Computational evolution of decision-making strategies

Most research on adaptive decision-making takes a strategy-first approach, proposing a method of solving a problem and then examining whether it can be implemented in the brain and in what environments it succeeds. We present a method for studying strategy development based on computational evolution that takes the opposite approach, allowing strategies to develop in response to the decision-making environment via Darwinian evolution. We apply this approach to a dynamic decision-making problem where artificial agents make decisions about the source of incoming information. In doing so, we show that the complexity of the brains and strategies of evolved agents are a function of the environment in which they develop. More difficult environments lead to larger brains and more information use, resulting in strategies resembling a sequential sampling approach. Less difficult environments drive evolution toward smaller brains and less information use, resulting in simpler heuristic-like strategies.Comment: Conference paper, 6 pages / 3 figure

Bundled Journals : From Big Deals to Fair Deals

Intervention à la préconférence au congrès LIBER 2004 organisée par SPARC Europe. Une analyse des différentes formes de "bouquets" de revues en ligne et de leurs conséquences pour les bibliothèques-clientes

A heparin-mimicking polymer conjugate stabilizes basic fibroblast growth factor.

Basic fibroblast growth factor (bFGF) is a protein that plays a crucial role in diverse cellular functions, from wound healing to bone regeneration. However, a major obstacle to the widespread application of bFGF is its inherent instability during storage and delivery. Here, we describe the stabilization of bFGF by covalent conjugation with a heparin-mimicking polymer, a copolymer consisting of styrene sulfonate units and methyl methacrylate units bearing poly(ethylene glycol) side chains. The bFGF conjugate of this polymer retained bioactivity after synthesis and was stable to a variety of environmentally and therapeutically relevant stressors--such as heat, mild and harsh acidic conditions, storage and proteolytic degradation--unlike native bFGF. Following the application of stress, the conjugate was also significantly more active than the control conjugate system in which the styrene sulfonate units were omitted from the polymer structure. This research has important implications for the clinical use of bFGF and for the stabilization of heparin-binding growth factors in general

Transport infrastructure evaluation using cost-benefit analysis: improvements to valuing the asset through residual value—a case study

Residual value (RV) is an important component of Cost Benefit Analysis (CBA), often valued at 20% to 50% of total construction costs. It is often overlooked which can artificially depress the project?s returns. The treatment of RV is inadequate and needs further research. Residual value represents the value of the infrastructure at the end of its project lifetime and the value that the asset generates from then on. We analyze three methods for calculating RV: straight-line depreciation, annuity/perpetuity and component. The straight-line depreciation method is the most commonly used; it is simple and quick to produce and it typically uses a percent of the total construction cost rather than real value. The perpetuity/annuity method ignores the actual value of the asset. It reflects the difference of costs and benefits between economic and useful life (annuity method) or assumes an infinite economic life (perpetuity method). The component method is the most detailed and difficult to calculate method. It gives the actual value of the physical asset at the end of project appraisal by infrastructure component. We assume three scenarios for the future for the component method. We use the case study of the Portuguese High Speed Rail project to calculate and compare each method. As expected, the perpetuity has the highest RV and net present value (NPV), followed by the annuity method and then the component method. The straight-line method produces the lowest values (other than one scenario for the component method). Sensitivity analysis is performed ceteris paribus for the demand, construction cost and discount rate factors. We conclude that RV is important in situations when the benefit-cost ratio is close to 1 and the method selected can have a large impact on the size (and sign) of the NPV

New York and Vermont Corn Silage Hybrid Trials

The corn silage hybrid evaluation program expanded to 77 hybrids in 2018. Hybrid evaluation at multiple environments helps in decision making and expands the reach of this type of data to more farmers. With this in mind Cornell, UVM, and seed companies collaborate to bring this robust evaluation. This year, hybrids were either entered into the 80-95 day relative maturity (RM) group (Early-Mid) and were tested at two locations in NY (n = 20; Hu-Lane Farm in Albion and the Willsboro Research Farm in Willsboro) and one location in VT (n = 20; Borderview Farm in Alburgh) or were entered into the 96-110 day relative maturity group (Mid-Late) and were tested at two locations in NY (n = 57; Greenwood Farms in Madrid and the Musgrave Research Farm in Aurora) and one location in VT (n = 55; Borderview Farm in Alburgh). The average Growing Degree Days (GDD; 86-50°F system) from May through August for years 2005 to 2018 is 2053 GGD at Albion, 2039 at Willsboro, 1979 at Alburgh, 2078 at Aurora and 1953 at Madrid (Table 1a and 1b)

Directional receiver for biomimetic sonar system

An ultrasonic localization method for a sonar system equipped with an emitter and two directional receivers and inspired by bat echolocation uses knowledge of the beam pattern of the receivers to estimate target orientation. Rousettus leschenaultii’s left ear constitutes the model for the design of the optimal receiver for this sonar system and 3D printing was used to fabricate receiver structures comprising of two truncated cones with an elliptical external perimeter and a parabolic flare rate in the upper part. Measurements show one receiver has a predominant lobe in the same region and with similar attenuation values as the bat ear model. The final sonar system is to be mounted on vehicular and aerial robots which require remote control for motion and sensors for estimation of each robot’s location

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics