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Found 16 papers, 5 papers with code
Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction
Based on our new StepEval-Audio-360 evaluation benchmark, Step-Audio achieves state-of-the-art performance in human evaluations, especially in terms of instruction following.
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model with 30B parameters and the ability to generate videos up to 204 frames in length.
Low carbon optimal scheduling of integrated energy system considering waste heat utilization under the coordinated operation of incineration power plant and P2G
In order to improve energy utilization and reduce carbon emissions, this paper presents a comprehensive energy system economic operation strategy of Incineration power plant Power-to-gas (P2G) with waste heat recovery.
General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following Control
However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies.
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
2) From the feature perspective, the simple Tail Interaction module implicitly enhances potential correlations among all samples from all source domains, facilitating the acquisition of domain-invariant representations across multiple domains for the model.
USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis
In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis.
Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation
Therefore, we propose a new automatic lung lobe segmentation framework, in which we urge the model to pay attention to the area around the pulmonary fissure during the training process, which is realized by a task-specific loss function.
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
However, the local operation of the convolution kernel makes the model focus too much on local representations (e. g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability.
Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals
In pulsar signal processing, two primary difficulties are (1) radio-frequency interference (RFI) mitigation and (2) information loss due to preprocessing and mitigation itself.
PP-MSVSR: Multi-Stage Video Super-Resolution
Different from the Single Image Super-Resolution(SISR) task, the key for Video Super-Resolution(VSR) task is to make full use of complementary information across frames to reconstruct the high-resolution sequence.
One-shot Weakly-Supervised Segmentation in Medical Images
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.
The first evidence for three-dimensional spin-velocity alignment in pulsars
Observations have shown a strong tendency for alignment of the pulsar space velocity and spin axis in young pulsars but, up to now, these comparisons have been restricted to two dimensions.
Astrophysics of Galaxies
Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games
In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking.
Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation
In this paper, we propose a fully-automated framework to segment left atrium in gadolinium-enhanced MR volumes.
A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions.
