantial performance improvement for both DeepGaze II and DeepGaze III and sets a new state of the art for freeviewing fixation prediction and freeviewing scanpath prediction across all commonly used metrics. We further use the improved DeepGaze III model to better understand human scanpaths. For example, we quantify the effects of scene content and scanpath history on human scanpaths. We find that, on the MIT1003 dataset, scene content has a substantially larger effect on fixation selection than scanpath history and that there are only very subtle but measurable interactions between scene content and scanpath history that go beyond a scalar saliency measure. Furthermore, we are able to disentangle the central fixation bias into contributions that are driven by image content, by the initial central fixation, and by a remaining effect that cannot be explained from these two sources. Taken together, the improved DeepGaze models allow us to analyze human scanpaths in ways that are not possible without high-performing deep learning models.
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).
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