Investigating the Impact of Marijuana Legalization on Income, Education, and Depression

Over the past two decades, marijuana has been the most widely used illicit drug by adolescents in the US. The drug continues to soar in popularity as both a recreational and medicinal drug despite mounting scientific research that marijuana consumption may impair cognitive function including deficits in learning, memory, motivation, and attention. Marijuana use has also been linked to exacerbation of depression and anxiety symptoms. Though federal laws still classify marijuana as an illegal substance, recent state-level legislation has sparked national debate over its legal status. In fact, 23 states and the District of Columbia have legalized marijuana for medical use and four—Colorado, Washington, Alaska, and Oregon-have legalized marijuana for recreational use. This paper investigates the impact that marijuana legalization has on income, education, and depression using cross-sectional and time-series data from the 1996-2013 (not including 2002) Behavioral Risk Factor Surveillance System Survey and 1995-2013 Current Population Survey. The regressions indicated that marijuana legalization had an effect on several of the outcome variables. Those living in states that permitted marijuana dispensaries had wage premiums and higher self-employment, but males had higher high school dropout rates and females had more depressive days. States that permitted home cultivation were also affected, with increases in depressive days and self-employment for both genders. Finally, states that legalized marijuana for recreational use showed wage penalties for females and decreases in self-employment for both genders. However, there was no evidence that marijuana legalization had an effect on unemployment

Audio-Driven Dubbing for User Generated Contents via Style-Aware Semi-Parametric Synthesis

Existing automated dubbing methods are usually designed for Professionally Generated Content (PGC) production, which requires massive training data and training time to learn a person-specific audio-video mapping. In this paper, we investigate an audio-driven dubbing method that is more feasible for User Generated Content (UGC) production. There are two unique challenges to design a method for UGC: 1) the appearances of speakers are diverse and arbitrary as the method needs to generalize across users; 2) the available video data of one speaker are very limited. In order to tackle the above challenges, we first introduce a new Style Translation Network to integrate the speaking style of the target and the speaking content of the source via a cross-modal AdaIN module. It enables our model to quickly adapt to a new speaker. Then, we further develop a semi-parametric video renderer, which takes full advantage of the limited training data of the unseen speaker via a video-level retrieve-warp-refine pipeline. Finally, we propose a temporal regularization for the semi-parametric renderer, generating more continuous videos. Extensive experiments show that our method generates videos that accurately preserve various speaking styles, yet with considerably lower amount of training data and training time in comparison to existing methods. Besides, our method achieves a faster testing speed than most recent methods.Comment: TCSVT 202

Emission charge and liner shipping network configuration ‐ an economic investigation of the Asia‐Europe route

This paper models shipping lines’ operational costs and CO2 emissions under alternative geographic network configurations when an emission charge is imposed on operations from Asia to Europe. Our modeling results suggest that shipping firms’ network configuration is influenced by emission charge, fuel price, port loading and unloading cost, and demand pattern of cargo transport across different markets. Total emission will be reduced by an EU emission charge scheme. However, if the charge is above a threshold, carriers will reconfigure shipping networks to minimize their costs including emission charge payments. This will offset part of the emission reduction achieved by the emission scheme. As a result, a higher charge does not always lead to a higher emission reduction. In addition, the performance of major ports along the Asia-Europe routes will be influenced in different ways, leading to conflicting views from regional governments. These findings reveal possible market distortions associated with regional emission systems, and highlight the complex effects of international environmental policies when market dynamics are considered

UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation

Human generation has achieved significant progress. Nonetheless, existing methods still struggle to synthesize specific regions such as faces and hands. We argue that the main reason is rooted in the training data. A holistic human dataset inevitably has insufficient and low-resolution information on local parts. Therefore, we propose to use multi-source datasets with various resolution images to jointly learn a high-resolution human generative model. However, multi-source data inherently a) contains different parts that do not spatially align into a coherent human, and b) comes with different scales. To tackle these challenges, we propose an end-to-end framework, UnitedHuman, that empowers continuous GAN with the ability to effectively utilize multi-source data for high-resolution human generation. Specifically, 1) we design a Multi-Source Spatial Transformer that spatially aligns multi-source images to full-body space with a human parametric model. 2) Next, a continuous GAN is proposed with global-structural guidance and CutMix consistency. Patches from different datasets are then sampled and transformed to supervise the training of this scale-invariant generative model. Extensive experiments demonstrate that our model jointly learned from multi-source data achieves superior quality than those learned from a holistic dataset.Comment: Accepted by ICCV2023. Project page: https://unitedhuman.github.io/ Github: https://github.com/UnitedHuman/UnitedHuma

