Bis(μ-naphthalene-1,8-dicarboxyl­ato-κ2 O 1:O 8)bis­[aqua­bis­(N,N′-dimethyl­formamide-κO)copper(II)]

In the centrosymmetric dinuclear title complex, [Cu2(C12H6O4)2(C3H7NO)4(H2O)2], the coordination environment of each Cu(II) atom displays a distorted CuO5 square-pyramidal geometry, which is formed by two carboxyl­ate O atoms of two μ-1,8-nap ligands (1,8-nap is naphthalene-1,8-dicarboxyl­ate), two O atoms of two DMF (DMF is N,N′-dimethyl­formamide) and one coordinated water mol­ecule. The Cu—O distances involving the four O atoms in the square plane are in the range 1.9501 (11)–1.9677 (11) Å, with the Cu atom lying nearly in the plane [deviation = 0.0726 (2) Å]. The axial O atom occupies the peak position with a Cu—O distance of 2.885 (12) Å, which is significantly longer than the rest of the Cu—O distances. Each 1,8-nap ligand acts as bridge, linking two CuII atoms into a dinuclear structure. Inter­molecular O—H⋯O and C—H⋯O hydrogen-bonding inter­actions consolidate the structure

Diagnosis and treatment characteristics of radioactive optic neuropathy

AIM: To explore the diagnosis and treatment methods of radioaction-induced optic neuritis(RION)through the clinical dates of 17 patients. <p>METHODS: It was a retrospective case series study. From August 2008 to October 2013, 17 cases(24 eyes)of Rion clinical dates from Chinese PLA General Hospital were studied. The diagnosis methods including visual acuity, pupil, fundus, visual field, fundus fluorescein angiography(FFA), visual electrophysiological testing, and head MRI. To analysis the clinical date of patients with diagnosis of RION by statistical description.<p>RESULTS: The deterioration degree of vision: 13 eyes were classified as Ⅳ, 9 eyes as Ⅲ, 2 eyes as Ⅱ. Ten eyes RAPD(+), visual electrophysiology is extinguished. The retina of 5 eyes showed flame hemorrhages and cotton wool spots exudation. Optic nerve head edema in one eye. T1-weighted MRI enhanced in 19 eyes which showed optic nerve of the intracranial and intratubal segments abnormal changed, optic chiasm and pituitary stalk signal abnormalities and enhancement of the optic nerve. Tortuous optic nerves and rough edges were observed in 5 eyes. Treatment effect: 4 eyes of visual acuity improved, 1 eye from blindness to light perception,1 eye from 0.08 to 0.2, 1 eye from 0.4 to 0.6,1 eye from 0.04 to 0.15, the rest of the cases did not see any improvement.<p>CONCLUSION: The unique clinical manifestation of RION can provide objective basis for clinical diagnosis in time, but there have not been proven any effective treatments

Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD

CDR: Conservative Doubly Robust Learning for Debiased Recommendation

In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation