Action recognition using single-pixel time-of-flight detection

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject's privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network

Dominant and Complementary Emotion Recognition from Still Images of Faces

Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition

The possible functions of duplicated ets (GGAA) motifs located near transcription start sites of various human genes

Transcription is one of the most fundamental nuclear functions and is an enzyme complex-mediated reaction that converts DNA sequences into mRNA. Analyzing DNA sequences of 5′-flanking regions of several human genes that respond to 12-O-tetradecanoyl-phorbol-13-acetate (TPA) in HL-60 cells, we have identified that the ets (GGAA) motifs are duplicated, overlapped, or clustered within a 500-bp distance from the most 5′-upstream region of the cDNA. Multiple protein factors including Ets family proteins are known to recognize and bind to the GGAA containing sequences. In addition, it has been reported that the ets motifs play important roles in regulation of various promoters. Here, we propose a molecular mechanism, defined by the presence of duplication and multiplication of the GGAA motifs, that is responsible for the initiation of transcription of several genes and for the recruitment of binding proteins to the transcription start site (TSS) of TATA-less promoters

The impact of transposable element activity on therapeutically relevant human stem cells

Human stem cells harbor significant potential for basic and clinical translational research as well as regenerative medicine. Currently ~ 3000 adult and ~ 30 pluripotent stem cell-based, interventional clinical trials are ongoing worldwide, and numbers are increasing continuously. Although stem cells are promising cell sources to treat a wide range of human diseases, there are also concerns regarding potential risks associated with their clinical use, including genomic instability and tumorigenesis concerns. Thus, a deeper understanding of the factors and molecular mechanisms contributing to stem cell genome stability are a prerequisite to harnessing their therapeutic potential for degenerative diseases. Chemical and physical factors are known to influence the stability of stem cell genomes, together with random mutations and Copy Number Variants (CNVs) that accumulated in cultured human stem cells. Here we review the activity of endogenous transposable elements (TEs) in human multipotent and pluripotent stem cells, and the consequences of their mobility for genomic integrity and host gene expression. We describe transcriptional and post-transcriptional mechanisms antagonizing the spread of TEs in the human genome, and highlight those that are more prevalent in multipotent and pluripotent stem cells. Notably, TEs do not only represent a source of mutations/CNVs in genomes, but are also often harnessed as tools to engineer the stem cell genome; thus, we also describe and discuss the most widely applied transposon-based tools and highlight the most relevant areas of their biomedical applications in stem cells. Taken together, this review will contribute to the assessment of the risk that endogenous TE activity and the application of genetically engineered TEs constitute for the biosafety of stem cells to be used for substitutive and regenerative cell therapiesS.R.H. and P.T.R. are funded by the Government of Spain (MINECO, RYC-2016- 21395 and SAF2015–71589-P [S.R.H.]; PEJ-2014-A-31985 and SAF2015–71589- P [P.T.R.]). GGS is supported by a grant from the Ministry of Health of the Federal Republic of Germany (FKZ2518FSB403)