113 research outputs found
Tab-CoT: Zero-shot Tabular Chain of Thought
The chain-of-though (CoT) prompting methods were successful in various
natural language processing (NLP) tasks thanks to their ability to unveil the
underlying complex reasoning processes. Such reasoning processes typically
exhibit implicitly structured steps. Recent efforts also started investigating
methods to encourage more explicitly structured reasoning procedures to be
captured. In this work, we propose Tab-CoT, a novel tabular-format CoT
prompting method, which allows the complex reasoning process to be explicitly
modelled in a highly structured manner. Despite its simplicity, we show that
our approach is capable of performing reasoning across multiple dimensions
(i.e., both rows and columns). We demonstrate our approach's strong zero-shot
and few-shot capabilities through extensive experiments on a range of reasoning
tasks.Comment: accepted by ACL 2023 Findin
Sustainable digital marketing under big data: an AI random forest model approach
Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies
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Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
Multimodal contrastive learning aims to train a general-purpose feature
extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text
data. This can greatly benefit various complex downstream tasks, including
cross-modal image-text retrieval and image classification. Despite its
promising prospect, the security issue of cross-modal pre-trained encoder has
not been fully explored yet, especially when the pre-trained encoder is
publicly available for commercial use.
In this work, we propose AdvCLIP, the first attack framework for generating
downstream-agnostic adversarial examples based on cross-modal pre-trained
encoders. AdvCLIP aims to construct a universal adversarial patch for a set of
natural images that can fool all the downstream tasks inheriting the victim
cross-modal pre-trained encoder. To address the challenges of heterogeneity
between different modalities and unknown downstream tasks, we first build a
topological graph structure to capture the relevant positions between target
samples and their neighbors. Then, we design a topology-deviation based
generative adversarial network to generate a universal adversarial patch. By
adding the patch to images, we minimize their embeddings similarity to
different modality and perturb the sample distribution in the feature space,
achieving unviersal non-targeted attacks. Our results demonstrate the excellent
attack performance of AdvCLIP on two types of downstream tasks across eight
datasets. We also tailor three popular defenses to mitigate AdvCLIP,
highlighting the need for new defense mechanisms to defend cross-modal
pre-trained encoders.Comment: This paper has been accepted by the ACM International Conference on
Multimedia (ACM MM '23, October 29-November 3, 2023, Ottawa, ON, Canada
Tunable luminescent lead bromide complexes
Lead halides are used extensively to prepare perovskite-based devices but it is less known that lead halides can also form luminescent complexes in solvents. Using polyethylene glycol as a solvent, a lead bromide complex with a photoluminescence quantum yield over 20% is obtained and the photoluminescence peak can be shifted around 50 nm with different alkylammonium bromides
Downstream-agnostic Adversarial Examples
Self-supervised learning usually uses a large amount of unlabeled data to
pre-train an encoder which can be used as a general-purpose feature extractor,
such that downstream users only need to perform fine-tuning operations to enjoy
the benefit of "large model". Despite this promising prospect, the security of
pre-trained encoder has not been thoroughly investigated yet, especially when
the pre-trained encoder is publicly available for commercial use.
