We show that faces contain much more information about sexual orientation than can be perceived or interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial ...images. These features were entered into a logistic regression aimed at classifying sexual orientation. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71% of cases for women. Human judges achieved much lower accuracy: 61% for men and 54% for women. The accuracy of the algorithm increased to 91% and 83%, respectively, given five facial images per person. Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style). Consistent with the prenatal hormone theory of sexual orientation, gay men and women tended to have gender-atypical facial morphology, expression, and grooming styles. Prediction models aimed at gender alone allowed for detecting gay males with 57% accuracy and gay females with 58% accuracy. Those findings advance our understanding of the origins of sexual orientation and the limits of human perception. Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people's intimate traits, our findings expose a threat to the privacy and safety of gay men and women.
Background Sarcopenia, a relatively new syndrome referring to the age-related decline of muscle strength and degenerative loss of skeletal muscle mass and function, often resulting in frailty, ...disability, and mortality. Osteoarthritis, as a prevalent joint degenerative disease, is affecting over 250 million patients worldwide, and it is the fifth leading cause of disability. Despite the high prevalence of osteoarthritis, there are still lack of efficient treatment potions in clinics, partially due to the heterogeneous and complexity of osteoarthritis pathology. Previous studies revealed the association between sarcopenia and osteoarthritis, but the conclusions remain controversial and the prevalence of sarcopenia within osteoarthritis patients still needs to be elucidated. To identify the current evidence on the prevalence of sarcopenia and its association with osteoarthritis across studies, we performed this systematic review and meta-analysis that would help us to further confirm the association between these two diseases. Methods and analysis Electronic sources including PubMed, Embase, and Web of Science will be searched systematically following appropriate strategies to identify relevant studies from inception up to 28 February 2022 with no language restriction. Two investigators will evaluate the preselected studies independently for inclusion, data extraction and quality assessment using a standardized protocol. Meta-analysis will be performed to pool the estimated effect using studies assessing an association between sarcopenia and osteoarthritis. Subgroup analyses will also be performed when data are sufficient. Heterogeneity and publication bias of included studies will be investigated. PROSPERO registration number CRD42020155694.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, ...occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.
The wavelet frame systems have been extensively studied due to their capability of sparsely approximating piecewise smooth functions, such as images, and the corresponding wavelet frame-based image ...restoration models are mostly based on the penalization of the ℓ 1 norm of wavelet frame coefficients for sparsity enforcement. In this paper, we focus on the image inpainting problem based on the wavelet frame, propose a weighted sparse restoration model, and develop a corresponding efficient algorithm. The new algorithm combines the idea of iterative support detection method, first proposed by Wang and Yin for sparse signal reconstruction, and the split Bregman method for wavelet frame ℓ 1 model of image inpainting, and more important, naturally makes use of the specific multilevel structure of the wavelet frame coefficients to enhance the recovery quality. This new algorithm can be considered as the incorporation of prior structural information of the wavelet frame coefficients into the traditional ℓ 1 model. Our numerical experiments show that the proposed method is superior to the original split Bregman method for wavelet frame-based ℓ 1 norm image inpainting model as well as some typical ℓ p (0 ≤ p <; 1) norm-based nonconvex algorithms such as mean doubly augmented Lagrangian method, in terms of better preservation of sharp edges, due to their failing to make use of the structure of the wavelet frame coefficients.
Cancer stemness and immune evasion are closely associated, and play critical roles in tumor development and resistance to immunotherapy. However, little is known about the underlying molecular ...mechanisms that coordinate this association. Here, it is reported that elevated circular RNA FAT1 (circFAT1) in squamous cell carcinoma (SCC) unifies and regulates the positive association between cancer stemness and immune evasion by promoting STAT3 activation. circFAT1 knockdown (KD) reduces tumorsphere formation of SCC cells in vitro and tumor growth in vivo. Bioinformatic analysis reveals that circFAT1 KD impairs the cancer stemness signature and activates tumor cell‐intrinsic immunity. Mechanistically, circFAT1 binding to STAT3 in the cytoplasm prevents STAT3 dephosphorylation by SHP1 and promotes STAT3 activation, resulting in inhibition of STAT1‐mediated transcription. Moreover, circFAT1 KD significantly enhances PD1 blockade immunotherapy by promoting CD8+ cell infiltration into tumor microenvironment. Taken together, the results demonstrate that circFAT1 is an important regulator of cancer stemness and antitumor immunity.
Cancer stemness and immune evasion are tightly associated with tumor development and immunotherapy resistance. This study shows that circFAT1 controls cancer stemness and immune evasion by regulating STAT3 activation. CircFAT1 binds to STAT3 and prevents STAT3 dephosphorylation by SHP1, resulting in the promotion of STAT3 activation. Knockdown of circFAT1 impairs cancer stemness and enhances PD1 blockade immunotherapy.
We propose, analyze, and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new ...half-quadratic model applicable to not only the anisotropic but also the isotropic forms of TV discretizations. The per-iteration computational complexity of the algorithm is three fast Fourier transforms. We establish strong convergence properties for the algorithm including finite convergence for some variables and relatively fast exponential (or $q$-linear in optimization terminology) convergence for the others. Furthermore, we propose a continuation scheme to accelerate the practical convergence of the algorithm. Extensive numerical results show that our algorithm performs favorably in comparison to several state-of-the-art algorithms. In particular, it runs orders of magnitude faster than the lagged diffusivity algorithm for TV-based deblurring. Some extensions of our algorithm are also discussed.
Graphene and other layered materials, such as transition metal dichalcogenides, have rapidly established themselves as exceptional building blocks for optoelectronic applications because of their ...unique properties and atomically thin nature. The ability to stack them into van der Waals (vdWs) heterostructures with new functionality has opened a new platform for fundamental research and device applications. Nevertheless, near-infrared (NIR) photodetectors based on layered semiconductors are rarely realized. In this work, we fabricate a graphene–MoTe2–graphene vertical vdWs heterostructure on a SiO2/p+-Si substrate by a facile and reliable site-controllable transfer method and apply it for photodetection from the visible to NIR wavelength range. Compared to the layered semiconductor photodetectors reported thus far, the graphene–MoTe2–graphene photodetector has a superior performance, including high photoresponsivity (∼110 mA W–1 at 1064 nm and 205 mA W–1 at 473 nm), high external quantum efficiency (EQE; ∼12.9% at 1064 nm and ∼53.8% at 473 nm), rapid response and recovery processes (a rise time of 24 μs and a fall time of 46 μs under 1064 nm illumination), and free from an external source–drain power supply. We have employed scanning photocurrent microscopy to investigate the photocurrent generation in this heterostructure under various back-gate voltages and found that the two Schottky barriers between the graphenes and MoTe2 play an important role in the photocurrent generation. In addition, the vdWs heterostructure has a uniform photoresponsive area. The photoresponsivity and EQE of the photodetector can be modulated by the back-gate (p+-Si) voltage. We compared the responsivities of thin and thick flakes and found that the responsivity had a strong dependence on the thickness. The heterostructure has promising applications in future novel optoelectronic devices, enabling next-generation high-responsivity, high-speed, flexible, and transparent NIR devices.
Abstract During the early merger of the Milky Way, intermediate-mass black holes (BHs) in merged dwarf galaxies may have been ejected from the center of their host galaxies due to gravitational ...waves, carrying some central stars along. This process can lead to the formation of hypercompact star clusters, potentially hosting BHs in the mass range of 10 4 –10 5 solar masses. These clusters are crucial targets for identifying and investigating intermediate-mass BHs. However, no hypercompact star clusters in the Milky Way have been identified so far. In this paper, taking advantage of the high spatial resolution power of Gaia, we used data from Gaia Early Data Release (EDR) 3 and Large-Area Multi-Object Fiber Optic Spectroscopic Telescope Data Release 7, along with additional data from Pan-STARRS and the Sloan Digital Sky Survey, to conduct an initial screening of 6,138,049 sources using various parameters of Gaia EDR3. A total of 4786 sources were selected for in-depth analysis. Each of these sources was meticulously scrutinized by examining their images, spectra, and nearby celestial objects to exclude various false positives, such as contaminations, galaxies, wide binaries, or wrong matches. We finally identified one likely hypercompact star cluster candidate in the Milky Way, laying the foundation for further high-resolution imaging and spectral verification.
The group IV–VI compound tin selenide (SnSe) has recently attracted particular interest due to its unexpectedly low thermal conductivity and high power factor and shows great promise for ...thermoelectric applications. With an orthorhombic lattice structure, SnSe displays intriguing anisotropic properties due to the low symmetry of the puckered in-plane lattice structure. When thermoelectric materials, such as SnSe, have decreased dimensionality, their thermoelectric conversion efficiency may be improved due to increased power factor and decreased thermal conductivity. Therefore, it is necessary to elucidate the complete optical and electrical anisotropies of SnSe nanostructures in realizing the material's advantages in high-performance devices. Here, we synthesize single-crystal SnSe nanoplates (NPs) using the chemical vapor deposition method. The SnSe NPs’ polarized Raman spectra exhibit an angular dependence that reveals the crystal’s anomalous anisotropic light–matter interaction. The Raman’s anisotropic response has a dependence upon the incident light polarization, photon, and phonon energy, arising from the anisotropic electron–photon and electron–phonon interactions in the SnSe NPs. Finally, angle-resolved charge-transport measurements indicate strong anisotropic conductivity of the SnSe NPs, fully elucidating the anisotropic properties necessary for ultrathin SnSe in electronic, thermoelectric, and optoelectronic devices.
Variational models with $\ell_1$-norm based regularization, in particular total variation (TV) and its variants, have long been known to offer superior image restoration quality, but processing speed ...remained a bottleneck, preventing their widespread use in the practice of color image processing. In this paper, by extending the grayscale image deblurring algorithm proposed in Y. Wang, J. Yang, W. Yin, and Y. Zhang, SIAM J. Imaging Sci., 1 (2008), pp. 248-272, we construct a simple and efficient algorithm for multichannel image deblurring and denoising, applicable to both within-channel and cross-channel blurs in the presence of additive Gaussian noise. The algorithm restores an image by minimizing an energy function consisting of an $\ell_2$-norm fidelity term and a regularization term that can be either TV, weighted TV, or regularization functions based on higher-order derivatives. Specifically, we use a multichannel extension of the classic TV regularizer (MTV) and derive our algorithm from an extended half-quadratic transform of Geman and Yang IEEE Trans. Image Process., 4 (1995), pp. 932-946. For three-channel color images, the per-iteration computation of this algorithm is dominated by six fast Fourier transforms. The convergence results in Y. Wang, J. Yang, W. Yin, and Y. Zhang, SIAM J. Imaging Sci., 1 (2008), pp. 248-272 for single-channel images, including global convergence with a strong $q$-linear rate and finite convergence for some quantities, are extended to this algorithm. We present numerical results including images recovered from various types of blurs, comparisons between our results and those obtained from the deblurring functions in MATLAB's Image Processing Toolbox, as well as images recovered by our algorithm using weighted MTV and higher-order regularization. Our numerical results indicate that the processing speed, as attained by the proposed algorithm, of variational models with TV-like regularization can be made comparable to that of less sophisticated but widely used methods for color image restoration.
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