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In statistics, log-concave density estimation is a central problem within the field of nonparametric inference under shape constraints. Despite great progress in recent years on the statistical theory of the canonical estimator, namely the log-concave maximum likelihood estimator, adoption of this method has been hampered by the complexities of the non-smooth convex optimization problem that underpins its computation. We provide enhanced understanding of the structural properties of this optimization problem, which motivates the proposal of new algorithms, based on both randomized and Nesterov smoothing, combined with an appropriate integral discretization of increasing accuracy. We prove that these methods enjoy, both with high probability and in expectation, a convergence rate of order 1/T up to logarithmic factors on the objective function scale, where T denotes the number of iterations. The benefits of our new computational framework are demonstrated on both synthetic and real data, and our implementation is available in a github repository LogConcComp (Log-Concave Computation). [ABSTRACT FROM AUTHOR]

A target-triggered strand displacement-assisted target recycling based on carbon dots-based fluorescent probe and mesoporous silica nanoparticles@polydopamine (MSNs@PDA) was established to detect miRNA. The surface of MSNs rich in mesopores was coated with a layer of PDA, which can adsorb and quench the fluorescence of single-stranded Fuel DNA with fluorescent carbon dots (CDs) modified at the end through fluorescence resonance energy transfer (FRET). After adding double-stranded DNA-gold nanoparticles (dsDNA-AuNPs) and target let-7a, it will trigger two toehold-mediated strand displacement reactions (TSDR), leading to the recovery of fluorescence and the recycling of target let-7a (excitation wavelength: 380 nm; emission wavelength: 458 nm). The recovery value of fluorescence is proportional to the logarithm of the target microRNA let-7a concentration, thus realizing the sensitivity amplification detection of disease markers. The MSNs@PDA@Fuel DNA-CDs/dsDNA-AuNPs nanoplatform based on the strategy of "on–off–on" and TSDR cyclic amplification may hold great potential as an effective and safe nanoprobe for accurate fluorescence imaging of diseases related to miRNA with low abundances. [ABSTRACT FROM AUTHOR]

Alcohol abuse and addiction is a public health issue of global concern. Wastewater-based epidemiology (WBE) is a forceful and effective complementary tool for investigating chemical consumption. This study examined alcohol consumption in major cities of China via WBE and compared WBE estimates with other data sources. A simple and valid ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed for the determination of two alcohol metabolites, ethyl glucuronide (EtG) and ethyl sulfate (EtS) in wastewater. The optimized method was applied to 62 sewage samples collected from wastewater treatment plants (WWTPs) in 31 provincial capital cities across China in the fourth quarter of 2020. The methodology established in this study was validated with the lower limit of quantification (LLOQ) up to 0.1 μg/L, good linearity in the range of 0.1–50 μg/L, intra-day and inter-day precision less than 5.58% and 5.55%, respectively, and the recoveries of the extracts were higher than 97.14%. The consumption range of alcohol estimated via WBE was 6.09 ± 4.56 ethanol/person/day (EPD) in the capital cities of China. Alcohol consumption varies significantly between cities in China, with WBE estimating lower alcohol consumption than WHO and lower than foreign countries. Investing in alcohol consumption based on WBE has great potential to accurately and efficiently estimate alcohol consumption. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the intra-examination agreement between multi-echo gradient echo (MEGE) and confounder-corrected chemical shift-encoded (CSE) sequences for liver T2*/R2* estimations in a wide range of T2*/R2* and proton density fat fraction (PDFF) values. Exploratorily, to search for the T2*/R2* value where the agreement line breaks and examine differences between regions of low and high agreement. Methods: Consecutive patients at risk for liver iron overload who underwent MEGE and CSE sequences within the same exam at 1.5 T were retrospectively selected. Regions of interest were drawn in the right and one in the left liver lobes on post-processed images for R2*(sec−1) and PDFF (%) estimation. Agreement between MEGE-R2* and CSE-R2* was evaluated using intra-class correlation coefficient (ICC) and Bland–Altman analysis. 95% confidence intervals (CI) were computed. Segment-and-regression analysis was performed to find the point where the agreement between sequences is interrupted. Regions of low and high agreement were examined using tree-based partitioning analyses. Results: 49 patients were included. Mean MEGE-R2* was 94.2 s−1 (range: 31.0–737.1) and mean CSE-R2* 87.7 (29.7–748.1). Mean CSE-PDFF was 9.12% (0.1–43.3). Agreement was strong for R2* estimations (ICC: 0.992,95%CI 0.987,0.996), but the relation was nonlinear and possibly heteroskedastic. Lower agreement occurred when MEGE-R2* > 235 s−1, with MEGE-R2* values consistently lower than CSE-R2*. Higher agreement was observed when PDFF < 14%. Conclusion: MEGE-R2* and CSE-R2* strongly agree, though at higher iron content, MEGE-R2* is consistently lower than CSE-R2*. In this preliminary dataset, a breaking point for agreement was found at R2* > 235. Lower agreement was observed in patients with moderate to severe liver steatosis. [ABSTRACT FROM AUTHOR]

