152,450 research outputs found
The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance
Recent work addressing model reliability and generalization has resulted in a
variety of methods that seek to proactively address differences between the
training and unknown target environments. While most methods achieve this by
finding distributions that will be invariant across environments, we will show
they do not necessarily find the same distributions which has implications for
performance. In this paper we unify existing work on prediction using stable
distributions by relating environmental shifts to edges in the graph underlying
a prediction problem, and characterize stable distributions as those which
effectively remove these edges. We then quantify the effect of edge deletion on
performance in the linear case and corroborate the findings in a simulated and
real data experiment
Power Modelling for Heterogeneous Cloud-Edge Data Centers
Existing power modelling research focuses not on the method used for
developing models but rather on the model itself. This paper aims to develop a
method for deploying power models on emerging processors that will be used, for
example, in cloud-edge data centers. Our research first develops a hardware
counter selection method that appropriately selects counters most correlated to
power on ARM and Intel processors. Then, we propose a two stage power model
that works across multiple architectures. The key results are: (i) the
automated hardware performance counter selection method achieves comparable
selection to the manual selection methods reported in literature, and (ii) the
two stage power model can predict dynamic power more accurately on both ARM and
Intel processors when compared to classic power models.Comment: 10 pages,10 figures,conferenc
Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data
Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients – basic parameters required for implementing the MIIA – are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite
- …