Distribution Characteristics of Geo-hazards in a Reservoir Area, South Gansu Province, China

233-240In the process of water storage, due to water level fluctuations and base level erosion, reservoirs also play an important role in the occurrence of geological disasters. Taking a reservoir valley type in South Gansu Province, China as a case study, we investigated in depth the development and distribution of geological hazards and their influencing factors. The geological environment had changed considerably after reservoir impoundment with an increase in geological disasters. Furthermore, the main types of geological disasters were also analyzed systematically. Slope angle, altitude, slope aspect, proximity to earthquake faults, reservoir water storage, slope body structure, rock mass structure, and their combination features influenced the development and distribution of geological disasters in reservoir area. Close proximity to rivers also increases the likelihood of geological disasters. Landslides and collapses are closely related to the geo-hazards and their triggers include earthquakes, torrential rainfall, and fluctuations in reservoir water level. We also identified 2 types of debris which flow into the reservoir: gulch development and slope liquefaction

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization

Uplift modeling has shown very promising results in online marketing. However, most existing works are prone to the robustness challenge in some practical applications. In this paper, we first present a possible explanation for the above phenomenon. We verify that there is a feature sensitivity problem in online marketing using different real-world datasets, where the perturbation of some key features will seriously affect the performance of the uplift model and even cause the opposite trend. To solve the above problem, we propose a novel robustness-enhanced uplift modeling framework with adversarial feature desensitization (RUAD). Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our RUAD in online marketing. In addition, we also demonstrate the robustness of our RUAD to the feature sensitivity, as well as the compatibility with different uplift models

Relating the composition of continental margin surface sediments from the Ross Sea to the Amundsen Sea, West Antarctica, to modern environmental conditions

Investigating the multiple proxies involving productivity, organic geochemistry, and trace element (TE) enrichment in surface sediments could be used as paleoenvironment archives to gain insights into past and future environmental conditions changes. We present redox-sensitive TEs (Mn, Ni, Cu, U, P, Mo, Co, V, Zn, and Cd), productivity-related proxies (total organic carbon and opal), and total nitrogen and CaCO3 contents of bulk surface sediments of this area. The productivity proxies from the shelf and coastal regions of the Ross and the Amundsen seas showed that higher productivity was affiliated with an area of nutrient-rich deep water upwelling. The upwelling of weakly corrosive deep water may be beneficial for preserving CaCO3, while highly corrosive dense water, if it forms on the shelf near the coastal region (coastal polynya), could limit the preservation of CaCO3 in modern conditions. There were no oxic or anoxic conditions in the study area, as indicated by the enrichment factors of redox-sensitive TEs (Mn, Co, and U). The enrichment factor of Cd, which is redox-sensitive, indicated suboxic redox conditions in sediment environments because of high primary productivity and organic matter preservation/decomposition. The enrichment factors of other redox-sensitive TEs (P, Ni, Cu, V, and Zn) and the correlations between the element/Ti ratio with productivity and nutrient proxies indicated that the organic matter decomposed, and there was massive burial of phytoplankton biomass. There was variation in the enrichment, such that sediments were enriched in P, Mo, and Zn, but depleted in Ni, Cu, and V

A Survey on Large Language Model based Autonomous Agents

Autonomous agents have long been a prominent research topic in the academic community. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from the human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating autonomous agents based on LLMs. To harness the full potential of LLMs, researchers have devised diverse agent architectures tailored to different applications. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of autonomous agents from a holistic perspective. More specifically, our focus lies in the construction of LLM-based agents, for which we propose a unified framework that encompasses a majority of the previous work. Additionally, we provide a summary of the various applications of LLM-based AI agents in the domains of social science, natural science, and engineering. Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository for the related references at https://github.com/Paitesanshi/LLM-Agent-Survey.Comment: 32 pages, 3 figure

Characteristics and causes analysis of Nandianzi landslide in Lingtai County, Gansu Province

