We study universal aspects of thermalization induced by Trotterization, a procedure routinely used in gate-based quantum computation. We use the reduced-Bardeen-Cooper-Schrieffer model-quantum integrable with a classically integrable mean-field limit-where the effects of Trotter chaos are expected to be particularly stark. The resulting Trotterized chaotic dynamics is characterized by its Lyapunov spectrum and rescaled Kolmogorov-Sinai entropy. The chaos quantifiers depend on the Trotterization time step τ. We observe a Trotter transition at a finite step value τ_{c}≈sqrt[N]. While the dynamics is weakly chaotic for time steps τ≪τ_{c}, the regime of large Trotterization steps is characterized by short temporal correlations. We derive two different scaling laws for the two different regimes by numerically fitting the maximum Lyapunov exponent data. The scaling law of the large τ limit agrees well with the one derived from the kicked top map. Beyond its relevance to current quantum computers, our work opens other directions-such as probing observables like the Loschmidt echo, which lie beyond standard mean-field description--across the Trotter transition we uncover.
We propose searching for physics beyond the Standard Model in the low-transverse-momentum tracks accompanying hard-scatter events at the LHC. TeV-scale resonances connected to a dark QCD sector could be enhanced by selecting events with anomalies in the track distributions. As a benchmark, a quirk model with microscopic string lengths is developed, including a setup for event simulation. For this model, strategies are presented to enhance the sensitivity compared to inclusive resonance searches: a simple cut-based selection, a supervised search, and a model-agnostic weakly supervised anomaly search with the CATHODE method. Expected discovery potentials and exclusion limits are shown for 140 fb − 1 of 13 TeV proton-proton collisions at the LHC.
Although the OGD initiative has gained global momentum over the past two decades, the lack of a machine-readable format for much financial OGD in the U.S. hinders stakeholders' ability to use this information. This study draws on the "last mile problem"-a term that originally symbolizes inefficiencies in the final stage of delivering goods or services to end-users-to describe the difficulties that stakeholders face when analyzing PDFtype financial OGD. Following a design science methodology, this study proposes a report analysis framework to address this problem, develop processes, and evaluate its performance through a GASB standard-setting process (PIR). Results indicate that this framework achieves a 95.8 percent accuracy rate for data extraction from governmental reports and is four times faster than GASB's existing approach. This research contributes to the government accounting literature by applying accounting information system technologies to enhance the usability of OGD for various accounting users.
To unlock access to stronger winds, the offshore wind industry is advancing toward significantly larger and taller wind turbines. This massive upscaling motivates a departure from wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE-a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate nonstationary kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high-dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from offshore wind energy areas in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.
While the research on digital sustainability highlights how new technologies address environmental challenges, we lack a systematic understanding of the pathways through which digital technologies deliver environmentally sustainable outcomes. This study fills the gap by advancing a conceptual model of "digital sustainability solutions" (DSSs)-action pathways through which digital affordances are actualized for environmental sustainability. Our model makes three contributions. First, it advances our understanding of the multiple ways in which digital technologies deliver environmentally sustainable outcomes. Second, our synthesis illustrates how four classes of technology affordances (impact assessment, informed collective formation, behavioral modification, and spatial transcendence) actualize through four action domains (structure, resources, mindset, and interactions) toward three environmental sustainability goals (resource efficiency, waste control, and emission reduction). Third, our use of a real-world example, along with the identification of boundary conditions, illustrates the utility of DSSs as a toolkit to turn organizations and their relationships into a sustainable ecosystem.
