Glassy phases in organic semiconductors

Organic semiconductors may be processed from fluids using graphical arts printing and patterning techniques to create complex circuitry. Because organic semiconductors are weak van der Waals solids, the creation of glassy phases during processing is quite common. Because structural disorder leads to electronic disorder, it is necessary to understand these phases to optimize and control the electronic properties of these materials. Here we review the significance of glassy phases in organic semiconductors. We examine challenges in the measurement of the glass transition temperature and the accurate classification of phases in these relatively rigid materials. Device implications of glassy phases are discussed. Processing schemes that are grounded in the principles of glass physics and sound glass transition temperature measurement will more quickly achieve desired structure and electronic characteristics, accelerating the exciting progress of organic semiconductor technology development.     

Exploration of channel width scaling and edge states in transition metal dichalcogenides

We explore the impact of edge states in three types of transition metal dichalcogenides (TMDs), namely metallic Td-phase WTe₂ and semiconducting 2H-phase MoTe₂ and MoS₂, by patterning thin flakes into ribbons with varying channel widths. No obvious charge depletion at the edges is observed for any of these three materials, in contrast to observations made for graphene nanoribbon devices. The semiconducting ribbons are characterized in a three-terminal field-effect transistor (FET) geometry. In addition, two ribbon array designs have been carefully investigated and found to exhibit current levels higher than those observed for conventional one-channel devices. Our results suggest that device structures incorporating a high number of edges can improve the performance of TMD FETs. This improvement is attributed to a higher local electric field, resulting from the edges, increasing the effective number of charge carriers, and the absence of any detrimental edge-related scattering.

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Podcasting in education: Are students as ready and eager as we think they are?

Instructors in higher education are disseminating instructional content via podcasting, as many rally behind the technology’s potential benefits. Others have expressed concern about the risks of deleterious effects that might accompany the adoption of podcasting, such as lower class attendance. Yet, relatively few studies have investigated students’ perceptions of podcasting for educational purposes, especially in relation to different podcasting forms: repetitive and supplemental. The present study explored students’ readiness and attitudes towards these two forms of podcasting to provide fundamental information for future researchers and educators. The results indicated that students may not be as ready or eager to use podcasting for repetitive or supplemental educational purposes as much as we think they are, but they could be persuaded.     

Privacy policies for shared content in social network sites

Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today’s most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users’ understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair.     

Development of a pit filling algorithm for LiDAR canopy height models

LiDAR canopy height models (CHMs) can exhibit unnatural looking holes or pits, i.e., pixels with a much lower digital number than their immediate neighbors. These artifacts may be caused by a combination of factors, from data acquisition to post-processing, that not only result in a noisy appearance to the CHM but may also limit semi-automated tree-crown delineation and lead to errors in biomass estimates. We present a highly effective semi-automated pit filling algorithm that interactively detects data pits based on a simple user-defined threshold, and then fills them with a value derived from their neighborhood. We briefly describe this algorithm and its graphical user interface, and show its result in a LiDAR CHM populated with data pits. This method can be rapidly applied to any CHM with minimal user interaction. Visualization confirms that our method effectively and quickly removes data pits.     

The Chinese Room: Visualization and Interaction to Understand and Correct Ambiguous Machine Translation

We present The Chinese Room , a visualization interface that allows users to explore and interact with a multitude of linguistic resources in order to decode and correct poor machine translations. The target users of The Chinese Room are not bilingual and are not familiar with machine translation technologies. We investigate the ability of our system to assist such users in decoding and correcting faulty machine translations. We found that by collaborating with our application, end-users can overcome many difficult translation errors and disambiguate translated passages that were otherwise baffling. We also examine the utility of our system to machine translation researchers. Anecdotal evidence suggests that The Chinese Room can help such researchers develop better machine translation systems.     

Fast Global Illumination on Dynamic Height Fields

We present a real-time method for rendering global illumination effects from large area and environmental lights on dynamic height fields. In contrast to previous work, our method handles inter-reflections (indirect lighting) and non-diffuse surfaces. To reduce sampling, we construct one multi-resolution pyramid for height variation to compute direct shadows, and another pyramid for each indirect bounce of incident radiance to compute inter-reflections. The basic principle is to sample the points blocking direct light, or shedding indirect light, from coarser levels of the pyramid the farther away they are from a given receiver point. We unify the representation of visibility and indirect radiance at discrete azimuthal directions (i.e., as a function of a single elevation angle) using the concept of a "casting set" of visible points along this direction whose contributions are collected in the basis of normalized Legendre polynomials. This analytic representation is compact, requires no precomputation, and allows efficient integration to produce the spherical visibility and indirect radiance signals. Sub-sampling visibility and indirect radiance, while shading with full-resolution surface normals, further increases performance without introducing noticeable artifacts. Our method renders 512×512 height fields (> 500K triangles) at 36Hz.     

A Stronger Model of Dynamic Programming Algorithms

We define a formal model of dynamic programming algorithms which we call Prioritized Branching Programs (pBP). Our model is a generalization of the BT model of Alekhnovich et al. (IEEE Conference on Computational Complexity, pp. 308–322, 2005), which is in turn a generalization of the priority algorithms model of Borodin, Nielson and Rackoff. One of the distinguishing features of these models is that they not only capture large classes of algorithms generally considered to be greedy, backtracking or dynamic programming algorithms, but they also allow characterizations of their limitations. Hence they give meaning to the statement that a given problem can or cannot be solved by dynamic programming. After defining the model, we prove three main results: (i) that certain types of natural restrictions of our seemingly more powerful model can be simulated by the BT model; (ii) that in general our model is stronger than the BT model—a fact which is witnessed by the classical shortest paths problem; (iii) that our model has very real limitations, namely that bipartite matching cannot be efficiently computed in it, hence suggesting that there are problems that can be solved efficiently by network flow algorithms and by simple linear programming that cannot be solved by natural dynamic programming approaches.     

Dynamic visualization of coexpression in systems genetics data

Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes residing in critical positions in networks and pathways. By using a graph as a universal representation of correlation in gene expression, our system employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by compound queries, dynamic level-of-detail abstraction, and template-based fuzzy classification. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.