94 research outputs found
GLOTTAL EXCITATION EXTRACTION OF VOICED SPEECH - JOINTLY PARAMETRIC AND NONPARAMETRIC APPROACHES
The goal of this dissertation is to develop methods to recover glottal flow pulses, which contain biometrical information about the speaker. The excitation information estimated from an observed speech utterance is modeled as the source of an inverse problem. Windowed linear prediction analysis and inverse filtering are first used to deconvolve the speech signal to obtain a rough estimate of glottal flow pulses. Linear prediction and its inverse filtering can largely eliminate the vocal-tract response which is usually modeled as infinite impulse response filter. Some remaining vocal-tract components that reside in the estimate after inverse filtering are next removed by maximum-phase and minimum-phase decomposition which is implemented by applying the complex cepstrum to the initial estimate of the glottal pulses. The additive and residual errors from inverse filtering can be suppressed by higher-order statistics which is the method used to calculate cepstrum representations. Some features directly provided by the glottal source\u27s cepstrum representation as well as fitting parameters for estimated pulses are used to form feature patterns that were applied to a minimum-distance classifier to realize a speaker identification system with very limited subjects
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Graph neural networks (GNNs) have demonstrated a significant boost in
prediction performance on graph data. At the same time, the predictions made by
these models are often hard to interpret. In that regard, many efforts have
been made to explain the prediction mechanisms of these models from
perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works
present systematic frameworks to interpret GNNs, a holistic review for
explainable GNNs is unavailable. In this survey, we present a comprehensive
review of explainability techniques developed for GNNs. We focus on explainable
graph neural networks and categorize them based on the use of explainable
methods. We further provide the common performance metrics for GNNs
explanations and point out several future research directions
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
Large language models (LLMs) have been widely used as agents to complete
different tasks, such as personal assistance or event planning. While most work
has focused on cooperation and collaboration between agents, little work
explores competition, another important mechanism that fosters the development
of society and economy. In this paper, we seek to examine the competition
behaviors in LLM-based agents. We first propose a general framework to study
the competition between agents. Then, we implement a practical competitive
environment using GPT-4 to simulate a virtual town with two types of agents,
including restaurant agents and customer agents. Specifically, restaurant
agents compete with each other to attract more customers, where the competition
fosters them to transform, such as cultivating new operating strategies. The
results of our experiments reveal several interesting findings ranging from
social learning to Matthew Effect, which aligns well with existing sociological
and economic theories. We believe that competition between agents deserves
further investigation to help us understand society better. The code will be
released soon.Comment: Technical report; 21 page
Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations
In oncology research, accurate 3D segmentation of lesions from CT scans is
essential for the modeling of lesion growth kinetics. However, following the
RECIST criteria, radiologists routinely only delineate each lesion on the axial
slice showing the largest transverse area, and delineate a small number of
lesions in 3D for research purposes. As a result, we have plenty of unlabeled
3D volumes and labeled 2D images, and scarce labeled 3D volumes, which makes
training a deep-learning 3D segmentation model a challenging task. In this
work, we propose a novel model, denoted a multi-dimension unified Swin
transformer (MDU-ST), for 3D lesion segmentation. The MDU-ST consists of a
Shifted-window transformer (Swin-transformer) encoder and a convolutional
neural network (CNN) decoder, allowing it to adapt to 2D and 3D inputs and
learn the corresponding semantic information in the same encoder. Based on this
model, we introduce a three-stage framework: 1) leveraging large amount of
unlabeled 3D lesion volumes through self-supervised pretext tasks to learn the
underlying pattern of lesion anatomy in the Swin-transformer encoder; 2)
fine-tune the Swin-transformer encoder to perform 2D lesion segmentation with
2D RECIST slices to learn slice-level segmentation information; 3) further
fine-tune the Swin-transformer encoder to perform 3D lesion segmentation with
labeled 3D volumes. The network's performance is evaluated by the Dice
similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D
lesion dataset with 593 lesions extracted from multiple anatomical locations.
