Financial Capital or Social Capital: Evidence From the Survival Analysis of Online P2P Lending Platforms

In this paper, we draw upon the bank survival literature and that in the information management area in identifying the key factors behind the survival of Chinese online P2P lending platforms. In particular, we are interested in determining whether the traditional financial capital or the social capital, associated with the online nature of these innovative lending platforms, plays a more essential role. We implement a flexible proportional odds model with a baseline spline function to analyze survival patterns and also consider potential fractional polynomial transformation and time-dependent effect of variables. Using a hand-collected dataset of 6190 platforms from June 2007 to June 2017, we provide robust evidence that although financial capital variables play an important role in driving platform survival, they are less significant or become insignificance in the presence of social capital variables. These findings contribute to both the literature and the development of this innovative and fast-growing industry of financial inclusio

Concise and Effective Network for 3D Human Modeling from Orthogonal Silhouettes

In this paper, we revisit the problem of 3D human modeling from two orthogonal silhouettes of individuals (i.e., front and side views). Different from our prior work {\cite{wang2003virtual}}, a supervised learning approach based on \textit{convolutional neural network} (CNN) is investigated to solve the problem by establishing a mapping function that can effectively extract features from two silhouettes and fuse them into coefficients in the shape space of human bodies. A new CNN structure is proposed in our work to exact not only the discriminative features of front and side views and also their mixed features for the mapping function. 3D human models with high accuracy are synthesized from coefficients generated by the mapping function. Existing CNN approaches for 3D human modeling usually learn a large number of parameters (from {8.5M} to {355.4M}) from two binary images. Differently, we investigate a new network architecture and conduct the samples on silhouettes as input. As a consequence, more accurate models can be generated by our network with only {2.4M} coefficients. The training of our network is conducted on samples obtained by augmenting a publicly accessible dataset. Learning transfer by using datasets with a smaller number of scanned models is applied to our network to enable the function of generating results with gender-oriented (or geographical) patterns

AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose

How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of plausible and appropriate hand-object interactions. In this work, we present AffordPose, a large-scale dataset of hand-object interactions with affordance-driven hand pose. We first annotate the specific part-level affordance labels for each object, e.g. twist, pull, handle-grasp, etc, instead of the general intents such as use or handover, to indicate the purpose and guide the localization of the hand-object interactions. The fine-grained hand-object interactions reveal the influence of hand-centered affordances on the detailed arrangement of the hand poses, yet also exhibit a certain degree of diversity. We collect a total of 26.7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses. The comprehensive data analysis shows the common characteristics and diversity of hand-object interactions per affordance via the parameter statistics and contacting computation. We also conduct experiments on the tasks of hand-object affordance understanding and affordance-oriented hand-object interaction generation, to validate the effectiveness of our dataset in learning the fine-grained hand-object interactions. Project page: https://github.com/GentlesJan/AffordPose.Comment: Accepted by ICCV 202

Expression of Ets-1, Ang-2 and maspin in ovarian cancer and their role in tumor angiogenesis

<p>Abstract</p> <p>Background</p> <p>Various angiogenic regulators are involved in angiogenesis cascade. Transcription factor Ets-1 plays important role in angiogenesis, remodeling of extracellular matrix, and tumor metastasis. Ets-1 target genes involved in various stages of new blood vessel formation include angiopoietin, matrix metalloproteinases (MMPs) and the protease inhibitor maspin.</p> <p>Methods</p> <p>We used immunohistochemistry (IHC) to detect the expression of Ets-1, angiopoietin-2 (Ang-2) and maspin in ovarian tumor and analyzed the relationship between the expression of these proteins and the clinical manifestation of ovarian cancer.</p> <p>Results</p> <p>Ets-1 expression was much stronger in ovarian cancer compared to benign tumors, but had no significant correlation with other pathological parameters of ovarian cancer. However, Ang-2 and maspin expression had no obvious correlation with pathological parameters of ovarian cancer. Ets-1 had a positive correlation with Ang-2 which showed their close relationship in angiogenesis. Although microvessel density (MVD) value had no significant correlation with the expression of Ets-1, Ang-2 or maspin, strong nuclear expression of maspin appeared to be correlated with high grade and MVD.</p> <p>Conclusions</p> <p>The expression of Ets-1, Ang2 and maspin showed close relationship with angiogenesis in ovarian cancer and expression of maspin appeared to be correlated with high grade and MVD. The mechanisms underlying the cross-talk of the three factors need further investigations.</p

