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3,293 research outputs found

Uniform Consistency for Nonparametric Estimators in Null Recurrent Time Series

Author: Dag Tjostheim
Jiti Gao
Degui Li
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Field of study

This paper establishes several results for uniform convergence of nonparametric kernel density and regression estimates for the case where the time series regressors concerned are nonstationary nullÂ–recurrent Markov chains. Under suitable conditions, certain rates of convergence are also established for these estimates. Our results can be viewed as an extension of some wellÂ–known uniform consistency results for the stationary time series to the nonstationary time series case.betaÂ–null recurrent Markov chain; nonparametric estimation; rate of convergence, uniform consistency

Research Papers in Economics

PsdR-expressing strain exhibits LasR-null-like phenotypes.

Author: Huifang Qiu (714750)
Huali Chen (2261914)
Yuanhao Li (141505)
Ajai A. Dandekar (14642659)
Weijun Dai (3585854)
Min Yuan (202757)
Publication venue
Publication date: 2024
Field of study

(A-C) PsdR-expressing strain produces metabolites to the levels similar to the LasR-null mutant. (A) Pyocyanin production (OD695/OD600 values) in shown strains. (B) Elastase production (OD495/OD600 values) in these strains. (C) The relative concentrations of hydrogen cyanide in shown strains. A one-way ANOVA with Bonferroni posttest was used for statistical analysis (n.s., not significant). (TIF)</p

The Francis Crick Institute

ESPnet2 pretrained model, Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave, fs=8k, lang=en

Author: Chenda Li
Publication venue
Publication date: 04/02/2021
Field of study

ZENODO

ESPnet2 pretrained model, Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave, fs=8k, lang=en

Author: Chenda Li
Publication venue
Publication date: 04/02/2021
Field of study

ZENODO

Intra-cavity spectroscopy using amplified spontaneous emission in fiber lasers

Author: Norhana Arsad
Min Li
Arsad Norhana
Johnstone Walter
Min Li
George Stewart
Walter Johnstone
Stewart George
Publication venue
Publication date: 01/03/2011
Field of study

Fiber laser sources offer interesting possibilities for gas sensors since they can operate over an extended wavelength range, encompassing the near-IR absorption lines of a number of important gases but a major problem is that overtone absorption lines of gases in the near-IR are relatively weak. In order to enhance sensitivity, we present here a simple method of intra-cavity absorption spectroscopy (ICAS) which makes use of the amplified spontaneous emission (ASE) already present within a fiber laser cavity. The ASE also provides a convenient broadband source for the simultaneous interrogation of several gases within the gain-bandwidth of the fiber laser. The key principle is based on adjusting the cavity attenuation to select an appropriate inversion level where the fiber gain curve is flat. Under this condition, the ASE undergoes multiple circulations within the fiber laser cavity, enhancing the effective path-length of a gas cell placed within the laser cavity. A theoretical model of system operation is given and we have experimentally demonstrated the principle of operation with acetylene and carbon dioxide using a simple erbium fiber laser system containing a 6 cm path-length, fiber coupled, intra-cavity, micro-optic gas cell. We have experimentally simultaneously observed 16 absorption lines for 1% acetylene gas in the 1530 nm region and detected the very weak carbon dioxide lines in this same wavelength region. A path length enhancement of in the linear regime has been demonstrated transforming the 6 cm micro-optic cell into an effective path length of m. We also demonstrate how the enhancement factor may be calibrated by use of a simple fiber-optic interferometer. Apart from the OSA, all components are inexpensive and the system is very simple to construct and operate

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University of Strathclyde Institutional Repository

Testing the Parametric Specification of the Diffusion Function in a Diffusion Process

Author: Fuchun Li
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Publication date
Field of study

A new consistent test is proposed for the parametric specification of the diffusion function in a diffusion process without any restrictions on the functional form of the drift function. The data are assumed to be sampled discretely in a time interval that can be fixed or lengthened to infinity. The test statistic is shown to follow an asymptotic normal distribution under the null hypothesis that the parametric diffusion function is correctly specified. Monte Carlo simulations are conducted to examine the finite-sample performance of the test, revealing that the test has good size and power.Econometric and statistical methods; Interest rates

Research Papers in Economics

ESPnet2 pretrained model, Chenda Li/wsj0_2mix_joint_train_asr_transformer_raw_char_aux_valid.acc.ave, fs=8k, lang=en

