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The equity-efficiency trade-off and cumulative return theories predict larger returns to school spending in areas with higher previous investment in children. Equity—not efficiency—is therefore used to justify progressive school funding: spending more in communities with fewer financial resources. Yet it remains unclear how returns to school spending vary across areas by previous investment. Using county-level panel data for 2009–18 from the Stanford Education Data Archive, the Census Finance Survey, and National Vital Statistics, the authors estimate achievement returns to school spending and test whether returns vary between counties with low and high levels of initial human capital (measured as birth weight), child poverty, and previous spending. Spending returns are higher among counties with low previous investment (counties that also have a high percentage of Black students). Evidence of diminishing returns by previous investment documents another way that schools increase equality and establishes another argument for progressive school funding: efficiency.

Flavonol synthase (FLS) is a key enzyme of the flavonoid biosynthetic pathway, which catalyzes the conversion of dihydroflavonols into flavonols. In this study, the FLS gene IbFLS1 was cloned and characterized from sweet potato. The resulting IbFLS1 protein showed a high similarity with other plant FLSs. The conserved amino acids (HxDxnH motifs) binding ferrous iron and residues (RxS motifs) binding 2-oxoglutarate were found in IbFLS1 at conserved positions, as in other FLSs, suggesting that IbFLS1 belongs to the 2-oxoglutarate-dependent dioxygenases (2-ODD) superfamily. qRT-PCR analysis showed an organ-specific pattern of expression of the IbFLS1 gene, which was predominantly expressed in young leaves. The recombinant IbFLS1 protein could catalyze the conversion of dihydrokaempferol and dihydroquercetin to kaempferol and quercetin, respectively. The results of subcellular localization studies indicated that IbFLS1 was found mainly in the nucleus and cytomembrane. Furthermore, silencing the IbFLS gene in sweet potato changed the color of the leaves to purple, substantially inhibiting the expression of IbFLS1 and upregulating the expression of genes involved in the downstream pathway of anthocyanin biosynthesis (i.e., DFR, ANS, and UFGT). The total anthocyanin content in the leaves of the transgenic plants was dramatically increased, whereas the total flavonol content was significantly reduced. Thus, we conclude that IbFLS1 is involved in the flavonol biosynthetic pathway and is a potential candidate gene of color modification in sweet potato.

Additional file 1: Table S1. The detailed clinical characteristics of patients in the TCGA and CGGA cohorts.

Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones. We find that although the only previous OWOD work constructively puts forward to the OWOD definition, the experimental settings are unreasonable with the illogical benchmark, confusing metric calculation, and inappropriate method. In this paper, we rethink the OWOD experimental setting and propose five fundamental benchmark principles to guide the OWOD benchmark construction. Moreover, we design two fair evaluation protocols specific to the OWOD problem, filling the void of evaluating from the perspective of unknown classes. Furthermore, we introduce a novel and effective OWOD framework containing an auxiliary Proposal ADvisor (PAD) and a Class-specific Expelling Classifier (CEC). The non-parametric PAD could assist the RPN in identifying accurate unknown proposals without supervision, while CEC calibrates the over-confident activation boundary and filters out confusing predictions through a class-specific expelling function. Comprehensive experiments conducted on our fair benchmark demonstrate that our method outperforms other state-of-the-art object detection approaches in terms of both existing and our new metrics. Our benchmark and code are available at https://github.com/RE-OWOD/RE-OWOD.

Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradient-based explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We propose an optimization framework that derives global invariance from unlabeled data using self-learning to promote the stability of local explanations tailored for specific query-reference pairs. The optimization problems can be solved using gradient descent-ascent (GDA) for constrained optimization, or SGD for KL-divergence regularized unconstrained optimization, with convergence proofs, especially when the objective functions are nonconvex due to the Siamese architecture. Quantitative results and case studies on tabular and graph data from neuroscience and chemical engineering show that the framework respects the self-learned invariance while robustly optimizing the faithfulness and simplicity of the explanation. We further demonstrate the convergence of GDA experimentally., Comment: Accepted to ICDM 2021

This study sought to determine the effect of feeding three levels of the novel feedstuff fresh Phyllanthus emblica (amla) fruit on nutrient digestibility, fermentation parameters, and milk production of lactating dairy cows. Eight ruminally cannulated mid-lactation dairy cows were randomly assigned to two treatment groups in a repeated crossover design. There was a 14-d adaptation phase followed by the start of period 1. In this period, the first group of 4 cows received the control diet, whereas the other group was supplemented with FAF at increasing levels (200, 400, 600 g/d), sequentially, at 14-d intervals. Each period consisted of a cow as the main plot, with three different amla doses representing subplots. In period 2, the control and supplemented groups were exchanged. For each sub-period, the first ten days were adjusted for diet adaptation, and the last four days were assigned to sample milk, feed, rumen, and feces. Supplementation of FAF had no effect (P = 0.92) on feed intake. While, the lowest (P

Stowage planning is the core of ship planning. It directly influences the seaworthiness of container ship and the handling efficiency of container terminal. As the latter step of container ship stowage plan, terminal stowage planning optimizes terminal cost according to pre-plan. Group-Bay stowage planning is the smallest sub problem of terminal stowage planning problem. A group-bay stowage planning model is formulated to minimize relocation, crane movement and target weight gap satisfying both ship owner and container terminal. A GA-A* hybrid algorithm is designed to solve this problem. Numerical experiment shown the validity and the efficiency.

