Question: I am building a model to predict the transmission quality of optical paths in an optical network, but I am facing problems with model training due to the limited number of available samples. Can you recommend any papers on training optical path QoT prediction models under small sample conditions?

Answer: Domain adversarial adaptation framework for few-shot QoT estimation in optical networks

Answer: Meta-learning-aided QoT estimator provisioning for a dynamic VNT configuration in optical networks

Answer: Sample-distribution-matching-based transfer learning for QoT estimation in optical networks

Answer: Evolutionary neuron-level transfer learning for QoT estimation in optical networks

Answer: Reducing probes for quality of transmission estimation in optical networks with active learning

Answer: Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks [Invited]

Answer: Lightpath QoT computation in optical networks assisted by transfer learning

Answer: Model transfer of QoT prediction in optical networks based on artificial neural networks

Answer: Demonstration of distributed collaborative learning with end-to-end QoT estimation in multi-domain elastic optical networks

Answer: Assessment of cross-train machine learning techniques for QoT-estimation in agnostic optical networks

Answer: Transfer Learning Using ANN for G-OSNR Estimation in WDM Network Topologies

Answer: Machine-learning-based lightpath QoT estimation and forecasting

Answer: Accurate Quality of Transmission Estimation With Machine Learning

Answer: Privacy-Preserving Distributed Learning Framework for 6G Telecom Ecosystems

Answer: Meta Learning based QoT Estimation of lightpaths with few samples for Optical Networks

Answer: Iterative Transfer Learning Approach for QoT Prediction of Lightpath in Optical Networks

Answer: Integrating Knowledge Distillation and Transfer Learning for Enhanced QoT-Estimation in Optical Networks

Answer: On the benefits of domain adaptation techniques for quality of transmission estimation in optical networks

Answer: Toward Deployments of ML Applications in Optical Networks

Answer: Active learning of optical path classification

Answer: A Meta-Learning-Assisted Training Framework for Physical Layer Modeling in Optical Networks

Answer: Transfer learning aided qot computation in network operating with the 400zr standard

Answer: Data Efficient Estimation for Quality of Transmission Through Active Learning in Fiber-Wireless Integrated Network

Answer: Fast adaptation of multi-task meta-learning for optical performance monitoring

Answer: Adaptive alarm prediction in optical network based on model generalization in cross-layer AI

Answer: Lifelong QoT prediction: an adaptation to real-world optical networks

Answer: Digital-twin-assisted meta learning for soft-failure localization in ROADM-based optical networks

Answer: Learning quantile QoT models to address uncertainty over unseen lightpaths

Answer: Learning Process for Reducing Uncertainties on Network Parameters and Design Margins

Answer: Machine learning regression for QoT estimation of unestablished lightpaths

Engineering and Technology


Question: Could you help me retrieve some papers that focus on machine learning-based optical path QoT prediction methods in optical networks, specifically addressing the small sample size problem in model training, but excluding methods that use sample generation for model training?

Answer: Domain adversarial adaptation framework for few-shot QoT estimation in optical networks

Answer: Meta-learning-aided QoT estimator provisioning for a dynamic VNT configuration in optical networks

Answer: Sample-distribution-matching-based transfer learning for QoT estimation in optical networks

Answer: Evolutionary neuron-level transfer learning for QoT estimation in optical networks

Answer: On the benefits of domain adaptation techniques for quality of transmission estimation in optical networks

Answer: Reducing probes for quality of transmission estimation in optical networks with active learning

Answer: Assessment of Domain Adaptation Approaches for QoT Estimation in Optical Networks

Answer: Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks [Invited]

Answer: Model transfer of QoT prediction in optical networks based on artificial neural networks

Answer: Evol-TL: Evolutionary Transfer Learning for QoT Estimation in Multi-Domain Networks

Answer: Lifelong QoT prediction: an adaptation to real-world optical networks

Answer: QoT Estimation with Margin-Driven Transfer Learning in Time-Varying Optical Networks

Answer: Transfer Learning Using ANN for G-OSNR Estimation in WDM Network Topologies

Answer: Machine-learning-based lightpath QoT estimation and forecasting

Answer: Iterative Transfer Learning Approach for QoT Prediction of Lightpath in Optical Networks

Answer: Integrating Knowledge Distillation and Transfer Learning for Enhanced QoT-Estimation in Optical Networks

Answer: Toward Deployments of ML Applications in Optical Networks

Answer: A QoT prediction technique based on machine learning and NLSE for QoS and new lightpaths in optical communication networks

Answer: A Meta-Learning-Assisted Training Framework for Physical Layer Modeling in Optical Networks

Answer: Transfer learning aided qot computation in network operating with the 400zr standard

Answer: Application of ML Algorithms for Prediction of the QoT in Optical Networks with Imbalanced and Incomplete Data

Answer: Fast adaptation of multi-task meta-learning for optical performance monitoring

Answer: Adaptive alarm prediction in optical network based on model generalization in cross-layer AI

Engineering and Technology


Question: I am curating a new exhibition for a university museum which focus on science and technology, but I can not find theories which focus on how technology is presented in museums, would you please help me to find some?

Answer: Employing AI-Based Tools to Support Exhibition Design: A Science and Technology Museum Case Study

Answer: Embodied ephemeralities: Methodologies and historiographies for investigating the display and spatialization of science and technology in the twentieth century

Answer: Designing for Embodied Proximity

Answer: Design-Based Research on Digital Media in a Museum Environment

Answer: The Mediterranean forest in a science museum: engaging children through drawings that come to life in a virtual world

Answer: The development and evaluation of Chinese digital science and technology museum

Answer: Somatosensory Interaction of the Science Exhibition Based on Flow Theory

Answer: Designing for a socioenactive experience: A case study in an educational workshop on deep time

Answer: Reaching Broad Audiences from a Science Center or Museum Setting

Answer: Exhibiting Science: Changing Conceptions of Science Museum Display

Answer: ‘I know how it works!’ student engagement with exhibits in a science museum

Answer: Engaging the Public in Public Policy

Other


Question: Could you please find papers which disagree the Sharon Macdonald's ANT theory?

Answer: Curating actor-network theory: testing object-oriented sociology in the Science Museum

Answer: The role of technologies in cultural mediation in museums: an Actor-Network Theory view applied in France

Other


Question: I am currently writing a review on the application of dynamic magnetic resonance imaging in the flexion-extension position of the spine. I need to integrate various imaging methods, studies targeting specific diseases, and data stratified by age groups. However, I lack relevant literature for analysis. Could you recommend some relevant papers so that I can simplify the multivariate analysis while not losing the collaborative insights of different elements?

Answer: Dynamic flexion-extension magnetic resonance imaging of the cervical spine: an Evolutionary Tool for diagnosis and management of cervical spondylotic myelopathy

Answer: MRI‐based measurement of in vivo disc mechanics in a young population due to flexion, extension, and diurnal loading

Answer: Factors affecting dynamic foraminal stenosis in the lumbar spine

Answer: Safety of dynamic magnetic resonance imaging of the cervical spine in children performed without neurosurgical supervision

Answer: Dynamic bulging of intervertebral discs in the degenerative lumbar spine

Answer: Analysis of the relationship between the facet fluid sign and lumbar spine motion of degenerative spondylolytic segment using Kinematic MRI

Answer: Risk factors for missed dynamic canal stenosis in the cervical spine

Answer: High reliability and accuracy of dynamic magnetic resonance imaging in the diagnosis of cervical Spondylotic myelopathy: a multicenter study

Answer: Flexion/Extension Cervical Magnetic Resonance Imaging: A Potentially Useful Tool for Decision-Making in Patients with Symptomatic Degenerative Cervical Spine

Answer: Dynamic changes of the spinal canal in patients with cervical spondylosis at flexion and extension using magnetic resonance imaging

Answer: Radiological and Clinical Significance of Cervical Dynamic Magnetic Resonance Imaging for Cervical Spondylotic Myelopathy

Answer: Real-time dynamic 3-T MRI assessment of spine kinematics: a feasibility study utilizing three different fast pulse sequences

Answer: Feasibility and Safety of Cervical Kinematic Magnetic Resonance Imaging in Patients with Cervical Spinal Cord Injury without Fracture and Dislocation

Answer: The Role of Dynamic Magnetic Resonance Imaging in Cervical Spondylotic Myelopathy

Answer: The contribution of cervical dynamic magnetic resonance imaging to the surgical treatment of cervical spondylotic myelopathy

Answer: The value of dynamic MRI in cervical spondylotic myelopathy: About 24 cases

Medicine and Life Sciences


Question: Could you help identify clinical trials that investigate minimally invasive surgical techniques for colorectal cancer liver metastases requiring radical resection but NOT including radiofrequency ablation, specifically for patients with bilobar disease but excluding those who have received prior hepatic artery infusion chemotherapy, focusing on laparoscopic approaches rather than open surgery?

