Loss Of Precision Research Articles

392-P: Optimizing Duration and Threshold of Sensor Glucose for Identifying Symptomatic Hypoglycemia in Insulin-Treated Diabetes—Hypo-METRICS Study

Many episodes of hypoglycemia defined by the ATTD consensus are asymptomatic. We investigated if optimizing the glucose threshold and duration for sensor detected hypoglycemia (SDH) improves precision and sensitivity for identifying symptomatic hypoglycaemia from continuous glucose monitoring (CGM) data. We analysed 10 weeks of blinded CGM (Libre 2) and FitBit data from 435 participants [217 type 1, 218 type 2] with intact hypoglycemic awareness (Gold score <4). They self-reported symptomatic hypoglycemia on the Hypo-METRICS smartphone app. We used particle Markov chain Monte Carlo optimisation to generate the threshold and duration of SDH that maximizes detection of symptomatic hypoglycemia across our population and assessed impact of sleep status and diabetes type. The optimized threshold and duration were 70mg/dl for 27 min, with 2% gain of precision but 8% loss of sensitivity vs level 1 SDH (70mg/dl >15 min). Both level 1 SDH and our optimized definition had 3-fold increased sensitivity vs level 2 SDH (54mg/dl >15 min), with minimal loss of precision (Table). Our data validate ATTD level 1 hypoglycemia. Overall precision for detection of symptomatic hypoglycemia remains low despite optimization. Disclosure P.Divilly: None. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi, Vertex Pharmaceuticals Incorporated. R.J.Mccrimmon: Advisory Panel; Sanofi, Speaker's Bureau; Novo Nordisk A/S. J.K.Mader: Advisory Panel; Novo Nordisk A/S, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Medtronic, Becton, Dickinson and Company, Pharmasense, embecta, Research Support; A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Dexcom, Inc., Profusa, Inc., Speaker's Bureau; Novo Nordisk A/S, A. Menarini Diagnostics, Abbott Diabetes, Roche Diabetes Care, Eli Lilly and Company, Sanofi, Boehringer Ingelheim Inc., Becton, Dickinson and Company, Ypsomed AG, Viatris Inc., Servier Laboratories, Medtrust, Stock/Shareholder; Decide Clinical Software GmbH. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. S.A.Amiel: Advisory Panel; Medtronic, Other Relationship; Sanofi, Novo Nordisk. P.Choudhary: Advisory Panel; Medtronic, Novo Nordisk, Dexcom, Inc., MannKind Corporation, Insulet Corporation, Research Support; Abbott Diabetes, Speaker's Bureau; Sanofi, Lilly. Hypo-resolve consortium: n/a. Z.Mahmoudi: Employee; Novo Nordisk. G.Martine-edith: Other Relationship; Novo Nordisk A/S. D.Boiroux: Employee; Novo Nordisk A/S. N.Zaremba: None. U.Soeholm: Employee; Novo Nordisk A/S. M.Gomes: Employee; Novo Nordisk A/S. A.A.Vaag: None. B.E.De galan: Research Support; Novo Nordisk. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460)

Two-grid fully discrete finite element algorithms on temporal graded meshes for nonlinear multi-term time-fractional diffusion equations with variable coefficient

In this paper, we are concerned with two-grid fully discrete finite element algorithms for solving the nonlinear multi-term time-fractional diffusion equation with a variable coefficient. Taking into account the weak singularity of the solution at the initial time t=0, we employ graded meshes in time, which is an effective method to compensate for the lack of smoothness. A fully discrete implicit finite element scheme is established with the L1 method on temporally graded meshes; the stability and optimal error estimates are theoretically proved. To improve the computational efficiency, we propose a two-grid algorithm based on the fully discrete scheme, and then the second algorithm is constructed to further improve the optimal convergence order. Similar properties as standard FEM are presented for the two algorithms. A significant fact is that this technique reduces the computation time without loss of numerical precision. Several numerical examples are provided to support our theoretical findings and verify the performance of the proposed algorithms.

A post-training quantization method applied to the game-environment-based Dueling-DQN model

Reinforcement learning is a common method that focuses on the best behaviors that agents can get the highest rewards for in one particular environment. It was recently combined with deep learning to improve information perception. However, the traditional deep reinforcement model DQN is considered to be as computationally intensive as its layer deplumation. As a result, this paper proposes to include a post-training quantization method in an improved version of DQN to see if it is worthwhile to include. After some experiments, the outputs exactly prove that post-training quantization can deduce the computation with a tiny precision loss.

Non-linear differential equation for reactivity monitoring

This paper presents a new formulation for inverse point kinetics that produces a non-linear differential equation for the calculation of real-time reactivity, without the need of the nuclear power history. A non-linear differential equation (NLDE) results from a process of integration by parts of an existing integral in the classic inverse point kinetics equation and from the use of the prompt-jump approximation. The use of a NLDE has been shown to yield a significant gain in relation to the classic methods found in the literature, as the use of the nuclear power history became unnecessary, allowing the recovery of the reactivity calculation at any point in time without the loss of precision, after some unexpected interruption of a reactimeter. Apart from this, the formulation proposed favored a sampling of the data on nuclear power with a minimum 1-second time interval, to allow the calculation of reactivity in real time.

Non-redundant identification of geometric errors in multilateration of rotary axis

The multilateration measurement acquires sufficient data for the identification of the position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs) of the rotary axis. However, because of redundancy in the used intermediate parameters, existing identification methods suffer from a loss of accuracy and precision caused by the relatively lower stability to the influences of random errors. Meanwhile, this may result in a more complicated process if additional constraints are added to reinforce stability. This paper proposes a non-redundant method for identifying both the PIGEs and PDGEs of the rotary axis. Firstly, the identification model, in which the rotating component pose errors are fully and non-redundantly described by the twists, is established to identify the pose errors caused by geometric errors. Then, by deriving the relationship between the pose error twists and the PIGE & PDGE parameters, the sequential decoupling algorithm applying the least squares condition, together with identification models, for both the PIGEs and PDGEs is developed. Comparative simulations and experiments are carried out. With no redundancy, the proposed method exhibits smaller discrepancies between the identified and preset test lengths in the cone path test simulation, yielding an average improvement of 15.73% and 56.97% in two identification modes, respectively. Furthermore, the method demonstrates smaller discrepancies between the predicted and measured test data in the cross-validation experiment with an average improvement of 80.21% and 86.87% in the two modes, respectively. These results jointly verify the improvement in accuracy for identifying the PIGE and PDGE parameters. In addition, an overall reduction of the PIGE and PDGE uncertainties is observed in the Monte-Carlo simulations, which verifies the precision improvement.

Context Sensitivity without Contexts: A Cut-Shortcut Approach to Fast and Precise Pointer Analysis

Over the past decades, context sensitivity has been considered as one of the most effective ideas for improving the precision of pointer analysis for Java. Different from the extremely fast context-insensitivity approach, context sensitivity requires every program method to be analyzed under different contexts for separating the static abstractions of different dynamic instantiations of the method’s variables and heap objects, and thus reducing spurious object flows introduced by method calls. However, despite great precision benefits, as each method is equivalently cloned and analyzed under each context, context sensitivity brings heavy efficiency costs. Recently, numerous selective context-sensitive approaches have been put forth for scaling pointer analysis to large and complex Java programs by applying contexts only to the selected methods while analyzing the remaining ones context-insensitively; however, because the selective approaches do not fundamentally alter the primary methodology of context sensitivity (and do not thus remove its efficiency bottleneck), they produce much improved but still limited results. In this work, we present a fundamentally different approach called Cut-Shortcut for fast and precise pointer analysis for Java. Its insight is simple: the main effect of cloning methods under different contexts is to filter spurious object flows that have been merged inside a callee method; from the view of a typical pointer flow graph (PFG), such effect can be simulated by cutting off (Cut) the edges that introduce precision loss to certain pointers and adding Shortcut edges directly from source pointers to the target ones circumventing the method on PFG. As a result, we can achieve the effect of context sensitivity without contexts. We identify three general program patterns and develop algorithms based on them to safely cut off and add shortcut edges on PFG, formalize them and formally prove the soundness. To comprehensively validate Cut-Shortcut’s effectiveness, we implement two versions of Cut-Shortcut for two state-of-the-art pointer analysis frameworks for Java, one in Datalog for the declarative Doop and the other in Java for the imperative Tai-e, and we consider all the large and complex programs used in recent literatures that meet the experimental requirements. The evaluation results are extremely promising: Cut-Shortcut is even able to run faster than context insensitivity for most evaluated programs while obtaining high precision that is comparable to context sensitivity (if scalable) in both frameworks. This is for the first time that we have been able to achieve such a good efficiency and precision trade-off for those hard-to-analyze programs, and we hope Cut-Shortcut could offer new perspectives for developing more effective pointer analysis for Java in the future.

