Miracle-dz/UniFAR-FTUs huggingface dataset:
Meson resonance gas in a relativistic quark model: scalar vs vector confinement and semishort range correlations

Smooth transitions from hadronic matter to hot and dense matter of quantum chromodynamics accompany continuous transformations in effective degrees of freedom. The microscopic descriptions should include relativistic quarks interacting inside of hadrons. In this work we construct a schematic constituent quark model with relativistic kinematics which captures the global trends of meson spectra in the light, strange, and charm quark sectors. We examine the roles of the scalar- and vector-confining potentials as well as semishort range correlations in estimating the strength of central, spin-spin, and spin-orbit interactions. The quark dynamics in low-lying mesons is very sensitive to relativistic kinematics and short range interactions, while in high-lying mesons are sensitive to the composition of scalar- and vector-confinement. After expressing mesons in terms of quark wave functions, we use them to describe the quark occupation probability in a meson resonance gas, and discuss how it can be related to its counterpart in a quark-gluon-plasma.
Mixtures of Gaussian Process Experts with SMC2

Gaussian processes are a key component of many ﬂexible statistical and machine learning models. However, they exhibit cubic computational complexity and high memory constraints due to the need of inverting and storing a full covariance matrix. To circumvent this, mixtures of Gaussian process experts have been considered where data points are assigned to independent experts, reducing the complexity by allowing inference based on smaller, local covariance matrices. Moreover, mixtures of Gaussian process experts sub-stantially enrich the model’s ﬂexibility, allowing for behaviors such as non-stationarity, heteroscedasticity, and discontinuities. In this work, we construct a novel inference approach based on nested sequential Monte Carlo samplers to simultaneously infer both the gating network and Gaussian process expert parameters. This greatly improves inference compared to importance sampling, particularly in settings when a stationary Gaussian process is inappropriate, while still being thoroughly parallelizable.
Measuring design compliance using neural language models: an automotive case study

As the modern vehicle becomes more software-defined, it is beginning to take significant effort to avoid serious regression in software design. This is because automotive software architects rely largely upon manual review of code to spot deviations from specified design principles. Such an approach is both inefficient and prone to error. In recent days, neural language models pre-trained on source code are beginning to be used for automating a variety of programming tasks. In this work, we extend the application of such a Programming Language Model (PLM) to automate the assessment of design compliance. Using a PLM, we construct a system that assesses whether a set of query programs comply with Controller-Handler, a design pattern specified to ensure hardware abstraction in automotive control software. The assessment is based upon measuring whether the geometrical arrangement of query program embeddings, extracted from the PLM, aligns with that of a set of known implementations of the pattern. The level of alignment is then transformed into an interpretable measure of compliance. Using a controlled experiment, we demonstrate that our technique determines compliance with a precision of 92%. Also, using expert review to calibrate the automated assessment, we introduce a protocol to determine the nature of the violation, helping eventual refactoring. Results from this work indicate that neural language models can provide valuable assistance to human architects in assessing and fixing violations in automotive software design.
A Comprehensive IoT Node Proposal Using Open Hardware. A Smart Farming Use Case to Monitor Vineyards

The last decade has witnessed a significant reduction in prices and an increased performance of electronic components, coupled with the influence of the shift towards the generation of open resources, both in terms of knowledge (open access), programs (open-source software), and components (open hardware). This situation has produced different effects in today’s society, among which is the empowerment of citizens, called makers, who are themselves able to generate citizen science or build assembly developments. Situated in the context described above, the current study follows a Do-It-Yourself (DIY) approach. In this way, it attempts to define a conceptual design of an Internet of Things (IoT) node, which is reproducible at both physical and behavioral levels, to build IoT nodes which can cover any scenario. To test this conceptual design, this study proposes a sensorization node to monitor meteorological phenomena. The node is called SEnviro (node) and features different improvements such as: the possibility of remote updates using Over-the-Air (OTA) updates; autonomy, using 3G connectivity, a solar panel, and applied energy strategies to prolong its life; and replicability, because it is made up of open hardware and other elements such as 3D-printed pieces. The node is validated in the field of smart agriculture, with the aim of monitoring different meteorological phenomena, which will be used as input to disease detection models to detect possible diseases within vineyards.
Spin Josephson effects of spin–orbit-coupled Bose–Einstein condensates in a non-Hermitian double well

