8,819 research outputs found
New molecular candidates: X(1910), X(2200), and X(2350)
Assuming the newly observed resonant structures X(1910), X(2200), and X(2350)
as , , and molecular states respectively,
we compute their mass values in the framework of QCD sum rules. The numerical
results are for state,
for state, and for state, which
coincide with the experimental values of X(1910), X(2200), and X(2350),
respectively. This supports the statement that X(1910), X(2200), and X(2350)
could be , , and molecular candidates
respectively.Comment: 9 pages, 9 eps figures; the name of X(2000) changed to X(1910)
according to the updated data of experiments; more references and discussions
added; accepted for publication in PRD. arXiv admin note: substantial text
overlap with arXiv:1211.2277, arXiv:1201.341
Developing a Low-Cost Force Treadmill via Dynamic Modeling
By incorporating force transducers into treadmills, force platform-instrumented treadmills (commonly called force treadmills) can collect large amounts of gait data and enable the ground reaction force (GRF) to be calculated. However, the high cost of force treadmills has limited their adoption. This paper proposes a low-cost force treadmill system with force sensors installed underneath a standard exercise treadmill. It identifies and compensates for the force transmission dynamics from the actual GRF applied on the treadmill track surface to the force transmitted to the force sensors underneath the treadmill body. This study also proposes a testing procedure to assess the GRF measurement accuracy of force treadmills. Using this procedure in estimating the GRF of “walk-on-the-spot motion,” it was found that the total harmonic distortion of the tested force treadmill system was about 1.69%, demonstrating the effectiveness of the approach
Assessing Postural Stability Via the Correlation Patterns of Vertical Ground Reaction Force Components
Background Many methods have been proposed to assess the stability of human postural balance by using a force plate. While most of these approaches characterize postural stability by extracting features from the trajectory of the center of pressure (COP), this work develops stability measures derived from components of the ground reaction force (GRF). Methods In comparison with previous GRF-based approaches that extract stability features from the GRF resultant force, this study proposes three feature sets derived from the correlation patterns among the vertical GRF (VGRF) components. The first and second feature sets quantitatively assess the strength and changing speed of the correlation patterns, respectively. The third feature set is used to quantify the stabilizing effect of the GRF coordination patterns on the COP. Results In addition to experimentally demonstrating the reliability of the proposed features, the efficacy of the proposed features has also been tested by using them to classify two age groups (18–24 and 65–73 years) in quiet standing. The experimental results show that the proposed features are considerably more sensitive to aging than one of the most effective conventional COP features and two recently proposed COM features. Conclusions By extracting information from the correlation patterns of the VGRF components, this study proposes three sets of features to assess human postural stability during quiet standing. As demonstrated by the experimental results, the proposed features are not only robust to inter-trial variability but also more accurate than the tested COP and COM features in classifying the older and younger age groups. An additional advantage of the proposed approach is that it reduces the force sensing requirement from 3D to 1D, substantially reducing the cost of the force plate measurement system
Implicit-explicit Integrated Representations for Multi-view Video Compression
With the increasing consumption of 3D displays and virtual reality,
multi-view video has become a promising format. However, its high resolution
and multi-camera shooting result in a substantial increase in data volume,
making storage and transmission a challenging task. To tackle these
difficulties, we propose an implicit-explicit integrated representation for
multi-view video compression. Specifically, we first use the explicit
representation-based 2D video codec to encode one of the source views.
Subsequently, we propose employing the implicit neural representation
(INR)-based codec to encode the remaining views. The implicit codec takes the
time and view index of multi-view video as coordinate inputs and generates the
corresponding implicit reconstruction frames.To enhance the compressibility, we
introduce a multi-level feature grid embedding and a fully convolutional
architecture into the implicit codec. These components facilitate
coordinate-feature and feature-RGB mapping, respectively. To further enhance
the reconstruction quality from the INR codec, we leverage the high-quality
reconstructed frames from the explicit codec to achieve inter-view
compensation. Finally, the compensated results are fused with the implicit
reconstructions from the INR to obtain the final reconstructed frames. Our
proposed framework combines the strengths of both implicit neural
representation and explicit 2D codec. Extensive experiments conducted on public
datasets demonstrate that the proposed framework can achieve comparable or even
superior performance to the latest multi-view video compression standard MIV
and other INR-based schemes in terms of view compression and scene modeling
Understanding Programs by Exploiting (Fuzzing) Test Cases
Semantic understanding of programs has attracted great attention in the
community. Inspired by recent successes of large language models (LLMs) in
natural language understanding, tremendous progress has been made by treating
programming language as another sort of natural language and training LLMs on
corpora of program code. However, programs are essentially different from texts
after all, in a sense that they are normally heavily structured and
syntax-strict. In particular, programs and their basic units (i.e., functions
and subroutines) are designed to demonstrate a variety of behaviors and/or
provide possible outputs, given different inputs. The relationship between
inputs and possible outputs/behaviors represents the functions/subroutines and
profiles the program as a whole. Therefore, we propose to incorporate such a
relationship into learning, for achieving a deeper semantic understanding of
programs. To obtain inputs that are representative enough to trigger the
execution of most part of the code, we resort to fuzz testing and propose fuzz
tuning to boost the performance of program understanding and code
representation learning, given a pre-trained LLM. The effectiveness of the
proposed method is verified on two program understanding tasks including code
clone detection and code classification, and it outperforms current
state-of-the-arts by large margins. Code is available at
https://github.com/rabbitjy/FuzzTuning.Comment: Findings of the Association for Computational Linguistics: ACL 202
A Conceptual Artificial Intelligence Application Framework in Human Resource Management
This study proposes a conceptional framework of artificial intelligence (AI) technology application for human resource management (HRM). Based on the theory of the six basic dimensions of human resource management, which includes human resource strategy and planning, recruitment, training and development process, performance management, salary evaluation, and the employee relationship management, is combine with its potential corresponding AI technology application. With the cases analysis on recruitment of leap.ai and online training of Baidu, the recruitment dimension and training dimension with AI are further explored. Finally, the practical implication and future study are supplemented. This AIHRM conceptual model provides suggestions and directions for the development of AI in enterprise human resource management
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