Social Influence-Maximizing Group Recommendation

In this paper, we revisit the group recommendation problem, by taking into consideration the information diffusion in a social network, as one of the main criteria that must be maximised. While the well-known influence maximization problem has the objective to select k users (spread seeds) from a social network, so that a piece of information can spread to the largest possible number of people in the network, in our setting the seeds are known (given as a group), and we must decide which k items (pieces of information) should be recommended to them. Therefore, the recommended items should at the same time be the best match for that group's preferences, and have the potential to spread as much as possible in an underlying diffusion network, to which the group members (the seeds) belong. This problem is directly motivated by group recommendation scenarios where social networking is an inherent dimension that must be taken into account when assessing the potential impact of a certain recommendation. We present the model and formulate the problem of influence-aware group recommendation as a multiple objective optimization problem. We then describe a greedy approach for this problem and we design an optimisation approach, by adapting the top-k algorithms NRA and TA. We evaluate all these methods experimentally, in three different recommendation scenarios, for movie, micro-blog and book recommendations, based on real-world datasets from Flixster, Twitter, and Douban respectively. Unsurprisingly, with the introduction of information diffusion as an optimization criterion for group recommendation, the recommendation problem becomes more complex. However, we show that our algorithms enable spread efficiency without loss of recommendation precision, under reasonable latency.

Distributed Computation for Marginal Likelihood based Model Choice

We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only accessed locally by individual workers and no data is shared between the workers. We approximate the model evidence for the full data set through Monte Carlo sampling from the posterior on every subset generating a model evidence per subset. The results are combined using a novel approach which corrects for the splitting using summary statistics of the generated samples. Our divide-and-conquer approach enables Bayesian model choice in the large data setting, exploiting all available information but limiting communication between workers. We derive theoretical error bounds that quantify the resulting trade-off between computational gain and loss in precision. The embarrassingly parallel nature yields important speed-ups when used on massive data sets as illustrated by our real world experiments. In addition, we show how the suggested approach can be extended to model choice within a reversible jump setting that explores multiple feature combinations within one run.

RPIM-RITSS Method for Large Deformation Analysis Using ABAQUS

In the standard Lagrangian finite-element framework, the “remeshing and interpolation technique with the small strain” (RITSS) method has been applied increasingly in geotechnical engineering. However, the mapping algorithm of the RITSS method suffers from either the onerous division of unique element, such as the modified unique element divisional method, or frequent mapping of the stress and other information between integration points and nodes, resulting in the loss of precision, such as superconvergent patch recovery (SPR) and recovery by equilibration of patches. In this study, within the original RITSS, the radial point interpolation method (RPIM) mapping algorithm is implemented to improve accuracy and convenience. The RPIM-RITSS method is integrated into the ABAQUS (6.14) commercial software package. With a master Python script, the whole analysis process is performed automatically and continuously without any intervention from the user. The efficiency and robustness of the RPIM-RITSS method are verified through three numerical examples: penetration of the strip footing, the pullout and the rotation of the anchor plate, and deep penetration of the T-Bar. Consequently, the RPIM-RITSS method is an alternative, powerful, and easily extensible way to tackle large deformation problems in geotechnical engineering.

