222 research outputs found
System Dynamics Modeling-based Study of Contingent Sourcing under Supply Disruptions
AbstractIn this paper, using the methodology of system dynamics modeling, we separately build two models for a supply chain under two circumstances of supply disruptions, without backup supplier, and with a contingent supplier. The retailer's total profits are also compared under these two circumstances of supply disruptions to help the decision-makers better understanding the backup purchasing strategy. The supply chain studied only involves one retailer and two independent suppliers that are referred to as major supplier and backup supplier. The paper contributes to the literature by providing a better understanding of the impacts of supply disruptions on the system performance and by shedding insights into the value of a backup supply
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance
Relevance module plays a fundamental role in e-commerce search as they are
responsible for selecting relevant products from thousands of items based on
user queries, thereby enhancing users experience and efficiency. The
traditional approach models the relevance based product titles and queries, but
the information in titles alone maybe insufficient to describe the products
completely. A more general optimization approach is to further leverage product
image information. In recent years, vision-language pre-training models have
achieved impressive results in many scenarios, which leverage contrastive
learning to map both textual and visual features into a joint embedding space.
In e-commerce, a common practice is to fine-tune on the pre-trained model based
on e-commerce data. However, the performance is sub-optimal because the
vision-language pre-training models lack of alignment specifically designed for
queries. In this paper, we propose a method called Query-LIFE (Query-aware
Language Image Fusion Embedding) to address these challenges. Query-LIFE
utilizes a query-based multimodal fusion to effectively incorporate the image
and title based on the product types. Additionally, it employs query-aware
modal alignment to enhance the accuracy of the comprehensive representation of
products. Furthermore, we design GenFilt, which utilizes the generation
capability of large models to filter out false negative samples and further
improve the overall performance of the contrastive learning task in the model.
Experiments have demonstrated that Query-LIFE outperforms existing baselines.
We have conducted ablation studies and human evaluations to validate the
effectiveness of each module within Query-LIFE. Moreover, Query-LIFE has been
deployed on Miravia Search, resulting in improved both relevance and conversion
efficiency
Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction
Demystifying the delay propagation mechanisms among multiple airports is
fundamental to precise and interpretable delay prediction, which is crucial
during decision-making for all aviation industry stakeholders. The principal
challenge lies in effectively leveraging the spatiotemporal dependencies and
exogenous factors related to the delay propagation. However, previous works
only consider limited spatiotemporal patterns with few factors. To promote more
comprehensive propagation modeling for delay prediction, we propose
SpatioTemporal Propagation Network (STPN), a space-time separable graph
convolutional network, which is novel in spatiotemporal dependency capturing.
From the aspect of spatial relation modeling, we propose a multi-graph
convolution model considering both geographic proximity and airline schedule.
From the aspect of temporal dependency capturing, we propose a multi-head
self-attentional mechanism that can be learned end-to-end and explicitly reason
multiple kinds of temporal dependency of delay time series. We show that the
joint spatial and temporal learning models yield a sum of the Kronecker
product, which factors the spatiotemporal dependence into the sum of several
spatial and temporal adjacency matrices. By this means, STPN allows cross-talk
of spatial and temporal factors for modeling delay propagation. Furthermore, a
squeeze and excitation module is added to each layer of STPN to boost
meaningful spatiotemporal features. To this end, we apply STPN to multi-step
ahead arrival and departure delay prediction in large-scale airport networks.
To validate the effectiveness of our model, we experiment with two real-world
delay datasets, including U.S and China flight delays; and we show that STPN
outperforms state-of-the-art methods. In addition, counterfactuals produced by
STPN show that it learns explainable delay propagation patterns.Comment: 14 pages,8 figure
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
Multivariate time series forecasting poses an ongoing challenge across
various disciplines. Time series data often exhibit diverse intra-series and
inter-series correlations, contributing to intricate and interwoven
dependencies that have been the focus of numerous studies. Nevertheless, a
significant research gap remains in comprehending the varying inter-series
correlations across different time scales among multiple time series, an area
that has received limited attention in the literature. To bridge this gap, this
paper introduces MSGNet, an advanced deep learning model designed to capture
the varying inter-series correlations across multiple time scales using
frequency domain analysis and adaptive graph convolution. By leveraging
frequency domain analysis, MSGNet effectively extracts salient periodic
patterns and decomposes the time series into distinct time scales. The model
incorporates a self-attention mechanism to capture intra-series dependencies,
while introducing an adaptive mixhop graph convolution layer to autonomously
learn diverse inter-series correlations within each time scale. Extensive
experiments are conducted on several real-world datasets to showcase the
effectiveness of MSGNet. Furthermore, MSGNet possesses the ability to
automatically learn explainable multi-scale inter-series correlations,
exhibiting strong generalization capabilities even when applied to
out-of-distribution samples.Comment: 13 pages, 12 figure
March in Chat: Interactive Prompting for Remote Embodied Referring Expression
Many Vision-and-Language Navigation (VLN) tasks have been proposed in recent
years, from room-based to object-based and indoor to outdoor. The REVERIE
(Remote Embodied Referring Expression) is interesting since it only provides
