Search Results for author:
Found 123 papers, 55 papers with code
Semantic Sentence Matching via Interacting Syntax Graphs
In this paper, we formalize the task of semantic sentence matching as a problem of graph matching in which each sentence is represented as a directed graph according to its syntactic structures.
Optimal Partial Transport Based Sentence Selection for Long-form Document Matching
Enjoying the partial transport properties of OPT, the selected key sentences can not only effectively enhance the matching accuracy, but also be explained as the rationales for the matching results.
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis
To facilitate research in this new task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE, and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets.
AI-Generated Images Introduce Invisible Relevance Bias to Text-Image Retrieval
This invisible relevance bias is prevalent across retrieval models with varying training data and architectures.
MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images
Cone Beam Computed Tomography (CBCT) plays a key role in dental diagnosis and surgery.
4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters
The illumination of improperly exposed photographs has been widely corrected using deep convolutional neural networks or Transformers.
Do LLMs Implicitly Exhibit User Discrimination in Recommendation? An Empirical Study
Recently, Large Language Models (LLMs) have enhanced user interaction, enabling seamless information retrieval and recommendations.
LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts
Surprisingly, our findings indicate that neural retrieval models tend to rank LLM-generated documents higher. We refer to this category of biases in neural retrieval models towards the LLM-generated text as the \textbf{source bias}.
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution
The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by simultaneously performing video frame interpolation (VFI) and video super-resolution (VSR).
Space-time Video Super-resolution
Video Frame Interpolation
+1
Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning
To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning.
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
In the video recommendation, watch time is commonly adopted as an indicator of user interest.
HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation
In real-world streaming recommender systems, user preferences often dynamically change over time (e. g., a user may have different preferences during weekdays and weekends).
LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback.
4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network
4D scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
Lightweight Improved Residual Network for Efficient Inverse Tone Mapping
But the majority of media images on the internet remain in 8-bit standard dynamic range (SDR) format.
KuaiSAR: A Unified Search And Recommendation Dataset
We believe this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the S&R services in practical, real-world applications.
Controllable Multi-Objective Re-ranking with Policy Hypernetworks
In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights.
When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm
We argue that these models present significant opportunities for reliable user simulation, and have the potential to revolutionize traditional study paradigms in user behavior analysis.
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors.
Uncovering ChatGPT's Capabilities in Recommender Systems
The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond.
A Dynamic Multi-Scale Voxel Flow Network for Video Prediction
The performance of video prediction has been greatly boosted by advanced deep neural networks.
Ranked #1 on
Video Prediction
on Cityscapes
Dually Enhanced Propensity Score Estimation in Sequential Recommendation
According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data.
P-MMF: Provider Max-min Fairness Re-ranking in Recommender System
In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem.
Lattice piecewise affine approximation of explicit nonlinear model predictive control with application to trajectory tracking of mobile robot
Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC.
PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation
Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy.
Law Article-Enhanced Legal Case Matching: a Causal Learning Approach
We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models.
A Brief History of Recommender Systems
Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry.
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems.
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark
Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images.
Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.
Dynamic Message Propagation Network for RGB-D Salient Object Detection
This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects.
Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks
Existing studies on person-job fit, however, mainly focus on calculating the similarity between the candidate resumes and the job postings on the basis of their contents, without taking the recruiters' experience (i. e., historical successful recruitment records) into consideration.
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch.
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals
For example, users have determined the departure, the destination, and the travel time for booking a flight.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Reinforcement Re-ranking with 2D Grid-based Recommendation Panels
Then, it defines \emph{the MDP discrete time steps as the ranks in the initial ranking list, and the actions as the prediction of the user-item preference and the selection of the slots}.
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
An extension to the pairwise neural ranking is also developed.
FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation
For image exposure enhancement, the tasks of Single-Exposure Correction (SEC) and Multi-Exposure Fusion (MEF) are widely studied in the image processing community.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results.
Complexity-Oriented Per-shot Video Coding Optimization
Current per-shot encoding schemes aim to improve the compression efficiency by shot-level optimization.
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis
Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other.
A Multi-user Oriented Live Free-viewpoint Video Streaming System Based On View Interpolation
As an important application form of immersive multimedia services, free-viewpoint video(FVV) enables users with great immersive experience by strong interaction.
Error-free approximation of explicit linear MPC through lattice piecewise affine expression
The performance of the proposed approximation strategy is tested through two simulation examples, and the result shows that with a moderate number of sample points, we can construct lattice PWA approximations that are equivalent to optimal control law of the explicit linear MPC.
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm
Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.
Discovering Dialog Structure Graph for Coherent Dialog Generation
Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.
NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement
Then a minimum fusion strategy on the results of these two transforms is utilized to achieve more natural illumination component enhancement.
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer.
Ranked #5 on
Discourse Parsing
on STAC
MetaKernel: Learning Variational Random Features with Limited Labels
Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting.
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos.
Privacy-preserving Channel Estimation in Cell-free Hybrid Massive MIMO Systems
We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with $K$ users and $M$ access points (APs), each with $N_a$ antennas and $N_r< N_a$ radio frequency (RF) chains.
Low-Rank Matrix Completion
Information Theory
Signal Processing
Information Theory
Discovering Dialog Structure Graph for Open-Domain Dialog Generation
Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation.
MobileSal: Extremely Efficient RGB-D Salient Object Detection
Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD.
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection
Intra-saliency and inter-saliency cues have been extensively studied for co-saliency detection (Co-SOD).
Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains
Four popular text matching methods have been exploited in the paper.
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval
Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.
Pre-training for Video Captioning Challenge 2020 Summary
The Pre-training for Video Captioning Challenge 2020 Summary: results and challenge participants' technical reports.
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient
Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency.
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift.
Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training
In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding.
Interactive Knowledge Distillation
Knowledge distillation is a standard teacher-student learning framework to train a light-weight student network under the guidance of a well-trained large teacher network.
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation
To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog.
Learning to Learn Kernels with Variational Random Features
We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable.
Bilateral Attention Network for RGB-D Salient Object Detection
To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task.
Ranked #3 on
RGB-D Salient Object Detection
on RGBD135
Gradient-Induced Co-Saliency Detection
Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images.
Ranked #6 on
Co-Salient Object Detection
on CoSOD3k
Conditional Variational Image Deraining
In this paper, we propose a Conditional Variational Image Deraining (CVID) network for better deraining performance, leveraging the exclusive generative ability of Conditional Variational Auto-Encoder (CVAE) on providing diverse predictions for the rainy image.
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity.
Deep Hough Transform for Semantic Line Detection
In addition to the proposed method, we design an evaluation metric to assess the quality of line detection and construct a large scale dataset for the line detection task.
Ranked #2 on
Line Detection
on NKL
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.
Non-negative Sparse and Collaborative Representation for Pattern Classification
In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification.
RANet: Ranking Attention Network for Fast Video Object Segmentation
Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning
Then the proposed HorNet can learn the visual and language representation from both the images and captions jointly, and thus enhance the performance of person re-ID.
Semi-Supervised Self-Growing Generative Adversarial Networks for Image Recognition
There are two main problems in label inference: how to measure the confidence of the unlabeled data and how to generalize the classifier.
Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile
Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications.
Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
Synthesis planning is the process of recursively decomposing target molecules into available precursors.
SAVIOR: Towards Bug-Driven Hybrid Testing
Unlike the existing hybrid testing tools, SAVIOR prioritizes the concolic execution of the seeds that are likely to uncover more vulnerabilities.
Software Engineering
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance.
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images.
STAR: A Structure and Texture Aware Retinex Model
A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.
Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection
In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses.
PTrix: Efficient Hardware-Assisted Fuzzing for COTS Binary
This poor performance is caused by the slow extraction of code coverage information from highly compressed PT traces.
Software Engineering Cryptography and Security
Efficient hinging hyperplanes neural network and its application in nonlinear system identification
It is proved that for every EHH neural network, there is an equivalent adaptive hinging hyperplanes (AHH) tree, which was also proposed based on the model of HH and find good applications in system identification.
swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor
The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability.
Pre-training of Context-aware Item Representation for Next Basket Recommendation
In the offline phase, IERT pre-trains deep item representations conditioning on their transaction contexts.
HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering
However, the performances of such models are not so good as that in the RC task.
Multivariate Time Series Imputation with Generative Adversarial Networks
Multivariate time series usually contain a large number of missing values, which hinders the application of advanced analysis methods on multivariate time series data.
SeriesNet:A Generative Time Series Forecasting Model
This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals.
Scaled Simplex Representation for Subspace Clustering
However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations.
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising
Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN).
Ranked #3 on
Denoising
on Darmstadt Noise Dataset
Learning to Control the Specificity in Neural Response Generation
In conversation, a general response (e. g., {``}I don{'}t know{''}) could correspond to a large variety of input utterances.
Tailored Sequence to Sequence Models to Different Conversation Scenarios
In this paper, we propose two tailored optimization criteria for Seq2Seq to different conversation scenarios, i. e., the maximum generated likelihood for specific-requirement scenario, and the conditional value-at-risk for diverse-requirement scenario.
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization.
A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping
State-of-the-art tone mapping algorithms mostly decompose an image into a base layer and a detail layer, and process them accordingly.
A PID Controller Approach for Stochastic Optimization of Deep Networks
We first reveal the intrinsic connections between SGD-Momentum and PID based controller, then present the optimization algorithm which exploits the past, current, and change of gradients to update the network parameters.
Modeling Diverse Relevance Patterns in Ad-hoc Retrieval
The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document.
A Tree Search Algorithm for Sequence Labeling
Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word.
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server
One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchange the model gradients between the workers and servers during the training iterations.
Real-world Noisy Image Denoising: A New Benchmark
In order to promote the study on this problem while implementing the concurrent real-world image denoising datasets, we construct a new benchmark dataset which contains comprehensive real-world noisy images of different natural scenes.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
Locally Smoothed Neural Networks
The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields.
DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval
This paper concerns a deep learning approach to relevance ranking in information retrieval (IR).
A Deep Investigation of Deep IR Models
Therefore, it is necessary to identify the difference between automatically learned features by deep IR models and hand-crafted features used in traditional learning to rank approaches.
Spherical Paragraph Model
Representing texts as fixed-length vectors is central to many language processing tasks.
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising
Most of the existing denoising algorithms are developed for grayscale images, while it is not a trivial work to extend them for color image denoising because the noise statistics in R, G, B channels can be very different for real noisy images.
Ranked #5 on
Denoising
on Darmstadt Noise Dataset
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising
We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real-world noisy image denoising.
A Unified Architecture for Semantic Role Labeling and Relation Classification
This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence.
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle
To achieve the information efficiently, we propose a path planning approach for the UAV based on a Markov decision process (MDP) model.
A Study of MatchPyramid Models on Ad-hoc Retrieval
Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it.
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
In this paper we present MSR-VTT (standing for "ABC-Video to Text") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text.
Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i. e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position.
Semantic Regularities in Document Representations
Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language.
Text Matching as Image Recognition
An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising
PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior.
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.
