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Found 48 papers, 16 papers with code
MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance
To improve the efficiency of video representation, we propose Meta Neural Representations for Videos, named MetaNeRV, a novel framework for fast NeRV representation for unseen videos.
Automatically Planning Optimal Parallel Strategy for Large Language Models
The number of parameters in large-scale language models based on transformers is gradually increasing, and the scale of computing clusters is also growing.
Less is More: Towards Green Code Large Language Models via Unified Structural Pruning
The extensive application of Large Language Models (LLMs) in generative coding tasks has raised concerns due to their high computational demands and energy consumption.
Populating cellular metamaterials on the extrema of attainable elasticity through neuroevolution
The trade-offs between different mechanical properties of materials pose fundamental challenges in engineering material design, such as balancing stiffness versus toughness, weight versus energy-absorbing capacity, and among the various elastic coefficients.
Unseen Horizons: Unveiling the Real Capability of LLM Code Generation Beyond the Familiar
We use official test suites of these projects to evaluate the generated code.
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Specifically, we adopt INR to parameterize time series data as a continuous function and employ a transformer-based architecture to predict the INR of given data.
KA$^2$ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation
Then, the input data for all challenging tasks are encoded in the foundation model and the expert models, respectively, and their backbone features are jointly projected into the adaptive amalgamation layer.
NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction
We contend that the key to addressing these challenges lies in accurately decoding both high-level semantics and low-level perception flows, as perceived by the brain in response to video stimuli.
Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes
Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG).
Attention Beats Linear for Fast Implicit Neural Representation Generation
Unlike gradient-based methods, which exhibit lower efficiency in inference, the adoption of hyper-network for generating parameters in Multi-Layer Perceptrons (MLP), responsible for executing INR functions, has surfaced as a promising and efficient alternative.
Floating Anchor Diffusion Model for Multi-motif Scaffolding
Motif scaffolding seeks to design scaffold structures for constructing proteins with functions derived from the desired motif, which is crucial for the design of vaccines and enzymes.
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
Additionally, we introduce a token merging method guided by comprehensive semantics from the frequency and spatial domains.
Real-Time Go-Around Prediction: A case study of JFK airport
In this paper, we employ the long-short-term memory model (LSTM) to predict the real-time go-around probability as an arrival flight is approaching JFK airport and within 10 nm of the landing runway threshold.
Excess Delay from GDP: Measurement and Causal Analysis
Ground Delay Programs (GDPs) have been widely used to resolve excessive demand-capacity imbalances at arrival airports by shifting foreseen airborne delay to pre-departure ground delay.
Airport Delay Prediction with Temporal Fusion Transformers
Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research.
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments
This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports.
MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks.
Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
In real-world scenarios, multimodal federated learning often faces the practical challenge of intricate modality missing, which poses constraints on building federated frameworks and significantly degrades model inference accuracy.
Lite-Mind: Towards Efficient and Robust Brain Representation Network
The limited data availability and the low signal-to-noise ratio of fMRI signals lead to the challenging task of fMRI-to-image retrieval.
Image change detection with only a few samples
This paper considers image change detection with only a small number of samples, which is a significant problem in terms of a few annotations available.
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs.
Unified Matrix Factorization with Dynamic Multi-view Clustering
In typical recommendation scenarios, the user-item interaction paradigm is usually a two-stage process and requires static clustering analysis of the obtained user and item representations.
Contrastive Knowledge Amalgamation for Unsupervised Image Classification
Current methods focus on coarsely aligning teachers and students in the common representation space, making it difficult for the student to learn the proper decision boundaries from a set of heterogeneous teachers.
S2SNet: A Pretrained Neural Network for Superconductivity Discovery
To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures.
$E(2)$-Equivariant Vision Transformer
To address this issue, we design a Group Equivariant Vision Transformer (GE-ViT) via a novel, effective positional encoding operator.
Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning
Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is a trending yet challenging and computationally expensive task in the real world.
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
User Modeling plays an essential role in industry.
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling
However, existing researches have little ability to model universal user representation based on lifelong behavior sequences since user registration.
Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies
Classification sub-module supplies classifying of images according to the eras, nationalities and garment types; Parsing sub-network supplies the semantic for person contours, clothing and background in the image to achieve more accurate colorization of clothes and persons and prevent color overflow.
ChemiRise: a data-driven retrosynthesis engine
We have developed an end-to-end, retrosynthesis system, named ChemiRise, that can propose complete retrosynthesis routes for organic compounds rapidly and reliably.
Semantic Segmentation by Improved Generative Adversarial Networks
Besides, the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth.
Molecular modeling with machine-learned universal potential functions
Molecular modeling is an important topic in drug discovery.
Machine-Learned Phase Diagrams of Generalized Kitaev Honeycomb Magnets
We use a recently developed interpretable and unsupervised machine-learning method, the tensorial kernel support vector machine (TK-SVM), to investigate the low-temperature classical phase diagram of a generalized Heisenberg-Kitaev-$\Gamma$ ($J$-$K$-$\Gamma$) model on a honeycomb lattice.
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information
Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity.
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids
Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases.
The Regularization of Small Sub-Constraint Satisfaction Problems
This paper describes a new approach on optimization of constraint satisfaction problems (CSPs) by means of substituting sub-CSPs with locally consistent regular membership constraints.
Exploring Properties of Icosoku by Constraint Satisfaction Approach
Icosoku is a challenging and interesting puzzle that exhibits highly symmetrical and combinatorial nature.
Identification of hidden order and emergent constraints in frustrated magnets using tensorial kernel methods
Machine-learning techniques have proved successful in identifying ordered phases of matter.
Strongly Correlated Electrons Statistical Mechanics
Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state
We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.
High Energy Physics - Experiment
Learning multiple order parameters with interpretable machines
Furthermore, we discuss an intrinsic parameter of SVM, the bias, which allows for a special interpretation in the classification of phases, and its utility in diagnosing the existence of phase transitions.
Statistical Mechanics Strongly Correlated Electrons Computational Physics
Probing hidden spin order with interpretable machine learning
The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism.
Chemi-net: a graph convolutional network for accurate drug property prediction
Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery.
Prediction of amino acid side chain conformation using a deep neural network
A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy.
Adaptive Mesh Representation and Restoration of Biomedical Images
The triangulation of images has become an active research area in recent years for its compressive representation and ease of image processing and visualization.