high-level instructions to the agent, which are closer to human commands in
practice. Nevertheless, this poses more challenges than other VLN tasks since
it requires agents to infer a navigation plan only based on a short
instruction. Large Language Models (LLMs) show great potential in robot action
planning by providing proper prompts. Still, this strategy has not been
explored under the REVERIE settings. There are several new challenges. For
example, the LLM should be environment-aware so that the navigation plan can be
adjusted based on the current visual observation. Moreover, the LLM planned
actions should be adaptable to the much larger and more complex REVERIE
environment. This paper proposes a March-in-Chat (MiC) model that can talk to
the LLM on the fly and plan dynamically based on a newly proposed
Room-and-Object Aware Scene Perceiver (ROASP). Our MiC model outperforms the
previous state-of-the-art by large margins by SPL and RGSPL metrics on the
REVERIE benchmark.Comment: Accepted by ICCV 202
A Unified Object Counting Network with Object Occupation Prior
The counting task, which plays a fundamental role in numerous applications
(e.g., crowd counting, traffic statistics), aims to predict the number of
objects with various densities. Existing object counting tasks are designed for
a single object class. However, it is inevitable to encounter newly coming data
with new classes in our real world. We name this scenario as \textit{evolving
object counting}. In this paper, we build the first evolving object counting
dataset and propose a unified object counting network as the first attempt to
address this task. The proposed model consists of two key components: a
class-agnostic mask module and a class-incremental module. The class-agnostic
mask module learns generic object occupation prior via predicting a
class-agnostic binary mask (e.g., 1 denotes there exists an object at the
considering position in an image and 0 otherwise). The class-incremental module
is used to handle new coming classes and provides discriminative class guidance
for density map prediction. The combined outputs of class-agnostic mask module
and image feature extractor are used to predict the final density map. When new
classes come, we first add new neural nodes into the last regression and
classification layers of class-incremental module. Then, instead of retraining
the model from scratch, we utilize knowledge distillation to help the model
remember what have already learned about previous object classes. We also
employ a support sample bank to store a small number of typical training
samples of each class, which are used to prevent the model from forgetting key
information of old data. With this design, our model can efficiently and
effectively adapt to new coming classes while keeping good performance on
already seen data without large-scale retraining. Extensive experiments on the
collected dataset demonstrate the favorable performance.Comment: Under review; The dataset and code will be available at:
https://github.com/Tanyjiang/EOC
Teacher Agent: A Non-Knowledge Distillation Method for Rehearsal-based Video Incremental Learning
With the rise in popularity of video-based social media, new categories of
videos are constantly being generated, creating an urgent need for robust
incremental learning techniques for video understanding. One of the biggest
challenges in this task is catastrophic forgetting, where the network tends to
forget previously learned data while learning new categories. To overcome this
issue, knowledge distillation is a widely used technique for rehearsal-based
video incremental learning that involves transferring important information on
similarities among different categories to enhance the student model.
Therefore, it is preferable to have a strong teacher model to guide the
students. However, the limited performance of the network itself and the
occurrence of catastrophic forgetting can result in the teacher network making
inaccurate predictions for some memory exemplars, ultimately limiting the
student network's performance. Based on these observations, we propose a
teacher agent capable of generating stable and accurate soft labels to replace
the output of the teacher model. This method circumvents the problem of
knowledge misleading caused by inaccurate predictions of the teacher model and
avoids the computational overhead of loading the teacher model for knowledge
distillation. Extensive experiments demonstrate the advantages of our method,
yielding significant performance improvements while utilizing only half the
resolution of video clips in the incremental phases as input compared to recent
state-of-the-art methods. Moreover, our method surpasses the performance of
joint training when employing four times the number of samples in episodic
memory.Comment: Under review; Do We Really Need Knowledge Distillation for
Class-incremental Video Learning
TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss
Time series is a special type of sequence data, a sequence of real-valued
random variables collected at even intervals of time. The real-world
multivariate time series comes with noises and contains complicated local and
global temporal dynamics, making it difficult to forecast the future time
series given the historical observations. This work proposes a simple and
effective framework, coined as TimeSQL, which leverages multi-scale patching
and smooth quadratic loss (SQL) to tackle the above challenges. The multi-scale
patching transforms the time series into two-dimensional patches with different
length scales, facilitating the perception of both locality and long-term
correlations in time series. SQL is derived from the rational quadratic kernel
and can dynamically adjust the gradients to avoid overfitting to the noises and
outliers. Theoretical analysis demonstrates that, under mild conditions, the
effect of the noises on the model with SQL is always smaller than that with
MSE. Based on the two modules, TimeSQL achieves new state-of-the-art
performance on the eight real-world benchmark datasets. Further ablation
studies indicate that the key modules in TimeSQL could also enhance the results
of other models for multivariate time series forecasting, standing as
plug-and-play techniques
Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch under Cyber-Attacks
False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation
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