CIFAR-10-L T, CIFAR-100-L T, ImageNet-100-L T, and Places-L T are 5, 80, 50, and 182 respectively. Our default training set of each dataset is summarized in Table 8.

CIFAR-10-L T, CIFAR-100-L T, ImageNet-100-L T, and Places-L T are 5, 80, 50, and 182 respectively. Our default training set of each dataset is summarized in Table 8.

Q2: Please summarize your review in 1-2 sentences The paper proposes a modified SVM learning algorithm in which the loss function is modified by a per-example weight. However, example dependent costs are already widely used in machine learning.

In many situations we have some measurement of confidence on "positiveness" for a binary label. The "positiveness" is a continuous value whose range is a bounded interval.

In many situations we have some measurement of confidence on "positiveness" for a binary label. The "positiveness" is a continuous value whose range is a bounded interval.

In many situations we have some measurement of confidence on positiveness for a binary label. Thepositiveness" is a continuous value whose range is a bounded interval. We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the positiveness" instead of a binary label of each training sample is available. Our e-SVM algorithm can also be readily extended to learn segment classifiers under weak supervision where the exact positiveness value of each training example is unobserved. In experiments, we show that the e-SVM algorithm can effectively address the segment proposal classification task under both strong supervision (e.g. the pixel-level annotations are available) and the weak supervision (e.g.

This study examines whether positive news about firms increases their stock prices and, moreover, whether it increases stock prices of the firms' suppliers and customers, using a large sample of publicly listed firms across the world and another of Japanese listed firms. The level of positiveness of each news article is determined by FinBERT, a natural language processing model fine-tuned specifically for financial information. Supply chains of firms across the world are identified mostly by financial statements, while those of Japanese firms are taken from large-scale firm-level surveys. We find that positive news increases the change rate of stock prices of firms mentioned in the news before its disclosure, most likely because of diffusion of information through informal channels. Positive news also raises stock prices of the firms' suppliers and customers before its disclosure, confirming propagation of market values through supply chains. In addition, we generally find a larger post-news effect on stock prices of the mentioned firms and their suppliers and customers than the pre-news effect. The positive difference between the post- and pre-news effects can be considered as the net effect of the disclosure of positive news, controlling for informal information diffusion. However, the post-news effect on suppliers and customers in Japan is smaller than the pre-news effect, a result opposite to those from firms across the world. This notable result is possibly because supply chain links of Japanese firms are stronger than global supply chains while such knowledge is restricted to selected investors.

In many situations we have some measurement of confidence on "positiveness" for a binary label. The "positiveness" is a continuous value whose range is a bounded interval.

Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate contextual knowledge to improve the completeness of class activation maps (CAM). In this work, we argue that the knowledge bias between instances and contexts affects the capability of the prototype to sufficiently understand instance semantics. Inspired by prototype learning theory, we propose leveraging prototype awareness to capture diverse and fine-grained feature attributes of instances. The hypothesis is that contextual prototypes might erroneously activate similar and frequently co-occurring object categories due to this knowledge bias. Therefore, we propose to enhance the prototype representation ability by mitigating the bias to better capture spatial coverage in semantic object regions. With this goal, we present a Context Prototype-Aware Learning (CPAL) strategy, which leverages semantic context to enrich instance comprehension. The core of this method is to accurately capture intra-class variations in object features through context-aware prototypes, facilitating the adaptation to the semantic attributes of various instances. We design feature distribution alignment to optimize prototype awareness, aligning instance feature distributions with dense features. In addition, a unified training framework is proposed to combine label-guided classification supervision and prototypes-guided self-supervision. Experimental results on PASCAL VOC 2012 and MS COCO 2014 show that CPAL significantly improves off-the-shelf methods and achieves state-of-the-art performance. The project is available at https://github.com/Barrett-python/CPAL.

In many situations we have some measurement of confidence on positiveness for a binary label. The positiveness" is a continuous value whose range is a bounded interval. We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the positiveness" instead of a binary label of each training sample is available. Our e-SVM algorithm can also be readily extended to learn segment classifiers under weak supervision where the exact positiveness value of each training example is unobserved. In experiments, we show that the e-SVM algorithm can effectively address the segment proposal classification task under both strong supervision (e.g. the pixel-level annotations are available) and the weak supervision (e.g.