10,620 research outputs found
Single item stochastic lot sizing problem considering capital flow and business overdraft
This paper introduces capital flow to the single item stochastic lot sizing
problem. A retailer can leverage business overdraft to deal with unexpected
capital shortage, but needs to pay interest if its available balance goes below
zero. A stochastic dynamic programming model maximizing expected final capital
increment is formulated to solve the problem to optimality. We then investigate
the performance of four controlling policies: (), (), () and
(, , ); for these policies, we adopt simulation-genetic
algorithm to obtain approximate values of the controlling parameters. Finally,
a simulation-optimization heuristic is also employed to solve this problem.
Computational comparisons among these approaches show that policy and
policy provide performance close to that of optimal
solutions obtained by stochastic dynamic programming, while
simulation-optimization heuristic offers advantages in terms of computational
efficiency. Our numerical tests also show that capital availability as well as
business overdraft interest rate can substantially affect the retailer's
optimal lot sizing decisions.Comment: 18 pages, 3 figure
Emotion Expression Extraction Method for Chinese Microblog Sentences
With the rapid spread of Chinese microblog, a large number of microblog topics are being generated in real-time. More and more users pay attention to emotion expressions of these opinionated sentences in different topics. It is challenging to label the emotion expressions of opinionated sentences manually. For this endeavor, an emotion expression extraction method is proposed to process millions of user-generated opinionated sentences automatically in this paper. Specifically, the proposed method mainly contains two tasks: emotion classification and opinion target extraction. We first use a lexicon-based emotion classification method to compute different emotion values in emotion label vectors of opinionated sentences. Then emotion label vectors of opinionated sentences are revised by an unsupervised emotion label propagation algorithm. After extracting candidate opinion targets of opinionated sentences, the opinion target extraction task is performed on a random walk-based ranking algorithm, which considers the connection between candidate opinion targets and the textual similarity between opinionated sentences, ranks candidate opinion targets of opinionated sentences. Experimental results demonstrate the effectiveness of algorithms in the proposed method
A multi-period multi-product stochastic inventory problem with order-based loan
This paper investigates a multi-product stochastic inventory problem in which
a cash-constrained online retailer can adopt order-based loan provided by some
Chinese e-commerce platforms to speed up its cash recovery for deferred
revenue. We first build deterministic models for the problem and then develop
the corresponding stochastic programming models to maximize the retailers'
expected profit over the planning horizon. The uncertainty of customer demand
is represented by scenario trees, and a scenario reduction technique is used to
solve the problem when the scenario trees are too large. We conduct numerical
tests based on real data crawling from an online store. The results show that
the stochastic model outperforms the deterministic model, especially when the
retailer is less cash-constrained. Moreover, the retailer tends to choose using
order-based loan when its initial available cash is small or facing long
receipt delay length
BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling
Like masked language modeling (MLM) in natural language processing, masked
image modeling (MIM) aims to extract valuable insights from image patches to
enhance the feature extraction capabilities of the underlying deep neural
network (DNN). Contrasted with other training paradigms like supervised
learning and unsupervised contrastive learning, masked image modeling (MIM)
pretraining typically demands significant computational resources in order to
manage large training data batches (e.g., 4096). The significant memory and
computation requirements pose a considerable challenge to its broad adoption.
To mitigate this, we introduce a novel learning framework,
termed~\textit{Block-Wise Masked Image Modeling} (BIM). This framework involves
decomposing the MIM tasks into several sub-tasks with independent computation
patterns, resulting in block-wise back-propagation operations instead of the
traditional end-to-end approach. Our proposed BIM maintains superior
performance compared to conventional MIM while greatly reducing peak memory
consumption. Moreover, BIM naturally enables the concurrent training of
numerous DNN backbones of varying depths. This leads to the creation of
multiple trained DNN backbones, each tailored to different hardware platforms
with distinct computing capabilities. This approach significantly reduces
computational costs in comparison with training each DNN backbone individually.
Our framework offers a promising solution for resource constrained training of
MIM
Quantum Anomalous Hall Effect in Graphene Proximity Coupled to an Antiferromagnetic Insulator
We propose realizing the quantum anomalous Hall effect by proximity coupling
graphene to an antiferromagnetic insulator that provides both broken
time-reversal symmetry and spin-orbit coupling. We illustrate our idea by
performing ab initio calculations for graphene adsorbed on the (111) surface of
BiFeO3. In this case, we find that the proximity-induced exchange field in
graphene is about 70 meV, and that a topologically nontrivial band gap is
opened by Rashba spin-orbit coupling. The size of the gap depends on the
separation between the graphene and the thin film substrate, which can be tuned
experimentally by applying external pressure.Comment: 5pages, 5 figure
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