334 research outputs found
Low-Cost Compressive Sensing for Color Video and Depth
A simple and inexpensive (low-power and low-bandwidth) modification is made
to a conventional off-the-shelf color video camera, from which we recover
{multiple} color frames for each of the original measured frames, and each of
the recovered frames can be focused at a different depth. The recovery of
multiple frames for each measured frame is made possible via high-speed coding,
manifested via translation of a single coded aperture; the inexpensive
translation is constituted by mounting the binary code on a piezoelectric
device. To simultaneously recover depth information, a {liquid} lens is
modulated at high speed, via a variable voltage. Consequently, during the
aforementioned coding process, the liquid lens allows the camera to sweep the
focus through multiple depths. In addition to designing and implementing the
camera, fast recovery is achieved by an anytime algorithm exploiting the
group-sparsity of wavelet/DCT coefficients.Comment: 8 pages, CVPR 201
Adaptive Temporal Compressive Sensing for Video
This paper introduces the concept of adaptive temporal compressive sensing
(CS) for video. We propose a CS algorithm to adapt the compression ratio based
on the scene's temporal complexity, computed from the compressed data, without
compromising the quality of the reconstructed video. The temporal adaptivity is
manifested by manipulating the integration time of the camera, opening the
possibility to real-time implementation. The proposed algorithm is a
generalized temporal CS approach that can be incorporated with a diverse set of
existing hardware systems.Comment: IEEE Interonal International Conference on Image Processing
(ICIP),201
CAT: Causal Audio Transformer for Audio Classification
The attention-based Transformers have been increasingly applied to audio
classification because of their global receptive field and ability to handle
long-term dependency. However, the existing frameworks which are mainly
extended from the Vision Transformers are not perfectly compatible with audio
signals. In this paper, we introduce a Causal Audio Transformer (CAT)
consisting of a Multi-Resolution Multi-Feature (MRMF) feature extraction with
an acoustic attention block for more optimized audio modeling. In addition, we
propose a causal module that alleviates over-fitting, helps with knowledge
transfer, and improves interpretability. CAT obtains higher or comparable
state-of-the-art classification performance on ESC50, AudioSet and UrbanSound8K
datasets, and can be easily generalized to other Transformer-based models.Comment: Accepted to ICASSP 202
Self-reported Rates of Abuse, Neglect, and Bullying Experienced by Transgender and Gender-Nonbinary Adolescents in China
open access articleImportance This is the first comprehensive national study reporting the rates of abuse, neglect, and bullying from family and classmates or teachers among Chinese transgender and gender-nonbinary adolescents and identifying risk factors associated with poor mental health in this population.
Objective To assess the rates of abuse, neglect, and bullying and their association with poor mental health among Chinese transgender and gender nonbinary adolescents.
Design, Setting, and Participants This national survey study used an online self-selecting survey conducted between January 1, 2017, and September 29, 2017, in China. Eligibility criteria included reporting being aged 12 to 18 years and being transgender or gender nonbinary. Data analysis was performed from March 25 to 28, 2019.
Main Outcomes and Measures The main outcome was self-reported poor mental health, including depressive symptoms, anxiety symptoms, and suicidal ideation. Depressive symptoms were measured using the Center for Epidemiological Studies Depression 9-item scale. Anxiety symptoms were measured using the 7-item General Anxiety Disorder scale. Suicidal ideation was measured using standardized questions adapted from previous Chinese studies. Abuse, neglect, and bullying were measured using specifically designed questions.
Results Of 564 responses collected, 385 respondents (mean [SD] age, 16.7 [1.2] years) met inclusion criteria, including 109 (28.3%) transgender adolescent boys, 167 (43.4%) transgender adolescent girls, and 109 (28.3%) gender-nonbinary adolescents. Among 319 respondents who reported that their parents were aware of their gender identity, 296 (92.8%) reported having experienced parental abuse or neglect. Among the full cohort, 295 respondents (76.6%) reported having experienced abuse or bullying owing to being transgender or gender nonbinary in school from classmates or teachers. There were 173 respondents (44.9%) with Center for Epidemiological Studies Depression 9-item scale scores indicating they were at risk of major depressive disorder, and 148 respondents (38.4%) had 7-item General Anxiety Disorder scale scores indicating they were at risk of an anxiety disorder. In univariate analysis, reporting experiences of bullying from a classmate or teacher was significantly associated with suicidal ideation (odds ratio, 1.68 [95% CI, 1.04-2.70]; P = .03), but the association was no longer statistically significant after controlling for level of educational attainment, aversion to assigned sex, and depressed mood at the onset of puberty (odds ratio, 1.63 [95% CI, 0.97-2.73]; P = .06).
Conclusions and Relevance In this survey study, transgender and gender-nonbinary adolescents in China reported high rates of abuse, neglect, and bullying at home and in school and high rates of symptoms associated with poor mental health. This study highlights the importance of reducing home- and school-based abuse, neglect, and bullying of transgender and gender-nonbinary adolescents in China to improve mental health outcomes; however, broader change in the social environment may be required to address the prejudice and stigma aimed at gender minorities
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