The traditional approaches for improving corrosion resistance of alloys typically lead to the sacrifice of mechanical properties because the microstructures needed for improving corrosion resistance often contradict those for high strength. Here we demonstrate that selected laser melting (SLM), a net-shape additive manufacture technique, can maintain good mechanical properties while double the corrosion-resistance of a N-doped CoCrFeNi HEA. The SLM processed sample possesses a heterogeneous microstructure with 3D dislocation cells inside each grain. The SLM-induced 3D dislocation cell structure can provide effective diffusion paths to significantly promote Cr outward segregation, forming a thick protective Cr oxide layer, which renders excellent corrosion resistance. Furthermore, Cr segregation along cell boundaries provides numerous sites for nucleation of oxides, and stabilizes the cell structure for good mechanical properties. The strategy discovered here may also be applied to other HEAs with multiple strengthening mechanisms. [ABSTRACT FROM AUTHOR]

Objective: Cancer stem cells (CSCs) are the main driving force of tumorigenesis, metastasis, recurrence, and drug resistance in epithelial ovarian cancer (EOC). The current study aimed to explore the regulatory effects of ring finger protein 144A (RNF144A), an E3 ubiquitin ligase, in the maintenance of CSC properties and tumor development in EOC. Methods: The expressions of RNF144A in EOC tissue samples and cells were examined. The knockdown or overexpression of a target gene was achieved by transfecting EOC cells with short hairpin RNA or adenoviral vectors. A mouse xenograft model was constructed by inoculating nude mice with EOC cells. Co-immunoprecipitation was used to determine the interaction between RNF144A and LIN28B. Results: Downregulated RNF144A expression was observed in ovarian tumor tissues and EOC cells. Low RNF144A expression was positively associated with poor survival of EOC patients. RNF144A knockdown significantly enhanced sphere formation and upregulated stem cell markers in EOC cells, while RNF144A overexpression prevented EOC cells from acquiring stem cell properties. Also, the upregulation of RNF144A inhibited ovarian tumor growth and aggressiveness in cell culture and mouse xenografts. Further analysis revealed that RNF144A induced LIN28B degradation through ubiquitination in EOC cells. LIN28B upregulation restored the expressions of stem cell pluripotency-associated transcription factors in EOC cells overexpressing RNF144A. Conclusion: Taken together, our findings highlight the therapeutic potential of restoring RNF144A expression and thereby suppressing LIN28B-associated oncogenic signaling for EOC treatment. [ABSTRACT FROM AUTHOR]

Solvent refining with Al-Si alloy is a promising purification method for the production of solar-grade silicon (SoG-Si) feedstock owing to the advantages of low production cost and high impurity removal efficiency. In this process, larger refined Si primary dendrites should be easily collected after acid leaching, which is favorable to recovery, thereby reducing the production cost. Hence, the growth behavior of the precipitated Si crystal must be investigated systematically. In the present work, the morphology evolution of solidified Al-Si alloys with a wide range of Si content (30 ~ 70 wt%) was analyzed. The typical plate-like Si primary dendrites grown following the twin plane re-entrance edge (TPRE) mechanism formed in all alloy compositions. As increasing the Si content from 30 wt% to 50 wt%, the Si primary dendrites underwent a coarsening process attributed to the preferred growth along with < 211 > and < 111 > directions, leading to an increase in the experimental recovery rate. However, the preferred growth along < 211 > direction was inhibited when the Si content is higher than 55 wt%. Moreover, the broken effect originating from grain collision and thermal stress on the Si primary dendrites was enhanced by further increasing the Si content, resulting in a decrease in the experimental recovery rate. Therefore, the optimum composition is determined as Al-50 ~ 55 wt% Si for solvent refining solution, based on the cost reduction consideration. [ABSTRACT FROM AUTHOR]

The currently predominant token-to-token attention mechanism has demonstrated its ability to capture word dependencies in neural machine translation. This mechanism treats a sequence as bag-of-words tokens and compute the similarity between tokens without considering their intrinsic interactions. In this paper, we argue that this attention mechanism may miss opportunity of take advantage of the state information through multiple time steps. Thus, we propose a Gated State Network which manipulates the state information flow with sequential characteristics. We also incorporate a Focal Adaptive Attention Network which utilizes a Gaussian distribution to concentrate the attention distribution to a predicted focal position and its neighborhood. Experimental results on WMT'14 English–German and WMT'17 Chinese–English translation tasks demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We cannot overemphasize the essence of contextual information in most natural language processing (NLP) applications. The extraction of context yields significant improvements in many NLP tasks, including emotion recognition from texts. The paper discusses transformer-based models for NLP tasks. It highlights the pros and cons of the identified models. The models discussed include the Generative Pre-training (GPT) and its variants, Transformer-XL, Cross-lingual Language Models (XLM), and the Bidirectional Encoder Representations from Transformers (BERT). Considering BERT's strength and popularity in text-based emotion detection, the paper discusses recent works in which researchers proposed various BERT-based models. The survey presents its contributions, results, limitations, and datasets used. We have also provided future research directions to encourage research in text-based emotion detection using these models. [ABSTRACT FROM AUTHOR]

Background: Secreted frizzled-related protein 2 (SFRP2) in circulating tumor DNA (ctDNA) is related to gastric cancer (GC) proliferation. However, whether methylated SFRP2 in ctDNA serves as the biomarker in GC patients remains unknown. Aims: To investigate the relationship between methylated SFRP2 and the clinical outcomes of GC patients. Methods: One hundred and forty-eight GC patients receiving systemic chemotherapy were collected during 2015–2017. Aberrant SFRP2 methylation was detected before and after chemotherapy by digital PCR-based technologies. Results: Baseline SFRP2 methylation positively correlated with lymph node status (P < 0.001), distant metastasis (P < 0.001) and TNM stage (P = 0.005). The top 50% methylated SFRP2 had shorter progression-free survival (PFS) and overall survival (OS) than those with bottom 50% in stage III GC patients (median PFS, 11.0 vs. NR months; HR 13.05; 95% CI 3.05–55.95; median OS 17.0 vs. NR months; HR 7.80; 95% CI 1.81–33.60) and stage IV GC patients (median PFS, 4.0 vs. 7.0 months; HR 2.74; 95% CI 1.58–4.78; median OS 12.0 vs. 16.0 months; HR 3.14; 95% CI 1.67–5.92). Besides, the increased methylated SFPR2 from baseline to post-treatment was related to the worse PFS and OS among stage IV patients (median PFS, 5.0 vs. 7.0 months; HR 2.17; 95% CI 1.25–3.76; median OS 12.0 vs. 15.5 months; HR 3.51; 95% CI 1.94–6.35), but not to stage III patients. Conclusions: SFRP2 methylation and dynamic change are associated with GC prognosis. Our findings provide potential biomarker detection approach in ctDNA for prognosis prediction and dynamic monitoring among GC patients. [ABSTRACT FROM AUTHOR]

