Session-Guided Attention in Continuous Learning With Few Samples.

Few-shot class-incremental learning (FSCIL) aims to learn from a sequence of incremental data sessions with a limited number of samples in each class. The main issues it encounters are the risk of forgetting previously learned data when introducing new data classes, as well as not being able to adapt the old model to new data due to limited training samples. Existing state-of-the-art solutions normally utilize pre-trained models with fixed backbone parameters to avoid forgetting old knowledge.

DyCR: A Dynamic Clustering and Recovering Network for Few-Shot Class-Incremental Learning.

Few-shot class-incremental learning (FSCIL) aims to continually learn novel data with limited samples. One of the major challenges is the catastrophic forgetting problem of old knowledge while training the model on new data. To alleviate this problem, recent state-of-the-art methods adopt a well-trained static network with fixed parameters at incremental learning stages to maintain old knowledge.

Self-Supervised Lie Algebra Representation Learning via Optimal Canonical Metric.

Learning discriminative representation with limited training samples is emerging as an important yet challenging visual categorization task. While prior work has shown that incorporating self-supervised learning can improve performance, we found that the direct use of canonical metric in a Lie group is theoretically incorrect. In this article, we prove that a valid optimization measurement should be a canonical metric on Lie algebra.

The executive pay gap and stock price crash risk: Promotion or suppression?

Background: In recent years, cases of stock price crash have continued to emerge. However, yet little research to date has investigated the compensation incentives of top management team (TMT) affect the risk of stock price crash. Nor has research considered the impact of the executive pay gap on the stock price crash risk.

Can the social trust promote corporate green innovation? Evidence from China.

Corporate green innovation has played a crucial role in balancing profitability and environmental protection. The existing research on determinant factors of green innovation has its main defects in emphasizing excessively enterprise's formal institutional environment and neglecting the informal institutional environment, causing an incomplete understanding of the relationship between institutional environments and corporate green innovation. To bridge this gap, using a sample of Shanghai and Shenzhen A-share listed firms in manufacturing industry during the period of 2010-2016, we investigate how social trust, an important informal institutions, affects corporate green innovation.

Harder than diamond: superior indentation strength of wurtzite BN and lonsdaleite.

Recent indentation experiments indicate that wurtzite BN (w-BN) exhibits surprisingly high hardness that rivals that of diamond. Here we unveil a novel two-stage shear deformation mechanism responsible for this unexpected result. We show by first-principles calculations that large normal compressive pressures under indenters can compel w-BN into a stronger structure through a volume-conserving bond-flipping structural phase transformation during indentation which produces significant enhancement in its strength, propelling it above diamond's.

Colossal shear-strength enhancement of low-density cubic BC2N by nanoindentation.

Recently synthesized low-density cubic BC2N exhibits surprisingly high shear strength inferred by nanoindentation in stark contrast to its relatively low elastic moduli. We show by first-principles calculation that this intriguing phenomenon can be ascribed to a novel structural hardening mechanism due to the compressive stress beneath the indenter. It significantly strengthens the weak bonds connecting the shear planes, yielding a colossal enhancement in shear strength.