宾夕法尼亚大学（University of Pennsylvania ），简称宾大（UPenn），位于宾夕法尼亚州最大城市费城，是一所全球顶尖的私立研究型大学，著名的八所常春藤盟校之一，美国大学协会14所创始成员之一。宾大由本杰明·富兰克林创建于1740年，是美国第四古老的高等教育机构，也是美国第一所从事科学技术和人文教育的现代高等学校。1765年成立全美第一所医学院、1881年成立全美第一所商学院、1896成立全美第一个学生会组织，在至今数百年的学校历史中，宾大一次又一次在教育以及科学技术上推动了人类文明的发展，诞生了人类历史上第一台通用电子计算机ENIAC，被誉为现代计算机科学文明的发源地，以及风疹疫苗、乙肝疫苗、认知心理疗法等的发明挽救了无数生命。宾大拥有全世界最强大的校友网络之一，包括数十位在各自领域的最高奖项获得者（如诺贝尔奖、图灵奖等）以及许多美国国家院士。宾夕法尼亚大学名列2019福布斯美国大学排行榜第6名，2021U.S. News美国最佳大学排名第8名 ，2021U.S. News世界大学排名第14名 ，2021泰晤士高等教育世界大学排名第10名 ，2015年被普林斯顿评论评为十大梦想学校之一 。根据研究经费及师生质量等学术指标，2014年，大学评定中心把宾大评为研究实力一级研究型大学 [6] 。根据2017年公布的福布斯富豪榜，宾大培养的亿万富豪校友数量位居全美第一。
Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we introduce a novel post-processing approach. This approach feeds the output from the last hidden layer of a pre-trained large-scale DNN model into a stochastic neural network (StoNet), then trains the StoNet with a sparse penalty on a validation dataset and constructs prediction intervals for future observations. We establish a theoretical guarantee for the validity of this approach; in particular, the parameter estimation consistency for the sparse StoNet is essential for the success of this approach. Comprehensive experiments demonstrate that the proposed approach can construct honest confidence intervals with shorter interval lengths compared to conformal methods and achieves better calibration compared to other post-hoc calibration techniques. Additionally, we show that the StoNet formulation provides us with a platform to adapt sparse learning theory and methods from linear models to DNNs.
Line-Intensity Mapping (LIM) has emerged as a powerful technique for studying large-scale structure and the high-redshift universe, enabling three-dimensional maps of line emission across vast cosmological volumes. In this review, we summarize the LIM framework, its key scientific goals, and its future prospects. We describe the landscape of emission line tracers, theoretical modeling approaches, anticipated signals, and data-analysis methodologies. We also discuss experimental challenges, particularly those posed by astrophysical foregrounds, and review possible mitigation strategies. Further, we highlight a range of cross-correlation science cases, linking LIM with other cosmological surveys. Finally, we summarize current and upcoming experiments and early results, including recent first detections, while outlining the outlook for future discoveries. Specifically, LIM may offer new insights into galaxy formation and evolution and cosmology, while revealing the Epoch of Reionization, Cosmic Dawn, and possibly the Cosmic Dark Ages. LIM enables cosmological measurements that complement other probes and provide unique access to the high-redshift universe, potentially shedding light on dark matter, dark energy, and cosmic inflation.
In this work, we propose a correction-function-based kernel-free boundary integral (CF-KFBI) method for solving Stokes- and Brinkman-type interface problems. We begin by recasting the original interface problem with discontinuous coefficients as boundary integral equations, in which the integral operators can be interpreted as boundary data for potential functions that satisfy simpler interface problems without coefficient discontinuities. Each such interface problem is discretized using a corrected Marker-and-Cell (MAC) scheme. Within a narrow band around the interface, we introduce a local correction function that represents the solution jump, leading to a local Cauchy problem. This problem is solved with a collocation method, for which we provide criteria for a minimal choice of collocation points and prove solvability. Several numerical experiments, including both fixed- and moving-interface problems, are presented to demonstrate the accuracy and efficiency of the proposed method.
Negotiation research was among the first to document zero-sum thinking, in the form of the fixed-pie bias: treating negotiations as win-lose interactions and thus missing win-win opportunities. Recently, research on zero-sum thinking has reemerged, largely outside negotiation, documenting zero-sum perceptions of status, social relations, and even everyday exchange. We offer a three-part framework that brings these advances back to negotiation. First, we review evidence linking zero-sum thinking to negotiation avoidance, in part through the fear of conflict. Second, we propose that the desire to “win” may lead negotiators to accept deals that are worse than their outside options and to reject deals that are better. Third, we propose that zero-sum thinking may lead negotiators to undervalue the relationships on which a deal's value depends. These arguments suggest that zero-sum thinking in negotiation extends well beyond its best-known form, the fixed-pie bias.
