Field-effect mobility enhanced by tuning the Fermi level into the band gap of Bi2Se3

By eliminating normal fabrication processes, we preserve the bulk insulating state of calcium-doped Bi2Se3 single crystals in suspended nanodevices, as indicated by the activated temperature dependence of the resistivity at low temperatures. We perform low-energy electron beam irradiation (<16 keV) and electrostatic gating to control the carrier density and therefore the Fermi level position in the nanodevices. In slightly p-doped Bi2-xCaxSe3 devices, continuous tuning of the Fermi level from the bulk valence band to the band-gap reveals dramatic enhancement (> a factor of 10) in the field-effect mobility, which suggests suppressed backscattering expected for the Dirac fermion surface states in the gap of topological insulators

Semantic role labeling for protein transport predicates

<p>Abstract</p> <p>Background</p> <p>Automatic semantic role labeling (SRL) is a natural language processing (NLP) technique that maps sentences to semantic representations. This technique has been widely studied in the recent years, but mostly with data in newswire domains. Here, we report on a SRL model for identifying the semantic roles of biomedical predicates describing protein transport in GeneRIFs – manually curated sentences focusing on gene functions. To avoid the computational cost of syntactic parsing, and because the boundaries of our protein transport roles often did not match up with syntactic phrase boundaries, we approached this problem with a word-chunking paradigm and trained support vector machine classifiers to classify words as being at the beginning, inside or outside of a protein transport role.</p> <p>Results</p> <p>We collected a set of 837 GeneRIFs describing movements of proteins between cellular components, whose predicates were annotated for the semantic roles AGENT, PATIENT, ORIGIN and DESTINATION. We trained these models with the features of previous word-chunking models, features adapted from phrase-chunking models, and features derived from an analysis of our data. Our models were able to label protein transport semantic roles with 87.6% precision and 79.0% recall when using manually annotated protein boundaries, and 87.0% precision and 74.5% recall when using automatically identified ones.</p> <p>Conclusion</p> <p>We successfully adapted the word-chunking classification paradigm to semantic role labeling, applying it to a new domain with predicates completely absent from any previous studies. By combining the traditional word and phrasal role labeling features with biomedical features like protein boundaries and MEDPOST part of speech tags, we were able to address the challenges posed by the new domain data and subsequently build robust models that achieved F-measures as high as 83.1. This system for extracting protein transport information from GeneRIFs performs well even with proteins identified automatically, and is therefore more robust than the rule-based methods previously used to extract protein transport roles.</p

Pore Structure Characterization and Transport Performance Simulation of Cement Hydration Based on Irregular Particles

Based on the CEMHYD3D hydration model, the irregular cement particles were introduced into the model, and three 3D micro structures under different water cement ratio (0.23, 0.35, 0.53) were obtained. Numerous physical models for calculating the characteristic parameters of pore structure are established and the characteristic parameters of pore structure obtained from the physical models. The characteristic parameters of pore structure include the total porosity (referred to as porosity), the porosity of continuous pore, isolated pore and dead-end pore, connectivity, pore size distribution and tortuosity. Finally, the transmission coefficient of each micro structure is calculated by the electric simulation method

TSGBench: Time Series Generation Benchmark

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides have been made in this field, existing methods exhibit three key limitations: (1) They often benchmark against similar model types, constraining a holistic view of performance capabilities. (2) The use of specialized synthetic and private datasets introduces biases and hampers generalizability. (3) Ambiguous evaluation measures, often tied to custom networks or downstream tasks, hinder consistent and fair comparison. To overcome these limitations, we introduce \textsf{TSGBench}, the inaugural Time Series Generation Benchmark, designed for a unified and comprehensive assessment of TSG methods. It comprises three modules: (1) a curated collection of publicly available, real-world datasets tailored for TSG, together with a standardized preprocessing pipeline; (2) a comprehensive evaluation measures suite including vanilla measures, new distance-based assessments, and visualization tools; (3) a pioneering generalization test rooted in Domain Adaptation (DA), compatible with all methods. We have conducted comprehensive experiments using \textsf{TSGBench} across a spectrum of ten real-world datasets from diverse domains, utilizing ten advanced TSG methods and twelve evaluation measures. The results highlight the reliability and efficacy of \textsf{TSGBench} in evaluating TSG methods. Crucially, \textsf{TSGBench} delivers a statistical analysis of the performance rankings of these methods, illuminating their varying performance across different datasets and measures and offering nuanced insights into the effectiveness of each method.Comment: Accepted and to appear in VLDB 202

When poignant stories outweigh cold hard facts: A meta-analysis of the anecdotal bias

The objective of this paper is to resolve mixed findings about which type of evidence is more persuasive—statistical or anecdotal information. In a meta-analysis of 61 papers exploring the persuasive impact of evidence type, we establish that, in situations where emotional engagement is high (e.g., an issue associated with a severe threat, involving a health issue, or affecting oneself), statistical evidence is less influential than anecdotal evidence. However, in situations where emotional engagement is relatively low (e.g., an issue associated with low threat severity, involving a non-health issue, or affecting others), statistical evidence is more persuasive than anecdotal evidence. We discuss the theoretical and practical implications of these findings, and how to improve persuasive messaging by considering the contextual effectiveness of both anecdotes and statistics

Compositional coding for collaborative filtering

National Research Foundation (NRF) Singapore under its AI Singapore Programm

No bursts detected from FRB121102 in two 5-hour observing campaigns with the Robert C. Byrd Green Bank Telescope

Here, we report non-detection of radio bursts from Fast Radio Burst FRB 121102 during two 5-hour observation sessions on the Robert C. Byrd 100-m Green Bank Telescope in West Virginia, USA, on December 11, 2017, and January 12, 2018. In addition, we report non-detection during an abutting 10-hour observation with the Kunming 40-m telescope in China, which commenced UTC 10:00 January 12, 2018. These are among the longest published contiguous observations of FRB 121102, and support the notion that FRB 121102 bursts are episodic. These observations were part of a simultaneous optical and radio monitoring campaign with the the Caltech HIgh- speed Multi-color CamERA (CHIMERA) instrument on the Hale 5.1-m telescope.Comment: 1 table, Submitted to RN of AA

Suspension and Measurement of Graphene and Bi2Se3 Atomic Membranes

Coupling high quality, suspended atomic membranes to specialized electrodes enables investigation of many novel phenomena, such as spin or Cooper pair transport in these two dimensional systems. However, many electrode materials are not stable in acids that are used to dissolve underlying substrates. Here we present a versatile and powerful multi-level lithographical technique to suspend atomic membranes, which can be applied to the vast majority of substrate, membrane and electrode materials. Using this technique, we fabricated suspended graphene devices with Al electrodes and mobility of 5500 cm^2/Vs. We also demonstrate, for the first time, fabrication and measurement of a free-standing thin Bi2Se3 membrane, which has low contact resistance to electrodes and a mobility of >~500 cm^2/Vs