Comrades or Foes: Did the Russians Break the Law or New Ground for the First Amendment?

This Article discusses the recent decision by the United States Federal Government to indict more than a dozen Russian nationals for conspiracy to defraud the United States of America. The Government accused the Russians of staging protests, distributing false propaganda, and spreading political messages and ideologies online in an effort to affect the outcome of the 2016 Presidential Election. We argue that while the Defendants violated several other laws, the majority of the acts the Government classifies as a conspiracy to defraud the United States should not be considered criminal. Rather, these acts are protected political speech under the First Amendment of the United States Constitution because the Russians engaged in conduct that is crucial to political discourse in a Democracy and which the Founding Fathers intended to protect. Therefore, prosecution of the Russian Defendants on that basis should cease

A Second Opinion: Can Windsor v. United States Survive President Trump’s Supreme Court?

This Article examines President Donald Trump’s recent recomposition of the United States Supreme Court and the potential effects on Windsor v. United States and its progeny. The Article considers whether the shifting balance of the Court may lead to reconsideration of Windsor, particularly via attempted exploits of the weaknesses in the standard of review applied to reach the decision. The Article will conclude that while revolutionary, Windsor lacked the doctrinal clarity of its offspring, Obergefell v. Hodges, and therefore may be at greatest risk of reversal by the increasingly conservative Court. In particular, the Court may rely on the conflict between Windsor and preceding jurisprudence regarding the rational basis review standard to draw the conclusion that Windsor should have been decided differently under the state of the law in 2013

Comrades or Foes: Did the Chinese Break the Law or New Ground Ground for the First Amendment

Prior to exiting the White House, President Trump placed a variety of restrictions on Chinese-owned social media applications, TikTok and WeChat, threatening to greatly curtail their influence in the United States. While couching his actions in the context of national security, the former president engaged in viewpoint discrimination in plain violation of the First Amendment to the United States Constitution. The court rulings in favor of TikTok and WeChat were encouraging and should stem the tide of future government regulations of social media platforms. This article discusses how the decisions fit into the greater context of First Amendment jurisprudence and shows that government regulations of internet communication platforms is almost assuredly unconstitutional, whether the platform is foreign or domestic. Therefore, current and future proposals for state and federal regulations should be viewed with skepticism, as they would ultimately fail constitutional scrutiny

When Congress Passes the Buck: How Russia’s Invasion of Ukraine Exposed Flaws in Granting the President Sanctioning Powers

The United States (U.S.) Constitution provides few limitations on endowing the Executive Branch with powers to govern foreign trade, which was initially granted to the Legislature. In a world where global trade dominates, the power over foreign trade can be more important than the power over domestic matters. Leaving unrestrained trade authority to the Executive Branch may cause hazards for Americans and foreigners alike. Russia’s war in Ukraine demonstrates the flaws in permitting the Executive Branch to unilaterally sanction foreign states. This Article demonstrates how reactive Executive Branch policies infringed on the welfare and safety of American citizens and foreigners alike

Who Watches the Watchmen? Character and Fitness Panels and the Onerous Demands Imposed on Bar Applicants

This Article discusses the onerous requirements that state bars sometimes impose on bar applicants to prove good moral character despite the vague definition of the term and the apparently limitless amount of evidence that a character and fitness panel can rely on to deny or delay admission. We present recent examples of decisions that beg the question of whether state bars are really preventing the entry of the unethical into the profession or simply screening out applicants that panelists dislike. We also discuss at least one potential problem highlighted within the process by COVID-19. This Article argues that while most bar applicants pass the character and fitness portion of their bar application without problems, history shows that the potential for arbitrary decisions is so high that this potential should be eliminated. The changes we propose should come either voluntarily, as state bars appropriately adjust their rules to notify applicants of what conduct is truly prohibited, or via a ruling from the Supreme Court of the United States, which has already established some constitutional requirements for the process that have apparently been forgotten

The Alarming Legality of Security Manipulation Through Shareholder Proposals

Shareholder proposals attract attention from scholars in finance and economics because they present an opportunity to study both quasidemocratic decision-making at the corporate level and the impact of this decision-making on firm outcomes. These studies capture the effect of various proposals but rarely address whether regulations should allow many of them in the first place due to the possibility of stock price manipulation. Recent changes to shareholder proposal rules, adopted in September 2020, sought to address the potential for exploitation that some proposals create (but ultimately failed to do so). This Article shows the potential for apparently legal stock price manipulation if shareholder proposals remain relatively unregulated. We propose improvements to decrease this risk of stock price manipulation, which should help the government prosecute the offenders

