Latent Embeddings for Collective Activity Recognition

Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments.Comment: 6pages, accepted by IEEE-AVSS201

Development of Functional Chemical Probes for the Study of Viscosity, Fe(II), and Ferroptosis and Photo-triggered Drug Delivery

Small molecule probes are useful tools for the study of biology. In particular, the dye derived fluorescence probes enable to spatiotemporally monitor the events of analytes of interest. The noninvasive feature is particularly attractive for the biological studies in live cells. The challenge is to develop chemical probes capable of detection of the analyte of interest with high specificity. Toward this end, my Ph. D. study centers on the development of novel chemical probes for the study and understanding of the alternation of important cellar contents and substances and their functions and relationship between normal and disease states. In the first effort, new far-red organelle-targeting probes have been developed. They display excellent selectivity and sensitivity in response to viscosity change in cell imaging studies. The probes are further applied for the investigation of the correlation of the elevated level of mitochondrial viscosity with mitochondrial damage in cellular and animal models. In the second effort, to investigate the relationship between Fe(II) and mitochondrial damage in ischemic stroke, a new mitochondrial targeting Fe(II) probe is designed, synthesized and evaluated by using a Fe(II) induced cleavage of N-O bond chemistry. The relationship between the elevated level of Fe(II) and ROS tied with the ischemia is uncovered by the probe using cellular and animal models. In the third study, based on the unique N-O bond chemistry, a novel photo-triggered ketone prodrug release system is designed and studied. The strategy can be used as a general approach to spatiotemporally deliver drugs. Lastly, the role of iron (Fe(II) and Fe(III)) in the ferroptosis process is elucidated. The studies reveal that that labile Fe(III) is the real ferroptosis inducer instead of labile Fe(II) for the first time. Furthermore, hydroxylamines as new ferroptosis inhibitors through the reduction of Fe(III) to Fe(II) are developed inspired by Fe(II) reduced specific cleavage of N-O bond chemistry

Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity

Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solutions can be connected by a linear path in the parameter space while maintaining near-constant training and test losses. In this work, we introduce a stronger notion of linear connectivity, Layerwise Linear Feature Connectivity (LLFC), which says that the feature maps of every layer in different trained networks are also linearly connected. We provide comprehensive empirical evidence for LLFC across a wide range of settings, demonstrating that whenever two trained networks satisfy LMC (via either spawning or permutation methods), they also satisfy LLFC in nearly all the layers. Furthermore, we delve deeper into the underlying factors contributing to LLFC, which reveal new insights into the spawning and permutation approaches. The study of LLFC transcends and advances our understanding of LMC by adopting a feature-learning perspective.Comment: 25 pages, 23 figure

Development genetic and stability classification of seasonal glacial lakes in a tectonically active area—A case study in Niangmuco, east margin of the Eastern Himalayan Syntaxis

The Niangmuco region on the east margin of the Eastern Himalayan Syntaxis features a large number of glacial lakes. The development process and stability classification of glacial lakes is of great significance to the study of seasonal glaciers in the eastern Himalayan margin, with implications for economic development and disaster prevention. Based on Landsat remote sensing image data from 2000 to 2021, this study analyzed the development and change characteristics of glacial lakes in the Niangmuco region during the past 21 years, and classified the stability of lakes with areas >0.02 km2 using the fuzzy consistent matrix method. In this area, 126 glacial lakes were identified within an elevation range of 3044–4156 m with a total area of 10.94 km2. These lakes primarily included glacial erosion lakes, valley lakes, tectonic lakes, and landslide dam lakes. Specifically, glacial erosion lakes accounted for 88.9% of the total number of lakes and 60.3% of the total lake area, followed by valley lakes with 6.3% and 23.7%, respectively. From 2000 to 2010, the total area of glacial lakes decreased from 10.53 km2 to 10.09 km2, which may be attributable to climate fluctuations. Subsequently, the area of lakes increased significantly to 10.94 km2 in 2021, an increase of 0.41 km2. Compared with 2000, among the lakes with a growth rate of 0.019 km2/a in 21 years, glacial erosion lakes exhibited the largest change. Among the classified glacial lakes in the study area, 95.7% were stable and relatively stable, mostly comprising glacial erosion lakes at high altitudes between 3468 and 4156 m. Only 4 unstable and extremely unstable glacial lakes were identified, and they were located near a fault zone. The findings show that the development and the change of glacial lakes in the area are primarily controlled by temperature and precipitation, and the topography and fault activity have important influences on the stability of glacial lakes

Skywork: A More Open Bilingual Foundation Model

In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves \emph{state of the art} performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs