Optimal allocation of finite sampling capacity in accumulator models of multi-alternative decision making

When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here we study the problem of how to optimally allocate limited sampling time to multiple options, modelled as accumulators of noisy evidence, to determine the most profitable one. We show that the effective sampling capacity of an agent increases with both available time and the discriminability of the options, and optimal policies undergo a sharp transition as a function of it. For small capacity, it is best to allocate time evenly to exactly five options and to ignore all the others, regardless of the prior distribution of rewards. For large capacities, the optimal number of sampled accumulators grows sub-linearly, closely following a power law for a wide variety of priors. We find that allocating equal times to the sampled accumulators is better than using uneven time allocations. Our work highlights that multi-alternative decisions are endowed with breadth-depth tradeoffs, demonstrates how their optimal solutions depend on the amount of limited resources and the variability of the environment, and shows that narrowing down to a handful of options is always optimal for small capacities

Complex behavior from intrinsic motivation to occupy action-state path space

Most theories of behavior posit that agents tend to maximize some form of reward or utility. However, animals very often move with curiosity and seem to be motivated in a reward-free manner. Here we abandon the idea of reward maximization, and propose that the goal of behavior is maximizing occupancy of future paths of actions and states. According to this maximum occupancy principle, rewards are the means to occupy path space, not the goal per se; goal-directedness simply emerges as rational ways of searching for resources so that movement, understood amply, never ends. We find that action-state path entropy is the only measure consistent with additivity and other intuitive properties of expected future action-state path occupancy. We provide analytical expressions that relate the optimal policy and state-value function, and prove convergence of our value iteration algorithm. Using discrete and continuous state tasks, including a high--dimensional controller, we show that complex behaviors such as `dancing', hide-and-seek and a basic form of altruistic behavior naturally result from the intrinsic motivation to occupy path space. All in all, we present a theory of behavior that generates both variability and goal-directedness in the absence of reward maximization.Comment: Extended results, main ones: high dimensional, continuous control, experiment from Gymnasium; and detailed comparison with Empowerment and Free Energy Principle. Updated all main figure

Experience with the use of Rituximab for the treatment of rheumatoid arthritis in a tertiary Hospital in Spain: RITAR study

There is evidence supporting that there are no relevant clinical differences between dosing rituximab 1000 mg or 2000 mg per cycle in rheumatoid arthritis (RA) patients in clinical trials, and low-dose cycles seem to have a better safety profile. Our objective was to describe the pattern of use of rituximab in real-life practice conditions. Methods: Rituximab for RA in clinical practice (RITAR) study is a retrospective cohort study from 2005 to 2015. Eligibility criteria were RA adults treated with rituximab for active articular disease. Response duration was the main outcome defined as months elapsed from the date of rituximab first infusion to the date of flare. A multivariable analysis was performed to determine the variables associated with response duration. Results: A total of 114 patients and 409 cycles were described, 93.0% seropositive and 80.7% women. Rituximab was mainly used as second-line biological therapy. On demand retreatment was used in 94.6% of cases versus fixed 6 months retreatment in 5.4%. Median response duration to on demand rituximab cycles was 10 months (interquartile range, 7–13). Multivariable analysis showed that age older than 65 years, number of rituximab cycles, seropositivity, and first- or second-line therapy were associated with longer response duration. The dose administered at each cycle was not significantly associated with response duration. Conclusions: Our experience suggests that 1000 mg rituximab single infusion on demand is a reasonable schedule for long-term treatment of those patients with good response after the first cycles, especially in seropositive patients and when it is applied as a first- or second-line biological therap

Experimental investigations on the influence of ice floating in an internal melt ice-on-coil tank

