Compensation effect in carbon nanotube quantum dots coupled to polarized electrodes in the presence of spin-orbit coupling

We study theoretically the Kondo effect in carbon nanotube quantum dot attached to polarized electrodes. Since both spin and orbit degrees of freedom are involved in such a system, the electrode polarization contains the spin- and orbit-polarizations as well as the Kramers polarization in the presence of the spin-orbit coupling. In this paper we focus on the compensation effect of the effective fields induced by different polarizations by applying magnetic field. The main results are i) while the effective fields induced by the spin- and orbit-polarizations remove the degeneracy in the Kondo effect, the effective field induced by the Kramers polarization enhances the degeneracy through suppressing the spin-orbit coupling; ii) while the effective field induced by the spin-polarization can not be compensated by applying magnetic field, the effective field induced by the orbit-polarization can be compensated; and iii) the presence of the spin-orbit coupling does not change the compensation behavior observed in the case without the spin-orbit coupling. These results are observable in an ultraclean carbon-nanotube quantum dot attached to ferromagnetic contacts under a parallel applied magnetic field along the tube axis and it would deepen our understanding on the Kondo physics of the carbon nanotube quantum dot.Comment: 8 pages, 6 figure

Peripheral Direct Adjacent Lobe Invasion Non-small Cell Lung Cancer Has a Similar Survival to That of Parietal Pleural Invasion T3 Disease

IntroductionThe postoperative prognosis of peripheral adjacent lobe invasion non-small cell lung cancer (NSCLC) is unclear. The purpose of this study was to determine the postoperative prognosis of NSCLC with direct adjacent lobe invasion by comparing it with that of visceral pleural invasion (primary lobe) T2 disease, and parietal pleural invasion T3 disease, and hence determine its most appropriate T category.MethodsA retrospective analysis was conducted to assess the survival of patients with peripheral direct adjacent lobe invasion NSCLC (group A), and it was compared with that of patients with visceral pleural invasion of the primary lobe (group B) and parietal pleural invasion (group C). All patients were node-negative on pathologic examination. Kaplan-Meier method was used to compare the postoperative survival between groups.ResultsA total of 263 patients were analyzed. The overall survival rates in groups A (n = 28), B (n = 167), and C (n = 68) at 5 years were 40.7, 54.6, and 41.9%, respectively; corresponding median survival in three groups were 53, 71, and 40 months, respectively. The survival difference among three groups was statistically significant (p = 0.031). A similar survival was observed between groups A and C, whereas group B had a much better survival than other groups.ConclusionsPeripheral adjacent lobe invasion NSCLC has a similar survival prognosis with that of parietal pleural invasion T3 disease and hence should be classified as T3 rather than T2. However, further studies are warranted

SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition

Error correction in automatic speech recognition (ASR) aims to correct those incorrect words in sentences generated by ASR models. Since recent ASR models usually have low word error rate (WER), to avoid affecting originally correct tokens, error correction models should only modify incorrect words, and therefore detecting incorrect words is important for error correction. Previous works on error correction either implicitly detect error words through target-source attention or CTC (connectionist temporal classification) loss, or explicitly locate specific deletion/substitution/insertion errors. However, implicit error detection does not provide clear signal about which tokens are incorrect and explicit error detection suffers from low detection accuracy. In this paper, we propose SoftCorrect with a soft error detection mechanism to avoid the limitations of both explicit and implicit error detection. Specifically, we first detect whether a token is correct or not through a probability produced by a dedicatedly designed language model, and then design a constrained CTC loss that only duplicates the detected incorrect tokens to let the decoder focus on the correction of error tokens. Compared with implicit error detection with CTC loss, SoftCorrect provides explicit signal about which words are incorrect and thus does not need to duplicate every token but only incorrect tokens; compared with explicit error detection, SoftCorrect does not detect specific deletion/substitution/insertion errors but just leaves it to CTC loss. Experiments on AISHELL-1 and Aidatatang datasets show that SoftCorrect achieves 26.1% and 9.4% CER reduction respectively, outperforming previous works by a large margin, while still enjoying fast speed of parallel generation.Comment: AAAI 202

