Miracle-dz/UniFAR-FTUs huggingface dataset:
Coulomb screening correction to the Q value of the triple-alpha process in thermal plasmas

The triple-alpha reaction is key to $^{12}$C production and is expected to occur in weakly coupled thermal plasmas as encountered in normal stars. We investigate how Coulomb screening affects the structure of a system of three alpha particles in such a plasma environment by precise three-body calculations within the Debye–Hückel approximation. A three-alpha model that has the Coulomb interaction modified in the Yukawa form is employed. Precise three-body wave functions are obtained by a superposition of correlated Gaussian basis with the aid of the stochastic variational method. The energy shifts of the Hoyle state due to the Coulomb screening are obtained as a function of the Debye screening length. The results, which automatically incorporate the finite-size effect of the Hoyle state, are consistent with the conventional result based on the Coulomb correction to the chemical potentials of ions that are regarded as point charges in a weakly coupled thermal plasma. We have given a theoretical basis to the conventional point-charge approach to the Coulomb screening problem relevant for nuclear reactions in normal stars by providing the first evaluation of the Coulomb corrections to the $Q$ value of the triple-alpha process that produces a finite-size Hoyle state.
Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format

There exists a natural tension between encouraging a diverse ecosystem of open-source search engines and supporting fair, replicable comparisons across those systems. To balance these two goals, we examine two approaches to providing interoperability between the inverted indexes of several systems. The first takes advantage of internal abstractions around index structures and building wrappers that allow one system to directly read the indexes of another. The second involves sharing indexes across systems via a data exchange specification that we have developed, called the Common Index File Format (CIFF). We demonstrate the first approach with the Java systems Anserini and Terrier, and the second approach with Anserini, JASSv2, OldDog, PISA, and Terrier. Together, these systems provide a wide range of implementations and features, with different research goals. Overall, we recommend CIFF as a low-effort approach to support independent innovation while enabling the types of fair evaluations that are critical for driving the field forward.
Validity of SMEFT studies of VH and VV Production at NLO

The production of $W^{\pm}H$, $ZH$, $W^+W^-$, and $W^\pm Z$ pairs probes non-Standard-Model interactions of quarks, gauge bosons, and the Higgs boson. New effects can be parameterized in terms of an effective field theory (EFT) where the Lagrangian is expanded in terms of higher-dimension operators suppressed by increasing powers of a high scale $\Lambda$. We examine the importance of including next-to-leading-order QCD corrections in global fits to the coefficients of the EFT. The numerical implications on the fits due to different approaches to enforcing the validity of the EFT are quantified. We pay particular attention to the dependence of the fits on the expansion in $1/\Lambda^2$ since the differences between results calculated at ${\cal {O}}(1/\Lambda^2)$ and ${\cal{O}}(1/\Lambda^4)$ may give insight into the possible significance of dimension-8 effects.
Chandra Resolves the Double FU Orionis System RNO 1B/1C in X-Rays

We present new Chandra X-ray observations of the close pair of young stars RNO 1B and 1C (6″ separation) located in the L1287 cloud. RNO 1B erupted in 1978–1990 and is classified as an FU Orionis star (FUor). RNO 1C also shows most of the properties of an FUor but no eruption has been seen yet. Only a few dozen FUors are known and the presence of two such objects with a small angular separation is rare, suggesting a common origin. Both stars were faintly detected by Chandra and we summarize their X-ray properties within the framework of other previously detected FUors. We also report other X-ray detections in L1287 including the deeply embedded young star RNO 1G, the jet-like radio source VLA 3, and an enigmatic hard flaring source with no Two Micron All Sky Survey counterpart that was only detected in the second of the two Chandra exposures.
Teacher-Student Domain Adaptation for Biosensor Models

We present an approach to domain adaptation, addressing the case where data from the source domain is abundant, labelled data from the target domain is limited or non-existent, and a small amount of paired source-target data is available. The method is designed for developing deep learning models that detect the presence of medical conditions based on data from consumer-grade portable biosensors. It addresses some of the key problems in this area, namely, the difficulty of acquiring large quantities of clinically labelled data from the biosensor, and the noise and ambiguity that can affect the clinical labels. The idea is to pre-train an expressive model on a large dataset of labelled recordings from a sensor modality for which data is abundant, and then to adapt the model's lower layers so that its predictions on the target modality are similar to the original model's on paired examples from the source modality. We show that the pre-trained model's predictions provide a substantially better learning signal than the clinician-provided labels, and that this teacher-student technique significantly outperforms both a naive application of supervised deep learning and a label-supervised version of domain adaptation on a synthetic dataset and in a real-world case study on sleep apnea. By reducing the volume of data required and obviating the need for labels, our approach should reduce the cost associated with developing high-performance deep learning models for biosensors.
Generating Electronic Health Records with Multiple Data Types and Constraints

