174 research outputs found
Broadly, independent-tunable, dual-wavelength mid-infrared ultrafast optical parametric oscillator
We demonstrate a two-crystal mid-infrared dual-wavelength optical parametric
oscillator, synchronously pumped by a high power femtosecond Yb:fiber laser.
The singly-resonant ring cavity, containing two periodically poled lithium
niobate crystals, is capable of generating two synchronized idler wavelengths,
independently tunable over 30 THz in the 2.9 - 4.2 {\mu}m wavelength region,
due to the cascaded quadratic nonlinear effect. The independent tunability of
the two idlers makes the optical parametric oscillator a promising source for
ultrafast pulse generation towards the THz wavelength region, based on
different frequency generation. In addition, the observed frequency doubled
idler within the crystal indicates the possibility to realize a broadband
optical self-phase locking between pump, signal, idler and higher order
generated parametric lights
Effects of Government R&D Grants on IT Entrepreneurial Firm Performance: A New Perspective on Exploration vs. Exploitation
Governments keep subsidizing R&D of IT entrepreneurial firms greatly. However, the effect of these grants remains unclear. Acknowledging this gap, this study provides a nuanced perspective to understand the influence of government R&D grants on IT entrepreneurial firm performance. Based on the literature on organizational learning, we categorize government R&D grants into two types: explorative vs. exploitative. Moreover, drawing on resource complementarity theory, we articulate how the two types of government R&D grants interact with firms’ private R&D resources. In particular, we hypothesize that in the innovation stage, government explorative R&D grants complement a firm’s internal exploration in influencing innovation performance, but substitute a firm’s external exploration. We further posit that in the commercialization stage, government exploitative R&D grants complement a firm’s innovation performance and internal exploitation in impacting financial performance, but substitute a firm’s external exploitation. We advance a theory of public-private R&D interaction for IT entrepreneurial firms
Automated Static Warning Identification via Path-based Semantic Representation
Despite their ability to aid developers in detecting potential defects early
in the software development life cycle, static analysis tools often suffer from
precision issues (i.e., high false positive rates of reported alarms). To
improve the availability of these tools, many automated warning identification
techniques have been proposed to assist developers in classifying false
positive alarms. However, existing approaches mainly focus on using
hand-engineered features or statement-level abstract syntax tree token
sequences to represent the defective code, failing to capture semantics from
the reported alarms. To overcome the limitations of traditional approaches,
this paper employs deep neural networks' powerful feature extraction and
representation abilities to generate code semantics from control flow graph
paths for warning identification. The control flow graph abstractly represents
the execution process of a given program. Thus, the generated path sequences of
the control flow graph can guide the deep neural networks to learn semantic
information about the potential defect more accurately. In this paper, we
fine-tune the pre-trained language model to encode the path sequences and
capture the semantic representations for model building. Finally, this paper
conducts extensive experiments on eight open-source projects to verify the
effectiveness of the proposed approach by comparing it with the
state-of-the-art baselines.Comment: 17 pages, in Chinese language, 9 figure
A novel approach for bilevel programs based on Wolfe duality
This paper considers a bilevel program, which has many applications in
practice. To develop effective numerical algorithms, it is generally necessary
to transform the bilevel program into a single-level optimization problem. The
most popular approach is to replace the lower-level program by its KKT
conditions and then the bilevel program can be reformulated as a mathematical
program with equilibrium constraints (MPEC for short). However, since the MPEC
does not satisfy the Mangasarian-Fromovitz constraint qualification at any
feasible point, the well-developed nonlinear programming theory cannot be
applied to MPECs directly. In this paper, we apply the Wolfe duality to show
that, under very mild conditions, the bilevel program is equivalent to a new
single-level reformulation (WDP for short) in the globally and locally optimal
sense. We give an example to show that, unlike the MPEC reformulation, WDP may
satisfy the Mangasarian-Fromovitz constraint qualification at its feasible
points. We give some properties of the WDP reformulation and the relations
between the WDP and MPEC reformulations. We further propose a relaxation method
for solving WDP and investigate its limiting behavior. Comprehensive numerical
experiments indicate that, although solving WDP directly does not perform very
well in our tests, the relaxation method based on the WDP reformulation is
quite efficient
SAGA: Summarization-Guided Assert Statement Generation
Generating meaningful assert statements is one of the key challenges in
automated test case generation, which requires understanding the intended
functionality of the tested code. Recently, deep learning-based models have
shown promise in improving the performance of assert statement generation.
However, existing models only rely on the test prefixes along with their
corresponding focal methods, yet ignore the developer-written summarization.
Based on our observations, the summarization contents usually express the
intended program behavior or contain parameters that will appear directly in
the assert statement. Such information will help existing models address their
current inability to accurately predict assert statements. This paper presents
a novel summarization-guided approach for automatically generating assert
statements. To derive generic representations for natural language (i.e.,
summarization) and programming language (i.e., test prefixes and focal
methods), we leverage a pre-trained language model as the reference
architecture and fine-tune it on the task of assert statement generation. To
the best of our knowledge, the proposed approach makes the first attempt to
leverage the summarization of focal methods as the guidance for making the
generated assert statements more accurate. We demonstrate the effectiveness of
our approach on two real-world datasets when compared with state-of-the-art
models.Comment: Preprint, to appear in the Journal of Computer Science and Technology
(JCST
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning
Privacy-sensitive data is stored in autonomous vehicles, smart devices, or
sensor nodes that can move around with making opportunistic contact with each
other. Federation among such nodes was mainly discussed in the context of
federated learning with a centralized mechanism in many works. However, because
of multi-vendor issues, those nodes do not want to rely on a specific server
operated by a third party for this purpose. In this paper, we propose a
wireless ad hoc federated learning (WAFL) -- a fully distributed cooperative
machine learning organized by the nodes physically nearby. WAFL can develop
generalized models from Non-IID datasets stored in distributed nodes locally by
exchanging and aggregating them with each other over opportunistic node-to-node
contacts. In our benchmark-based evaluation with various opportunistic
networks, WAFL has achieved higher accuracy of 94.8-96.3% than the
self-training case of 84.7%. All our evaluation results show that WAFL can
train and converge the model parameters from highly-partitioned Non-IID
datasets over opportunistic networks without any centralized mechanisms.Comment: 14 pages, 8 figures, 2 table
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing
In recent years, Transformer networks are beginning to replace pure
convolutional neural networks (CNNs) in the field of computer vision due to
their global receptive field and adaptability to input. However, the quadratic
computational complexity of softmax-attention limits the wide application in
image dehazing task, especially for high-resolution images. To address this
issue, we propose a new Transformer variant, which applies the Taylor expansion
to approximate the softmax-attention and achieves linear computational
complexity. A multi-scale attention refinement module is proposed as a
complement to correct the error of the Taylor expansion. Furthermore, we
introduce a multi-branch architecture with multi-scale patch embedding to the
proposed Transformer, which embeds features by overlapping deformable
convolution of different scales. The design of multi-scale patch embedding is
based on three key ideas: 1) various sizes of the receptive field; 2)
multi-level semantic information; 3) flexible shapes of the receptive field.
Our model, named Multi-branch Transformer expanded by Taylor formula
(MB-TaylorFormer), can embed coarse to fine features more flexibly at the patch
embedding stage and capture long-distance pixel interactions with limited
computational cost. Experimental results on several dehazing benchmarks show
that MB-TaylorFormer achieves state-of-the-art (SOTA) performance with a light
computational burden. The source code and pre-trained models are available at
https://github.com/FVL2020/ICCV-2023-MB-TaylorFormer.Comment: ICCV 202
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