Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting binding energies of astrochemically relevant molecules via machine learning

The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is thus an important parameter for many astrochemical studies. This parameter is usually determined with time-consuming experiments, computationally expensive quantum chemical calculations, or the inexpensive, but inaccurate, linear addition method. In this work we propose a new method based on machine learning for predicting binding energies that is accurate, yet computationally inexpensive. A machine learning model based on Gaussian Process Regression is created and trained on a database of binding energies of molecules collected from laboratory experiments presented in the literature. The molecules in the database are categorized by their features, such as mono- or multilayer coverage, binding surface, functional groups, valence electrons, and H-bond acceptors and donors. The performance of the model is assessed with five-fold and leave-one-molecule-out cross validation. Predictions are generally accurate, with differences between predicted and literature binding energies values of less than $\pm$20\%. The validated model is used to predict the binding energies of twenty one molecules that have recently been detected in the interstellar medium, but for which binding energy values are not known. A simplified model is used to visualize where the snowlines of these molecules would be located in a protoplanetary disk. This work demonstrates that machine learning can be employed to accurately and rapidly predict binding energies of molecules. Machine learning complements current laboratory experiments and quantum chemical computational studies. The predicted binding energies will find use in the modelling of astrochemical and planet-forming environments.
Design and Experimental Comparison of Energy Management Strategies for Hybrid Electric Buses Based on Test-Bench Simulation

Energy management in hybrid and electric vehicles is a key factor to improve the operational performance and meet the efficiency objectives defined in the transport sector. Thus, optimized energy management strategies (EMSs), before being integrated in a real system, need to be experimentally validated in test-bench platforms in order to identify the possible deviations from the expected simulation-based performance while minimizing the implementation time and field-test on the real application. An economical and flexible mean of validating these strategies is the hardware-in-the-loop simulation. Therefore, this work aims to present the design approach and comparison, by means of experimental tests, of two optimized (simulation-based) EMSs proposed for a hybrid electric bus (HEB) focusing on the real-time operational performance. Both EMSs handle the proper power split behavior of the vehicle demand among a genset (internal combustion engine + electric generator) and a hybrid energy storage system (combining Li-ion batteries with supercapacitors). The experimental platform consists of a scaled test bench emulating the electrical dc grid of a HEB. This test bench, combined with software models to control the power electronic devices, allows us to emulate the real behavior of the genset, battery, supercapacitor, traction, and auxiliary loads.
Emergence of community structures through biased random walks rewiring

Community structures have been identified in various complex real-world networks, for example, communication, information, internet and shareholder networks. The scaling of community size distribution indicates the heterogeneity in the topological structures of the network. The current network generating or growing models can reproduce some properties, including degree distributions, large clustering coefficients and communities. However, the scaling behaviour of the community size lacks investigation, especially from the perspectives of local interactions. Based on the assumption that heterogeneous nodes behave differently and result in different topological positions of the networks, we propose a model of designed random walks in directed networks to explain the features in the observed networks. The model highlights that two different dynamics can mimic the local interactions, and a hidden layer is essential when reproducing the characteristics of real complex networks. The key features the model can explain include community size distribution, degree distribution, percolation properties, distribution of average path length and dependence of the above properties on the labels of nodes in the data.
2n-Stream Conservative Scattering

We show how to use matrix methods of quantum mechanics to eﬃciently and accurately calculate axially symmetric radiation transfer in clouds, with conservative scattering of arbitrary anisotropy. Analyses of conservative scattering, where the single scattering albedo is ˜ ω = 1 and no energy is exchanged between the radiation and scatterers, began with work by Schwarzschild, Milne, Eddington and others[1] on radiative transfer in stars. There the scattering is isotropic or nearly so. It has been diﬃcult to extend tradi-tional methods to highly anisotropic scattering, like that of sunlight in Earth’s clouds. The 2 n -stream method described here is a practical way to handle highly anisotropic, conservative scattering. The basic ideas of the 2 n -stream method are an extension of Wick’s seminal work on transport of thermal neutrons by isotropic scattering[2] to scattering with arbitrary anisotropy. How to do this for ﬁnite absorption and ˜ ω < 1 was described in a previous paper[3]. But those methods fail for conservative scattering, when ˜ ω = 1. Here we show that minor modiﬁcations to the fundamental 2 n -scattering theory for ˜ ω < 1 make it suitable for ˜ ω = 1.
A Step in understanding the 
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 tension