Effects of soil flooding on photosynthesis and growth of Zea mays L. seedlings under different light intensities

Soil flooding is one of the major abiotic stresses that repress maize (Zea mays L.) growth and yield, and other environmental factors often influence soil flooding stress. This paper reports an experimental test of the hypothesis that light intensity can influence the responses of maize seedlings to soil flooding. In this experiment, maize seedlings were subjected to soil flooding at the two-leaf stage under control light (600 μmol m-2 s-1) or low light (150 μmol m-2 s-1) conditions. Under control light growth conditions, the average photosynthetic rate (PN), transpiration rate (E) and water use efficiency (WUE) were 70, 26 and 59%, respectively, higher in non-flooded than in flooded seedlings; and the average chlorophyll a (Chl a), chlorophyll b (Chl b) and Chl a+b were 31, 42 and 34%, respectively, higher in non-flooded than in flooded seedlings; and the average belowground biomass and total biomass were 52 and 34%, respectively, higher in non-flooded than in flooded seedlings. There was a slight decrease of seedling biomass in six days flooded seedlings under low light growth conditions. The effects of flooding on photosynthetic, seedling growth and shoot/root ratio were more pronounced under control light growth conditions than under low light growth conditions, which indicate that even for maize which is a C4 plant, relatively high light intensity still aggravated soil flooding stress, while low light growth condition mitigated soil flooding stress, and suggests that light effects should be considered when we study maize responses to soil flooding.Keywords: Biomass accumulation, gas exchange, light limitation, maize, stres

Fermentation of tomato juice improves \u3cem\u3ein vitro\u3c/em\u3e bioaccessibility of lycopene

The impact of fermentation (Saccharomyces cerevisiae ATCC 9763) on the bioaccessibility of lycopene in a model tomato juice was examined. The physicochemical and structural properties of the tomato tissue were determined after fermentation and the bioaccessibility of lycopene was monitored using a simulated gastrointestinal tract. A lycopene concentration of 45.1 mg/100 g was obtained under optimal fermentation conditions. The cell walls of the tomato cells were hydrolyzed and disrupted by fermentation. Cell disruption decreased the pectin content and reduced the tissue fragment size, thereby reducing gravitational separation and facilitating lycopene release. The lycopene bioaccessibility in the tomato juices increased in the following order: unfermented (8.5%) \u3c fermented (11.4%) \u3c unfermented-emulsified (13.6%) \u3c fermented-emulsified (22.7%). These effects were attributed to a combination of greater tomato tissue disruption and enhanced mixed micelle formation. Our results may be useful for the development of functional foods and beverages with improved health benefits

Online low-rank representation learning for joint multi-subspace recovery and clustering

Benefiting from global rank constraints, the lowrank representation (LRR) method has been shown to be an effective solution to subspace learning. However, the global mechanism also means that the LRR model is not suitable for handling large-scale data or dynamic data. For large-scale data, the LRR method suffers from high time complexity, and for dynamic data, it has to recompute a complex rank minimization for the entire data set whenever new samples are dynamically added, making it prohibitively expensive. Existing attempts to online LRR either take a stochastic approach or build the representation purely based on a small sample set and treat new input as out-of-sample data. The former often requires multiple runs for good performance and thus takes longer time to run, and the latter formulates online LRR as an out-ofsample classification problem and is less robust to noise. In this paper, a novel online low-rank representation subspace learning method is proposed for both large-scale and dynamic data. The proposed algorithm is composed of two stages: static learning and dynamic updating. In the first stage, the subspace structure is learned from a small number of data samples. In the second stage, the intrinsic principal components of the entire data set are computed incrementally by utilizing the learned subspace structure, and the low-rank representation matrix can also be incrementally solved by an efficient online singular value decomposition (SVD) algorithm. The time complexity is reduced dramatically for large-scale data, and repeated computation is avoided for dynamic problems. We further perform theoretical analysis comparing the proposed online algorithm with the batch LRR method. Finally, experimental results on typical tasks of subspace recovery and subspace clustering show that the proposed algorithm performs comparably or better than batch methods including the batch LRR, and significantly outperforms state-of-the-art online methods