instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to-peer applications. We introduce probabilistic available bandwidth, which is defined in terms of ingress rates and egress rates of traffic on a path, rather than in terms of capacity and utilization of the constituent links of the path like the standard available bandwidth metric. In this paper, we describe a distributed algorithm, based on a probabilistic graphical model and Bayesian active learning, for simultaneously estimating the probabilistic available bandwidth of multiple paths through a network. Our procedure exploits the fact that each packet train provides information not only about the path it traverses, but also about any path that shares a link with the monitored path. Simulations and PlanetLab experiments indicate that this process can dramatically reduce the number of probes required to generate accurate estimates.
answer:
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning

question:
Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higherorder automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logicbased systems and that features derived from the proofs are effective for learning textual similarity.
answer:
Determining Semantic Textual Similarity using Natural Deduction Proofs

question:
Within the framework of ADABOOST.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem toK binary one-againstall classifications. The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length K and label-independent scalar classifier. At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions. The algorithm retains the conceptual elegance, power, and computational efficiency of binary ADABOOST. In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLOGITBOOST, and it is significantly better than other known implementations of ADABOOST.MH.
answer:
The return of ADABOOST.MH: multi-class Hamming trees