Models of dark sectors with a mass threshold can have important cosmological signatures. If, in the era prior to recombination, a relativistic species becomes nonrelativistic and is then depopulated in equilibrium, there can be measurable impacts on the cosmic microwave background as the entropy is transferred to lighter relativistic particles. In particular, if this ``step'' occurs near $z\sim 20,000$, the model can naturally accommodate larger values of $H_0$. If this stepped radiation is additionally coupled to dark matter, there can be a meaningful impact on the matter power spectrum as dark matter can be coupled via a species that becomes nonrelativistic and depleted. This can naturally lead to suppressed power at scales inside the sound horizon before the step, while leaving conventional cold dark matter signatures for power outside the sound horizon. We study these effects and show such models can naturally provide lower values of $S_8$ than scenarios without a step. This suggests these models may provide an interesting framework to address the $S_8$ tension, both in concert with the $H_0$ tension and without.
Distributed Ranging SLAM for Multiple Robots with Ultra-WideBand and Odometry Measurements

To accomplish task efficiently in a multiple robots system, a problem that has to be addressed is Simultaneous Localization and Mapping (SLAM). LiDAR (Light Detection and Ranging) has been used for many SLAM solutions due to its superb accuracy, but its performance degrades in featureless environments, like tunnels or long corridors. Centralized SLAM solves the problem with a cloud server, which requires a huge amount of computational resources and lacks robustness against central node failure. To address these issues, we present a distributed SLAM solution to estimate the trajectory of a group of robots using Ultra-WideBand (UWB) ranging and odometry measurements. The proposed approach distributes the processing among the robot team and significantly mitigates the computation concern emerged from the centralized SLAM. Our solution determines the relative pose (also known as loop closure) between two robots by minimizing the UWB ranging measurements taken at different positions when the robots are in close proximity. UWB provides a good distance measure in line-of-sight conditions, but retrieving a precise pose estimation remains a challenge, due to ranging noise and unpredictable path traveled by the robot. To deal with the suspicious loop closures, we use Pairwise Consistency Maximization (PCM) to examine the quality of loop closures and perform outlier rejections. The filtered loop closures are then fused with odometry in a distributed pose graph optimization (DPGO) module to recover the full trajectory of the robot team. Extensive experiments are conducted to validate the effectiveness of the proposed approach.
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems

The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects. While the definition of anomaly strictly depends on the domain framework, it is often impractical or too time consuming to obtain a fully labelled dataset. The use of unsupervised models to overcome the lack of labels often fails to catch domain specific anomalies as they rely on general definitions of outlier. This paper suggests a new active learning based approach, ALIF, to solve this problem by reducing the number of required labels and tuning the detector towards the definition of anomaly provided by the user. The proposed approach is particularly appealing in the presence of a Decision Support System (DSS), a case that is increasingly popular in real-world scenarios. While it is common that DSS embedded with anomaly detection capabilities rely on unsupervised models, they don't have a way to improve their performance: ALIF is able to enhance the capabilities of DSS by exploiting the user feedback during common operations. ALIF is a lightweight modification of the popular Isolation Forest that proved superior performances with respect to other state-of-art algorithms in a multitude of real anomaly detection datasets.
Ground-state phase diagram of spin-
S
 Kitaev-Heisenberg models

The Kitaev model, whose ground state is a quantum spin liquid (QSL), was originally conceived for spin S = 1 / 2 moments on a honeycomb lattice. In recent years, the model has been extended to higher S from both theoretical and experimental interests, but the stability of the QSL ground state has not been systematically clariﬁed for general S , especially in the presence of other additional interactions, which inevitably exist in candidate materials. Here we study the spin- S Kitaev-Heisenberg models by using an extension of the pseudofermion functional renormalization group method to general S . We show that, similar to the S = 1 / 2 case, the phase diagram for higher S contains the QSL phases in the vicinities of the pristine ferromagnetic and antiferromagnetic Kitaev models, in addition to four magnetically ordered phases. We ﬁnd, however, that the QSL phases shrink rapidly with increasing S , becoming vanishingly narrow for S ≥ 2, whereas the phase boundaries between the ordered phases remain almost intact. Our results provide a reference for the search of higher- S Kitaev materials.
The ACII 2022 Affective Vocal Bursts Workshop & Competition