IoT enabled prediction of agriculture's plant disease using improvedϖ quantum whale optimization DRDNN approach

The population of the earth is increasing at a very rapid rate. Ensuring access to nutritious food is essential to the survival of the world's population, given this continued growth. Agriculture also plays a critical role in developing countries economies. The difference in supply and demand for food or agriculture has an impact on the overall state of the economy. Plant diseases pose a serious risk to food yield, which could lead to hunger and economic slowdown. The proposed Improved Quantum Whale Optimization with Principle Component Analysis (IQWO-PCA) on using a Machine Learning (ML) model to evaluate a collection of images of tomato disease to take the appropriate preventative measures to deal with this agricultural catastrophe. The dataset used in this study was derived from an easily available plant village dataset. The network is developed after the hyper-parameters are optimized in the systematic review. Four trained prototypes are used to develop the transmission learning-based DNN: Alexnet, VGG16, ResNet50, and DenseNet121.Using an approach to optimize composite building blocks, the main features of the data set are retrieved. To better classify tomato diseases, the recovered data are loaded into a deep neuronal network. The proposed model is then evaluated using traditional ML methodologies to determine its superiority in terms of precision measurements and loss rates.

IFDS-based Context Debloating for Object-Sensitive Pointer Analysis

Object-sensitive pointer analysis, which separates the calling contexts of a method by its receiver objects, is known to achieve highly useful precision for object-oriented languages such as Java. Despite recent advances, all object-sensitive pointer analysis algorithms still suffer from the scalability problem due to the combinatorial explosion of contexts in large programs. In this article, we introduce a new approach, Conch , that can be applied to debloat contexts for all object-sensitive pointer analysis algorithms, thereby improving significantly their efficiency while incurring a negligible loss of precision. Our key insight is to approximate a recently proposed set of two necessary conditions for an object in a program to be context-sensitive, i.e., context-dependent (whose precise verification is undecidable) with a set of three linearly verifiable conditions in terms of the number of edges in the pointer assignment graph (PAG) representation of the program. These three linearly verifiable conditions, which turn out to be almost always necessary in practice, are synthesized from three key observations regarding context-dependability for the objects created and used in real-world object-oriented programs. To develop a practical implementation for Conch , we introduce an IFDS-based algorithm for reasoning about object reachability in the PAG of a program, which runs linearly in terms of the number of edges in the PAG. By debloating contexts for three representative object-sensitive pointer analysis algorithms, which are applied to a set of representative Java programs, Conch can speed up these three baseline algorithms substantially at only a negligible loss of precision (less than 0.1%) with respect to several commonly used precision metrics. In addition, Conch also improves their scalability by enabling them to analyze substantially more programs to completion than before (under a time budget of 12 hours). Conch has been open-sourced (http://www.cse.unsw.edu.au/~corg/tools/conch), opening up new opportunities for other researchers and practitioners to further improve this research. To demonstrate this, we introduce one extension of Conch to accelerate further the three baselines without losing any precision, providing further insights on extending Conch to make precision-efficiency tradeoffs in future research.

