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

Comparative Evaluation of Cooling Systems for Farrowing Sows

The field studies reported here compare the performance of three cooling systems for relieving farrowing/lactating sows of heat stress under the warm and humid production climate in southern China. The comparative systems included (1) tunnel ventilation (TV) with vertical head-zone ventilation (HZV) vs. TV with HZV and drip cooling (DC), (2) TV only vs. TV with DC, and (3) horizontal air mixing (HAM) only vs. HAM and DC. For the HZV, a perforated overhead air duct was used to create an air velocity of 0.6 to 0.8 m/s (118 to 157 ft/min) in the head zone of the sow. The paired tests were conducted successively in an experimental commercial farrowing barn housing 42 sows. Body temperature (Tb) and respiration rate (RR) of the sows were used to evaluate the efficacy of the systems. The results indicate that sows under TV + DC or TV + HZV + DC had significantly lower Tb than those under TV only or TV + HZV (P \u3c 0.01 and P \u3c 0.001, respectively). DC under HAM was less effective for Tb reduction (P \u3e 0.05). DC reduced RR in all cases, 42% under TV (P \u3c 0.01), 41% under TV + HZV (P \u3c 0.01), and 22% under HAM (P \u3e 0.05). It was concluded that TV with DC provides the most cost-effective cooling scheme

The growing inequality between firms

Globalisation, technological progress and a range of policies and institutions are driving ‘Great Divergences’ in wages and productivity, write Giuseppe Berlingieri, Patrick Blanchenay and Chiara Criscuol

Influence of diabetes on cardiac resynchronization therapy in heart failure patients: a meta-analysis

BACKGROUND: Diabetes mellitus is an independent risk factor of increased morbidity and mortality in patients with heart failure. Cardiac resynchronization therapy (CRT), a pacemaker-based therapy for dyssynchronous heart failure, improves cardiac performance and quality of life, but its effect on mortality in patients with diabetes is uncertain. METHODS: We performed a meta-analysis of results from randomized controlled trials (RCTs) of the long-term outcome of cardiac resynchronization therapy for heart failure in diabetic and non-diabetic patients. Literature search of MEDLINE via Pubmed for reports of randomized controlled trials of Cardiac resynchronization for chronic symptomatic left-ventricular dysfunction in patients with and without diabetes mellitus, with death as the outcome. Relevant data were analyzed by use of a random-effects model. Reports published from 1994 to 2011 that described RCTs of CRT for treating chronic symptomatic left ventricular dysfunction in patients with and without diabetes, with all-cause mortality as an outcome. RESULTS: A total of 5 randomized controlled trials met the inclusion criteria, for 2,923 patients. The quality of studies was good to moderate. Cardiac resynchronization significantly reduced the mortality for heart failure patients with or without diabetes mellitus. Mortality was 24.3% for diabetic patients with heart failure and 20.4 % for non-diabetics (odds ratio 1.28, 95% confidence interval 1.06–1.55; P = 0.010). CONCLUSIONS: Cardiac resynchronization therapy (CRT) may reduce mortality from progressive heart failure in patients with or without diabetes mellitus, but mortality may be higher for patients with than without diabetes after CRT for heart failure

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

OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds

Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they usually utilized heavy correlation operations to capture category-level characteristics only, and overlook the inherent merit of arbitrariness in contrast to multiple object tracking. In this work, we propose a radically novel one-stream network with the strength of the instance-level encoding, which avoids the correlation operations occurring in previous Siamese network, thus considerably reducing the computational effort. In particular, the proposed method mainly consists of a Template-aware Transformer Module (TTM) and a Multi-scale Feature Aggregation (MFA) module capable of fusing spatial and semantic information. The TTM stitches the specified template and the search region together and leverages an attention mechanism to establish the information flow, breaking the previous pattern of independent \textit{extraction-and-correlation}. As a result, this module makes it possible to directly generate template-aware features that are suitable for the arbitrary and continuously changing nature of the target, enabling the model to deal with unseen categories. In addition, the MFA is proposed to make spatial and semantic information complementary to each other, which is characterized by reverse directional feature propagation that aggregates information from shallow to deep layers. Extensive experiments on KITTI and nuScenes demonstrate that our method has achieved considerable performance not only for class-specific tracking but also for class-agnostic tracking with less computation and higher efficiency.Comment: 12 pages,9 figure