Internet applications remain exposed to pervasive Domain Name System (DNS)–based threats. Blockchain technologies provide a new way for tackling DNS vulnerability issues, and have been highlighted recently. However, traditional blockchain is still not well suited for big data applications such as DNS, because the performance of blockchain consensus greatly limits its practical adoption. In this paper, we present DagGridLedger, a sharded directed acyclic graph (DAG) blockchain that provides scalable big data architecture for trustful DNS management. To achieve this goal, DagGridLedger proposes a radical new architecture that combines blockchain sharding and DAG techniques on the DNS resolver side, thereby making it a promising solution to enhance the security and stability of large-scale DNS system. To be specific, DagGridLedger provides a blockchain structure targeting DNS application, which employs a high-performance DAG consensus algorithm named DagGrid. DagGrid consensus realizes a multi-DNS negotiation mechanism through block sharding in generating a block. With an improved asynchronous leaderless Byzantine consensus, DagGrid implements total order determination, which guarantees the trustful DNS management. Further experiments verified the performance of DagGridLedger as well as the applicability of the proposed blockchain architecture in traditional DNS. To this end, DagGridLedger consistently achieves a big data architecture for secure DNS record management, with a novel shared DAG consensus designed for high throughput. This makes DagGridLedger a promising architecture for highly secure and efficient DNS solution. [ABSTRACT FROM AUTHOR]

With only global image-level annotations, weakly supervised learning of deep convolutional neural networks has shown enough capacity in classification and localization but lack of ability to present the detection explicitly. In this work, we propose a novel spatial division network, which is applied to detect bounding boxes only with weak supervision. The essence of our model is two innovative differentiable modules, determination network and parameterized division, which perform the spatial division in feature maps of classification networks. After training, the learned parameters of the spatial division would correspond to a set of predicted bounding box coordinates. To demonstrate the effectiveness of our model for multi-label classification and weakly supervised detection, we conduct extensive experiments on the multi-MNIST dataset. Experimental results show our spatial division networks can (1) help improve the accuracy of multi-label classification, (2) implement in an end-to-end way only with the image-level annotations, and (3) output accurate bounding box coordinate, thereby achieving multi-digits detection. [ABSTRACT FROM AUTHOR]

At present, there are many studies on the dynamic response of overlapped tunnels, but most of them are through numerical simulation. Very few has been conducted by the experimental model research and frequency domain analysis. Combined with the model test and the numerical simulation, the dynamic response of the tunnel lining structure under the action of different train speeds and different tunnel clear distances are investigated, which can remedy the deficiencies in the relevant fields and verify the accuracy of numerical simulation results. The results show that as the train speed increases, the amplitude of the energy spectra of the vibration loads decrease significantly. The tunnel response at the sidewalls is smaller than that at the tunnel crown and at the invert in the lower tunnel. As the net distance increases, the amplitudes of the acceleration frequency spectrum and the energy spectra of the lower tunnel decrease, but with a diminishing rate. The dynamic coefficients of circumferential of the upper tunnel under a train load are larger than those in the longitudinal direction in the invert. [ABSTRACT FROM AUTHOR]

