Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds

Single object tracking in point clouds has been attracting more and more attention owing to the presence of LiDAR sensors in 3D vision. However, the existing methods based on deep neural networks focus mainly on training different models for different categories, which makes them unable to perform well in real-world applications when encountering classes unseen during the training phase. In this work, we investigate a more challenging task in the LiDAR point clouds, class-agnostic tracking, where a general model is supposed to be learned for any specified targets of both observed and unseen categories. In particular, we first investigate the class-agnostic performances of the state-of-the-art trackers via exposing the unseen categories to them during testing, finding that a key factor for class-agnostic tracking is how to constrain fused features between the template and search region to maintain generalization when the distribution is shifted from observed to unseen classes. Therefore, we propose a feature decorrelation method to address this problem, which eliminates the spurious correlations of the fused features through a set of learned weights and further makes the search region consistent among foreground points and distinctive between foreground and background points. Experiments on the KITTI and NuScenes demonstrate that the proposed method can achieve considerable improvements by benchmarking against the advanced trackers P2B and BAT, especially when tracking unseen objects

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

Small Object Tracking in LiDAR Point Cloud: Learning the Target-awareness Prototype and Fine-grained Search Region

Single Object Tracking in LiDAR point cloud is one of the most essential parts of environmental perception, in which small objects are inevitable in real-world scenarios and will bring a significant barrier to the accurate location. However, the existing methods concentrate more on exploring universal architectures for common categories and overlook the challenges that small objects have long been thorny due to the relative deficiency of foreground points and a low tolerance for disturbances. To this end, we propose a Siamese network-based method for small object tracking in the LiDAR point cloud, which is composed of the target-awareness prototype mining (TAPM) module and the regional grid subdivision (RGS) module. The TAPM module adopts the reconstruction mechanism of the masked decoder to learn the prototype in the feature space, aiming to highlight the presence of foreground points that will facilitate the subsequent location of small objects. Through the above prototype is capable of accentuating the small object of interest, the positioning deviation in feature maps still leads to high tracking errors. To alleviate this issue, the RGS module is proposed to recover the fine-grained features of the search region based on ViT and pixel shuffle layers. In addition, apart from the normal settings, we elaborately design a scaling experiment to evaluate the robustness of the different trackers on small objects. Extensive experiments on KITTI and nuScenes demonstrate that our method can effectively improve the tracking performance of small targets without affecting normal-sized objects

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