Carbon Nanocone: A Promising Thermal Rectifier

With molecular dynamics simulations, we demonstrate very obvious thermal rectification in large temperature range from 200 to 400 K in nanocone. We also observe that the rectification of nanocone does not depend on the length very sensitively, which is in stark contrast with the nanotube thermal rectifier in which the rectification decreases dramatically as the length increases. Our work demonstrates that carbon nanocone is a promising practical phononic device

Thermal Rectification In Asymmetric Graphene Ribbons

In this paper, heat flux in graphene nano ribbons has been studied by using molecular dynamics simulations. It is found that the heat flux runs preferentially along the direction of decreasing width, which demonstrates significant thermal rectification effect in the asymmetric graphene ribbons. The dependence of rectification ratio on the vertex angle and the length are also discussed. Compared to the carbon nanotube based one-dimensional thermal rectifier, graphene nano ribbons have much higher rectification ratio even in large scale. Our results demonstrate that asymmetric graphene ribbon might be a promising structure for practical thermal (phononics) device

Imagination Based Sample Construction for Zero-Shot Learning

Zero-shot learning (ZSL) which aims to recognize unseen classes with no labeled training sample, efficiently tackles the problem of missing labeled data in image retrieval. Nowadays there are mainly two types of popular methods for ZSL to recognize images of unseen classes: probabilistic reasoning and feature projection. Different from these existing types of methods, we propose a new method: sample construction to deal with the problem of ZSL. Our proposed method, called Imagination Based Sample Construction (IBSC), innovatively constructs image samples of target classes in feature space by mimicking human associative cognition process. Based on an association between attribute and feature, target samples are constructed from different parts of various samples. Furthermore, dissimilarity representation is employed to select high-quality constructed samples which are used as labeled data to train a specific classifier for those unseen classes. In this way, zero-shot learning is turned into a supervised learning problem. As far as we know, it is the first work to construct samples for ZSL thus, our work is viewed as a baseline for future sample construction methods. Experiments on four benchmark datasets show the superiority of our proposed method.Comment: Accepted as a short paper in ACM SIGIR 201

Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus

Mobile UI understanding is important for enabling various interaction tasks such as UI automation and accessibility. Previous mobile UI modeling often depends on the view hierarchy information of a screen, which directly provides the structural data of the UI, with the hope to bypass challenging tasks of visual modeling from screen pixels. However, view hierarchies are not always available, and are often corrupted with missing object descriptions or misaligned structure information. As a result, despite the use of view hierarchies could offer short-term gains, it may ultimately hinder the applicability and performance of the model. In this paper, we propose \textit{Spotlight}, a vision-only approach for mobile UI understanding. Specifically, we enhance a vision-language model that only takes the screenshot of the UI and a region of interest on the screen -- the focus -- as the input. This general architecture is easily scalable and capable of performing a range of UI modeling tasks. Our experiments show that our model establishes SoTA results on several representative UI tasks and outperforms previous methods that use both screenshots and view hierarchies as inputs. Furthermore, we explore multi-task learning and few-shot prompting capacities of the proposed models, demonstrating promising results in the multi-task learning direction

t→cVt\to cV via SUSY FCNC couplings in the unconstrained MSSM

We recalculate the branching ratios for $t\to cV$ ($V=g,\gamma,Z$) induced by SUSY FCNC couplings within the general unconstrained MSSM framework using mass eigenstate approach. Our results show that the branching ratios for these decays are larger than ones reported in previous literatures in the MSSM with R-parity conservation, and they can reach $\sim 10^{-4}$, $\sim 10^{-6}$, and $\sim 10^{-6}$, respectively, for favorable parameter values allowed by current precise experiments. Thus, the branching ratios for $t\to cg$ and $t\to c\gamma$ may be measurable at the LHC.Comment: 15 pages, 3 figures, minor changs in the Table