A Lightweight Radar Ship Detection Framework with Hybrid Attentions

One of the current research areas in the synthetic aperture radar (SAR) processing fields is deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR images has achieved continuous breakthroughs in detection precision. However, determining how to strike a better balance between the precision and complexity of the algorithm is very meaningful for real-time object detection in real SAR application scenarios, and has attracted extensive attention from scholars. In this paper, a lightweight object detection framework for radar ship detection named multiple hybrid attentions ship detector (MHASD) with multiple hybrid attention mechanisms is proposed. It aims to reduce the complexity without loss of detection precision. First, considering that the ship features in SAR images are not inconspicuous compared with other images, a hybrid attention residual module (HARM) is developed in the deep-level layer to obtain features rapidly and effectively via the local channel attention and the parallel self-attentions. Meanwhile, it is also capable of ensuring high detection precision of the model. Second, an attention-based feature fusion scheme (AFFS) is proposed in the model neck to further heighten the features of the object. Meanwhile, AFFS constructs and develops a fresh hybrid attention feature fusion module (HAFFM) upon the local channel and spatial attentions to guarantee the applicability of the detection model. The Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) experimental results demonstrate that MHASD can balance detection speed and precision (improving average precision by 1.2% and achieving 13.7 GFLOPS). More importantly, extensive experiments on the SAR Ship Detection Dataset (SSDD) demonstrate that the proposed method is less affected by the background such as ports and rocks.

An Automated Method of 3D Facial Soft Tissue Landmark Prediction Based on Object Detection and Deep Learning.

Three-dimensional facial soft tissue landmark prediction is an important tool in dentistry, for which several methods have been developed in recent years, including a deep learning algorithm which relies on converting 3D models into 2D maps, which results in the loss of information and precision. This study proposes a neural network architecture capable of directly predicting landmarks from a 3D facial soft tissue model. Firstly, the range of each organ is obtained by an object detection network. Secondly, the prediction networks obtain landmarks from the 3D models of different organs. The mean error of this method in local experiments is 2.62±2.39, which is lower than that in other machine learning algorithms or geometric information algorithms. Additionally, over 72% of the mean error of test data falls within ±2.5 mm, and 100% falls within 3 mm. Moreover, this method can predict 32 landmarks, which is higher than any other machine learning-based algorithm. According to the results, the proposed method can precisely predict a large number of 3D facial soft tissue landmarks, which gives the feasibility of directly using 3D models for prediction.

RADIOCARBON CALIBRATION: FROM BANE TO BLESSING

ABSTRACT Temporal and spatial variation in radiocarbon (14C) in the atmosphere has been the subject of investigation from the first pioneering work of Libby and Arnold. However, as the precision of measurements has improved, now by almost two orders of magnitude, what constitutes a significant variation has also changed. Furthermore, it has become possible to test degrees of variation over much longer timescales and with ever wider geographic coverage. As knowledge has improved, the interpretation of 14C measurements has had to be revised. These re-evaluations, and the loss of chronological precision that comes with accurate calibration, have often been seen as an unfortunate drawback in the 14C dating method. However, these problems have stimulated extensive research in global 14C records, statistical methods for dealing with complex 14C data, and measurement methods. This research has provided a wealth of information useful for other scientific challenges, most notably the quantification of the global carbon cycle, but also enabled, in the right circumstances, measurement precision an order of magnitude better than if there had been no variation in atmospheric 14C. Challenges remain but the research undertaken for 14C calibration has, through its ingenuity and innovation, provided rich scientific dividends in both chronology and broader geoscience.

Immobilization of zeolitic imidazolate framework‐90 onto modified stainless steel wire via covalent bonding for solid‐phase microextraction of biomarkers in exhalation of lung cancer patients

AbstractThe improvement of the strong force between the coating and the substrate is important to make the coating for solid‐phase microextraction with good stability, durability, and reproducibility during thermal and solvent desorption. In this paper, zeolitic imidazolate frameworks‐90 coating was immobilized on the stainless‐steel fiber via the chemical‐bonded method with the help of polydopamine. The fiber was used for the solid‐phase microextraction of lung cancer biomarkers prior to gas chromatography‐mass spectrometry analysis. Several extraction and desorption conditions were optimized. Under the optimized conditions, the established method exhibited high enrichment factors (434–7560), low limits of detection (2.6–4.2 ng/L), wide linear ranges (0.02–30 μg/L), good repeatability (3.46%–9.37% for intra‐day, 4.11%–9.18% for inter‐day, n = 6) and reproducibility (5.37%–7.02%, n = 3). Moreover, the coated fiber is thermally stable and can be reused at least 100 cycles of extraction/desorption/conditioning without significant loss of extraction efficiency and precision. Eventually, the self‐made coated fiber combined with gas chromatography‐mass spectrometry was successfully applied to the extraction and detection of five biomarkers of lung cancer from real breath samples with a satisfactory recovery (96.6%–117.6%).

Representer Theorem for Learning Koopman Operators

In this work, we consider the problem of learning the Koopman operator for discrete-time autonomous systems. The learning problem is generally formulated as a constrained regularized empirical loss minimization in the infinite-dimensional space of linear operators. We show that a representer theorem holds for the learning problem under certain but general conditions. This allows convex reformulation of the problem in a finite-dimensional space without any approximation and loss of precision. We discuss the inclusion of various forms of regularization and constraints in the learning problem, such as the operator norm, the Frobenius norm, the operator rank, the nuclear norm, and the stability. Subsequently, we derive the corresponding equivalent finite-dimensional problem. Furthermore, we demonstrate the connection between the proposed formulation and the extended dynamic mode decomposition. We present several numerical examples to illustrate the theoretical results and verify the performance of regularized learning of the Koopman operators.

A method to address beam cross coupling errors in phased array Doppler sonar

Performance of Doppler sonar is degraded by beam cross coupling errors. This performance degradation presents itself as the loss of precision and bias of velocity estimates output by the system. A model is proposed here to reveal the physical essence of the beam cross coupling. Specifically, the model can analyze the effects of environmental conditions and vehicle attitude on the coupling bias. Based on this model, a phase assignment method is also proposed to reduce the beam cross coupling bias. The results obtained for various settings validate the efficacy of the proposed method.

Lidar Pose Tracking of a Tumbling Spacecraft Using the Smoothed Normal Distribution Transform

Lidar sensors enable precise pose estimation of an uncooperative spacecraft in close range. In this context, the iterative closest point (ICP) is usually employed as a tracking method. However, when the size of the point clouds increases, the required computation time of the ICP can become a limiting factor. The normal distribution transform (NDT) is an alternative algorithm which can be more efficient than the ICP, but suffers from robustness issues. In addition, lidar sensors are also subject to motion blur effects when tracking a spacecraft tumbling with a high angular velocity, leading to a loss of precision in the relative pose estimation. This work introduces a smoothed formulation of the NDT to improve the algorithm’s robustness while maintaining its efficiency. Additionally, two strategies are investigated to mitigate the effects of motion blur. The first consists in un-distorting the point cloud, while the second is a continuous-time formulation of the NDT. Hardware-in-the-loop tests at the European Proximity Operations Simulator demonstrate the capability of the proposed methods to precisely track an uncooperative spacecraft under realistic conditions within tens of milliseconds, even when the spacecraft tumbles with a significant angular rate.

Lipid lowering effects and safety of evolocumab in Chinese patients at very high cardiovascular risk: a single-center study.

Lipid lowering effects and safety of evolocumab in Chinese patients at very high cardiovascular risk: a single-center study.

Reduced memory precision in older age is associated with functional and structural differences in the angular gyrus

Decreased fidelity of mnemonic representations plays a critical role in age-related episodic memory deficits, yet the brain mechanisms underlying such reductions remain unclear. Using functional and structural neuroimaging, we examined how changes in two key nodes of the posterior-medial network, the hippocampus and the angular gyrus (AG), might underpin loss of memory precision in older age. Healthy young and older adults completed a memory task that involved reconstructing object features on a continuous scale. Investigation of blood-oxygen-level-dependent (BOLD) activity during retrieval revealed an age-related reduction in activity reflecting successful recovery of object features in the hippocampus, whereas trial-wise modulation of BOLD signal by graded memory precision was diminished in the AG. Gray matter volume of the AG further predicted individual differences in memory precision in older age, beyond likelihood of successful retrieval. These findings provide converging evidence for a role of functional and structural integrity of the AG in constraining the fidelity of episodic remembering in older age, yielding new insights into parietal contributions to age-related episodic memory decline.