Bi-Directional Generation for Unsupervised Domain Adaptation

Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. Specifically, two cross-domain generators are employed to synthesize one domain conditioned on the other. The performance of our proposed method can be further enhanced by the consistent classifiers and the cross-domain alignment constraints. We also design two classifiers which are jointly optimized to maximize the consistency on target sample prediction. Extensive experiments verify that our proposed model outperforms the state-of-the-art on standard cross domain visual benchmarks.Comment: 9 pages, 4 figure

UniM2^2AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving

Masked Autoencoders (MAE) play a pivotal role in learning potent representations, delivering outstanding results across various 3D perception tasks essential for autonomous driving. In real-world driving scenarios, it's commonplace to deploy multiple sensors for comprehensive environment perception. While integrating multi-modal features from these sensors can produce rich and powerful features, there is a noticeable gap in MAE methods addressing this integration. This research delves into multi-modal Masked Autoencoders tailored for a unified representation space in autonomous driving, aiming to pioneer a more efficient fusion of two distinct modalities. To intricately marry the semantics inherent in images with the geometric intricacies of LiDAR point clouds, the UniM$^2$AE is proposed. This model stands as a potent yet straightforward, multi-modal self-supervised pre-training framework, mainly consisting of two designs. First, it projects the features from both modalities into a cohesive 3D volume space, ingeniously expanded from the bird's eye view (BEV) to include the height dimension. The extension makes it possible to back-project the informative features, obtained by fusing features from both modalities, into their native modalities to reconstruct the multiple masked inputs. Second, the Multi-modal 3D Interactive Module (MMIM) is invoked to facilitate the efficient inter-modal interaction during the interaction process. Extensive experiments conducted on the nuScenes Dataset attest to the efficacy of UniM$^2$AE, indicating enhancements in 3D object detection and BEV map segmentation by 1.2\%(NDS) and 6.5\% (mIoU), respectively. Code is available at https://github.com/hollow-503/UniM2AE.Comment: Code available at https://github.com/hollow-503/UniM2A

Beam energy distribution influences on density modulation efficiency in seeded free-electron lasers

The beam energy spread at the entrance of undulator system is of paramount importance for efficient density modulation in high-gain seeded free-electron lasers (FELs). In this paper, the dependences of high harmonic micro-bunching in the high-gain harmonic generation (HGHG), echo-enabled harmonic generation (EEHG) and phase-merging enhanced harmonic generation (PEHG) schemes on the electron energy spread distribution are studied. Theoretical investigations and multi-dimensional numerical simulations are applied to the cases of uniform and saddle beam energy distributions and compared to a traditional Gaussian distribution. It shows that the uniform and saddle electron energy distributions significantly enhance the performance of HGHG-FELs, while they almost have no influence on EEHG and PEHG schemes. A numerical example demonstrates that, with about 84keV RMS uniform and/or saddle slice energy spread, the 30th harmonic radiation can be directly generated by a single-stage seeding scheme for a soft x-ray FEL facility

FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models

Few-shot class-incremental learning (FSCIL) aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data. While the Contrastive Vision-Language Pre-Training (CLIP) model has been effective in addressing 2D few/zero-shot learning tasks, its direct application to 3D FSCIL faces limitations. These limitations arise from feature space misalignment and significant noise in real-world scanned 3D data. To address these challenges, we introduce two novel components: the Redundant Feature Eliminator (RFE) and the Spatial Noise Compensator (SNC). RFE aligns the feature spaces of input point clouds and their embeddings by performing a unique dimensionality reduction on the feature space of pre-trained models (PTMs), effectively eliminating redundant information without compromising semantic integrity. On the other hand, SNC is a graph-based 3D model designed to capture robust geometric information within point clouds, thereby augmenting the knowledge lost due to projection, particularly when processing real-world scanned data. Considering the imbalance in existing 3D datasets, we also propose new evaluation metrics that offer a more nuanced assessment of a 3D FSCIL model. Traditional accuracy metrics are proved to be biased; thus, our metrics focus on the model's proficiency in learning new classes while maintaining the balance between old and new classes. Experimental results on both established 3D FSCIL benchmarks and our dataset demonstrate that our approach significantly outperforms existing state-of-the-art methods