In this paper, the discharge of an experimental ice-storage tank is analyzed. The storage tank is an internal melt-ice-on-coil system. The discharge process has been studied for different mass flow rates and supply temperatures in the range from 10 ºC to 15 ºC. The results indicate that once the ice breaks and floats toward the top of the tank, convection in the ice/water mixture is enhanced and the heat transfer fluid in the top coils becomes colder than in the bottom coils. Thus, an increase of the cooling power is generally observed around the ice-breaking point. Two correlations have been developed to reproduce the effect of the mass flow rate and supply temperature on the discharge duration and the mean cooling power.The authors gratefully acknowledge ACCIONA Infraestructuras for the financing support and collaboration.López Navarro, A.; Biosca Taronger, J.; Torregrosa Jaime, B.; Corberán Salvador, JM.; Bote García, J.; Payá Herrero, J. (2013). Experimental investigations on the influence of ice floating in an internal melt ice-on-coil tank. Energy and Buildings. 57:20-25. doi:10.1016/j.enbuild.2012.10.040S20255

Optimizing PV use through active demand side management

With recent technological developments within the field of power conditioning and the progressive decrease of incentives for PV electricity in grid-connected markets, new operation modes for PV systems should be explored beyond the traditional maximization of PV electri city feed-in. An example can be found in the domestic sector, where the use of modern PV hybrid systems combin ed with efficient electrical appliances and demand side management strategies can significantly enhance the PV value for the user. This paper presents an active demand side management system able to displace the consumer’s load curve in response to local (PV hybrid system, user) and external conditions (external grid). In this way, th e consumer becomes an “active consumer” that can also cooperate with others and the grid, increasing even more the PV value for the electrical system

Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (_0.0141) sensitivity and 0.8094 (_0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (_0.0197) sensitivity and 0.7865 (_0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.This work was partially supported by PICCOLO project. This project has received funding from the European Union’s Horizon2020 Research and Innovation Programme under grant agreement No. 732111. This research has also received funding from the Basque Government’s Industry Department under the ELKARTEK program’s project ONKOTOOLS under agreement KK-2020/00069 and the industrial doctorate program UC- DI14 of the University of Cantabria

Frailty is associated with objectively assessed sedentary behaviour patterns in older adults: Evidence from the Toledo Study for Healthy Aging (TSHA)

OBJECTIVE: The aim of this study was to examine the association of sedentary behaviour patterns with frailty in older people. SETTING: Clinical setting. DESIGN: Cross-sectional, observational study. PARTICIPANTS AND MEASUREMENTS: A triaxial accelerometer was used in a subsample from the Toledo Study for Healthy Aging (519 participants, 67-97 years) to assess several sedentary behaviour patterns including sedentary time per day, the number and duration (min) of breaks in sedentary time per day, and the proportion of the day spent in sedentary bouts of 10 minutes or more. Frailty was assessed using the Frailty Trait Scale (FTS). Regression analysis was used to ascertain the associations between sedentary behaviour patterns and frailty. RESULTS: Sedentary time per day and the proportion of the day spent in sedentary bouts of 10 minutes or more, were positively associated with frailty in the study sample. Conversely, the time spent in breaks in sedentary time was negatively associated with frailty. CONCLUSION: In summary, breaking up sedentary time and time spent in sedentary behaviour are associated with frailty in older people

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Optimal allocation of finite sampling capacity in accumulator models of multialternative decision making

When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here, we study the problem of how to optimally allocate limited sampling time to multiple options, modeled as accumulators of noisy evidence, to determine the most profitable one. We show that the effective sampling capacity of an agent increases with both available time and the discriminability of the options, and optimal policies undergo a sharp transition as a function of it. For small capacity, it is best to allocate time evenly to exactly five options and to ignore all the others, regardless of the prior distribution of rewards. For large capacities, the optimal number of sampled accumulators grows sublinearly, closely following a power law as a function of capacity for a wide variety of priors. We find that allocating equal times to the sampled accumulators is better than using uneven time allocations. Our work highlights that multialternative decisions are endowed with breadth–depth tradeoffs, demonstrates how their optimal solutions depend on the amount of limited resources and the variability of the environment, and shows that narrowing down to a handful of options is always optimal for small capacities.This work is supported by the Howard Hughes Medical Institute (HHMI, ref 55008742), MINECO (Spain; BFU2017-85936-P), and ICREA Academia (2016) to R.M.-B, and MINECO/ESF (Spain; PRE2018-084757) to J.R.-R