Answer: Long-term oncologic outcomes after laparoscopic versus open resection for colorectal liver metastases: a randomized trial

Answer: One-stage laparoscopic parenchymal sparing liver resection for bilobar colorectal liver metastases: safety, recurrence patterns and oncologic outcomes

Answer: Radiofrequency ablation in the treatment of liver metastases from colorectal cancer

Answer: Combined regional and systemic chemotherapy by a mini-invasive approach for the treatment of colorectal liver metastases

Answer: Laparoscopic versus open resection for colorectal liver metastases: the OSLO-COMET randomized controlled trial

Answer: Effect of PET before liver resection on surgical management for colorectal adenocarcinoma metastases: a randomized clinical trial

Answer: Hepatic arterial infusion in combination with systemic chemotherapy in patients with hepatic metastasis from colorectal cancer: a randomized phase II study - (NCT05103020) - study protocol

Answer: Hepatic arterial infusion chemotherapy for colorectal cancer metastasis to the liver

Answer: Long-Term Oncologic Outcomes After Laparoscopic Versus Open Resection for Colorectal Liver Metastases

Answer: Two-stage laparoscopic liver resection for bilateral colorectal liver metastasis

Answer: The role of the laparoscopic approach in two-stage hepatectomy for colorectal liver metastases: a single-center experience

Answer: Recent Advances in Minimally Invasive Liver Resection for Colorectal Cancer Liver Metastases—A Review

Answer: Robotic versus Laparoscopic Liver Resections for Colorectal Metastases: A Systematic Review and Meta-Analysis

Answer: Laparoscopic versus open resection of primary colorectal cancers and synchronous liver metastasis: a systematic review and meta-analysis

Answer: Robotic and laparoscopic liver surgery for colorectal liver metastases: an experience from a German Academic Center

Answer: Systematic Review Comparing the Effectiveness of Robotic verse Laparoscopic Liver Surgery in Colorectal Liver Metastasis (CRLM)

Answer: Simultaneous robotic-assisted resection of colorectal cancer and synchronous liver metastases: a systematic review

Answer: Robotic synchronous resection of rectal cancer and liver metastases: Current evidence and review

Answer: Results after simultaneous surgery and RFA liver ablation for patients with colorectal carcinoma and synchronous liver metastases

Answer: Safety and long-term prognosis of simultaneous versus staged resection in synchronous colorectal cancer with liver metastasis: a systematic review and meta-analysis

Answer: Interventional Oncology for Colorectal Liver Metastases: From Local Cure to Salvage Therapy

Answer: Optimizing treatment of hepatic metastases from colorectal cancer: Resection or resection plus ablation?

Answer: Simultaneous resection for colorectal cancer with synchronous liver metastases: current state-of-the-art

Medicine and Life Sciences


Question: I’m working on atmospheric error correction for InSAR (Interferometric Synthetic Aperture Radar) using deep learning methods, but I’ve encountered several persistent challenges, such as limited training data, generalizability and interpretability. Please help me search for papers published before November 1, 2025, that propose deep learning-based solutions specifically targeting InSAR atmospheric error correction, with a focus on improving cross-region adaptability, leveraging limited or noisy labeled data, enhancing model interpretability, or effectively modeling temporal atmospheric patterns. Please exclude papers that only apply traditional statistical or numerical weather models without a core deep learning or machine learning component, or those focused solely on other InSAR errors (e.g., deformation, topography) without explicit atmospheric correction.

Answer: InSAR spatial-heterogeneity tropospheric delay correction in steep mountainous areas based on deep learning for landslides monitoring

Answer: Removing InSAR topography-dependent atmospheric effect based on deep learning

Answer: ARU-net: Reduction of atmospheric phase screen in SAR interferometry using attention-based deep residual U-net

Answer: Improving time-series InSAR deformation estimation for city clusters by deep learning-based atmospheric delay correction

Answer: An attempt at improving atmospheric corrections in InSAR using cycle-consistent adversarial networks

Answer: Using generative adversarial networks for extraction of insar signals from large-scale Sentinel-1 interferograms by improving tropospheric noise correction

Answer: Time-series InSAR with deep-learning-based topography-dependent atmospheric delay correction for potential landslide detection

Answer: Recurrent neural networks for atmospheric noise removal from InSAR time series with missing values

Answer: An Interferometric Synthetic Aperture Radar Tropospheric Delay Correction Method Based on a Global Navigation Satellite System and a Backpropagation Neural Network: More Suitable for Areas with Obvious Terrain Changes

Answer: Improving tropospheric corrections on large-scale Sentinel-1 interferograms using a machine learning approach for integration with GNSS-derived zenith total delay (ZTD)

Answer: TropoDeep: a deep learning-based model for InSAR tropospheric correction on large-scale interferograms using GNSS and WRF outputs

Answer: Improving Atmospheric Noise Correction from InSAR Time Series Using Variational Autoencoder with Clustering (VAE-Clustering) Method

Answer: Removing Atmospheric Noise from InSAR Interferograms in Mountainous Regions with a Convolutional Neural Network

Answer: Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning

Answer: Deep Learning-Based Coseismic Deformation Estimation From InSAR Interferograms

Answer: The Influence of Topography-Dependent Atmospheric Delay for the InSAR Time-Series Results and the Deep Neural Network Correction

Fundamental Sciences


Question: I'm working on hyperspectral image classification using Recurrent Neural Networks (RNN), particularly LSTM and GRU architectures, and encountering several persistent challenges, Spectral-Spatial Feature Integration Bottleneck, High Computational Cost, Limited Performance Gain, Vanishing/Exploding Gradients in Deep Spectral Processing, Fixed-Length Input Restriction. I'm looking for papers published before 2023 that propose novel RNN-based approaches specifically for hyperspectral image classification, Please exclude papers that: Focus primarily on other network types (like pure CNNs or transformers) with only marginal RNN components, Don't specifically target hyperspectral image classification as the primary application, Are published after 2023 or are review/survey papers without novel methodological contributions.

Answer: Cascaded recurrent neural networks for hyperspectral image classification

Answer: GRU with spatial prior for hyperspectral image classification

Answer: Guided filter based deep recurrent neural networks for hyperspectral image classification

Answer: Hyperspectral image classification based on long short term memory network

Answer: Spectral-spatial classification for hyperspectral image based on a single GRU

Answer: Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks

Answer: Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification

Answer: Hyperspectral Image Classification Based on Bidirectional Gated Recurrent Units

Answer: Hyperspectral image classification using attention-based bidirectional long short-term memory network

Answer: Geometry-aware deep recurrent neural networks for hyperspectral image classification

Answer: Convolutional Recurrent Neural Networks for Hyperspectral Data Classification

Answer: Hyperspectral Image Classification Using Spectral-Spatial LSTMs

Answer: A Band Grouping Based LSTM Algorithm for Hyperspectral Image Classification

Answer: Deep Recurrent Neural Networks for Hyperspectral Image Classification

Answer: Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification

Answer: Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification

Answer: Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks

Answer: Multi-scale hierarchical recurrent neural networks for hyperspectral image classification

Answer: 3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images

Answer: A Spectral-Spatial Cascaded 3D Convolutional Neural Network with a Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification

Answer: TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification

Answer: Hyperspectral Image Classification via a Novel Spectral–Spatial 3D ConvLSTM-CNN

Answer: Deep LSTM with Guided Filter for Hyperspectral Image Classification

Answer: Bi-directional LSTM with multi-scale dense attention mechanism for hyperspectral image classification

Answer: Fast Sequential Feature Extraction for Recurrent Neural Network-Based Hyperspectral Image Classification

Answer: Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification

Answer: Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery

Answer: Multiscanning Strategy-Based Recurrent Neural Network for Hyperspectral Image Classification

Answer: Hyperspectral image classification by integrating attention-based LSTM and hybrid spectral networks

Answer: Spatial-Spectral Network for Hyperspectral Image Classification: A 3-D CNN and Bi-LSTM Framework

Answer: A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification

Answer: Spectral-Spatial Attention Networks for Hyperspectral Image Classification

Answer: Pooled hybrid-spectral for hyperspectral image classification

Answer: Learning Multiscale Temporal–Spatial–Spectral Features via a Multipath Convolutional LSTM Neural Network for Change Detection With Hyperspectral Images

Answer: Hyperspectral Image Features Classification Using Deep Learning Recurrent Neural Networks

Answer: Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning

Answer: Hyperspectral Image Classification: An Analysis Employing CNN, LSTM, Transformer, and Attention Mechanism

Answer: Grouped Bidirectional LSTM Network and Multistage Fusion Convolutional Transformer for Hyperspectral Image Classification

Answer: Deep Learning for Classification of Hyperspectral Data: A Comparative Review

Answer: An optimal model using hybrid LCNN- GRU for efficient hyperspectral image classification

Answer: HSI Classification: A Comprehensive Review, Dataset, and Future Trends

Answer: Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model

Answer: Spatial-Spectral Transformer for Hyperspectral Image Denoising

Fundamental Sciences


Question: I am developing a Retrieval-Augmented Generation (RAG) system specifically for the medical domain. Standard metrics like BLEU or ROUGE are insufficient for measuring safety. Can you recommend papers published between 2023 and 2025 that propose specific benchmarks or evaluation frameworks designed to detect hallucinations or factual errors in medical LLMs or medical RAG systems?

