Prodigality and myopia. Two rationales for social security.

Among the rationales for social security, there is the fact that some people have to be forced to save. To explain undersaving, rational prodigality and hyperbolic preferences are often cited but treated separably. In this paper we study those two particular behaviors that lead to forced saving within an optimal income tax second-best setting.social security, myopia, dual-self model, prodigality.

Retirement as a hedge

This paper explores the effect of letting individuals choose their retirement age in a world of uncertainty where there exist both defined benefit (DB) and de?ned contribution (DC) pension plans. The paper shows that giving individuals the flexibility to determine when to retire is an important tool for them when they are hedging against future uncertainty. It also finds that it is preferable to let people make their retirement decision after rather than before the uncertainty is lifted. Finally, it shows that shifting from DB to DC plans fosters the rate of activity of elderly workers.retirement decision, defined benefit defined contribution

Simple groups without lattices

We show that the group of almost automorphisms of a d-regular tree does not admit lattices. As far as we know this is the first such example among (compactly generated) simple locally compact groups.Comment: 17 pages. Revised according to referee's repor

Interaction of defined benefit pension plans and social security

This paper explores the shift from defined benefit to defined contribution pension plans when the payout rate from social security is set optimally. This paper shows that when employees are receiving more of their private pensions from defined contribution plans one should be raising the payout rate from traditional social security rather than trying to privatize part of it.social security, defined benefit, defined contribution.

An End-to-End CNN with Attentional Mechanism Applied to Raw EEG in a BCI Classification Task

Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based motor-imagery classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing. Approach. To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, Brain-Computer Interface (BCI) Competition IV 2a and 2b. BCI. Main results. Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning. Significance. Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI

Permian rifting and isolation of New Caledonia: evidence from detrital zircon geochronology

The island of New Caledonia is the second largest rock exposure of the continent Zealandia. The New Caledonian basement rocks have been interpreted as representing a late Paleozoic to Mesozoic intra-oceanic arc system that was possibly correlative to contemporaneous terranes in eastern Australia and New Zealand. In order to understand tectonic relationships between the basement rocks of New Caledonia and other eastern Gondwanan terranes, we obtained >2200 new U-Pb ages of detrital zircon grains from New Caledonia. Our new results, combined with a synthesis of previously published geochronological data, show abundant pre-Mesozoic zircon ages, but an absence of Early Permian to Middle Triassic ages characteristic of eastern Gondwana magmatism. The results thus suggest that the detritus of the New Caledonian basement was derived from a local Paleozoic continental fragment that was rifted from the margin of Gondwana, most likely in the Early Permian. The results imply that dispersal of the Gondwanan margins started earlier than the Late Cretaceous opening of the Tasman and Coral seas, consistent with the Mesozoic endemism of both New Caledonia and New Zealand

Wirtschaftsbeziehungen zwischen Ost und West: Perspektiven und Probleme

Vorwort 3 I. Einleitung 5 II. Ost-West-Handel 8 a. Entwicklung 8 b. Aussichten für die nähere Zukunft 12 c. Der Handel mit China 14 d. Der sowjetische Ferne Osten 15 e. Komparative Vorteile im Ost-West-Süd-Handel . . 16 f. Gewinne aus dem Handel 17 III. Technologietransfer 20 IV. Die Finanzierung des Handels: Das Problem der Verschuldung . 26 V. Institutionelle Probleme 33 VI. Zusammenfassung und Schlußfolgerungen 37 Verzeichnis der Tabellen ^9 Literaturverzeichnis 4

Ten Years’ Experience with Alendronate for Osteoporosis in Postmenopausal Women

Background Antiresorptive agents are widely used to treat osteoporosis. We report the results of a multinational randomized, double-blind study, in which postmenopausal women with osteoporosis were treated with alendronate for up to 10 years. Methods The initial three-year phase of the study compared three daily doses of alendronate with placebo. Women in the original placebo group received alendronate in years 4 and 5 and then were discharged. Women in the original active-treatment groups continued to receive alendronate during the initial extension (years 4 and 5). In two further extensions (years 6 and 7, and 8 through 10), women who had received 5 mg or 10 mg of alendronate daily continued on the same treatment. Women in the discontinuation group received 20 mg of alendronate daily for two years and 5 mg daily in years 3, 4, and 5, followed by five years of placebo. Randomized group assignments and blinding were maintained throughout the 10 years. We report results for the 247 women who participated in all four phases of the study. Results Treatment with 10 mg of alendronate daily for 10 years produced mean increases in bone mineral density of 13.7 percent at the lumbar spine (95 percent confidence interval, 12.0 to 15.5 percent), 10.3 percent at the trochanter (95 percent confidence interval, 8.1 to 12.4 percent), 5.4 percent at the femoral neck (95 percent confidence interval, 3.5 to 7.4 percent), and 6.7 percent at the total proximal femur (95 percent confidence interval, 4.4 to 9.1 percent) as compared with base-line values; smaller gains occurred in the group given 5 mg daily. The discontinuation of alendronate resulted in a gradual loss of effect, as measured by bone density and biochemical markers of bone remodeling. Safety data, including fractures and stature, did not suggest that prolonged treatment resulted in any loss of benefit. Conclusions The therapeutic effects of alendronate were sustained, and the drug was well tolerated over a 10-year period. The discontinuation of alendronate resulted in the gradual loss of its effects

An Automated Machine Learning-based Model Predicts Postoperative Mortality Using Readily-Extractable Preoperative Electronic Health Record Data

Background Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. Methods We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. Results Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910–0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598–0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658–0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829–0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917–0.955). Conclusions This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period