The intersection structure, composed of twin shield tunnels and a transverse passage, is ubiquitous in the metros around the world. However, the dynamic characteristics of this kind of intersection structure have not been studied a lot yet. By carrying out the corresponding model tests and numerical simulations, the dynamic characteristics of this kind of intersection structure are studied in this paper, which is very helpful for the determination of the ground vibration from this kind of underground structure. The results show that the vibration of the transverse passage is more severe than the main tunnel, manifested that the frequency response function (FRF) of the transverse passage has greater magnitudes than that of the main tunnel at the locations with the same distance from the vibration source. The peak frequencies of the acceleration responses at different locations of the intersection structure under the moving train load are all located in the frequency band 44.9–56.1 Hz where the corresponding 1/3 octave band spectra of FRF have the largest values in the considered frequency band 31.5–250 Hz, indicating that the natural frequencies of the intersection structure in the frequency band 44.9~56.1 Hz have significant influences on its acceleration responses below 250 Hz induced by the moving train. [ABSTRACT FROM AUTHOR]

Computer vision and natural language processing have been some of the long-standing challenges in artificial intelligence. In this paper, we explore a generative automatic image annotation model, which utilizes recent advances on both fronts. Our approach makes use of a deep-convolutional neural network to detect image regions, which later will be fed to recurrent neural network that is trained to maximize the likely-hood of the target sentence description of the given image. During our experimentation we found that better accuracy and training was achieved when the image representation from our detection model is coupled with the input word embedding, we also found out most of the information from the last layer of detection model vanishes when it is fed as thought vector for our LSTM decoder. This is mainly because the information within the last fully connected layer of the YOLO model represents the class probabilities for the detected objects and their bounding box and this information is not rich enough. We trained our model on coco benchmark for 60 h on 64,000 training and 12,800-validation dataset achieving 23% accuracy. We also realized a significant training speed drop when we changed the number of hidden units in the LSTM layer from 1470 to 4096. [ABSTRACT FROM AUTHOR]

Although the parallel mechanisms have the advantages of high accuracy, velocity, stiffness, and payload capacity, the shortcomings of the space utilization and workspace limit the applications in the confined space. A novel 3 degrees of freedom spatial parallel manipulator 3- PSR-O (prismatic-spherical-revolute) is proposed, which possesses a compact architecture and extended workspace while maintaining the inherent advantages of the parallel mechanisms. The direct-inverse position, singularity and workspace are investigated. The mapping method is adopted in the position analysis, and the closed form solution is derived in the form of a six order equation. The singularity analysis of the mechanism is also carried out based on the geometrical constraints, including six singularity boundaries. A feature boundary, which is independent of the prismatic joints' stroke limit, is obtained by integrating the six singularity boundaries. According to the formation of the reachable workspace, a concept of basic workspace is also introduced and presented in the analytical way. By demarcating the basic workspace along the central height with the feature boundary, the reachable workspace can be derived and analyzed more efficiently. Finally, a comparative study on the space utilization between the 3- PSP parallel mechanism and the new mechanism is also presented. The area of feature boundary of the new mechanism is about 140% of the 3- PSP parallel mechanism, while its installation radius is only 1/2 of the 3- PSP parallel mechanism. The proposed parallel mechanism shows great space utilization, and is ideally suited for applications in confined space occasions such as immersion lithography, nano-imprint etc. [ABSTRACT FROM AUTHOR]

Modeling energy-minimizing curves have many applications and are a basic problem of Geometric Modeling. In this paper, we propose the method for geometric design of energy-minimizing Bézier curves. Firstly, the necessary and sufficient condition on the control points for Bézier curves to have minimal internal energy is derived. Based on this condition, we propose the geometric constructions of three kinds of Bézier curves with minimal internal energy including stretch energy, strain energy and jerk energy. Given some control points, the other control points can be determined as the linear combination of the given control points. We compare the three kinds of energy-minimizing Bézier curves via curvature combs and curvature plots, and present the collinear properties of quartic energy-minimizing Bézier curves. We also compare the proposed method with previous methods on efficiency and accuracy. Finally, several applications of the curve generation technique, such as curve interpolation with geometric constraints and modeling of circle-like curves are discussed. [ABSTRACT FROM AUTHOR]