Page 3 - Paper Library - AiPaper

This paper addresses performance bottlenecks in sequential recommendation by proposing userspecific optimization, analyzing each user's behavior independently, and integrating realtime inference to enhance efficiency and model stability, using KL divergence for individual seque

Robust deep learning–based protein sequence design using ProteinMPNN

Published:9/15/2022

Deep Learning-Based Protein Sequence DesignProteinMPNNProtein Structure PredictionExperimental Protein Design MethodsMulti-Chain Amino Acid Coupling

The study introduces ProteinMPNN, a deep learningbased protein sequence design method, achieving 52.4% sequence recovery, outperforming Rosetta's 32.9%. It can handle amino acid coupling in single and multichains, successfully rescuing previously failed protein designs, showcas

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Generative Sparse-View Gaussian Splatting

Published:6/10/2025

Generative Sparse-View Gaussian Splatting3D/4D Scene ReconstructionImage Diffusion ModelsView Consistency EnhancementGenerative Models for Limited Observations

The paper presents Generative Sparseview Gaussian Splatting (GSGS) to enhance 3D/4D scene reconstruction under limited observations, leveraging pretrained image diffusion models to improve view consistency and rendering quality, outperforming existing stateoftheart methods.

Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach

Published:5/25/2025

Personalized Safety in Large Language ModelsPENGUIN BenchmarkUser-Context-Based Safety EnhancementRAISE Agent FrameworkSafety Evaluation Methods

This paper introduces personalized safety and presents the PENGUIN benchmark, showing a 43.2% safety score improvement from personalized user information. It also develops the RAISE framework, enhancing safety scores by 31.6% without the need for model retraining, highlighting th

探究老年糖尿病夜间低血糖的预防及护理

Elderly Diabetes CareHypoglycemia Prevention MeasuresNighttime Hypoglycemia ManagementDiabetes Clinical Treatment

The paper explores the causes and nursing measures for nocturnal hypoglycemia in elderly diabetics, highlighting their diminished awareness. It presents six causes and five preventive strategies to guide clinical practitioners.

Aligning LLMs with Individual Preferences via Interaction

Published:10/4/2024

LLM Personalized AlignmentMulti-Turn Preference LearningPersonalized User Behavior ModelingPersonalized Preference DatasetInteraction-Based LLM Fine-Tuning

This study introduces a method for aligning large language models (LLMs) with individual preferences through multiturn dialogues, using a diverse persona pool and a multiturn conversation dataset. The approach enhances model adaptability via supervised finetuning and reinforce

老年糖尿病患者夜间低血糖的识别与护理防范措施

Management of Nocturnal Hypoglycemia in Elderly Diabetic PatientHypoglycemia Identification and Care StrategiesBlood Glucose Monitoring and Individualized TreatmentNursing Interventions for Elderly Patients

This paper explores the characteristics and identification methods of nocturnal hypoglycemia in elderly diabetics. It finds that comprehensive nursing strategies can significantly reduce risks, highlighting the importance of a systematic approach to enhance patient quality of lif

Dynamic Discounted Counterfactual Regret Minimization (CFR) is a family of iterative algorithms showing promising results in solving imperfect-information games. Recent novel CFR variants (e.g., CFR+, DCFR) have significantly improved the convergence rate of the vanilla CFR. The key to these CFR variants’ performance is weighting each iteration non-uniformly, i.e., discounting earlier iterations. However, these algorithms use a fixed, manually-specified scheme to weight each iteration, which eno

Dynamic Discounted Counterfactual Regret MinimizationPolicy Optimization in Markov Decision ProcessesSolving Imperfect-Information GamesImproving Convergence of Iterative AlgorithmsDynamic Weight Learning

This paper introduces Dynamic Discounted CFR (DDCFR), the first framework to dynamically learn discounting for iterations. By formalizing the CFR process as a Markov decision process, DDCFR achieves faster convergence and high performance, significantly improving efficiency in so

STUDY OF HYPOGLYCEMIA IN ELDERLY DIABETES MELLITUS PATIENTS

Published:2/5/2022

Study of Hypoglycemia in Elderly Diabetes Mellitus PatientsDiabetes ManagementClinical PharmacologyHypoglycemic SymptomsUse of Sulphonylureas

This study examines hypoglycemia in elderly diabetic patients at Raja Muthiah Medical College, finding Glimipride as the most common sulphonylurea used, with over 30% of patients asymptomatic during hypoglycemic episodes, highlighting monitoring challenges.

EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models

Published:10/26/2025

Empathetic Dialogue Evaluation BenchmarkEvaluation of Speech Language Model CapabilitiesSpoken Content Understanding and Non-Lexical CuesMulti-Level Dialogue Capability AssessmentEmotionally Intelligent Dialogue Systems

The paper introduces EchoMind, a multilevel benchmark to evaluate empathetic Speech Language Models (SLMs), integrating spoken content understanding, vocalcue perception, reasoning, and response generation. Findings indicate significant weaknesses in advanced models' empathic r

CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models

Published:12/16/2024

Streaming Speech SynthesisApplication of Large Language ModelsMultilingual DatasetOptimization of Speech Generation ModelsProgressive Semantic Decoding

CosyVoice 2 is an enhanced streaming speech synthesis model that optimizes token utilization with finitescalar quantization, simplifies the LM architecture using pretrained large language models, and employs chunkaware causal flow matching for humanlevel naturalness and virtu

5G Vehicle-to-Everything Services: Gearing Up for Security and Privacy

Published:11/13/2019

5G V2X Security and PrivacyVehicle-to-Everything Communication5G Network Services and ApplicationsVehicle Sensor NetworksLow Latency Communication

The paper provides a comprehensive review of security and privacy issues in 5G V2X services, analyzing architecture, use cases, and potential trust threats, while exploring recent protection strategies and highlighting future research directions to advance this field.

A Component Architecture for the Internet of Things

Published:9/1/2018

Component Architecture for Internet of ThingsConcurrent Time-Stamped Discrete-Event SemanticsAsynchronous Atomic Callbacks (AAC)Secure Swarm Toolkit (SST)Proxies and Services Interaction

This paper presents a componentbased architecture for IoT, featuring proxies called "accessors" that interact under concurrent, timestamped discreteevent semantics, leveraging asynchronous atomic callbacks (AAC) and the actor model to enhance efficiency and security.

UniMMVSR: A Unified Multi-Modal Framework for Cascaded Video Super-Resolution

Published:10/9/2025

Cascaded Video Super-ResolutionMultimodal Video GenerationLatent Video Diffusion ModelCondition Injection StrategiesMultimodal Condition Utilization

The paper introduces UniMMVSR, a unified framework for video superresolution that handles multiple input modalities. It explores condition injection strategies and demonstrates superior performance in detail and conformity to multimodal conditions, enabling 4K video generation.

iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides

Published:3/28/2020

Bitter Peptide Prediction ModelScoring Card MethodAmino Acid Propensity ScoringDietary Drug DevelopmentComparison of Machine Learning Classifiers

iBitterSCM is a novel computational model that predicts bitter peptides based on amino acid sequences using the scoring card method with propensity scores. It achieved 84.38% accuracy on independent datasets, outperforming other classifiers and serving as a significant tool for

Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension

Published:11/26/2024

Hotspot-Driven Peptide DesignAutoregressive Generation ModelsPeptide Drug DevelopmentFragment-Based Peptide GenerationEnergy Density Model

PepHAR is introduced as a hotspotdriven autoregressive model for peptide design, focusing on residues with higher interaction potential. It effectively combines fragment extension and optimization to generate peptides with correct geometries, advancing peptide drug development.

From prediction to design: Revealing the mechanisms of umami peptides using interpretable deep learning, quantum chemical simulations, and module substitution

Interpretable Deep LearningModule Substitution StrategyUmami Peptide DesignQuantum Chemical SimulationsVirtual Hydrolysis

This study uses interpretable deep learning and module substitution to efficiently screen and design umami peptides, achieving 0.94 accuracy. It identifies various umami peptides, explores module substitution mechanisms, and highlights essential amino acids for taste enhancement.

Mip-Splatting: Alias-free 3D Gaussian Slatting

3D Gaussian SplattingHigh-Frequency Artifact EliminationMip Filtering3D Smoothing Filter

The paper introduces a 3D smoothing filter that eliminates artifacts in 3D Gaussian Splatting by constraining the size of 3D Gaussian primitives based on sampling frequency, and effectively mitigates aliasing issues using a 2D Mip filter.

Mip-Splatting: Alias-free 3D Gaussian SRecently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. However, strong artifacts can be observed when changing the sampling rate, e.g., by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter. To address this problem, we introduce a 3D smoothing filter which

3D Gaussian Splatting RenderingAlias-Free 3D Smoothing FilterNovel View SynthesisHigh-Frequency Artifact Elimination3D Frequency Constraints

The paper introduces a 3D smoothing filter to eliminate highfrequency artifacts in novel view synthesis by constraining the size of 3D Gaussian primitives. Replacing the 2D dilation filter with a 2D Mip filter effectively mitigates aliasing issues.

Inference Performance of Large Language Models on a 64-core RISC-V CPU with Silicon-Enabled Vectors

LLM Reasoning Capacity EnhancementRISC-V Based Hardware OptimizationSilicon-Enabled Vector ComputingEnergy-Efficient Computing ArchitecturesMatrix Multiplication Performance Benchmark

This study evaluates LLM inference performance on a 64core RISCV CPU with SiliconEnabled Vectors, revealing significant throughput and energy efficiency improvements, particularly for smaller models. It offers practical insights for deploying LLMs on future heterogeneous compu

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