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This paper reconciles the asymptotic disagreement between Bayesian and frequentist inference in set-identified models by adopting a multiple-prior (robust) Bayesian approach. We propose new tools for Bayesian inference in set-identified models and show that they have a well-defined posterior interpretation in finite samples and are asymptotically valid from the frequentist perspective. The main idea is to construct a prior class that removes the source of the disagreement: the need to specify an unrevisable prior for the structural parameter given the reduced-form parameter. The corresponding class of posteriors can be summarized by reporting the ‘posterior lower and upper probabilities’ of a given event and/or the ‘set of posterior means’and the associated ‘robust credible region’. We show that the set of posterior means is a consistent estimator of the true identified set and the robust credible region has the correct frequentist asymptotic coverage for the true identified set if it is convex. Otherwise, the method provides posterior inference about the convex hull of the identified set. For impulse-response analysis in set-identified Structural Vector Autoregressions, the new tools can be used to overcome or quantify the sensitivity of standard Bayesian inference to the choice of an unrevisable prior.

The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. Additional speedups were achieved by exploiting the mixed precision capabilities of the newer GPUs and by using lookup tables. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries. It may become a powerful tool for many applications that need to perform a large number of acoustic simulations, such as training machine learning systems for audio signal processing, or for real-time room acoustics simulations for immersive multimedia systems, such as augmented or virtual reality.

This paper presents an algorithm for immersive processing of a multichannel recording for headphone listening. The material listened to with headphones should evoke impressions in the listener that are identical to those experienced when listening from a multi-speaker system. In order to allow the processing system to be adapted to the individual anatomical characteristics of the listener, an algorithm was developed, which is based on data in the form of directional room impulse responses acquired with an intensity probe, in the form of classical room pressure impulse responses at the excitation emitted by the individual loudspeakers of the listening system. The listening room characteristics recorded in this way are supplemented with data from the Head Related Transfer Function (HRTF) databases, which can be selected according to the listener's perception. The study compared the effects of an impulse response segmentation algorithm using publicly available HRTF averaging databases with the classic approach using individualized binaural room impulse responses (BRIR). Reference was also made to available binauralization algorithms using dummy head.

Impulse‐response functions (IRFs) are mathematical functions that represent the response of the coupled carbon‐climate system to different trajectories of fossil‐fuel emissions and land‐use. They help understand the time‐scales of the Earth system response to perturbations and have played a prominent role in climate policy. However, there are limitations due to assumptions of linearity and time/state‐invariance in obtaining IRFs. Recent research has brought new mathematical and computational techniques to address non‐linearity and to obtain spatially explicit state‐aware IRFs to tackle a new set of questions in carbon‐climate science. One path forward is to integrate the IRF framework with data‐driven methods, such as machine learning, to learn these state dependencies in a hybrid approach. Specifically, we present a prototype of a state‐aware carbon‐climate system emulator. Additionally, we discuss how to compare IRFs with observed radiocarbon data and how to apply the convolution of IRFs in climate change attribution studies. Plain Language Summary Scientists use “impulse response functions” (IRFs) to understand how the Earth's climate system responds to changes in greenhouse gas emissions and land use. These tools have been helpful in developing key metrics for climate policies, but they have limitations. For example, they assume the climate system responds in a linear and consistent way, which isn't always true. New mathematical and computational techniques have been developed to overcome these limitations. We suggest using these new techniques to answer key challenges in climate science, such as comparing IRF results with real‐world data and extend IRFs to account for state changes in space and time. In this context, we introduce a prototype for an emulator of Earth's climate system that is aware of such state changes. This emulator accurately replicates the full response of the climate system to changes in greenhouse gas emissions, laying the groundwork for tools to rapidly develop more effective policies to mitigate climate change. Key Points Impulse‐response functions (IRFs) are an effective modeling tool for representing carbon‐climate interactions A new set of methods can be used for obtaining spatially and temporally explicit IRFs removing linearity and state invariance assumptions These new methods offer new possibilities for improving climate change attribution studies

The blood oxygen level‐dependent (BOLD) signal has been instrumental in characterizing brain activity. While the spatial resolution of fMRI continues to improve, relatively few methods have focused on enhancing and leveraging temporal resolution to investigate the spatiotemporal dynamics of the hemodynamic response. In this study, we applied a reordering method to achieve ultra‐high temporal resolution (60 ms) in data acquired during a somatosensory stimulation paradigm. We then used a finite impulse response model (FIR) for each participant (N = 31) to preserve the temporal dynamics in the statistical analysis. At the group level, we employed an ANOVA combined with 4D nonparametric permutation testing to identify significant signal changes in time across the whole brain. Our results characterize the hemodynamic response in terms of both its temporal and spatial patterns and reveal distinct differences in response shapes within the somatosensory system. This method introduces a time‐resolved approach to BOLD signal analysis, drawing inspiration from grand‐average techniques commonly used in EEG research. This study employed a novel combination of methodological approaches to examine the spatiotemporal dynamics of the somatosensory‐evoked BOLD signal across the whole brain with high temporal resolution (60 ms). By applying a time‐resolved hemodynamic analysis, this approach allows for inspection of 4D spatiotemporal dynamics of the BOLD signal.

Impulse response functions (IRF), the response in a climate parameter to an emission pulse of CO2, are used to characterize Earth system response timescales and to calculate Global Warming Potentials (GWPs). GWPs are widely used to compare emissions of different greenhouse gases and to compute CO2 equivalent emissions as reported by governments to the United Nations Framework Convention on Climate Change (UNFCCC). The GWP of any gas x is the absolute GWP of gas x Absolute and relative Global Warming Potential (AGWPx) divided by AGWP of CO2. Ideally, AGWP CO2 and GWPx would be independent of atmospheric CO2 and climate. However, AGWP CO2 , and, in turn, GWPx change under rising atmospheric CO2 and global warming, affecting the emission reporting under the UNFCCC. Here, we apply perturbed parameter ensemble simulations, constrained in a Bayesian approach by observational data, to investigate how AGWP CO2 and IRF vary under different atmospheric background CO2 levels (CO 2,bg ). We provide analytical formulations to compute AGWP CO2 and IRF for CO2, ocean and land carbon uptake, global mean surface air temperature, steric sea level, and ocean heat content, and to adjust these metrics to different CO 2,bg . AGWP CO2 , given by the time-integrated response in CO2 at year 100 multiplied by its radiative efficiency, is 101.8(±13.5) 10−15 yr W m−2 kg-CO 2−1 for CO 2,bg = 425 ppm and decreases by 7% for CO 2,bg = 500 ppm. The decrease is driven by a decrease in the radiative efficiency of CO2, partly canceled by a concomitant increase of IRF CO2 due to muted ocean and land carbon uptake under higher CO2 levels. We recommend regularly adjusting AGWP CO2 and, in turn, GWPs of long-lived gases to contemporary atmospheric CO2 and climate.

Golden jackal optimization (GJO) is inspired by the cooperative attacking behavior of golden jackals and mainly simulates searching for prey, stalking and enclosing prey, and pouncing on prey to solve complicated optimization problems. However, the basic GJO has the disadvantages of premature convergence, a slow convergence rate and low computation precision. To enhance the overall search and optimization abilities, an enhanced golden jackal optimization (EGJO) method with the elite opposition-based learning technique and the simplex technique is proposed to address adaptive infinite impulse response system identification. The intention is to minimize the error fitness value and obtain the appropriate control parameters. The elite opposition-based learning technique boosts population diversity, enhances the exploration ability, extends the search range and avoids search stagnation. The simplex technique accelerates the search process, enhances the exploitation ability, improves the computational precision and increases the optimization depth. EGJO can not only achieve complementary advantages to avoid search stagnation but also balance exploration and exploitation to arrive at the best value. Three sets of experiments are used to verify the effectiveness and feasibility of EGJO. The experimental results clearly demonstrate that the optimization efficiency and recognition accuracy of EGJO are superior to those of AOA, GTO, HHO, MDWA, RSO, WOA, TSA and GJO. EGJO has a faster convergence rate, higher computation precision, better control parameters and better fitness value, and it is stable and resilient in solving the IIR system identification problem.

This paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling, and reflector position estimation. The dataset is provided with software utilities to easily access, manipulate, and visualize the data as well as baseline methods for echo-related tasks.

Following the emergence of the Internet, the Internet of things (IOT) brought about another wave of technological and economic revolutions. Through the lens of the production process, this study utilises the dynamic network slack-based measure model in data envelopment analysis to evaluate 32 IOT companies in Taiwan in terms of their innovation efficiency, operational efficiency and overall efficiency for the period of 2007–2017. Empirical results reveal that the average operational and overall efficiencies of IOT companies in Taiwan have been decreasing considerably since 2008. However, their average innovation efficiency remains stable over the sample period owing to government reductions in enterprise research and development (R&D) tax credit incentives. Through the impulse response function method, this study further confirms that the Statute for Industrial Innovation, which was implemented in 2010 and revised and reimplemented in 2016, specifically, policies concerning enterprise R&D tax credits, affect the efficiencies of IOT companies in Taiwan. Overall, this study reveals the performance evaluation process of IOT companies by showing that their innovation capability affects their operational efficiency. Thus, the government is advised to incorporate innovation measures into relevant industrial policies to achieve policy effectiveness.

Rendering immersive spatial audio in virtual reality (VR) and video games demands a fast and accurate generation of room impulse responses (RIRs) to recreate auditory environments plausibly. However, the conventional methods for simulating or measuring long RIRs are either computationally intensive or challenged by low signal-to-noise ratios. This study is propelled by the insight that direct sound and early reflections encapsulate sufficient information about room geometry and absorption characteristics. Building upon this premise, we propose a novel task termed \"RIR completion,\" aimed at synthesizing the late reverberation given only the early portion (50 ms) of the response. To this end, we introduce DECOR , D eep E xponential C ompletion O f R oom impulse responses, a deep neural network structured as an encoder-decoder designed to predict multi-exponential decay envelopes of filtered noise sequences. The proposed method is compared against a much larger adapted state-of-the-art network, and comparable performance shows promising results supporting the feasibility of the RIR completion task. The RIR completion can be widely adapted to enhance RIR generation tasks where fast late reverberation approximation is required.