The ACII Affective Vocal Bursts Workshop & Com-petition is focused on understanding multiple affective dimensions of vocal bursts: laughs, gasps, cries, screams, and many other non-linguistic vocalizations central to the expression of emotion and to human communication more generally. This year's com-petition comprises four tracks using a large-scale and in-the-wild dataset of 59,299 vocalizations from 1,702 speakers. The first, the A- VB-HIGH task, requires competition participants to perform a multi-label regression on a novel model for emotion, utilizing ten classes of richly annotated emotional expression intensities, including; Awe, Fear, and Surprise. The second, the A-VB-Two task, utilizes the more conventional 2-dimensional model for emotion, arousal, and valence. The third, the A- VB-CULTURE task, requires participants to explore the cultural aspects of the dataset, training native-country dependent models. Finally, for the fourth task, A - VB - TYPE, participants should recognize the type of vocal burst (e.g., laughter, cry, grunt) as an 8-class classification. This paper describes the four tracks and baseline systems, which use state-of-the-art machine learning methods. The baseline performance for each track is obtained by utilizing an end-to-end deep learning model and is as follows: for A- VB-HIGH, a mean (over the 10-dimensions) Concordance Correlation Coefficient (CCC) of 0.5687 CCC is obtained; for A- VB- Two, a mean (over the 2-dimensions) CCC of 0.5084 is obtained; for A-VB − CULTURE, a mean CCC from the four cultures of 0.4401 is obtained; and for A-VB-TYPE, the baseline Unweighted Average Recall (UAR) from the 8-classes is 0.4172 UAR.
Using Quantile Forecasts for Dynamic Equivalents of Active Distribution Grids under Uncertainty

While distribution networks (DNs) turn from consumers to active and responsive intelligent DNs, the question of how to represent them in large-scale transmission network (TN) studies is still under investigation. The standard approach that uses aggregated models for the inverter-interfaced generation and conventional load models introduces significant errors to the dynamic modeling that can lead to instabilities. This paper presents a new approach based on quantile forecasting to represent the uncertainty originating in DNs at the TN level. First, we aquire a required rich dataset employing Monte Carlo simulations of a DN. Then, we use machine learning (ML) algorithms to not only predict the most probable response but also intervals of potential responses with predefined confidence. These quantile methods represent the variance in DN responses at the TN level. The results indicate excellent performance for most ML techniques. The tuned quantile equivalents predict accurate bands for the current at the TN/DN-interface, and tests with unseen TN conditions indicate robustness. A final assessment that compares the MC trajectories against the predicted intervals highlights the potential of the proposed method.
ErgoTac-Belt: Anticipatory Vibrotactile Feedback to Lead Centre of Pressure during Walking

Balance and gait disorders are the second leading cause of falls, which, along with consequent injuries, are reported as major public health problems all over the world. For patients who do not require mechanical support, vibrotactile feedback interfaces have proven to be a successful approach in restoring balance. Most of the existing strategies assess trunk or head tilt and velocity or plantar forces, and are limited to the analysis of stance. On the other hand, central to balance control is the need to maintain the body's centre of pressure (CoP) within feasible limits of the support polygon (SP), as in standing, or on track to a new SP, as in walking. Hence, this paper proposes an exploratory study to investigate whether vibrotactile feedback can be employed to lead human CoP during walking. The ErgoTac-Belt vibrotactile device is introduced to instruct the users about the direction to take, both in the antero-posterior and medio-lateral axes. An anticipatory strategy is adopted here, to give the users enough time to react to the stimuli. Experiments on ten healthy subjects demonstrated the promising capability of the proposed device to guide the users' CoP along a predefined reference path, with similar performance as the one achieved with visual feedback. Future developments will investigate our strategy and device in guiding the CoP of elderly or individuals with vestibular impairments, who may not be aware of or, able to figure out, a safe and ergonomic CoP path.
TF-GNN: Graph Neural Networks in TensorFlow

TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. In addition to enabling machine learning researchers and advanced developers, TF-GNN offers low-code solutions to empower the broader developer community in graph learning. Many production models at Google use TF-GNN, and it has been recently released as an open source project. In this paper we describe the TF-GNN data model, its Keras message passing API, and relevant capabilities such as graph sampling and distributed training.
Learning High-quality Proposals for Acne Detection

Acne detection is crucial for interpretative diagnosis and precise treatment of skin disease. The arbitrary boundary and small size of acne lesions lead to a significant number of poor-quality proposals in two-stage detection. In this paper, we propose a novel head structure for Region Proposal Network to improve the proposals' quality in two ways. At first, a Spatial Aware Double Head(SADH) structure is proposed to disentangle the representation learning for classification and localization from two different spatial perspectives. The proposed SADH ensures a steeper classification confidence gradient and suppresses the proposals having low intersection-over-union(IoU) with the matched ground truth. Then, we propose a Normalized Wasserstein Distance prediction branch to improve the correlation between the proposals' classification scores and IoUs. In addition, to facilitate further research on acne detection, we construct a new dataset named AcneSCU, with high-resolution imageries, precise annotations, and fine-grained lesion categories. Extensive experiments are conducted on both AcneSCU and the public dataset ACNE04, and the results demonstrate the proposed method could improve the proposals' quality, consistently outperforming state-of-the-art approaches. Code and the collected dataset are available in https://github.com/pingguokiller/acnedetection.
Local approximation for perfect discrimination of quantum states

