Recessions and Retirement: New Evidence from the COVID-19 Pandemic

The COVID-19 pandemic disrupted the US labor market, leading to an unprecedented loss of 22 million jobs in March and April 2020. Evidence from past recessions indicates that economic downturns are typically associated with an increase in retirements. In this study, we revisit the relationship between recessions and retirement in the COVID-19 era, using data from the Current Population Survey (CPS) supplemented by other data on economic and COVID conditions. We find that higher unemployment is associated with an increase in the probability of transitioning from employment to being out of the labor force during the pre-pandemic period, consistent with previous studies. Surprisingly, however, retirement transitions during the pandemic have been insensitive to local labor market conditions. Our finding that the probability of retirement increased during the pandemic but that retirements are largely unrelated to local economic or COVID conditions points to a potential role for common national factors such as generalized health concerns, government policies, or stock market gains

To What Extent Is the Security Dilemma an Inescapable Feature of International Security?

This essay attempts to explore to what extent the security dilemma is an inescapable feature of international security. The three main schools of thought in international relations theory offer different perspectives on this issue. Realism asserts that the security dilemma is entirely inescapable. Liberalism, on the other hand, acknowledges its inescapability but argues that it can be mitigated through international cooperation mechanisms. Constructivism takes a different approach, suggesting that the constructed “security dilemma” can be fundamentally overcome by changing interactive behaviors. While liberalism and constructivism challenge realism’s conclusion, neither perspective can successfully refute the notion that the security dilemma is an inherent feature of international security. Liberalism is more applicable to economic matters and lacks explanatory power in the realm of international security, while constructivism tends to be overly idealistic and lacks the ability to effectively address real-world problems

Impact of Controlling Shareholder Equity Pledge on Corporate Value

As an innovative financing behavior, equity pledge breaks the limit of traditional financing, and broadens the financing channels of companies and major shareholders. This paper comprehensively considers the impact of controlling shareholder equity pledge on corporate value from three research perspectives. The main conclusions are as follows: (1) When the equity pledge is not considered, the cash flow rights and voting rights of the company owned by the controlling shareholder are positively correlated with corporate value. That is, this presents incentive effect, but the existence of the separation of the two powers brings the second type of agency problem and reduces corporate value. (2) When considering the equity pledge, the controlling shareholder’s equity pledge may weaken the incentive effect and strengthen the encroachment effect which causing a reduction of corporate value. (3) Based on the accounting point of view, the controlling shareholder’s equity pledge is negatively correlated with the corporate performance, while the concentration of ownership dilutes this negative effect. (4) The balance of equity weakens the negative effect of the controlling shareholder’s equity pledge on corporate value, thereby reduces the negative impact of the equity pledge

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table

Analysis of five deep-sequenced trio-genomes of the Peninsular Malaysia Orang Asli and North Borneo populations

BackgroundRecent advances in genomic technologies have facilitated genome-wide investigation of human genetic variations. However, most efforts have focused on the major populations, yet trio genomes of indigenous populations from Southeast Asia have been under-investigated.ResultsWe analyzed the whole-genome deep sequencing data (30x) of five native trios from Peninsular Malaysia and North Borneo, and characterized the genomic variants, including single nucleotide variants (SNVs), small insertions and deletions (indels) and copy number variants (CNVs). We discovered approximately 6.9 million SNVs, 1.2 million indels, and 9000 CNVs in the 15 samples, of which 2.7% SNVs, 2.3% indels and 22% CNVs were novel, implying the insufficient coverage of population diversity in existing databases. We identified a higher proportion of novel variants in the Orang Asli (OA) samples, i.e., the indigenous people from Peninsular Malaysia, than that of the North Bornean (NB) samples, likely due to more complex demographic history and long-time isolation of the OA groups. We used the pedigree information to identify de novo variants and estimated the autosomal mutation rates to be 0.81x10(-8) - 1.33x10(-8), 1.0x10(-9) - 2.9x10(-9), and 0.001 per site per generation for SNVs, indels, and CNVs, respectively. The trio-genomes also allowed for haplotype phasing with high accuracy, which serves as references to the future genomic studies of OA and NB populations. In addition, high-frequency inherited CNVs specific to OA or NB were identified. One example is a 50-kb duplication in DEFA1B detected only in the Negrito trios, implying plausible effects on host defense against the exposure of diverse microbial in tropical rainforest environment of these hunter-gatherers. The CNVs shared between OA and NB groups were much fewer than those specific to each group. Nevertheless, we identified a 142-kb duplication in AMY1A in all the 15 samples, and this gene is associated with the high-starch diet. Moreover, novel insertions shared with archaic hominids were identified in our samples.ConclusionOur study presents a full catalogue of the genome variants of the native Malaysian populations, which is a complement of the genome diversity in Southeast Asians. It implies specific population history of the native inhabitants, and demonstrated the necessity of more genome sequencing efforts on the multi-ethnic native groups of Malaysia and Southeast Asia

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS2), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS2 and t-WS2 monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young’s modulus. We find that the mechanical properties of h-WS2 surpass those of t-WS2 due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young’s modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS2 (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS2 type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model

OCC-VO: Dense Mapping via 3D Occupancy-Based Visual Odometry for Autonomous Driving

Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge being the lack of depth information in camera images. This paper introduces OCC-VO, a novel framework that capitalizes on recent advances in deep learning to transform 2D camera images into 3D semantic occupancy, thereby circumventing the traditional need for concurrent estimation of ego poses and landmark locations. Within this framework, we utilize the TPV-Former to convert surround view cameras' images into 3D semantic occupancy. Addressing the challenges presented by this transformation, we have specifically tailored a pose estimation and mapping algorithm that incorporates Semantic Label Filter, Dynamic Object Filter, and finally, utilizes Voxel PFilter for maintaining a consistent global semantic map. Evaluations on the Occ3D-nuScenes not only showcase a 20.6% improvement in Success Ratio and a 29.6% enhancement in trajectory accuracy against ORB-SLAM3, but also emphasize our ability to construct a comprehensive map. Our implementation is open-sourced and available at: https://github.com/USTCLH/OCC-VO.Comment: 7pages, 3 figure

COEM: Cross-Modal Embedding for MetaCell Identification

Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal \textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient sequencing coverage. COEM outperforms the state-of-the-art method SEACells by efficiently identifying accurate and well-separated metacells across datasets with continuous and discrete cell types. Furthermore, COEM significantly improves peak-to-gene association analyses, and facilitates complex gene regulatory inference tasks.Comment: 5 pages, 2 figures, ICML workshop on computational biolog