Missing Block Research Articles

Insulator-to-Metal Transition and Isotropic Gigantic Magnetoresistance in Layered Magnetic Semiconductors.

Magnetotransport, the response of electrical conduction to external magnetic field, acts as an important tool to reveal fundamental concepts behind exotic phenomena and plays a key role in enabling spintronic applications. Magnetotransport is generally sensitive to magnetic field orientations. In contrast, efficient and isotropic modulation of electronic transport, which is useful in technology applications such as omnidirectional sensing, is rarely seen, especially for pristine crystals. Here a strategy is proposed to realize extremely strong modulation of electron conduction by magnetic field which is independent of field direction. GdPS, a layered antiferromagnetic semiconductor with resistivity anisotropies, supports a field-driven insulator-to-metal transition with a paradoxically isotropic gigantic negative magnetoresistance insensitive to magnetic field orientations. This isotropic magnetoresistance originates from the combined effects of a near-zero spin-orbit coupling of Gd3+-based half-filling f-electron system and the strong on-site f-d exchange coupling in Gd atoms. These results not only providea novel material system with extraordinary magnetotransport that offers a missing block for antiferromagnet-based ultrafast and efficient spintronic devices, but also demonstrate the key ingredients for designing magnetic materials with desired transport properties for advanced functionalities.

PD-L1 expression in fine-needle aspiration cell blocks of head and neck squamous-cell carcinoma and its cytohistological concordance.

PD-L1 immunoexpression in head and neck squamous-cell carcinoma (HNSCC) determines immunotherapy eligibility. Patients are often diagnosed using fine-needle aspiration (FNA) of metastatic lymph nodes, however, the cytohistologic correlation of the combined positive score (CPS) is largely unknown. This study retrospectively identified 96 paired histologic (HS) and cytologic specimens (CyS), between 2016 and 2020, diagnosed with HNSCC. Cases with <100 tumor cells (n = 54) or missing block(s) (n = 8) were excluded. All 34 case pairs were scored with CPS using the PD-L1 22C3 pharmDx assay at clinically relevant cut-offs of <1%, 1%-19%, and ≥20% independently by three observers blinded to the case pairs (CyS with corresponding HS). The CPS (<1/1-19/≥20) for CyS and HS were as follows: 10(29.4%)/10(29.4%)/14(41.2%) and 2(5.9%)/13(38.2%)/19(55.9%), respectively. There was fair overall cytohistologic agreement (OA) of 76.5% (k = 0.261) at the CPS cut-off of 1. The OA did not differ significantly between site-matched (n = 13) and -unmatched (n = 21) case pairs (p = .4653). CyS has a specificity and positive predictive value (PPV) of 100% but a negative predictive value (NPV) of only 20% as compared to its paired HS. Our study demonstrates fair CPS cytohistologic correlation in HNSCC specimens using the PD-L1 IHC 22C3 pharmDx assay with high PPV but low NPV. This suggest that determining PD-L1 status in FNA specimens can play an important role in the clinical management of HNSCC patients.

Variable selection for high‐dimensional generalized linear model with block‐missing data

AbstractIn modern scientific research, multiblock missing data emerges with synthesizing information across multiple studies. However, existing imputation methods for handling block‐wise missing data either focus on the single‐block missing pattern or heavily rely on the model structure. In this study, we propose a single regression‐based imputation algorithm for multiblock missing data. First, we conduct a sparse precision matrix estimation based on the structure of block‐wise missing data. Second, we impute the missing blocks with their means conditional on the observed blocks. Theoretical results about variable selection and estimation consistency are established in the context of a generalized linear model. Moreover, simulation studies show that compared with existing methods, the proposed imputation procedure is robust to various missing mechanisms because of the good properties of regression imputation. An application to Alzheimer's Disease Neuroimaging Initiative data also confirms the superiority of our proposed method.

MPGI‐Terminal defect detection based on M‐FRCNN

Micro-precision glass insulated terminal (MPGI-Terminal) is a high-precision and widely used micro-precision component. The MPGI-Terminal is small in size with 2–5 mm in diameter. It is difficult to distinguish missing blocks on the surface during manual inspection, which is prone to false detection and missing detection. The advantages of deep learning methods in the field of object detection have gradually highlighted. They can be used to construct the missing block detection model of MPGI-Terminal. A Max-Min Intersection over Union (MIoU) loss function based on overlap area, distance and aspect ratio is proposed, which helps the model to perform bounding box regression more reasonably, so as to obtain the object position more accurately. In addition, the Spatial Attention Module (SAM) and the Region of Interest Alignment (RoI Align) module are introduced to further improve the model performance. Based on the above modules, a MPGI-Terminal missing block detection model based on M-FRCNN is constructed. The results show that the proposed model can meet the requirements of fast and high accuracy of MPGI-Terminal missing block detection.

Weighted multiple blockwise imputation method for high-dimensional regression with blockwise missing data

Blockwise missing data present a great challenge for data analysis because of their special missing structure. In this article, we propose a novel weighted multiple blockwise imputation method to target the problem of high-dimensional regression with blockwise missing data. Specifically, we first apply a multiple blockwise imputation technique to impute the missing blocks in the design matrix, after which a weighted sum formula is used to integrate the resulting imputation schemes and estimate the regression coefficients. The proposed method can make full use of the available information to perform imputation; hence, it shows superior performance compared to some previous methods. Simulations illustrate the advantageous numerical performance of our method in terms of variable selection, parameter estimation, and prediction ability. A real application is also presented to demonstrate its practical merit.

Moving towards aṇgasuddhi and saustabham with a conscious bodymind: Embodied imagery, metaphor and sensory awareness in Odissi dance training

This article investigates the usage of embodied imagery, metaphor and sensory awareness in the teaching and learning process of Odissi dance, an Indian classical dance from the eastern state of Odisha. It analyses examples of Odissi dance training used by chosen dance institutes and dancers in India. The discussion is undertaken in correlation with the psychophysical performers’, dance scholars’, somatic movement practitioners’, dance anthropologists’ and philosophers’ study of bodymind and embodiment. It proposes a shift from the objectified to a subjective approach to the dancer’s body that empowers students/dancers to reclaim the ownership of their bodies and movements. Altogether, it highlights a missing block in the training process that enables dance students to move towards the socioculturally imagined level of ‘perfection’, however, with a healthy, thinking, feeling, moving and agentive bodymind.

Missing value imputation on multidimensional time series

We present DeepMVI, a deep learning method for missing value imputation in multidimensional time-series datasets. Missing values are commonplace in decision support platforms that aggregate data over long time stretches from disparate sources, whereas reliable data analytics calls for careful handling of missing data. One strategy is imputing the missing values, and a wide variety of algorithms exist spanning simple interpolation, matrix factorization methods like SVD, statistical models like Kalman filters, and recent deep learning methods. We show that often these provide worse results on aggregate analytics compared to just excluding the missing data. DeepMVI expresses the distribution of each missing value conditioned on coarse and fine-grained signals along a time series, and signals from correlated series at the same time. Instead of resorting to linearity assumptions of conventional matrix factorization methods, DeepMVI harnesses a flexible deep network to extract and combine these signals in an end-to-end manner. To prevent over-fitting with high-capacity neural networks, we design a robust parameter training with labeled data created using synthetic missing blocks around available indices. Our neural network uses a modular design with a novel temporal transformer with convolutional features, and kernel regression with learned embeddings. Experiments across ten real datasets, five different missing scenarios, comparing seven conventional and three deep learning methods show that DeepMVI is significantly more accurate, reducing error by more than 50% in more than half the cases, compared to the best existing method. Although slower than simpler matrix factorization methods, we justify the increased time overheads by showing that DeepMVI provides significantly more accurate imputation that finally impacts quality of downstream analytics.

Defect Detection of Micro-Precision Glass Insulated Terminals

Micro-precision Glass Insulated Terminals (referred to as glass terminals) are the core components used in precision electronic equipment and are often used for electrical connections between modules. As a glass terminal, its quality has a great influence on the performance of precision electronic equipment. Due to the limitations of materials and production processes, some of the glass terminals produced have defects, such as missing blocks, pores and cracks. At present, most of the defect detection of glass terminals is done by manual inspection, and rapid detection easily causes eye fatigue, so it is difficult to ensure product quality and production efficiency. The traditional defect detection technology is difficult to effectively detect the very different defects of the glass terminal. Therefore, this paper proposes to use deep learning technology to detect missing blocks. First, preprocess the sample pictures of the missing block defects of the glass terminal, and then train the improved Faster Region-CNN deep learning network for defect detection. According to the test results, the accuracy of the algorithm in detecting missing defects in the glass terminal is as high as 93.52%.

Analytical Modeling of Modulated Rotating-Blade Noise : the Skipping Rope and the Darrieus Wind Turbine

The present study is about the modulations of the aerodynamic broadband noise heard from slowly rotating rotors with few blades. It is aimed at producing a fast-running prediction tool that could be used to assess the nuisance of Vertical-Axis Wind Turbines (VAWT) in the context of urban installation. The simpler case of a skipping rope that only involves part of the modulation effect is addressed as a first step. The rope is split into segments for which an instantaneous sound-radiation model exists to yield an overall spectrogram. Finally, its time signature is reconstructed by additive synthesis. After inspection of existing databases from oscillatory-airfoil experiments, needs for data representative of the dynamic stall of VAWT blades are seen as the missing block to reconstruct a synthetic spectrogram.

Analysis of a Cooperative Caching-Multicast Strategy in D2D-Aided Networks

Due to characteristics of video request, i.e., request-repeatability and group-orientation, we propose a cooperative caching-multicast strategy in typical Device-to-Device (D2D)-aided networks to improve the timeliness and space coverage of video services. First, based on the request cumulative time window (RCTW), a multicast-scale based caching resource allocation scheme is presented and videos whose corresponding multicast groups have larger probabilities to be non-empty are allocated more caching spaces. Second, a collaborative transmission scheme is presented, where base station opportunistically multicasts blocks of the requested video to group while failed receivers can obtain the missing blocks from successful receivers with the aid of D2D joint transmission. Finally, the tractable expressions for the average transmission coverage rate and the system average transmission delay are derived. The effectiveness of the proposed strategy when the video popularity distribution is in the general dip range is verified by numerical examples. Simulation results demonstrate that a reasonable RCTW can balance the relationship between the service probability of more users through one multicast transmission and the system transmission delay.

Radiographic portal or superior mesenteric vein invasion is an independent prognostic factor in non-metastatic pancreatic ductal adenocarcinoma: A missing block of clinical T staging?

BackgroundVenous invasion is not included in the pancreatic ductal adenocarcinoma (PDAC) staging, and its correlation with prognosis remains unclear. We evaluated the prognostic impact of radiographic portal/superior mesenteric vein (PV/SMV) invasion, and its possibility of complementing T staging. MethodsWe identified patients with non-metastatic PDAC using our institutional cohort, and divided them according to PV/SMV invasion at imaging, defined as >180-degree tumor-vessel interface or contour deformity. We conducted Cox proportional hazard regression, and compared survival in the original and 1:1 propensity score matched datasets. ResultsWe identified 454 patients [PV/SMV(+): 172; PV/SMV(−): 282]. In the multivariate analysis, PV/SMV invasion, age (≥70 years), performance status, tumor size (2–4, >4 cm), lymph nodes >4, and arterial invasion was correlated with prognosis. The PV/SMV(+) group had a shorter overall survival (OS) than the PV/SMV(−) group in the original (14.4 vs. 20.9 months; P < 0.001) and matched datasets (14.3 vs. 17.2 months; P = 0.009). Among patients without arterial invasion (cT1–cT3), the PV/SMV(+) group had a shorter OS (15.9 vs. 21.2 months; P = 0.002). Moreover, their OS did not differ from that of patients with arterial invasion (cT4) (15.9 vs. 14.4 months; P = 0.907). Patients with vessel (artery/vein) invasion had a shorter OS than those without vessel invasion (14.5 vs. 21.2 months; P < 0.001). ConclusionsRadiographic PV/SMV invasion in non-metastatic PDAC was correlated with a poor prognosis. It could identify a group with shorter OS among patients without arterial invasion (cT1–cT3). It is suggested that inclusion of PV/SMV invasion in clinical T4 criteria should be considered.

Scalable recovery of missing blocks in time series with high and low cross-correlations

Missing values are very common in real-world data including time-series data. Failures in power, communication or storage can leave occasional blocks of data missing in multiple series, affecting not only real-time monitoring but also compromising the quality of data analysis. Traditional recovery (imputation) techniques often leverage the correlation across time series to recover missing blocks in multiple series. These recovery techniques, however, assume high correlation and fall short in recovering missing blocks when the series exhibit variations in correlation. In this paper, we introduce a novel approach called CDRec to recover large missing blocks in time series with high and low correlations. CDRec relies on the centroid decomposition (CD) technique to recover multiple time series at a time. We also propose and analyze a new algorithm called Incremental Scalable Sign Vector to efficiently compute CD in long time series. We empirically evaluate the accuracy and the efficiency of our recovery technique on several real-world datasets that represent a broad range of applications. The results show that our recovery is orders of magnitude faster than the most accurate algorithm while producing superior results in terms of recovery.

Hybrid Robotic Reinforcement Learning for Inspection/Correction Tasks

The ability to rapidly program robots for complex tasks is an important precursor to wider adoption of robotics in industry. Robot programming is often time consuming and brittle to unanticipated variations in processing. Automated robot task learning is a solution to this problem. Reinforcement Learning (RL) is a commonly used approach for a robot to autonomously learn simple tasks. In RL, rewards are used to guide the robot towards learning an optimal plan or control policy. RL, however, has proven to be of limited value for problems with large-state spaces and considerable environmental variability. In this paper, we investigate formulation of the RL approach for inspect/correct types of tasks, specifically a misplaced block in a simple grid-world environment (requiring searching the gird world to identify a missing block and returning the missing block back to the target). We use a hybrid method, combining the SARSA algorithm and a model of the environment. The model of the environment is used as a reference model to reduce the state space, avoiding unnecessary exploration of the environment. A main focus of this research is the impact of task variability on RL performance.

Van der Waals epitaxial growth of single crystal α-MoO3 layers on layered materials growth templates

Since the isolation of graphene, various two-dimensional (2D) materials have been extensively investigated. Nevertheless, only few 2D oxides have been reported to date due to difficulties in their synthesis. However, it is expected that the layered transition-metal oxides (TMOs) could be missing blocks for van der Waals heterostructures and essential elements for 2D electronics. Herein, the crystal structure and band structure of van der Waals epitaxially grown α-MoO3 nanosheets on various 2D growth templates are characterized. Monolayer and multilayer α-MoO3 nanosheets are successfully grown on a 2D substrate by simply evaporating amorphous molybdenum oxide thin film in ambient conditions. A single-crystal α-MoO3 nanosheet without grain boundary is epitaxially grown on various 2D substrates despite a large lattice mismatch. During growth, the quasi-stable monolayer α-MoO3 first covers the 2D substrate, then additional layers are continuously grown on the first monolayer α-MoO3. The band gap of the α-MoO3 increases from 2.9 to 3.2 eV as the thickness decreases. Furthermore, due to oxygen vacancies and surface adsorbates, the synthesized α-MoO3 is highly n-doped with a small work function. Therefore, α-MoO3 field-effect transistors (FETs) exhibit a typical n-type conductance. This work shows the great potential of ultra-thin α-MoO3 in 2D-material-based electronics.

Nonlinear Viscoelastic Modeling of Adhesive Failure for Polyacrylate Pressure-Sensitive Adhesives

We investigate experimentally the adherence energy $\Gamma$ of model polyacrylate Pressure Sensitive Adhesives (PSAs) with combined large strain rheological measurements in uniaxial extension and an instrumented peel test. We develop a nonlinear model for such peel test which captures the dependence of $\Gamma(V)$ with peeling rate $V$ revealing the key role played by the extensional rheology. Our model explains in particular why traditional linear viscoelastic approaches correctly predict the slope of $\Gamma(V)$ curves for sufficiently elastic PSAs characterized by a simple rate-independent debonding criterion. However, for more viscoelastic adhesives, we identified a more complex rate-dependent debonding criterion yielding a significant modification of the $\Gamma(V)$ curves, an effect that has been largely overlooked so far. This investigation opens the way towards the understanding of fibrils debonding, which is the main missing block to predict the adherence of PSAs.

Differentiating lava slip planes from tectonic faults: A key issue in structural geology

Abstract The correct geological and structural survey of rock successions containing lava deposits may be biased by the possible confusion between tectonic faults and slip planes produced by lava flow sectors moving at different velocities during emplacement. Microtectonic features on slip planes resulting from lava emplacement, here named “lava slip planes”, can have a very similar appearance to slickensides formed under tectonic deformation. In order to come up with criteria to distinguish between the two types of structures, slip planes of various extent from recent trachytic lava flows on Ischia Island (Italy), and basaltic lavas at Mt. Etna (Italy) and along the Northern Volcanic Zone in Iceland, have been studied in detail in the field, sampled, and analysed also with the Scanning Electrone Microscope. Strike-slip planes dominate along flow margins while reverse slip planes dominate near eruptive centers. Normal slip planes can also be found at the sides and at the front of lava flows. Slip planes form at thermal boundaries that enable plastic/brittle deformation and favour the development of shear zones. The rheological properties of the lava should be such that the material has developed a shear strength. Although the best criterion to recognize a lava slip plane is the evaluation of its confinement to a single depositional unit, this approach can be rarely used, due to the frequently limited (decimetre- to meter-sized) outcrop of slip planes. The best criterion, which can be used directly in the outcrop as well as by means of a microscope, is the individuation of burrs, which are sub-millimeter to centimeter-size portions of the slip surface that get dragged away in the direction of movement of the missing block.

Edge-aware spatial-frequency extrapolation for consecutive block loss.

To improve the spatial error concealment (SEC) for consecutive block loss, an edge-aware spatial-frequency extrapolation (ESFE) algorithm and its edge-guided parametric model are proposed by selectively incorporating the Hough-based edge synthesis into the frequency-based extrapolation architecture. The dominant edges that cross the missing blocks are firstly identified by the Canny detector, and then the robust Hough transformation is utilized to systematically connect these discontinuous edges. During the generation of edge-guided parametric model, the synthesized edges are utilized to divide the missing blocks into the structure-preserving regions, and thus the residual error is reliably reduced. By successively minimizing the weighted residual error and updating the parametric model, the known samples are approximated by a set of basis functions which are distributed in a region containing both known and unknown samples. Compared with other state-of-the-art SEC algorithms, experimental results show that the proposed ESFE algorithm can achieve better reconstruction quality for consecutive block loss while keeping relatively moderate computational complexity.

Wave impacts on structures with rectangular geometries: Part 1. Seawalls

This paper considers steep wave impact on seawalls of various geometries. A simple analytical model for the pressure impulse due to a wave of idealized geometry and dynamics is developed and applied to the following geometries: (a) vertical seawall with a berm, (b) vertical seawall with a ditch at its base and (c) vertical seawall with a block missing (damaged condition).The method uses eigenfunction expansions in each of the rectangular regions that satisfy some of the rigid surface conditions and a simplified free-surface condition. Their unknown coefficients are determined from the impact boundary condition, rigid wall conditions and by matching the values and the horizontal derivatives of the solutions in each rectangular region at their mutual boundary. The method yields the pressure impulse throughout the entire region. The overall impulse and moment impulse on the seawall and a simple model for the uprush of the spray jet after the impact are also presented. The effects of different impact regions and different geometries can therefore be quickly estimated and used to show trends in the results. It is shown that berms generally have a beneficial effect on reducing the impulse, moment impulse and uprush, but not the maximum pressure impulse on the seawall, whereas ditches are generally and sometimes strongly detrimental for all effects except uprush. A missing block in the seawall gives an almost constant or linearly decreasing value inside the gap (depending on the boundary condition applied at the rear of the gap being hard or soft respectively); the soft case can affect the pressure impulse on the front face of the seawall, thereby affecting the impulse and moment impulse.

Dental research: The missing block in the dental institutions

Dental research: The missing block in the dental institutions

More than Drivers in Motor Drives: Closing the Loop

This paper presents the problematic associated to the design of a complete motor drive, exposing all available and missing blocks at this moment. We proceed by dividing the power drive into basic functions – control, different power supplies, isolated communication, controller interfacing, current monitoring – and we present for each of these functions an implementation example. Implementation examples shown are based on high-temperature X-REL parts such as XTR26020 Isolated Intelligent Gate Driver, XTR40010 Isolated Two Channel Transceiver, XTR30010 PWM Controller, XTR70010 and XTR70020 Low-dropout Linear Regulators and XTR50010 Bidirectional Multichannel Level Translator. A drive solution based on the XTR26020, presented for the first time in this paper, is explained and compared against previous art. Main characteristics of the new linear regulator XTR70020 are presented, showing the best-in-class dropout voltage, which outperforms the closest competitor by a factor of three. For all parts featured, tests results at an ambient temperature up to 230°C (even higher in some cases) are presented.