Sharing electronic health records (EHRs) on a large scale may lead to privacy intrusions. Recent research has shown that risks may be mitigated by simulating EHRs through generative adversarial network (GAN) frameworks. Yet the methods developed to date are limited because they 1) focus on generating data of a single type (e.g., diagnosis codes), neglecting other data types (e.g., demographics, procedures or vital signs), and 2) do not represent constraints betweenfeatures. In this paper, we introduce a method to simulate EHRs composed of multiple data types by 1) refining the GAN model, 2) accounting for feature constraints, and 3) incorporating key utility measures for such generation tasks. Our analysis with over 770,000 EHRs from Vanderbilt University Medical Center demonstrates that the new model achieves higher performance in terms ofretaining basic statistics, cross-feature correlations, latent structural properties, feature constraints and associated patterns from real data, without sacrificing privacy.
A New Path Division Multiple Access for the Massive MIMO-OTFS Networks

This article focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal matching pursuit algorithm is utilized for the extraction of the UL channel parameters, including channel gains, directions of arrival, delays, and Doppler frequencies, over the antenna-time-frequency domain. Thirdly, we carefully analyze energy dispersion and power leakage of the 3D angle-delay-Doppler channels. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides and to assure that the observation regions for different users do not overlap over the 3D cubic area, i.e., angle-delay-Doppler domain. After scheduling, different users can map their respective data to the scheduled delay-Doppler domain grids, and simultaneously send the data to base station (BS) without inter-user interference in the same OTFS block. Correspondingly, the signals at desired grids within the 3D resource space of BS are separately collected to implement the 3D channel estimation and maximal ratio combining-based data recovery over the angle-delay-Doppler domain. Then, we construct a low complexity beamforming scheme over the angle-delay-Doppler domain to achieve inter-user interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.
Inference of Gas-liquid Flowrate using Neural Networks

The metering of gas-liquid flows is difficult due to the non-linear relationship between flow regimes and fluid properties, flow orientation, channel geometry, etc. In fact, a majority of commercial multiphase flow meters have a low accuracy, limited range of operation or require a physical separation of the phases. We introduce the inference of gas-liquid flowrates using a neural network model that is trained by wire-mesh sensor (WMS) experimental data. The WMS is an experimental tool that records high-resolution high-frequency 3D void fraction distributions in gas-liquid flows. The experimental database utilized spans over two orders of superficial velocity magnitude and multiple flow regimes for a vertical small-diameter pipe. Our findings indicate that a single network can provide accurate and precise inference with below a 7.5% MAP error across all flow regimes. The best performing networks have a combination of a 3D-Convolution head, and an LSTM tail. The finding indicates that the spatiotemporal features observed in gas-liquid flows can be systematically decomposed and used for inferring phase-wise flowrate. Our method does not involve any complex pre-processing of the void fraction matrices, resulting in an evaluation time that is negligible when contrasted to the input time-span. The efficiency of the model manifests in a response time two orders of magnitude lower than the current state-of-the-art.
Optimal Image Smoothing and Its Applications in Anomaly Detection in Remote Sensing

This paper is focused on deriving an optimal image smoother. The optimization is done through the minimization of the norm of the Laplace operator in the image coordinate system. Discretizing the Laplace operator and using the method of Euler-Lagrange result in a weighted average scheme for the optimal smoother. Satellite imagery can be smoothed by this optimal smoother. It is also very fast and can be used for detecting the anomalies in the image. A real anomaly detecting problem is considered for the Qom region in Iran. Satellite image in different bands are smoothed. Comparing the smoothed and original images in different bands, the maps of anomalies are presented. Comparison between the derived method and the existing methods reveals that it is more efficient in detecting anomalies in the region.
Dynamic Distribution-Sensitive Point Location