Answer: Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK

Answer: Detecting and evaluating medical hallucinations in large vision language models

Answer: DAHL: Domain-specific automated hallucination evaluation of long-form text through a benchmark dataset in biomedicine

Answer: Benchmarking retrieval-augmented generation for medicine

Answer: Medical hallucinations in foundation models and their impact on healthcare

Answer: Medhalu: Hallucinations in responses to healthcare queries by large language models

Answer: Halo: Hallucination analysis and learning optimization to empower llms with retrieval-augmented context for guided clinical decision making

Answer: Rule: Reliable multimodal rag for factuality in medical vision language models

Answer: Enhancing Health Information Retrieval with RAG by prioritizing topical relevance and factual accuracy

Answer: Addressing hallucinations with rag and nmiss in italian healthcare llm chatbots

Answer: VeriFact: Verifying Facts in LLM-Generated Clinical Text with Electronic Health Records

Answer: MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models

Answer: MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks

Answer: MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models

Answer: Med-HALT: Medical Domain Hallucination Test for Large Language Models

Answer: A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation

Answer: FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence

Answer: imapScore: Medical Fact Evaluation Made Easy

Answer: Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs

Answer: A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models

Answer: A dataset for evaluating clinical research claims in large language models

Answer: Medical Hallucination in Foundation Models and Their Impact on Healthcare

Answer: HealthBench: Evaluating Large Language Models Towards Improved Human Health

Answer: LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation

Answer: RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models

Answer: Process Reward Models for Sentence-Level Verification of LVLM Radiology Reports

Answer: Evaluating Retrieval-Augmented Generation Variants for Clinical Decision Support: Hallucination Mitigation and Secure On-Premises Deployment

Answer: Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts

Answer: Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering

Answer: Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

Answer: Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain

Answer: Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

Answer: MIRA: A Novel Framework for Fusing Modalities in Medical RAG

Answer: RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

Answer: Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models

Answer: Almanac: Retrieval-Augmented Language Models for Clinical Medicine

Answer: Development and Evaluation of a Retrieval-Augmented Generation Chatbot for Orthopedic and Trauma Surgery Patient Education: Mixed-Methods Study

Answer: Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots

Answer: Enhancing Large Language Models for Improved Accuracy and Safety in Medical Question Answering: Comparative Study

Answer: BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science

Answer: Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy

Answer: Retrieval Augmented Generation Evaluation for Health Documents

Answer: Trustworthy Medical Question Answering: An Evaluation-Centric Survey

Answer: MedCite: Can Language Models Generate Verifiable Text for Medicine?

Answer: Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments

Answer: MedExQA: Medical Question Answering Benchmark with Multiple Explanations

Answer: Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases

Answer: Medical Hallucinations in Foundation Models and Their Impact on Healthcare

Answer: Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models

Artificial Intelligence and Computing


Question: Please provide papers on Parameter-Efficient Fine-Tuning (PEFT) methods for Large Language Models aimed at improving performance over the original LoRA. However, I am NOT interested in quantization-based methods (like QLoRA). The papers should be published in 2024 or 2025.

Answer: Splitlora: A split parameter-efficient fine-tuning framework for large language models

Answer: DoRA: Weight-Decomposed Low-Rank Adaptation

Answer: DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution

Artificial Intelligence and Computing


Question: I am currently interested in Latent Profile Analysis (LPA). As a researcher in the field of foreign language education, my academic focus lies particularly in cognitive and affective factors. I would like to know about research papers in foreign language education that incorporate cognitive and affective constructs and adopt the LPA methodology. Could you recommend some relevant articles to me?

Answer: A latent profile analysis of Chinese EFL learners’ enjoyment and anxiety in reading and writing: associations with imaginative capacity and story continuation writing performance

Answer: A latent profile analysis of L2 writing emotions and their relations to writing buoyancy, motivation and proficiency

Answer: Exploring the profiles of foreign language learners’ writing self-regulation: focusing on individual differences

Answer: A latent profile analysis of Informal Digital Learning of English: Emotion regulation strategies as predictors and willingness to communicate as outcomes

Answer: How teacher support influences EFL learners’ emotions? A Latent Profile Transition Analysis

Answer: Profiling EFL Students’ Motivation and Anxiety in Collaborative Learning: Associations With Teachers’ Support, Self‐Regulated Learning, and Achievement

Answer: A latent profile analysis of students’ motivation of engaging in one-to-one computing environment for English learning

Answer: GROWTH, FIXED, AND MIXED MINDSETS

Answer: Unpacking the relationship between achievement goals, grit and engagement among Chinese university students: variable- and person-centered approaches

Answer: A latent profile analysis of future selves and grit among multilingual learners: Associations with language learning engagement

Answer: Students’ L2 grit, foreign language anxiety and language learning achievement: a latent profile and mediation analysis

Answer: Profiles of motivation and engagement in foreign language learning: Associations with emotional factors, academic achievement, and demographic features

Answer: A latent profile analysis of Chinese high school students’ control and value appraisals: Associations with enjoyment, boredom, and language achievement

Answer: A latent profile analysis of emotional intelligence and its relationship with L2 student writing feedback literacy

Answer: Latent profile analysis reveals heterogeneity related to motivation, effort, and outside of class behaviors in digital environments among Chinese university students

Answer: A latent profile analysis of EFL learners’ self-efficacy: Associations with academic emotions and language proficiency

Answer: EFL students’ profiles of English reading self-efficacy: Relations with reading enjoyment, engagement, and performance

Answer: Beyond emotional exhaustion: Mapping proximal profiles and distal triggers of second language burnout in Chinese high school students

Answer: Motivation and self-regulated learning profiles: A person-centered perspective of English learning and achievement in an Asia context

Answer: Deep Learning Engagement as a Predictor of Academic Self-Efficacy and Language Performance

Answer: Effects of AI Affordances on Student Engagement in EFL Classrooms: A Structural Equation Modelling and Latent Profile Analysis

Answer: Applying the online language learners' characteristics model in connection with various personality traits: A latent profile analysis

Answer: Exploring latent profiles in the relationship between learning atmosphere, negative emotions, and willingness to communicate among EFL learners

Answer: Exploring Engagement, Self-Efficacy, and Anxiety in Large Language Model EFL Learning: A Latent Profile Analysis of Chinese University Students

Answer: Profiles of the Future L2 Selves in Foreign Language Learning: Associations With Emotions, Willingness to Communicate, and Personality Traits

Answer: Understanding self-regulation, achievement emotions, and mindset of undergraduates in emergency remote teaching: a latent profile analysis

Answer: Latent profiles of learning engagement and anxiety in high school students: the mediating role of academic self-efficacy

Answer: Typologies of teaching strategies in classrooms and students' metacognition and motivation: a latent profile analysis of the Greek PISA 2018 data

Other


Question: As a researcher focusing on foreign language emotions, could you recommend peer-reviewed academic papers in the field of university-level foreign language education that center on the three core emotional variables—foreign language enjoyment (FLE), foreign language anxiety (FLA/FLCA), and foreign language boredom (FLB)—with the requirements that the papers must be published between 2012 and 2025 and exclude any content related to foreign language burnout?

Answer: Enjoymen, Boredom, and Anxiety in the Spanish Language Classroom

Answer: Teacher perceptions of teaching enjoyment and student foreign language enjoyment and anxiety

Answer: A review of foreign language learners' emotions

Answer: The predictive effects of foreign language anxiety and boredom on willingness to communicate among Chinese struggling EFL learners

Answer: Predictors of foreign language proficiency: Emotion regulation, foreign language enjoyment, or academic stress?

Answer: Classroom environment and willingness to communicate in English: The mediating role of emotions experienced by university students in China

Answer: A control–value theory approach to boredom in English classes among university students in China

Answer: Investigating factors responsible for boredom in English classes: The case of advanced learners

Answer: How unique is the foreign language classroom enjoyment and anxiety of Chinese EFL learners?

Answer: Teacher enthusiasm and students’ social-behavioral learning engagement: The mediating role of student enjoyment and boredom in Chinese EFL classes

Answer: Modelling boredom in the EFL context: An investigation of the role of coping self-efficacy, mindfulness, and foreign language enjoyment

Answer: The predictive effects of classroom environment and trait emotional intelligence on foreign language enjoyment and anxiety

Answer: Foreign language enjoyment, anxiety, and boredom among Chinese L3 learners: the predictive effects of learner and teacher variables

Answer: Emotions in English language classrooms among Chinese top university students

Answer: Modeling the associations between L2 teacher support and EFL learners’ reading motivation: The mediating impact of reading enjoyment, anxiety, and boredom

Answer: The predictive effects of foreign language enjoyment, anxiety, and boredom on general and domain-specific English achievement in online English classrooms

Answer: The Impact of EFL Learners' Negative Emotional Orientations on (Un)Willingness to Communicate in In-person and Online L2 Learning Contexts

Answer: The Relationship Between Chinese English Major Students' Learning Anxiety and Enjoyment in an English Language Classroom: A Positive Psychology Perspective

Answer: Foreign language enjoyment and foreign language classroom anxiety among Chinese learners of German

Answer: Foreign language enjoyment and anxiety in the communicative classroom

Answer: Foreign Language Enjoyment and Anxiety as the Correlates of the Ideal L2 Self in the English as a Foreign Language Context

Answer: Foreign language learning boredom: conceptualization and measurement

Answer: Unpacking boredom factors of Chinese foreign language major students in translation classes: A sequential mixed methods study

Answer: Boredom in online classes in the Iranian EFL context: Sources and solutions

Answer: Activity-induced boredom in online EFL classes

Answer: The antecedents of boredom in L2 classroom learning

Answer: A Longitudinal Analysis of Informal Digital Learning of English, Willingness to Communicate and Foreign Language Boredom: A Latent Change Score Mediation Model

Answer: A Comparative Study of the Experience of Boredom in the L2 and L3 Classroom

Answer: The Effect of Foreign Language Boredom on Willingness to Communicate in Classroom

Answer: The predictive power of sociobiographical and linguistic variables on foreign language anxiety of Chinese university students

Answer: Predictive Effects of English Classroom Anxiety and Motivation on Chinese Undergraduate EFL Learners’ English Achievement

Answer: The Effect of Foreign Language Anxiety on College Students Online Learning

Answer: Classroom Social Climate and Student Engagement in English as a Foreign Language Learning: The Mediating Roles of Academic Buoyancy and Academic Emotions

Answer: Exploring the Effect of Language-related Academic Emotions on Foreign Language Achievement: A Systematic Review and Meta Analysis

Answer: Modeling the Contribution of Anxiety, Enjoyment, and Classroom Environment to Boredom among Students of English as a Foreign Language Students

Answer: Online Foreign Language Learning in Higher Education and Its Correlates during the COVID-19 Pandemic

Answer: Exploring the relationships among emotions, willingness to communicate and flow experience in Chinese EFL learners: a structural equation modelling approach

Answer: Foreign Language Enjoyment and Anxiety of Chinese EFL Learners at the Tertiary Level: A Pseudo-Longitudinal Investigation

Answer: Language Attitude and GenAI Attitude of Struggling EFL Learners: A Moderated Mediation Model of Enjoyment and Anxiety

Answer: Relations among and Predictive Effects of Anxiety, Enjoyment and Self-Efficacy on Chinese Interpreting Majors' Self-Rated Interpreting Competence

Answer: Foreign language learning boredom and enjoyment: The effects of learner variables and teacher variables

Answer: Enhancing foreign language enjoyment through online cooperative learning: a longitudinal study of EFL learners

Answer: How skills-based classroom activities shape learners' foreign language enjoyment: a mixed-modelling longitudinal examination

Answer: From Global to Local: Investigating the Role of Enjoyment and Anxiety in Interpreting Achievement

Answer: Emotional dynamics in L3 Japanese learning: a pseudo-longitudinal study of enjoyment and boredom across different skill levels

Answer: FLE, FLCA, and FL Perceived Competence and FL Learning Effort in Saudi Military Cadets: A Structural Equation Modeling Approach

Other


Question: Please identify papers that propose a SLAM (Simultaneous Localization and Mapping) system utilizing 3D Gaussian Splatting as the underlying scene representation. The papers must be formally published in the proceedings of CVPR 2024 (Conference on Computer Vision and Pattern Recognition).

Answer: Gaussian Splatting SLAM

Answer: SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM

Answer: GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting

Artificial Intelligence and Computing


Question: I am looking for research papers on "jailbreaking" attacks against Large Language Models (LLMs) that were published in ICLR 2024. Specifically, I need automated black-box attack methods (using prompt engineering or optimization). Please EXCLUDE any methods that require white-box access (i.e., exclude gradient-based attacks like GCG).

Answer: BlackDAN: A Black-Box Multi-Objective Approach to Effective and Contextual Jailbreaking of Language Models

Answer: AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

Answer: BlackDAN: A Black-Box Multi-Objective Approach for Effective and Contextual Jailbreaking of Large Language Models

Answer: Jailbreaking Black Box Large Language Models in Twenty Queries

Answer: Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning

Answer: Untargeted Jailbreak Attack

Artificial Intelligence and Computing


Question: I am developing an LLM-based agent system for long-horizon scientific literature search, but I find that agents often hallucinate citations or fail to verify claims against the original papers. I am looking for research that proposes evaluation frameworks or benchmarks specifically designed to test LLM or agent-based systems on citation grounding, claim verification, or hallucination detection, where the evaluation requires checking model outputs against real academic papers, rather than synthetic QA pairs.

Answer: CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation

Answer: CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?

Answer: Towards fine-grained citation evaluation in generated text: A comparative analysis of faithfulness metrics

Answer: CiteME: Can Language Models Accurately Cite Scientific Claims?

Answer: AttributionBench: How Hard is Automatic Attribution Evaluation?

Answer: ExpertQA: Expert-Curated Questions and Attributed Answers

Answer: SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers

Answer: Automatic Evaluation of Attribution by Large Language Models

Answer: WICE: Real-World Entailment for Claims in Wikipedia

Answer: SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification

Answer: Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim $\rightarrow$ Evidence Reasoning

Answer: SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature

Answer: A scalable framework for evaluating multiple language models through cross-domain generation and hallucination detection

Answer: Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science

Answer: ResearchArena: Benchmarking Large Language Models' Ability to Collect and Organize Information as Research Agents

Artificial Intelligence and Computing


Question: Could you help me find papers published at ACL or EMNLP (2022–2024) that introduce benchmarks or evaluation datasets for large language models, where the benchmarks are specifically designed to evaluate long-context understanding or multi-document reasoning, and the first author is affiliated with a university rather than an industry lab?

Answer: LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Artificial Intelligence and Computing


Question: I am building an LLM-based research assistant for academic writing, but I notice that the model often generates incorrect or fabricated citations. I want to find research that proposes benchmarks or evaluation frameworks to assess hallucination, factual consistency, or citation correctness in LLM-generated texts, where the evaluation requires verifying model outputs against real-world documents or real academic papers, rather than synthetic datasets.

Answer: Med-halt: Medical domain hallucination test for large language models

Answer: Halueval-wild: Evaluating hallucinations of language models in the wild

Answer: Examining the Reliability of ChatGPT: Identifying Retracted Scientific Literature and Ensuring Accurate Citations and References

Answer: Can LLMs Write Faithfully? An Agent-Based Evaluation of LLM-generated Islamic Content

Answer: Survey on factuality in large language models

Answer: RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking

Answer: DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence

Answer: CiteGuard: Faithful Citation Attribution for LLMs via Retrieval-Augmented Validation

Answer: FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation

Answer: RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models

Answer: HalluLens: LLM Hallucination Benchmark

Answer: CiteME: Can Language Models Accurately Cite Scientific Claims?

Answer: Enabling Large Language Models to Generate Text with Citations

Answer: CitaLaw: Enhancing LLM with Citations in Legal Domain

Answer: Evaluating Verifiability in Generative Search Engines

Answer: Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses

Answer: AttributionBench: How Hard is Automatic Attribution Evaluation?

Answer: FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence

Answer: Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Answer: ExpertQA: Expert-Curated Questions and Attributed Answers

Answer: HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution

Answer: An Evaluation Framework for Attributed Information Retrieval using Large Language Models

Answer: Automatic Evaluation of Attribution by Large Language Models

Answer: Understanding Retrieval Augmentation for Long-form Question Answering

Answer: The Extractive-Abstractive Spectrum: Uncovering Verifiability Trade-offs in LLM Generations

Answer: MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents

Answer: Factcheck-GPT: End-to-End Fine-Grained Document-Level Fact-Checking and Correction of LLM Output

Answer: RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models

Answer: FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Answer: VERISCORE: Evaluating the factuality of verifiable claims in long-form text generation

Answer: VeriFastScore: Speeding up long-form factuality evaluation

Answer: Long-form factuality in large language models

Answer: WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries

Answer: FELM: Benchmarking Factuality Evaluation of Large Language Models

Answer: WICE: Real-World Entailment for Claims in Wikipedia

Answer: Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors

Answer: LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation

Answer: A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains

Answer: Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization

Answer: LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA

Answer: Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models

Answer: DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis

Answer: DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents

Answer: LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild

Answer: How well do LLMs cite relevant medical references? An evaluation framework and analyses

Answer: RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

Answer: PaperQA: Retrieval-Augmented Generative Agent for Scientific Research

Answer: Valsci: an open-source, self-hostable literature review utility for automated large-batch scientific claim verification using large language models

Answer: Attribute or Abstain: Large Language Models as Long Document Assistants

Answer: The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

Answer: ChatGPT Hallucinates when Attributing Answers

Answer: Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models

Answer: Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics

Answer: WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research

Answer: OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs

Answer: A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Answer: Training Language Models to Generate Text with Citations via Fine-grained Rewards

Answer: When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research

Artificial Intelligence and Computing


Question: Can you find papers published at ACL or EMNLP between 2022 and 2024 that introduce benchmarks or evaluation datasets for large language models, where the benchmarks are specifically designed to evaluate long-context understanding or multi-document reasoning, and where the first author is affiliated with a university rather than an industry lab?

Answer: LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Answer: ∞Bench: Extending Long Context Evaluation Beyond 100K Tokens

Answer: LongAlign: A Recipe for Long Context Alignment of Large Language Models

Artificial Intelligence and Computing


Question: I am developing a decision-support system for public health surveillance, but I want to ensure that the system’s recommendations are grounded in real epidemiological evidence rather than fabricated or outdated information. I am looking for public health research that proposes evaluation frameworks or benchmark datasets to assess data quality, evidence consistency, or reliability of surveillance and risk assessment models, where the evaluation requires validating model outputs against real-world public health data sources such as national disease surveillance systems, cohort studies, or official health statistics.

