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Found 54 papers, 23 papers with code
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation
We present a comprehensive evaluation framework for assessing Large Language Models' (LLMs) capabilities in suicide prevention, focusing on two critical aspects: the Identification of Implicit Suicidal ideation (IIS) and the Provision of Appropriate Supportive responses (PAS).
An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation
In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews.
SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPs
Despite significant advancements in general-purpose AI agents, several challenges still hinder their practical application in real-world scenarios.
Rethinking Model Redundancy for Low-light Image Enhancement
Inspired by the rethinking, we propose two innovative techniques to mitigate model redundancy while improving the LLIE performance: Attention Dynamic Reallocation (ADR) and Parameter Orthogonal Generation (POG).
Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising
RDC renoises the predicted denoised image by the predicted noise to construct multiple noisy images, preserving all the information of the original image.
Learning to Hash for Recommendation: A Survey
In this survey, we present a comprehensive review of current HashRec algorithms.
Towards the efficacy of federated prediction for epidemics on networks
Epidemic prediction is of practical significance in public health, enabling early intervention, resource allocation, and strategic planning.
A Survey on Human-Centric LLMs
The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, and social interaction.
Prompt-Guided Internal States for Hallucination Detection of Large Language Models
By utilizing appropriate prompts to guide changes in the structure related to text truthfulness within the LLM's internal states, we make this structure more salient and consistent across texts from different domains.
Testing the effects of an unobservable factor: Do marriage prospects affect college major choice?
Motivated by studying the effects of marriage prospects on students' college major choices, this paper develops a new econometric test for analyzing the effects of an unobservable factor in a setting where this factor potentially influences both agents' decisions and a binary outcome variable.
A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts
Creativity involves not only generating new ideas from scratch but also redefining existing concepts and synthesizing previous insights.
Prosody of speech production in latent post-stroke aphasia
We also investigated the diagnostic classification of latent aphasia versus neurotypical control using random forest, aiming to build a fast and reliable tool to assist with the identification of latent aphasia.
System States Forecasting of Microservices with Dynamic Spatio-Temporal Data
This paper introduces STMformer, a model tailored for forecasting system states in microservices environments, capable of handling multi-node and multivariate time series.
BadActs: A Universal Backdoor Defense in the Activation Space
Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks (DNNs) during their development stage.
CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation
To achieve coherence between segments, we design a Past Guided Generation Module (PGG) to maintain the consistency of customized listener attributes through the motion prior, and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation.
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs.
Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation
For the first time, our framework explores three types of bilateral entanglements within AVS: pixel entanglement, modality entanglement, and temporal entanglement.
Keyword spotting -- Detecting commands in speech using deep learning
Speech recognition has become an important task in the development of machine learning and artificial intelligence.
You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception
Human-centric perception (e. g. detection, segmentation, pose estimation, and attribute analysis) is a long-standing problem for computer vision.
Ranked #3 on
Human Instance Segmentation
on OCHuman
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support.
A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities
Especially satellite imagery is a potential data source for studying sustainable urban development.
Joint adjustment image steganography networks
Thus, the security and quality of stego and revealed secret images still have much room for promotion, especially for large-capacity image steganography.
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding.
Ranked #1 on
Color Image Denoising
on ImageNet sigma250
Carbon emissions and sustainability of launching 5G mobile networks in China
We show that the decline in carbon efficiency leads to a carbon efficiency trap, estimated to cause additional carbon emissions of 23. 82 +- 1. 07 megatons in China.
A Sequence-to-Sequence&Set Model for Text-to-Table Generation
Specifically, in addition to a text encoder encoding the input text, our model is equipped with a table header generator to first output a table header, i. e., the first row of the table, in the manner of sequence generation.
Improving Knowledge Distillation via Transferring Learning Ability
Existing knowledge distillation methods generally use a teacher-student approach, where the student network solely learns from a well-trained teacher.
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection
In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model.
