716 research outputs found
Behavior-Driven Model Design: A Deep Learning Recommendation Model Jointing Users and Products Reviews
Data-driven is widely mentioned, but the data is generated by user behavior. Our work aims to utilize a behavior-driven model design pattern to improve accuracy and provide explanations in review-based recommendations. Review-based recommendation introduces review text to overcome the sparseness and unexplainably of rating or scores-based model. Driven by users rating behavior and human cognitive abilities, we proposed a deep learning recommendation model jointing users and products reviews (DLRM-UPR) to learn user preferences and product characteristics adaptively. The DLRM-UPR consists of word, text, and context co-attention layers considering the interaction between each user-product-context pair. Extensive experiments on real datasets demonstrate that DLRM-UPR outperforms existing state-of-the-art models. In addition, the relevant information in the reviews and the suggestion for improving the user experience can be highlighted to explain the recommendation results
Efficient and Accurate Measurement of Absorption Cross Section of a Lossy Object in Reverberation Chamber Using Two One-Antenna Methods
Quantum Imitation Learning
Despite remarkable successes in solving various complex decision-making
tasks, training an imitation learning (IL) algorithm with deep neural networks
(DNNs) suffers from the high computation burden. In this work, we propose
quantum imitation learning (QIL) with a hope to utilize quantum advantage to
speed up IL. Concretely, we develop two QIL algorithms, quantum behavioural
cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL).
Q-BC is trained with a negative log-likelihood loss in an off-line manner that
suits extensive expert data cases, whereas Q-GAIL works in an inverse
reinforcement learning scheme, which is on-line and on-policy that is suitable
for limited expert data cases. For both QIL algorithms, we adopt variational
quantum circuits (VQCs) in place of DNNs for representing policies, which are
modified with data re-uploading and scaling parameters to enhance the
expressivity. We first encode classical data into quantum states as inputs,
then perform VQCs, and finally measure quantum outputs to obtain control
signals of agents. Experiment results demonstrate that both Q-BC and Q-GAIL can
achieve comparable performance compared to classical counterparts, with the
potential of quantum speed-up. To our knowledge, we are the first to propose
the concept of QIL and conduct pilot studies, which paves the way for the
quantum era.Comment: Manuscript submitted to a journal for review on January 5, 202
Learning to Reduce Information Bottleneck for Object Detection in Aerial Images
Object detection in aerial images is a fundamental research topic in the
domain of geoscience and remote sensing. However, advanced progresses on this
topic are mainly focused on the designment of backbone networks or header
networks, but surprisingly ignored the neck ones. In this letter, we first
analyse the importance of the neck network in object detection frameworks from
the theory of information bottleneck. Then, to alleviate the information loss
problem in the current neck network, we propose a global semantic network,
which acts as a bridge from the backbone to the head network in a bidirectional
global convolution manner. Compared to the existing neck networks, our method
has advantages of capturing rich detailed information and less computational
costs. Moreover, we further propose a fusion refinement module, which is used
for feature fusion with rich details from different scales. To demonstrate the
effectiveness and efficiency of our method, experiments are carried out on two
challenging datasets (i.e., DOTA and HRSC2016). Results in terms of accuracy
and computational complexity both can verify the superiority of our method.Comment: 5 pages, 3 figure
Practical and Secure Circular Range Search on Private Spatial Data
With the location-based services (LBS) booming, the volume of spatial data inevitably explodes. In order to reduce local storage and computational overhead, users tend to outsource data and initiate queries to the cloud. However, sensitive data or queries may be compromised if cloud server has access to raw data and plaintext token. To cope with this problem, searchable encryption for geometric range is applied. Geometric range search has wide applications in many scenarios, especially the circular range search.
In this paper, a practical and secure circular range search scheme (PSCS) is proposed to support searching for spatial data in a circular range. With our scheme, a semi-honest cloud server will return data for a given circular range correctly without uncovering index privacy or query privacy. We propose a polynomial split algorithm which can decompose the inner product calculation neatly. Then, we define the security of our PSCS formally and prove that it is secure under same-closeness-pattern chosen-plaintext attacks (CLS-CPA) in theory. In addition, we demonstrate the efficiency and accuracy through analysis and experiments compared with existing schemes
Generalization and Hallucination of Large Vision-Language Models through a Camouflaged Lens
Large Vision-Language Model (LVLM) has seen burgeoning development and
increasing attention recently. In this paper, we propose a novel framework,
camo-perceptive vision-language framework (CPVLF), to explore whether LVLM can
generalize to the challenging camouflaged object detection (COD) scenario in a
training-free manner. During the process of generalization, we find that due to
hallucination issues within LVLM, it can erroneously perceive objects in
camouflaged scenes, producing counterfactual concepts. Moreover, as LVLM is not
specifically trained for the precise localization of camouflaged objects, it
exhibits a degree of uncertainty in accurately pinpointing these objects.
Therefore, we propose chain of visual perception, which enhances LVLM's
perception of camouflaged scenes from both linguistic and visual perspectives,
reducing the hallucination issue and improving its capability in accurately
locating camouflaged objects. We validate the effectiveness of CPVLF on three
widely used COD datasets, and the experiments show the potential of LVLM in the
COD task
Sex-specific prevalence and risk factors of metabolic-associated fatty liver disease among 75,570 individuals in eastern China
BackgroundMetabolic-associated fatty liver disease (MAFLD) is a newly proposed definition and there is limited data on MAFLD prevalence. We aimed to investigate the prevalence of MAFLD in an eastern Chinese population.MethodsThis cross-sectional study included participants from an eastern Chinese population who underwent regular health checkups. Based on current diagnostic criteria, MAFLD was diagnosed in individuals with both hepatic steatosis and metabolic disorders. The overall and stratified prevalence derived based on sex, age, body mass index (BMI), and various metabolic disorders were estimated. Multivariate logistic regression analysis was used to determine the risk factors for MAFLD.ResultsAmong the 75,570 participants, the overall prevalence of MAFLD was 37.32%, with higher rates in men (45.66%) than in women (23.91%). MAFLD prevalence was highest in men aged 40–49 years (52.21%) and women aged 70–79 years (44.77%). In all the BMI subgroups, the prevalence was higher in men than in women. In both sexes, the prevalence of MAFLD increased as BMI levels increased. Furthermore, MAFLD was associated with metabolic disorders, especially in the female participants with severe obesity (odds ratio 58.318; 95% confidence interval: 46.978–72.397).ConclusionMAFLD is prevalent in the general adult population in eastern China. Sex-specific differences in MAFLD prevalence were identified based on age, BMI, and metabolic disorders. MAFLD is associated with metabolic disorders, particularly obesity
EyeLS: Shadow-Guided Instrument Landing System for Intraocular Target Approaching in Robotic Eye Surgery
Robotic ophthalmic surgery is an emerging technology to facilitate
high-precision interventions such as retina penetration in subretinal injection
and removal of floating tissues in retinal detachment depending on the input
imaging modalities such as microscopy and intraoperative OCT (iOCT). Although
iOCT is explored to locate the needle tip within its range-limited ROI, it is
still difficult to coordinate iOCT's motion with the needle, especially at the
initial target-approaching stage. Meanwhile, due to 2D perspective projection
and thus the loss of depth information, current image-based methods cannot
effectively estimate the needle tip's trajectory towards both retinal and
floating targets. To address this limitation, we propose to use the shadow
positions of the target and the instrument tip to estimate their relative depth
position and accordingly optimize the instrument tip's insertion trajectory
until the tip approaches targets within iOCT's scanning area. Our method
succeeds target approaching on a retina model, and achieves an average depth
error of 0.0127 mm and 0.3473 mm for floating and retinal targets respectively
in the surgical simulator without damaging the retina.Comment: 10 page
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