In this paper, we investigate the spin and tunneling dynamics of a spin–orbit-coupled noninteracting Bose–Einstein condensate in a periodically driven non-Hermitian double-well potential. Under high-frequency driving, we obtain the effective time-averaged Hamiltonian by using the standard time-averaging method, and analytically calculate the Floquet quasienergies, revealing that the parity-time ( )-breaking phase transition appears even for arbitrarily small non-Hermitian parameters when the spin–orbit coupling strength takes half-integer value, irrespective of the values of other parameters used. When the system is -symmetric with balanced gain and loss, we find numerically and analytically that in the broken -symmetric regions, there will exist the net spin current together with a vanishing atomic current, if we drop the contribution of the exponential growth of the norm to the current behaviors. When the system is non- -symmetric, though the quasienergies are partial complex, a stable net spin current can be generated by controlling the periodic driving field, which is accompanied by a spatial localization of the condensate in the well with gain. The results deepen the understanding of non-Hermitian physics and could be useful for engineering a variety of devices for spintronics.
YOLOX-PAI: An Improved YOLOX Version by PAI

We develop an all-in-one computer vision tool-box named EasyCV to facilitate the use of various SOTA computer vision methods. Recently, we add YOLOX-PAI, an improved version of YOLOX, into EasyCV. We conduct ablation studies to investigate the inﬂuence of some detection methods on YOLOX. We also provide an easy use for PAI-Blade which is used to accelerate the inference process based on BladeDISC and TensorRT. Finally, we receive 42.8 mAP on COCO dateset within 1.0 ms on a single NVIDIA V100 GPU, which is a bit faster than YOLOv6. A simple but efﬁ-cient predictor api is also designed in EasyCV to conduct end2end object detection. Codes and models are now available at: https://github. com/alibaba/EasyCV.
Randomised Composition and Small-Bias Minimax

We prove1 two results about randomised query complexity $\mathbf{R}(f)$. First, we introduce a linearised complexity measure LR and show that it satisfies an inner-optimal composition theorem: $\mathbf{R}(f^{\circ} g)\geq\Omega(\mathbf{R}(f)\mathbf{L R}(g))$ for all partial f and g, and moreover, LR is the largest possible measure with this property. In particular, LR can be polynomially larger than previous measures that satisfy an inner composition theorem, such as the max-conflict complexity of Gavinsky, Lee, Santha, and Sanyal (ICALP 2019). Our second result addresses a question of Yao (FOCS 1977). He asked if $\epsilon$-error expected query complexity $\overline{\mathbf{R}}_{\epsilon}(f)$ admits a distributional characterisation relative to some hard input distribution. Vereshchagin (TCS 1998) answered this question affirmatively in the bounded-error case. We show that an analogous theorem fails in the small-bias case $\epsilon=1/2-o(1)$. 1This is an extended abstract. For the full version of this article, please refer to [BDBGM22].
Chosen methods of improving object recognition of small objects with weak recognizable features

Many object detection models struggle with several problematic aspects of small object detection including the low number of samples, lack of diversity and low features representation. Taking into account that GANs belong to generative models class, their initial objective is to learn to mimic any data distribution. Using the proper GAN model would enable augmenting low precision data increasing their amount and diversity. This solution could potentially result in improved object detection results. Additionally, incorporating GAN-based architecture inside deep learning model can increase accuracy of small objects recognition. In this work the GAN-based method with augmentation is presented to improve small object detection on VOC Pascal dataset. The method is compared with different popular augmentation strategies like object rotations, shifts etc. The experiments are based on FasterRCNN model.
Improving Electricity Market Economy via Closed-Loop Predict-and-Optimize

—The electricity market clearing is usually implemented via an open-loop predict-then-optimize (O-PO) process: it ﬁrst predicts the available power of renewable energy sources (RES) and the system reserve requirements; then, given the predictions, the markets are cleared via optimization models, i.e., unit com- mitment (UC) and economic dispatch (ED), to pursue the optimal electricity market economy. However, the market economy could suffer from the open-loop process because its predictions may be overly myopic to the optimizations, i.e., the predictions seek to improve the immediate statistical forecasting errors instead of the ultimate market economy. To this end, this paper proposes a closed-loop predict-and-optimize (C-PO) framework based on the tri-level mixed-integer programming, which trains economy- oriented predictors tailored for the market-clearing optimization to improve the ultimate market economy. Speciﬁcally, the upper level trains the economy-oriented RES and reserve predictors according to their induced market economy; the middle and lower levels, with given predictions, mimic the market-clearing process and feed the induced market economy results back to the upper level. The trained economy-oriented predictors are then embedded into the UC model, forming a prescriptive UC model that can simultaneously provide RES-reserve predictions and UC decisions with enhanced market economy. Numerical case studies on an IEEE 118-bus system illustrate potential economic and practical advantages of C-PO over O-PO, robust UC, and stochastic UC.
OSF: An Open-Source Framework for Synchronous Flooding over Multiple Physical Layers

Flooding protocols based on concurrent transmissions are regarded as the most reliable way to collect or disseminate data across a multi-hop low-power wireless mesh network. Recent works have shown that such protocols are effective for narrowband communication not only over IEEE 802.15. 4, but also over the BLE 5 physical layers (PHYs). However, to date, existing literature has only built synchronous flooding solutions on top of a single PHY, and there has been no attempt to leverage different PHYs at runtime to increase performance. This paper fills this gap and presents OSF, an open-source framework that enables the design of multi-PHY synchronous flooding solutions thanks to a novel radio driver and middle-ware architecture capable of dynamically switching the underlying physical layer. This allows exploitation of the specific benefits of each PHY (e.g., higher data-rate, increased robustness) on-demand during each flood, increasing performance. We tailor OSF to the off-the-shelf nRF52840 platform, and showcase its benefits by comparing single-PHY and multi-PHY synchronous flooding solutions on a real-world testbed.
Morphologies of Galaxies at z ≳ 9 Uncovered by JWST/NIRCam Imaging: Cosmic Size Evolution and an Identification of an Extremely Compact Bright Galaxy at z ∼ 12

We present morphologies of galaxies at z ≳ 9 resolved by JWST/NIRCam 2–5 μm imaging. Our sample consists of 22 galaxy candidates identified by stringent dropout and photo-z criteria in GLASS, CEERS, SMACS J0723, and Stephan’s Quintet flanking fields, one of which has been spectroscopically identified at z = 11.44. We perform surface brightness (SB) profile fitting with GALFIT for six bright galaxies with a signal-to-noise ratio = 10–40 on an individual basis and for stacked faint galaxies with secure point-spread functions (PSFs) of the NIRCam real data, carefully evaluating systematics by Monte Carlo simulations. We compare our results with those of previous JWST studies, and confirm that the effective radii r e of our measurements are consistent with those of previous measurements at z ∼ 9. We obtain r e ≃ 200–300 pc with the exponential-like profiles, Sérsic indexes of n ≃ 1–1.5, for galaxies at z ∼ 12–16, indicating that the relation of r e ∝ (1 + z) s for s=−1.22−0.16+0.17 explains cosmic evolution over z ∼ 0–16 for ∼Lz=3* galaxies. One bright (M UV = −21 mag) galaxy at z ∼ 12, GL-z12-1, has an extremely compact profile with r e = 39 ± 11 pc that is surely extended over the PSF. Even in the case that the GL-z12-1 SB is fit by active galactic nuclei + galaxy composite profiles, the best-fit galaxy component is again compact, re=48−15+38 pc, which is significantly (>5σ) smaller than the typical r e value at z ∼ 12. Compared with numerical simulations, we find that such a compact galaxy naturally forms at z ≳ 10, and that frequent mergers at the early epoch produce more extended galaxies following the r e ∝ (1 + z) s relation.
An efficient solution procedure for solving higher-codimension Hopf and Bogdanov-Takens bifurcations

In solving real world systems for higher-codimension bifurcation problems, one often faces the difficulty in computing the normal form or the focus values associated with generalized Hopf bifurcation, and the normal form with unfolding for higher-codimension Bogdanov-Takens bifurcation. The difficulty is not only coming from the teadious symbolic computation of focus values, but also due to the restriction on the system parameters, which frequently leads to failure of the conventional approach used in the computation even for simple $2$-dimensional nonlinear dynamical systems. In this paper, we use a simple 2-dimensional epidemic model, for which the conventional approach fails in analyzing the stability of limit cycles arising from Hopf bifurcation, to illustrate how our method can be efficiently applied to determine the codimension of Hopf bifurcation. Further, we apply the simplest normal form theory to consider codimension-3 Bogdanov-Takens bifurcation and present an efficient one-step transformation approach, compared with the classical six-step transformation approach to demonstrate the advantage of our method.
Multiparty Spohn's theorem for mixed local Markovian and non-Markovian quantum dynamics

We obtain the Gorini-Kossakowski-Sudarshan-Lindblad master equation for two or more quantum systems connected locally to a combination of Markovian and non-Markovian heat baths. We analyze the thermodynamic quantities for such a mixed set of local environments, and derive a modiﬁed form of the Spohn’s theorem for that setup. The modiﬁcation of the theorem naturally leads to a witness as well as an easily computable quantiﬁer of non-Markovianity. Furthermore, we ﬁnd that for multiparty situations, where a combination of Markovian and non-Markovian heat baths are active, the response in thermodynamic system characteristics due to non-Markovian baths is prominent at times close to the initial time of evolution, whereas the long-time behavior is predominantly controlled by the Markovian ones.
Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review

This paper reviews the state of the art in offshore wind farm operations and maintenance with a focus on decision support models for the scheduling of maintenance. Factors influential to maintenance planning are collected from the literature and their inclusion in state-of-the-art models is discussed. Methods for modeling and optimization are presented. The methods currently used and possible alternatives are discussed. The existing models are already able to aid the decision-making process. They can be improved by applying more advanced mathematical methods, including uncertainties in the input, regarding more of the influential factors, and by collecting, analyzing, and subsequently using more accurate data.
University of Birmingham Comprehensive mapping of O-glycosylation in flagellin from Campylobacter jejuni 11168

Glycosylation of ﬂagellin is essential for the virulence of Campylobacter jejuni , a leading cause of bacterial gastroenteritis. Here, we demonstrate comprehensive mapping of the O-glycosylation of ﬂagellin from Campylobacter jejuni 11168 by use of a bottom-up proteomics approach that incorporates differential ion mobility spectrometry (also known as high ﬁeld asymmetric waveform ion mobility spectrometry or FAIMS) together with proteolysis with proteinase K. Proteinase K provides complementary sequence coverage to that achieved following trypsin proteolysis. The use of FAIMS increased the number of glycopeptides identiﬁed. Novel glycans for this strain were identiﬁed (pseudaminic acid and either acetamidino pseudaminic acid or legionaminic acid), as were novel glycosylation sites: Thr208, Ser343, Ser348, Ser349, Ser395, Ser398, Ser423, Ser433, Ser436, Ser445, Ser448, Ser451, Ser452, Ser454, Ser457 and Thr465. Multiply glycosylated peptides were observed, as well as variation at individual residues in the nature of the glycan and its presence or absence. Such extreme heterogeneity in the pattern of glycosylation has not been reported previously, and suggests a novel dimension in molecular variation within a bacterial population that may be signiﬁcant in persistence of the organism in its natural environment. These results demonstrate the usefulness of differential ion mobility in proteomics investigations of PTMs. Additional supporting information may be found in the online version of this article at the publisher’s web-site
CLOUD BASED SECURITY CENTRALIZED DATABASE FOR STORAGE OF VOTER’S INFORMATION USING CRYPTOGRAPHIC TECHNIQUE

