Non-invasive progressive optimization for in-memory databases

Progressive optimization introduces robustness for database workloads against wrong estimates, skewed data, correlated attributes, or outdated statistics. Previous work focuses on cardinality estimates and rely on expensive counting methods as well as complex learning algorithms. In this paper, we utilize performance counters to drive progressive optimization during query execution. The main advantages are that performance counters introduce virtually no costs on modern CPUs and their usage enables a non-invasive monitoring. We present fine-grained cost models to detect differences between estimates and actual costs which enables us to kick-start reoptimization. Based on our cost models, we implement an optimization approach that estimates the individual selectivities of a multi-selection query efficiently. Furthermore, we are able to learn properties like sortedness, skew, or correlation during run-time. In our evaluation we show, that the overhead of our approach is negligible, while performance improvements are convincing. Using progressive optimization, we improve runtime up to a factor of three compared to average run-times and up to a factor of 4,5 compared to worst case run-times. As a result, we avoid costly operator execution orders and; thus, making query execution highly robust

GRIP: Generating Interaction Poses Using Latent Consistency and Spatial Cues

Hands are dexterous and highly versatile manipulators that are central to how humans interact with objects and their environment. Consequently, modeling realistic hand-object interactions, including the subtle motion of individual fingers, is critical for applications in computer graphics, computer vision, and mixed reality. Prior work on capturing and modeling humans interacting with objects in 3D focuses on the body and object motion, often ignoring hand pose. In contrast, we introduce GRIP, a learning-based method that takes, as input, the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction. As a preliminary step before synthesizing the hand motion, we first use a network, ANet, to denoise the arm motion. Then, we leverage the spatio-temporal relationship between the body and the object to extract two types of novel temporal interaction cues, and use them in a two-stage inference pipeline to generate the hand motion. In the first stage, we introduce a new approach to enforce motion temporal consistency in the latent space (LTC), and generate consistent interaction motions. In the second stage, GRIP generates refined hand poses to avoid hand-object penetrations. Given sequences of noisy body and object motion, GRIP upgrades them to include hand-object interaction. Quantitative experiments and perceptual studies demonstrate that GRIP outperforms baseline methods and generalizes to unseen objects and motions from different motion-capture datasets.Comment: The project has been started during Omid Taheri's internship at Adobe and as a collaboration with the Max Planck Institute for Intelligent System

BEACON:A Summary Framework to Overcome Potential Reimbursement Hurdles

Objective To provide a framework for addressing payers' criteria during the development of pharmaceuticals.Methods A conceptual framework was presented to an international health economic expert panel for discussion. A structured literature search (from 2010 to May 2015), using the following databases in Ovid: Medline((R)) and Medline((R)) In-Process (PubMed), Embase (Ovid), EconLit (EBSCOhost) and the National Health Service Economic Evaluation Database (NHS EED), and a 'grey literature' search, were conducted to identify existing criteria from the payer perspective. The criteria assessed by existing frameworks and guidelines were collated; the most commonly reported criteria were considered for inclusion in the framework. A mnemonic was conceived as a memory aide to summarise these criteria.Results Overall, 41 publications were identified as potentially relevant to the objective. Following further screening, 26 were excluded upon full-text review on the basis of no framework presented (n = 13), redundancy (n = 11) or abstract only (n = 2). Frameworks that captured criteria developed for or utilised by the pharmaceutical industry (n = 5) and reimbursement guidance (n = 10) were reviewed. The most commonly identified criteria-unmet need/patient burden, safety, efficacy, quality-of-life outcomes, environment, evidence quality, budget impact and comparator-were incorporated into the summary framework. For ease of communication, the following mnemonic was developed: BEACON (Burden/target population, Environment, Affordability/value, Comparator, Outcomes, Number of studies/quality of evidence).Conclusions The BEACON framework aims to capture the 'essence' of payer requirements by addressing the most commonly described criteria requested by payers regarding the introduction of a new pharmaceutical.</p

Conformal Transformations of the Wigner Function and Solutions of the Quantum Corrected Vlasov Equation

We study conformal properties of the quantum kinetic equations in curved spacetime. A transformation law for the covariant Wigner function under conformal transformations of a spacetime is derived by using the formalism of tangent bundles. The conformal invariance of the quantum corrected Vlasov equation is proven. This provides a basis for generating new solutions of the quantum kinetic equations in the presence of gravitational and other external fields. We use our method to find explicit quantum corrections to the class of locally isotropic distributions, to which equilibrium distributions belong. We show that the quantum corrected stress--energy tensor for such distributions has, in general, a non--equilibrium structure. Local thermal equilibrium is possible in quantum systems only if an underlying spacetime is conformally static (not stationary). Possible applications of our results are discussed.Comment: 30 page

Hit-and-run trophallaxis of small hive beetles

Some parasites of social insects are able to exploit the exchange of food between nestmates via trophallaxis, because they are chemically disguised as nestmates. However, a few parasites succeed in trophallactic solicitation although they are attacked by workers. The underlying mechanisms are not well understood. The small hive beetle (=SHB), Aethina tumida, is such a parasite of honey bee, Apis mellifera, colonies and is able to induce trophallaxis. Here, we investigate whether SHB trophallactic solicitation is innate and affected by sex and experience. We quantified characteristics of the trophallactic solicitation in SHBs from laboratory-reared individuals that were either bee-naïve or had 5 days experience. The data clearly show that SHB trophallactic solicitation is innate and further suggest that it can be influenced by both experience and sex. Inexperienced SHB males begged more often than any of the other groups had longer breaks than their experienced counterparts and a longer soliciting duration than both experienced SHB males and females, suggesting that they start rather slowly and gain more from experience. Successful experienced females and males were not significantly different from each other in relation to successful trophallactic interactions, but had a significantly shorter soliciting duration compared to all other groups, except successful inexperienced females. Trophallactic solicitation success, feeding duration and begging duration were not significantly affected by either SHB sex or experience, supporting the notion that these behaviors are important for survival in host colonies. Overall, success seems to be governed by quality rather than quantity of interactions, thereby probably limiting both SHB energy investment and chance of injury (<1%). Trophallactic solicitation by SHBs is a singular example for an alternative strategy to exploit insect societies without requiring chemical disguise. Hit-and-run trophallaxis is an attractive test system to get an insight into trophallaxis in the social insects