Answer: Impact of Primary Health Care Data Quality on Infectious Disease Surveillance in Brazil: Case Study

Answer: A methodological framework for the evaluation of syndromic surveillance systems: a case study of England

Answer: Real-world evidence from the first online healthcare analytics platform—Livingstone. Validation of its descriptive epidemiology module

Answer: Developing a Comprehensive Framework for Real-World Data Case Validation in Vaccine Safety Monitoring: The VAC4EU experience

Answer: Data quality assessment approaches for event-based surveillance systems

Answer: INSIGHT: A Tool for Fit-for-Purpose Evaluation and Quality Assessment of Standardized Observational Data Sources for Real World Evidence on Medicine and Vaccine Safety

Answer: Enhancing the accuracy and integrity of immunization registry data using scalable cloud-based validation frameworks

Answer: Population Health Metrics Research Consortium gold standard verbal autopsy validation study: design, implementation, and development of analysis datasets

Answer: Artificial Intelligence Based Models for Predicting Foodborne Pathogen Risk In Public Health Systems

Answer: A Standard Framework for Evaluating Large Health Care Data and Related Resources

Answer: Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group

Answer: Bio-ALIRT Biosurveillance Detection Algorithm Evaluation

Answer: A framework for evaluating epidemic forecasts

Answer: The Zoltar forecast archive, a tool to standardize and store interdisciplinary prediction research

Answer: The United States COVID-19 Forecast Hub dataset

Answer: Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

Answer: Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Answer: A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States

Answer: Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016

Answer: Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

Answer: Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S

Answer: Evaluating infectious disease forecasts with allocation scoring rules

Answer: When are predictions useful? A new method for evaluating epidemic forecasts

Answer: The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance

Answer: Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future

Answer: A simulation study comparing aberration detection algorithms for syndromic surveillance

Answer: Quantifying the determinants of outbreak detection performance through simulation and machine learning

Answer: An indicator framework for the monitoring and evaluation of event-based surveillance systems

Answer: Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events

Answer: Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis

Answer: An evaluation framework for comparing epidemic intelligence systems

Answer: Evaluation Challenges for Syndromic Surveillance --- Making Incremental Progress

Answer: Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009–2014

Answer: Nowcasting (Short-Term Forecasting) of Influenza Epidemics in Local Settings, Sweden, 2008–2019

Answer: Nowcasting (Short-Term Forecasting) of COVID-19 Hospitalizations Using Syndromic Healthcare Data, Sweden, 2020

Answer: Nowcasting Influenza Incidence with CDC Web Traffic Data: A Demonstration Using a Novel Data Set

Answer: α-Satellite: An AI-driven System and Benchmark Datasets for Hierarchical Community-level Risk Assessment to Help Combat COVID-19

Answer: On the accuracy of short-term COVID-19 fatality forecasts

Answer: A systematic review and comparative evaluation to develop and validate a comprehensive framework for cancer surveillance systems

Answer: Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning

Answer: Unlocking the Potential of Public Datasets: Wastewater-Based Epidemiological Forecasting During COVID-19

Answer: Towards a Verification and Validation Framework for COVID-19 Forecast Models

Answer: The Certainty Framework for Assessing Real‐World Data in Studies of Medical Product Safety and Effectiveness

Answer: A real-world data validation of the value of early-stage SIR modelling to public health

Answer: Public health utility of cause of death data: applying empirical algorithms to improve data quality

Answer: Evaluating Model Performance in Medical Datasets Over Time

Answer: The Role of Surveillance Systems in the Early Detection and Response to Outbreaks of Emerging and Reemerging Infectious Diseases: A Comprehensive Review

Answer: Medical device surveillance with electronic health records

Answer: Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance

Answer: Precision information extraction for rare disease epidemiology at scale

Answer: GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making

Answer: Foundation time series models for forecasting and policy evaluation in infectious disease epidemics

Answer: Performance analysis of mathematical methods used to forecast the 2022 New York City Mpox outbreak

Answer: SEIR‐driven semantic integration framework: Internet of Things‐enhanced epidemiological surveillance in COVID‐19 outbreaks using recurrent neural networks

Answer: A survey of using EHR as real-world evidence for discovering and validating new drug indications

Answer: Using machine learning to predict disease outbreaks and enhance public health surveillance

Answer: AI for pandemic preparedness and infectious disease surveillance: predicting outbreaks, modeling transmission, and optimizing public health interventions

Answer: Real-World Data and Real-World Evidence in Healthcare in the United States and Europe Union

Answer: A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning–Based Risk Prediction Models

Answer: An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Answer: Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study

Answer: Artificial intelligence for modelling infectious disease epidemics

Answer: Is the SMART risk prediction model ready for real-world implementation? A validation study in a routine care setting of approximately 380 000 individuals

Answer: Benchmarking Large Language Models from Open and Closed Source Models to Apply Data Annotation for Free-Text Criteria in Healthcare

Medicine and Life Sciences


Question: I am looking for peer-reviewed or authoritative public health publications published between 2015 and 2022 that explicitly propose evaluation frameworks or standardized guidelines for assessing the performance, data quality, or usefulness of public health surveillance systems. The work must focus on evaluation methodology rather than disease modeling, and be issued by recognized public health institutions or journals, such as the CDC, WHO, or American Journal of Public Health.

Answer: A methodological framework for the evaluation of syndromic surveillance systems: a case study of England

Answer: Evaluating Behavioral Health Surveillance Systems

Answer: Quality criteria for the evaluation of climate-informed early warning systems for infectious diseases

Answer: Early warning alert and response network in emergencies: evaluation protocol

Answer: Data quality review: module 1: framework and metrics

Answer: Data quality review: module 3: data verification and system assessment

Answer: Developing monitoring and evaluation tools for event-based surveillance: experience from Vietnam

Answer: National AMR surveillance systems and participation in the GLASS: a guide to planning, implementation, and monitoring and evaluation

Answer: International Health Regulations (2005): IHR monitoring and evaluation framework

Answer: Joint external evaluation tool: International Health Regulations (2005), 3rd ed

Answer: Joint external evaluation tool: International Health Regulations (2005), 2nd ed

Answer: Joint external evaluation tool: International Health Regulations

Answer: Integrated Disease Surveillance and Response Technical Guidelines, Booklet One: Introduction Section, 3rd ed

Answer: Data quality assessment of national and partner HIV treatment and patient monitoring data and systems implementation tool

Answer: Malaria surveillance, monitoring and evaluation: a reference manual

Answer: Development of a District-Level Programmatic Assessment Tool for Risk of Measles Virus Transmission

Answer: Surveillance systems to track progress towards polio eradication worldwide, 2015–2016– Systèmes de surveillance pour suivre les progress accomplis dans le monde vers l’éradication de la poliomyélite, 2015-2016

Answer: European external quality assessment programme for influenza virus – 2018

Answer: One Health Evaluation of Antimicrobial Use and Resistance Surveillance: A Novel Tool for Evaluating Integrated, One Health Antimicrobial Resistance and Antimicrobial Use Surveillance Programs

Answer: Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework

Answer: Strengthening surveillance of and response to foodborne diseases: a practical manual. Stage 2: strengthening indicator-based surveillance

Answer: Improving the quality and use of immunization and surveillance data: Summary report of the Working Group of the Strategic Advisory Group of Experts on Immunization

Medicine and Life Sciences


Question: Could you help me find the article about lithium-ion battery electrolyte additives that was published in an Elsevier journal?

Answer: Electrolyte additives for Li-ion batteries: classification by elements

Answer: (Pentafluorophenyl)diphenylphosphine as a dual-functional electrolyte additive for LiNi0.5Mn1.5O4 cathodes in high-voltage lithium-ion batteries

Engineering and Technology


Question: Could you help find papers that design LLM-based code generation methods for Python programming tasks, but these methods do NOT rely on pre-trained code libraries. The evaluation should use HumanEval benchmark, but exclude papers that only adopt automatic evaluation metrics without human evaluation

Answer: Humanevalcomm: Benchmarking the communication competence of code generation for llms and llm agents

Answer: Human-Like Code Quality Evaluation through LLM-based Recursive Semantic Comprehension

Answer: A survey on large language models for code generation

Answer: BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution

Answer: Codejudge: Evaluating code generation with large language models

Answer: Taskeval: Assessing difficulty of code generation tasks for large language models

Answer: The RealHumanEval: Evaluating Large Language Models’ Abilities to Support Programmers

Answer: LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation

Answer: Enhancing Code Generation via Bidirectional Comment-Level Mutual Grounding

Answer: The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers

Answer: Low-Cost Language Models: Survey and Performance Evaluation on Python Code Generation

Answer: DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Answer: ReCode: Robustness Evaluation of Code Generation Models

Answer: Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Answer: Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models

Answer: The Stack: 3 TB of permissively licensed source code

Answer: LLM-Assisted Code Cleaning For Training Accurate Code Generators

Answer: PyGen: A Collaborative Human-AI Approach to Python Package Creation

Answer: Refactoring Programs Using Large Language Models with Few-Shot Examples

Answer: PAL: Program-aided Language Models

Answer: Teaching Large Language Models to Self-Debug

Answer: Code Needs Comments: Enhancing Code LLMs with Comment Augmentation

Answer: Aligning Offline Metrics and Human Judgments of Value for Code Generation Models

Answer: Exploring the Potential of Offline LLMs in Data Science: A Study on Code Generation for Data Analysis

Engineering and Technology


Question:
Please retrieve papers related to "Application Progress and Future Prospects of Artificial Intelligence
 and Synthetic Biology in the Development of Antibiotic Alternatives". Focus on the following specific technical directions: 1. Artificial Intelligence: machine learning for predicting antibacterial activity, computer-aided design of antimicrobial peptides; 2. Synthetic Biology: engineering strains to synthesize antibiotic alternative molecules, analysis of microbiota-metabolite-host interaction
 networks; 3. Also cover practical application progress in the development of antibiotic
alternative products such as microecological preparations and antimicrobial peptides.

