Installment Land Contracts in Purchaser Bankruptcy

The executory contract analysis under § 365 of the Bankruptcy Code has long challenged judges, practitioners, and scholars. The challenge of understanding the purpose of § 365 and reaching an equitable result thereafter is most profound when confronting installment land contracts. The parties to an installment land contract, typically the purchaser, can become insolvent and enter bankruptcy, and consequently, the rights of the parties may be altered dramatically as a result of applying bankruptcy law

Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface

Deep learning has shown promise in decoding brain signals, such as electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs). However, the non-stationary characteristics of EEG signals pose challenges for training neural networks to acquire appropriate knowledge. Inconsistent EEG signals resulting from these non-stationary characteristics can lead to poor performance. Therefore, it is crucial to investigate and address sample inconsistency to ensure robust performance in spontaneous BCIs. In this study, we introduce the concept of sample dominance as a measure of EEG signal inconsistency and propose a method to modulate its effect on network training. We present a two-stage dominance score estimation technique that compensates for performance degradation caused by sample inconsistencies. Our proposed method utilizes non-parametric estimation to infer sample inconsistency and assigns each sample a dominance score. This score is then aggregated with the loss function during training to modulate the impact of sample inconsistency. Furthermore, we design a curriculum learning approach that gradually increases the influence of inconsistent signals during training to improve overall performance. We evaluate our proposed method using public spontaneous BCI dataset. The experimental results confirm that our findings highlight the importance of addressing sample dominance for achieving robust performance in spontaneous BCIs.Comment: 5 pages, 2 figure

Offline Imitation Learning by Controlling the Effective Planning Horizon

In offline imitation learning (IL), we generally assume only a handful of expert trajectories and a supplementary offline dataset from suboptimal behaviors to learn the expert policy. While it is now common to minimize the divergence between state-action visitation distributions so that the agent also considers the future consequences of an action, a sampling error in an offline dataset may lead to erroneous estimates of state-action visitations in the offline case. In this paper, we investigate the effect of controlling the effective planning horizon (i.e., reducing the discount factor) as opposed to imposing an explicit regularizer, as previously studied. Unfortunately, it turns out that the existing algorithms suffer from magnified approximation errors when the effective planning horizon is shortened, which results in a significant degradation in performance. We analyze the main cause of the problem and provide the right remedies to correct the algorithm. We show that the corrected algorithm improves on popular imitation learning benchmarks by controlling the effective planning horizon rather than an explicit regularization.Comment: Preprin

Gate modulation of the long-range magnetic order in a vanadium-doped WSe2 semiconductor

We demonstrate the gate-tunability of the long-range magnetic order in a p-type V-doped WSe2 monolayer using ab initio calculations. We found that at a low V-doping concentration limit, the long-range ferromagnetic order is enhanced by increasing the hole density. In contrast, the short-range antiferromagnetic order is manifested at a high electron density by full compensation of the p-type V doping concentration. The hole-mediated long-range magnetic exchange is ~70 meV, thus strongly suggesting the ferromagnetism in V-doped WSe2 at room temperature. Our findings on strong coupling between charge and spin order in V-doped WSe2 provide plenty of room for multifunctional gate-tunable spintronics.Comment: 13 pages, 4 figures, 1 tabl

Psychometric properties of a short self-reported measure of medication adherence among patients with hypertension treated in a busy clinical setting in Korea.

BackgroundWe examined the psychometric properties of the Korean version of the 8-item Morisky Medication Adherence Scale (MMAS-8) among adults with hypertension.MethodsA total of 373 adults with hypertension were given face-to-face interviews in 2 cardiology clinics at 2 large teaching hospitals in Seoul, South Korea. Blood pressure was measured twice, and medical records were reviewed. About one-third of the participants (n = 109) were randomly selected for a 2-week test-retest evaluation of reliability via telephone interview.ResultsInternal consistency reliability was moderate (Cronbach α = 0.56), and test-retest reliability was excellent (intraclass correlation = 0.91; P < 0.001), although a ceiling effect was detected. The correlation of MMAS-8 scores with scores for the original 4-item scale indicated that convergent validity was good (r = 0.92; P < 0.01). A low MMAS-8 score was significantly associated with poor blood pressure control (χ(2) = 29.86; P < 0.001; adjusted odds ratio = 5.08; 95% CI, 2.56-10.08). Using a cut-off point of 6, sensitivity and specificity were 64.3% and 72.9%, respectively. Exploratory factor analysis identified 3 dimensions of the scale, with poor fit for the 1-dimensional construct using confirmatory factory analysis.ConclusionsThe MMAS-8 had satisfactory reliability and validity and thus might be suitable for assessment and counseling regarding medication adherence among adults with hypertension in a busy clinical setting in Korea