On October 3, 2021, a large landslide occurred at the loess-mudstone interface in Nandianzi, Lingtai, its successful early warning measures preventing casualties. In order to investigate the occurrence mechanism behind the Nandianzi landslide, a basic investigation was conducted, covering the topography, lithology, hydrogeological conditions, and human engineering activities related to the landslide. Based on the characteristics and differences of the crack development of the landslide, the landslide mass was divided into five blocks. The characteristics of each block were thoroughly analyzed through qualitative and quantitative analysis. The specific sliding situation of different parts of the landslide was analyzed, and further evidence was provided for the objective rationality of landslide classification and zoning, as well as the analysis of landslide mechanisms. Ultimately, it is concluded that the main causes of landslide disasters are as follows: (1) Large-scale excavation and earthwork activities at the lower and middle parts of the slope and the toe, leading to slope steepening and reduced resistance to sliding; (2) Formation of slope depressions, causing inadequate drainage and softening of the rock layer contact surface, thereby diminishing slope stability; and (3) Prolonged heavy rainfall that leads to instability and causes significant loss. While the Nandianzi landslide in Lintai county represents a successfully averted disaster, it serves as a noteworthy case study and a cautionary example for scientifically and standardizedly approaching urban construction and rural revitalization in China. This study holds significance value for monitoring, early warning,risk assessmen, and engineering treatment in comparable regions

When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm

User behavior analysis is crucial in human-centered AI applications. In this field, the collection of sufficient and high-quality user behavior data has always been a fundamental yet challenging problem. An intuitive idea to address this problem is automatically simulating the user behaviors. However, due to the subjective and complex nature of human cognitive processes, reliably simulating the user behavior is difficult. Recently, large language models (LLM) have obtained remarkable successes, showing great potential to achieve human-like intelligence. 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. In this paper, we take recommender system as an example to explore the potential of using LLM for user simulation. Specifically, we regard each user as an LLM-based autonomous agent, and let different agents freely communicate, behave and evolve in a virtual simulator called RecAgent. For comprehensively simulation, we not only consider the behaviors within the recommender system (\emph{e.g.}, item browsing and clicking), but also accounts for external influential factors, such as, friend chatting and social advertisement. Our simulator contains at most 1000 agents, and each agent is composed of a profiling module, a memory module and an action module, enabling it to behave consistently, reasonably and reliably. In addition, to more flexibly operate our simulator, we also design two global functions including real-human playing and system intervention. To evaluate the effectiveness of our simulator, we conduct extensive experiments from both agent and system perspectives. In order to advance this direction, we have released our project at {https://github.com/RUC-GSAI/YuLan-Rec}.Comment: 26 pages, 9 figure

Magnetic Resonance Imaging of Bone Marrow Cell-Mediated Interleukin-10 Gene Therapy of Atherosclerosis

A characteristic feature of atherosclerosis is its diffuse involvement of arteries across the entire human body. Bone marrow cells (BMC) can be simultaneously transferred with therapeutic genes and magnetic resonance (MR) contrast agents prior to their transplantation. Via systemic transplantation, these dual-transferred BMCs can circulate through the entire body and thus function as vehicles to carry genes/contrast agents to multiple atherosclerosis. This study was to evaluate the feasibility of using in vivo MR imaging (MRI) to monitor BMC-mediated interleukin-10 (IL-10) gene therapy of atherosclerosis.For in vitro confirmation, donor mouse BMCs were transduced by IL-10/lentivirus, and then labeled with a T2-MR contrast agent (Feridex). For in vivo validation, atherosclerotic apoE(-/-) mice were intravenously transplanted with IL-10/Feridex-BMCs (Group I, n = 5) and Feridex-BMCs (Group II, n = 5), compared to controls without BMC transplantation (Group III, n = 5). The cell migration to aortic atherosclerotic lesions was monitored in vivo using 3.0T MRI with subsequent histology correlation. To evaluate the therapeutic effect of BMC-mediated IL-10 gene therapy, we statistically compared the normalized wall indexes (NWI) of ascending aortas amongst different mouse groups with various treatments.Of in vitro experiments, simultaneous IL-10 transduction and Feridex labeling of BMCs were successfully achieved, with high cell viability and cell labeling efficiency, as well as IL-10 expression efficiency (≥90%). Of in vivo experiments, MRI of animal groups I and II showed signal voids within the aortic walls due to Feridex-created artifacts from the migrated BMCs in the atherosclerotic plaques, which were confirmed by histology. Histological quantification showed that the mean NWI of group I was significantly lower than those of group II and group III (P<0.05).This study has confirmed the possibility of using MRI to track, in vivo, IL-10/Feridex-BMCs recruited to atherosclerotic lesions, where IL-10 genes function to prevent the progression of atherosclerosis

