arXiv subjects

Sheng Shen

Publications and source records attributed to Sheng Shen.

78 records · Page 5Linked to original sources

arXiv2019

Pragmatically Informative Text Generation

We improve the informativeness of models for conditional text generation using techniques from computational pragmatics. These techniques formulate language production as a game between speakers and listeners, in which a speaker should generate output text that a listener can use to correctly identify the original input that the text describes. While such approaches are widely used in cognitive science and grounded language learning, they have received less attention for more standard language generation tasks. We consider two pragmatic modeling methods for text generation: one where pragmatics is imposed by information preservation, and another where pragmatics is imposed by explicit modeling of distractors. We find that these methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations.

cs.CL↗

arXiv2019

Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any other languages, which creates a considerable inequality in the quality of related information services received by users speaking different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language, and thus compromises the accuracy of the downstream classification task. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification. The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce.

cs.IR↗

arXiv2017

Towards Release Strategy Optimization for Apps in Google Play

In the appstore-centric ecosystem, app developers have an urgent requirement to optimize their release strategy to maximize the success opportunity of their apps. To address this problem, we introduce an approach to assisting developers to select the proper release opportunity based on the purpose of the update and current condition of the app. Before that, we propose the interval of an update to its previous update to characterize release patterns, and find significance of the release opportunity through empirical analysis. We mined the update-history data of 17,820 apps from 33 categories in Google Play, over a period of 105 days. With 41,028 releases identified from these apps, we reveal important characteristics of update intervals and how these factors can influence update effects. We suggest developers to synthetically consider app ranking, rating trend, and what to update in addition to the opportunity before releasing an app version. We propose a Multinomial Naive Bayes model to help decide an optimal release opportunity to gain better user adoption.

cs.SE↗

arXiv2016

Quasi-Normal Mode Theory for Thermal Radiation from Lossy and Dispersive Optical Resonators

Although optical resonators are widely used for controlling and engineering thermal radiation, what has been lacking is a general theoretical framework to elucidate the thermal emission of optical resonators, and guide the design and application of thermally driven optical resonators. We developed a general and self-consistent formalism to describe the thermal radiation from arbitrary optical resonators made by lossy and dispersive materials like metals, with the only assumption that the resonators have a single predominant resonance mode. Our formalism demonstrates that the thermal emission of an optical resonator is maximized when the mode losses to the emitter and the absorber (or far-field background) are matched, and meanwhile the predominant resonant modes are electrically quasi-static. By efficiently calculating the resonant modes using the finite element methods, our formalism serves as a general principle of designing the arbitrary optical resonator thermal emitters with perfect or maximized emission.

physics.optics↗

arXiv2014

Temperature Gated Thermal Rectifier

Heat flow control is essential for widespread applications of heating, cooling, energy conversion and utilization. Here we demonstrate the first observation of temperature-gated thermal rectification in vanadium dioxide beams, in which an environment temperature actively modulates asymmetric heat flow. In this three terminal device, there are two switchable states, which can be accessed by global heating: Rectifier state and Resistor state. In the Rectifier state, up to 28% thermal rectification is observed. In the Resistor state, the thermal rectification is significantly suppressed (below 4%). This temperature-gated rectifier can have substantial implications ranging from autonomous thermal management of micro/nanoscale devices to thermal energy conversion and storage.

cond-mat.mtrl-sci↗

arXiv2009

Near-field radiative heat transfer between a sphere and a substrate

Near-field force and energy exchange between two objects due to quantum electrodynamic fluctuations give rise to interesting phenomena such as Casimir and van der Waals forces, and thermal radiative transfer exceeding Planck's theory of blackbody radiation. Although significant progress has been made in the past on the precise measurement of Casimir force related to zero-point energy, experimental demonstration of near-field enhancement of radiative heat transfer is difficult. In this work, we present a sensitive technique of measuring near-field radiative transfer between a microsphere and a substrate using a bi-material atomic force microscope (AFM) cantilever, resulting in "heat transfer-distance" curves. Measurements of radiative transfer between a sphere and a flat substrate show the presence of strong near-field effects resulting in enhancement of heat transfer over the predictions of the Planck blackbody radiation theory.

cond-mat.mes-hall↗