Tensor Bounds on the Hidden Universe

During single clock inflation, hidden fields (i.e. fields coupled to the inflaton only gravitationally) in their adiabatic vacua can ordinarily only affect observables through virtual effects. After renormalizing background quantities (fixed by observations at some pivot scale), all that remains are logarithmic runnings in correlation functions that are both Planck and slow roll suppressed. In this paper we show how a large number of hidden fields can partially compensate this suppression and generate a potentially observable running in the tensor two point function, consistently inferable courtesy of a large $N$ resummation. We detour to address certain subtleties regarding loop corrections during inflation, extending the analysis of [1]. Our main result is that one can extract bounds on the hidden field content of the universe from bounds on violations of the consistency relation between the tensor spectral index and the tensor to scalar ratio, were primordial tensors ever detected. Such bounds are more competitive than the naive bound inferred from requiring inflation to occur below the strong coupling scale of gravity if deviations from the consistency relation can be bounded to within the sub-percent level. We discuss how one can meaningfully constrain the parameter space of various phenomenological scenarios and constructions that address naturalness with a large number of species (such as `N-naturalness') with CMB observations up to cosmic variance limits, and possibly future 21cm and gravitational wave observations.Comment: 14 pages, 4 figures, 3 appendices. Version accepted to JHEP; references added, updated bounds on $r$ incorporate

Simultaneous Process Design and Control Optimization using Reinforcement Learning

With the ever-increasing numbers in population and quality in healthcare, it is inevitable for the demand of energy and natural resources to rise. Therefore, it is important to design highly efficient and sustainable chemical processes in the pursuit of sustainability. The performance of a chemical plant is highly affected by its design and control. A design cannot be evaluated without its controls and vice versa. To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this, is intractable. However, by computing an optimal policy using reinforcement learning, a controller with close-form expression can be found and embedded into the mathematical program. In this work, an approach using a policy gradient method along with mathematical programming to solve the problem simultaneously is proposed. The approach was tested in two case studies and the performance of the controller was evaluated. It was shown that the proposed approach outperforms current state-of-the-art control strategies. This opens a whole new range of possibilities to address the simultaneous design and control of engineering systems

HI Observations of Early-Type Galaxies

We present high resolution (15") HI observations of the S0 galaxy NGC 404. We derive an HI mass of MHi = 6.7 x 10???, in good agreement with previous measurements. The HI is distributed in a broad annulus (a doughnut) and extends out to a diameter of 9', well beyond the optical diameter of 6'. The velocity field is regular and shows, surprisingly, a declining rotation curve. This decline is purely Keplerian, strongly suggesting that all the mass is contained within the inner 200"

Gross morphometry of the heart of the Common marmoset

  The Callithrix jacchus is a Brazilian endemic species that has been widely used asan experimental model in biomedical research. Anatomical data are necessary to support experimental studies with this species. Eleven hearts of C. jacchus from the German Primate Centre (DPZ) have been studied in order to characterize their gross morphometry and compare them with other animal models and human. Biometric data were also obtained. The mean values for morphometry of the hearts did not show any significant difference between male and female. The relative heart weight was similar to human, bovine and equine species. Considering those aspects, the C. jacchus could be used as non-human primate experimental modelfor biomedical studies on heart.

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model‐based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low‐quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics‐based and data‐driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high‐quality data by correcting raw process measurements via a physics‐based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data‐driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re‐fitting the simple kinetic model (soft sensor) using the data‐driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed‐batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open‐loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application

The Energetic Costs of Cellular Computation

Cells often perform computations in response to environmental cues. A simple example is the classic problem, first considered by Berg and Purcell, of determining the concentration of a chemical ligand in the surrounding media. On general theoretical grounds (Landuer's Principle), it is expected that such computations require cells to consume energy. Here, we explicitly calculate the energetic costs of computing ligand concentration for a simple two-component cellular network that implements a noisy version of the Berg-Purcell strategy. We show that learning about external concentrations necessitates the breaking of detailed balance and consumption of energy, with greater learning requiring more energy. Our calculations suggest that the energetic costs of cellular computation may be an important constraint on networks designed to function in resource poor environments such as the spore germination networks of bacteria.Comment: 9 Pages (including Appendix); 4 Figures; v3 corrects even more typo

