Sparse Estimation of Cox Proportional Hazards Models via Approximated Information Criteria

We propose a new sparse estimation method for Cox (1972) proportional hazards models by optimizing an approximated information criterion. The main idea involves approximation of the inline image norm with a continuous or smooth unit dent function. The proposed method bridges the best subset selection and regularization by borrowing strength from both. It mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. We further reformulate the problem with a reparameterization step so that it reduces to one unconstrained nonconvex yet smooth programming problem, which can be solved efficiently as in computing the maximum partial likelihood estimator (MPLE). Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing postselection inference. The oracle property of the proposed method is established. Both simulated experiments and empirical examples are provided for assessment and illustration

Image Super-Resolution Reconstruction Based on L1/2 Sparsity

Based on image sparse representation in the shearlet domain, we proposed a L1/2 sparsity regularized unconvex variation model for image super-resolution. The L1/2 regularizer term constrains the underlying image to have a sparse representation in shearlet domain. The fidelity term restricts the consistency with the measured imaged in terms of the data degradation model. Then, the variable splitting algorithm is used to break down the model into a series of constrained optimization problems which can be solved by alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms

Rational Herding in Microloan Markets

Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel data set that tracks the funding dynamics of borrower listings on Prosper.com, the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listing heterogeneity and payoff externalities. Moreover, instead of passively mimicking their peers (irrational herding), lenders engage in active observational learning (rational herding); they infer the creditworthiness of borrowers by observing peer lending decisions and use publicly observable borrower characteristics to moderate their inferences. Counterintuitively, obvious defects (e.g., poor credit grades) amplify a listing's herding momentum, as lenders infer superior creditworthiness to justify the herd. Similarly, favorable borrower characteristics (e.g., friend endorsements) weaken the herding effect, as lenders attribute herding to these observable merits. Follow-up analysis shows that rational herding beats irrational herding in predicting loan performance

The Sound of Silence: Observational Learning in the Us Kidney Market.

Mere observation of others' choices can be informative about product quality. This paper develops an individual-level dynamic model of observational learning and applies it to a novel data set from the U.S. kidney market, where transplant candidates on a waiting list sequentially decide whether to accept a kidney offer. We find strong evidence of observational learning: patients draw negative quality inferences from earlier refusals in the queue, thus becoming more inclined towards refusal themselves. This self-reinforcing chain of inferences leads to poor kidney utilization despite the continual shortage in kidney supply. Counterfactual policy simulations show that patients would have made more efficient use of kidneys had the concerns behind earlier refusals been shared. This study yields a set of marketing implications. In particular, we show that observational learning and information sharing shape consumer choices in markedly different ways. Optimal marketing strategies should take into account how consumers learn from others

αV Integrin Induces Multicellular Radioresistance in Human Nasopharyngeal Carcinoma via Activating SAPK/JNK Pathway

BACKGROUND:Tumor cells acquire the capacity of resistance to chemotherapy or radiotherapy via cell-matrix and cell-cell crosstalk. Integrins are the most important cell adhesion molecules, in which αV integrin mainly mediating the tight contact between tumor cells. METHODOLOGY/PRINCIPAL FINDINGS:To investigate the role of αV integrin in multi-cellular radioresistance (MCR) of human nasopharyngeal carcinoma (NPC), we performed immunohistochemistry and Western blotting to find that the expression of αV integrin in the tumor tissue of radioresistant patients is much higher than that in radiosensitive patients. In vitro, we cultured human NPC cell line CNE-2 cells as multi-cellular spheroids (MCSs) or as monolayer cells (MCs), and found that the expression of αV integrin in MCSs is significantly higher than that in MCs. MTT, flow cytometry and clonogenic survival assays showed that MCSs are less sensitive to X-ray irradiation than MCs while blocking of αV integrin in MCSs dramatically reversed their radioresistance. Furthermore, as detected by Western blotting, MCSs displayed sustained activation of the stress-activated protein kinase/c-Jun NH2-terminal kinase (SAPK/JNK) pathway in presence of irradiation. Blocking of αV integrin in MCSs decreased the expression of phosphorylated JNK. Additionally, blocking of SAPK/JNK signaling pathway synergistically induced apoptosis of MCSs exposed to irradiation by increasing the expression of cleaved caspase-3. In vivo, we found that irradiation combined with αV integrin blocking treatment significantly enhanced the radiosensitivity of NPC xenografts. CONCLUSIONS:Our results indicate a novel role of αV integrin in multi-cellular radioresistance of NPCs

Non-destructive in-situ condition assessment of plastic pipe using ultrasound

Pipelines in potable water distribution system are a vital part of modern infrastructure, providing one of the most important services for society. This vital, complex infrastructure is endemic to our urban environments but is ageing, with current average age of around 70 years and with current replacement rates an inferred serviceable asset life of hundreds of years. Hence it is important that we develop technology that will enable pipeline condition assessment without service interruption. Due to environmental and operational stresses acting upon these pipelines, the common structural health problems include stress corrosion, thermal degradation, cracks or even leaks [1]. In particular, it has been suggested that void formation external to buried pipe wall is a crucial factor in pipe breakages due to lack of structural support [1, 2]. This paper presents the development and laboratory testing of ultrasonic non-destructive inspection technology for the condition assessment of plastic pipes, provide a measure of the structural integrity of the pipe, as well as 'looking' through the pipe wall to assess void formation and critical loss of support. Ultrasonic detection results are presented for grooves and cracks with two common plastic pipe materials, HDPE (High-density polyethylene) and PVC (Polyvinyl chloride) in order to simulate material loss in pipe wall. In addition, four voids in the ground external to plastics with varying shapes and dimensions were detected. Tested soils include two particle sized sands and two particle sized gravels. The study demonstrates the feasibility of developing a new technique for condition and health assessing for buried water plastic pipes