In this paper, we propose AdvEncoder, the first framework for generating
downstream-agnostic universal adversarial examples based on the pre-trained
encoder. AdvEncoder aims to construct a universal adversarial perturbation or
patch for a set of natural images that can fool all the downstream tasks
inheriting the victim pre-trained encoder. Unlike traditional adversarial
example works, the pre-trained encoder only outputs feature vectors rather than
classification labels. Therefore, we first exploit the high frequency component
information of the image to guide the generation of adversarial examples. Then
we design a generative attack framework to construct adversarial
perturbations/patches by learning the distribution of the attack surrogate
dataset to improve their attack success rates and transferability. Our results
show that an attacker can successfully attack downstream tasks without knowing
either the pre-training dataset or the downstream dataset. We also tailor four
defenses for pre-trained encoders, the results of which further prove the
attack ability of AdvEncoder.Comment: This paper has been accepted by the International Conference on
Computer Vision (ICCV '23, October 2--6, 2023, Paris, France
The Mechanism of Knowledge Associated Integration of Interdisciplinary Research Team Based on Absorption Ability
Interdisciplinary research team (IDRT) to solve complex, major multidisciplinary problem, is an important organization form. Members thought fusion and multidisciplinary knowledge integration is the key of implementing interdisciplinary team sustainable competitive advantage. According to the theory of knowledge absorptive capacity, this paper has analyzed knowledge associated integration mechanism of interdisciplinary team. First, the paper analyses the characteristics of absorbing ability cognitive mediation, subject link together mechanism, dimension conversion mechanism and reciprocal decision mechanism. Second, this paper builds an absorption evolution model of knowledge associated integration of interdisciplinary research team. Finally, it simulates and analyzes the evolution characteristics of interdisciplinary team knowledge network, and proposes management strategy
Evaluation of Arctic sea ice simulation of CMIP6 models from China
Nine coupled climate models from China participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were evaluated in terms of their capability in ensemble historical Arctic sea ice simulation in the context of 56 CMIP6 models. We evaluated these nine models using satellite observations from 1980 to 2014. This evaluation was conducted comprehensively using 12 metrics covering different aspects of the seasonal cycle and long-term trend of sea ice extent (SIE) and sea ice concentration (SIC). The nine Chinese models tended to overestimate SIE, especially in March, and underestimate its long-term decline trend. There was less spread in model skill in reproducing the spatial pattern of March SIC than in reproducing the spatial pattern of September SIC. The error of March SIC simulation was distributed at the margins of sea ice cover, such as in the Nordic Seas, the Barents Sea, the Labrador Sea, the Bering Sea, and the Sea of Okhotsk. However, the error of September SIC was distributed both at the margins of sea ice cover and in the central part of the Arctic Basin. Five of these nine models had capabilities comparable with the majority of the CMIP6 models in reproducing the seasonal cycle and long-term trend of Arctic sea ice
Comprehensive Information Integration Modeling Framework for Video Titling
In e-commerce, consumer-generated videos, which in general deliver consumers'
individual preferences for the different aspects of certain products, are
massive in volume. To recommend these videos to potential consumers more
effectively, diverse and catchy video titles are critical. However,
consumer-generated videos seldom accompany appropriate titles. To bridge this
gap, we integrate comprehensive sources of information, including the content
of consumer-generated videos, the narrative comment sentences supplied by
consumers, and the product attributes, in an end-to-end modeling framework.
Although automatic video titling is very useful and demanding, it is much less
addressed than video captioning. The latter focuses on generating sentences
that describe videos as a whole while our task requires the product-aware
multi-grained video analysis. To tackle this issue, the proposed method
consists of two processes, i.e., granular-level interaction modeling and
abstraction-level story-line summarization. Specifically, the granular-level
interaction modeling first utilizes temporal-spatial landmark cues, descriptive
words, and abstractive attributes to builds three individual graphs and
recognizes the intra-actions in each graph through Graph Neural Networks (GNN).
Then the global-local aggregation module is proposed to model inter-actions
across graphs and aggregate heterogeneous graphs into a holistic graph
representation. The abstraction-level story-line summarization further
considers both frame-level video features and the holistic graph to utilize the
interactions between products and backgrounds, and generate the story-line
topic of the video. We collect a large-scale dataset accordingly from
real-world data in Taobao, a world-leading e-commerce platform, and will make
the desensitized version publicly available to nourish further development of
the research community...Comment: 11 pages, 6 figures, to appear in KDD 2020 proceeding
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Convolutional neural networks excel in histopathological image
classification, yet their pixel-level focus hampers explainability. Conversely,
emerging graph convolutional networks spotlight cell-level features and medical
implications. However, limited by their shallowness and suboptimal use of
high-dimensional pixel data, GCNs underperform in multi-class histopathological
image classification. To make full use of pixel-level and cell-level features
dynamically, we propose an asymmetric co-training framework combining a deep
graph convolutional network and a convolutional neural network for multi-class
histopathological image classification. To improve the explainability of the
entire framework by embedding morphological and topological distribution of
cells, we build a 14-layer deep graph convolutional network to handle cell
graph data. For the further utilization and dynamic interactions between
pixel-level and cell-level information, we also design a co-training strategy
to integrate the two asymmetric branches. Notably, we collect a private
clinically acquired dataset termed LUAD7C, including seven subtypes of lung
adenocarcinoma, which is rare and more challenging. We evaluated our approach
on the private LUAD7C and public colorectal cancer datasets, showcasing its
superior performance, explainability, and generalizability in multi-class
histopathological image classification
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