451 research outputs found
Approximate finite-dimensional ODE temperature model for microwave heating
In this paper, a finite-dimensional ordinary differential equation (ODE) model is proposed for predicting the temperature profile with microwave heating to accomplish lower computing complexity. The traditional parabolic partial different equation (PDE) model with integrating Maxwell's equation and heat transport equation is not suitable for designing the on-line controller. Based on the obstruction, using an auxiliary function derives an intermediate model, which is analyzed and discussed for model reduction by employing the parameter separation method and Galerkin's method. The simulation experiments on one-dimensional waveguide and cavity demonstrate that the proposed approximate model is effective
An Image Dataset for Benchmarking Recommender Systems with Raw Pixels
Recommender systems (RS) have achieved significant success by leveraging
explicit identification (ID) features. However, the full potential of content
features, especially the pure image pixel features, remains relatively
unexplored. The limited availability of large, diverse, and content-driven
image recommendation datasets has hindered the use of raw images as item
representations. In this regard, we present PixelRec, a massive image-centric
recommendation dataset that includes approximately 200 million user-image
interactions, 30 million users, and 400,000 high-quality cover images. By
providing direct access to raw image pixels, PixelRec enables recommendation
models to learn item representation directly from them. To demonstrate its
utility, we begin by presenting the results of several classical pure ID-based
baseline models, termed IDNet, trained on PixelRec. Then, to show the
effectiveness of the dataset's image features, we substitute the itemID
embeddings (from IDNet) with a powerful vision encoder that represents items
using their raw image pixels. This new model is dubbed PixelNet.Our findings
indicate that even in standard, non-cold start recommendation settings where
IDNet is recognized as highly effective, PixelNet can already perform equally
well or even better than IDNet. Moreover, PixelNet has several other notable
advantages over IDNet, such as being more effective in cold-start and
cross-domain recommendation scenarios. These results underscore the importance
of visual features in PixelRec. We believe that PixelRec can serve as a
critical resource and testing ground for research on recommendation models that
emphasize image pixel content. The dataset, code, and leaderboard will be
available at https://github.com/westlake-repl/PixelRec
DREAM: Domain-free Reverse Engineering Attributes of Black-box Model
Deep learning models are usually black boxes when deployed on machine
learning platforms. Prior works have shown that the attributes (, the
number of convolutional layers) of a target black-box neural network can be
exposed through a sequence of queries. There is a crucial limitation: these
works assume the dataset used for training the target model to be known
beforehand and leverage this dataset for model attribute attack. However, it is
difficult to access the training dataset of the target black-box model in
reality. Therefore, whether the attributes of a target black-box model could be
still revealed in this case is doubtful. In this paper, we investigate a new
problem of Domain-agnostic Reverse Engineering the Attributes of a black-box
target Model, called DREAM, without requiring the availability of the target
model's training dataset, and put forward a general and principled framework by
casting this problem as an out of distribution (OOD) generalization problem. In
this way, we can learn a domain-agnostic model to inversely infer the
attributes of a target black-box model with unknown training data. This makes
our method one of the kinds that can gracefully apply to an arbitrary domain
for model attribute reverse engineering with strong generalization ability.
Extensive experimental studies are conducted and the results validate the
superiority of our proposed method over the baselines
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Learning a recommender system model from an item's raw modality features
(such as image, text, audio, etc.), called MoRec, has attracted growing
interest recently. One key advantage of MoRec is that it can easily benefit
from advances in other fields, such as natural language processing (NLP) and
computer vision (CV). Moreover, it naturally supports transfer learning across
different systems through modality features, known as transferable recommender
systems, or TransRec.
However, so far, TransRec has made little progress, compared to
groundbreaking foundation models in the fields of NLP and CV. The lack of
large-scale, high-quality recommendation datasets poses a major obstacle. To
this end, we introduce NineRec, a TransRec dataset suite that includes a
large-scale source domain recommendation dataset and nine diverse target domain
recommendation datasets. Each item in NineRec is represented by a text
description and a high-resolution cover image. With NineRec, we can implement
TransRec models in an end-to-end training manner instead of using pre-extracted
invariant features. We conduct a benchmark study and empirical analysis of
TransRec using NineRec, and our findings provide several valuable insights. To
support further research, we make our code, datasets, benchmarks, and
leaderboards publicly available at https://github.com/westlake-repl/NineRec
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Adapters, a plug-in neural network module with some tunable parameters, have
emerged as a parameter-efficient transfer learning technique for adapting
pre-trained models to downstream tasks, especially for natural language
processing (NLP) and computer vision (CV) fields. Meanwhile, learning
recommendation models directly from raw item modality features -- e.g., texts
of NLP and images of CV -- can enable effective and transferable recommender
systems (called TransRec). In view of this, a natural question arises: can
adapter-based learning techniques achieve parameter-efficient TransRec with
good performance?
To this end, we perform empirical studies to address several key
sub-questions. First, we ask whether the adapter-based TransRec performs
comparably to TransRec based on standard full-parameter fine-tuning? does it
hold for recommendation with different item modalities, e.g., textual RS and
visual RS. If yes, we benchmark these existing adapters, which have been shown
to be effective in NLP and CV tasks, in the item recommendation settings.
Third, we carefully study several key factors for the adapter-based TransRec in
terms of where and how to insert these adapters? Finally, we look at the
effects of adapter-based TransRec by either scaling up its source training data
or scaling down its target training data. Our paper provides key insights and
practical guidance on unified & transferable recommendation -- a less studied
recommendation scenario. We promise to release all code & datasets for future
research
Brain pericyte biology: from physiopathological mechanisms to potential therapeutic applications in ischemic stroke
Pericytes play an indispensable role in various organs and biological processes, such as promoting angiogenesis, regulating microvascular blood flow, and participating in immune responses. Therefore, in this review, we will first introduce the discovery and development of pericytes, identification methods and functional characteristics, then focus on brain pericytes, on the one hand, to summarize the functions of brain pericytes under physiological conditions, mainly discussing from the aspects of stem cell characteristics, contractile characteristics and paracrine characteristics; on the other hand, to summarize the role of brain pericytes under pathological conditions, mainly taking ischemic stroke as an example. Finally, we will discuss and analyze the application and development of pericytes as therapeutic targets, providing the research basis and direction for future microvascular diseases, especially ischemic stroke treatment
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