Kondo effect of an adatom in graphene and its scanning tunneling spectroscopy

We study the Kondo effect of a single magnetic adatom on the surface of graphene. It was shown that the unique linear dispersion relation near the Dirac points in graphene makes it more easy to form the local magnetic moment, which simply means that the Kondo resonance can be observed in a more wider parameter region than in the metallic host. The result indicates that the Kondo resonance indeed can form ranged from the Kondo regime, to the mixed valence, even to the empty orbital regime. While the Kondo resonance displays as a sharp peak in the first regime, it has a peak-dip structure and/or an anti-resonance in the remaining two regimes, which result from the Fano resonance due to the significant background leaded by dramatically broadening of the impurity level in graphene. We also study the scanning tunneling microscopy (STM) spectra of the adatom and they show obvious particle-hole asymmetry when the chemical potential is tuned by the gate voltages applied to the graphene. Finally, we explore the influence of the direct tunneling channel between the STM tip and the graphene on the Kondo resonance and find that the lineshape of the Kondo resonance is unaffected, which can be attributed to unusual large asymmetry factor in graphene. Our study indicates that the graphene is an ideal platform to study systematically the Kondo physics and these results are useful to further stimulate the relevant experimental studies on the system.Comment: 8 pages, 5 figure

Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation

Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem with machine learning and fragmentation-based methods, however the two approaches fail to give a satisfactory description of long-range and many-body interactions, respectively. Inspired by fragmentation-based methods, we propose the Long-Short-Range Message-Passing (LSR-MP) framework as a generalization of the existing equivariant graph neural networks (EGNNs) with the intent to incorporate long-range interactions efficiently and effectively. We apply the LSR-MP framework to the recently proposed ViSNet and demonstrate the state-of-the-art results with up to $40\%$ error reduction for molecules in MD22 and Chignolin datasets. Consistent improvements to various EGNNs will also be discussed to illustrate the general applicability and robustness of our LSR-MP framework

IL21R and PTH May Underlie Variation of Femoral Neck Bone Mineral Density as Revealed by a Genome-wide Association Study

Bone mineral density (BMD) measured at the femoral neck (FN) is the most important risk phenotype for osteoporosis and has been used as a reference standard for describing osteoporosis. The specific genes influencing FN BMD remain largely unknown. To identify such genes, we first performed a genome-wide association (GWA) analysis for FN BMD in a discovery sample consisting of 983 unrelated white subjects. We then tested the top significant single-nucleotide polymorphisms (SNPs; 175 SNPs with p < 5 × 10−4) for replication in a family-based sample of 2557 white subjects. Combing results from these two samples, we found that two genes, parathyroid hormone (PTH) and interleukin 21 receptor (IL21R), achieved consistent association results in both the discovery and replication samples. The PTH gene SNPs, rs9630182, rs2036417, and rs7125774, achieved p values of 1.10 × 10−4, 3.24 × 10−4, and 3.06 × 10−4, respectively, in the discovery sample; p values of 6.50 × 10−4, 5.08 × 10−3, and 5.68 × 10−3, respectively, in the replication sample; and combined p values of 3.98 × 10−7, 9.52 × 10−6, and 1.05 × 10−5, respectively, in the total sample. The IL21R gene SNPs, rs8057551, rs8061992, and rs7199138, achieved p values of 1.51 × 10−4, 1.53 × 10−4, and 3.88 × 10−4, respectively, in the discovery sample; p values of 2.36 × 10−3, 6.74 × 10−3, and 6.41 × 10−3, respectively, in the replication sample; and combined p values of 2.31 × 10−6, 8.62 × 10−6, and 1.41 × 10−5, respectively, in the total sample. The effect size of each SNP was approximately 0.11 SD estimated in the discovery sample. PTH and IL21R both have potential biologic functions important to bone metabolism. Overall, our findings provide some new clues to the understanding of the genetic architecture of osteoporosis. © 2010 American Society for Bone and Mineral Research

FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER) than original ASR outputs. Previous works usually use a sequence-to-sequence model to correct an ASR output sentence autoregressively, which causes large latency and cannot be deployed in online ASR services. A straightforward solution to reduce latency, inspired by non-autoregressive (NAR) neural machine translation, is to use an NAR sequence generation model for ASR error correction, which, however, comes at the cost of significantly increased ASR error rate. In this paper, observing distinctive error patterns and correction operations (i.e., insertion, deletion, and substitution) in ASR, we propose FastCorrect, a novel NAR error correction model based on edit alignment. In training, FastCorrect aligns each source token from an ASR output sentence to the target tokens from the corresponding ground-truth sentence based on the edit distance between the source and target sentences, and extracts the number of target tokens corresponding to each source token during edition/correction, which is then used to train a length predictor and to adjust the source tokens to match the length of the target sentence for parallel generation. In inference, the token number predicted by the length predictor is used to adjust the source tokens for target sequence generation. Experiments on the public AISHELL-1 dataset and an internal industrial-scale ASR dataset show the effectiveness of FastCorrect for ASR error correction: 1) it speeds up the inference by 6-9 times and maintains the accuracy (8-14% WER reduction) compared with the autoregressive correction model; and 2) it outperforms the popular NAR models adopted in neural machine translation and text edition by a large margin.Comment: NeurIPS 2021. Code URL: https://github.com/microsoft/NeuralSpeec