Answer: Computer-aided design of antimicrobial peptides: are we generating effective drug candidates?

Answer: Accelerating antibiotic discovery through artificial intelligence

Answer: A deep learning approach to antibiotic discovery

Answer: A machine learning tool to predict the antibacterial capacity of nanoparticles

Answer: The application of machine learning techniques to innovative antibacterial discovery and development

Answer: Generative AI for designing and validating easily synthesizable and structurally novel antibiotics

Answer: Antibiotic discovery in the artificial intelligence era

Answer: Hybrid Quorum Sensing and Machine Learning Systems for Precision Gene Regulation: Revolutionizing Synthetic Biology and Autonomous Therapeutic Applications

Answer: AI-Driven Antimicrobial Peptide Discovery: Mining and Generation

Answer: A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria

Answer: Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model

Answer: Synthetic antimicrobial peptides: Combatting antimicrobial resistance for sustainable aquaculture

Answer: Synthesis and Evaluation of Aquatic Antimicrobial Peptides Derived from Marine Metagenomes Using a High-Throughput Screening Approach

Answer: Progress in the Identification and Design of Novel Antimicrobial Peptides Against Pathogenic Microorganisms

Answer: Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery

Answer: Rational design of stapled antimicrobial peptides

Answer: Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Answer: Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

Answer: Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence

Answer: Machine learning for antimicrobial peptide identification and design

Answer: SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification

Answer: AI Methods for Antimicrobial Peptides: Progress and Challenges

Answer: Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model

Answer: Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review

Answer: How Machine Learning Helps in Combating Antimicrobial Resistance: A Review of AMP Analysis and Generation Methods

Answer: De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks

Answer: Artificial intelligence-driven antimicrobial peptide discovery

Answer: Advanced Strategies in Phage Research: Innovations, Applications, and Challenges

Answer: Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis

Answer: Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks

Answer: Advances in Artificially Designed Antibacterial Active Antimicrobial Peptides

Answer: Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning

Other


Question:
I am conducting cutting-edge interdisciplinary research under the background of "reducing and
replacing antibiotics", aiming to combine artificial intelligence and synthetic biology to break
through bottlenecks in traditional antibiotic alternative development. However, I am unclear
 about the implementation difficulties and future trends of current technical routes. Could
 you recommend papers that review the application progress, technical bottlenecks, and
 future prospects of this field, especially those involving machine learning for predicting
antibacterial activity and engineering strains to synthesize antibiotic alternative molecules?

Answer: Machine learning for antimicrobial peptide identification and design

Answer: Synthetic biology and computer-based frameworks for antimicrobial peptide discovery

Answer: Artificial intelligence-driven antimicrobial peptide discovery

Answer: Recent Advancements in AI-Assisted Drug Design and Discovery Systems

Answer: Deep learning tools to accelerate antibiotic discovery

Answer: AI-Driven Antimicrobial Peptide Discovery: Mining and Generation

Answer: A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria

Answer: The convergence of AI and synthetic biology: the looming deluge

Answer: Tutorial: guidelines for the use of machine learning methods to mine genomes and proteomes for antibiotic discovery

Answer: AI for antibiotic design

Answer: Harnessing Innovations in Antimicrobial Peptide Design: From AI-Driven Discovery to Precision Targeting Mechanisms

Answer: Advanced Strategies in Phage Research: Innovations, Applications, and Challenges

Answer: Synthetic antimicrobial peptides: Combatting antimicrobial resistance for sustainable aquaculture

Answer: Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Answer: Progress and future of the computational design of antimicrobial peptides (AMPs): bio-inspired functional molecules

Answer: Artificial Intelligence and Antibiotic Discovery

Answer: Application of artificial intelligence in microbial drug discovery: Unlocking new frontiers in biotechnology

Answer: A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation

Answer: How Machine Learning Helps in Combating Antimicrobial Resistance: A Review of AMP Analysis and Generation Methods

Answer: Reviewing on AI‐Designed Antibiotic Targeting Drug‐Resistant Superbugs by Emphasizing Mechanisms of Action

Answer: AI Methods for Antimicrobial Peptides: Progress and Challenges

Answer: Next-Generation Wastewater Treatment: Omics and AI-Driven Microbial Strategies for Xenobiotic Bioremediation and Circular Resource Recovery

Answer: Microbiome Engineering to Enhance Disease Resistance in Aquaculture: Current Strategies and Future Directions

Answer: Accelerating antibiotic discovery through artificial intelligence

Answer: Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era

Other


Question:
Please retrieve review papers published between 2020 and 2025 in top journals related to
aquaculture, aquatic animal disease prevention and control, or microbiology, with the
theme of "Application Progress and Future Prospects of Artificial Intelligence and
Synthetic Biology in the Development of Antibiotic Alternatives". These papers
 should focus on hot topics such as engineering modification of antimicrobial
peptides, phage therapy, and nanodelivery systems.

Answer: Machine learning for antimicrobial peptide identification and design

Answer: Artificial intelligence-driven antimicrobial peptide discovery

Answer: Recent progress in the discovery and design of antimicrobial peptides using traditional machine learning and deep learning

Answer: Progress and future of the computational design of antimicrobial peptides (AMPs): bio-inspired functional molecules

Answer: Advances in antimicrobial peptide discovery via machine learning and delivery via nanotechnology

Answer: Deep learning in antimicrobial peptide prediction

Answer: Phage engineering using synthetic biology and artificial intelligence to enhance phage applications in food industry

Answer: Advanced Strategies in Phage Research: Innovations, Applications, and Challenges

Answer: Advances in the field of phage-based therapy with special emphasis on computational resources

Answer: Enhancing phage therapy through synthetic biology and genome engineering

Answer: AI Methods for Antimicrobial Peptides: Progress and Challenges

Answer: Synthetic antimicrobial peptides: Combatting antimicrobial resistance for sustainable aquaculture

Answer: Deep Learning for Novel Antimicrobial Peptide Design

Answer: Phage–nanomaterial platforms for precision antimicrobial therapy: from design to therapeutic application

Answer: Phage therapy: From biological mechanisms to future directions

Answer: Optimizing phage therapy with artificial intelligence: a perspective

Answer: Synthetic cells for phage therapy: a perspective

Answer: Antimicrobial peptides: An alternative to traditional antibiotics

Other


Question: Please retrieve papers related to "Application Progress and Future Prospects of Artificial Intelligence and Synthetic Biology in the Development of Antibiotic Alternatives" in the aquaculture field. Focus on the application of interdisciplinary technologies in natural antibiotic alternative products such as microecological preparations, antimicrobial peptides, or phages. Exclude studies that only discuss chemically synthesized alternatives, single technical directions (e.g., using only AI or only synthetic biology), as well as those focusing on livestock or human subjects.

Answer: Microbiome engineering to enhance disease resistance in aquaculture: current strategies and future directions

Answer: Synthesis and Evaluation of Aquatic Antimicrobial Peptides Derived from Marine Metagenomes Using a High-Throughput Screening Approach

Answer: Artificial intelligence-driven antimicrobial peptide discovery

Answer: Streptococcosis in aquaculture: Advances, challenges, and future directions in disease control and prevention

Answer: Beyond the Resistome: Molecular Insights, Emerging Therapies, and Environmental Drivers of Antibiotic Resistance

Answer: The Strategic Role of Biotechnology in Aquaculture: Integrating Upstream and Downstream Processes for Sustainable Fish Production

Answer: Artificial intelligence in microbiome research and beyond: connecting human health, animal husbandry, and aquaculture

Answer: Engineering the fish gut microbiome: could it serve as future-proof strategy for sustainable aquaculture?

Answer: Advanced strategies in phage research: innovations, applications, and challenges

Answer: Synthetic antimicrobial peptides: Combatting antimicrobial resistance for sustainable aquaculture

Answer: AI Methods for Antimicrobial Peptides: Progress and Challenges

Answer: Video-Driven Artificial Intelligence for Predictive Modelling of Antimicrobial Peptide Generation: Literature Review on Advances and Challenges

Answer: Unlocking Antimicrobial Peptides from Marine Invertebrates: A Comprehensive Review of Antimicrobial Discovery

Other


Question: I am using LNP to deliver DNA, but my current formulation isn't working. Please help me look for papers with established formulations.

Answer: The 60-year evolution of lipid nanoparticles for nucleic acid delivery

Answer: Nanodelivery of nucleic acids

Answer: Safer non-viral DNA delivery using lipid nanoparticles loaded with endogenous anti-inflammatory lipids

Answer: Improving the safety of lipid nanoparticle-based DNA delivery for extended gene expression

Answer: Multi-step screening of DNA/lipid nanoparticles and co-delivery with siRNA to enhance and prolong gene expression

Answer: Structuring lipid nanoparticles, DNA, and protein corona into stealth bionanoarchitectures for in vivo gene delivery

Answer: Nanoparticle-based DNA vaccine protects against SARS-CoV-2 variants in female preclinical models

Answer: Lipid Nanoparticle Formulation Increases Efficiency of DNA-Vectored Vaccines/Immunoprophylaxis in Animals Including Transchromosomic Bovines

Answer: Optimal self-assembly of lipid nanoparticles (LNP) in a ring micromixer

Answer: Lipid nanoparticle-mediated efficient delivery of CRISPR/Cas9 for tumor therapy

Answer: Role of liposome and peptide in the synergistic enhancement of transfection with a lipopolyplex vector

Answer: Novel Nanosomes for Gene Delivery to Plasmodium falciparum-infected Red Blood Cells

Medicine and Life Sciences


Question: Please help me find articles in the Molecular Therapy series journal about the treatment of hemophilia A.

