Bellwether counties are mostly a matter of chance and are now poor predictors of presidential election results.

Those who believe that Donald Trump won the 2020 presidential election have held up his victories in many bellwether counties as evidence of electoral fraud. In new research, Bernard Grofman and Haotian Chen argue that the evidence shows that this claim is laughable. They write that the electoral record shows that not only are bellwether counties poor predictors of who will win a presidential election, rising polarization means that their ability to predict long streaks of elections has been in decline over the last two decades

Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust

Legal judgment Prediction (LJP), aiming to predict a judgment based on fact descriptions, serves as legal assistance to mitigate the great work burden of limited legal practitioners. Most existing methods apply various large-scale pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent improvements. However, we discover the fact that the state-of-the-art (SOTA) model makes judgment predictions according to wrong (or non-casual) information, which not only weakens the model's generalization capability but also results in severe social problems like discrimination. Here, we analyze the causal mechanism misleading the LJP model to learn the spurious correlations, and then propose a framework to guide the model to learn the underlying causality knowledge in the legal texts. Specifically, we first perform open information extraction (OIE) to refine the text having a high proportion of causal information, according to which we generate a new set of data. Then, we design a model learning the weights of the refined data and the raw data for LJP model training. The extensive experimental results show that our model is more generalizable and robust than the baselines and achieves a new SOTA performance on two commonly used legal-specific datasets

Multi-stage prognosis of COVID-19 using a clinical event-based stratification of disease severity

The COVID-19 disease has shown remarkable diversity in its manifestation. Precise anticipation of these manifestations is important to enable earlier intervention for high-risk patients and efficient deployment of medical resources. In this thesis, a multi-stage prognostic framework is developed for assessing COVID-19 patients at hospital admission and during disease progression. The analysis is conducted upon 10,123 COVID-19 patients treated at Rush University Medical Center at Chicago between 03/17/2020 and 08/07/2020. In order to characterize the patients with different severity, a stratification scheme is first established to assign patients to different stages of disease severity based on discrete clinical events (i.e., admission to hospital, admission to ICU, mechanical ventilation, and death). Then two prognostic frameworks were developed to predict the progression of COVID-19 through these stages: 1) a baseline model which uses the measurements collected at hospital admission to predict disease escalation to severe stages; 2) a progressive model which uses the measurements collected at the patient’s latest stage to predict further escalation. It is found that future clinical stages can be predicted using baseline measurements with clinically significant accuracy. Finally, key risk factors are identified using Least Absolute Shrinkage and Selection Operator (LASSO) and decision tree algorithms. The developed multi-stage framework can be used to anticipate COVID-19 disease progression, allowing earlier interventions as well as better management of hospital resources

MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning

Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work well in tasks with relatively slight difference. However, when the task distribution becomes wider, it would be quite inefficient to directly learn such a meta-policy. In this paper, we propose a new meta-RL algorithm called Meta Goal-generation for Hierarchical RL (MGHRL). Instead of directly generating policies over primitive action space for new tasks, MGHRL learns to generate high-level meta strategies over subgoals given past experience and leaves the rest of how to achieve subgoals as independent RL subtasks. Our empirical results on several challenging simulated robotics environments show that our method enables more efficient and generalized meta-learning from past experience.Comment: Accepted to the ICLR 2020 workshop: Beyond tabula rasa in RL (BeTR-RL

On Validity of Gyrokinetic Theory

We study the validity of gyrokinetic theory by examining the destruction of magnetic moment adiabatic invariant in the presence of fluctuations. Contrary to common assertions, it is shown for the first time that the gyrokinetic theory rests not only on the magnetic moment conservation, but also on the fact that the particle dynamics constitutes a boundary layer problem. For low frequency fluctuations, there exists a quantitative, frequency independent threshold below which the adiabaticity is preserved, allowing thereby the general validity of gyrokinetic theory. The adiabaticity threshold in the high frequency regime, however, depends sensitively on frequency, which questions the generalization of gyrokinetic equation to arbitrary frequencies. Further analyses suggest that it is not feasible to construct a reduced kinetic equation based on superadiabaticity

Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming. In particular, PlenOctree (POT)[1], an explicit hierarchical multi-scale octree representation, has emerged as a structural and influential framework. However, POT's fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly. To address this issue, we propose the dynamic PlenOctree DOT, which adaptively refines the sample distribution to adjust to changing scene complexity. Specifically, DOT proposes a concise yet novel hierarchical feature fusion strategy during the iterative rendering process. Firstly, it identifies the regions of interest through training signals to ensure adaptive and efficient refinement. Next, rather than directly filtering out valueless nodes, DOT introduces the sampling and pruning operations for octrees to aggregate features, enabling rapid parameter learning. Compared with POT, our DOT outperforms it by enhancing visual quality, reducing over $55.15$/$68.84\%$ parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $\&$ Temples, respectively. Project homepage:https://vlislab22.github.io/DOT. [1] Yu, Alex, et al. "Plenoctrees for real-time rendering of neural radiance fields." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.Comment: Accepted by ICCV202