Secure communications have become a requirement for virtually all kind of applications. Currently, two distant parties can generate shared random secret keys by using public key cryptography. However, quantum computing represents one of the greatest threats for the finite complexity of the mathematics behind public key cryptography. In contrast, Quantum Key Distribution (QKD) relies on properties of quantum mechanics, which enables eavesdropping detection and guarantees the security of the key. Among QKD systems, polarization encoded QKD has been successfully tested in laboratory experiments and recently demonstrated in closed environments. The main drawback of QKD is its high cost, which comes, among others, from: i) the requirements for the quantum transmitters and receivers; and ii) the need of carefully selecting the fibers supporting the quantum channel to minimize the environmental effects that could dramatically change the polarization state of photons. In this paper, we propose a Machine Learning(ML)-based polarization tracking and compensation that is able to keep shared secret key exchange to high rates even under large fiber stressing events. Exhaustive results using both synthetic and experimental data show remarkable performance, which can simplify the design of both quantum transmitter and receiver, as well as enable the use of aerial optical cables, thus reducing total QKD system cost.
Index Terms— Machine learning | polarization-encoded quantum key distribution.

A major problem faced by government as trustee of society charged with conserving the nation’s forest envi- ronmental asset is that the standard Economic Account for Forestry (EAF) fails to measure the contribution of nature to total forest incomes and environmental assets. In the context of this government mission, the debate arises with regard to how to uncover the contribution of nature to the total forest incomes enjoyed by people through a refined accounting framework which extends the EAF. The latter is applied by the statistics office to estimate the values added of timber, firewood, cork, resin, industrial nut and other non-woody final products of the forest at national/sub-national scale. Bearing in mind this narrow scope of the EAF, this research proposes the application of the experimental Agroforestry Accounting System (AAS), which extends the forest incomes and environmental asset estimates by applying simulated exchange values stated/revealed by consumers for non- market public goods and services. We apply the EAF and AAS frameworks to 12 large publicly-owned pro- tected conifer forest farms which are not available for sale on the competitive land market and which cover an area of 47,262 ha in Andalusia-Spain. In this conifer farm case study, the EAF considers the economic activities of timber, firewood, aromatic plants and residential service. The AAS adds to the EAF activities those of grazing, conservation forestry, hunting, livestock, agricultural crops, livestock-keeper private amenity, fire services, free access recreation, mushrooms, carbon, landscape conservation, threatened wild biodiversity and water supply runoff stored lower down the watershed in public reservoirs. The objectives of this conifer farm case study are, first, to compare the final products and incomes estimated by applying the EAF and AAS frameworks and, second, to measure the sensitivity of conifer farm environmental assets to changes in land ownership rights and dis- counting rates in accordance with the AAS results. The conifer farm results show total income measured by the AAS is 38 times higher than the EAF net value added (NVA) for the 2010 period. The AAS economic activities of forestry conservation, fire services and landscape conservation activities generate 71% of the conifer farm labour compensation. The AAS opening environmental asset measured at the assumed competitive real baseline dis- counting rate of 3% is 6371.6 €/ha, which is 3.7 times lower than it would be if the conifer farm was available for sale on the competitive land market. The change in the baseline discounting rate chosen, from 3% to 1.5%, would lead to an increase of 116% in the value of the opening environmental asset. The above results reveal the inconsistent EAF measurement of total conifer farm incomes. The sensitivity analysis underlines the important effects on the environmental asset arising from changes in land ownership rights and discounting rates.
keywords: حساب اقتصادی برای جنگلداری | سیستم حساب های ملی | سیستم حسابداری جنگلداری | خدمات محیط زیستی | درآمد زیست محیطی | Economic account for forestry | System of national accounts | Agroforestry accounting system | Ecosystem services | Environmental income

We show that the current account balance (CA) is systematically distorted by an inflation effect , which arises because income on debt is recorded as nominal interest in the currency of denomination. Since nominal interest includes compensations for expected inflation, in- creases in the latter must impact the CA. Guided by the relevant international accounting rules, we impute the inflation effect for 50 economies between 1991 and 2017. When ad- justing for the inflation effect , the absolute value of yearly CAs drops by 0.13% of GDP on average. Over the full period, the reduction is a sizable 22.85% of initial GDP for the aver- age country (26.4% for the U.S.). As the flip side of the CA distortions, the inflation effect contributes systematically to the well-known valuation effect of net foreign assets. For the average country, the inflation effect accounts for a twelfth of the valuation effect, for the U.S., it accounts for well over a half.
keywords: تورم | حساب جاری | اثر ارزش گذاری | Inflation | Current account | Valuation effect

A prominent trend in the functional brain imaging literature is that older adults exhibit increased brain activity compared to young adults to perform a given task. This phenomenon has been extensively stud- ied for cognitive tasks, with the field converging on interpretations described in two alternative accounts. One account interprets over-activation in older adults as reflecting neural dysfunction (increased brain activity – indicates poorer performance), whereas another interprets it as neural compensation (in- creased brain activity - supports better performance). Here we review studies that have recorded brain activity and walking measurements in older adults, and we categorize their findings as reflecting either neural dysfunction or neural compensation. Based on this synthesis, we recommend including multiple task difficulty levels in future work to help differentiate if and when compensation fails as the locomo- tion task becomes more difficult. Using multiple task difficulty levels with neuroimaging will lead to a more advanced understanding of how age-related changes in locomotor brain activity fit with existing accounts of brain aging and support the development of targeted neural rehabilitation techniques.
keywords: فعالیت مغز | پیاده روی | سالخورده | جبران خسارت | اختلال عملکرد | کنترل عصبی | Brain activity | Walking | Aging | Compensation | Dysfunction | Neural control

We investigate the feasibility of inferring economic choices from simple biometric non- choice data. We employ a machine learning approach to assess whether biometric data acquired during sleep, naturally occurring daily chores and participation in an experiment can reveal preferences for competitive and team-based compensation schemes. We find that biometric data acquired using wearable devices enable equally accurate out-of- sample prediction for compensation-scheme choice as gender and performance. Our results demonstrate the feasibility of inferring economic choices from simple biometric data without observing past decisions. However, we find that biometric data recorded in naturally occurring environments during daily chores and sleep add little value to out-of- sample predictions.© 2021 Elsevier B.V. All rights reserved.
Keywords: Compensation schemes | Competition | Team | Experiment | Gender | Heart rate variability | Non-choice data