1,392 research outputs found
Selected Papers from the 8th Annual Conference of Energy Economics and Management
This collection represents successful invited submissions from the papers presented at the 8th Annual Conference of Energy Economics and Management held in Beijing, China, 22–24 September 2017. With over 500 participants, the conference was co-hosted by the Management Science Department of National Natural Science Foundation of China, the Chinese Society of Energy Economics and Management, and Renmin University of China on the subject area of “Energy Transition of China: Opportunities and Challenges”. The major strategies to transform the energy system of China to a sustainable model include energy/economic structure adjustment, resource conservation, and technology innovation. Accordingly, the conference and its associated publications encourage research to address the major issues faced in supporting the energy transition of China. Papers published in this collection cover the broad spectrum of energy economics issues, including building energy efficiency, industrial energy demand, public policies to promote new energy technologies, power system control technology, emission reduction policies in energy-intensive industries, emission measurements of cities, energy price movement, and the impact of new energy vehicle
Variational Metric Scaling for Metric-Based Meta-Learning
Metric-based meta-learning has attracted a lot of attention due to its
effectiveness and efficiency in few-shot learning. Recent studies show that
metric scaling plays a crucial role in the performance of metric-based
meta-learning algorithms. However, there still lacks a principled method for
learning the metric scaling parameter automatically. In this paper, we recast
metric-based meta-learning from a Bayesian perspective and develop a
variational metric scaling framework for learning a proper metric scaling
parameter. Firstly, we propose a stochastic variational method to learn a
single global scaling parameter. To better fit the embedding space to a given
data distribution, we extend our method to learn a dimensional scaling vector
to transform the embedding space. Furthermore, to learn task-specific
embeddings, we generate task-dependent dimensional scaling vectors with
amortized variational inference. Our method is end-to-end without any
pre-training and can be used as a simple plug-and-play module for existing
metric-based meta-algorithms. Experiments on mini-ImageNet show that our
methods can be used to consistently improve the performance of existing
metric-based meta-algorithms including prototypical networks and TADAM. The
source code can be downloaded from
https://github.com/jiaxinchen666/variational-scaling.Comment: AAAI202
Frost Durability and Strength of Concrete Prepared with Crushed Sand of Different Characteristics
The influences of fines content, methylene blue (MB) value, and lithology of crushed sand (CS) on frost durability and strength of concrete were investigated, and the frost durability and strength of crushed sand concrete (CSC) and river sand concrete (RSC) were compared. The results show that inclusion of fines improves CSC compressive strength and reduces frost durability of C30 CSC when fines content reaches 10%, whereas it has little negative influence on frost durability of C60 CSC. Increasing MB value does not negatively affect compressive strength of C30 CSC but decreases compressive strength of C60 CSC and frost durability of CSC, and the reduction is more pronounced when MB value exceeds 1.0. Lithology has no prominent influence on frost durability and compressive strength of CSC within the lithologies (dolomite, limestone, granite, basalt, and quartz) studied. Though compressive strength of CSC is a little higher than RSC under equal water to cement ratio, frost durability of CSC is no better than RSC especially for C30 CSC, and air-entraining agent is suggested for enhancing frost durability of C30 CSC exposed to freezing environment
A framework for evaluating the performance of sustainable service supply chain management under uncertainty
Developing and accessing a measure of sustainable service supply chain management (SSSCM) performance is currently a key challenge. The main contributions of this study are two-fold. First, this paper provides valuable support for SSSCM regarding the nature of network hierarchical relations with qualitative and quantitative scales. Second, this study indicates the practical implementation and enhances management effectiveness for SSSCM. The literature on SSSCM is very limited and performance measures need to have a systematic framework. The purpose of this study is to develop and evaluate the SSSCM importance based on aspects i.e., environmentally conscious design, environmental service operations design and environmentally sustainable design. This paper developed a hierarchical network for SSSCM in a closed-loop hierarchical structure. A generalized quantitative evaluation model based on the Fuzzy Delphi Method and Analytical Network Process were then used to consider both the interdependence among measures and the fuzziness of subjective measures in SSSCM. The results indicate that the top-ranking aspect to consider is that of environmental service operation design, and the top criteria is reverse logistics integrated into service packag
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression
Quantizing floating-point neural network to its fixed-point representation is
crucial for Learned Image Compression (LIC) because it ensures the decoding
consistency for interoperability and reduces space-time complexity for
implementation. Existing solutions often have to retrain the network for model
quantization which is time consuming and impractical. This work suggests the
use of Post-Training Quantization (PTQ) to directly process pretrained,
off-the-shelf LIC models. We theoretically prove that minimizing the mean
squared error (MSE) in PTQ is sub-optimal for compression task and thus develop
a novel Rate-Distortion (R-D) Optimized PTQ (RDO-PTQ) to best retain the
compression performance. Such RDO-PTQ just needs to compress few images (e.g.,
10) to optimize the transformation of weight, bias, and activation of
underlying LIC model from its native 32-bit floating-point (FP32) format to
8-bit fixed-point (INT8) precision for fixed-point inference onwards.
Experiments reveal outstanding efficiency of the proposed method on different
LICs, showing the closest coding performance to their floating-point
counterparts. And, our method is a lightweight and plug-and-play approach
without any need of model retraining which is attractive to practitioners
Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models
Replicating the innate human ability to detect all objects based on free-form
texts at any granularity remains a formidable challenge for Vision-Language
models. Current Large Vision Language Models (LVLMs) are predominantly
constrained to grounding a single, pre-existing object, relying solely on data
from Referring Expression Comprehension tasks. The limitation leads to a
compromise in model design, necessitating the introduction of visual expert
models or the integration of customized head structures. Beyond these
constraints, our research delves into the untapped potential of LVLMs and
uncover their inherent capability for basic object perception, allowing them to
accurately identify and locate objects of interest. Building on this insight,
we introduce a novel language-prompted localization dataset designed to fully
unleash the capabilities of LVLMs in integrating fine-grained object perception
with precise location awareness. More importantly, we present
, a purely LVLM-based baseline, which does not require the
introduction of any special tokens, expert models, or additional detection
modules. It simply maintains a consistent structure with popular LVLMs by
unifying data formats across various localization-related scenarios and is
trained end-to-end through a well-designed pipeline. Comprehensive experiments
demonstrate that not only achieves state-of-the-art
performance on the fine-grained RefCOCO series but also approaches the
capabilities of the expert model Faster RCNN on the detection benchmark MSCOCO.Comment: Technical report. The codes and dataset will be released soon at
https://github.com/jefferyZhan/Griffo
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