Miracle-dz/UniFAR-FTUs huggingface dataset:
Excited Sounds Augmented by Gestural Control

An acoustic interface to create excitation signals for digital resonators (waveguides, lumped models, modal synthesis and sample convolution) in synchronicity with augmenting control signals is presented. It is described how a direct acoustic excitation creates an intimate and intuitive interaction. Multiple prototypes and the lessons learned from them are documented in this paper.
Analysis of the Application and Control of the Distributed Energy Storage System in Microgrid

Because of the intermittence and randomness of renewable energy and the continuous variability of load. By configuring a certain capacity of energy storage in microgrid, the active and reactive power throughput can be flexibly and quickly adjusted to smooth load fluctuations and reliability of power supply improvement. It can improve the power quality and keep the system stable. Therefore, the application potential of distributed energy storage system in microgrid and distribution system is huge. In terms of give full play to the performance advantages of energy storage through distribution system, it is necessary to study the core technologies of modular integration, topology, capacity design and control of energy storage through distribution system. The feasibility and value of energy storage through distribution system in power system engineering are demonstrated. Based on the analysis of relevant requirements, a energy storage through distribution system based on micro energy storage unit is proposed in this paper. This structure is very suitable for energy storage components to be connected to the micro-grid to achieve plug and play, high efficiency, high reliability and high flexibility of energy storage system applications.
Research and Application of Relation Extraction based on Triple Relation Graph Convolutional Networks

In recent years, data in non-Euclidean spaces is becoming more and more. Traditional methods cannot perform feature extraction on these data. Most of existing methods just extract contextual semantic features from relational instances. Their structural features in corpora are ignored. To solve this problem, the paper proposed a relation extraction method based on triple relation graph convolutional networks (TRGCN). Based on the extraction of semantic features of sentences using convolutional neural networks, this method used the concept of triple relation graphs to represent structural features. In other words, triple relation graphs were formed by considering triples formed by the relation between two entities in one sentence as nodes and triples with common entities and same relations as edges. Finally, multiple-layer graph convolutional networks were used for training. As shown by experimental results, the method proposed in this paper achieved an F1 value of 86.8% on the SemEval 2010 Tesk 8 data set, indicating that it is better than mainstream convolutional neural networks and recurrent neural networks.
The Effectivity of Thymus vulgaris Extract on IL-1 Level and Bacterial Count in The Heart of Balb-c Mice Infected with MRSA

Article Info History Received: 21 July 2021 Accepted: 10 Dec 2021 Available: 31 Dec 2021 Abstract Background: The increasing phenomenon resistance of bacteria to various types of antibiotics, for example MRSA (Methicillin Resistant Staphylococcus aureus) became a serious problem. Over the last few decades S. aureus has become the dominant cause of endocarditis. MRSA data in Indonesia are still rarely reported. This has led to the importance of extracting natural substances that have anti-bacterial effects or that modulate immune response such as Thymus vulgaris (TV). It was hypothesized that TV can overcome MRSA infection by increasing IL-1, which is the initial cytokine in a natural immune response stimulated by the presence of S. aureus. Objective: This study was to investigate the effect of Thymus vulgaris extract on IL1 and bacterial counts in the heart of balb-c mice infected with MRSA. Methods: There were 30 male Balb-c mice randomly divided into six groups. Group K (mice infected with MRSA without treatment),P1(mice treated with TV extract 7 days later infected with MRSA), P2(mice infected and treated with vancomycin), P3(mice infected and treated with TV+vancomycin extract), P4(mice infected and treated with amoxicillin), and P5 (mice infected and treated with TV extract+amoxicillin). IL-1 and bacterial count was assessed by ELISA and microbiological culture. Results: The result showed that there was a significant increase in IL-1 between groups (p=0.001). The median decrease in the bacterial count in the heart was 0 in almost treatment group except for P4 group which its median was 26.000 (p=0.161). The correlation between IL-1 levels and MRSA count in the heart had a weak negative correlation (r=-0.182). Conclusion: Thymus vulgaris extract was significantly effective to increase IL-1 levels but not proven to decrease MRSA count in the heart of balb-c mice.
ExoCAM: A 3D Climate Model for Exoplanet Atmospheres

