Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs

Optimal power allocation for orthogonal frequency division multiplexing (OFDM) wiretap channels with Gaussian channel inputs has already been studied in some previous works from an information theoretical viewpoint. However, these results are not sufficient for practical system design. One reason is that discrete channel inputs, such as quadrature amplitude modulation (QAM) signals, instead of Gaussian channel inputs, are deployed in current practical wireless systems to maintain moderate peak transmission power and receiver complexity. In this paper, we investigate the power allocation and artificial noise design for OFDM wiretap channels with discrete channel inputs. We first prove that the secrecy rate function for discrete channel inputs is nonconcave with respect to the transmission power. To resolve the corresponding nonconvex secrecy rate maximization problem, we develop a low-complexity power allocation algorithm, which yields a duality gap diminishing in the order of O(1/\sqrt{N}), where N is the number of subcarriers of OFDM. We then show that independent frequency-domain artificial noise cannot improve the secrecy rate of single-antenna wiretap channels. Towards this end, we propose a novel time-domain artificial noise design which exploits temporal degrees of freedom provided by the cyclic prefix of OFDM systems {to jam the eavesdropper and boost the secrecy rate even with a single antenna at the transmitter}. Numerical results are provided to illustrate the performance of the proposed design schemes.Comment: 12 pages, 7 figures, accepted by IEEE Transactions on Wireless Communications, Jan. 201

SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate "ties" due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to $\sqrt{M/N}$, where $M$ and $N$ are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.Comment: This paper has been accepted in AAAI'2

Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models

Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-world applications, it is common and necessary to generate a series of coherent images for story-stelling. In this work, we mainly focus on story visualization and continuation tasks and propose AR-LDM, a latent diffusion model auto-regressively conditioned on history captions and generated images. Moreover, AR-LDM can generalize to new characters through adaptation. To our best knowledge, this is the first work successfully leveraging diffusion models for coherent visual story synthesizing. Quantitative results show that AR-LDM achieves SoTA FID scores on PororoSV, FlintstonesSV, and the newly introduced challenging dataset VIST containing natural images. Large-scale human evaluations show that AR-LDM has superior performance in terms of quality, relevance, and consistency.Comment: Technical Repor

Tailoring silk fibroin fibrous architecture by a high‐yield electrospinning method for fast wound healing possibilities

In this study, a novel array electrospinning collector was devised to generate two distinct regenerated silk fibroin (SF) fibrous membranes: ordered and disordered. Leveraging electrostatic forces during the electrospinning process allowed precise control over the orientation of SF fiber, resulting in the creation of membranes comprising both aligned and randomly arranged fiber layers. This innovative approach resulted in the development of large‐area membranes featuring exceptional stability due to their alternating patterned structure, achievable through expansion using the collector, and improving the aligned fiber membrane mechanical properties. The study delved into exploring the potential of these membranes in augmenting wound healing efficiency. Conducting in vitro toxicity assays with adipose tissue‐derived mesenchymal stem cells (AD‐MSCs) and normal human dermal fibroblasts (NHDFs) confirmed the biocompatibility of the SF membranes. We use dual perspectives on exploring the effects of different conditioned mediums produced by cells and structural cues of materials on NHDFs migration. The nanofibers providing the microenvironment can directly guide NHDFs migration and also affect the AD‐MSCs and NHDFs paracrine effects, which can improve the chemotaxis of NHDFs migration. The ordered membrane, in particular, exhibited pronounced effectiveness in guiding directional cell migration. This research underscores the revelation that customizable microenvironments facilitated by SF membranes optimize the paracrine products of mesenchymal stem cells and offer valuable physical cues, presenting novel prospects for enhancing wound healing efficiency

Sorting out inherent features of head-to-head gene pairs by evolutionary conservation

BACKGROUND: A ‘head-to-head’ (h2h) gene pair is defined as a genomic locus in which two adjacent genes are divergently transcribed from opposite strands of DNA. In our previous work, this gene organization was found to be ancient and conserved, which subjects functionally related genes to transcriptional co-regulation. However, some of the biological features of h2h pairs still need further clarification. RESULTS: In this work, we assorted human h2h pairs into four sequentially inclusive sets of gradually incremental conservation, and examined whether those previously asserted features were conserved or sharpened in the more conserved h2h pair sets in order to identify the inherent features of the h2h gene organization. The features of TSS distance, expression correlation within h2h pairs and among h2h genes, transcription factor association and functional similarities of h2h genes were examined. Our conservation-based analyses found that the bi-directional promoters of h2h gene pairs are most likely shorter than 100 bp; h2h gene pairs generally have only significant positive expression correlation but not negative correlation, and remarkably high positive expression correlations exist among h2h genes, as well as between h2h pairs observed in our previous study; h2h paired genes tend to share transcription factors. In addition, expression correlation of h2h pairs is positively related with the TF-sharing and functional coordination, while not related with TSS distance. CONCLUSIONS: Our findings remove the uncertainties of h2h genes about TSS distance, expression correlation and functional coordination, which provide insights into the study on the molecular mechanisms and functional consequences of the transcriptional regulation based on this special gene organization