Inundaciones y cambio climático

Las inundaciones de los ríos han sucedido de forma tradicional en numerosos ríos de la Península Ibérica, aportando grandes beneficios en la agricultura y en la disponibilidad de recursos hídricos, pero en la historia más reciente han originado graves daños en personas y bienes haciendo que hoy día predomine una percepción de las inundaciones como “catástrofes”. En el origen del incremento de la frecuencia de las inundaciones hay que mencionar la intensificación del uso del territorio, sellando y compactando los suelos haciendo que aumenten las escorrentías rápidas, y la alteración hidromorfológica de los ríos, concentrando las aguas y favoreciendo la ocurrencia de avenidas y desbordamientos. En el incremento exponencial de las pérdidas que las inundaciones han originado en los últimos años hay que referirse a la intensa ocupación de las riberas de los ríos y sus llanuras de inundación por personas y actividades económicas, con un desarrollo en dichas zonas no compatible con la dinámica fluvial. El cambio climático es considerado un factor de riesgo adicional muy variable según las regiones, y la estimación de sus efectos sobre las inundaciones presenta todavía numerosas incertidumbres. Atendiendo a ello se revisan algunos estudios e informes relacionados con el fenómeno de las inundaciones y su posible relación con el cambio climático, y se propone la restauración de los sistemas fluviales y la restricción de usos en las zonas inundables como estrategias más acertadas para hacer frente a la mencionada problemática de las inundaciones y a la incertidumbre creada con el cambio climático. El análisis de los sucesivos paradigmas históricos planteados por el hombre frente a las inundaciones de los ríos pone en evidencia el interés de cambiar unas estrategias de “defensa” en contra de ellas, tratando de evitar que ocurran, por otras de “convivencia” con las mismas gestionando de la forma más apropiada el riesgo de los daños que pueden generar, atendiendo al espíritu de las Directivas europeas Marco del Agua y de evaluación y gestión del riesgo de la inundación

Microglial regulation of satiety and cognition

Microglia have been known for decades as key immune cells that shape the central nervous system (CNS) during development and respond to brain pathogens and injury in adult life. Recent findings now suggest that these cells also play a highly complex role in several other functions of the CNS. In this review, we provide a brief overview of the established microglial functions in development and disease. We also discuss emerging research suggesting that microglia are important for both cognitive function and the regulation of food intake. With respect to cognitive function, current data suggest microglia are not indispensable for neurogenesis, synaptogenesis or cognition in the healthy young adult, although they crucially modulate and support these functions. In doing so, they are likely important in supporting the balance between apoptosis and survival of newborn neurones and in orchestrating appropriate synaptic remodelling in response to a learning stimulus. We also explore the possibility of a role for microglia in feeding and satiety. Microglia have been implicated in both appetite suppression with sickness and obesity and in promoting feeding under some conditions and we discuss these findings here, highlighting the contribution of these cells to healthy brain function

Recurrent Focal Segmental Glomerulosclerosis in Renal Allograft Recipients: Role of Human Leukocyte Antigen Mismatching and Other Clinical Variables

Recurrence of focal segmental glomerulosclerosis (FSGS) after renal transplantation impacts long-term graft survival and limits access to transplantation. We hypothesized that HLA donor/recipient matching could be used as a surrogate marker of recurrence. In a retrospective study of 42 pediatric and 77 adult subjects with primary FSGS, transplanted from 1990 to 2007 at a single center, we analyzed the degree of donor/recipient HLA compatibility and other clinical variables associated with FSGS recurrence. There were total of 131 allografts for primary FSGS (11 subjects were transplanted twice, and 1 had a third allograft) with 20 cases of FSGS recurrence (17 children) in the primary allograft, and two children who had FSGS recurrence in the second allograft. Fifty-two subjects (40%) were African American, and 66 (50%) Caucasians. Recurrent FSGS and controls were not different for age at transplant, gender, donor source, acute/chronic rejection episodes, and HLA matches. Recurrent FSGS was not associated with HLA mismatches; power equals 83%. Immunosuppressive regimen had no effect on recurrence of FSGS, P = .75. Recurrent FSGS is not associated with HLA mismatching, acute cellular or vascular rejection, and occurs primarily in the pediatric population

Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty

Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real time. The main bottleneck in the industrial application of DRTO is the presence of uncertainty. Many stochastic systems present the following obstacles: 1) plant-model mismatch, 2) process disturbances, 3) risks in violation of process constraints. To accommodate these difficulties, we present a constrained reinforcement learning (RL) based approach. RL naturally handles the process uncertainty by computing an optimal feedback policy. However, no state constraints can be introduced intuitively. To address this problem, we present a chance-constrained RL methodology. We use chance constraints to guarantee the probabilistic satisfaction of process constraints, which is accomplished by introducing backoffs, such that the optimal policy and backoffs are computed simultaneously. Backoffs are adjusted using the empirical cumulative distribution function to guarantee the satisfaction of a joint chance constraint. The advantage and performance of this strategy are illustrated through a stochastic dynamic bioprocess optimization problem, to produce sustainable high-value bioproducts