— Network Security is the most vital component in information security because it is responsible for securing all information passed through networked computers. Cloud is an Internet-based computing technology, where shared resources such as software, platform, storage and information are provided to customers on demand. Cloud Computing is a computing platform for sharing resources that include infrastructures, software, applications, and business processes. Cloud Computing is a virtual pool of computing resources. It provides computing resources in the pool for users through internet, Cloud computing as an emerging computing paradigm aims to share storage, computation and services transparently among massive users. Current Cloud computing systems poses serious limitation in protecting users’ data confidentiality. Since users’ sensitive data is presented in unencrypted forms to remote machines owned and operated by third party service providers, the risks of unauthorized disclosure of the users’ sensitive data by service providers may be quite high. There are many techniques for protecting users’ data from outside attackers. An approach is presented to protecting the confidentiality of users’ data from service providers, and ensures service providers cannot collect users’ confidential data while the data is processed and stored in Cloud computing systems. Cloud computing systems provide various internet based data storage and services. Due to its many major benefits, including cost effectiveness and high scalability and flexibility, Cloud computing is gaining significant momentum recently as a new paradigm of distributed computing for various applications, especially for business applications along with the rapid growth of the Internet. With the rise of the era of “Cloud computing”, concerns about “Internet Security” continue to increase. Cryptography, together with suitable communication protocols, can provide a high degree of protection in digital communications against intruder attacks as far as the communication between two different computers is concerned.
Empagliflozin in acute myocardial infarction: the EMMY trial

Abstract Aims Sodium–glucose co-transporter 2 inhibition reduces the risk of hospitalization for heart failure and for death in patients with symptomatic heart failure. However, trials investigating the effects of this drug class in patients following acute myocardial infarction are lacking. Methods and results In this academic, multicentre, double-blind trial, patients (n = 476) with acute myocardial infarction accompanied by a large creatine kinase elevation (>800 IU/L) were randomly assigned to empagliflozin 10 mg or matching placebo once daily within 72 h of percutaneous coronary intervention. The primary outcome was the N-terminal pro-hormone of brain natriuretic peptide (NT-proBNP) change over 26 weeks. Secondary outcomes included changes in echocardiographic parameters. Baseline median (interquartile range) NT-proBNP was 1294 (757–2246) pg/mL. NT-proBNP reduction was significantly greater in the empagliflozin group, compared with placebo, being 15% lower [95% confidence interval (CI) −4.4% to −23.6%] after adjusting for baseline NT-proBNP, sex, and diabetes status (P = 0.026). Absolute left-ventricular ejection fraction improvement was significantly greater (1.5%, 95% CI 0.2–2.9%, P = 0.029), mean E/e′ reduction was 6.8% (95% CI 1.3–11.3%, P = 0.015) greater, and left-ventricular end-systolic and end-diastolic volumes were lower by 7.5 mL (95% CI 3.4–11.5 mL, P = 0.0003) and 9.7 mL (95% CI 3.7–15.7 mL, P = 0.0015), respectively, in the empagliflozin group, compared with placebo. Seven patients were hospitalized for heart failure (three in the empagliflozin group). Other predefined serious adverse events were rare and did not differ significantly between groups. Conclusion In patients with a recent myocardial infarction, empagliflozin was associated with a significantly greater NT-proBNP reduction over 26 weeks, accompanied by a significant improvement in echocardiographic functional and structural parameters. ClinicalTrials.gov registration NCT03087773.