Topic models provide insights into document collections, and their supervised extensions also capture associated document-level metadata such as sentiment. However, inferring such models from data is often slow and cannot scale to big data. We build upon the “anchor” method for learning topic models to capture the relationship between metadata and latent topics by extending the vector-space rep...

This work describes a process to extract Named Entity (NE) translations from the text available in web links (anchor texts). It translates a NE by retrieving a list of web documents in the target language, extracting the anchor texts from the links to those documents and finding the best translation from the anchor texts, using a combination of features, some of which, are specific to anchor te...

Due to the un-consideration of random distribution of DV-Hop algorithm in wireless sensor network, a big error caused by using of hop count between unknown and anchor nodes and the nearest anchor nodes’ average hop distance instead of the average hop distance of all the anchor nodes. An improved DV-HOP localization algorithm is proposed based on the traditional DVHOP localization algorithm aime...

The anchoring-and-adjustment heuristic has been used to account for a wide variety of numerical judgments. Five studies show that adjustment away from a numerical anchor is smaller if the anchor is precise than if it is rounded. Evidence suggests that precise anchors, compared with rounded anchors, are represented on a subjective scale with a finer resolution. If adjustment consists of a series...

We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisy relative measurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents positions are estimated with respect to the anchor. We...

This paper analyzes the source of the localization error in the least square localization (LSL) algorithm, and introduces the thought of distance clustering to select the reference anchor nodes, and gives a new principle to select the benchmark anchor node. Based on the above analysis, this paper proposes an improved least square localization algorithm based on the distance clustering and the s...

In Section 3 of the paper, we use a reduced form model to account for how consumers select their anchor node. In this section, we provide a formal micro-model of a process that provides justi cation for our assumptions. We also consider the possibility that some consumers may switch their anchor nodes when encountering a link to a higher quality site and show that including this feature in our ...

We report a strong compression of space around a visual anchor presented in the near visual periphery (<5°). While subjects kept fixation, a salient visual stimulus (from now on referred to as "anchor") was presented, followed by a brief whole-field mask. At various times around mask onset a probe dot was flashed. Subjects estimated the position of the probe dot in relation to a subsequently pr...