Space-Time Transmit-Receive Design for Colocated MIMO Radar

This chapter deals with the design of multiple input multiple-output (MIMO) radar space-time transmit code (STTC) and space-time receive filter (STRF) to enhance moving targets detection in the presence of signal-dependent interferences, where we assume that some knowledge of target and clutter statistics are available for MIMO radar system according to a cognitive paradigm by using a site-specific (possible dynamic) environment database. Thus, an iterative sequential optimization algorithm with ensuring the convergence is proposed to maximize the signal to interference plus noise ratio (SINR) under the similarity and constant modulus constraints on the probing waveform. In particular, each iteration of the proposed algorithm requires to solve the hidden convex problems. The computational complexity is linear with the number of iterations and polynomial with the sizes of the STTW and the STRF. Finally, the gain and the computation time of the proposed algorithm also compared with the available methods are evaluated

QueryForm: A Simple Zero-shot Form Entity Query Framework

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes both the document schema and a specific entity type into a query, which is used to prompt a Transformer model to perform a single entity extraction task. Furthermore, we propose to leverage large-scale query-entity pairs generated from form-like webpages with weak HTML annotations to pre-train QueryForm. By unifying pre-training and fine-tuning into the same query-based framework, QueryForm enables models to learn from structured documents containing various entities and layouts, leading to better generalization to target document types without the need for target-specific training data. QueryForm sets new state-of-the-art average F1 score on both the XFUND (+4.6%~10.1%) and the Payment (+3.2%~9.5%) zero-shot benchmark, with a smaller model size and no additional image input.Comment: Accepted to Findings of ACL 202

Therapeutic targets and limits of minocycline neuroprotection in experimental ischemic stroke

<p>Abstract</p> <p>Background</p> <p>Minocycline, a second-generation tetracycline with anti-inflammatory and anti-apoptotic properties, has been shown to promote therapeutic benefits in experimental stroke. However, equally compelling evidence demonstrates that the drug exerts variable and even detrimental effects in many neurological disease models. Assessment of the mechanism underlying minocycline neuroprotection should clarify the drug's clinical value in acute stroke setting.</p> <p>Results</p> <p>Here, we demonstrate that minocycline attenuates both <it>in vitro </it>(oxygen glucose deprivation) and <it>in vivo </it>(middle cerebral artery occlusion) experimentally induced ischemic deficits by direct inhibition of apoptotic-like neuronal cell death involving the anti-apoptotic Bcl-2/cytochrome c pathway. Such anti-apoptotic effect of minocycline is seen in neurons, but not apparent in astrocytes. Our data further indicate that the neuroprotection is dose-dependent, in that only low dose minocycline inhibits neuronal cell death cascades at the acute stroke phase, whereas the high dose exacerbates the ischemic injury.</p> <p>Conclusion</p> <p>The present study advises our community to proceed with caution to use the minimally invasive intravenous delivery of low dose minocycline in order to afford neuroprotection that is safe for stroke.</p

FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction

The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. The graph contrastive objective maximizes the agreement of multimodal representations, providing a natural interplay for all modalities without special customization. In addition, we extract image features within the bounding box that joins a pair of tokens connected by a graph edge, capturing more targeted visual cues without loading a sophisticated and separately pre-trained image embedder. FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size.Comment: Accepted to ACL 202

Circulating Long Noncoding RNAs as Biomarkers for Predicting Head and Neck Squamous Cell Carcinoma

Background/Aims: The anatomical complexity of the head and neck region and the lack of sufficiently specific and sensitive biomarkers often lead to the diagnosis of head and neck squamous cell carcinoma (HNSCC) at advanced stages. To identify novel biomarkers for early diagnosis of primary HNSCC through a minimally invasive method, we investigated circulating long noncoding RNA (lncRNA) levels in plasma of HNSCC patients. Methods: The global lncRNA expression profiles of HNSCC patients were measured using microarray and next-generation RNA-sequencing (RNA-seq) data from both circulating and tissue samples. The diagnosis prediction model based on the lncRNA signatures and clinical features was evaluated by multi-stage validation and risk score analysis. Results: The data showed that 432 lncRNA transcripts were differentially expressed by fold changes of &#x3e; 4 in circulating samples and 333 in tissues samples, respectively. Only 12 lncRNAs consistently emerged in these two kinds of samples. After the risk score analysis including a multistage validation, we identified three lncRNAs, namely, HOXA11-AS, LINC00964 and MALAT1, which were up-regulated in the plasma of HNSCC patients compared with those in healthy controls with merged areas under the curve (AUCs) in training and validation sets of 0.925 and 0.839, respectively. Conclusion: HOXA11-AS, LINC00964 and MALAT1 might be potential circulating biomarkers for the early detection of HNSCC in the future

