Transport spectroscopy of chemical nanostructures: the case of metallic single-walled carbon nanotubes

Transport spectroscopy, a technique based on current-voltage measurements of individual nanostructures in a three-terminal transistor geometry, has emerged as a powerful new tool to investigate the electronic properties of chemically derived nanostructures. In this review, we discuss the utility of this approach using the recent studies of single-nanotube transistors as an example. Specifically, we discuss how transport measurements can be used to gain detailed insight into the electronic motion in metallic single-walled carbon nanotubes in several distinct regimes, depending on the coupling strength of the contacts to the nanotubes. Measurements of nanotube devices in these different conductance regimes have enabled a detailed analysis of the transport properties, including the experimental determination of all Hartree-Fock parameters that govern the electronic structure of metallic nanotubes and the demonstration of Fabry-Perot resonators based on the interference of electron waves

Shell Filling and Exchange Coupling in Metallic Single-Walled Carbon Nanotubes

We report the characterization of electronic shell filling in metallic single-walled carbon nanotubes by low-temperature transport measurements. Nanotube quantum dots with average conductance ∼(1–2)e^2/h exhibit a distinct four-electron periodicity for electron addition as well as signatures of Kondo and inelastic cotunneling. The Hartree-Fock parameters that govern the electronic structure of metallic nanotubes are determined from the analysis of transport data using a shell-filling model that incorporates the nanotube band structure and Coulomb and exchange interactions

Capturing Evolution Genes for Time Series Data

The modeling of time series is becoming increasingly critical in a wide variety of applications. Overall, data evolves by following different patterns, which are generally caused by different user behaviors. Given a time series, we define the evolution gene to capture the latent user behaviors and to describe how the behaviors lead to the generation of time series. In particular, we propose a uniform framework that recognizes different evolution genes of segments by learning a classifier, and adopt an adversarial generator to implement the evolution gene by estimating the segments' distribution. Experimental results based on a synthetic dataset and five real-world datasets show that our approach can not only achieve a good prediction results (e.g., averagely +10.56% in terms of F1), but is also able to provide explanations of the results.Comment: a preprint version. arXiv admin note: text overlap with arXiv:1703.10155 by other author

The Effect of Training Dataset Size on SAR Automatic Target Recognition Using Deep Learning

Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us the minimum size of the training set for a particular classification accuracy

Pharmacokinetics of oleracimine in rats by ultra-high-performance liquid chromatography

The novel alkaloid, oleracimine, presented remarkable anti-inflammatory bioactivity, and therefore, its pharmacokinetics was investigated in rat plasma after intravenous and oral administration by using a rapid ultra-high-performance liquid chromatography (UHPLC) method with UV detection at 270 nm. The analysis was performed on a shim-pack ODS column (75 mm×2 mm, 1.6 µm particle size, Shimadzu, Japan) column using isocratic elution with a mobile phase consisting of methanol-water (62:38, v/v) within 3 min. The results indicated that oleracimine was rapidly distributed with Tmax for 11.7 min after oral administration, which presented the double-peak phenomenon in the pharmacokinetic profile with a higher oral absolute bioavailability of 55.1% ± 7.83%

Transmission electron microscopy analysis of some transition metal compounds for energy storage and conversion

This work was preliminarily supported by the National Key Research Program of China (2016YFA0202604), the Natural Science Foundation of China (21476271), NSFC-RGC (21461162003) and Natural Science Foundation (2014KTSCX004 and 2014A030308012) of Guangdong Province, China.Recently, transition metal compounds (TMCs) have been employed as high-performance electrode materials for lithium ion batteries (LIBs) and supercapacitors (SCs) owing to their high specific capacities, high electrical conductivity, and high chemical and thermal stability. While the characterization of electrochemical properties of TMC anodes is well developed, new challenges arise in understanding the structure-property relationships. Transmission electron microscopy (TEM) is a powerful tool for studying microstructural characteristics. With TEM and related techniques, fundamental understanding of how the microstructures affect the properties of the TMC nanostructured anodes can be improved. In this article, the application of TEM in characterization of some typical TMC anode materials optimized through structural engineering, elemental doping, surface modification, defect-control engineering, morphological control, etc. is reviewed. Emphasis is given on analyzing the microstructures, including surface structures, various defects, local chemical compositions and valence states of transition metals, aimed at illustrating a structure-property relationship. The contribution and future development of the TEM techniques to elucidation of the electrochemical properties of the TMC anodes are highlighted.PostprintPeer reviewe

Learning to Rank in Generative Retrieval

Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generation models and represents a new paradigm distinct from traditional learning-to-rank methods. However, despite its rapid development, current generative retrieval methods are still limited. They typically rely on a heuristic function to transform predicted identifiers into a passage rank list, which creates a gap between the learning objective of generative retrieval and the desired passage ranking target. Moreover, the inherent exposure bias problem of text generation also persists in generative retrieval. To address these issues, we propose a novel framework, called LTRGR, that combines generative retrieval with the classical learning-to-rank paradigm. Our approach involves training an autoregressive model using a passage rank loss, which directly optimizes the autoregressive model toward the optimal passage ranking. This framework only requires an additional training step to enhance current generative retrieval systems and does not add any burden to the inference stage. We conducted experiments on three public datasets, and our results demonstrate that LTRGR achieves state-of-the-art performance among generative retrieval methods, indicating its effectiveness and robustness

MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation

Developing conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially in the time of COVID-19 Pandemic. However, the training of end-to-end neural-based medical dialogue system is restricted by an insufficient quantity of medical dialogue corpus. In this work, we make the first attempt to build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG, with more than 17K conversations collected from the online health consultation community. Five different categories of entities, including diseases, symptoms, attributes, tests, and medicines, are annotated in each conversation of MedDG as additional labels. To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. To acquire a clear comprehension on these two medical dialogue tasks, we implement several state-of-the-art benchmarks, as well as design two dialogue models with a further consideration on the predicted entities. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset, and the response quality can be enhanced with the help of auxiliary entity information. From human evaluation, the simple retrieval model outperforms several state-of-the-art generative models, indicating that there still remains a large room for improvement on generating medically meaningful responses.Comment: Data and code are available at https://github.com/lwgkzl/MedD