Appearance can affect social interaction, which in turn affects personality development. There is ample evidence that facial morphology and social cues provide information about human personality and behavior. In this study, we focused on the relationship between self-reported personality characteristics and facial features. We propose a new approach for predicting college students’ personality characteristics (on the basis of the Big Five personality characteristics) with static facial images. First, we construct a dataset containing 13,347 data pairs composed of facial images and personality characteristics. Second, we train a deep neural network with 10,667 sample pairs from the dataset and use the remaining samples to test (1335 pairs) and validate (1335 pairs) self-reported Big Five personalities. We trained a series of deep neural networks on a large, labeled dataset to predict the self-reported Big Five personality trait scores. This novel work applies deep learning to this topic. We also verify the network’s advanced nature on the publicly available database with obvious personality characteristics. The experimental results show that 1) personality traits can be reliably predicted from facial images with an accuracy that exceeds 70%. In five-character tag classification, the recognition accuracy of neuroticism and extroversion was the most accurate, and the prediction accuracy exceeded 90%. 2) Deep learning neural network features are better than traditional manual features in predicting personality characteristics. The results strongly support the application of neural networks trained on large-scale labeled datasets in multidimensional personality feature prediction from static facial images. 3) There are some differences in the personality traits of college students with different academic backgrounds. Future research can explore the relative contribution of other facial image features in predicting other personality characteristics.

We propose and experimentally demonstrate an ultraefficient broadband photonic channelizer. By polarization-division multiplexing the received signal and using dual-output Hartley image-reject receiving topology, 4N channels can be generated with only (N + 1)-line signal optical frequency comb (OFC) and N-line local oscillator (LO) OFC, and the frequency interval of the OFC is reduced to half of the typical value. With the use of the integrated dual-polarization coherent detection receiver (DP-CDR), the required quadrature optical hybrid (QOH) and the balanced photodetectors (BPDs) are reduced to half. The system size is reduced and stability is improved. Besides, assisted with a digital signal processor (DSP) algorithm, the IQ imbalance can be compensated, and approximately, ideal image rejection can be realized. A proof-of-concept experiment is carried out. A 2-14-GHz wideband signal is successfully divided into 12 channels with 1-GHz bandwidth by applying four-line signal OFC and three-line LO OFC. The image rejection ratio (IRR), channel isolation (CI), and spurious-free dynamic range (SFDR) are also measured and discussed.

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development. In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template. All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it.

Synthetic aperture radar (SAR) and optical sensing are two important means of Earth observation. SAR-to-optical image translation (S2OIT) can integrate the advantages of both and assist SAR image interpretation under all-day and all-weather conditions. The existing S2OIT methods generally follow a paired training paradigm, which is difficult when dealing with the unpaired S2OIT application scenarios. Moreover, the generator and discriminator in current S2OIT methods have insufficient scene memory for SAR images, resulting in regional landform deformation in the generated images. To address these issues, we propose a novel generative adversarial network capable of memorizing scene for unpaired S2OIT called MS-GAN. The cycle learning framework based on cycle generative adversarial network (CycleGAN) for unpaired S2OIT is designed to construct the translation mapping between unpaired SAR and optical images. The multi-scale representation generator (MRG) is constructed for multi-scale fusion and utilization of scene features of SAR images. The proposed multi-receptive field discriminator (MFD) has the ability to enhance scene memory and generate higher quality optical images in different landforms. In addition, the designed sub-bands shrinkage denoising (SSD) module can further suppress the effect of speckle noise in SAR images on the quality of the generated results. Extensive experiments conducted on three challenging datasets SEN1-2, WHU-SEN-City, and QXS-SAROPT demonstrate that the proposed MS-GAN outperforms the state-of-the-art methods on both subjective and objective evaluation metrics.

Microwave photonic channelized receiver can break through the electronic bottleneck, improve the throughput of satellite payload, and is expected to be widely used in broadband satellite communications. To solve the self-interference problem in the full-duplex satellite relay system, a microwave photonic channelized receiver with a self-interference cancellation (SIC) function is proposed in this paper. The equal interval division of broadband radio frequency (RF) signals is realized through polarization multiplexing technology and optical frequency shifting technology. In addition, by constructing a reference interference signal and matching the delay and amplitude in the optical domain, the self-interference signal is eliminated to avoid the receiver blockage caused by interference leakage. Theoretical derivation and experiment research are carried out, and a wideband signal with 3-GHz bandwidth is down-converted to six sub-channels with a frequency range of 0.5 GHz–1 GHz. Under the single-tone test, the isolation between sub-channels exceeds 31 dB, and the SIC depth exceeds 40 dB. When the sub-channels carry the 16quadrature amplitude modulation (QAM) signals with the bandwidth of 50 MHz, the SIC depth over 26 dB is achieved, and the error vector magnitude (EVM) is 7.6%. Under the two-tone test, the SFDR of the sub-channel is 93.5 dB.Hz ^2/3 .