Quantum state discrimination involves identifying a given state out of a set of possible states. When the states are mutually orthogonal, perfect state discrimination is always possible using a global measurement. In the case of multipartite systems when the parties are constrained to use multiple rounds of local operations and classical communication (LOCC), perfect state discrimination is often impossible even with the use of \emph{asymptotic LOCC}, wherein an error is allowed but must vanish in the limit of an infinite number of rounds. Utilizing our recent results on asymptotic LOCC, we derive a lower bound on the error probability for LOCC discrimination of any given set of mutually orthogonal pure states. Informed by the insights gained from this lower bound, we are able to prove necessary conditions for perfect state discrimination by asymptotic LOCC. We then illustrate by example the power of these necessary conditions in significantly simplifying the determination of whether perfect discrimination of a given set of states can be accomplished arbitrarily closely using LOCC. The latter examples include a proof that perfect discrimination by asymptotic LOCC is impossible for a certain subset of \emph{minimal} unextendible product bases (UPB), where minimal means that for the given multipartite system, no UPB with a smaller number of states can exist. We also give a simple proof that what has been called \emph{strong nonlocality without entanglement} is considerably stronger than had previously been demonstrated.
Using the Swiss ball in labor

OBJECTIVES: To characterize the use of the Swiss ball for the care of laboring women in obstetric care services linked to the Unified Health System in Sao Paulo, and to identify the characteristics of its use in assisting laboring women by nurse-midwives. METHODS: A descriptive study based on structured interviews with 35 nurses who were providing assistance to laboring women. RESULTS: We found that 100% of Normal Birthing Centers and 40.9% of obstetric centers owned the Swiss ball. The indications for the use of Swiss ball were: promoting fetal descent (32.4%), relaxation (19.7%), progression of labor (17.1%), exercise of the perineum (14.5%), pain relief (11.8%), psychological benefits and maternal movement. Nearly all of the institutions visited (96.8%) had no protocol for its use. CONCLUSION: The study found that nurses ascribe benefits to using the Swiss ball during labor. Clinical trials are needed to evaluate its effects and support the development of guidelines for its use.
RF-DC conversion efficiency improvement for microwave transmission with pulse modulation

Microwave power transfer (MPT) can solve certain types of problems. For example, Internet of Things requires a flexible configuration of sensor networks, which is hindered by wired-charging sensors. This problem can be overcome by MPT techniques. However, the transmission efficiency of MPT is lower than that of wired transmission. This study focuses on the operation of rectifiers having a pulse-modulated input signal. Although a pulse-modulated wave is effective for improving the RF-DC conversion efficiency, the output voltage waves of rectifiers have a high ripple content. Moreover, the harmonic balance method cannot be used to simulate the operation of a pulse-modulated rectifier. To reduce the ripple content, a smoothing capacitor should be connected in parallel to an output load. We investigated the influence of a smoothing capacitor, the general characteristics of rectifiers under pulse-modulated waves, and the effectiveness of using pulse-modulated waves for improving RF-DC conversion efficiency. In conclusion, we reveal a necessary condition of the smoothing capacitor for improvement, demonstrate the effectiveness of pulse modulation, and show that the optimum impedance with a pulse-modulated wave input is an inverse of duty ratio times as compared to that with continuous wave input.
A national audit of pancreatic enzyme prescribing in pancreatic cancer from 2015 to 2023 in England using OpenSAFELY-TPP

Background: Cancer treatments were variably disrupted during the COVID-19 pandemic. Despite UK national guidelines recommending pancreatic enzyme replacement therapy to all people with unresectable pancreatic cancer, observational studies demonstrate under-prescribing. Aim: To investigate the impact of the COVID-19 pandemic on the prescribing of pancreatic enzyme replacement to people with unresectable pancreatic cancer. Methods: With the approval of NHS England, we conducted a cohort study using 24 million health records through the OpenSAFELY-TPP research platform. We modelled the effect of COVID-19 with multivariable linear regression. Results: We found no reduction in pancreatic enzyme replacement therapy during the COVID-19 pandemic. Overall, since 2015, the rates of prescribing increased steadily over time by 1% every year. The national rates ranged from 41% in 2015 to 48% in 2022. There was substantial regional variation. The highest rates of 50% to 60% were in the West Midlands and lowest (20% to 30%) in London. Conclusions: In contrast to many other treatments, prescribing of pancreatic enzyme replacement therapy was not affected during the COVID-19 pandemic. Although overall rates increased over time, substantial under-prescribing existed at the end of this study (March 2022). At just under 50% in 2022, the rates were still below the recommended 100% standard. Prior work evaluating quality of care in this area relied on manual audits which come at increased cost and reduced frequency of updates. With the methodological advantage of OpenSAFELY, we established an automated audit which allows for regular updates.