5,034 research outputs found
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR
2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4:
correction, update reference baseline results according to their latest post;
V5: minor correction; V6: Identification results using NIH data splits and
various image model
Optimal Power Flow in Stand-alone DC Microgrids
Direct-current microgrids (DC-MGs) can operate in either grid-connected or
stand-alone mode. In particular, stand-alone DC-MG has many distinct
applications. However, the optimal power flow problem of a stand-alone DC-MG is
inherently non-convex. In this paper, the optimal power flow (OPF) problem of
DC-MG is investigated considering convex relaxation based on second-order cone
programming (SOCP). Mild assumptions are proposed to guarantee the exactness of
relaxation, which only require uniform nodal voltage upper bounds and positive
network loss. Furthermore, it is revealed that the exactness of SOCP relaxation
of DC-MGs does not rely on either topology or operating mode of DC-MGs, and an
optimal solution must be unique if it exists. If line constraints are
considered, the exactness of SOCP relaxation may not hold. In this regard, two
heuristic methods are proposed to give approximate solutions. Simulations are
conducted to confirm the theoretic results
Improving Code Generation by Dynamic Temperature Sampling
Recently, Large Language Models (LLMs) have shown impressive results in code
generation. However, existing decoding strategies are designed for Natural
Language (NL) generation, overlooking the differences between NL and
programming languages (PL). Due to this oversight, a better decoding strategy
for code generation remains an open question. In this paper, we conduct the
first systematic study to explore a decoding strategy specialized in code
generation. With an analysis of loss distributions of code tokens, we find that
code tokens can be divided into two categories: challenging tokens that are
difficult to predict and confident tokens that can be easily inferred. Among
them, the challenging tokens mainly appear at the beginning of a code block.
Inspired by the above findings, we propose a simple yet effective method:
Adaptive Temperature (AdapT) sampling, which dynamically adjusts the
temperature coefficient when decoding different tokens. We apply a larger
temperature when sampling for challenging tokens, allowing LLMs to explore
diverse choices. We employ a smaller temperature for confident tokens avoiding
the influence of tail randomness noises. We apply AdapT sampling to LLMs with
different sizes and conduct evaluations on two popular datasets. Results show
that AdapT sampling significantly outperforms state-of-the-art decoding
strategy
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