Fairness-aware Cross-Domain Recommendation

Cross-Domain Recommendation (CDR) is an effective way to alleviate the cold-start problem. However, previous work severely ignores fairness and bias when learning the mapping function, which is used to obtain the representations for fresh users in the target domain. To study this problem, in this paper, we propose a Fairness-aware Cross-Domain Recommendation model, called FairCDR. Our method achieves user-oriented group fairness by learning the fairness-aware mapping function. Since the overlapping data are quite limited and distributionally biased, FairCDR leverages abundant non-overlapping users and interactions to help alleviate these problems. Considering that each individual has different influence on model fairness, we propose a new reweighing method based on Influence Function (IF) to reduce unfairness while maintaining recommendation accuracy. Extensive experiments are conducted to demonstrate the effectiveness of our model.Comment: Reorganize the logical structure of the manuscript and supplement with necessary experiment

DynDL: Scheduling Data-Locality-Aware Tasks with Dynamic Data Transfer Cost for Multicore-Server-Based Big Data Clusters

Network bandwidth is a scarce resource in big data environments, so data locality is a fundamental problem for data-parallel frameworks such as Hadoop and Spark. This problem is exacerbated in multicore server-based clusters, where multiple tasks running on the same server compete for the server&#8217;s network bandwidth. Existing approaches solve this problem by scheduling computational tasks near the input data and considering the server&#8217;s free time, data placements, and data transfer costs. However, such approaches usually set identical values for data transfer costs, even though a multicore server&#8217;s data transfer cost increases with the number of data-remote tasks. Eventually, this hampers data-processing time, by minimizing it ineffectively. As a solution, we propose DynDL (Dynamic Data Locality), a novel data-locality-aware task-scheduling model that handles dynamic data transfer costs for multicore servers. DynDL offers greater flexibility than existing approaches by using a set of non-decreasing functions to evaluate dynamic data transfer costs. We also propose online and offline algorithms (based on DynDL) that minimize data-processing time and adaptively adjust data locality. Although DynDL is NP-complete (nondeterministic polynomial-complete), we prove that the offline algorithm runs in quadratic time and generates optimal results for DynDL&#8217;s specific uses. Using a series of simulations and real-world executions, we show that our algorithms are 30% better than algorithms that do not consider dynamic data transfer costs in terms of data-processing time. Moreover, they can adaptively adjust data localities based on the server&#8217;s free time, data placement, and network bandwidth, and schedule tens of thousands of tasks within subseconds or seconds

Influence of hydrodynamic conditions on the fate of halogenated flame retardants along salinity gradients in a highly polluted micro-tidal estuary

Halogenated flame retardants (HFRs) have properties similar to those of hydrophobic organic pollutants (HOPs). How-ever, the understanding of their environmental fate in tidal estuaries remains limited. This study aims to bridge knowl-edge gaps regarding the land-sea transport of HFRs through riverine discharge into coastal waters. HFR levels were significantly influenced by tidal movement, and decabromodiphenyl ethane (DBDPE) was the predominant compound with a median concentration of 3340 pg L-1 in the Xiaoqing River estuary (XRE), whereas BDE209 had a median con-centration of 1370 pg L-1. The Mihe River tributary plays a key role in transporting pollution to the downstream estuary of the XRE in summer, and the increasing suspended particulate matter (SPM) by resuspension in winter significantly affects HFR levels. These concentrations were inversely proportional to diurnal tidal oscillations. Tidal asymmetry caused an increase in SPM during an ebb tide, which increased HFR levels in a micro-tidal estuary such as the Xiaoqing River. The location of the point source and flow velocity influences the HFR concentrations during tidal fluctuations. Tidal asymmetry increases the likelihood of some HFRs being adsorbed by particles exported to the adjacent coast, and some settled down in areas with low hydrodynamic conditions, hindering their flow to the ocean