Dynamic metasurface lens based on MEMS Technology

In the recent years, metasurfaces, being flat and lightweight, have been designed to replace bulky optical components with various functions. We demonstrate a monolithic Micro-Electro-Mechanical System (MEMS) integrated with a metasurface-based flat lens that focuses light in the mid-infrared spectrum. A two-dimensional scanning MEMS platform controls the angle of the lens along the two orthogonal axes (tip-tilt) by +-9 degrees, thus enabling dynamic beam steering. The device can compensate for off-axis incident light and thus correct for aberrations such as coma. We show that for low angular displacements, the integrated lens-on-MEMS system does not affect the mechanical performance of the MEMS actuators and preserves the focused beam profile as well as the measured full width at half maximum. We envision a new class of flat optical devices with active control provided by the combination of metasurfaces and MEMS for a wide range of applications, such as miniaturized MEMS-based microscope systems, LIDAR scanners, and projection systems

Learning a reactive potential for silica-water through uncertainty attribution

The reactivity of silicates in an aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the $\omega$-B97XD def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.Comment: 26 pages, 4 figures, 1 supplementary figur

An integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits

Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity. However, a highly detailed system characterization required to understand the underlying errors is an arduous process and impractical with increasing chip size. Open-loop optimal control techniques allow for the improvement of gates but are limited by the models they are based on. To rectify the situation, we provide a new integrated open-source tool-set for Control, Calibration and Characterization ($C^3$), capable of open-loop pulse optimization, model-free calibration, model fitting and refinement. We present a methodology to combine these tools to find a quantitatively accurate system model, high-fidelity gates and an approximate error budget, all based on a high-performance, feature-rich simulator. We illustrate our methods using fixed-frequency superconducting qubits for which we learn model parameters to an accuracy of $<1\%$ and derive a coherence limited cross-resonance (CR) gate that achieves $99.6\%$ fidelity without need for calibration.Comment: Source code available at http://q-optimize.org; added reference

Soluble ST2 levels and left ventricular structure and function in patients with metabolic syndrome

Background: A biomarker that is of great interest in relation to adverse cardiovascular events is soluble ST2 (sST2), a member of the interleukin family. Considering that metabolic syndrome (MetS) is accompanied by a proinflammatory state, we aimed to assess the relationship between sST2 and left ventricular (LV) structure and function in patients with MetS. Methods: A multicentric, cross-sectional study was conducted on180 MetS subjects with normal LV ejection fraction as determined by echocardiography. LV hypertrophy (LVH) was defined as an LV mass index greater than the gender-specific upper limit of normal as determined by echocardiography. LV diastolic dysfunction (DD) was assessed by pulse-wave and tissue Doppler imaging. sST2 was measured by using a quantitative monoclonal ELISA assay. Results: LV mass index (β=0.337, P<0 .001, linear regression) was independently associated with sST2 concentrations. Increased sST2 was associated with an increased likelihood of LVH [Exp (B)=2.20, P=0.048, logistic regression] and increased systolic blood pressure [Exp (B)=1.02, P=0.05, logistic regression]. Comparing mean sST2 concentrations (adjusted for age, body mass index, gender) between different LV remodeling patterns, we found the greatest sST2 level in the group with concentric hypertrophy. There were no differences in sST2 concentration between groups with and without LV DD. Conclusions: Increased sST2 concentration in patients with MetS was associated with a greater likelihood of exhibiting LVH. Our results suggest that inflammation could be one of the principal triggering mechanisms for LV remodeling in MetS

Predictable convergence in hemoglobin function has unpredictable molecular underpinnings

To investigate the predictability of genetic adaptation, we examined the molecular basis of convergence in hemoglobin function in comparisons involving 56 avian taxa that have contrasting altitudinal range limits. Convergent increases in hemoglobin-oxygen affinity were pervasive among high-altitude taxa, but few such changes were attributable to parallel amino acid substitutions at key residues.Thus, predictable changes in biochemical phenotype do not have a predictable molecular basis. Experiments involving resurrected ancestral proteins revealed that historical substitutions have context-dependent effects, indicating that possible adaptive solutions are contingent on prior history. Mutations that produce an adaptive change in one species may represent precluded possibilities in other species because of differences in genetic background

Phenolic Characterization of Cabernet Sauvignon Wines From Different Geographical Indications of Mendoza, Argentina: Effects of Plant Material and Environment

The chemical and sensory characteristics of the wines are related to the geographical origin of the grape, as a result of the interplay between the plant material (G), its acclimatization to the environment (E) and the human factor that influences both the vineyard and the winery. The range of phenotypes that a single genotype can express depending on its environment is known as phenotypic plasticity and is the result of G × E interaction. The present study evaluated the independent and interactive effects of Cabernet Sauvignon plant materials (G: Clone 7 and Mount Eden) implanted in different geographical indications of Mendoza, Argentina (E: Agrelo, Pampa El Cepillo, Altamira and Gualtallary) according to fruit yield and phenolic profiles of wines. The experiment was carried out during 2018 and 2019 vintages using a multifactorial design. When berries reached 24 °Brix, the clusters were harvested, analyzed and wines elaborated by a standardized procedure. Then, the anthocyanin and non-anthocyanin phenolic profiles of wines were determined by high-performance liquid chromatography with diode array and fluorescence detection (HPLC-DAD–FLD). The results revealed significant G × E interactions for yield traits, including the number of clusters per plant. Differential chemical composition and quality parameters of the resulting wines, markedly affected by E, were observed; that is the geographical location of the vineyards. There were similarities in the phenolic composition between Pampa El Cepillo and Altamira, while larger differences between Agrelo and Gualtallary were observed. Gualtallary presented the highest levels of anthocyanins, quercetin and trans-resveratrol. The increased amount of these compounds in Gualtallary was associated with an increased UV-B exposure of plants at this high altitude environment. This is the first report that characterizes the effects of plant material and environment for Cabernet Sauvignon. These results are of oenological and viticulture interest for the wine industry demonstrating that the selection of the plant material and the vineyard location for Cabernet Sauvignon can considerably affect the quality attributes of wines.Fil: Muñoz, Flavio Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Urvieta, Roy Alexander. Catena Institute Of Wine.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Buscema, Fernando. Catena Institute Of Wine.; ArgentinaFil: Rasse, Manuel. School of Agriculture Ingenieers; FranciaFil: Fontana, Ariel Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; ArgentinaFil: Berli, Federico Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; Argentin

SIGIR 2021 E-Commerce Workshop Data Challenge

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.Comment: SIGIR eCOM 2021 Data Challeng