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As an efficient watermark attack method, geometric distortions destroy the synchronization between the watermark encoder and decoder. Local geometric distortion is a considerable challenge in the watermarking field. Although many geometric distortion resilient watermarking schemes have been proposed, few perform well against local geometric distortions, such as random bending attacks (RBAs). To address this problem, this paper proposes a novel watermark synchronization process and a corresponding watermarking scheme. In our scheme, the watermark bits are represented by random patterns. The message is encoded to obtain a watermark unit, and the watermark unit is flipped to generate a symmetrical watermark. Then, the symmetrical watermark is additively embedded into the spatial domain of the host image. In watermark extraction, we first obtain the theoretical mean-square error minimized estimation of the watermark. Then, an autoconvolution function is applied to this estimation to detect the symmetry and obtain a watermark unit map. According to this map, the watermark can be accurately synchronized, and then extraction can be performed. Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortions, global geometric distortions, common image processing operations, and some kinds of combined attacks. [ABSTRACT FROM AUTHOR]

Background: Tumor‐infiltrating immune cells participate in the initiation and progression of prostate adenocarcinoma (PRAD). However, it is not fully known how immune infiltration affects the development of PRAD and its clinical presentation. Methods: Herein, we investigated the immune infiltration phenotypes in PRAD based on transcriptome profiles, methylation profiles, somatic mutation, and copy number variations. We also developed an immune prognostic model (IPM) to identify unfavorable prognosis. To verify this model, immunohistochemistry staining was performed on a cohort of PRAD samples. Moreover, we constructed a nomogram to assess the survival of PRAD incorporating immune infiltration and other clinical features. Results: We categorized PRAD patients into high and low‐level clusters based on immune infiltration phenotypes. The patients in the high‐level clusters had worse survival than their low‐level counterparts. Gene set enrichment analysis indicated that both anti‐ and pro‐tumor terms were enriched in high‐level cluster. Moreover, we identified a positive correlation between anti‐ and pro‐tumor immune cells in PRAD microenvironment. Notably, Somatic mutation analysis showed patients in high‐level cluster had a higher somatic mutation burden of KMT2D, HSPA8, CHD7, and MAP1A. In addition, we developed an IPM with robust predictive ability. The model can distinguish high‐risk PRAD patients with poor prognosis from low‐risk PRAD patients in both training and another three independent validation datasets. Besides, we constructed a nomogram incorporating Gleason score, pathological T stage, and IPM for the prognosis prediction of PRAD patients, which displayed robust predictive ability and might contribute to clinical practice. Conclusion: Our work illustrated the immune infiltration phenotypes strongly related to the poor prognosis of PRAD patients, and highlighted the potential of the IPM to identify unfavorable tumor features. [ABSTRACT FROM AUTHOR]

mRNA‐based molecular subtypes have implications for bladder cancer prognosis and clinical benefit from certain therapies. Whether small extracellular vesicles (sEVs) can reflect bladder cancer molecular subtypes is unknown. We performed whole transcriptome RNA sequencing for formalin fixed paraffin embedded (FFPE) tumour tissues and sEVs separated from matched tissue explants, urine and plasma in patients with bladder cancer. sEVs were separated using size‐exclusion chromatography, and characterized by transmission electron microscopy, nano flow cytometry and western blots, respectively. High yield of sEVs were obtained using approximately 1 g of tissue, incubated with media for 30 min. FFPE tumour tissue and tumour tissue‐derived sEVs demonstrated good concordance in molecular subtype classification. All urinary sEVs were classified as luminal subtype, while all plasma sEVs were classified as Ba/Sq subtype, regardless of the molecular subtypes indicated by their matched FFPE tumour tissue. The comparison within urine sEVs, which may exclude the sample type specific background, could pick up the different biology between NMIBC and MIBC, as well as the signature genes related to molecular subtypes. Four candidate sEV‐related bladder cancer‐specific mRNA biomarkers, FAM71E2, OR4K5, FAM138F and KRTAP26‐1, were identified by analysing matched urine sEVs, tumour tissue derived sEVs, and adjacent normal tissue derived sEVs. Compared to sEVs separated from biofluids, tissue‐derived sEVs may reflect more tissue‐ or disease‐specific biological features. Urine sEVs are promising biomarkers to be used for liquid biopsy‐based molecular subtype classification, but the current algorithm needs to be modified/adjusted. Future work is needed to validate the four new bladder cancer‐specific biomarkers in large cohorts. [ABSTRACT FROM AUTHOR]

