From oscillation dip to oscillation valley in atmospheric neutrino experiments

AbstractAtmospheric neutrino experiments can show the “oscillation dip” feature in data, due to their sensitivity over a large L/E range. In experiments that can distinguish between neutrinos and antineutrinos, like INO, oscillation dips can be observed in both these channels separately. We present the dip-identification algorithm employing a data-driven approach – one that uses the asymmetry in the upward-going and downward-going events, binned in the reconstructed L/E of muons – to demonstrate the dip, which would confirm the oscillation hypothesis. We further propose, for the first time, the identification of an “oscillation valley” in the reconstructed ($$E_\mu $$ E μ ,$$\,\cos \theta _\mu $$ cos θ μ ) plane, feasible for detectors like ICAL having excellent muon energy and direction resolutions. We illustrate how this two-dimensional valley would offer a clear visual representation and test of the L/E dependence, the alignment of the valley quantifying the atmospheric mass-squared difference. Owing to the charge identification capability of the ICAL detector at INO, we always present our results using $$\mu ^{-}$$ μ - and $$\mu ^{+}$$ μ + events separately. Taking into account the statistical fluctuations and systematic errors, and varying oscillation parameters over their currently allowed ranges, we estimate the precision to which atmospheric neutrino oscillation parameters would be determined with the 10-year simulated data at ICAL using our procedure.

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A new approach to probe non-standard interactions in atmospheric neutrino experiments

Journal of High Energy Physics ◽

10.1007/jhep04(2021)159 ◽

2021 ◽

Vol 2021 (4) ◽

Author(s):

Anil Kumar ◽

Amina Khatun ◽

Sanjib Kumar Agarwalla ◽

Amol Dighe

Keyword(s):

Neutral Current ◽

Atmospheric Neutrino ◽

Simulated Data ◽

Systematic Errors ◽

Muon Energy ◽

New Approach ◽

Flavor Changing ◽

Statistical Fluctuations ◽

The Difference ◽

Neutrino Experiments

Abstract We propose a new approach to explore the neutral-current non-standard neutrino interactions (NSI) in atmospheric neutrino experiments using oscillation dips and valleys in reconstructed muon observables, at a detector like ICAL that can identify the muon charge. We focus on the flavor-changing NSI parameter εμτ, which has the maximum impact on the muon survival probability in these experiments. We show that non-zero εμτ shifts the oscillation dip locations in L/E distributions of the up/down event ratios of reconstructed μ− and μ+ in opposite directions. We introduce a new variable ∆d representing the difference of dip locations in μ− and μ+, which is sensitive to the magnitude as well as the sign of εμτ, and is independent of the value of $$ \Delta {m}_{32}^2 $$ Δ m 32 2 . We further note that the oscillation valley in the (E, cos θ) plane of the reconstructed muon observables bends in the presence of NSI, its curvature having opposite signs for μ− and μ+. We demonstrate the identification of NSI with this curvature, which is feasible for detectors like ICAL having excellent muon energy and direction resolutions. We illustrate how the measurement of contrast in the curvatures of valleys in μ− and μ+ can be used to estimate εμτ. Using these proposed oscillation dip and valley measurements, the achievable precision on |εμτ| at 90% C.L. is about 2% with 500 kt·yr exposure. The effects of statistical fluctuations, systematic errors, and uncertainties in oscillation parameters have been incorporated using multiple sets of simulated data. Our method would provide a direct and robust measurement of εμτ in the multi-GeV energy range.

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Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

JAMIA Open ◽

10.1093/jamiaopen/ooab011 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Fuchiang R Tsui ◽

Lingyun Shi ◽

Victor Ruiz ◽

Neal D Ryan ◽

Candice Biernesser ◽

...

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Suicide Attempt ◽

Language Processing ◽

Suicide Attempts ◽

Large Data ◽

Structured Data ◽

Data Driven ◽

Data Driven Approach ◽

First Time

Abstract Objective Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. Methods This case-control study included patients aged 10–75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort). Results The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P < .001). The best-predicting model utilized 1726 variables with AUC = 0.932 (95% CI, 0.922–0.941). The model was robust across multiple prediction windows and subgroups by demographics, points of historical most recent clinical contact, and depression diagnosis history. Conclusions Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.

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Mild cognitive impairment understanding: an empirical study by data-driven approach

BMC Bioinformatics ◽

10.1186/s12859-019-3057-1 ◽

2019 ◽

Vol 20 (S15) ◽

Author(s):

Liyuan Liu ◽

Bingchen Yu ◽

Meng Han ◽

Shanshan Yuan ◽

Na Wang

Keyword(s):

Risk Factors ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Sleep Time ◽

Data Driven ◽

Control And Prevention ◽

Data Driven Approach ◽

Personal Welfare ◽

First Time

Abstract Background Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy. Results In this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined. Conclusion By incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.

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Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management

Complexity ◽

10.1155/2018/9642892 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 7

Author(s):

Kailong Liu ◽

Kang Li ◽

Qiao Peng ◽

Yuanjun Guo ◽

Li Zhang

Keyword(s):

Thermal Management ◽

Estimation Error ◽

Lithium Ion ◽

Data Driven ◽

State Function ◽

Identification Algorithm ◽

Battery Management System ◽

Internal Temperature ◽

Battery Thermal Management ◽

Data Driven Approach

Temperature is a crucial state to guarantee the reliability and safety of a battery during operation. The ability to estimate battery temperature, especially the internal temperature, is of paramount importance to the battery management system for monitoring and thermal control purposes. In this paper, a data-driven approach combining the RBF neural network (NN) and the extended Kalman filter (EKF) is proposed to estimate the internal temperature for lithium-ion battery thermal management. To be specific, the suitable input terms and the number of hidden nodes for the RBF NN are first optimized by a two-stage stepwise identification algorithm (TSIA). Then, the teaching-learning-based optimization (TLBO) algorithm is developed to optimize the centres and widths in every neuron of basis function. After optimizing the RBF NN model, a battery lumped thermal model is adopted as the state function with the EKF to filter out the outliers of the RBF model and reduce the estimation error. This data-driven approach is validated under four different conditions in comparison with the linear NN models. The experimental results demonstrate that the proposed RBF data-driven approach outperforms the other approaches and can be extended to other types of batteries for thermal monitoring and management.

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History of rare diseases and their genetic causes - a data driven approach

10.1101/595819 ◽

2019 ◽

Author(s):

Friederike Ehrhart ◽

Egon L. Willighagen ◽

Martina Kutmon ◽

Max van Hoften ◽

Nasim Bahram Sangani ◽

...

Keyword(s):

Rare Disease ◽

Rare Diseases ◽

Scientific Publication ◽

Data Driven ◽

Semantic Model ◽

Genetic Causes ◽

Monogenic Diseases ◽

Data Driven Approach ◽

History Of ◽

First Time

AbstractThis dataset provides information about monogenic, rare diseases with a known genetic cause supplemented with manually extracted provenance of both the disease and the discovery of the underlying genetic cause of the disease.We collected 4166 rare monogenic diseases according to their OMIM identifier, linked them to 3163 causative genes which are annotated with Ensembl identifiers and HGNC symbols. The PubMed identifier of the scientific publication, which for the first time describes the rare disease, and the publication which found the gene causing this disease were added using information from OMIM, Wikipedia, Google Scholar, Whonamedit, and PubMed. The data is available as a spreadsheet and as RDF in a semantic model modified from DisGeNET.This dataset relies on publicly available data and publications with a PubMed IDs but this is to our knowledge the first time this data has been linked and made available for further study under a liberal license. Analysis of this data reveals the timeline of rare disease and causative genes discovery and links them to developments in methods and databases.

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Data-driven approach to using individual cattle weights to estimate mean adult dairy cattle weight

Veterinary Record ◽

10.1136/vr.105471 ◽

2019 ◽

Vol 185 (17) ◽

pp. 540-540 ◽

Cited By ~ 1

Author(s):

Hannah Schubert ◽

Sarah Wood ◽

Kristen Reyher ◽

Harriet Mills

Keyword(s):

Dairy Cattle ◽

Data Driven ◽

Average Weight ◽

Evidence Based ◽

Antimicrobial Use ◽

Data Driven Approach ◽

The Uk ◽

On Farm ◽

First Time ◽

Lactating Dairy Cattle

BackgroundKnowledge of accurate weights of cattle is crucial for effective dosing of individual animals and for reporting antimicrobial usage. For the first time, we provide an evidence-based estimate of the average weight of UK dairy cattle to better inform farmers, veterinarians and the scientific community.MethodsData were collected for 2747 lactating dairy cattle from 20 farms in the UK. Data were used to calculate a mean weight for lactating dairy cattle by breed and a UK-specific mean weight. Trends in weight by lactation number and production level were also explored.ResultsMean weight for adult dairy cattle in this study was 617 kg (sd=85.6 kg). Mean weight varied across breeds, with a range of 466 kg (sd=56.0 kg, Jersey) to 636 kg (sd=84.1, Holsteins). When scaled to UK breed proportions, the estimated UK-specific mean weight was 620 kg.ConclusionThis study is the first to calculate a mean weight of adult dairy cattle in the UK based on on-farm data. Overall mean weight was higher than that most often proposed in the literature (600 kg). Evidence-informed weights are crucial as the UK works to better monitor and report metrics to measure antimicrobial use and are useful to farmers and veterinarians to inform dosing decisions.

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Amplifying the Neural Power Spectrum

10.1101/659268 ◽

2019 ◽

Author(s):

J. Andrew Doyle ◽

Paule-Joanne Toussaint ◽

Alan C. Evans

Keyword(s):

Time Domain ◽

Random Noise ◽

Power Spectra ◽

Data Driven ◽

Background Signal ◽

Data Driven Approach ◽

Novel Method ◽

Human Faces ◽

First Time ◽

Scientific Hypotheses

AbstractWe introduce a novel method that employs a parametric model of human electroen-cephalographic (EEG) brain signal power spectra to evaluate cognitive science experiments and test scientific hypotheses. We develop the Neural Power Amplifier (NPA), a data-driven approach to EEG pre-processing that can replace current filtering strategies with a principled method based on combining filters with log-arithmic and Gaussian magnitude responses. Presenting the first time domain evidence to validate an increasingly popular model for neural power spectra [1], we show that filtering out the 1/f background signal and selecting peaks improves a time-domain decoding experiment for visual stimulus of human faces versus random noise.

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