Connectivity based Real-Time fMRI Neurofeedback Training in Youth with a History of Major Depressive Disorder
Therefore, the aim of present study is to investigate the use of connectivity between amygdala and prefrontal regions as the target of neurofeedback training in healthy individuals and subjects with a life-time history of major depressive disorder (MDD) performing an emotion regulation task.
Single Depth-image 3D Reflection Symmetry and Shape Prediction
In this paper, we present Iterative Symmetry Completion Network (ISCNet), a single depth-image shape completion method that exploits reflective symmetry cues to obtain more detailed shapes.
UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression
Naturally, we also present a unified multi-task Geometric Transformer framework, Geoformer, to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation.
Ranked #5 on
Mathematical Reasoning
on PGPS9K
Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Image
Given a single RGB-D image, our method first predicts its semantic segmentation map and goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view RGB-D and segmentation map, and integrating all RGB-D and segmentation maps into the point cloud.
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph.
Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing for Facial Expression Recognition
On the other hand, GUS is introduced to suppress the feature ambiguity in the representation space.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis
We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm.
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint
Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drastically reduce manual efforts to condense this data into reports.
A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases
This paper proposes a scalable workflow which leverages both structured data and unstructured textual notes from EHRs with techniques including NLP, AutoML and Clinician-in-the-Loop mechanism to build machine learning classifiers to identify patients at scale with given diseases, especially those who might currently be miscoded or missed by ICD codes.
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization
TS-PostDiff takes a Bayesian approach to mixing TS and Uniform Random (UR): the probability a participant is assigned using UR allocation is the posterior probability that the difference between two arms is 'small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained.
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
Contextualised word embeddings is a powerful tool to detect contextual synonyms.
Distributional Effects with Two-Sided Measurement Error: An Application to Intergenerational Income Mobility
This paper considers identification and estimation of distributional effect parameters that depend on the joint distribution of an outcome and another variable of interest ("treatment") in a setting with "two-sided" measurement error -- that is, where both variables are possibly measured with error.
Two Sample Unconditional Quantile Effect
This paper proposes a new framework to evaluate unconditional quantile effects (UQE) in a data combination model.
Policy Evaluation during a Pandemic
National and local governments have implemented a large number of policies in response to the Covid-19 pandemic.
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images
Specifically, the encoder of a DL model that is pre-trained on the source domain is used to initialize the encoder of a reconstruction model.
Document-aligned Japanese-English Conversation Parallel Corpus
Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation.
Microscopic origin of reflection-asymmetric nuclear shapes
Background: The presence of nuclear ground states with stable reflection-asymmetric shapes is supported by rich experimental evidence.
Nuclear Theory
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
In order to address these limitations, we introduce the concept of cumulative accessibility functions, which measure the reachability of a goal from a given state within a specified horizon.
Designing the Business Conversation Corpus
While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems.
Ranked #1 on
Machine Translation
on Business Scene Dialogue JA-EN
(using extra training data)
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records
The goal is to facilitate the learning process in the target segments by leveraging the learned knowledge from data-sufficient source segments.
Evaluating Policies Early in a Pandemic: Bounding Policy Effects with Nonrandomly Missing Data
During the early part of the Covid-19 pandemic, national and local governments introduced a number of policies to combat the spread of Covid-19.
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly.
Edge Intelligence: Architectures, Challenges, and Applications
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence.
On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives
In addition, our analysis also pinpoints the importance of choosing a diverging scaling parameter when using Gaussian kernels and suggests a data-driven choice of the scaling parameter that yields tests optimal, up to an iterated logarithmic factor, over a wide range of smooth alternatives.
Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation
Human-object interactions (HOI) recognition and pose estimation are two closely related tasks.
Cellular Controlled Delay TCP (C2TCP)
To cope with these challenges, we present C2TCP, a flexible end-to-end solution targeting interactive applications requiring high throughput and low delay in cellular networks.
Networking and Internet Architecture
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
The reproducing kernel Hilbert space (RKHS) embedding of distributions offers a general and flexible framework for testing problems in arbitrary domains and has attracted considerable amount of attention in recent years.