We propose a dynamic data structure for the distribution-sensitive point location problem in the plane. Suppose that there is a fixed query distribution within a convex subdivision S, and we are given an oracle that can return in O(1) time the probability of a query point falling into a polygonal region of constant complexity. We can maintain S such that each query is answered in Oopt(S)) expected time, where opt (S) is the expected time of the best linear decision tree for answering point location queries in S. The space and construction time are O(nlog2n), where n is the number of vertices of S. An update of S as a mixed sequence of k edge insertions and deletions takes O(klog4 n) amortized time. As a corollary, the randomized incremental construction of the Voronoi diagram of n sites can be performed in O(nlog4 n) expected time so that, during the incremental construction, a nearest neighbor query at any time can be answered optimally with respect to the intermediate Voronoi diagram at that time.
Data-driven forward discretizations for Bayesian inversion

This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governing the discretization as part of the unknown to be estimated within the Bayesian machinery. We numerically show that in a variety of inverse problems arising in mechanical engineering, signal processing and the geosciences, the observations contain useful information to guide the choice of discretization.
A computational study of viscoelastic blood flow in an arteriovenous fistula

A finite element analysis of flows of an Oldroyd-B fluid is developed, to simulate blood flow in an arteriovenous fistula. The model uses a combination of a standard conforming finite element approximation for the momentum equation, and the discontinuous Galerkin method, with upwinding, for the equation governing the evolution of the extra stress. The model is verified for a range of values of Weissenberg number We by applying it to the benchmark problem of flow past a cylinder in a channel. The main application is to flow in an arteriovenous fistula, the geometry of which is based on patient-specific data. Results for Oldroyd-B fluids are compared with those for a Newtonian fluid as well as with data from patient-specific velocity MRI scans. Features such as streamlines and regions of recirculation are similar across a range of values of We and the Newtonian case. There is however a strong dependence of maximum wall shear stress on We, with values for the viscoelastic fluid in all cases being higher than that for the Newtonian case.
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá

The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countries. Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way. Additionally, to alleviate some of the negative effects of the errors in automatic annotation, noise-handling methods can be integrated. Pretrained word embeddings are another key component of most neural named entity classifiers. With the advent of more complex contextual word embeddings, an interesting trade-off between model size and performance arises. While these techniques have been shown to work well in high-resource settings, we want to study how they perform in low-resource scenarios. In this work, we perform named entity recognition for Hausa and Yor\`ub\'a, two languages that are widely spoken in several developing countries. We evaluate different embedding approaches and show that distant supervision can be successfully leveraged in a realistic low-resource scenario where it can more than double a classifier's performance.
Getting to 99% Accuracy in Interactive Segmentation

Interactive object cutout tools are the cornerstone of the image editing workflow. Recent deep-learning based interactive segmentation algorithms have made significant progress in handling complex images and rough binary selections can typically be obtained with just a few clicks. Yet, deep learning techniques tend to plateau once this rough selection has been reached. In this work, we interpret this plateau as the inability of current algorithms to sufficiently leverage each user interaction and also as the limitations of current training/testing datasets. We propose a novel interactive architecture and a novel training scheme that are both tailored to better exploit the user workflow. We also show that significant improvements can be further gained by introducing a synthetic training dataset that is specifically designed for complex object boundaries. Comprehensive experiments support our approach, and our network achieves state of the art performance.
Ford Multi-AV Seasonal Dataset

This article presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles (AVs) at different days and times during 2017–2018. The vehicles traversed an average route of 66 km in Michigan that included a mix of driving scenarios such as the Detroit airport, freeways, city centers, university campus, and suburban neighborhoods. Each vehicle used in this data collection was a Ford Fusion outfitted with an Applanix POS-LV GNSS/INS system, four HDL-32E Velodyne 3D-lidar scanners, six Point Grey 1.3 MP cameras arranged on the rooftop for 360° coverage, and one Point Grey 5 MP camera mounted behind the windshield for the forward field of view. We present the seasonal variation in weather, lighting, construction, and traffic conditions experienced in dynamic urban environments. We also include data from multiple AVs that were driven in close proximity. This dataset can help design robust algorithms for AVs and multi-agent systems. Each log in the dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground-truth pose, and 3D maps. All data is available in rosbag format that can be visualized, modified, and applied using the open-source Robot Operating System (ROS). We also provide the output of reflectivity-based localization for bench-marking purposes. The dataset can be freely downloaded at avdata.ford. com.
Silver Tape