A New Perspective for Dipolarization Front Dynamics: Electromagnetic Effects of Velocity Inhomogeneity

The stability of a quasi‐static near‐Earth dipolarization front (DF) is investigated with a two‐dimensional electromagnetic particle‐in‐cell model. Strongly localized ambipolar electric fields self‐consistently generate a highly sheared dawnward E→×B→ electron drift on the kinetic scale in the DF. Electromagnetic particle‐in‐cell simulations based on the observed DF thickness and gradients of plasma/magnetic field parameters reveal that the DF is susceptible to the kinetic electron‐ion hybrid (EIH) instability driven by the strong velocity inhomogeneity. The excited waves show a broadband spectrum in the lower hybrid (LH) frequency range, which has been often observed at DFs. The wavelength is comparable to the shear scale length, and the growth rate is also in the LH frequency range, which are consistent with the EIH theory. As a result of the LH wave emissions, the velocity shear is relaxed, and the DF is broadened. When the plasma beta increases, the wave mode shifts to longer wavelengths with reduced growth rates and enhanced magnetic fluctuations although the wave power is mostly in the electrostatic regime. This study highlights the role of velocity inhomogeneity in the dynamics of DF which has been long neglected. The EIH instability is suggested to be an important mechanism for the wave emissions and steady‐state structure at the DF.Key PointsMagnetotail DF contains a substantial velocity shear in the tangential electron driftThe sheared flow is susceptible to the EIH instability and can broaden the DF by emitting broadband LH wavesThe EIH emissions become more electromagnetic as plasma beta increasesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152027/1/jgra55215_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152027/2/jgra55215.pd

Redshift: Manipulating Signal Propagation Delay via Continuous-Wave Lasers

We propose a new laser injection attack Redshift that manipulates signal propagation delay, allowing for precise control of oscillator frequencies and other behaviors in delay-sensitive circuits. The target circuits have a significant sensitivity to light, and a low-power continuous-wave laser, similar to a laser pointer, is sufficient for the attack. This is in contrast to previous fault injection attacks that use highpowered laser pulses to flip digital bits. This significantly reduces the cost of the attack and extends the range of possible attackers. Moreover, the attack potentially evades sensor-based countermeasures configured for conventional pulse lasers. To demonstrate Redshift, we target ring-oscillator and arbiter PUFs that are used in cryptographic applications. By precisely controlling signal propagation delays within these circuits, an attacker can control the output of a PUF to perform a state-recovery attack and reveal a secret key. We finally discuss the physical causality of the attack and potential countermeasures

An Ap star catalog based on LAMOST DR9

We present a sample of 2700 Ap stars in LAMOST DR9. The candidates are first selected to be in a temperature range typical of Ap stars by using the BP-RP color index from Gaia DR3. Then the 5200\,Å flux depression features characteristic of Ap stars are visually checked in LAMOST DR9 spectra. The detailed spectral features are given by applying a modified spectral classification program, MKCLASS. Stellar parameters of these Ap stars such as Teff, logg, [Fe/H], [Si/H], and vsini are either extracted from a hot star catalog or derived through empirical relations and then a statistical analysis is carried out. The evolutionary stages are also discussed. Finally, we discuss the rotation and pulsation features of those who have TESS or Kepler light curves. Among these Ap stars we find 7 new rotation variables, 1 new roAp star, and new δ Scuti pulsation of a previously known roAp star

OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, reconstruction, and generation in the real world, we propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties: 1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations. 2) Rich Annotations: Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 3) Realistic Scans: The professional scanners support highquality object scans with precise shapes and realistic appearances. With the vast exploration space offered by OmniObject3D, we carefully set up four evaluation tracks: a) robust 3D perception, b) novel-view synthesis, c) neural surface reconstruction, and d) 3D object generation. Extensive studies are performed on these four benchmarks, revealing new observations, challenges, and opportunities for future research in realistic 3D vision.Comment: Project page: https://omniobject3d.github.io