Spatial Distribution Modelling of Prothonotary Warbler (Protonotaria citrea) on Breeding Grounds

Ecological niche modeling is used to predict a species’ distribution in a geographic area based on abiotic and biotic variables. Understanding a species’ range is important for conservation and restoration efforts. As anthropogenic forces may alter or deplete habitat, it is important to know the ecological requirements of a species to understand how and what habitat to protect. With the increasing threat of climate change and rising temperature and precipitation, the suitable habitat and the distribution for many species is expected to shift. Migratory species are particularly at risk of these changes as they require suitable habitat not only on their wintering and stopover grounds, but on their breeding grounds. Without suitable breeding grounds, reproductive success is guaranteed to decline for a species. Understanding how these changes affect the range and distribution of a species allows researchers and conservationist to better formulate effective species management plan

Traité d'arithmétique théoripratique, 1844

Livro com 215 páginas. Disponível no seguinte link: https://gallica.bnf.fr/ark:/12148/bpt6k1192364g/f9.item.r=Cours%20d%20'%20arithm%C3%A9tiqu

Quantum device fine-tuning using unsupervised embedding learning

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min

Chromatin Signature Identifies Monoallelic Gene Expression Across Mammalian Cell Types

Monoallelic expression of autosomal genes (MAE) is a widespread epigenetic phenomenon which is poorly understood, due in part to current limitations of genome-wide approaches for assessing it. Recently, we reported that a specific histone modification signature is strongly associated with MAE and demonstrated that it can serve as a proxy of MAE in human lymphoblastoid cells. Here, we use murine cells to establish that this chromatin signature is conserved between mouse and human and is associated with MAE in multiple cell types. Our analyses reveal extensive conservation in the identity of MAE genes between the two species. By analyzing MAE chromatin signature in a large number of cell and tissue types, we show that it remains consistent during terminal cell differentiation and is predominant among cell-type specific genes, suggesting a link between MAE and specification of cell identity

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies

A Comparison of Treatment-Seeking Behavioral Addiction Patients with and without Parkinson's Disease

The administration of dopaminergic medication to treat the symptoms of Parkinson's disease (PD) is associated with addictive behaviors and impulse control disorders. Little is known, however, on how PD patients differ from other patients seeking treatments for behavioral addictions. The aim of this study was to compare the characteristics of behavioral addiction patients with and without PD. N = 2,460 treatment-seeking men diagnosed with a behavioral addiction were recruited from a university hospital. Sociodemographic, impulsivity [Barratt Impulsiveness Scale (BIS-11)], and personality [Temperament and Character Inventory-Revised (TCI-R)] measures were taken upon admission to outpatient treatment. Patients in the PD group were older and had a higher prevalence of mood disorders than patients without PD. In terms of personality characteristics and impulsivity traits, PD patients appeared to present a more functional profile than PD-free patients with a behavioral addiction. Our results suggest that PD patients with a behavioral addiction could be more difficult to detect than their PD-free counterparts in behavioral addiction clinical setting due to their reduced levels of impulsivity and more standard personality traits. As a whole, this suggests that PD patients with a behavioral addiction may have different needs from PD-free behavioral addiction patients and that they could potentially benefit from targeted interventions

Report of the Scientific Council Meeting 01 -15 June 2017

Council met at the Sobey Building, Saint Mary’s University, Halifax, NS, Canada, during 01 – 15 June 2017, to consider the various matters in its Agenda. Representatives attended from Canada, Denmark (in respect of Faroe Islands and Greenland), the European Union (France, Germany (via WebEx), Portugal, Spain, the United Kingdom and the European Commission), Japan, the Russian Federation and the United States of America. Observers from the Ecology Action Centre and Dalhousie University were also present. The Executive Secretary, Scientific Council Coordinator and other members of the Secretariat were in attendance. The Executive Committee met prior to the opening session of the Council to discuss the provisional agenda and plan of work. The Council was called to order at 1000 hours on 01 June 2017. The provisional agenda was adopted with modification. The Scientific Council Coordinator was appointed the rapporteur. The Council was informed that the meeting was quorate and authorization had been received by the Executive Secretary for proxy votes from the European Union, Denmark (in respect of Faroe Islands and Greenland), Iceland, Japan, Republic of Korea, and Norway. The opening session was adjourned at 1200 hours on 01 June 2017. Several sessions were held throughout the course of the meeting to deal with specific items on the agenda. The Council considered adopted the STACFEN report on 8 June 2017, and the STACPUB, STACFIS and STACREC reports on 15 June 2017. The concluding session was called to order at 0830 hours on 15 June 2017. The Council considered and adopted the report the Scientific Council Report of this meeting of 01 -15 June 2017. The Chair received approval to leave the report in draft form for about two weeks to allow for minor editing and proof-reading on the usual strict understanding there would be no substantive changes. The meeting was adjourned at 1430 hours on 15 June 2017. The Reports of the Standing Committees as adopted by the Council are appended as follows: Appendix I - Report of the Standing Committee on Fisheries Environment (STACFEN), Appendix II - Report of Standing Committee on Publications (STACPUB), Appendix III - Report of Standing Committee on Research Coordination (STACREC), and Appendix IV - Report of Standing Committee on Fisheries Science (STACFIS). The Agenda, List of Research (SCR) and Summary (SCS) Documents, and List of Representatives, Advisers and Experts, are given in Appendix V-VII. The Council’s considerations on the Standing Committee Reports, and other matters addressed by the Council follow in Sections II-XV

Stability of long-sustained oscillations induced by electron tunneling

Self-oscillations are the result of an efficient mechanism generating periodic motion from a constant power source. In quantum devices, these oscillations may arise due to the interaction between single electron dynamics and mechanical motion. Due to the complexity of this mechanism, these self-oscillations may irrupt, vanish, or exhibit a bistable behavior causing hysteresis cycles. We observe these hysteresis cycles and characterize the stability of different regimes in single and double quantum dot configurations. In particular cases, we find these oscillations stable for over 20 seconds, many orders of magnitude above electronic and mechanical characteristic timescales, revealing the robustness of the mechanism at play. The experimental results are reproduced by our theoretical model that provides a complete understanding of bistability in nanoelectromechanical devices.Comment: 11 pages, 10 figures, includes the complete paper and the Supplemental Materia