How Much Temporal Long-Term Context is Needed for Action Segmentation?

Modeling long-term context in videos is crucial for many fine-grained tasks including temporal action segmentation. An interesting question that is still open is how much long-term temporal context is needed for optimal performance. While transformers can model the long-term context of a video, this becomes computationally prohibitive for long videos. Recent works on temporal action segmentation thus combine temporal convolutional networks with self-attentions that are computed only for a local temporal window. While these approaches show good results, their performance is limited by their inability to capture the full context of a video. In this work, we try to answer how much long-term temporal context is required for temporal action segmentation by introducing a transformer-based model that leverages sparse attention to capture the full context of a video. We compare our model with the current state of the art on three datasets for temporal action segmentation, namely 50Salads, Breakfast, and Assembly101. Our experiments show that modeling the full context of a video is necessary to obtain the best performance for temporal action segmentation.Comment: ICCV 202

Deep-Temporal LSTM for Daily Living Action Recognition

In this paper, we propose to improve the traditional use of RNNs by employing a many to many model for video classification. We analyze the importance of modeling spatial layout and temporal encoding for daily living action recognition. Many RGB methods focus only on short term temporal information obtained from optical flow. Skeleton based methods on the other hand show that modeling long term skeleton evolution improves action recognition accuracy. In this work, we propose a deep-temporal LSTM architecture which extends standard LSTM and allows better encoding of temporal information. In addition, we propose to fuse 3D skeleton geometry with deep static appearance. We validate our approach on public available CAD60, MSRDailyActivity3D and NTU-RGB+D, achieving competitive performance as compared to the state-of-the art.Comment: Submitted in conferenc

Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection

International audienceIn this paper, we address the detection of daily living activities in long-term untrimmed videos. The detection of daily living activities is challenging due to their long temporal components, low inter-class variation and high intra-class variation. To tackle these challenges, recent approaches based on Temporal Convolutional Networks (TCNs) have been proposed. Such methods can capture long-term temporal patterns using a hierarchy of temporal convolutional filters, pooling and up sampling steps. However, as one of the important features of con-volutional networks, TCNs process a local neighborhood across time which leads to inefficiency in modeling the long-range dependencies between these temporal patterns of the video. In this paper, we propose Self-Attention-Temporal Convolutional Network (SA-TCN), which is able to capture both complex activity patterns and their dependencies within long-term untrimmed videos. We evaluate our proposed model on DAily Home LIfe Activity Dataset (DAHLIA) and Breakfast datasets. Our proposed method achieves state-of-the-art performance on both DAHLIA and Breakfast dataset

Assessment of dermal absorption of beryllium and copper contained in temple tips of eyeglasses

Dermal exposure to hazardous substances such as chemicals, toxics, metallic items and other contaminants may present substantial danger for health. Beryllium (Be) is a hazardous metal, especially when inhaled and/or in direct contact with the skin, associated with chronic beryllium disease (CBD) and Be sensitization (BeS). The objective of this study was to investigate the percutaneous penetration of beryllium and copper contained in metallic items as eyeglass temple tips (specifically BrushCAST (R) Copper Beryllium Casting Alloys containing Be 0.35 < 2.85%; Cu 95.3-98.7%), using Franz diffusion cells. This work demonstrated that the total skin absorption of Cu was higher (8.86%) compared to Be (4.89%), which was expected based on the high percentage of Cu contained in the eyeglass temple tips. However, Be accumulated significantly in the epidermis and dermis (up to 0.461 mu g/cm(2)) and, to a lesser extent, in the stratum corneum (up to 0.130 mu g/cm2) with a flux of permeation of 3.52 +/- 4.5 mu g/cm(2)/hour and lag time of 2.3 +/- 1.3 h, after cutaneous exposure of temple tip into 1.0 mL artificial sweat for 24 h. Our study highlights the importance of avoiding the use of Be alloys in items following long-term skin contact

A Critical Review of Experimental Investigations about Convective Heat Transfer Characteristics of Nanofluids under Turbulent and Laminar Regimes with a Focus on the Experimental Setup

In this study, several experimental investigations on the effects of nanofluids on the con- vective heat transfer coefficient in laminar and turbulent conditions were analyzed. The aim of this work is to provide an overview of the thermal performance achieved with the use of nanofluids in various experimental systems. This review covers both forced and natural convection phenomena, with a focus on the different experimental setups used to carry out the experimental campaigns. When possible, a comparison was performed between different experimental campaigns to provide an analysis of the possible common points and differences. A significant increase in the convective heat transfer coefficient was found by using nanofluids instead of traditional heat transfer fluids, in general, even with big data dispersion from one case to another that depended on boundary condi- tions and the particular experimental setup. In particular, a general trend shows that once a critic value of the Reynolds number or nanoparticle concentrations is reached, the heat transfer perfor- mance of the nanofluid decreases or has no appreciable improvement. As a research field still under development, nanofluids are expected to achieve even higher performance and their use will be crucial in many industrial and civil sectors to increase energy efficiency and, thus, mitigate the en- vironmental impact

In vitro dermal penetration of nickel nanoparticles.

Nickel nanoparticles (NiNPs) represent a new type of occupational exposure because, due to the small size/high surface, they can release more Ni ions compared to bulk material. It has been reported a case of a worker who developed sensitization while handling nickel nanopowder without precautions. Therefore there is the need to assess whether the skin absorption of NiNPs is higher compared to bulk nickel. Two independent in vitro experiments were performed using Franz diffusion cells. Eight cells for each experiment were fitted using intact and needle-abraded human skin. The donor phase was a suspension of NiNPs with mean size of 77.7 \ub1 24.1 nm in synthetic sweat. Ni permeated both types of skin, reaching higher levels up to two orders of magnitude in the damaged skin compared to intact skin (5.2 \ub1 2.0 vs 0.032 \ub1 0.010 \u3bcg cm(-2), p = 0.006) at 24 h. Total Ni amount into the skin was 29.2 \ub1 11.2 \u3bcg cm(-2) in damaged skin and 9.67 \ub1 2.70 \u3bcg cm(-2) in intact skin (mean and SD, p = 0.006). Skin abrasions lead to doubling the Ni amount in the epidermis and to an increase of ten times in the dermis. This study demonstrated that NiNPs applied on skin surface cause an increase of nickel content into the skin and a significant permeation flux through the skin, higher when a damaged skin protocol was used. Preventive measures are needed when NiNPs are produced and used due to their higher potential to enter in our body compared to bulk nickel