Energy Consumption and Habit Formation: Evidence from High Frequency Thermostat Data

Using minute-by-minute data from over 60,000 smart thermostats in households distributed across the United States, we analyze the persistence of energy consumption behaviors in response to external weather shocks. The analysis examines habitual behavior and provides insight into what affects long term change and what triggers the decision to reconsider one’s passive choices. Our preferences for indoor temperatures demonstrate habituation to outdoor temperatures. This habituation is asymmetrical between positive and negative changes and non-linear at the extremes. While our indoor temperature preferences habituate to match small outdoor changes, our preferences revert to long term means in response to extreme temperature change. We also find people are more likely to make active choices when outdoor temperature is salient. Finally, we show there is heterogeneity in how preferences respond as a function of social norms, political preferences, and change costs. Results provide guidance on how conservation policies impact energy use–failure to understand the influence of habit on decision making can lead us to over-estimate the impact of short term policy nudges but underestimate the long run impact of small changes. Our results also inform how changing average temperatures and changing cultural attitudes may affect energy conservation behaviors

The mandatory and voluntary approaches to sustainability: BASIX vs BEAM Plus

Many assessment systems have been introduced to measure the environmental sustainability of buildings that aim to reduce energy consumption and carbon emissions over the last decade. Examples are the BRE Environmental Assessment Method (BREEAM) in the UK, Leadership in Energy and Environmental Design (LEED) in the US and Canada, the Green Star and Building Sustainability Index (BASIX) in Australia, and the Building Environmental Assessment Method (BEAM) Plus in Hong Kong. Some of the systems, such as BASIX, apply a mandatory approach for implementation; others, such as BEAM Plus, are voluntary with incentives. This paper aims to compare the difference between BASIX and BEAM Plus and discuss their different approaches to building sustainability. The comparison is important because it would then be possible to evaluate the implications of the environmental assessment policy tools in which two different approaches are used. The paper will first study and compare both the BASIX and BEAM Plus assessment systems. Second, the advantages and pitfalls of the mandatory and voluntary approaches will be identified and discussed. The paper is based on desk research. The impacts of the environmental policy tools, determined through case studies that will be conducted, should reveal if a voluntary-with-incentives approach is the stronger motivation for the building industry to improve its environmental performance

PHL 6625: A Minor Merger-Associated QSO Behind NGC 247

PHL 6625 is a luminous quasi-stellar object (QSO) at z = 0.3954 located behind the nearby galaxy NGC 247 (z = 0.0005). Hubble Space Telescope (HST) observations revealed an arc structure associated with it. We report on spectroscopic observations with the Very Large Telescope (VLT) and multiwavelength observations from the radio to the X-ray band for the system, suggesting that PHL 6625 and the arc are a close pair of merging galaxies, instead of a strong gravitational lens system. The QSO host galaxy is estimated to be (4-28) x 10^10 M_sun, and the mass of the companion galaxy of is estimated to be M_* = (6.8 +/- 2.4) x 10^9 M_sun, suggesting that this is a minor merger system. The QSO displays typical broad emission lines, from which a black hole mass of about (2-5) x 10^8 M_sun and an Eddington ratio of about 0.01-0.05 can be inferred. The system represents an interesting and rare case where a QSO is associated with an ongoing minor merger, analogous to Arp 142.Comment: ApJ to appea

Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach

The literature on using yield curves to forecast recessions customarily uses 10-year--three-month Treasury yield spread without verification on the pair selection. This study investigates whether the predictive ability of spread can be improved by letting a machine learning algorithm identify the best maturity pair and coefficients. Our comprehensive analysis shows that, despite the likelihood gain, the machine learning approach does not significantly improve prediction, owing to the estimation error. This is robust to the forecasting horizon, control variable, sample period, and oversampling of the recession observations. Our finding supports the use of the 10-year--three-month spread

MAP3Kinase-dependent SnRK2-kinase activation is required for abscisic acid signal transduction and rapid osmotic stress response.

Abiotic stresses, including drought and salinity, trigger a complex osmotic-stress and abscisic acid (ABA) signal transduction network. The core ABA signalling components are snf1-related protein kinase2s (SnRK2s), which are activated by ABA-triggered inhibition of type-2C protein-phosphatases (PP2Cs). SnRK2 kinases are also activated by a rapid, largely unknown, ABA-independent osmotic-stress signalling pathway. Here, through a combination of a redundancy-circumventing genetic screen and biochemical analyses, we have identified functionally-redundant MAPKK-kinases (M3Ks) that are necessary for activation of SnRK2 kinases. These M3Ks phosphorylate a specific SnRK2/OST1 site, which is indispensable for ABA-induced reactivation of PP2C-dephosphorylated SnRK2 kinases. ABA-triggered SnRK2 activation, transcription factor phosphorylation and SLAC1 activation require these M3Ks in vitro and in plants. M3K triple knock-out plants show reduced ABA sensitivity and strongly impaired rapid osmotic-stress-induced SnRK2 activation. These findings demonstrate that this M3K clade is required for ABA- and osmotic-stress-activation of SnRK2 kinases, enabling robust ABA and osmotic stress signal transduction

Robust Online Monitoring of Signal Temporal Logic

Signal Temporal Logic (STL) is a formalism used to rigorously specify requirements of cyberphysical systems (CPS), i.e., systems mixing digital or discrete components in interaction with a continuous environment or analog com- ponents. STL is naturally equipped with a quantitative semantics which can be used for various purposes: from assessing the robustness of a specification to guiding searches over the input and parameter space with the goal of falsifying the given property over system behaviors. Algorithms have been proposed and implemented for offline computation of such quantitative semantics, but only few methods exist for an online setting, where one would want to monitor the satisfaction of a formula during simulation. In this paper, we formalize a semantics for robust online monitoring of partial traces, i.e., traces for which there might not be enough data to decide the Boolean satisfaction (and to compute its quantitative counterpart). We propose an efficient algorithm to compute it and demonstrate its usage on two large scale real-world case studies coming from the automotive domain and from CPS education in a Massively Open Online Course (MOOC) setting. We show that savings in computationally expensive simulations far outweigh any overheads incurred by an online approach

HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation

Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs. In this context, Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively high decoding speed compared to other methods. However, existing INR-based methods have failed to deliver rate quality performance comparable with the state of the art in video compression. This is mainly due to the simplicity of the employed network architectures, which limit their representation capability. In this paper, we propose HiNeRV, an INR that combines light weight layers with novel hierarchical positional encodings. We employs depth-wise convolutional, MLP and interpolation layers to build the deep and wide network architecture with high capacity. HiNeRV is also a unified representation encoding videos in both frames and patches at the same time, which offers higher performance and flexibility than existing methods. We further build a video codec based on HiNeRV and a refined pipeline for training, pruning and quantization that can better preserve HiNeRV's performance during lossy model compression. The proposed method has been evaluated on both UVG and MCL-JCV datasets for video compression, demonstrating significant improvement over all existing INRs baselines and competitive performance when compared to learning-based codecs (72.3% overall bit rate saving over HNeRV and 43.4% over DCVC on the UVG dataset, measured in PSNR)

Quantum Phase Transition of Spin-2 Cold Bosons in an Optical Lattice

The Bose-Hubbard Hamiltonian of spin-2 cold bosons with repulsive interaction in an optical lattice is proposed. After neglecting the hopping term, the site-independent Hamiltonian and its energy eigenvalues and eigenstates are obtained. We consider the hopping term as a perturbation to do the calculations in second order and draw the phase diagrams for different cases. The phase diagrams show that there is a phase transition from Mott insulator with integer number bosons to superfluid when the ratio $c_0/t$ ($c_0$ is the spin-independent on-site interaction and $t$ the hopping matrix element between adjacent lattice sites) is decreased to a critical value and that there is different phase boundary between superfluid and Mott insulator phase for different Zeeman level component in some ground states. We find that the position of phase boundary for different Zeeman level component is related to its average population in the Mott ground state.Comment: 16 pages, 6 figure

Thermocurrents and their Role in high Q Cavity Performance

Over the past years it became evident that the quality factor of a superconducting cavity is not only determined by its surface preparation procedure, but is also influenced by the way the cavity is cooled down. Moreover, different data sets exists, some of them indicate that a slow cool-down through the critical temperature is favourable while other data states the exact opposite. Even so there where speculations and some models about the role of thermo-currents and flux-pinning, the difference in behaviour remained a mystery. In this paper we will for the first time present a consistent theoretical model which we confirmed by data that describes the role of thermo-currents, driven by temperature gradients and material transitions. We will clearly show how they impact the quality factor of a cavity, discuss our findings, relate it to findings at other labs and develop mitigation strategies which especially addresses the issue of achieving high quality factors of so-called nitrogen doped cavities in horizontal test

Design of PDC Controllers by Matrix Reversibility for Synchronization of Yin

This paper investigates the synchronization of Yin and Yang chaotic T-S fuzzy Henon maps via PDC controllers. Based on the Chinese philosophy, Yin is the decreasing, negative, historical, or feminine principle in nature, while Yang is the increasing, positive, contemporary, or masculine principle in nature. Yin and Yang are two fundamental opposites in Chinese philosophy. The Henon map is an invertible map; so the Henon maps with increasing and decreasing argument can be called the Yang and Yin Henon maps, respectively. Chaos synchronization of Yin and Yang T-S fuzzy Henon maps is achieved by PDC controllers. The design of PDC controllers is based on the linear invertible matrix theory. The T-S fuzzy model of Yin and Yang Henon maps and the design of PDC controllers are novel, and the simulation results show that the approach is effective