arXiv subjects

Chengfeng Zhang

Publications and source records attributed to Chengfeng Zhang.

7 recordsLinked to original sources

arXiv2025

AudioCodecBench: A Comprehensive Benchmark for Audio Codec Evaluation

Multimodal Large Language Models (MLLMs) have been widely applied in speech and music. This tendency has led to a focus on audio tokenization for Large Models (LMs). Unlike semantic-only text tokens, audio tokens must both capture global semantic content and preserve fine-grained acoustic details. Moreover, they provide a discrete method for speech and music that can be effectively integrated into MLLMs. However, existing research is unsuitable in the definitions of semantic tokens and acoustic tokens. In addition, the evaluation of different codecs typically concentrates on specific domains or tasks, such as reconstruction or Automatic Speech Recognition (ASR) task, which prevents fair and comprehensive comparisons. To address these problems, this paper provides suitable definitions for semantic and acoustic tokens and introduces a systematic evaluation framework. This framework allows for a comprehensive assessment of codecs' capabilities which evaluate across four dimensions: audio reconstruction metric, codebook index (ID) stability, decoder-only transformer perplexity, and performance on downstream probe tasks. Our results show the correctness of the provided suitable definitions and the correlation among reconstruction metrics, codebook ID stability, downstream probe tasks and perplexity.

cs.SD↗

arXiv2025

Unsaturated Dinitrogen Difluoride under Pressure: toward high-Energy Density Polymerized NF Chains

Based on first-principles calculations and ab initio molecular dynamics simulations, the polymerisation of the unsaturated cis dinitrogen-difluoride (cis-N2F2) molecular compound is investigated. The thermodynamic, dynamical and thermal stabilities of the nitrogen fluorine NF system are investigated at conditions of 0-3000 K and 0-200 GPa. The cis-N2F2 molecule is a suitable precursor to obtain one-dimensional polymerized nitrogen-fluorine (poly-NF) chains at a pressure above 90 GPa and at a temperature around 1900 K. Importantly, these poly-NF chains can be quenched to room conditions, and potentially serve as a High-energy-density materials (HEDM). It has been established that when Al is utilised as a reducing agent, poly-NF chains exhibit a gravimetric energy density of 13.55 kJ/g, which exceeds that of cubic gauche nitrogen (cg-N, 9.70 kJ/g). This is attributable to the presence of both polymerised nitrogen and strong oxidising F atoms.

cond-mat.mtrl-sci↗

arXiv2024

Realized Stable BP-N at Ambient Pressure by Phosphorus Doping

Black phosphorus nitrogen (BP-N) is an attractive high-energy-density material. However, high-pressure synthesized BP-N will decompose at low-pressure and cannot be quenched to ambient conditions. Finding a method to stabilize it at 0 GPa is of great significance for its practical applications. However, unlike cg-N, LP-N, and HLP-N, it is always a metastable phase at high-pressure up to 260 GPa, and decomposes into chains at 23 GPa. Here, based on the first-principles simulations, we find that P atom doping can effectively reduce the synthesis pressure of BP-N and maintain its stability at 0 GPa. Uniform distribution of P atom dopants within the layer helps maintain the structural stability of BP-N layer at 0 GPa, while interlayer electrostatic interaction induced by N-P dipoles enhances its dynamic stability by eliminating interlayer slipping. Furthermore, pressure is conducive to enhancing the stability of BP-N and its doped forms by suppressing N-chain dissociation. For the configuration with 12.5% doping concentration, a gravimetric energy density of 8.07 kJ/g can be realized, which is nearly two times higher than TNT.

cond-mat.mtrl-sci↗

arXiv2023

Sample size calculation based on the difference in restricted mean time lost for clinical trials with competing risks

Computation of sample size is important when designing clinical trials. The presence of competing risks makes the design of clinical trials with time-to-event endpoints cumbersome. A model based on the subdistribution hazard ratio (SHR) is commonly used for trials under competing risks. However, this approach has some limitations related to model assumptions and clinical interpretation. Considering such limitations, the difference in restricted mean time lost (RMTLd) is recommended as an alternative indicator. In this paper, we propose a sample size calculation method based on the RMTLd for the Weibull distribution (RMTLdWeibull) for clinical trials, which considers experimental conditions such as equal allocation, uniform accrual, uniform loss to follow-up, and administrative censoring. Simulation results show that sample size calculation based on the RMTLdWeibull can generally achieve a predefined power level and maintain relative robustness. Moreover, the performance of the sample size calculation based on the RMTLdWeibull is similar or superior to that based on the SHR. Even if the event time does not follow the Weibull distribution, the sample size calculation based on the RMTLdWeibull still performs well. The results also verify the performance of the sample size calculation method based on the RMTLdWeibull. From the perspective of the results of this study, clinical interpretation, application conditions and statistical performance, we recommend that when designing clinical trials in the presence of competing risks, the RMTLd indicator be applied for sample size calculation and subsequent effect size measurement.

stat.ME↗

arXiv2023

Time-varying effect in the competing risks based on restricted mean time lost

Patients with breast cancer tend to die from other diseases, so for studies that focus on breast cancer, a competing risks model is more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor. To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale. Through Monte Carlo simulation, we validated the dynamic effects estimated by the proposed regression having low bias and a coverage rate of around 95%. Applying this model to an elderly early-stage breast cancer cohort, we found that most factors had different patterns of dynamic effects, revealing meaningful physiological mechanisms underlying diseases. Moreover, from the perspective of prediction, the mean C-index in external validation reached 0.78. Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

stat.ME↗

arXiv2023

Analysis of dynamic restricted mean survival time based on pseudo-observations

In clinical follow-up studies with a time-to-event end point, the difference in the restricted mean survival time (RMST) is a suitable substitute for the hazard ratio (HR). However, the RMST only measures the survival of patients over a period of time from the baseline and cannot reflect changes in life expectancy over time. Based on the RMST, we study the conditional restricted mean survival time (cRMST) by estimating life expectancy in the future according to the time that patients have survived, reflecting the dynamic survival status of patients during follow-up. In this paper, we introduce the estimation method of cRMST based on pseudo-observations, the construction of test statistics according to the difference in the cRMST (cRMSTd), and the establishment of the robust dynamic prediction model using the landmark method. Simulation studies are employed to evaluate the statistical properties of these methods, which are also applied to two real examples. The simulation results show that the estimation of the cRMST is accurate and the cRMSTd test performs well. In addition, the dynamic RMST model has high accuracy in coefficient estimation and better predictive performance than the static RMST model. The hypothesis test proposed in this paper has a wide range of applicability, and the dynamic RMST model can predict patients' life expectancy from any prediction time, considering the time-dependent covariates and time-varying effects of covariates.

stat.ME↗

arXiv2022

Restricted mean survival time regression model with time-dependent covariates

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions.

stat.ME↗