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) project was initiated to compare 3D climate models that are commonly used for predicting theoretical climates of habitable zone extrasolar planets. One of the core models studied as part of THAI is ExoCAM, an independently curated exoplanet branch of the National Center for Atmospheric Research Community Earth System Model (CESM), version 1.2. 1. ExoCAM has been used for studying atmospheres of terrestrial extrasolar planets around a variety of stars. To accompany the THAI project and provide a primary reference, here we describe ExoCAM and what makes it unique from standard configurations of CESM. Furthermore, we also conduct a series of intramodel sensitivity tests of relevant moist physical tuning parameters while using the THAI protocol as our starting point. A common criticism of 3D climate models used for exoplanet modeling is that cloud and convection routines often contain free parameters that are tuned to the modern Earth, and thus may be a source of uncertainty in evaluating exoplanet climates. Here, we explore sensitivities to numerous configuration and parameter selections, including a recently updated radiation scheme, a different cloud and convection physics package, different cloud and precipitation tuning parameters, and a different sea ice albedo. Improvements to our radiation scheme and the modification of cloud particle sizes have the largest effects on global mean temperatures, with variations up to ∼10 K, highlighting the requirement for accurate radiative transfer and the importance of cloud microphysics for simulating exoplanetary climates. However, for the vast majority of sensitivity tests, climate differences are small. For all cases studied, intramodel differences do not bias general conclusions regarding climate states and habitability.
SU(2) hyper-clocks: Quantum engineering of spinor interferences for time and frequency metrology

In 1949, Ramsey’s method of separated oscillating ﬁelds was elaborated boosting over many decades metrological performances of atomic clocks and becoming the standard technique for very high precision spectroscopic measurements. A generalization of this interferometric method is presented replacing the two single coherent excitations by arbitrary composite laser pulses. The rotation of the state vector of a two-level system under the eﬀect of a single pulse is described using the Pauli matrices basis of the SU(2) group. It is then generalized to multiple excitation pulses by a recursive Euler-Rodrigues-Gibbs algorithm describing a composition of rotations with diﬀerent rotation axes. A general analytical formula for the phase-shift associated with the clock’s interferometric signal is derived. As illustrations, hyper-clocks based on three-pulse and ﬁve-pulse interrogation protocols are studied and shown to exhibit nonlinear cubic and quintic sensitivities to residual probe-induced light-shifts. The presented formalism is well suited to optimize composite phase-shifts produced by tailored quantum algorithms in order to design a new generation of optical frequency standards and robust engineering control of atomic interferences in AMO physics with cold matter and anti-matter.
Oscillatory Flow in the Inlet Region of a Circular Tube

The velocity distributions of laminar xisymmetric os illatory flows in the inletregion of a long circular tube in case the flowrate changes tosinuous status are measured with laser Doppler velocimeter, and the unsteady incompressible NaviesStokes equations expressed interms of voracity and stream functions are also *naked numerically as to the internal oscillatory flow. The numerical results, as compared with the experimental results, proved in close agreement with each other as regards velocity profile. It was furthermore shown that the behavior fits variation ismuch characterized by Strauhal Numbers defined by mean flowrate, frequency and tube diameter.
High prevalence of olfactory disorders 18 months after contracting COVID-19

