A Multi-Period Curve Fitting Model for Short-Term Prediction of the COVID-19 Spread in the U.S. Metropolitans.

The COVID-19 has wreaked havoc upon the world with over 248 million confirmed cases and a death toll of over 5 million. It is alarming that the United States contributes over 18% of these confirmed cases and 14% of the deaths. Researchers have proposed many forecasting models to predict the spread of COVID-19 at the national, state, and county levels.

Biogeochemical Fe(II) generators as a new strategy for limiting Cd uptake by rice and its implication for agricultural sustainability.

This work has developed a new strategy of biogeochemical Fe(II) generators for activating microbial Fe(II) generation to immobilize Cd in soils through protons scavenging and coprecipitation. A new biochar modified magnetite (FeBC15) has been fabricated through a top-down method, with which microbial respiration can be stimulated in paddy soil. The FeBC15 exhibits a higher adsorption capacity for Cd than pristine magnetite (1.

Control of the interface graphitized/amorphous carbon of biomass-derived carbon microspheres for symmetric supercapacitors.

Rational interface control of porous carbon electrode materials is of significance for achieving efficient supercapacitors. Herein, biomass-derived carbon microspheres with a highly graphitized porous surface and amorphous subsurface were well constructed a flexible coupled catalysis-activation process. The unique structure not only endows the carbon microspheres with rapid electron transfer but also an ultra-high specific surface area.

Phase Compatible NiFeO Coating Tunes Oxygen Redox in Li-Rich Layered Oxide.

Li-rich layered oxides have attracted intense attention for lithium-ion batteries, as provide substantial capacity from transition metal cation redox simultaneous with reversible oxygen-anion redox. However, unregulated irreversible oxygen-anion redox leads to critical issues such as voltage fade and oxygen release. Here, we report a feasible NiFeO (NFO) surface-coating strategy to turn the nonbonding coordination of surface oxygen into metal-oxygen decoordination.

Exploring Natural Allelic Variations of the β-Triketone Herbicide Resistance Gene HIS1 for Application in indica Rice and Particularly in Two-Line Hybrid Rice.

Background: Benzobicyclon (BBC) is a β-triketone herbicide (bTH) used in rice paddy fields. It has the advantages of high efficiency, low toxicity, high crop safety, and good environmental compatibility, and shows efficacy against paddy weeds resistant to other types of herbicides. However, as some important indica rice varieties are susceptible to BBC, BBC is currently only registered and applied in japonica rice cultivation areas.

Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging.

Multiple Kernel Learning (MKL) generalizes SVMs to the setting where one simultaneously trains a linear classifier and chooses an optimal combination of given base kernels. Model complexity is typically controlled using various norm regularizations on the base kernel mixing coefficients. Existing methods neither regularize nor exploit potentially useful information pertaining to how kernels in the input set 'interact'; that is, higher order kernel-pair relationships that can be easily obtained via unsupervised (similarity, geodesics), supervised (correlation in errors), or domain knowledge driven mechanisms (which features were used to construct the kernel?).

Scale Invariant cosegmentation for image groups.

Our primary interest is in generalizing the problem of Cosegmentation to a large group of images, that is, concurrent segmentation of common foreground region(s) from multiple images. We further wish for our algorithm to offer scale invariance (foregrounds may have arbitrary sizes in different images) and the running time to increase (no more than) near linearly in the number of images in the set. What makes this setting particularly challenging is that even if we ignore the scale invariance desiderata, the Cosegmentation problem, as formalized in many recent papers (except [1]), is already hard to solve optimally in the two image case.

Ensemble Clustering using Semidefinite Programming with Applications.

In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem.

Role adaptation of family caregivers for ventilator-dependent patients: transition from respiratory care ward to home.

Aim And Objectives: To explore the underlying theoretical framework for the role adaptation of family caregivers for ventilator-dependent patients after transferring from respiratory care ward to home.

Background: The number of ventilator-dependent patients has been increasing worldwide. Under Taiwan's National Health Insurance policy, if ventilator-dependent patients are stable, they should be transferred from an acute care hospital to a subacute unit or home.

Learning Kernels for variants of Normalized Cuts: Convex Relaxations and Applications.

We propose a new algorithm for learning kernels for variants of the Normalized Cuts (NCuts) objective - i.e., given a set of training examples with known partitions, how should a basis set of similarity functions be combined to induce NCuts favorable distributions.

Ensemble Clustering using Semidefinite Programming.

We consider the ensemble clustering problem where the task is to 'aggregate' multiple clustering solutions into a single consolidated clustering that maximizes the shared information among given clustering solutions. We obtain several new results for this problem. First, we note that the notion of agreement under such circumstances can be better captured using an agreement measure based on a 2 string encoding rather than voting strategy based methods proposed in literature.