Author: Chenda Li
Publication venue
Publication date: 18/06/2021
Field of study

This model was trained by Chenda Li using wsj0_2mix recipe in espnet. Python API See https://github.com/espnet/espnet_model_zoo Evaluate in the recipe git clone https://github.com/espnet/espnet cd espnet git checkout 7ee55a382e7b8087daf2434451711855c76aa699 pip install -e . cd egs2/wsj0_2mix/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model Chenda Li/wsj0_2mix_joint_train_asr_transformer_raw_char_aux_valid.acc.ave Results # RESULTS ## Environments - date: `Fri Jun 18 16:47:32 CST 2021` - python version: `3.7.6 (default, Jan 8 2020, 19:59:22) [GCC 7.3.0]` - espnet version: `espnet 0.9.9` - pytorch version: `pytorch 1.7.1` - Git hash: `7ee55a382e7b8087daf2434451711855c76aa699` - Commit date: `Fri Jun 18 15:14:28 2021 +0800` ## joint_train_asr_transformer_raw_char_aux ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| exp/joint_train_asr_transformer_raw_char_aux/decode_tt_max_8k_decode_lm_train_lm_char_valid.loss.best_asr_model_valid.acc.ave/score_wer/result.txt:|6000|98613|88.1|10.2|1.7|2.4|14.3|62.1| exp/joint_train_asr_transformer_raw_char_aux/decode_tt_max_8k_decode_lm_train_lm_char_valid.loss.best_asr_model_valid.acc.best/score_wer/result.txt:|6000|98613|84.1|13.5|2.4|3.3|19.2|68.5| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| exp/joint_train_asr_transformer_raw_char_aux/decode_tt_max_8k_decode_lm_train_lm_char_valid.loss.best_asr_model_valid.acc.ave/score_cer/result.txt:|6000|598296|94.8|2.9|2.3|2.5|7.7|63.6| exp/joint_train_asr_transformer_raw_char_aux/decode_tt_max_8k_decode_lm_train_lm_char_valid.loss.best_asr_model_valid.acc.best/score_cer/result.txt:|6000|598296|92.5|4.1|3.4|3.5|11.0|69.5| ASR config config: conf/tuning/train_asr_transformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/joint_train_asr_transformer_raw_char_aux ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 43100 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false collect_stats: false write_collected_feats: false max_epoch: 120 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max - - valid - loss - min keep_nbest_models: 10 grad_clip: 3 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 4 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/joint_stats_raw_8k_aux/train/speech_mix_shape - exp/joint_stats_raw_8k_aux/train/utt2category_shape - exp/joint_stats_raw_8k_aux/train/speech_ref1_shape - exp/joint_stats_raw_8k_aux/train/text_ref1_shape - exp/joint_stats_raw_8k_aux/train/speech_ref2_shape - exp/joint_stats_raw_8k_aux/train/text_ref2_shape valid_shape_file: - exp/joint_stats_raw_8k_aux/valid/speech_mix_shape - exp/joint_stats_raw_8k_aux/valid/utt2category_shape - exp/joint_stats_raw_8k_aux/valid/speech_ref1_shape - exp/joint_stats_raw_8k_aux/valid/text_ref1_shape - exp/joint_stats_raw_8k_aux/valid/speech_ref2_shape - exp/joint_stats_raw_8k_aux/valid/text_ref2_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 - 80000 - 80000 - 80000 - 80000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr_max_8k_train_si284/wav.scp - speech_mix - sound - - dump/raw/tr_max_8k_train_si284/utt2category - utt2category - text - - dump/raw/tr_max_8k_train_si284/spk1.scp - speech_ref1 - sound - - dump/raw/tr_max_8k_train_si284/text_spk1 - text_ref1 - text - - dump/raw/tr_max_8k_train_si284/spk2.scp - speech_ref2 - sound - - dump/raw/tr_max_8k_train_si284/text_spk2 - text_ref2 - text valid_data_path_and_name_and_type: - - dump/raw/cv_max_8k/wav.scp - speech_mix - sound - - dump/raw/cv_max_8k/utt2category - utt2category - text - - dump/raw/cv_max_8k/spk1.scp - speech_ref1 - sound - - dump/raw/cv_max_8k/text_spk1 - text_ref1 - text - - dump/raw/cv_max_8k/spk2.scp - speech_ref2 - sound - - dump/raw/cv_max_8k/text_spk2 - text_ref2 - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 eps: 1.0e-08 weight_decay: 1.0e-07 scheduler: warmuplr scheduler_conf: warmup_steps: 45000 enh_model_conf: loss_type: si_snr token_list: - - - - E - T - A - N - I - O - S - R - H - L - D - C - U - M - P - F - G - Y - W - B - V - K - . - X - '''' - J - Q - Z - - ',' - '-' - '"' - '*' - ':' - ( - ) - '?' - '!' - '&' - ; - '1' - '2' - '0' - / - $ - '{' - '}' - '8' - '9' - '6' - '3' - '5' - '7' - '4' - '~' - '`' - _ - - - \ - ^ - init: xavier_uniform input_size: null ctc_conf: reduce: true asr_model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false joint_model_conf: enh_weight: 0.8 end2end_train: true cal_enh_loss: true use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null enh_encoder: conv enh_encoder_conf: channel: 256 kernel_size: 20 stride: 10 enh_separator: tcn enh_separator_conf: num_spk: 2 layer: 6 stack: 3 bottleneck_dim: 256 hidden_dim: 384 kernel: 3 causal: false norm_type: gLN nonlinear: relu enh_decoder: conv enh_decoder_conf: channel: 256 kernel_size: 20 stride: 10 frontend: default frontend_conf: fs: 8000 n_fft: 256 win_length: 256 hop_length: 64 apply_stft: true specaug: null specaug_conf: {} normalize: utterance_mvn normalize_conf: {} encoder: transformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1536 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.2 attention_dropout_rate: 0.0 input_layer: conv2d normalize_before: true decoder: transformer decoder_conf: attention_heads: 4 linear_units: 1536 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 required: - output_dir - token_list version: 0.9.9 distributed: true LM config config: conf/tuning/train_lm.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/lm_train_lm_char ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 200 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 1 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null unused_parameters: false use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null pretrain_path: null init_param: [] freeze_param: [] num_iters_per_epoch: null batch_size: 1000 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/lm_stats/train/text_shape.char valid_shape_file: - exp/lm_stats/valid/text_shape.char batch_type: folded valid_batch_type: null fold_length: - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/srctexts_with_spk - text - text valid_data_path_and_name_and_type: - - dump/raw/valid_srctexts_with_spk - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: sgd optim_conf: lr: 0.1 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 3 token_list: - - - - E - T - A - N - I - O - S - R - H - L - D - C - U - M - P - F - G - Y - W - B - V - K - . - X - '''' - J - Q - Z - - ',' - '-' - '"' - '*' - ':' - ( - ) - '?' - '!' - '&' - ; - '1' - '2' - '0' - / - $ - '{' - '}' - '8' - '9' - '6' - '3' - '5' - '7' - '4' - '~' - '`' - _ - - - \ - ^ - init: null model_conf: ignore_id: 0 use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null lm: seq_rnn lm_conf: unit: 650 nlayers: 2 required: - output_dir - token_list version: 0.9.7 distributed: fals