We propose Silver Tape, a simple yet novel fabrication technique to transfer inkjet-printed silver traces from paper onto versatile substrates, without time-/space- consuming processes such as screen printing or heat sintering. This allows users to quickly implement silver traces with a variety of properties by exploiting a wide range of substrates. For instance, high flexibility can be achieved with Scotch tape, high transparency with polydimethylsiloxane (PDMS), heat durability with Kapton polyimide tape, water solubility with 3M water-soluble tape, and beyond. Many of these properties are not achievable with conventional substrates that are used for inkjet-printing conductive traces. Specifically, our technique leverages the commonly undesired low adhesion property of the inkjet printing films and repurposes these films as temporary transfer media. We describe our fabrication methods with a library of materials we can utilize, evaluate the mechanical and electrical properties of the transferred traces, and conclude with several demonstrative applications. We believe Silver Tape enriches novel interactions for the ubiquitous computing domain, by enabling digital fabrication of electronics on versatile materials, surfaces, and shapes.
Calm Commute

Commutes provide an opportune time and space for interventions that mitigate stress-particularly stress accumulated during the workday. In this study, we test the efficacy and safety of haptic guided slow breathing interventions of short duration while driving. We also present design and experimental implications for evolving these interventions from prior simulator to moving vehicle scenarios. We ran a controlled study (N=24) testing a haptic guided breathing system in a closed circuit under normal and stressful driving conditions. Results show the intervention to be successful in both user adoption and system effectiveness with an 82% rate of engagement in intervention and clear reduction of breathing rate and physiological arousal, with no effect on driving safety and minimal effect on performance. The haptic intervention received positive acceptance from the participants: all indicated a willingness to engage with the intervention in the future and all rated the intervention as safe for traffic applications. The results of this study encourage further investigations exploring the use of the intervention on public roads and monitoring for longitudinal health benefits.
FORMULATION AND CHARACTERIZATION OF CUCUMIS SATIVUS EXTRACT IN THE TREATMENT OF ACNE

Acne vulgaris is the most widespread skin disease and a persistent inflammatory disorder affecting 90-95% of adolescents. Inflammation is due to the over proliferation of Propionibacterium acnes, Staphylococcus aureus and Staphylococcus epidermidis. Many synthetic cosmetics have been developed for the treatment of acne. But they are known to have many side effects and are found to become resistant after some time. Thus, the present work was undertaken to investigate a natural medicinal plant, Cucumis sativus extracts, for their anti-oxidant, antimicrobial potential and its cosmetic value in treatment of acne. Results showed the presence of total phenolic content (19.25 µg and 6.23 µg GAE) and flavonoid content (1.82 µg and 6.24 µg catechin in fresh and dried extracts respectively. Fresh extract of Cucumis sativus indicated promising antibacterial potential and was mixed with tea tree (Melaleuca alternifolia) oil and linseed (Linum usitatissimum) oil to prepare polyherbal cream (O/W) formulations (F1-F5). Formulations were further evaluated for pH, viscosity and various physical parameters. F5 cream formulation showed pH in range of 5-6 with good spreadability, good consistency with ease of removal, and no evidence of phase separation.
Predicting Brain Functional Connectivity Using Mobile Sensing

Brain circuit functioning and connectivity between specific regions allow us to learn, remember, recognize and think as humans. In this paper, we ask the question if mobile sensing from phones can predict brain functional connectivity. We study the brain resting-state functional connectivity (RSFC) between the ventromedial prefrontal cortex (vmPFC) and the amygdala, which has been shown by neuroscientists to be associated with mental illness such as anxiety and depression. We discuss initial results and insights from the NeuroSence study, an exploratory study of 105 first year college students using neuroimaging and mobile sensing across one semester. We observe correlations between several behavioral features from students' mobile phones and connectivity between vmPFC and amygdala, including conversation duration (r=0.365, p<0.001), sleep onset time (r=0.299, p<0.001) and the number of phone unlocks (r=0.253, p=0.029). We use a support vector classifier and 10-fold cross validation and show that we can classify whether students have higher (i.e., stronger) or lower (i.e., weaker) vmPFC-amygdala RSFC purely based on mobile sensing data with an F1 score of 0.793. To the best of our knowledge, this is the first paper to report that resting-state brain functional connectivity can be predicted using passive sensing data from mobile phones.