492 research outputs found
Distance-Based Opportunistic Mobile Data Offloading.
Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS) content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliver the contents to the subscriber based on the distance information. If in the meantime a content owner has traveled further in the immediate past time than the distance between the owner and the subscriber, the content owner will send the content to the subscriber through opportunistic routing. Simulations provide an evaluation of the data traffic offloading efficiency of DOPS
DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
In e-commerce platforms, the relevant recommendation is a unique scenario
providing related items for a trigger item that users are interested in.
However, users' preferences for the similarity and diversity of recommendation
results are dynamic and vary under different conditions. Moreover, individual
item-level diversity is too coarse-grained since all recommended items are
related to the trigger item. Thus, the two main challenges are to learn
fine-grained representations of similarity and diversity and capture users'
dynamic preferences for them under different conditions. To address these
challenges, we propose a novel method called the Dynamic Preference-based and
Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in
relevant recommendations. Specifically, based on Attribute-aware Activation
Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR)
is designed to obtain similarity and diversity representations of user
interests and item information. Then Shallow and Deep Union-based Fusion (SDUF)
is proposed to capture users' dynamic preferences for the diverse degree of
recommendation results according to various conditions. DPAN has demonstrated
its effectiveness through extensive offline experiments and online A/B testing,
resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been
successfully deployed on our e-commerce platform serving the primary traffic
for relevant recommendations. The code of DPAN has been made publicly
available
A Location Prediction Algorithm for Mobile Communications Using Directional Antennas
A directional communication scheme, TRAC, is proposed in this paper to deal with issues in mobile directional communications. Directional communication can bring benefits in terms of spatial reuse, power consumption, and security. Using direction antennas implies that the transmitters must know the direction or location of the receiver. It is necessary to predict the receiver's location to keep the transmitter's antenna pointing in the right direction if nodes travel always. TRAC is composed of the location prediction and antenna adjustment. It predicts a possible circular region where the moving receiver may enter into in the near future. The transmitter points its antenna at the predicted circular region and adjusts the beam-width of its directional antenna to cover the predicted region. The authors validated the TRAC algorithm on some vehicles traces. The validation indicated that the algorithm efficiency of TRAC is larger than 96%. TRAC can be employed in mobile communications without nodes' history movement traces
Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT
In this paper, we aimed to provide a review and tutorial for researchers in
the field of medical imaging using language models to improve their tasks at
hand. We began by providing an overview of the history and concepts of language
models, with a special focus on large language models. We then reviewed the
current literature on how language models are being used to improve medical
imaging, emphasizing different applications such as image captioning, report
generation, report classification, finding extraction, visual question
answering, interpretable diagnosis, and more for various modalities and organs.
The ChatGPT was specially highlighted for researchers to explore more potential
applications. We covered the potential benefits of accurate and efficient
language models for medical imaging analysis, including improving clinical
workflow efficiency, reducing diagnostic errors, and assisting healthcare
professionals in providing timely and accurate diagnoses. Overall, our goal was
to bridge the gap between language models and medical imaging and inspire new
ideas and innovations in this exciting area of research. We hope that this
review paper will serve as a useful resource for researchers in this field and
encourage further exploration of the possibilities of language models in
medical imaging
Causal conditional hidden Markov model for multimodal traffic prediction
Multimodal traffic flow can reflect the health of the transportation system,
and its prediction is crucial to urban traffic management. Recent works
overemphasize spatio-temporal correlations of traffic flow, ignoring the
physical concepts that lead to the generation of observations and their causal
relationship. Spatio-temporal correlations are considered unstable under the
influence of different conditions, and spurious correlations may exist in
observations. In this paper, we analyze the physical concepts affecting the
generation of multimode traffic flow from the perspective of the observation
generation principle and propose a Causal Conditional Hidden Markov Model
(CCHMM) to predict multimodal traffic flow. In the latent variables inference
stage, a posterior network disentangles the causal representations of the
concepts of interest from conditional information and observations, and a
causal propagation module mines their causal relationship. In the data
generation stage, a prior network samples the causal latent variables from the
prior distribution and feeds them into the generator to generate multimodal
traffic flow. We use a mutually supervised training method for the prior and
posterior to enhance the identifiability of the model. Experiments on
real-world datasets show that CCHMM can effectively disentangle causal
representations of concepts of interest and identify causality, and accurately
predict multimodal traffic flow.Comment: 8 pages, 5 figure
SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery
Accurately perceiving location and scene is crucial for autonomous driving
and mobile robots. Recent advances in deep learning have made it possible to
learn egomotion and depth from monocular images in a self-supervised manner,
without requiring highly precise labels to train the networks. However,
monocular vision methods suffer from a limitation known as scale-ambiguity,
which restricts their application when absolute-scale is necessary. To address
this, we propose SelfOdom, a self-supervised dual-network framework that can
robustly and consistently learn and generate pose and depth estimates in global
scale from monocular images. In particular, we introduce a novel coarse-to-fine
training strategy that enables the metric scale to be recovered in a two-stage
process. Furthermore, SelfOdom is flexible and can incorporate inertial data
with images, which improves its robustness in challenging scenarios, using an
attention-based fusion module. Our model excels in both normal and challenging
lighting conditions, including difficult night scenes. Extensive experiments on
public datasets have demonstrated that SelfOdom outperforms representative
traditional and learning-based VO and VIO models.Comment: 14 pages, 8 figures, in submissio
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