Reduced olfactory function is the symptom with the highest prevalence in COVID-19 with nearly 70% of individuals with COVID-19 experiencing partial or total loss of their sense of smell at some point during the disease. Recent reports suggest that more than 7 months after recovering from COVID-19, a large proportion of these individuals still have olfactory dysfunction of some form. To establish the prevalence of olfactory dysfunction 18 months and beyond, we tested 100 individuals with established COVID-19 in the first wave of the pandemic using psychophysical full-scale testing of smell and taste functions as well as assessments of parosmia. Participants were recruited from an ongoing study, comprising healthcare workers at a hospital in Stockholm, Sweden, that are regularly tested for SARS-CoV-2 IgG antibodies since the start of the pandemic in Sweden. To assess potential skewed recruitment of individuals with prior olfactory dysfunction and assess normal rate of dysfunctions in the used population, 44 SARS-CoV-2 IgG naive individuals were also tested as a control group. One and a half year after COVID-19, more than one third of individuals recovered from COVID-19 demonstrated a clinical reduction in their sense of smell. Critically, nearly half of COVID-19-recovered individuals complaint about parosmia. Prevalence of gustatory dysfunction was, however, low (3%). In summary, a full 65% of individuals recovered from COVID-19 experience olfactory dysfunction of some form 18 months later. Given the amount of time since initial insult to the olfactory system, it is likely that these olfactory problems are permanent.
Are Sovereign Ratings Biased Against Africa?

Credit rating agencies play a crucial role in financial markets, but are often criticized for particular judgements they make. With debt sustainability in Africa coming under pressure during the COVID-19 pandemic, some market commentators renewed concerns about anti-African bias in sovereign credit ratings. Using ratings data from one of the largest agencies, and economic and fiscal data independently sourced from the IMF, this article formally tests for anti-African bias in sovereign ratings. In doing so, it focuses purely on quantitative explanatory factors, given the potential for any bias in ratings to be reflected in implicit bias within qualitative judgements made by rating analysts. However, there is no statistical evidence of bias against African sovereign over the data sample, which runs from 2016. As such, any future improvements in African sovereign ratings are likely to reflect sustained improvements in economic and fiscal strength and other factors influencing creditworthiness, rather than the removal of any prejudice against this group.
MEG II experiment status and prospect

The MEG II experiment at Paul Scherrer Institute (PSI) in Switzerland aims to achieve a sensitivity of 6 × 10−14 on the charged lepton flavor violating decay μ+ → e+γ. The current upper limit on this decay is 4.2× 10−13 at 90% Confidence Level (CL), set by the first phase of MEG. This result was achieved using the PSI muon beam at a reduced intensity, 3×107 μ+/s, to keep the background at a manageable level. The upgraded detectors in MEG II can cope with a higher intensity, thus the experiment is expected to run at a 7 × 107 μ+/s intensity. The new low mass, single volume, high granularity tracker, together with a new highly segmented timing counter, guarantees better resolutions for the positron detection. Moreover, the replacement of the old PhotoMultiplier Tubes (PMTs) with Multi-Pixel Photon Counters (MPPCs) in the inner face of the liquid xenon photon detector improved its performance. The details of the upgraded detectors and their present status will be discussed, together with the latest results from last year’s pre-engineering run and the perspective for the 2021 run, the first with all the detectors and electronics installed.
TOAST: Trajectory Optimization and Simultaneous Tracking Using Shared Neural Network Dynamics

Neural networks have been increasingly employed in Model Predictive Controller (MPC) to control nonlinear dynamic systems. However, MPC still poses a problem that an achievable update rate is insufficient to cope with model uncertainty and external disturbances. In this paper, we present a novel control scheme that can design an optimal tracking controller using the neural network dynamics of the MPC, making it possible to be applied as a plug-and-play extension for any existing model-based feedforward controller. We also describe how our method handles a neural network containing history information, which does not follow a general form of dynamics. The proposed method is evaluated by its performance in classical control benchmarks with external disturbances. We also extend our control framework to be applied in an aggressive autonomous driving task with unknown friction. In all experiments, our method outperformed the compared methods by a large margin. Our controller also showed low control chattering levels, demonstrating that our feedback controller does not interfere with the optimal command of MPC. Our video can be found at: https://youtu.be/YQG0yHE5jWw
The spatial scale dimension of speech processing in the human brain

