The stability of graphene band structures against an external periodic perturbation; Na on Graphene

We report that the $\pi$ band of graphene sensitively changes as a function of an external potential induced by Na especially when the potential becomes periodic at low temperature. We have measured the band structures from the graphene layers formed on the 6H-SiC(0001) substrate using angle-resolved photoemission spectroscopy with synchrotron photons. With increasing Na dose, the $\pi$ band appears to be quickly diffused into background at 85 K whereas it becomes significantly enhanced its spectral intensity at room temperature (RT). A new parabolic band centered at $k\sim$1.15 \AA$^{-1}$ also forms near Fermi energy with Na at 85 K while no such a band observed at RT. Such changes in the band structure are found to be reversible with temperature. Analysis based on our first principles calculations suggests that the changes of the $\pi$ band of graphene be mainly driven by the Na-induced potential especially at low temperature where the potential becomes periodic due to the crystallized Na overlayer. The new parabolic band turns to be the $\pi$ band of the underlying buffer layer partially filled by the charge transfer from Na adatoms. The five orders of magnitude increased hopping rate of Na adatoms at RT preventing such a charge transfer explains the absence of the new band at RT.Comment: 6 pages and 6 figure

The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

Language models (LMs) with less than 100B parameters are known to perform poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when solving unseen tasks. In this work, we aim to equip smaller LMs with the step-by-step reasoning capability by instruction tuning with CoT rationales. In order to achieve this goal, we first introduce a new instruction-tuning dataset called the CoT Collection, which augments the existing Flan Collection (including only 9 CoT tasks) with additional 1.84 million rationales across 1,060 tasks. We show that CoT fine-tuning Flan-T5 (3B & 11B) with CoT Collection enables smaller LMs to have better CoT capabilities on unseen tasks. On the BIG-Bench-Hard (BBH) benchmark, we report an average improvement of +4.34% (Flan-T5 3B) and +2.60% (Flan-T5 11B), in terms of zero-shot task accuracy. Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2.24% (Flan-T5 3B) and +2.37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until the max length by a +13.98% margin. Our code, the CoT Collection data, and model checkpoints are publicly available.Comment: EMNLP 2023 (Main Conference

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as “tactile avatar”. A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s−1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences. © 2021 The Authors. Advanced Science published by Wiley-VCH GmbH1

Exit option in hierarchical agency

We explain why organizations that limit the voice of their agents can benefit from granting them an exit option. We study a hierarchy with a principal, a productive supervisor and an agent. Communication is imperfect in that only the supervisor can communicate with the principal, while the agent has no direct voice to the principal. We show that the principal is better off if she grants the agent the option to walk away from the contract. By doing so, the principal is implicitly giving a "veto" power to the agent. This, in turn, restricts the manipulation of report by the supervisor. Thus, the exit option can be interpreted as a remedy for limits on communication. Our finding contrasts to the traditional result from the contract theory literature that the exit option reduces the principal's welfare, while protecting the agent. Our result is robust to the case of collusion between the supervisor and the agent. We also examine the optimal exit option, i.e. whether exit should entail a payment to or from the agent. © 2004 Elsevier B.V. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Designing Socially Acceptable Hand-to-Face Input

Wearable head-mounted displays combine rich graphical out- put with an impoverished input space. Hand-to-face gestures have been proposed as a way to add input expressivity while keeping control movements unobtrusive. To better understand how to design such techniques, we describe an elicitation study conducted in a busy public space in which pairs of users were asked to generate unobtrusive, socially acceptable hand- to-face input actions. Based on the results, we describe five design strategies: miniaturizing, obfuscating, screening, camouflaging and re-purposing. We instantiate these strategies in two hand-to-face input prototypes, one based on touches to the ear and the other based on touches of the thumbnail to the chin or cheek. Performance assessments characterize time and error rates with these devices. The paper closes with a validation study in which pairs of users experience the prototypes in a public setting and we gather data on the social acceptability of the designs and reflect on the effectiveness of the different strategies

PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation has become an important research topic in these days. The user check-in history used as the input to POI recommendation is very imbalanced and noisy because of sparse and missing check-ins. Although sample reweighting is commonly adopted for addressing this challenge with the input data, its fixed weighting scheme is often inappropriate to deal with different characteristics of users or POIs. Thus, in this paper, we propose PREMERE, an adaptive weighting scheme based on meta-learning. Because meta-data is typically required by meta-learning but is inherently hard to obtain in POI recommendation, we self-generate the meta-data via self-ensembling. Furthermore, the meta-model architecture is extended to deal with the scarcity of check-ins. Thorough experiments show that replacing a weighting scheme with PREMERE boosts the performance of the state-of-the-art recommender algorithms by 2.36–26.9% on three benchmark datasets

Highly transparent and flexible supercapacitors using graphene-graphene quantum dots chelate

Nowadays, transparent and flexible energy storage devices are attracting a great deal of research interest due to their great potential as integrated power sources. In order to take full advantage of transparent and flexible devices, however, their power sources also need to be transparent and flexible. In the present work we fabricated new transparent and flexible micro-supercapacitors using chelated graphene and graphene quantum dots (GQDs) by a simple electrophoretic deposition (EPD) method. Through a chelate formation between graphene and GQDs with metal ions, the GQD materials were strongly adhered on an interdigitated pattern of graphene (ipG-GQDs) and its resulting porous ipG-GQDs film was used as the active material in the micro-supercapacitors. Amazingly, these supercapacitor devices showed high transparency (92.97% at 550 nm), high energy storage (9.09 μF cm-2), short relaxation time (8.55 ms), stable cycle retention (around 100% for 10,000 cycles), and high stability even under severe bending angle 45° with 10,000 cycles. © 2016 Elsevier Ltd126301sciescopu