ZENODO

Interface-Engineered Li7La3Zr2O12-Based Garnet Solid Electrolytes with Suppressed Li-Dendrite Formation and Enhanced Electrochemical Performance

Author: Zhang Zhaoshuai
Yu C. (author)
Bo Xu
Wang Hongqiang (author)
Xu B. (author)
Zhang Long (author)
Zeng Hong (author)
Zhang Zhaoshuai (author)
Liu Yanyan (author)
Chuang Yu
Wang L.
Wang Hongqiang
Xu B.
Long Zhang
Wang L. (author)
Yanyan Liu
Hongqiang Wang
Zhang Long
Zeng Hong
Zhaoshuai Zhang
Hong Zeng
Liu Yanyan
Li‐min Wang
Yu C.
Publication venue
Publication date: 01/01/2018
Field of study

High grain-boundary resistance, Li-dendrite formation, and electrode/Li interfacial resistance are three major issues facing garnet-based solid electrolytes. Herein, interfacial architecture engineering by incorporating 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl) imide (BMP-TFSI) ionic liquid into a garnet oxide is proposed. The “soft” continuous BMP-TFSI coating with no added Li salt generates a conducting network facilitating Li+ transport and thus changes the ion conduction mode from point contacts to face contacts. The compacted microstructure suppresses Li-dendrite growth and shows good interfacial compatibility and interfacial wettability toward Li metal. Along with a broad electrochemical window larger than 5.5 V and an Li+ transference number that practically reaches unity, LiNi0.8Co0.1Mn0.1O2/Li and LiFePO4/Li solid-state batteries with the hybrid solid electrolyte exhibit superior cycling stability and low polarization, comparable to those with commercial liquid electrolytes, and excellent rate capability that is better than those of Li-salt-based ionic-liquid electrolytes.Accepted Author ManuscriptRST/Storage of Electrochemical Energ

Crossref

TU Delft Repository

Kaiso-deficient mice show resistance to intestinal cancer

Kaiso is a BTB domain protein that associates with the signaling molecule p120-catenin and binds to the methylated sequence mCGmCG or the nonmethylated sequence CTGCNA to modulate transcription. In Xenopus laevis, xKaiso deficiency leads to embryonic death accompanied by premature gene activation in blastulae and upregulation of the xWnt11 gene. Kaiso has also been proposed to play an essential role in mammalian synapse-specific transcription. We disrupted the Kaiso gene in mice to assess its role in mammalian development. Kaiso-null mice were viable and fertile, with no detectable abnormalities of development or gene expression. However, when crossed with tumor-susceptible Apc(Min/+) mice, Kaiso-null mice showed a delayed onset of intestinal tumorigenesis. Kaiso was found to be upregulated in murine intestinal tumors and is expressed in human colon cancers. Our data suggest that Kaiso plays a role in intestinal cancer and may therefore represent a potential target for therapeutic intervention

Crossref

Online Research @ Cardiff

Edinburgh Research Explorer

Enlighten

Li Yu xi qu zhong de li shi cheng xian yu min jian mian xiang

Author
Publication venue
Publication date: 01/01/2017
Field of study

M.Phil.As one of the most important playwrights in late imperial China, Li Yu and his plays have long been discussed by researchers. However, many of them have underestimated the significance of Li Yu’s double identity of being, simultaneously, a member of the literati and a professional playwright of the urban theatre. Focusing on the representations of history in Li Yu’s plays, this thesis aims to explore how he negotiated influence from the two identities in his works. It is argued that in most of these plays he managed to deliver his care for the commoner audience, including providing them with theatrical entertainment and a mirror of daily life during the Ming-Qing transition, while expressing his personal concerns and feelings as a literatus.There are seven chapters in this thesis. Chapter One reviews the research history on Li Yu in the past century and raises the above topic of negotiation in question. Chapter Two to Five focus on four of Li Yu’s plays in chronological order, namely Qingzhong pu, Qianzhong lu, Liang xumei and Wanli yuan. All of the four plays were written meticulously as a kind of history, local or national. These four chapters illustrate the ways in which Li Yu combined his personal feelings, reflections on history, empathy with the common people, and equally important, his dramatic artistry. In Chapter Six, Qilin ge and Niutou shan were treated as examples of how popular legends about heroic figures in history such as Qin Qiong and Yue Yun were recreated by Li Yu for the commercial urban theatre in Suzhou. Chapter seven concludes the above discussions.The personal feelings and social cares shown in Li Yu’s plays came from real-life issues. Li not only recorded historical changes and social problems of his time, but also expressed his attitude towards the Qing government after the fall of Ming. By examining Li Yu’s attitude, this research also sheds light on Li Yu’s state of mind in later life, which has seldom been mentioned in the study of this dramatist.李玉（約1611-1677）為明末清初最重要的劇作家之一，學界對其近一世紀的研究已有豐碩成果，然而關於李玉文人與職業劇作家雙重身分對其創作的影響卻未有詳細的討論。故此，本文將視野聚焦在李玉劇作中對歷史的呈現，從文本內部、觀眾接受、外在社會情況等方面，探討其雙重身分在呈現兩種不同的目的與關注時之協調，發掘劇中所寄託的個人情感與民間面向，並觀察當中的變化。本文第一章梳理李玉研究之發展脈絡，提出個人關注的問題。第二至第五章按脫稿時間之先後，分章討論《清忠譜》、《千忠錄》、《兩鬚眉》、《萬裡圓》四部「以曲為史」之作所寄託之個人感懷與反思，及當中所體現的對民間之關懷。第六章以《麒麟閣》與《牛頭山》為李玉以歷史故事入劇之代表，探討李玉如何以劇場重現著名歷史故事，為民間觀眾提供娛樂。第七章就前述各劇之間的內部聯繫，及李玉個人心境與民間面向的變化軌跡作總結。李玉劇作中的個人寄託與對民間的關懷來自對現實的關注。它們不僅見證其身處的時代與社會之轉變，入清後之作更承載個人對易代、對清廷態度的變化。本文欲藉對李玉心態之剖析，填補學界對其後半生心境變化討論上的空白，加深對李玉其人的認識，亦為李玉研究提出就其身分與心態作進一步發展的新方向。劉美宜.Thesis M.Phil. Chinese University of Hong Kong 2017.Includes bibliographical references (leaves 162-173).Abstracts in English and Chinese.Title from PDF title page (viewed on February 17, 2020).Liu Meiyi

CUHK Digital Repository