The proposed MDU-ST demonstrates significant improvement over the competing
models. The proposed method can be used to conduct automated 3D lesion
segmentation to assist radiomics and tumor growth modeling studies. This paper
has been accepted by the IEEE International Symposium on Biomedical Imaging
(ISBI) 2023
ELM of ELM-WD: An extremely low mass hot donor star discovered in LAMOST survey
The Extremely Low Mass White Dwarfs (ELM WDs) and pre-ELM WDs are helium core
white dwarfs with mass . They are formed in close binaries
and have lost over half of their initial masses via Common Envelope (CE)
ejection or stable Roche Lobe Over Flow (RLOF). Both evolution simulations and
observations show that a lower mass limit for ELM WDs exists at
. Here we report the discovery of an extremely low mass
ELM WD, ID70904216 in LAMOST survey, that may be lower than the ELM WD mass
limit. Based on LAMOST and P200 spectroscopic observations, ID70904216 shows
orbital period 0.219658 days and radial velocity semi-amplitude
, which gives the mass function of 0.73, indicating
the companion is a compact star. The low resolution spectra shows a F type star
with without emission features. The temperature is
consistent with that derived from SED fitting() and multi-color light
curve solution(). The optical light curves, in ZTF g, r and i bands and
Catalina V band, show ellipsoidal variability with amplitudes ,
suggesting that the visible companion is heavily tidal distorted. Combining
with the distance from Gaia survey, the WD code modeling estimates that the
mass of the visible star is , and the mass of
the invisible star is . The radius of the
visible donor is . The inclination angle is constrained
between 60 and 90. The observations indicate the system is
a pre-ELM WD + WD/NS binary system with an extremely low mass hot donor below
the theoretical limit.Comment: 16 pages, 10 figure
Orbital parameters for an ELM white dwarf with a white dwarf companion: LAMOST J033847.06+413424.2
Double white dwarf systems are of great astrophysical importance in the field
of gravitational wave and Type Ia supernova. While the binary fraction of CO
core white dwarf is about a few percents, the extremely low mass white dwarfs
are all thought to be within binary systems. In this work, we report the
orbital solution of a double degenerate system: J033847.06+413424.24, an
extremely low mass He core white dwarf orbiting a CO core white dwarf. With
LAMOST and P200, time domain spectroscopic observations have been made and
spectral atmosphere parameters are estimated to be K and
log dex. Combining Gaia parallax, 3D extinction, and evolution
tracks, we estimate a radius of and a mass of
. With the 37 single exposure spectra, the radial velocities are
measured and the orbital parameters are estimated to be days,
km/s and km/s. The radial velocity based system
ephemeris is also provided. The light curves from several photometric surveys
show no orbital modulation. The orbital solution suggests that the invisible
companion has a minimum mass of about 0.60 and is
for an inclination of , indicating most probably a CO
core white dwarf. The system is expected to merge in about 1 Gyr. With present
period and distance ( pc) it can not irradiate strong enough
gravitational wave for LISA. More double degenerate systems are expected to be
discovered and parameterized as the LAMOST survey goes on.Comment: 12 pages, 11 figure
KwaiYiiMath: Technical Report
Recent advancements in large language models (LLMs) have demonstrated
remarkable abilities in handling a variety of natural language processing (NLP)
downstream tasks, even on mathematical tasks requiring multi-step reasoning. In
this report, we introduce the KwaiYiiMath which enhances the mathematical
reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT)
and Reinforced Learning from Human Feedback (RLHF), including on both English
and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale
Chinese primary school mathematics test set (named KMath), consisting of 188
examples to evaluate the correctness of the problem-solving process generated
by the models. Empirical studies demonstrate that KwaiYiiMath can achieve
state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with
the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with
arXiv:2306.16636 by other author
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Building adaptive smart transport governance using citizen-centric data
With the increasing popularity of the concept “smart city”, many cities have adopted smart governance to address complex socio-economic and spatial issues in urban areas. Smart transport governance is applying innovations in the process of collective decision making in response to the technological and other changes in smart transport development. Governing smart transport, as a key priority in smart cities, faces old and new challenges such as managing complex uncertainties, considering alternative futures, involving citizens and correct analysis of their needs, as well as changing roles of governance. Robust theoretical and practical understandings of smart transport governance are useful for planners and policymakers to address these challenges and transform the urban mobility system towards accessible, sustainable, and innovative futures.
This PhD research explores the complexities in smart transport governance from theoretical, methodological, and practical aspects with a special focus on citizens’ needs. Four gaps in theory, methods, and practice are addressed in six chapters. In Chapter 2, a systematic literature review is performed to enhance the theoretical understanding of smart transport governance and its linkage with complexity theory in cities (CTC) and urban data science (UDS). A citizen-centric adaptive governance framework is proposed. Using the proposed framework to understand specific issues in smart transport governance, Chapters 3-5 conduct empirical studies. Chapter 3 first assesses the existing smart transport governance and development, using a new evaluation framework. Within English metropolitan areas, Greater London ranks first in smart transport development. Chapter 4 zooms into Greater London and applies novel methods to understand citizens’ activity-travel patterns with uncertainties. Typical activity-travel patterns before COVID-19 and the emerging self-organising changes when COVID-19 first hit London are identified. To supply quick insights into the pandemic’s impact on different sub-systems, Chapter 5 senses the public opinion towards different transport sub-systems through real-time social media big data. Dynamic behavioural changes and potential opportunities for smart transport transitions are found.
The outcomes of this research support the idea that CTC and UDS can enhance existing smart transport governance in terms of adaptive planning, robust analysis, and citizen involvement. We have identified and discussed emerging technologies and abrupt crises that add complexity to the urban transport sector on its way to transforming into smart transport. Adaptive understanding with the help of citizen-centric data is crucial for planning uncertain futures. Despite some limitations, the studies can provide theoretical and practical implications for smart transport governance in an increasingly complex world. The study also shows significant potential for future development and further applications of the adaptive governance framework
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