The existing sewer dredging method can not meet the needs of high-speed operation of the city. Through the research and analysis of the cutterhead of the shield tunneling machine, combined with the requirements of the sewer environment and dredging work, three-dimensional model, Ansys simulation analysis and physical experiments are adopted. In this way, a new type of spoke panel cutter head was designed. The cutter head adopts a new type of scraper. The structure is exquisite and the assembly is simple. It can effectively clear the sediment in the sewer environment and complete the initial recovery of the silt.

The drainage pipeline is responsible for the collection and transportation of urban sewage and wastewater, and is one of the necessary facilities for the normal operation of the city. Therefore, it is of great significance to unblock and prevent the sewer pipe. A new type of dredging machine is designed to overcome the defects of the existing dredging method. The machine consists of two parts: a mechanical module and a control module. The machine adopts the idea of mechatronics which realize the automatic operation of the multi-function dredging machine, and it has low cost and good market economy benefits.

Haoyu Ren,1 Yifan Shen,2 Daixing Hu,1 Wei He,1 Jing Zhou,1 Yijia Cao,1 Yu Mao,1 Yi Dou,1 Wei Xiong,1 Qi Xiao,1 Yuhong Zhang,2 Xinliang Su1 1Department of Endocrine and Breast Surgery, 2The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China Background: Mutations of BRAFV600E and TERT promoters are associated with thyroid cancer development. This study further investigated association of these mutations with clinicopathological characteristics from patients with papillary thyroid carcinoma (PTC). Methods: Tumor tissues from 342 PTC patients were obtained for DNA extraction and polymerase chain reaction amplification to detect the BRAFV600E mutation using amplification-refractory mutation system-polymerase chain reaction. TERT promoter mutations were assessed using Sanger DNA sequencing. The association of these gene mutations with clinicopathological characteristics was then statistically analyzed. Results: Two hundred and seventy of 342 (78.9%) PTC patients harbored the BRAFV600E mutation, which was associated with older age male patients. Moreover, TERT promoter mutations occurred in 12 of 342 (3.5 %) PTC patients, all of whom also had the BRAF mutation. One hundred thirty-three patients with papillary thyroid microcarcinoma (PTMC) had no TERT mutations. Statistically, the coexistence of BRAF and TERT promoter mutations were significantly associated with older age, larger tumor size, extrathyroidal extension, and advanced tumor stage, but not with central lymph node metastasis, lateral lymph node metastasis, numbers of lymph node metastasis >5, and numbers of involved/harvested lymph nodes (No. of LNs involved or harvested). The multivariate analyses showed older age (odds ratio [OR], 2.194; 95% CI: 1.117–4.311; p=0.023), larger tumor size (OR, 4.100; 95% CI: 2.257–7.450; p

In this paper, we propose a novel Vandermonde decomposition-based direction-of-arrival (DOA) estimation algorithm by using a coprime array, where increased number of degrees-of-freedom (DOFs) can be achieved in an off-grid manner. Specifically, the equivalent statistics corresponding to an augmented virtual uniform linear array are first derived from the coprime array received signals, and the resulting coprime coarray covariance matrix is capable to increase the DOFs. While the obtained coprime coarray covariance matrix follows a positive semi-definite Hermitian Toeplitz structure, Vandermonde decomposition can be incorporated to perform unique decomposition in the virtual domain. By matching the Vandermonde decomposition result and its theoretical version, closed-form solutions are formulated for estimating the DOA and power of each source. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

In this paper, a new numerical method is presented for solving the linear Poisson-Boltzmann equation. A mortar finite element method with fundamental solution and artificial boundary condition are used to deal with the difficulties in numerical simulation and optimal error estimates are obtained.

This paper proposes PTME, which is a probability-based trust management model for distributed e-commerce. PTME can establish trust relations between buyers and sellers in a distributed e-commerce application. By employing a recommendation framework which forms objectively reputation based on both direct observation and indirect recommendation, PTME inhibits the false accusation attack and collusion attack. Also, PTME uses a forgetting factor to expire old observations, which is of great help to defeat on-off attacks.