Artificial-Noise-Aided Secure Transmission with Directional Modulation based on Random Frequency Diverse Arrays

In this paper, a random frequency diverse array based directional modulation with artificial noise (RFDA-DMAN) scheme is proposed to enhance physical layer security of wireless communications. Specifically, we first design the RFDADM- AN scheme by randomly allocating frequencies to transmitantennas, thereby achieving two-dimensionally (i.e., angle and range) secure transmissions, and outperforming the state-of-theart one-dimensional (i.e., angle) phase array (PA) based DM scheme. Then we derive the closed-form expression of a lower bound on the ergodic secrecy capacity (ESC) of our RFDA-DMAN scheme. Based on the theoretical lower bound derived, we further optimize the transmission power allocation between the useful signal and artificial noise (AN) in order to improve the ESC. Simulation results show that 1) our RFDA-DM-AN scheme achieves a higher secrecy capacity than that of the PA based DM scheme, 2) the lower bound derived is shown to approach the ESC as the number of transmit antennas N increases and precisely matches the ESC when N is sufficiently large, and 3) the proposed optimum power allocation achieves the highest ESC of all power allocations schemes in the RFDA-DM-AN

Consistent Multimodal Generation via A Unified GAN Framework

We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model. The challenge is to produce outputs that are realistic, and also consistent with each other. Our solution builds on the StyleGAN3 architecture, with a shared backbone and modality-specific branches in the last layers of the synthesis network, and we propose per-modality fidelity discriminators and a cross-modality consistency discriminator. In experiments on the Stanford2D3D dataset, we demonstrate realistic and consistent generation of RGB, depth, and normal images. We also show a training recipe to easily extend our pretrained model on a new domain, even with a few pairwise data. We further evaluate the use of synthetically generated RGB and depth pairs for training or fine-tuning depth estimators. Code will be available at https://github.com/jessemelpolio/MultimodalGAN.Comment: In revie

Application progress of CT radiomics in gastrointestinal stromal tumor

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor in the gastrointestinal tract, with complex biological characteristics and varying risks, and the treatment methods and prognosis of patients with different risks are quite different; therefore, early diagnosis and risk assessment are crucial for its precision treatment. In recent years, CT radiomics, as an emerging imaging technology, can transform traditional CT image features into a large number of data, thereby reflecting the inherent heterogeneity of GIST and even correlating with its gene expression features. This paper reviews the research progress of CT radiomics in the diagnosis and prediction of GIST with the help of machine learning. The current CT radiomics can not only be used for the differential diagnosis of GIST and other gastric diseases, but also for the risk evaluation of GIST. Furthermore, pathological analysis and gene diagnosis can be performed based on CT images, and then the first-line treatment effect and long-term prognosis can be predicted. At present, various prediction models constructed by combination of CT radiomics and clinical information have been well verified in the specific practice of different clinical problems, showing broad application prospects. However, in the specific clinical application process, different methods of sample data collection and processing, differences in the selection of machine learning algorithms, and the selection of 2D or 3D images all affect the specific effectiveness of CT radiomics. Hence, unified and standardized application rules for radiomics has to be established

Synthesized spatiotemporal mode-locking and photonic flywheel in multimode mesoresonators

Dissipative Kerr soliton (DKS) frequency combs&mdash;also known as microcombs&mdash;have arguably created a new field in cavity nonlinear photonics, with a strong cross-fertilization between theoretical, experimental, and technological research. Spatiotemporal mode-locking (STML) not only adds new degrees of freedom to ultrafast laser technology, but also provides new insights for implementing analogue computers and heuristic optimizers with photonics. Here, we combine the principles of DKS and STML to demonstrate the STML DKS by developing an unexplored ultrahigh-quality-factor Fabry&ndash;P&eacute;rot (FP) mesoresonator based on graded index multimode fiber (GRIN-MMF). Complementing the two-step pumping scheme with a cavity stress tuning method, we can selectively excite either the eigenmode DKS or the STML DKS. Furthermore, we demonstrate an ultralow noise microcomb that enhances the photonic flywheel performance in both the fundamental comb linewidth and DKS timing jitter. The demonstrated fundamental comb linewidth of 400 mHz and DKS timing jitter of 500 attosecond (averaging times up to 25&thinsp;&mu;s) represent improvements of 25&times; and 2.5&times;, respectively, from the state-of-the-art. Our results show the potential of GRIN-MMF FP mesoresonators as an ideal testbed for high-dimensional nonlinear cavity dynamics and photonic flywheel with ultrahigh coherence and ultralow timing jitter. &nbsp;</p

Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset.

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications

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 ($f_{\rm HI} = M_{\rm HI}/M_{\star}$) is tightly and anti-correlated with black hole mass ($M_{\rm BH}$) with $f_{\rm HI} \propto M^{-\alpha}_{\rm BH}$ ($\alpha \sim 0.5-0.6$). This correlation is valid across five orders of magnitude in $M_{\rm BH}$. Once this correlation is taken into account, $f_{\rm HI}$ loses dependence on other galactic parameters, demonstrating that $M_{\rm BH}$ serves as the primary driver of $f_{\rm HI}$. 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

Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset.

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications