145 research outputs found
“Share for bargaining?”: A willingness model based on privacy computing theory
The use of mobile coupons to share for bargaining has become an important marketing method for merchants in the field of e-commerce. However, there are still some shortcomings in the existing research on consumers’ willingness to share mobile coupons. First of all, the use and sharing of mobile coupons are analyzed separately. Secondly, most of theories and models in this domain derive from the field of knowledge. Lastly, the influence of different platforms on consumers’ willingness to share are not considered. Therefore, this paper explores the influencing factors of consumers’ willingness to share mobile coupons in different platform scenarios from the perspective of privacy computing, and proposes six hypotheses to construct a structural equation model. Further analysis of 270 valid questionnaires obtained under five scenarios shows that users’ perceived economic benefits and perceived social benefits have a significant positive impact on users’ willingness to share for bargaining, users’ perceived privacy risks have no significant impact on users’ willingness to share for bargaining, and users’ perceived social risks have a significant negative impact on users’ willingness to share for bargaining. Low share for bargaining links will weaken the negative impact of perceived social risk on sharing willingness
The State of Diversity and Inclusion in Apache: A Pulse Check
Diversity and inclusion in open source software (OSS) is a multifaceted
concept that arises from differences in contributors' gender, seniority,
language, region, and other characteristics. D&I has received growing attention
in OSS ecosystems and projects, and various programs have been implemented to
foster contributor diversity. However, we do not yet know how the state of D&I
is evolving. By understanding the state of D&I in OSS projects, the community
can develop new and adjust current strategies to foster diversity among
contributors and gain insights into the mechanisms and processes that
facilitate the development of inclusive communities. In this paper, we report
and compare the results of two surveys of Apache Software Foundation (ASF)
contributors conducted over two years (n=624 & n=432), considering a variety of
D&I aspects. We see improvements in engagement among those traditionally
underrepresented in OSS, particularly those who are in gender minority or not
confident in English. Yet, the gender gap in the number of contributors
remains. We expect this study to help communities tailor their efforts in
promoting D&I in OSS.Comment: 11 pages, 1 figur
Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM
This paper describes an image based visual servoing (IBVS) system for a
nonholonomic robot to achieve good trajectory following without real-time robot
pose information and without a known visual map of the environment. We call it
trajectory servoing. The critical component is a feature-based, indirect SLAM
method to provide a pool of available features with estimated depth, so that
they may be propagated forward in time to generate image feature trajectories
for visual servoing. Short and long distance experiments show the benefits of
trajectory servoing for navigating unknown areas without absolute positioning.
Trajectory servoing is shown to be more accurate than pose-based feedback when
both rely on the same underlying SLAM system
Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance
Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation
Multi-modal 3D scene understanding has gained considerable attention due to
its wide applications in many areas, such as autonomous driving and
human-computer interaction. Compared to conventional single-modal 3D
understanding, introducing an additional modality not only elevates the
richness and precision of scene interpretation but also ensures a more robust
and resilient understanding. This becomes especially crucial in varied and
challenging environments where solely relying on 3D data might be inadequate.
While there has been a surge in the development of multi-modal 3D methods over
past three years, especially those integrating multi-camera images (3D+2D) and
textual descriptions (3D+language), a comprehensive and in-depth review is
notably absent. In this article, we present a systematic survey of recent
progress to bridge this gap. We begin by briefly introducing a background that
formally defines various 3D multi-modal tasks and summarizes their inherent
challenges. After that, we present a novel taxonomy that delivers a thorough
categorization of existing methods according to modalities and tasks, exploring
their respective strengths and limitations. Furthermore, comparative results of
recent approaches on several benchmark datasets, together with insightful
analysis, are offered. Finally, we discuss the unresolved issues and provide
several potential avenues for future research
ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores
Photonic tensor cores (PTCs) are essential building blocks for optical
artificial intelligence (AI) accelerators based on programmable photonic
integrated circuits. PTCs can achieve ultra-fast and efficient tensor
operations for neural network (NN) acceleration. Current PTC designs are either
manually constructed or based on matrix decomposition theory, which lacks the
adaptability to meet various hardware constraints and device specifications. To
our best knowledge, automatic PTC design methodology is still unexplored. It
will be promising to move beyond the manual design paradigm and "nurture"
photonic neurocomputing with AI and design automation. Therefore, in this work,
for the first time, we propose a fully differentiable framework, dubbed ADEPT,
that can efficiently search PTC designs adaptive to various circuit footprint
constraints and foundry PDKs. Extensive experiments show superior flexibility
and effectiveness of the proposed ADEPT framework to explore a large PTC design
space. On various NN models and benchmarks, our searched PTC topology
outperforms prior manually-designed structures with competitive matrix
representability, 2-30x higher footprint compactness, and better noise
robustness, demonstrating a new paradigm in photonic neural chip design. The
code of ADEPT is available at https://github.com/JeremieMelo/ADEPT using the
https://github.com/JeremieMelo/pytorch-onn (TorchONN) library.Comment: Accepted to ACM/IEEE Design Automation Conference (DAC), 202
Development and validation of prognostic index based on purine metabolism genes in patients with bladder cancer
BackgroundBladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and is associated with significant morbidity and mortality worldwide. Dysregulation of tumor metabolic pathways is closely linked to the initiation and proliferation of BLCA. Tumor cells exhibit distinct metabolic activities compared to normal cells, and the purine metabolism pathway, responsible for providing essential components for DNA and RNA synthesis, is believed to play a crucial role. However, the precise involvement of Purine Metabolism Genes (PMGs) in the defense mechanism against BLCA remains elusive.MethodsThe integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of PMGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and PMGs, unraveling their functional relevance and potential implications in BLCA.ResultsPMGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including CLDN6, CES1, SOST, SPRR2A, MYBPH, CGB5, and KRT1, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CRTAC1 was identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.ConclusionPMGs have been implicated in the etiology and progression of bladder cancer (BLCA). Prognostic models corresponding to this malignancy aid in the accurate prediction of patient outcomes. Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA
DOTA: A Dynamically-Operated Photonic Tensor Core for Energy-Efficient Transformer Accelerator
The wide adoption and significant computing resource consumption of
attention-based Transformers, e.g., Vision Transformer and large language
models, have driven the demands for efficient hardware accelerators. While
electronic accelerators have been commonly used, there is a growing interest in
exploring photonics as an alternative technology due to its high energy
efficiency and ultra-fast processing speed. Optical neural networks (ONNs) have
demonstrated promising results for convolutional neural network (CNN) workloads
that only require weight-static linear operations. However, they fail to
efficiently support Transformer architectures with attention operations due to
the lack of ability to process dynamic full-range tensor multiplication. In
this work, we propose a customized high-performance and energy-efficient
photonic Transformer accelerator, DOTA. To overcome the fundamental limitation
of existing ONNs, we introduce a novel photonic tensor core, consisting of a
crossbar array of interference-based optical vector dot-product engines, that
supports highly-parallel, dynamic, and full-range matrix-matrix multiplication.
Our comprehensive evaluation demonstrates that DOTA achieves a >4x energy and a
>10x latency reduction compared to prior photonic accelerators, and delivers
over 20x energy reduction and 2 to 3 orders of magnitude lower latency compared
to the electronic Transformer accelerator. Our work highlights the immense
potential of photonic computing for efficient hardware accelerators,
particularly for advanced machine learning workloads.Comment: The short version is accepted by Next-Gen AI System Workshop at MLSys
202
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