AdS Wormholes from AdS/Ricci-flat Correspondence

We discuss the Wormholes in general dimensions by studying the Einstein-phantom scalar field with and without the cosmological constant. Solving AdS wormholes in general dimension is hard due to the nonlinear nature of the theory. In this work, we implement the AdS/Ricci-flat correspondence, extended to include the axion field (the phantom scalar field), to construct AdS wormholes. Wormholes of Ellis-Bronnikov class are discussed in general dimensions.Comment: Corrected some typos in the Appendix in previous versio

Enhanced Sensitivity in Rydberg Atom Electric Field Sensors through Autler-Townes Effect and Two-Photon Absorption: A Theoretical Analysis Using Many-Mode Floquet Theory

In this paper, we present a comprehensive investigation into the sensitivity of a Rydberg atom electric field sensor, with a specific focus on the minimum detectable field (MDF) as a key metric. The study utilizes one-mode Floquet theory to calculate the Stark shift for selected Rydberg states when exposed to a signal electric field. The results are compared to those obtained using the rotating wave approximation (RWA). To enhance the sensor's sensitivity when the frequency of the signal electric field deviates from resonance frequencies between Rydberg states, we propose incorporating an extra coupling electric field and using many-mode Floquet theory, a generalization of one-mode Floquet theory, to theoretically analyze this kind of Rydberg atom electric field sensor. The Autler-Townes effect resulting from this coupling electric field causes Rydberg states to split into dressed states, effectively increasing sensitivity by modulating the frequencies of resonance peaks. Moreover, the phenomenon of two-photon absorption in the presence of the coupling electric field is explored. We demonstrate that by appropriately adjusting the coupling electric field's amplitude or frequency, one can control the occurrence of two-photon resonances, providing additional sensitivity enhancement for the Rydberg sensor within the significantly extended off-resonance domain. The study underscores the significance of coupling fields in enhancing the sensitivity of Rydberg atom electric field sensors. These insights hold promising implications for the development of more robust and versatile electric field sensing devices, applicable in diverse fields such as precision measurements and quantum information processing

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.ImportanceTo fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is usually limited by sampling size. Sequence conservation-based methods are further confounded by structural constraints and multifunctionality of proteins. Here we present a method that can systematically identify and annotate functional residues of a given protein. We used a high-throughput functional profiling platform to identify essential residues. Coupling it with homologous-structure comparison, we were able to annotate multiple functions of proteins. We demonstrated the method with the PB1 protein of influenza A virus and identified novel functional residues in addition to its canonical function as an RNA-dependent RNA polymerase. Not limited to virology, this method is generally applicable to other proteins that can be functionally selected and about which homologous-structure information is available

IPO-LDM: Depth-aided 360-degree Indoor RGB Panorama Outpainting via Latent Diffusion Model

Generating complete 360-degree panoramas from narrow field of view images is ongoing research as omnidirectional RGB data is not readily available. Existing GAN-based approaches face some barriers to achieving higher quality output, and have poor generalization performance over different mask types. In this paper, we present our 360-degree indoor RGB panorama outpainting model using latent diffusion models (LDM), called IPO-LDM. We introduce a new bi-modal latent diffusion structure that utilizes both RGB and depth panoramic data during training, but works surprisingly well to outpaint normal depth-free RGB images during inference. We further propose a novel technique of introducing progressive camera rotations during each diffusion denoising step, which leads to substantial improvement in achieving panorama wraparound consistency. Results show that our IPO-LDM not only significantly outperforms state-of-the-art methods on RGB panorama outpainting, but can also produce multiple and diverse well-structured results for different types of masks

GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping

The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects. Unlike conventional dexterous grasping, this task presents a more complex challenge as the policy needs to adapt to diverse user intentions, in addition to the object's geometry. We address this challenge by proposing an approach consisting of two sub-modules: a hand-object-conditional grasping primitive called Grasping Gradient Field~(GraspGF), and a history-conditional residual policy. GraspGF learns `how' to grasp by estimating the gradient from a success grasping example set, while the residual policy determines `when' and at what speed the grasping action should be executed based on the trajectory history. Experimental results demonstrate the superiority of our proposed method compared to baselines, highlighting the user-awareness and practicality in real-world applications. The codes and demonstrations can be viewed at "https://sites.google.com/view/graspgf"