Evaluating the Wisdom of Strangers: The Perceived Credibility of Online Consumer Reviews on Yelp

This study examined the effects of review valence, the reviewer profile, and the receiver's familiarity with the platform (user/nonuser) on the perceived credibility of a review on Yelp.com and on the receiver's attitude toward the reviewed object. The results demonstrated a difference in cue‐taking between users and nonusers. For users, there was an interaction effect of 2 profile cues (number of friends and number of reviews) on competence. Users interpreted the cues in combination, whereas non‐users were not influenced by them. The friends × reviews × platform familiarity interaction indirectly affected attitude through competence. Further, review valence was positively associated with perceived credibility and attitude. The findings support and extend the social information processing theory and cue combination literature.     

Convolutional Sparse Coding for High Dynamic Range Imaging

Current HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially‐varying pixel exposures. In this paper, we propose a novel algorithm to recover high‐quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently‐introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher‐quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.     

Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient‐specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter‐patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.     

Diffusive gradients in thin films predicts crop response better than calcium-acetate-lactate extraction

Nutrient Cycling in Agroecosystems - Soil P testing has been widely used to predict crop yields, P uptake, and fertilizer demands in agriculture. Diffusive gradients in thin films (DGT) provides a...     

Trial and Error

A pac-learning algorithm is -space bounded, if it stores at most examples from the sample at any time. We characterize the -space learnable concept classes. For this purpose we introduce the compression parameter of a concept class and design our Trial and Error Learning Algorithm. We show : is -space learnable if and only if the compression parameter of is at most . This learning algorithm does not produce a hypothesis consistent with the whole sample as previous approaches e.g. by Floyd, who presents consistent space bounded learning algorithms, but has to restrict herself to very special concept classes. On the other hand our algorithm needs large samples; the compression parameter appears as exponent in the sample size. We present several examples of polynomial time space bounded learnable concept classes: – all intersection closed concept classes with finite VC–dimension. – convex -gons in . – halfspaces in . – unions of triangles in . We further relate the compression parameter to the VC–dimension, and discuss variants of this parameter. Received May 24, 1994 / July 4, 1995     

Effect of catecholamine depletion on myocardial infarct size in dogs: role of catecholamines in ischemic preconditioning

OBJECTIVES: Cardioprotective adaptation to brief periods of ischemia and reperfusion is termed ischemic preconditioning (PC). Limitation of infarct size by preconditioning is associated with marked slowing of ischemic metabolism. The cause of metabolic slowing has not been determined but may involve either pro- or anti-adrenergic mechanisms. Hypothetically, adrenergic stimulation could signal the adaptive response. Alternatively, metabolic slowing during the sustained ischemic challenge could occur through a reduction in beta-adrenergic stimulation. This study was designed to test the role of cardiac norepinephrine (NE) in PC. METHODS: The effect of PC on myocardial infarct size was studied in control dogs and dogs depleted of catecholamines by pretreatment with reserpine (RES; 0.25 mg/kg i.v.). PC was induced by four cycles of 5 min of ischemia and 5 min of reperfusion. Infarcts were produced by 60 min of ischemia and 3 h of reperfusion. Cardiac NE depletion was verified by radioimmunoassay of tissue samples and by absence of hemodynamic response to a tyramine bolus (1.4 mg/kg) administered at the end of each experiment. Infarct size, expressed as percent of area at risk, was controlled for variation in collateral blood flow using analysis of covariance (ANCOVA). RESULTS: Adjusted mean infarct size was 25.5 +/- 3.2% in untreated controls vs. 19.1 +/- 3.3% in RES-treated controls (P = NS). PC limited infarct size in untreated dogs (7.4 +/- 1.8 vs. 25.5 +/- 3.2%; PC vs. control; P < 0.01) but not in RES-treated dogs (15.7 +/- 3.0% vs. 19.1 +/- 3.3%; RES + PC vs. RES; P = NS). Infarct size was larger in dogs with RES + PC than with PC alone, even though there was a trend toward a slight beneficial effect with RES alone. CONCLUSION: The cardioprotective effect of ischemic preconditioning cannot be explained entirely as an anti-adrenergic effect. On the contrary, adrenergic receptor stimulation may be required for the full expression of ischemic preconditioning in canine myocardium.