A comprehensive city-level final energy consumption dataset including renewable energy for China, 2005–2021

The role of China is increasingly pivotal in climate change mitigation, and the formulation of energy conservation and emission reduction policies requires city-level information. The effectiveness of national policy implementation is contingent upon the support and involvement of local governments. Accurate data on final energy consumption is vital to formulate and implement city-level energy transitions and energy conservation and emission reduction policies. However, there is a dearth of data sources pertaining to China’s city-level final energy consumption. To address these gaps, we developed computational modeling techniques along with top-down and downscaling methods to estimate China’s city-level final energy consumption. In this way, we compiled a final energy consumption inventory for 331 Chinese cities from 2005 to 2021, covering seven economic sectors, 30 fossil fuels, and four clean power sources. Moreover, we discussed the validity of the estimation results from multiple perspectives to enhance estimation accuracy. This dataset can be utilized for analysis in various cutting-edge research fields such as energy transition dynamics, transition risk management strategies, and policy formulation processes

A comprehensive city-level final energy consumption dataset including renewable energy for China, 2005–2021

The role of China is increasingly pivotal in climate change mitigation, and the formulation of energy conservation and emission reduction policies requires city-level information. The effectiveness of national policy implementation is contingent upon the support and involvement of local governments. Accurate data on final energy consumption is vital to formulate and implement city-level energy transitions and energy conservation and emission reduction policies. However, there is a dearth of data sources pertaining to China’s city-level final energy consumption. To address these gaps, we developed computational modeling techniques along with top-down and downscaling methods to estimate China’s city-level final energy consumption. In this way, we compiled a final energy consumption inventory for 331 Chinese cities from 2005 to 2021, covering seven economic sectors, 30 fossil fuels, and four clean power sources. Moreover, we discussed the validity of the estimation results from multiple perspectives to enhance estimation accuracy. This dataset can be utilized for analysis in various cutting-edge research fields such as energy transition dynamics, transition risk management strategies, and policy formulation processes

Comprehensive mendelian randomization reveals atrial fibrillation-breast cancer relationship and explores common druggable targets

BackgroundAtrial fibrillation (AF) and breast cancer pose significant risks to human health. The reasons behind the concurrent occurrence of AF and breast cancer remain unclear, leading to complex treatment approaches. Mendelian Randomization (MR) analyses aim to offer genetic evidence supporting the causation of AF and breast cancer and to investigate common druggable genes associated with both conditions.MethodsWe used two-samples of MR to sequentially explore the causal relationship between atrial fibrillation and breast cancer, and between atrial fibrillation and breast cancer therapeutic drugs, and verified the stability of the results through colocalization analysis. We utilized the Connectivity map database to infer the direction of drug effects on disease. Finally, we explored druggable genes that play a role in AF and breast cancer and performed a Phenome-wide MR analysis to analyze the potential side effects of drug targets.ResultsWe found 15 breast cancer therapeutic drugs that significantly support a causal association between AF and breast cancer through expression in blood and/or atrial appendage tissue. Among these, activation of ANXA5 by Docetaxel, inhibition of EIF5A by Fulvestrant, and inhibition of GNA12 by Tamoxifen increased the risk of AF, while inhibition of ANXA5 by Gemcitabine and Vinorebine and inhibition of PCGF6 by Paclitaxel reduced the risk of AF. Inhibition of MSH6 and SF3B1 by Cyclophosphamide, as well as inhibition of SMAD4 and PSMD2 and activation of ASAH1 and MLST8 by Doxorubicin can have bidirectional effects on AF occurrence. XBP1 can be used as a common druggable gene for AF and breast cancer, and there are no potential side effects of treatment against this target.ConclusionThis study did not find a direct disease causality between AF and breast cancer but identified 40 target genes for 15 breast cancer therapeutic drugs associated with AF, clarified the direction of action of 8 breast cancer therapeutic drugs on AF, and finally identified one common druggable target for AF and breast cancer