158 research outputs found
Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
The success of most advanced facial expression recognition works relies
heavily on large-scale annotated datasets. However, it poses great challenges
in acquiring clean and consistent annotations for facial expression datasets.
On the other hand, self-supervised contrastive learning has gained great
popularity due to its simple yet effective instance discrimination training
strategy, which can potentially circumvent the annotation issue. Nevertheless,
there remain inherent disadvantages of instance-level discrimination, which are
even more challenging when faced with complicated facial representations. In
this paper, we revisit the use of self-supervised contrastive learning and
explore three core strategies to enforce expression-specific representations
and to minimize the interference from other facial attributes, such as identity
and face styling. Experimental results show that our proposed method
outperforms the current state-of-the-art self-supervised learning methods, in
terms of both categorical and dimensional facial expression recognition tasks.Comment: Accepted to BMVC 202
Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems
Lithium-ion battery (LIB) sources have played an essential role in
self-sustained transportation energy systems and have been widely deployed in
the last few years. To realize reliable battery maintenance, identifying its
electrochemical parameters is necessary. However, the battery model contains
many parameters while the measurable states are only the current and voltage,
inducing the identification inherently an ill-conditioned problem. A parameter
identification approach is proposed, including the experiment, model, and
algorithm. Electrochemical parameters are first grouped manually based on the
physical properties and assigned to two sequenced tests for identification. The
two tests named the quasi-static test and the dynamic test, are compressed on
time for practical implementation. Proper optimization models and a
sensitivity-oriented stepwise (SSO) optimization algorithm are developed to
search for the optimal parameters efficiently. Typically, the Sobol method is
applied to conduct the sensitivity analysis. Based on the sensitivity indexes,
the SSO algorithm can decouple the mixed impacts of different parameters during
the identification. For validation, numerical experiments on a typical NCM811
battery at different life stages are conducted. The proposed approach saves
about half the time finding the proper parameter value. The identification
accuracy of crucial parameters related to battery degradation can exceed 95\%.
Case study results indicate that the identified parameters can not only improve
the accuracy of the battery model but also be used as the indicator of the
battery SOH
Immunoglobulin G Locus Events in Soft Tissue Sarcoma Cell Lines
Recently immunoglobulins (Igs) have been found to be expressed by cells other than B lymphocytes, including various human carcinoma cells. Sarcomas are derived from mesenchyme, and the knowledge about the occurrence of Ig production in sarcoma cells is very limited. Here we investigated the phenomenon of immunoglobulin G (IgG) expression and its molecular basis in 3 sarcoma cell lines. The mRNA transcripts of IgG heavy chain and kappa light chain were detected by RT-PCR. In addition, the expression of IgG proteins was confirmed by Western blot and immunofluorescence. Immuno-electron microscopy localized IgG to the cell membrane and rough endoplasmic reticulum. The essential enzymes required for gene rearrangement and class switch recombination, and IgG germ-line transcripts were also identified in these sarcoma cells. Chromatin immunoprecipitation results demonstrated histone H3 acetylation of both the recombination activating gene and Ig heavy chain regulatory elements. Collectively, these results confirmed IgG expression in sarcoma cells, the mechanism of which is very similar to that regulating IgG expression in B lymphocytes
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Monitoring of the central blood pressure waveform via a conformal ultrasonic device.
Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 ΞΌm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments
Analysis of Major Aroma Compounds in Fermented and Prepared Hawthorn Wine
In this study, liquid-liquid extraction-solvent assisted flavor evaporation (LLE-SAFE), headspace solid phase microextraction extraction (HS-SPME), gas chromatography-quadrupole-mass spectrometry (GC-Quadrupole-MS), gas chromatography-orbitrap-mass spectrometry (GC-Orbitrap-MS) and gas chromatography-olfactometry (GC-O) were used in combination to identify the volatile components in a fermented hawthorn wine (SBL-J) and a prepared hawthorn wine (FS), and the results of sensory analysis, modified frequency (MF) and odor activity value (OAV) were used to determine the key aroma compounds. Totally 89 aroma compounds were identified by LLE-SAFE/GC-O-MS. In addition, 29 and 38 aroma compounds with MF values of more than 20% were found in SBL-J and FS, respectively. A total of 123 volatile components were detected by HS-SPME/GC-Quadrupole-MS and HS-SPME/GC-Orbitrap-MS and there were 29 and 33 aroma compounds with OAV of greater than 1 (0.1 for esters) identified in SBL-J and FS, respectively. 2-Methybutyl acetate, ethyl hexanoate, ethyl octanoate and phenylethyl alcohol were the key aroma compounds in the two samples. To our knowledge, 2-methybutyl acetate, ethyl isovalerate, (E,E)-2,4-hexadienoic acid ethyl ester and ethyl butyrate, were identified for the first time as the key aroma components of hawthorn wine
Black holes regulate cold gas accretion in massive galaxies
Nearly every massive galaxy contains a supermassive black hole (BH) at its
center. For decades, both theory and numerical simulations have indicated that
BHs play a central role in regulating the growth and quenching of galaxies.
Specifically, BH feedback by heating or blowing out the interstellar medium
(ISM) serves as the groundwork for current models of massive galaxy formation.
However, direct evidence for such an impact on the galaxy-wide ISM from BHs has
only been found in some extreme objects. For general galaxy populations, it
remains unclear whether and how BHs impact the ISM. Here based on a large
sample of nearby galaxies with measurements of masses of both black holes and
atomic hydrogen, the major component of cold ISM, we reveal that the atomic
hydrogen content () is tightly and
anti-correlated with black hole mass () with (). This correlation is valid across
five orders of magnitude in . Once this correlation is taken into
account, loses dependence on other galactic parameters,
demonstrating that serves as the primary driver of .
These findings provide critical evidence for how the accumulated energy from BH
accretion impacts galaxy-wide ISM, representing a crucial step forward in our
understanding on the role of BHs in regulating the growth and quenching of
massive galaxies.Comment: 24 pages, 7 figures. Submitted to Natur
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