Single cell transcriptome profiling reveals cutaneous immune microenvironment remodeling by photodynamic therapy in photoaged skin

BackgroundThe immune microenvironment plays a critical role in maintaining skin homeostasis, which is closely related to the dysfunction in photoaged skin such as autoimmunity and tumorigenesis. Several recent studies have demonstrated the efficacy of 5-aminolevulinic acid photodynamic therapy (ALA-PDT) in alleviating photoaging and skin cancer. However, the underlying immune mechanisms and the immune microenvironment change by ALA-PDT remain largely unknown.MethodsTo illustrate the effects of ALA-PDT on immune microenvironment in photoaged skin, single cell RNA sequencing (scRNA-seq) analysis of photoaged skin on the extensor side of the human forearm before and after ALA-PDT was performed. R-packages of Seurat, clusterProfiler, Monocle, CellChat were used for cell clustering, differentially expressed genes analysis, functional annotation, pseudotime analysis and cell-cell communication analysis. The gene sets related to specific functions were extracted from the MSigDB database, which were used to score the functions of immune cells in different states. We also compared our result with published scRNA-seq data of photoaged skin of the eyelids.ResultsThe increase score of cellular senescence, hypoxia and reactive oxygen species pathway in immune cells and the decrease of immune receptor activity function and proportion of naive T cells were found in skin photoaging. Moreover, the function of T cell ribosomal synthesis was also impaired or down regulated and function of G2M checkpoint was up regulated. However, ALA-PDT showed promising results in reversing these effects, as it improved the above functions of T cells. The ratio of M1/M2 and percentage of Langerhans cells also decreased with photoaging and increased after ALA-PDT. Additionally, ALA-PDT restored the antigen presentation and migration function of dendritic cells and enhanced cell-cell communication among immune cells. These effects were observed to last for 6 months.ConclusionALA-PDT has potential to rejuvenate immune cells, partially reversed immunosenescence and improved the immunosuppressive state, ultimately remodelling the immune microenvironment in photoaged skin. These results provide an important immunological basis for further exploring strategies to reverse skin photoaging, chronological aging and potentially systemic aging

Robust Design of Constant Modulus Sequence and Receiver Filter in the Presence of Signal-dependent Clutter

In this paper, we focus on the detection of a moving point-like target embedded in uncertain signal-dependent clutter and develop robust transmit-code and receive-filter designs in slow-time. First, based on the Worst-case Signal-to-Interference-plus-Noise Ratio (W-SINR) when the second-order clutter statistics are uncertain, we establish a high-dimensional transmit-receive optimization model that considers the constant modulus constraint with non-convexity. Next, we propose an Iterative Sequential Optimization (ISO) algorithm. At each iteration, it converts a high-dimensional optimization into multiple one-dimensional fractional programming problems that can be efficiently solved using Dinkelbach’s method. Finally, we use numerical examples to confirm that the ISO can resist the uncertain knowledge of signal-dependent clutter, which enables the radar system to adapt to complicated environments. Moreover, compared to Semi-Definite Relaxation (SDR)-related and randomization methods, the proposed algorithm is superior with respect to both optimized W-SINR and computational time

Robust Capon Beamforming against Steering Vector Error Dominated by Large Direction-of-Arrival Mismatch for Passive Sonar

Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections

A Sequential Optimization Calibration Algorithm for Near-Field Source Localization

This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations of calibration sources involving the ranges and the azimuths by exploiting some imprecise a-priori knowledge of calibration sources. At each iteration of the proposed method, the source locations, and the gain and phase errors are obtained iteratively. Finally, at the analysis stage, we evaluate the effectiveness of the proposed technique using some numerical simulations. Results show that the proposed algorithm shares the capability to jointly estimate the source locations and the errors