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Found 26 papers, 12 papers with code
Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion
Short-PHD stabilizes the estimation of the previous PHD method for short texts by inserting off-topic content before the given input text and identifies LLM-generated text based on an established detection threshold.
A Systematic Review on Prompt Engineering in Large Language Models for K-12 STEM Education
Large language models (LLMs) have the potential to enhance K-12 STEM education by improving both teaching and learning processes.
Aligning Medical Images with General Knowledge from Large Language Models
Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising generalization ability.
Applying and Evaluating Large Language Models in Mental Health Care: A Scoping Review of Human-Assessed Generative Tasks
This scoping review aimed to assess the current generative applications of LLMs in mental health care, focusing on studies where these models were tested with human participants in real-world scenarios.
Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach
The efficacy of these methods depends on their ability to mitigate both covariate shift and concept shift between the infodemic domain and the domains from which they leverage labeled information.
A Watermark for Low-entropy and Unbiased Generation in Large Language Models
Moreover, we discuss the tradeoff between watermark strength and text quality for unbiased watermarks.
Federated Adaptation for Foundation Model-based Recommendations
It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy.
A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss
Low-light image enhancement aims to improve the perception of images collected in dim environments and provide high-quality data support for image recognition tasks.
Bias of AI-Generated Content: An Examination of News Produced by Large Language Models
We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles.
Proactive Resource Request for Disaster Response: A Deep Learning-based Optimization Model
Prior resource management research for disaster response overlooks the problem of deciding optimal quantities of resources requested by a local agency.
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Medical image segmentation is a fundamental task in the community of medical image analysis.
Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation
Recently, the advent of vision Transformer (ViT) has brought substantial advancements in 3D dataset benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg).
Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT
We draw on the medical literature to support depression detection using motion sensor data.
Exploiting Expert Knowledge for Assigning Firms to Industries: A Novel Deep Learning Method
To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account.
Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou
Customer Life Time Value (LTV) is the expected total revenue that a single user can bring to a business.
Diversity Preference-Aware Link Recommendation for Online Social Networks
Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications.
Posterior Consistency for Bayesian Relevance Vector Machines
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging.
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method.
2D-FFTLog: Efficient computation of real space covariance matrices for galaxy clustering and weak lensing
In this paper, we introduce a 2D-FFTLog algorithm for efficient, accurate and numerically stable computation of non-Gaussian real space covariances.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Beyond Limber: Efficient computation of angular power spectra for galaxy clustering and weak lensing
We present a new method based on our generalized FFTLog algorithm for the efficient computation of angular power spectra beyond the Limber approximation.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Recommendation with Attribute-aware Product Networks: A Representation Learning Model
Specifically, eRAN first maps items connected in attribute networks to low-dimensional embedding vectors through a deep autoencoder, and then an attention mechanism is applied to model the attractions of attributes to users, from which personalized item representation can be derived.
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature.
Beyond linear galaxy alignments
We include terms up to second order in the density and tidal fields and calculate the resulting IA contributions to two-point statistics at one-loop order.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
Uplift Modeling with Multiple Treatments and General Response Types
It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved.
FAST-PT II: an algorithm to calculate convolution integrals of general tensor quantities in cosmological perturbation theory
Cosmological perturbation theory is a powerful tool to predict the statistics of large-scale structure in the weakly non-linear regime, but even at 1-loop order it results in computationally expensive mode-coupling integrals.
Cosmology and Nongalactic Astrophysics
FAST-PT: a novel algorithm to calculate convolution integrals in cosmological perturbation theory
We present a novel algorithm, FAST-PT, for performing convolution or mode-coupling integrals that appear in nonlinear cosmological perturbation theory.
Cosmology and Nongalactic Astrophysics
