Sen Wu — Page 2 | Searcharxiv

arXiv2014

High-performance controllable ambipolar infrared phototransistors based on graphene-quantum dot hybrid

The field effect transistors (FETs) exhibited ultrahigh responsivity (107 A/W) to infrared light with great improvement of mobility in graphene / PbS quantum dot (QD) hybrid. These reported transistors are either unipolar or depletion mode devices. In this paper, we presented and fabricated conveniently-controlled grapheme / PbS QD hybrid FETs. Through the investigation on electric and optoelectronic properties, the ambipolar FETs (normally OFF) can be switched ON by raising gate voltage (VG) up to 3.7 V and -0.8 V in the first and third quadrants. Near these thresholds (VT) each carrier species shows comparable mobility (~ 300 cm2V-1s-1). Photo-responsivity reach ~ 107 A/W near each threshold and it will linearly increases with (VG-VT). These hybrid FETs become strongly competitive candidates for devices in flexible integrated circuits with low cost, large area, low-energy consumption and high performances.

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arXiv2014

Feature Engineering for Knowledge Base Construction

Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student. Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user "think about features---not algorithms." We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality.

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