In the past three decades, neuroimaging has provided important insights into structure-function relationships in the human brain. Recently, however, the methods for analyzing functional magnetic resonance imaging (fMRI) data have come under scrutiny, with studies questioning crosssoftware comparability, the validity of statistical inference and interpretation, and the influence of the spatial filter size on neuroimaging analyses. As most fMRI studies only use a single filter for analysis, much information on the size and shape of the BOLD signal in Gaussian scale space remains hidden and constrains the interpretation of fMRI studies. To investigate the influence of the spatial observation scale on fMRI analysis, we use a spatial multiscale analysis with a range of Gaussian filters from 1–20 mm (full width at half maximum) to analyze fMRI data from a speech repetition paradigm in 25 healthy subjects. We show that analyzing the fMRI data over a range of Gaussian filter kernels reveals substantial variability in the neuroanatomical localization and the average signal strength and size of suprathreshold clusters depending on the filter size. We also demonstrate how small spatial filters
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning

Self-supervised learning has not been fully explored for point cloud analysis. Current frameworks are mainly based on point cloud reconstruction. Given only 3D coordinates, such approaches tend to learn local geometric structures and contours, while failing in understanding high level semantic content. Consequently, they achieve unsatisfactory performance in downstream tasks such as classification, segmentation, etc. To fill this gap, we propose a generic Contour-Perturbed Reconstruction Network (CP-Net), which can effectively guide self-supervised reconstruction to learn semantic content in the point cloud, and thus promote discriminative power of point cloud representation. First, we introduce a concise contour-perturbed augmentation module for point cloud reconstruction. With guidance of geometry disentangling, we divide point cloud into contour and content components. Subsequently, we perturb the contour components and preserve the content components on the point cloud. As a result, self supervisor can effectively focus on semantic content, by reconstructing the original point cloud from such perturbed one. Second, we use this perturbed reconstruction as an assistant branch, to guide the learning of basic reconstruction branch via a distinct dual-branch consistency loss. In this case, our CP-Net not only captures structural contour but also learn semantic content for discriminative downstream tasks. Finally, we perform extensive experiments on a number of point cloud benchmarks. Part segmentation results demonstrate that our CP-Net (81.5% of mIoU) outperforms the previous self-supervised models, and narrows the gap with the fully-supervised methods. For classification, we get a competitive result with the fully-supervised methods on ModelNet40 (92.5% accuracy) and ScanObjectNN (87.9% accuracy). The codes and models will be released afterwards.
Existence, uniqueness and stability of steady vortex rings of small cross-section

This paper is concerned with steady vortex rings in an ideal fluid of uniform density, which are special global axi-symmetric solutions of the three-dimensional incompressible Euler equation. We systematically establish the existence, uniqueness and nonlinear orbital stability of steady vortex rings of small cross-section for which the potential vorticity is constant throughout the core. The latter two answer a long-standing question since the pioneering work of Fraenkel and Berger \cite{BF1} (Acta Math., 1974). To achieve our goal, we rescale the Stokes stream function of vortex ring by its cross-section radius, and expand it at the well-known Rankine vortex using Taylor's formula, where the estimates for coefficients are obtained by a decomposition according to Green's function and local Pohozaev identities. The main observations are: The stream function is even and has a translational invariance in $z$-direction; the zero point for the $r$-coefficient of linear term in its expansion determines the asymptotic location of vortex ring, which appears as the condition to eliminate the degenerate direction in Lyapunov-Schmidt reduction argument for existence; the non-vanishing condition of the second order $r$-coefficient at foresaid zero point is verified as one of the essential factors for uniqueness, while the negativity means that these vortex rings maximizes the functional composed of kinetic energy and impulse. By applying the Arnol'd's dual variational principle together with the uniqueness result, we are finally able to prove the nonlinear orbital stability of thin vortex rings. This result gives a large class of stable vortex rings supported on topological tori, which is different from Hill's spherical vortex discussed by Choi \cite{Choi20} (Comm. Pure Appl. Math., 2023).
Beyond the revised cardiac risk index: Validation of the hospital frailty risk score in non-cardiac surgery

