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Found 37 papers, 11 papers with code
Pulsed polarisation for robust DNP
We derive sequences theoretically and demonstrate experimentally that they are capable of efficient polarisation transfer from an optically polarised nitrogen-vacancy centre in diamond to the surrounding $^{13}$C nuclear spin bath even in the presence of control errors, making it an ideal tool for the realisation of the above NV centre based applications.
Quantum Physics
Image Captioning based on Deep Reinforcement Learning
However, in this paper, we propose a novel architecture for image captioning with deep reinforcement learning to optimize image captioning tasks.
Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images.
AIBench: An Industry Standard Internet Service AI Benchmark Suite
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification
Forensic audio analysis for speaker verification offers unique challenges due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings.
Spin-wave focusing induced skyrmion generation
We propose a new method to generate magnetic skyrmions through spin-wave focusing in chiral ferromagnets. A lens is constructed to focus spin waves by a curved interface between two ferromagnetic thin films with different perpendicular magnetic anisotropies.
Mesoscale and Nanoscale Physics
Geometric Electrostatic Particle-In-Cell Algorithm on Unstructured Meshes
We compare the energy conservation property of the geometric PIC algorithm derived from the discrete variational principle with that of previous PIC methods on unstructured meshes.
Plasma Physics
Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning
We propose a novel framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which replaces the traditional random sampling method with a teacher policy model to realize the dialogue policy for automatic curriculum learning.
Magnonic frequency comb through nonlinear magnon-skyrmion scattering
An optical frequency comb consists of a set of discrete and equally spaced frequencies and has found wide applications in the synthesis over broad spectral frequencies of electromagnetic wave and precise optical frequency metrology.
Mesoscale and Nanoscale Physics Optics
Three-dimensional charge density wave and robust zero-bias conductance peak inside the superconducting vortex core of a kagome superconductor CsV$_3$Sb$_5$
The transition-metal-based kagome metals provide a versatile platform for correlated topological phases hosting various electronic instabilities.
Superconductivity Strongly Correlated Electrons
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models.
Consensus-Based Decentralized Energy Trading for Distributed Energy Resources
In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy.
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
To combat the noisy labeling, we propose noise-resistant semi-supervised learning by quantifying the region uncertainty.
Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
Depth estimation is solved as a regression or classification problem in existing learning-based multi-view stereo methods.
Ranked #9 on
3D Reconstruction
on DTU
Impact of Naturalistic Field Acoustic Environments on Forensic Text-independent Speaker Verification System
Audio analysis for forensic speaker verification offers unique challenges in system performance due in part to data collected in naturalistic field acoustic environments where location/scenario uncertainty is common in the forensic data collection process.
Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?
We construct a framework for semi-supervised instance segmentation by assigning pixel-level pseudo labels.
A collaborative decomposition-based evolutionary algorithm integrating normal and penalty-based boundary intersection for many-objective optimization
On the other hand, the parallel reference lines of the parallel decomposition methods including the normal boundary intersection (NBI) might result in poor diversity because of under-sampling near the boundaries for MaOPs with concave frontiers.
Hybrid Physical Metric For 6-DoF Grasp Pose Detection
6-DoF grasp pose detection of multi-grasp and multi-object is a challenge task in the field of intelligent robot.
VTC-LFC: Vision Transformer Compression with Low-Frequency Components
Although Vision transformers (ViTs) have recently dominated many vision tasks, deploying ViT models on resource-limited devices remains a challenging problem.
Multi-source Domain Adaptation for Text-independent Forensic Speaker Recognition
A comprehensive set of experiments are conducted to demonstrate that: 1) diverse acoustic environments do impact speaker recognition performance, which could advance research in audio forensics, 2) domain adversarial training learns the discriminative features which are also invariant to shifts between domains, 3) discrepancy-minimizing adaptation achieves effective performance simultaneously across multiple acoustic domains, and 4) moment-matching adaptation along with dynamic distribution alignment also significantly promotes speaker recognition performance on each domain, especially for the LENA-field domain with noise compared to all other systems.
Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning
Automatic speaker verification systems are vulnerable to a variety of access threats, prompting research into the formulation of effective spoofing detection systems to act as a gate to filter out such spoofing attacks.
BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap
Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes.
Detecting Everything in the Open World: Towards Universal Object Detection
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category.
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization
When a conversation enters a dead-end state, regardless of the actions taken afterward, it will continue in a dead-end trajectory until the agent reaches a termination state or maximum turn.
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems.
Distributionally Robust Machine Learning with Multi-source Data
Classical machine learning methods may lead to poor prediction performance when the target distribution differs from the source populations.
Uni3DETR: Unified 3D Detection Transformer
In this paper, we propose Uni3DETR, a unified 3D detector that addresses indoor and outdoor 3D detection within the same framework.
BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs with Multi-turn Health Conversations Polished by ChatGPT
Large language models (LLMs) have performed well in providing general and extensive health suggestions in single-turn conversations, exemplified by systems such as ChatGPT, ChatGLM, ChatDoctor, DoctorGLM, and etc.
SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations
Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT).
SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM
Our results show that, with SmoothQuant+, the Code Llama-34B model can be quantized and deployed on a A100 40GB GPU, achieving lossless accuracy and a throughput increase of 1. 9 to 4. 0 times compared to the FP16 model deployed on two A100 40GB GPUs.
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout.
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information.
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation
Specifically, we propose the cycle-modality propagation, aimed at propagating knowledge bridging 2D and 3D modalities, to support the aforementioned functionalities.
SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera
Since the spike camera relies on temporal integration instead of temporal differentiation used by event cameras, our proposed TfS loss maintains manageable training costs.
Efficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy
Most of the existing dialogue policy methods rely on a single learning system, while the human brain has two specialized learning and memory systems, supporting to find good solutions without requiring copious examples.
