147 research outputs found
Higher Order Derivatives in Costa's Entropy Power Inequality
Let be an arbitrary continuous random variable and be an independent
Gaussian random variable with zero mean and unit variance. For , Costa
proved that is concave in , where the proof hinged on
the first and second order derivatives of . Specifically, these
two derivatives are signed, i.e., and . In this
paper, we show that the third order derivative of is
nonnegative, which implies that the Fisher information is
convex in . We further show that the fourth order derivative of
is nonpositive. Following the first four derivatives, we make
two conjectures on : the first is that
is nonnegative in if
is odd, and nonpositive otherwise; the second is that is
convex in . The first conjecture can be rephrased in the context of
completely monotone functions: is completely monotone in .
The history of the first conjecture may date back to a problem in mathematical
physics studied by McKean in 1966. Apart from these results, we provide a
geometrical interpretation to the covariance-preserving transformation and
study the concavity of , revealing its connection
with Costa's EPI.Comment: Second version submitted. https://sites.google.com/site/chengfancuhk
Split, Encode and Aggregate for Long Code Search
Code search with natural language plays a crucial role in reusing existing
code snippets and accelerating software development. Thanks to the
Transformer-based pretraining models, the performance of code search has been
improved significantly compared to traditional information retrieval (IR) based
models. However, due to the quadratic complexity of multi-head self-attention,
there is a limit on the input token length. For efficient training on standard
GPUs like V100, existing pretrained code models, including GraphCodeBERT,
CodeBERT, RoBERTa (code), take the first 256 tokens by default, which makes
them unable to represent the complete information of long code that is greater
than 256 tokens. Unlike long text paragraph that can be regarded as a whole
with complete semantics, the semantics of long code is discontinuous as a piece
of long code may contain different code modules. Therefore, it is unreasonable
to directly apply the long text processing methods to long code. To tackle the
long code problem, we propose SEA (Split, Encode and Aggregate for Long Code
Search), which splits long code into code blocks, encodes these blocks into
embeddings, and aggregates them to obtain a comprehensive long code
representation. With SEA, we could directly use Transformer-based pretraining
models to model long code without changing their internal structure and
repretraining. Leveraging abstract syntax tree (AST) based splitting and
attention-based aggregation methods, SEA achieves significant improvements in
long code search performance. We also compare SEA with two sparse Trasnformer
methods. With GraphCodeBERT as the encoder, SEA achieves an overall mean
reciprocal ranking score of 0.785, which is 10.1% higher than GraphCodeBERT on
the CodeSearchNet benchmark.Comment: 9 page
The effects of the rock bridge ligament angle and the confinement on crack coalescence in rock bridges: An experimental study and discrete element method
To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles
Explainable AI, in the context of autonomous systems, like self driving cars,
has drawn broad interests from researchers. Recent studies have found that
providing explanations for an autonomous vehicle actions has many benefits,
e.g., increase trust and acceptance, but put little emphasis on when an
explanation is needed and how the content of explanation changes with context.
In this work, we investigate which scenarios people need explanations and how
the critical degree of explanation shifts with situations and driver types.
Through a user experiment, we ask participants to evaluate how necessary an
explanation is and measure the impact on their trust in the self driving cars
in different contexts. We also present a self driving explanation dataset with
first person explanations and associated measure of the necessity for 1103
video clips, augmenting the Berkeley Deep Drive Attention dataset.
Additionally, we propose a learning based model that predicts how necessary an
explanation for a given situation in real time, using camera data inputs. Our
research reveals that driver types and context dictates whether or not an
explanation is necessary and what is helpful for improved interaction and
understanding.Comment: 9.5 pages, 7 figures, submitted to UIST202
Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques
Deep learning technique is an effective mean of processing complex data that has emerged in recent years, which has been applied to fault diagnosis of a wide range of equipment. In the present study, three types of deep learning techniques, namely, stacked autoencoder (SAE) network, long short term memory (LSTM) network, and convolutional neural network (CNN) are applied to fault diagnosis of a mixed-flow pump under cavitation conditions. Vibration signals of the mixed-flowed pump are collected from experiment measurements, and then employed as input datasets for deep learning networks. The operation status is clarified into normal, minor cavitation, and severe cavitation conditions according to visualized bubble density. The techniques of FFT and dropout algorithms are also applied to improve diagnosis accuracy. The results show that the diagnosis accuracy based on SAE and LSTM networks is lower than 50%, while is higher than 68% when using CNN. The maximum accuracy can reach 87.2% by mean of a combination of CNN, BN, MLP, and using frequency domain data by FFT as inputs, which validates the feasibility of applying CNN in mixed-flow pumps
OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis
Ultrasound (US) imaging is indispensable in clinical practice. To diagnose
certain diseases, sonographers must observe corresponding dynamic anatomic
structures to gather comprehensive information. However, the limited
availability of specific US video cases causes teaching difficulties in
identifying corresponding diseases, which potentially impacts the detection
rate of such cases. The synthesis of US videos may represent a promising
solution to this issue. Nevertheless, it is challenging to accurately animate
the intricate motion of dynamic anatomic structures while preserving image
fidelity. To address this, we present a novel online feature-decoupling
framework called OnUVS for high-fidelity US video synthesis. Our highlights can
be summarized by four aspects. First, we introduced anatomic information into
keypoint learning through a weakly-supervised training strategy, resulting in
improved preservation of anatomical integrity and motion while minimizing the
labeling burden. Second, to better preserve the integrity and textural
information of US images, we implemented a dual-decoder that decouples the
content and textural features in the generator. Third, we adopted a
multiple-feature discriminator to extract a comprehensive range of visual cues,
thereby enhancing the sharpness and fine details of the generated videos.
Fourth, we constrained the motion trajectories of keypoints during online
learning to enhance the fluidity of generated videos. Our validation and user
studies on in-house echocardiographic and pelvic floor US videos showed that
OnUVS synthesizes US videos with high fidelity.Comment: 14 pages, 13 figures and 6 table
- …