Sensitivity analysis in linear models

AbstractIn this work, we consider the general linear model or its variants with the ordinary least squares, generalised least squares or restricted least squares estimators of the regression coefficients and variance. We propose a newly unified set of definitions for local sensitivity for both situations, one for the estimators of the regression coefficients, and the other for the estimators of the variance. Based on these definitions, we present the estimators’ sensitivity results.We include brief remarks on possible links of these definitions and sensitivity results to local influence and other existing results.</jats:p

GriT-DBSCAN: A Spatial Clustering Algorithm for Very Large Databases

DBSCAN is a fundamental spatial clustering algorithm with numerous practical applications. However, a bottleneck of the algorithm is in the worst case, the run time complexity is $O(n^2)$. To address this limitation, we propose a new grid-based algorithm for exact DBSCAN in Euclidean space called GriT-DBSCAN, which is based on the following two techniques. First, we introduce a grid tree to organize the non-empty grids for the purpose of efficient non-empty neighboring grids queries. Second, by utilising the spatial relationships among points, we propose a technique that iteratively prunes unnecessary distance calculations when determining whether the minimum distance between two sets is less than or equal to a certain threshold. We theoretically prove that the complexity of GriT-DBSCAN is linear to the data set size. In addition, we obtain two variants of GriT-DBSCAN by incorporating heuristics, or by combining the second technique with an existing algorithm. Experiments are conducted on both synthetic and real-world data sets to evaluate the efficiency of GriT-DBSCAN and its variants. The results of our analyses show that our algorithms outperform existing algorithms

Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study

Abstract: System of panel models are popular models in applied sciences and the question of spatial errors has created the recent demand for spatial system estimation of panel models. Therefore we propose new diagnostic methods to explore if the spatial component will change significantly the outcome of non-spatial estimates of seemingly unrelated regression (SUR) systems. We apply a local sensitivity approach to study the behavior of generalized least squares (GLS) estimators in two spatial autoregression SUR system models: a SAR model with SUR errors (SAR-SUR) and a SUR model with spatial errors (SUR-SEM). Using matrix derivative calculus we establish a sensitivity matrix for spatial panel models and we show how a first order Taylor approximation of the GLS estimators can be used to approximate the GLS estimators in spatial SUR models. In a simulation study we demonstrate the good quality of our approximation results.

Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel query-enhanced text-image retrieval method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://sites.google.com/view/Flickr30K-cfq

Designing Practical Teaching System for Outside-school Practice Base

Abstract. A great deal of department in enterprise how to optimize practicing-teaching contents, and forms the practicing-teaching system, in building Beijing level Talent training base of outsideschool. The system consist of three parts which is the cognitive course content design, the hardware practice course content design and the software test theoretical teaching content design. We improved concepts, formed a detail enterprise practice curriculum program, and do it in practice. This practice teaching system is a featured program of our university. It has been carried out for 5 years, improving students&apos; engineering and practice skills and therefore fostering eligible persons with various abilities and qualities for the development and prosperity of our country

Multi-Core-shell structured LiFePO4@Na3V2(PO4)3@C composite for enhanced low-temperature performance of lithium ion batteries

In this work, a multi-core–shell-structured LiFePO4@Na3V2(PO4)3@C (LFP@NVP@C) composite was successfully designed and prepared to address inferior low-temperature performance of LiFePO4 cathode for lithium-ion batteries. Transmission electron microscopy (TEM) confirms the inner NVP and outer carbon layers co-existed on the surface of LFP particle. When evaluated at low-temperature operation, LFP@NVP@C composite exhibits an evidently enhanced electrochemical performance in term of higher capacity and lower polarization, compared with LFP@C. Even at − 10 °C with 0.5C, LFP@NVP@C delivers a discharge capacity of ca. 96.9 mAh·g−1 and discharge voltage of ca. 3.3 V, which is attributed to the beneficial contribution of NVP coating. NASICON-structured NVP with an open framework for readily insertion/desertion of Li+ will effectively reduce the polarization for the electrochemical reactions of the designed LFP@NVP@C composite

Improvements on the optical properties of Ge-Sb-Se chalcogenide glasses with iodine incorporation

International audienceDecreasing glass network defects and improving optical transmittance are essential work for material researchers. We studied the function of halogen iodine (I) acting as a glass network modifier in Ge–Sb–Se–based chalcogenide glass system. A systematic series of Ge20Sb5Se75-xIx (x = 0, 5, 10, 15, 20 at%) infrared (IR) chalcohalide glasses were investigated to decrease the weak absorption tail (WAT) and improve the mid-IR transparency. The mechanisms of the halogen I affecting the physical, thermal, and optical properties of Se-based chalcogenide glasses were reported. The structural evolutions of these glasses were also revealed by Raman spectroscopy and camera imaging. The progressive substitution of I for Se increased the optical bandgap. The WAT and scatting loss significantly decreased corresponding to the progressive decrease in structural defects caused by dangling bands and structure defects in the original Ge20Sb5Se75 glass. The achieved maximum IR transparency of Ge–Sb–Se–I glasses can reach up to 80% with an effective transmission window between 0.94 μm to 17 μm, whereas the absorption coefficient decreased to 0.029 cm-1 at 10.16 μm. Thus, these materials are promising candidates for developing low-loss IR fibers

Fabrication and characterization of Ge–Sb–Se–I glasses and fibers

International audienceChalcogenide glasses of the Ge20Sb5Se75−x I x (x = 0, 5, 10, 15, 20 at.%) system were prepared. This study was performed to examine some Ge–Sb–Se–I glass physical and optical properties, the structural evolution of the glass network, and the optical properties of the infrared glass fibers based on our previous studies. The variation process of the glass physical properties, such as transition temperature, glass density, and refractive index, was investigated from the glass of Ge20Sb5Se75 to the Ge20Sb5Se75−x I x glass series. The structural evolutions of these glasses were examined by Raman spectroscopy. The Ge20Sb5Se55I20 composition was selected for the preparation of the IR fiber. The Ge20Sb5Se55I20 glass was purified through distillation, and the intensity of the impurity absorption peaks caused by Ge–O, H2O, and Se–H was reduced or eliminated in the purified glasses. Then, Ge20Sb5Se55I20 chalcogenide glass fiber for mid-infrared transmission was fabricated using high-purity materials. The transmission loss of the Ge20Sb5Se55I20 fiber was greatly reduced compared with that of the Ge20Sb5Se75 glass fiber. The lowest losses obtained were 3.5 dB/m at 3.3 μm for Ge20Sb5Se75I20 fiber, which was remarkably improved compared with 48 dB/m of the unpurified Ge20Sb5Se75 fiber

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis

ObjectiveThis retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC).MethodsIn total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers—adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared.ResultsThe Multimodal-SVM model achieved the best predictive performance than the other models (P &lt; 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases.ConclusionThe ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions