4 research outputs found
Answering Layer 3 queries with DiscoSCMs
In the realm of causal inference, the primary frameworks are the Potential
Outcome (PO) and the Structural Causal Model (SCM), both predicated on the
consistency rule. However, when facing Layer 3 valuations, i.e., counterfactual
queries that inherently belong to individual-level semantics, they both seem
inadequate due to the issue of degeneration caused by the consistency rule. For
instance, in personalized incentive scenarios within the internet industry, the
probability of one particular user being a complier, denoted as , degenerates to a parameter that can only take values of 0 or 1. This
paper leverages the DiscoSCM framework to theoretically tackle the
aforementioned counterfactual degeneration problem, which is a novel framework
for causal modeling that combines the strengths of both PO and SCM, and could
be seen as an extension of them. The paper starts with a brief introduction to
the background of causal modeling frameworks. It then illustrates, through an
example, the difficulty in recovering counterfactual parameters from data
without imposing strong assumptions. Following this, we propose the DiscoSCM
with independent potential noise framework to address this problem.
Subsequently, the superior performance of the DiscoSCM framework in answering
counterfactual questions is demonstrated by several key results in the topic of
unit select problems. We then elucidate that this superiority stems from the
philosophy of individual causality. In conclusion, we suggest that DiscoSCM may
serve as a significant milestone in the causal modeling field for addressing
counterfactual queries
VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by Digital Signal Processing Synthesizer
End-to-end singing voice synthesis (SVS) model VISinger can achieve better
performance than the typical two-stage model with fewer parameters. However,
VISinger has several problems: text-to-phase problem, the end-to-end model
learns the meaningless mapping of text-to-phase; glitches problem, the harmonic
components corresponding to the periodic signal of the voiced segment occurs a
sudden change with audible artefacts; low sampling rate, the sampling rate of
24KHz does not meet the application needs of high-fidelity generation with the
full-band rate (44.1KHz or higher). In this paper, we propose VISinger 2 to
address these issues by integrating the digital signal processing (DSP) methods
with VISinger. Specifically, inspired by recent advances in differentiable
digital signal processing (DDSP), we incorporate a DSP synthesizer into the
decoder to solve the above issues. The DSP synthesizer consists of a harmonic
synthesizer and a noise synthesizer to generate periodic and aperiodic signals,
respectively, from the latent representation z in VISinger. It supervises the
posterior encoder to extract the latent representation without phase
information and avoid the prior encoder modelling text-to-phase mapping. To
avoid glitch artefacts, the HiFi-GAN is modified to accept the waveforms
generated by the DSP synthesizer as a condition to produce the singing voice.
Moreover, with the improved waveform decoder, VISinger 2 manages to generate
44.1kHz singing audio with richer expression and better quality. Experiments on
OpenCpop corpus show that VISinger 2 outperforms VISinger, CpopSing and
RefineSinger in both subjective and objective metrics.Comment: Submitted to ICASSP 202
Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow
To improve the distributed carbon emission optimization control capability of the smart distribution network system, thereby reducing the carbon emissions in the distribution process, it is a very important issue to comprehensively analyze the importance of the node carbon emission flow of the smart distribution network. This paper transforms the power grid into a carbon emission flow network through power flow calculations: Based on the complex network theory, it determines the coupling scale of the two networks by means of the correlation coefficient method and the correlation matrix method, and establishes a coupling network model based on the carbon emission flow network; Combining the different business characteristics of carbon emission flow and information flow, an evaluation index system considering the dual-network coupling scale is established, and a multi-indicator comprehensive evaluation method that combines the Topsis and grey relational analysis method, that can objectively evaluate indicators that contain subjective components was proposed; The obtained node importance values can be used to determine the relative key line, greater sum node importance values represent a greater carbon emission impact of the line, providing a sequential basis for the carbon reduction and restructuring of the distribution network; Taking the 3-machine 9-node system as an example, the carbon flow distribution in the corresponding network is calculated, and the comprehensive importance value of the coupling node is calculated to analyze the rationality of this method