Answer: Clinical immunogenicity outcomes from GENEr8-1, a phase 3 study of valoctocogene roxaparvovec, an AAV5-vectored gene therapy for hemophilia A

Answer: Early Phase Clinical Immunogenicity of Valoctocogene Roxaparvovec, an AAV5-Mediated Gene Therapy for Hemophilia A

Answer: Gene Therapy with BMN 270 Results in Therapeutic Levels of FVIII in Mice and Primates and Normalization of Bleeding in Hemophilic Mice

Answer: Preclinical assessment of an optimized AAV-FVIII vector in mice and non-human primates for the treatment of hemophilia A

Answer: Treatment of Hemophilia A Using Factor VIII Messenger RNA Lipid Nanoparticles

Answer: Bioengineered coagulation factor VIII enables long-term correction of murine hemophilia A following liver-directed adeno-associated viral vector delivery

Answer: The impact of minimally oversized adeno-associated viral vectors encoding human factor VIII on vector potency in vivo

Answer: Overexpression of factor VIII after AAV delivery is transiently associated with cellular stress in hemophilia A mice

Answer: Potential for cellular stress response to hepatic factor VIII expression from AAV vector

Answer: Sustained FVIII expression and phenotypic correction of hemophilia A in neonatal mice using an endothelial-targeted sleeping beauty transposon

Answer: Correction of the coagulation defect in hemophilia A mice through factor VIII expression in skin

Answer: Intraosseous delivery of lentiviral vectors targeting factor VIII expression in platelets corrects murine hemophilia A

Medicine and Life Sciences


Question: I'm developing a thermal energy storage system for concentrated solar power plants that needs to operate efficiently in desert environments with extreme temperature fluctuations (daily swings of 40°C+). Current molten salt systems we tested suffer from solidification issues during cold nights and thermal shock degradation. I need papers that address phase change material stability under cyclic thermal stress specifically for CSP applications, with solutions that maintain at least 90% storage efficiency after 1000+ cycles, published after 2020.

Answer: Assessing Mg84Cu16 as a novel PCM for high-temperature energy storage: Focus on thermal stability and material compatibility

Answer: A review of the performance and application of molten salt-based phase change materials in sustainable thermal energy storage at medium and high temperatures

Answer: High-temperature composite phase change material with “concrete-like” strength even beyond the eutectic temperature

Answer: Preparation of Al–Si microcapsules with high latent heat and durability via control of shell thickness

Answer: Innovative optimization of insulation for high-temperature thermal energy storage in generation 3 CSP systems using phase change materials (PCMs)

Answer: A technical and economic comparison between concrete and latent thermal energy storage for concentrated solar power applications

Answer: Thermal Expansion Challenges and Solution Strategies for Phase Change Material Encapsulation: A Comprehensive Review

Answer: Machine learning model for transient exergy performance of a phase change material integrated-concentrated solar thermoelectric generator

Answer: Towards resilient operation of photovoltaic-thermal collector with incorporated organic phase change material: Numerical and experimental investigation

Answer: Development Energy System of Iraq Harnessing the Power of Molten Salt by CSP Plant With Thermal Storage Energy

Engineering and Technology


Question: Find papers on waste heat recovery systems for industrial processes that achieve over 40% thermal efficiency, utilize organic Rankine cycles or Kalina cycles, but explicitly NOT using water/steam as the working fluid, must include economic analysis with payback period under 5 years, published between 2021 and October 2024.

Answer: Thermo-economic performance limit analysis of combined heat and power systems for optimal working fluid selections

Answer: Optimizing waste heat recovery with organic Rankine cycles: A novel graphical approach based on Exergy-Enthalpy diagrams

Answer: Waste Heat Recovery Technologies on Optimized CHP-BESS Plant: A Performance Comparison Between Organic Rankine Cycle and H2O-NH3 Absorption Plant

Answer: Comparative analysis of organic rankine cycle, Kalina cycle and thermoelectric generator to recover waste heat based on energy, exergy, economic and environmental analysis method

Answer: Techno-Economic Optimization of a Low-Temperature Organic Rankine System Driven by Multiple Heat Sources

Answer: Thermodynamic and economic analysis of a Kalina cycle-based combined heating and power system for low-temperature heat source utilization

Answer: Potential of Organic Rankine Cycle for waste heat recovery from piston engine-based power plants in isolated power systems

Answer: Parametric study and techno-economic analysis of a novel integration between thermoelectric generator and organic Rankine cycle onboard passenger ship

Answer: Technical and economic assessment of ORC and cogeneration including a combined variant – A case study for the Polish automotive fastener industry company

Answer: Energy, exergy analysis in a hybrid power and hydrogen production system using biomass and organic Rankine cycle

Answer: Multi-Objective Optimization of an Organic Rankine Cycle (ORC) for a Hybrid Solar–Waste Energy Plant

Answer: A techno-economic survey on high- to low-temperature waste heat recovery cycles for UK glass sector

Answer: Comparative Exergy Analysis of Series and Parallel Dual-Pressure Auto-Cascade Organic Rankine Cycles

Answer: Integration of district heating systems with small modular reactors and organic Rankine cycle including energy storage: Design and energy management optimization

Engineering and Technology


Question: I am a researcher studying mechanisms of cancer immune evasion. I have observed that in some tumors treated with PD-1/PD-L1 inhibitors, there is an upregulation of alternative immune checkpoints (such as LAG-3, TIGIT, or TIM-3) on tumor-infiltrating lymphocytes, which may contribute to acquired resistance. I need to find experimental research papers (not reviews) that provide direct in vivo or in vitro evidence demonstrating that the co-blockade of PD-1/PD-L1 with one of these alternative checkpoints can overcome resistance and enhance anti-tumor efficacy. The models should preferably involve syngeneic mouse models or patient-derived organoids. Please focus on papers published between 2021 and 2024.

Answer: A novel anti-LAG-3/TIGIT bispecific antibody exhibits potent anti-tumor efficacy in mouse models as monotherapy or in combination with PD-1 antibody

Answer: Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells

Answer: Dual targeting of TIGIT and PD-1 with a novel small molecule for cancer immunotherapy

Answer: TIGIT/CD155 blockade enhances anti-PD-L1 therapy in head and neck squamous cell carcinoma by targeting myeloid-derived suppressor cells

Answer: PD-L1/LAG-3 bispecific antibody enhances tumor-specific immunity

Answer: Mechanistic convergence of the TIGIT and PD-1 inhibitory pathways necessitates co-blockade to optimize anti-tumor CD8+ T cell responses

Answer: The search for an interesting partner to combine with PD-L1 blockade in mesothelioma: focus on TIM-3 and LAG-3

Answer: High plus low dose radiation strategy in combination with TIGIT and PD1 blockade to promote systemic antitumor responses

Answer: TIGIT and PD-L1 co-blockade promotes clonal expansion of multipotent, non-exhausted antitumor T cells by facilitating co-stimulation

Answer: Simultaneous Genetic Ablation of PD-1, LAG-3, and TIM-3 in CD8 T Cells Delays Tumor Growth and Improves Survival Outcome

Answer: Combination TIGIT/PD-1 blockade enhances the efficacy of neoantigen vaccines in a model of pancreatic cancer

Answer: Development of a bispecific antibody targeting PD-L1 and TIGIT with optimal cytotoxicity

Answer: Sequential administration of anti-PD-1 and anti-Tim-3 combined with an SA-GM-CSF-anchored vaccine overcomes adaptive immune resistance to reject established bladder cancer

Answer: Single-cell RNA sequencing reveals anti-tumor potency of CD56+ NK cells and CD8+ T cells in humanized mice via PD-1 and TIGIT co-targeting

Answer: PD-1 and LAG-3 blockade improve anti-tumor vaccine efficacy

Answer: Combined Blockade of TIGIT and PD-L1 Enhances Anti-Neuroblastoma Efficacy of GD2-Directed Immunotherapy with Dinutuximab Beta

Answer: COM902, a novel therapeutic antibody targeting TIGIT augments anti-tumor T cell function in combination with PVRIG or PD-1 pathway blockade

Answer: Co-blocking TIGIT and PVRIG Using a Novel Bispecific Antibody Enhances Antitumor Immunity

Medicine and Life Sciences


Question: Please help me find clinical trial research papers that evaluate the combination therapy of a CDK4/6 inhibitor (such as Palbociclib, Ribociclib, or Abemaciclib) with endocrine therapy as the first-line treatment for hormone receptor-positive (HR+), HER2-negative advanced breast cancer in postmenopausal women. The papers must be published in the journals The New England Journal of Medicine, The Lancet, or JAMA Oncology between 2020 and 2024.

