AAN: Attributes-Aware Network for Temporal Action Detection

The challenge of long-term video understanding remains constrained by the efficient extraction of object semantics and the modelling of their relationships for downstream tasks. Although the CLIP visual features exhibit discriminative properties for various vision tasks, particularly in object encoding, they are suboptimal for long-term video understanding. To address this issue, we present the Attributes-Aware Network (AAN), which consists of two key components: the Attributes Extractor and a Graph Reasoning block. These components facilitate the extraction of object-centric attributes and the modelling of their relationships within the video. By leveraging CLIP features, AAN outperforms state-of-the-art approaches on two popular action detection datasets: Charades and Toyota Smarthome Untrimmed datasets

Novel Insights in the Management of Vernal Keratoconjunctivitis (VKC): European Expert Consensus Using a Modified Nominal Group Technique

Introduction: Vernal keratoconjunctivitis (VKC) is a rare, severe allergic ocular disease, typically occurring in children and adolescents, that can have a significant impact on quality of life and lead to visual impairment. Long-term treatment may be necessary to tackle chronic inflammation and topical corticosteroid dependency must be minimised due to the risk of complications. There is a need for unified clinical guidance to aid the assessment, diagnosis and management of VKC across Europe. The aim of this expert panel (the EUR-VKC Group) was to provide clear guidance for primary care physicians and general ophthalmologists involved in the diagnosis and management of VKC. Methods: An expert group of seven European ophthalmologists was convened and a modified nominal group technique used to develop key recommendations on VKC management. The recommendations were subject to up to two rounds of voting using a 5-point Likert scale to ascertain consensus and the strength of each recommendation. Consensus was set at a predetermined threshold of ≥ 75.0% of experts selecting 'Strongly agree' or 'Agree'. Results: A total of 47 recommendations were developed relating to the assessment of key of VKC, guidance on who and when to refer, as well as treatment-escalation pathways, long-term follow-up, and supportive care and education. All recommendations reached consensus after two rounds. The group emphasise how timely diagnosis and treatment initiation that is appropriate to disease severity are crucial to benefit patients with VKC. Patients with signs ('red flags') indicating severe VKC, or persistent mild-to-moderate VKC that is non-responsive following 2-4 weeks of treatment, should be referred to a sub-specialist. Conclusion: The EUR-VKC Group provides recommendations on the assessment, diagnosis, management, referral and follow-up of patients with VKC. It also provides a framework to facilitate collaboration between primary care physicians, general ophthalmologists and sub-specialists to improve the outcomes for patients with VKC.info:eu-repo/semantics/publishedVersio

Semi-supervised Emotion Recognition using Inconsistently Annotated Data

International audienceExpression recognition remains challenging, predominantly due to (a) lack of sufficient data, (b) subtle emotion intensity, (c) subjective and inconsistent annotation, as well as due to (d) in-the-wild data containing variations in pose, intensity, and occlusion. To address such challenges in a unified framework, we propose a self-training based semi-supervised convolutional neural network (CNN) framework, which directly addresses the problem of (a) limited data by leveraging information from unannotated samples. Our method uses 'successive label smoothing' to adapt to the subtle expressions and improve the model performance for (b) low-intensity expression samples. Further, we address (c) inconsistent annotations by assigning sample weights during loss computation, thereby ignoring the effect of incorrect ground-truth. We observe significant performance improvement in in-the-wild datasets by leveraging the information from the in-the-lab datasets, related to challenge (d). Associated to that, experiments on four publicly available datasets demonstrate large performance gains in cross-database performance, as well as show that the proposed method achieves to learn different expression intensities, even when trained with categorical samples

Feasibility of nanofluid-based optical filters

In this article we report recent modeling and design work indicating that mixtures of nanoparticles in liquids can be used as an alternative to conventional optical filters. The major motivation for creating liquid optical filters is that they can be pumped in and out of a system to meet transient needs in an application. To demonstrate the versatility of this new class of filters, we present the design of nanofluids for use as long-pass, short-pass, and bandpass optical filters using a simple Monte Carlo optimization procedure. With relatively simple mixtures, we achieve filters with <15% mean-squared deviation in transmittance from conventional filters. We also discuss the current commercial feasibility of nanofluid-based optical filters by including an estimation of today's off-the-shelf cost of the materials. While the limited availability of quality commercial nanoparticles makes it hard to compete with conventional filters, new synthesis methods and economies of scale could enable nanofluid-based optical filters in the near future. As such, this study lays the groundwork for creating a new class of selective optical filters for a wide range of applications, namely communications, electronics, optical sensors, lighting, photography, medicine, and many more

Implementation of sub-nanosecond time-to-digital convertor in field-programmable gate array: applications to time-of-flight analysis in muon radiography

International audienceTime-of-flight (tof) techniques are standard techniques in high energy physics to determine particles propagation directions. Since particles velocities are generally close to c, the speed of light, and detectors typical dimensions at the meter level, the state-of-the-art tof techniques should reach sub-nanosecond timing resolution. Among the various techniques already available, the recently developed ring oscillator TDC ones, implemented in low cost FPGA, feature a very interesting figure of merit since a very good timing performance may be achieved with limited processing ressources. This issue is relevant for applications where unmanned sensors should have the lowest possible power consumption. Actually this article describes in details the application of this kind of tof technique to muon tomography of geological bodies. Muon tomography aims at measuring density variations and absolute densities through the detection of atmospheric muons flux's attenuation, due to the presence of matter. When the measured fluxes become very low, an identified source of noise comes from backwards propagating particles hitting the detector in a direction pointing to the geological body. The separation between through-going and backward-going particles, on the basis of the tof information is therefore a key parameter for the tomography analysis and subsequent previsions

Expression Recognition with Deep Features Extracted from Holistic and Part-based Models

International audienceFacial expression recognition aims to accurately interpret facial muscle movements in affective states (emotions). Previous studies have proposed holistic analysis of the face, as well as the extraction of features pertained only to specific facial regions towards expression recognition. While classically the latter have shown better performances, we here explore this in the context of deep learning. In particular, this work provides a performance comparison of holistic and part-based deep learning models for expression recognition. In addition, we showcase the effectiveness of skip connections, which allow a network to infer from both low and high-level feature maps. Our results suggest that holistic models outperform part-based models, in the absence of skip connections. Finally, based on our findings, we propose a data augmentation scheme, which we incorporate in a part-based model. The proposed multi-face multi-part (MFMP) model leverages the wide information from part-based data augmentation, where we train the network using the facial parts extracted from different face samples of the same expression class. Extensive experiments on publicly available datasets show a significant improvement of facial expression classification with the proposed MFMP framework

How Unique Is a Face: An Investigative Study

International audienceFace recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector. Surprisingly, while widely accepted, we still lack the understanding of uniqueness or distinctiveness of faces as biometric modality. In this work, we study the impact of factors such as image resolution, feature representation, database size, age and gender on uniqueness denoted by the Kullback-Leibler divergence between genuine and impostor distributions. Towards understanding the impact, we present experimental results on the datasets AT&T, LFW, IMDb-Face, as well as ND-TWINS, with the feature extraction algorithms VGGFace, VGG16, ResNet50, InceptionV3, MobileNet and DenseNet121, that reveal the quantitative impact of the named factors. While these are early results, our findings indicate the need for a better understanding of the concept of biometric uniqueness and its implication on face recognition

WEST Physics Basis

With WEST (Tungsten Environment in Steady State Tokamak) (Bucalossi et al 2014 Fusion Eng. Des. 89 907-12), the Tore Supra facility and team expertise (Dumont et al 2014 Plasma Phys. Control. Fusion 56 075020) is used to pave the way towards ITER divertor procurement and operation. It consists in implementing a divertor configuration and installing ITER-like actively cooled tungsten monoblocks in the Tore Supra tokamak, taking full benefit of its unique long-pulse capability. WEST is a user facility platform, open to all ITER partners. This paper describes the physics basis of WEST: the estimated heat flux on the divertor target, the planned heating schemes, the expected behaviour of the L-H threshold and of the pedestal and the potential W sources. A series of operating scenarios has been modelled, showing that ITER-relevant heat fluxes on the divertor can be achieved in WEST long pulse H-mode plasmas.EURATOM 63305

Toward understanding the dynamics of land change in Latin America : potential utility of a resilience approach for building archetypes of landsystems change

Rocha, Juan C. Stockholm University. Stockholm Resilience Centre. Stockholm, Suecia.Baraibar, Matilda M. Stockholm University. Department of Economic History and International Relations. Stockholm, Suecia.Deutsch, Lisa. Stockholm University. Stockholm Resilience Centre. Stockholm, Suecia.Bremond, Ariane de. University of Bern. Centre for Development and Environment. Bern, Suiza.Oestreicher, Jordan S. Universidade de Brasília. Centro de Desenvolvimento Sustentável. Distrito Federal, Brasil.Rositano, Florencia. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Cerealicultura. Buenos Aires, Argentina.Gelabert, Cecilia Corina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Economía, Desarrollo y Planeamiento Agrícola. Cátedra de Sistemas Agroalimentarios. Buenos Aires, Argentina.e17, 82 p.Climate change, financial shocks, and fluctuations in international trade are some of the reasons why resilience is increasingly invoked in discussions about land-use policy. However, resilience assessments come with the challenge of operationalization, upscaling their conclusions while considering the context-specific nature of land-use dynamics and the common lack of long-term data. We revisit the approach of system archetypes for identifying resilience surrogates and apply it to land-use systems using seven case studies spread across Latin America. The approach relies on expert knowledge and literature-based characterizations of key processes and patterns of land-use change synthesized in a data template. These narrative accounts are then used to guide development of causal networks, from which potential surrogates for resilience are identified. This initial test of the method shows that deforestation, international trade, technological improvements, and conservation initiatives are key drivers of land-use change, and that rural migration, leasing and land pricing, conflicts in property rights, and international spillovers are common causal pathways that underlie land-use transitions. Our study demonstrates how archetypes can help to differentiate what is generic from context dependant. They help identify common causal pathways and leverage points across cases to further elucidate how policies work and where, as well as what policy lessons might transfer across heterogeneous settings