The Implications of Marijuana Legalization on the Prevalence and Severity of Schizophrenia

The article discusses how regulation is vital to protect the small proportion of the population that will develop schizophrenia from marijuana use. Topics discussed include marijuana\u27s effects on a user\u27s psyche; triggers and symptoms of schizophrenia; and need to legalize marijuana for people who are at least twenty-five years old or who have been cleared by a psychologist

Pose Estimation and Segmentation for Rehabilitation

The global population is getting older and the aging demographic is increasing demands on health-care industry. This will drive the demand for post stroke, joint replacement, and chronic disease management rehabilitation. Currently physiotherapists rely on mostly subjective and observational tools for patient assessment and progress tracking. This thesis proposes methods to enable the use of non-intrusive, small, wearable, wireless sensors to estimate the pose of the lower body during rehabilitation and extract objective performance measures useful for therapists. Two different kinematic models of the human lower body are introduced. The first approach expresses the body position and orientation in the world frame using three prismatic and revolute joints, while the second switches the model's base between the right and the left ankle during gait. An Extended Kalman Filter (EKF) is set up to estimate the joint angles, velocities, and accelerations of the models using measurements from inertial measurement units. The state update model assumes constant joint acceleration and is linear. Measurement prediction, relating the joint positions, velocities and accelerations to the measured angular velocity and linear acceleration at each IMU, is done using forward kinematics, using one of the two proposed kinematic models. The approach is validated on healthy participant gait using motion capture studio data for ground truth comparison. The prismatic and revolute model achieves better Cartesian position accuracy in the swing leg due to a shorter kinematic chain, while the switching base model improves the stance leg Cartesian estimate and does not allow measurement noise to accumulate as drift in global position, knee joint angle root mean squared errors (RMSE) of 6.1 and 5.6 degrees are attained respectively by the models. Next the Rhythmic Extended Kalman Filter (R-EKF) algorithm is developed to improve pose estimation. It learns a model of rhythmic movement over time based on harmonic Fourier series and removes the constant acceleration assumption. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the EKF in simulation, on healthy participant data, and stroke patient monthly assessments. For the healthy participant marching dataset, the R-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37% respectively, estimates joint angles with 2.4 degree RMSE, and segments the motion into repetitions with 96% accuracy. While the proposed R-EKF effectively segments rhythmic rehabilitation movement such as gait, not all rehabilitation motions are rhythmic or may have uneven delays between repetitions by regimen design or due to fatigue. For such motions a time-series segmentation as data point classification algorithm is proposed. Common dimensionality reduction and classification techniques are applied to estimated joint angle data to classify each time-step as a segment or non-segment point. The algorithm is tested on five common rehabilitation exercises performed by healthy participants and achieves a segmentation accuracy of 82%

Human motion estimation and controller learning

Humans are capable of complex manipulation and locomotion tasks. They are able to achieve energy-efficient gait, reject disturbances, handle changing loads, and adapt to environmental constraints. Using inspiration from the human body, robotics researchers aim to develop systems with similar capabilities. Research suggests that humans minimize a task specific cost function when performing movements. In order to learn this cost function from demonstrations and incorporate it into a controller, it is first imperative to accurately estimate the expert motion. The captured motions can then be analyzed to extract the objective function the expert was minimizing. We propose a framework for human motion estimation from wearable sensors. Human body joints are modeled by matrix Lie groups, using special orthogonal groups SO(2) and SO(3) for joint pose and special Euclidean group SE(3) for base link pose representation. To estimate the human joint pose, velocity and acceleration, we provide the equations for employing the extended Kalman Filter on Lie Groups, thus explicitly accounting for the non-Euclidean geometry of the state space. Incorporating interaction constraints with respect to the environment or within the participant allows us to track global body position without an absolute reference and ensure viable pose estimate. The algorithms are extensively validated in both simulation and real-world experiments. Next, to learn underlying expert control strategies from the expert demonstrations we present a novel fast approximate multi-variate Gaussian Process regression. The method estimates the underlying cost function, without making assumptions on its structure. The computational efficiency of the approach allows for real time forward horizon prediction. Using a linear model predictive control framework we then reproduce the demonstrated movements on a robot. The learned cost function captures the variability in expert motion as well as the correlations between states, leading to a controller that both produces motions and reacts to disturbances in a human-like manner. The model predictive control formulation allows the controller to satisfy task and joint space constraints avoiding obstacles and self collisions, as well as torque constraints, ensuring operational feasibility. The approach is validated on the Franka Emika robot using real human motion exemplars