Frailty is an established risk factor for adverse outcomes following non-cardiac surgery. The Hospital Frailty Risk Score (HFRS) is a recently described frailty assessment tool that harnesses administrative data and is composed of 109 International Classification of Disease variables. We aimed to examine the incremental prognostic utility of the HFRS in a generalizable surgical population. Using linked administrative databases, a retrospective cohort of patients admitted for non-cardiac surgery between October 1st, 2008 and September 30th, 2019 in Alberta, Canada was created. Our primary outcome was a composite of death, myocardial infarction or cardiac arrest at 30-days. Multivariable logistic regression was undertaken to assess the impact of HFRS on outcomes after adjusting for age, sex, components of the Charlson Comorbidity Index (CCI), Revised Cardiac Risk Index (RCRI) and peri-operative biomarkers. The final cohort consisted of 712,808 non-cardiac surgeries, of which 55·1% were female and the average age was 53·4 +/- 22·4 years. Using the HFRS, 86.3% were considered low risk, 10·7% were considered intermediate risk and 3·1% were considered high risk for frailty. Intermediate and high HFRS scores were associated with increased risk of the primary outcome with an adjusted odds ratio of 1·61 (95% CI 1·50–1.74) and 1·55 (95% CI 1·38–1·73). Intermediate and high HFRS were also associated with increased adjusted odds of prolonged hospital stay, in-hospital mortality, and 1-year mortality. The HFRS is a minimally onerous frailty assessment tool that can complement perioperative risk stratification in identifying patients at high risk of short- and long-term adverse events.
Spitzer Publication Statistics

We present statistics on the number of refereed astronomy journal articles that used data from NASA’s Spitzer Space Telescope through the end of the calendar year 2020. We discuss the various types of science programs and science categories that were used to collect data during the mission and discuss how operational changes brought on by the depletion of cryogen in 2009 May, including the resulting budget cuts, impacted the publication rate. The post-cryogenic (warm) mission produced fewer papers than the cryogenic mission, but the percentage of the exposure time published did not appreciably change between the warm and cryogenic missions. This was mostly because in the warm mission the length of observations increased, so that each warm paper on average uses more data than the cryogenic papers. We also discuss the speed of publication, archival usage, and the tremendous efficacy of the Legacy and Exploration Science programs (large, coherent investigations), including the value of having well-advertised enhanced data products hosted in centralized archives. We also identify the observations that have been published the largest number of times, and sort them by a variety of metrics (including program type, instrument used, and observation length). Data that have the highest reuse rates in publications were taken early in the Spitzer mission, or belong to one of the large surveys (large either in number of objects, in number of hours observed, or in area covered on the sky). We also assess how often authors have cited the Spitzer fundamental papers or have correctly referenced the Spitzer data they used, finding that as many as 40% of papers have failed to cite the papers, and 15% have made it impossible to identify the data they used.
Strangeness ${S=-3}$ and ${-4}$ baryon-baryon interactions in chiral effective field theory

Studies of the baryon-baryon interaction involving hyperons within chiral effective field theory, so far performed up to next-to-leading order (NLO) in the chiral expansion, have shown that for the strangeness $S=-1$ ($\Lambda N$, $\Sigma N$) and $S=-2$ ($\Lambda \Lambda$, $\Xi N$) sectors a consistent and satisfactory description of the available scattering data and experimental constraints can be achieved based on the assumption of broken SU(3) flavor symmetry. We explore the possible extension of this approach at the NLO level to strangeness $S=-3$ and $S=-4$ baryon-baryon systems where empirical information is rather scarce. Specifically we address the question how measurements of two-body correlation functions in heavy-ion collisions and/or in high-energetic proton-proton collisions can help to constrain the interaction in channels like $\Xi\Lambda$ or $\Xi\Xi$.