This paper designs a novel underpainting based camera shooting resilient (CSR) document watermarking algorithm for dealing with the leak source tracking problem. By applying such algorithm, we can extract the authentication watermark information from the candid photographs. The watermarked underpainting contains three significant properties. 1) Inconspicuousness. The watermarked underpainting is inconspicuous and it will not easily be maliciously attacked. 2) Robustness. We propose DCT-based watermark embedding algorithm and distortion compensation based extracting algorithm, which make the watermark robust to camera shooting process. 3) Autocorrelation. We design the flip-based method to arrange the watermarked underpainting. So that a complete watermark region can be accurately located even if part of the document is recorded. Compared with previous watermarking algorithms, the proposed scheme guaranteed content independent embedding as well as the robustness to the camera shooting process. Besides, the proposed scheme satisfies the accuracy of extraction even when the captured document is incomplete. [ABSTRACT FROM AUTHOR]

As an important part of the new infrastructure, the cloud data center is developing rapidly, and its energy consumption problem is becoming more and more prominent. Therefore, research on energy-saving technologies for cloud data centers has attracted widespread attention from academia and industry. Some studies have reviewed energy-saving optimization methods and technologies for data centers, but recently, many state-of-the-art optimization methods of energy consumption and energy-saving technologies have sprung out, which are still worth analyzing and discussing. Depending on the in-depth investigation and analysis of related research status, this article firstly focuses on analyzing and discussing the energy-saving technologies of the two components: IT equipment and cooling systems, both of which bring about the largest energy consumption in cloud data centers. As for IT equipment, its energy-saving technologies mainly include the energy saving of servers, storage systems, and network systems. While as for cooling systems, airflow organization in the computer room, thermal-aware scheduling technology, and other new energy-saving technologies are involved. Secondly, on the basis of analyzing the energy-saving technologies of the two major components, a new optimization scheme of energy consumption for the jointing computing system and cooling system is explained. Throughout this work, various energy-saving strategies and technologies have been described and compared. Finally, the future trends and development directions of energy saving for data centers are further promoted, such as integral optimization of energy consumption jointing multiple components, energy saving using artificial intelligence methods, energy saving based on novel hardware equipment, hybrid cooling energy saving, and comprehensive energy conservation with various energy technologies. [ABSTRACT FROM AUTHOR]

Traditional watermarking algorithms have been extensively studied. As an important type of watermarking schemes, template-based approaches maintain a very high embedding rate. In such scheme, the message is often represented by some dedicatedly designed templates, and then the message embedding process is carried out by additive operation with the templates and the host image. To resist potential distortions, these templates often need to contain some special statistical features so that they can be successfully recovered at the extracting side. But in existing methods, most of these features are handcrafted and too simple, thus making them not robust enough to resist serious distortions unless very strong and obvious templates are used. Inspired by the powerful feature learning capacity of deep neural network, we propose the first deep template-based watermarking algorithm in this paper. Specifically, at the embedding side, we first design two new templates for message embedding and locating, which is achieved by leveraging the special properties of human visual system, i.e., insensitivity to specific chrominance components, the proximity principle and the oblique effect. At the extracting side, we propose a novel two-stage deep neural network, which consists of an auxiliary enhancing sub-network and a classification sub-network. Thanks to the power of deep neural networks, our method achieves both digital editing resilience and camera shooting resilience based on typical application scenarios. Through extensive experiments, we demonstrate that the proposed method can achieve much better robustness than existing methods while guaranteeing the original visual quality. [ABSTRACT FROM AUTHOR]