Answer: Overall Survival with Ribociclib plus Letrozole in Advanced Breast Cancer

Answer: Overall Survival with Ribociclib plus Fulvestrant in Advanced Breast Cancer

Answer: Fulvestrant-Palbociclib vs Letrozole-Palbociclib as Initial Therapy for Endocrine-Sensitive, Hormone Receptor–Positive, ERBB2-Negative Advanced Breast Cancer: A Randomized Clinical Trial

Medicine and Life Sciences


Question: I am working on world-model-based data generation for autonomous driving.However, when generating long-horizon, high-frame-rate videos, GPU memory usage becomes a major bottleneck and often leads to out-of-memory errors.Could you recommend papers that propose techniques to reduce memory consumption during long video or sequence generation without significantly degrading generation quality, such as through model architecture design, temporal decomposition, latent-space modeling, or memory-efficient training and inference strategies?

Answer: Ltx-video: Realtime video latent diffusion

Answer: Video probabilistic diffusion models in projected latent space

Answer: Latent video diffusion models for high-fidelity long video generation

Answer: Drama: Mamba-enabled model-based reinforcement learning is sample and parameter efficient

Answer: Drivedreamer: Towards real-world-drive world models for autonomous driving

Answer: Slowfast-vgen: Slow-fast learning for action-driven long video generation

Answer: LongLive: Real-time Interactive Long Video Generation

Answer: MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation

Answer: InfinityDrive: Breaking Time Limits in Driving World Models

Answer: Video World Models with Long-term Spatial Memory

Answer: WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception

Answer: Packing Input Frame Context in Next-Frame Prediction Models for Video Generation

Answer: StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text

Answer: Video-Infinity: Distributed Long Video Generation

Answer: Towards Chunk-Wise Generation for Long Videos

Answer: VFRTok: Variable Frame Rates Video Tokenizer with Duration-Proportional Information Assumption

Answer: Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction

Answer: LoViC: Efficient Long Video Generation with Context Compression

Answer: Mixture of Contexts for Long Video Generation

Answer: TokensGen: Harnessing Condensed Tokens for Long Video Generation

Answer: Pack and Force Your Memory: Long-form and Consistent Video Generation

Answer: MoGA: Mixture-of-Groups Attention for End-to-End Long Video Generation

Answer: Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models

Answer: Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers

Answer: Radial Attention: O(nlogn) Sparse Attention with Energy Decay for Long Video Generation

Answer: DSV: Exploiting Dynamic Sparsity to Accelerate Large-Scale Video DiT Training

Answer: SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention

Answer: Attention Surgery: An Efficient Recipe to Linearize Your Video Diffusion Transformer

Answer: LinVideo: A Post-Training Framework towards O(n) Attention in Efficient Video Generation

Answer: VidToMe: Video Token Merging for Zero-Shot Video Editing

Answer: Phenaki: Variable Length Video Generation From Open Domain Textual Description

Answer: Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing

Answer: Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity

Answer: VSA: Faster Video Diffusion with Trainable Sparse Attention

Answer: Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile

Answer: Training-free and Adaptive Sparse Attention for Efficient Long Video Generation

Answer: Long-Context State-Space Video World Models

Answer: SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces

Answer: Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers

Answer: Autoregressive Video Generation without Vector Quantization

Answer: From Slow Bidirectional to Fast Autoregressive Video Diffusion Models

Answer: MAGI-1: Autoregressive Video Generation at Scale

Answer: Real-Time Video Generation with Pyramid Attention Broadcast

Answer: Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation

Answer: NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation

Answer: MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control

Artificial Intelligence and Computing


Question: I’m working on autonomous driving scene reconstruction and street-level 3D/4D representation learning. I’m particularly interested in papers published in 2025 by Sida Peng (Zhejiang University) that propose methods for reconstructing or modeling driving scenes (e.g., street scenes, dynamic scenes, novel-view synthesis, 3D/4D reconstruction) from multi-view images and/or video sequences. Could you recommend and list his relevant 2025 works, prioritizing those evaluated on autonomous driving datasets (e.g., Waymo, KITTI) or realistic street-scene benchmarks?

Answer: Hierarchy UGP: Hierarchy Unified Gaussian Primitive for Large-Scale Dynamic Scene Reconstruction

Answer: StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models

Answer: ADGaussian: Generalizable Gaussian Splatting for Autonomous Driving with Multi-modal Inputs

Answer: Multi-view Reconstruction via SfM-guided Monocular Depth Estimation

Answer: LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation

Artificial Intelligence and Computing


Question: Could you help me find original, non-review research papers in the main Science journal that report scientific discoveries using liquid-cell transmission electron microscopy as a primary research tool?

Answer: Imaging rotational dynamics of nanoparticles in liquid by 4D electron microscopy

Answer: High-resolution EM of colloidal nanocrystal growth using graphene liquid cells

Answer: 3D structure of individual nanocrystals in solution by electron microscopy

Answer: Critical differences in 3D atomic structure of individual ligand-protected nanocrystals in solution

Answer: Single-particle mapping of nonequilibrium nanocrystal transformations

Answer: Facet development during platinum nanocube growth

Answer: In situ TEM imaging of CaCO3 nucleation reveals coexistence of direct and indirect pathways

Answer: Observation of Single Colloidal Platinum Nanocrystal Growth Trajectories

Answer: Real-Time Imaging of Pt3Fe Nanorod Growth in Solution

Answer: Direction-Specific Interactions Control Crystal Growth by Oriented Attachment

Answer: Dissolution enables dolomite crystal growth near ambient conditions

Fundamental Sciences


Question: Could you help me find research articles in the main Science journal that apply ultrafast transmission electron microscopy to study physical or chemical processes, excluding all studies focused on biological systems such as proteins, DNA, or cellular structures?

Answer: Imaging rotational dynamics of nanoparticles in liquid by 4D electron microscopy

Answer: Dynamics of chemical bonding mapped by energy-resolved 4D electron microscopy

Answer: 4D nanoscale diffraction observed by convergent-beam ultrafast electron microscopy

Answer: 4D imaging of transient structures and morphologies in ultrafast electron microscopy

Answer: 4D electron tomography

Answer: Subparticle ultrafast spectrum imaging in 4D electron microscopy

Answer: Ultrafast Electron Crystallography of Interfacial Water

Answer: Observing liquid flow in nanotubes by 4D electron microscopy

Fundamental Sciences


Question: How can sensor fusion and deep learning techniques enhance perception systems in autonomous vehicles under adverse weather conditions (e.g., heavy rain, snow, fog), with peer-reviewed publications

Answer: Deep camera–radar fusion with an attention framework for autonomous vehicle vision in foggy weather conditions

Answer: Object detection in adverse weather for autonomous vehicles based on sensor fusion and incremental learning

Answer: Improving Autonomous Vehicle Cognitive Robustness in Extreme Weather with Deep Learning and Thermal Camera Fusion

Answer: Autonomous vehicle driving in harsh weather: Adaptive fusion alignment modeling and analysis

Answer: Camera–Lidar sensor fusion for drivable area detection in winter weather using convolutional neural networks

Answer: Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Answer: Enhancing Autonomous Vehicle Perception in Adverse Weather: A Multi Objectives Model for Integrated Weather Classification and Object Detection

Artificial Intelligence and Computing


Question: list machine learning applications in medical diagnostics published in Nature Medicine by MIT researchers

Answer: A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Answer: Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary

Answer: Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Answer: A visual-language foundation model for computational pathology

Artificial Intelligence and Computing


Question: I am currently conducting research on the role of palmitoylation in renal ischemia-reperfusion. However, most studies on post-translational modifications of proteins in renal ischemia-reperfusion injury focus on modifications such as ubiquitination and phosphorylation. Could you recommend some papers that include palmitoylation that occurs under the condition of renal ischemia-reperfusion injury?

Answer: Palmitoyltransferase DHHC9 and acyl protein thioesterase APT1 modulate renal fibrosis through regulating β-catenin palmitoylation

Answer: Identification of biomarkers and mechanisms associated with palmitoylation in acute kidney injury using transcriptome and single cell data

Answer: DHHC21 deficiency attenuates renal dysfunction during septic injury

Answer: Enzymatic post-translational modifications of proteins in chronic kidney disease: mechanisms, regulation, and clinical significance

Answer: Lipid-Modified Bilirubin Nanocrystals for the Repair of Ischemia-Reperfusion-Induced Acute Kidney Injury

Answer: Alleviation of ischemia-reperfusion induced renal injury by chemically modified SOD2 mRNA delivered via lipid nanoparticles

Answer: DHHC6-Mediated SDHB Palmitoylation Protects against Tubular Cell Death and AKI

Answer: DHHC15 and APT2-Regulated S-Palmitoylation of Hsp90α Promotes Hypoxia-Inducible Factor (HIF)-1α Stabilization in Tubular Epithelial Cells and Renal Fibrosis

Medicine and Life Sciences


Question: Could you help me find the academic paper retrieval evaluation benchmark that can be retrieved in Pubmed and Web of Science?

Answer: Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Answer: Searching for scientific evidence in a pandemic: An overview of TREC-COVID

Answer: TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19

Answer: A large-scale dataset of patient summaries for retrieval-based clinical decision support systems

Answer: BioASQ-QA: A manually curated corpus for Biomedical Question Answering

Answer: An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition

Answer: Literature search sandbox: a large language model that generates search queries for systematic reviews

Answer: Automatic identification of high impact articles in PubMed to support clinical decision making

Answer: Clinical trial search: Using biomedical language understanding models for re-ranking

Answer: MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval

Answer: A comparative evaluation of biomedical similar article recommendation

Medicine and Life Sciences