We investigate the $O(N)$--symmetric $φ^6$ theory in three spacetime dimensions using dimensional regularisation and minimal subtraction. The predictions of other methods are scrutinised in a large-$N$ expansion. We show how the tricritical line of...
The article is devoted to double J/psi-meson production at LHC. Theoretical predictions for pp -> 2J/psi+X cross section at sqrt{s}=7 TeV for different kinematical restrictions are presented. Special attention is payed to possible contribution of nov...
Jan 7, 2021 · We could cross the global warming threshold as soon as 2027, according to a new model that can project the Earth's temperature until 2100 - and reduce prediction uncertainties by half.
Behavioral studies have shown that the memorability of images is similar across groups of people, suggesting that memorability is a function of the intrinsic properties of images, and is unrelated to people's individual experiences and traits. Deep l...
An R package for performing patient level prediction in an observational database in the OMOP Common Data Model. (⭐ 214)
Objective: The purpose of this study is to perform analysis through the low back pain open data set to predict the incidence of non-specific chronic low back pain (NSLBP) to obtain a more accurate and convenient sagittal spinopelvic parameter model....
Scientific and technological advances in numerical modelling have improved the quality of climate predictions over recent decades, but predictive skill remains limited in many aspects. Extreme events such as heat and cold waves, droughts, heavy rain...
We perform the two-loop analysis of the 1/Q power corrections to jet-shape variables. This step is necessary for producing reliable theoretical predictions for the relative magnitudes of genuine confinement effects. We show that the rescaling facto...
Antibiotic resistance predictions in minutes on a laptop (⭐ 50)
Antibiotic resistance prediction in minutes (⭐ 114)
:oncoming_automobile: "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API. (⭐ 926)
A very famous ``test'' of the General Theory of Relativity (GTR) is the advance of Mercury's perihelion (and of other planets too). To be more precise, this is not a prediction of General Relativity, since the anomaly was known in the XIXth century,...
Neural networks (NN) have become almost ubiquitous with image classification, but in their standard form produce point estimates, with no measure of confidence. Bayesian neural networks (BNN) provide uncertainty quantification (UQ) for NN predictions...
Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms...
The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks. We identify a mechanism by which non-stationary prediction targets can prevent...
Using machine learning algorithms, including deep learning, we studied the prediction of personal attributes from the text of tweets, such as gender, occupation, and age groups. We applied word2vec to construct word vectors, which were then used to v...
The high center-of-mass energy at the LHC provides the opportunity to test the predictions of some of the beyond the standard model theories. We provide an overview of a selected number of searches for new fermions and new bosons with the ATLAS and C...
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Article prediction is a task that has long defied accurate linguistic description. As such, this task is ideally suited to evaluate models on their ability to emulate native-speaker intuition. To this end, we compare the performance of native English...
Deep neural networks often produce overconfident predictions, undermining their reliability in safety-critical applications. This miscalibration is further exacerbated under distribution shift, where test data deviates from the training distribution...